ARTIFICIAL INTELLIGENCE
WHAT IS ARTIFICIAL INTELLIGENCE: TYPES, HISTORY, AND FUTURE
What Is Artificial Intelligence?
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.
Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.
The term "artificial intelligence" had previously been used to describe machines that mimic and display "human" cognitive skills that are associated with the human mind, such as "learning" and "problem-solving". This definition has since been rejected by major AI researchers who now describe AI in terms of rationality and acting rationally, which does not limit how intelligence can be articulated.
Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.
AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.
Key Takeaways
- Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines.
- The goals of artificial intelligence include computer-enhanced learning, reasoning, and perception.
- AI is being used today across different industries from finance to healthcare.
- Weak AI tends to be simple and single-task oriented, while strong AI carries on tasks that are more complex and human-like.
- Some critics fear that the extensive use of advanced AI can have a negative effect on society.
John McCarthy offers the following definition in this 2004 paper (PDF, 106 KB) , " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."
However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.
Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach , becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:
Human approach:
- Systems that think like humans
- Systems that act like humans
Ideal approach:
- Systems that think rationally
- Systems that act rationally
Alan Turing’s definition would have fallen under the category of “systems that act like humans.”
At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.
A lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartner’s hype cycle , product innovations like, self-driving cars and personal assistants, follow “a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovation’s relevance and role in a market or domain.”
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity.Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals.
https://en.wikipedia.org/wiki/Artificial_intelligence
Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC article predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.
CONTENTS
A Brief History of Artificial Intelligence
Types of Artificial Intelligence
AI Programming Cognitive Skills: Learning, Reasoning and Self-Correction
What Is Artificial Intelligence: Advantages and Disadvantages of Ai
Benefits & Risks of Artificial Intelligence
Applications of Artificial Intelligence
Business Use Cases for Artificial Intelligence Today
Augmented Intelligence vs. Artificial Intelligence
Different Artificial Intelligence Certifications
Further Understanding Artificial Intelligence (AI)
Ethical Use of Artificial Intelligence
Alan Turing and the Beginning of Ai
A Brief History of Artificial Intelligence
Here’s a brief timeline of the past six decades of how AI evolved from its inception.
1956 - John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.
1969 - Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.
1997 - Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.
2002 - The first commercially successful robotic vacuum cleaner was created.
2005 - 2019 - Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.
2020 - Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods.
The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used the tools and logic of their times to describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation.
The late 19th and first half of the 20th centuries brought forth the foundational work that would give rise to the modern computer. In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada Byron, Countess of Lovelace, invented the first design for a programmable machine.
Important events and milestones in the evolution of artificial intelligence include the following:
1940s. Princeton mathematician John Von Neumann conceived the architecture for the stored-program computer -- the idea that a computer's program and the data it processes can be kept in the computer's memory. And Warren McCulloch and Walter Pitts laid the foundation for neural networks.
1950s. With the advent of modern computers, scientists could test their ideas about machine intelligence. One method for determining whether a computer has intelligence was devised by the British mathematician and World War II code-breaker Alan Turing. The Turing Test focused on a computer's ability to fool interrogators into believing its responses to their questions were made by a human being.
1956. The modern field of artificial intelligence is widely cited as starting this year during a summer conference at Dartmouth College. Sponsored by the Defense Advanced Research Projects Agency (DARPA), the conference was attended by 10 luminaries in the field, including AI pioneers Marvin Minsky, Oliver Selfridge and John McCarthy, who is credited with coining the term artificial intelligence. Also in attendance were Allen Newell, a computer scientist, and Herbert A. Simon, an economist, political scientist and cognitive psychologist, who presented their groundbreaking Logic Theorist, a computer program capable of proving certain mathematical theorems and referred to as the first AI program.
1950s and 1960s. In the wake of the Dartmouth College conference, leaders in the fledgling field of AI predicted that a man-made intelligence equivalent to the human brain was around the corner, attracting major government and industry support. Indeed, nearly 20 years of well-funded basic research generated significant advances in AI: For example, in the late 1950s, Newell and Simon published the General Problem Solver (GPS) algorithm, which fell short of solving complex problems but laid the foundations for developing more sophisticated cognitive architectures; McCarthy developed Lisp, a language for AI programming that is still used today. In the mid-1960s MIT Professor Joseph Weizenbaum developed ELIZA, an early natural language processing program that laid the foundation for today's chatbots.
1970s and 1980s. But the achievement of artificial general intelligence proved elusive, not imminent, hampered by limitations in computer processing and memory and by the complexity of the problem. Government and corporations backed away from their support of AI research, leading to a fallow period lasting from 1974 to 1980 and known as the first "AI Winter." In the 1980s, research on deep learning techniques and industry's adoption of Edward Feigenbaum's expert systems sparked a new wave of AI enthusiasm, only to be followed by another collapse of government funding and industry support. The second AI winter lasted until the mid-1990s.
1990s through today. Increases in computational power and an explosion of data sparked an AI renaissance in the late 1990s that has continued to present times. The latest focus on AI has given rise to breakthroughs in natural language processing, computer vision, robotics, machine learning, deep learning and more. Moreover, AI is becoming ever more tangible, powering cars, diagnosing disease and cementing its role in popular culture. In 1997, IBM's Deep Blue defeated Russian chess grandmaster Garry Kasparov, becoming the first computer program to beat a world chess champion. Fourteen years later, IBM's Watson captivated the public when it defeated two former champions on the game show Jeopardy!. More recently, the historic defeat of 18-time World Go champion Lee Sedol by Google DeepMind's AlphaGo stunned the Go community and marked a major milestone in the development of intelligent machines.
- 1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing—famous for breaking the Nazi's ENIGMA code during WWII—proposes to answer the question 'can machines think?' and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.
- 1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.
- 1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.
- 1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.
- 1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).
- 2011: IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy!
- 2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.
- 2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.
AI as a service
Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings or providing access to artificial intelligence as a service (AIaaS) platforms. AIaaS allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment.
Popular AI cloud offerings include the following:
How does AI work?
As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
Learning processes. This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.
Self-correction processes. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
Why is artificial intelligence important?
AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.
This has helped fuel an explosion in efficiency and opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to predict when people are likely to need rides in certain areas, which helps proactively get drivers on the road before they're needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to understand how people use their services and then improving them. In 2017, the company's CEO, Sundar Pichai, pronounced that Google would operate as an "AI first" company.
Today's largest and most successful enterprises have used AI to improve their operations and gain advantage on their competitors.
What are the advantages and disadvantages of artificial intelligence?
Artificial neural networks and deep learning artificial intelligence technologies are quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions more accurately than humanly possible.
While the huge volume of data being created on a daily basis would bury a human researcher, AI applications that use machine learning can take that data and quickly turn it into actionable information. As of this writing, the primary disadvantage of using AI is that it is expensive to process the large amounts of data that AI programming requires.
Advantages
- Good at detail-oriented jobs;
- Reduced time for data-heavy tasks;
- Delivers consistent results; and
- AI-powered virtual agents are always available.
Disadvantages
- Expensive;
- Requires deep technical expertise;
- Limited supply of qualified workers to build AI tools;
- Only knows what it's been shown; and
- Lack of ability to generalize from one task to another.
Strong AI vs. weak AI
AI can be categorized as either weak or strong.
- Weak AI, also known as narrow AI, is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI.
- Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing Test and the Chinese room test.
Types of Artificial Intelligence
1. Purely Reactive
These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win.
2. Limited Memory
These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.
3. Theory of Mind
This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built.
4. Self-Aware
Self-aware machines are the future generation of these new technologies. They will be intelligent, sentient, and conscious.
How Does Artificial Intelligence Work?
Put simply, AI systems work by merging large with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and features in the analyzed data. Each time an Artificial Intelligence system performs a round of data processing, it tests and measures its performance and uses the results to develop additional expertise.
Ways of Implementing AI
Let’s explore the following ways that explain how we can implement AI:
Machine Learning
It is machine learning that gives AI the ability to learn. This is done by using algorithms to discover patterns and generate insights from the data they are exposed to.
Deep Learning
Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network. It can make sense of patterns, noise, and sources of confusion in the data.
Here we segregated the various kinds of images using deep learning. The machine goes through various features of photographs and distinguishes them with a process called feature extraction. Based on the features of each photo, the machine segregates them into different categories, such as landscape, portrait, or others.
Let us understand how deep learning works.
The above image depicts the three main layers of a neural network:
- Input Layer
- Hidden Layer
- Output Layer
Input Layer
The images that we want to segregate go into the input layer. Arrows are drawn from the image on to the individual dots of the input layer. Each of the white dots in the yellow layer (input layer) are a pixel in the picture. These images fill the white dots in the input layer.
We should have a clear idea of these three layers while going through this artificial intelligence tutorial.
Hidden Layer
The hidden layers are responsible for all the mathematical computations or feature extraction on our inputs. In the above image, the layers shown in orange represent the hidden layers. The lines that are seen between these layers are called ‘weights’. Each one of them usually represents a float number, or a decimal number, which is multiplied by the value in the input layer. All the weights add up in the hidden layer. The dots in the hidden layer represent a value based on the sum of the weights. These values are then passed to the next hidden layer.
You may be wondering why there are multiple layers. The hidden layers function as alternatives to some degree. The more the hidden layers are, the more complex the data that goes in and what can be produced. The accuracy of the predicted output generally depends on the number of hidden layers present and the complexity of the data going in.
Output Layer
The output layer gives us segregated photos. Once the layer adds up all these weights being fed in, it'll determine if the picture is a portrait or a landscape.
Example - Predicting Airfare Costs
This prediction is based on various factors, including:
- Airline
- Origin airport
- Destination airport
- Departure date
We begin with some historical data on ticket prices to train the machine. Once our machine is trained, we share new data that will predict the costs. Earlier, when we learned about four kinds of machines, we discussed machines with memory. Here, we talk about the memory only, and how it understands a pattern in the data and uses it to make predictions for the new prices as shown below:
AI Programming Cognitive Skills: Learning, Reasoning and Self-Correction
Artificial Intelligence emphasizes three cognitive skills of learning, reasoning, and self-correction, skills that the human brain possess to one degree or another. We define these in the context of AI as:
- Learning: The acquisition of information and the rules needed to use that information.
- Reasoning: Using the information rules to reach definite or approximate conclusions.
- Self-Correction: The process of continually fine-tuning AI algorithms and ensure that they offer the most accurate results they can.
However, researchers and programmers have extended and elaborated the goals of AI to the following:
1. Logical Reasoning
AI programs enable computers to perform sophisticated tasks. On February 10, 1996, IBM’s Deep Blue computer won a game of chess against a former world champion, Garry Kasparov.
2. Knowledge Representation
Smalltalk is an object-oriented, dynamically typed, reflective programming language that was created to underpin the “new world” of computing exemplified by “human-computer symbiosis.”
3. Planning and Navigation
The process of enabling a computer to get from point A to point B. A prime example of this is Google’s self-driving Toyota Prius.
4. Natural Language Processing
Set up computers that can understand and process language.
5. Perception
Use computers to interact with the world through sight, hearing, touch, and smell.
6. Emergent Intelligence
Intelligence that is not explicitly programmed, but emerges from the rest of the specific AI features. The vision for this goal is to have machines exhibit emotional intelligence and moral reasoning.
Some of the tasks performed by AI-enabled devices include:
- Speech recognition
- Object detection
- Solve problems and learn from the given data
- Plan an approach for future tests to be done
What is Artificial Intelligence: Advantages and Disadvantages of AI
Artificial intelligence has its pluses and minuses, much like any other concept or innovation. Here’s a quick rundown of some pros and cons.
Pros
- It reduces human error
- It never sleeps, so it’s available 24x7
- It never gets bored, so it easily handles repetitive tasks
- It’s fast
Cons
- It’s costly to implement
- It can’t duplicate human creativity
- It will definitely replace some jobs, leading to unemployment
- People can become overly reliant on it
Benefits & Risks of Artificial Intelligence
“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
Max Tegmark, President of the Future of Life Institute
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Why research AI safety?
In the near term, the goal of keeping AI’s impact on society beneficial motivates research in many areas, from economics and law to technical topics such as verification, validity, security and control. Whereas it may be little more than a minor nuisance if your laptop crashes or gets hacked, it becomes all the more important that an AI system does what you want it to do if it controls your car, your airplane, your pacemaker, your automated trading system or your power grid. Another short-term challenge is preventing a devastating arms race in lethal autonomous weapons.
In the long term, an important question is what will happen if the quest for strong AI succeeds and an AI system becomes better than humans at all cognitive tasks. As pointed out by I.J. Good in 1965, designing smarter AI systems is itself a cognitive task. Such a system could potentially undergo recursive self-improvement, triggering an intelligence explosion leaving human intellect far behind. By inventing revolutionary new technologies, such a superintelligence might help us eradicate war, disease, and poverty, and so the creation of strong AI might be the biggest event in human history. Some experts have expressed concern, though, that it might also be the last, unless we learn to align the goals of the AI with ours before it becomes superintelligent.
There are some who question whether strong AI will ever be achieved, and others who insist that the creation of superintelligent AI is guaranteed to be beneficial. At FLI we recognize both of these possibilities, but also recognize the potential for an artificial intelligence system to intentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negative consequences in the future, thus enjoying the benefits of AI while avoiding pitfalls.
How can AI be dangerous?
Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:
- The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.
- The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.
As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.
Why the recent interest in AI safety
Stephen Hawking, Elon Musk, Steve Wozniak, Bill Gates, and many other big names in science and technology have recently expressed concern in the media and via open letters about the risks posed by AI, joined by many leading AI researchers. Why is the subject suddenly in the headlines?
The idea that the quest for strong AI would ultimately succeed was long thought of as science fiction, centuries or more away. However, thanks to recent breakthroughs, many AI milestones, which experts viewed as decades away merely five years ago, have now been reached, making many experts take seriously the possibility of superintelligence in our lifetime. While some experts still guess that human-level AI is centuries away, most AI researches at the 2015 Puerto Rico Conference guessed that it would happen before 2060. Since it may take decades to complete the required safety research, it is prudent to start it now.
Because AI has the potential to become more intelligent than any human, we have no surefire way of predicting how it will behave. We can’t use past technological developments as much of a basis because we’ve never created anything that has the ability to, wittingly or unwittingly, outsmart us. The best example of what we could face may be our own evolution. People now control the planet, not because we’re the strongest, fastest or biggest, but because we’re the smartest. If we’re no longer the smartest, are we assured to remain in control?
FLI’s position is that our civilization will flourish as long as we win the race between the growing power of technology and the wisdom with which we manage it. In the case of AI technology, FLI’s position is that the best way to win that race is not to impede the former, but to accelerate the latter, by supporting AI safety research.
The Top Myths About Advanced AI
A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as: AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of of boring pseudo-controversies caused by people misunderstanding and talking past each other. To help ourselves focus on the interesting controversies and open questions — and not on the misunderstandings — let’s clear up some of the most common myths.
Timeline Myths
The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers:
On the other hand, a popular counter-myth is that we know we won’t get superhuman AI this century. Researchers have made a wide range of estimates for how far we are from superhuman AI, but we certainly can’t say with great confidence that the probability is zero this century, given the dismal track record of such techno-skeptic predictions. For example, Ernest Rutherford, arguably the greatest nuclear physicist of his time, said in 1933 — less than 24 hours before Szilard’s invention of the nuclear chain reaction — that nuclear energy was “moonshine.” And Astronomer Royal Richard Woolley called interplanetary travel “utter bilge” in 1956. The most extreme form of this myth is that superhuman AI will never arrive because it’s physically impossible. However, physicists know that a brain consists of quarks and electrons arranged to act as a powerful computer, and that there’s no law of physics preventing us from building even more intelligent quark blobs.
There have been a number of surveys asking AI researchers how many years from now they think we’ll have human-level AI with at least 50% probability. All these surveys have the same conclusion: the world’s leading experts disagree, so we simply don’t know. For example, in such a poll of the AI researchers at the 2015 Puerto Rico AI conference, the average (median) answer was by year 2045, but some researchers guessed hundreds of years or more.
There’s also a related myth that people who worry about AI think it’s only a few years away. In fact, most people on record worrying about superhuman AI guess it’s still at least decades away. But they argue that as long as we’re not 100% sure that it won’t happen this century, it’s smart to start safety research now to prepare for the eventuality. Many of the safety problems associated with human-level AI are so hard that they may take decades to solve. So it’s prudent to start researching them now rather than the night before some programmers drinking Red Bull decide to switch one on.
Controversy Myths
Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.
It may be that media have made the AI safety debate seem more controversial than it really is. After all, fear sells, and articles using out-of-context quotes to proclaim imminent doom can generate more clicks than nuanced and balanced ones. As a result, two people who only know about each other’s positions from media quotes are likely to think they disagree more than they really do. For example, a techno-skeptic who only read about Bill Gates’s position in a British tabloid may mistakenly think Gates believes superintelligence to be imminent. Similarly, someone in the beneficial-AI movement who knows nothing about Andrew Ng’s position except his quote about overpopulation on Mars may mistakenly think he doesn’t care about AI safety, whereas in fact, he does. The crux is simply that because Ng’s timeline estimates are longer, he naturally tends to prioritize short-term AI challenges over long-term ones.
Myths About the Risks of Superhuman AI
Many AI researchers roll their eyes when seeing this headline: “Stephen Hawking warns that rise of robots may be disastrous for mankind.” And as many have lost count of how many similar articles they’ve seen. Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because they’ve become conscious and/or evil. On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers don’t worry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, and robots.
If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car? Although this mystery of consciousness is interesting in its own right, it’s irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AI does, not how it subjectively feels.
The fear of machines turning evil is another red herring. The real worry isn’t malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans don’t generally hate ants, but we’re more intelligent than they are – so if we want to build a hydroelectric dam and there’s an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.
The consciousness misconception is related to the myth that machines can’t have goals. Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target. If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose. If that heat-seeking missile were chasing you, you probably wouldn’t exclaim: “I’m not worried, because machines can’t have goals!”
I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids, because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isn’t with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection – this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.
The robot misconception is related to the myth that machines can’t control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, it’s possible that we might also cede control.
The Interesting Controversies
Not wasting time on the above-mentioned misconceptions lets us focus on true and interesting controversies where even the experts disagree. What sort of future do you want? Should we develop lethal autonomous weapons? What would you like to happen with job automation? What career advice would you give today’s kids? Do you prefer new jobs replacing the old ones, or a jobless society where everyone enjoys a life of leisure and machine-produced wealth? Further down the road, would you like us to create superintelligent life and spread it through our cosmos? Will we control intelligent machines or will they control us? Will intelligent machines replace us, coexist with us, or merge with us? What will it mean to be human in the age of artificial intelligence? What would you like it to mean, and how can we make the future be that way? Please join the conversation!
computational aestheticsneural networkartificial intelligence programming languageexpert systemthree laws of robotics
B.J. Copeland• Alan Turing Benefits-risks-of-artificial-intelligence/
Applications of Artificial Intelligence
Machines and computers affect how we live and work. Top companies are continually rolling out revolutionary changes to how we interact with machine-learning technology.
DeepMind Technologies, a British artificial intelligence company, was acquired by Google in 2014. The company created a Neural Turing Machine, allowing computers to mimic the short-term memory of the human brain.
Google’s driverless cars and Tesla’s Autopilot features are the introductions of AI into the automotive sector. Elon Musk, CEO of Tesla Motors, has suggested via Twitter that Teslas will have the ability to predict the destination that their owners want to go via learning their pattern or behavior via AI.
Furthermore, Watson, a question-answering computer system developed by IBM, is designed for use in the medical field. Watson suggests various kinds of treatment for patients based on their medical history and has proven to be very useful.
Some of the more common commercial business uses of AI are:
1. Banking Fraud Detection
From extensive data consisting of fraudulent and non-fraudulent transactions, the AI learns to predict if a new transaction is fraudulent or not.
2. Online Customer Support
AI is now automating most of the online customer support and voice messaging systems.
3. Cyber Security
Using machine learning algorithms and ample sample data, AI can be used to detect anomalies and adapt and respond to threats.
4. Virtual Assistants
Siri, Cortana, Alexa, and Google now use voice recognition to follow the user's commands. They collect information, interpret what is being asked, and supply the answer via fetched data. These virtual assistants gradually improve and personalize solutions based on user preferences.
What are examples of AI technology and how is it used today?
AI is incorporated into a variety of different types of technology. Here are six examples:
- Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.
-
Machine
learning.
This is the science of getting a computer to act without programming. Deep
learning is a subset of machine learning that, in very simple terms, can be
thought of as the automation of predictive analytics. There are three types of
machine learning algorithms:
- Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.
- Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences.
- Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
- Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
- Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
- Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
- Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
Here are nine examples.
AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and can respond to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to predict, fight and understand pandemics such as COVID-19.
AI in business. Machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT analysts.
AI in education. AI can automate grading, giving educators more time. It can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. And it could change where and how students learn, perhaps even replacing some teachers.
AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street.
AI in law. The discovery process -- sifting through documents -- in law is often overwhelming for humans. Using AI to help automate the legal industry's labor-intensive processes is saving time and improving client service. Law firms are using machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents and natural language processing to interpret requests for information.
AI in manufacturing. Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed to perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors and other workspaces.
AI in banking. Banks are successfully employing chatbots to make their customers aware of services and offerings and to handle transactions that don't require human intervention. AI virtual assistants are being used to improve and cut the costs of compliance with banking regulations. Banking organizations are also using AI to improve their decision-making for loans, and to set credit limits and identify investment opportunities.
AI in transportation. In addition to AI's fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient.
Security. AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations. The maturing technology is playing a big role in helping organizations fight off cyber attacks.
Common Examples:
- Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.
- Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
- Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.
- Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
- Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
Business use cases for artificial intelligence today
Accelerate data preparation and improve data governance with artificial intelligence
Pain Point: Analyses are only as good as the data that it contains, and as a result, many data scientists and analysts spend an exorbitant amount of time cleaning and organizing data to provide business valueto their organizations. For example, raw data may not comply with your business taxonomy, and as a result, data may need to be classified in a specific way to provide context to stakeholders. Alternatively, you may not have a defined taxonomy for a new dataset yet; artificial intelligence can also help define clusters of data, segmenting groups based on shared characteristics. Either way, this can be a long, manual process for large datasets if left to an individual.
Solution: However, AI algorithms can assist in data classification tasks, reducing the cost and time to provide business insights. Labeled datasets can help categorize new data through supervised learning algorithms, while unsupervised learning can help businesses discover new clusters of data. We also asked businesses about this and we found that 39% of our survey participants identified data management and classification as a key use case of AI.
See how Regions Bank integrated AI to centralize data and insights
Streamline operational efficiency with artificial intelligence
Pain Point: As a business expands in size, organizational complexity can also grow with new employees and processes to help it scale. Over time, this can lead to operational inefficiencies, slowing businesses down from achieving their business goals.
Solution: Organizations can leverage log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas of improvement. Specialized algorithms provide insight into the root causes of deviations from the process norm, which in turn, arms managers with the information that they need to optimize them. When we dug deeper, we found that 42% of our survey participants identified IT operations as a key use case of AI.
See how Humana integrated AI to improve its operational efficiency
Forecast sales with artificial intelligence
Pain Point: Each year, business leaders are expected to set realistic goals for their organizations, setting the strategy for the incoming year. Publicly traded companies also have the additional responsibility of sharing these future objectives during earning calls to maintain market confidence. However, in an ever-changing business landscape, it can be difficult to make commitments on sales goals with certainty, especially within large, complex organizations, where there are a variety of factors that can impact bottom-line sales numbers.
Solution: While sales forecasting isn’t new to the business world, the factors that impact business sales have changed over the last decade, and they’ll continue to do so. In fact, many businesses are experiencing accelerated digital transformation efforts due to the COVID-19 pandemic, which means that new data systems (and more data) can be utilized as potential factors for sales growth. AI solutions allow data teams to adapt to changing circumstances, enabling them to make sales predictions with confidence. When we asked surveyors about this, we found that 36% of respondents leverage AI for sales forecasting.
Learn how IBM can apply AI to solve your business needs
Explore more use cases of AI in the enterprise
Augmented intelligence vs. artificial intelligence
Some industry experts believe the term artificial intelligence is too closely linked to popular culture, and this has caused the general public to have improbable expectations about how AI will change the workplace and life in general.
- Augmented intelligence. Some researchers and marketers hope the label augmented intelligence, which has a more neutral connotation, will help people understand that most implementations of AI will be weak and simply improve products and services. Examples include automatically surfacing important information in business intelligence reports or highlighting important information in legal filings.
- Artificial intelligence. True AI, or artificial general intelligence, is closely associated with the concept of the technological singularity -- a future ruled by an artificial superintelligence that far surpasses the human brain's ability to understand it or how it is shaping our reality. This remains within the realm of science fiction, though some developers are working on the problem. Many believe that technologies such as quantum computing could play an important role in making AGI a reality and that we should reserve the use of the term AI for this kind of general intelligence.
Different Artificial Intelligence Certifications
1. Introduction to Artificial Intelligence Course
Simplilearn's Artificial Intelligence basics program is designed to help learners decode the mystery of artificial intelligence and its business applications. The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised and reinforcement learning, be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.
2. Machine Learning Certification Course
Simplilearn’s AI and Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You'll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of a Machine Learning Engineer.
3. Artificial Intelligence Engineer Master’s Program
Simplilearn's Artificial Intelligence course, in collaboration with IBM, gives training on the skills required for a successful career in AI. Throughout this exclusive training program, you'll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain and kick-start your career in Artificial Intelligence.
4. Simplilearn’s Artificial Intelligence (AI) Capstone Project
Simplilearn’s Artificial Intelligence (AI) Capstone project will give you an opportunity to implement the skills you learned in the masters of AI. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. You'll learn various AI-based supervised and unsupervised techniques like Regression, Multinomial Naïve Bayes, SVM, Tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to employers.
Reasons to Get an Artificial Intelligence Certification: The Key Takeaways
Here are the top reasons why you should get a certification in AI if you’re looking to join this exciting and growing field:
1. Demand for Certified AI Professionals will Continue to Grow
The McKinsey Global Institute predicts that approximately 70 percent of businesses will be using at least one type of Artificial Intelligence technology by 2030, and about half of all big companies will embed a full range of Artificial Intelligence technology in their processes. AI will help companies offer customized solutions and instructions to employees in real-time. Therefore, the demand for professionals with skills in emerging technologies like AI will only continue to grow.
2. New and Unconventional Career Paths
A Future of Jobs Report released by the World Economic Forum in 2020 predicts that 85 million jobs will be lost to automation by 2025. However, it goes on to say that 97 new positions and roles will be created as industries figure out the balance between machines and humans.
Because of AI, new skill sets are required in the workforce, leading to new job opportunities. Some of the top AI roles include:
- AI/machine learning researcher - Researching to find improvements to machine learning algorithms.
- AI software development, program management, and testing - Developing systems and infrastructure that can apply machine learning to an input data set.
- Data mining and analysis - Deep investigation of abundant data sources, often creating and training systems to recognize patterns.
- Machine learning applications - Applying machine learning or AI framework to a specific problem in a different domain and for example, applying machine learning to gesture recognition, ad analysis, or fraud detection
3. Improve Your Earning Potential
Many of the top tech enterprises are investing in hiring talent with AI knowledge. The average Artificial Intelligence Engineer can earn $164,000 per year, and AI certification is a step in the right direction for enhancing your earning potential and becoming more marketable.
4. Higher Chances of a Discussion
If you are looking to join the AI industry, then becoming knowledgeable in Artificial Intelligence is just the first step; next, you need verifiable credentials. Certification earned after pursuing Simplilearn’s AI and ML Courses will help you reach the interview stage as you’ll possess skills that many people in the market do not. Certification will help convince employers that you have the right skills and expertise for a job, making you a valuable candidate.
Artificial Intelligence is emerging as the next big thing in technology. Organizations are adopting AI and budgeting for certified professionals in the field, thus the growing demand for trained and certified professionals. As this emerging field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries.
Karin Kelley What-is-artificial-intelligence
FURTHER UNDERSTANDING ARTIFICIAL INTELLIGENCE (AI)
When most people hear the term artificial intelligence, the first thing they usually think of is robots. That's because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgments that are subject to human experience.
As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.
AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, and more.
Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.
Types of Artificial Intelligence
Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, and it answers it for you.
Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.
Special Considerations
Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.
Another is that machines can hack into people's privacy and even be weaponized. Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.
Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.
Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people's skills obsolete.
The first artificial intelligence is thought to be a checkers-playing computer built by Oxford University (UK) computer scientists in 1951.1
How Is AI Used Today?
AI is used extensively across a range of applications today, with varying levels of sophistication. Recommendation algorithms that suggest what you might like next are popular AI implementations, as are chatbots that appear on websites or in the form of smart speakers (e.g., Alexa or Siri). AI is used to make predictions in terms of weather and financial forecasting, to streamline production processes, and to cut down on various forms of redundant cognitive labor (e.g., tax accounting or editing). AI is also used to play games, operate autonomous vehicles, process language, and much, much, more.
How Is AI Used in Healthcare?
In healthcare settings, AI is used to assist in diagnostics. AI is very good at identifying small anomalies in scans and can better triangulate diagnoses from a patient's symptoms and vitals. AI is also used to classify patients, maintain and track medical records, and deal with health insurance claims. Future innovations are thought to include AI-assisted robotic surgery, virtual nurses or doctors, and collaborative clinical judgment.
The Turing Test is a deceptively simple method of determining whether a machine can demonstrate human intelligence. more
Weak AI is a machine intelligence that is limited to a particular area. more
Fuzzy logic is a mathematical logic that solves problems with an open, imprecise data spectrum. Read how to obtain accurate conclusions with fuzzy logic. more
What Is Knowledge Engineering?
Knowledge engineering is a field of artificial intelligence (AI) that enables a system or machine to mimic the thought process of a human expert. more
Machine learning, a field of artificial intelligence (AI), is the idea that a computer program can adapt to new data independently of human action. more
A chatbot is a computer program that simulates human conversation through voice commands, text chats, or both. Read how chatbots can interact with humans. more
Jake Frankenfield Gordon Scott Artificial-intelligence-ai
ETHICAL USE OF ARTIFICIAL INTELLIGENCE
While AI tools present a range of new functionality for businesses, the use of artificial intelligence also raises ethical questions because, for better or worse, an AI system will reinforce what it has already learned.
This can be problematic because machine learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human being selects what data is used to train an AI program, the potential for machine learning bias is inherent and must be monitored closely.
Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning and generative adversarial network (GAN) applications.
Explainability is a potential stumbling block to using AI in industries that operate under strict regulatory compliance requirements. For example, financial institutions in the United States operate under regulations that require them to explain their credit-issuing decisions. When a decision to refuse credit is made by AI programming, however, it can be difficult to explain how the decision was arrived at because the AI tools used to make such decisions operate by teasing out subtle correlations between thousands of variables. When the decision-making process cannot be explained, the program may be referred to as black box AI.
Despite potential risks, there are currently few regulations governing the use of AI tools, and where laws do exist, they typically pertain to AI indirectly. For example, as previously mentioned, United States Fair Lending regulations require financial institutions to explain credit decisions to potential customers. This limits the extent to which lenders can use deep learning algorithms, which by their nature are opaque and lack explainability.
The European Union's General Data Protection Regulation (GDPR) puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.
In October 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered.
Crafting laws to regulate AI will not be easy, in part because AI comprises a variety of technologies that companies use for different ends, and partly because regulations can come at the cost of AI progress and development. The rapid evolution of AI technologies is another obstacle to forming meaningful regulation of AI. Technology breakthroughs and novel applications can make existing laws instantly obsolete. For example, existing laws regulating the privacy of conversations and recorded conversations do not cover the challenge posed by voice assistants like Amazon's Alexa and Apple's Siri that gather but do not distribute conversation -- except to the companies' technology teams which use it to improve machine learning algorithms. And, of course, the laws that governments do manage to craft to regulate AI don't stop criminals from using the technology with malicious intent.
Cognitive computing and AI
The terms AI and cognitive computing are sometimes used interchangeably, but, generally speaking, the label AI is used in reference to machines that replace human intelligence by simulating how we sense, learn, process and react to information in the environment.
The label cognitive computing is used in reference to products and services that mimic and augment human thought processes.
This was last updated in February 2022
Continue Reading About What is artificial intelligence (AI)?
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By Ed Burns Nicole Laskowski,Senior News Director Linda Tucci,Industry Editor -- CIO/IT Strategy AI-Artificial-Intelligence
WHAT IS INTELLIGENCE?
All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of Sphex—must include the ability to adapt to new circumstances.
Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.
Learning
There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the “add ed” rule and so form the past tense of jump based on experience with similar verbs.
Reasoning
To reason is to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive. An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore he is in the museum,” and of the latter, “Previous accidents of this sort were caused by instrument failure; therefore this accident was caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case the truth of the premise lends support to the conclusion without giving absolute assurance. Inductive reasoning is common in science, where data are collected and tentative models are developed to describe and predict future behaviour—until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic, where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.
There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, true reasoning involves more than just drawing inferences; it involves drawing inferences relevant to the solution of the particular task or situation. This is one of the hardest problems confronting AI.
Problem solving
Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot this might consist of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT, and MOVERIGHT—until the goal is reached.
Many diverse problems have been solved by artificial intelligence programs. Some examples are finding the winning move (or sequence of moves) in a board game, devising mathematical proofs, and manipulating “virtual objects” in a computer-generated world.
Perception
In perception the environment is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field.
At present, artificial perception is sufficiently well advanced to enable optical sensors to identify individuals, autonomous vehicles to drive at moderate speeds on the open road, and robots to roam through buildings collecting empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving television eye and a pincer hand, constructed at the University of Edinburgh, Scotland, during the period 1966–73 under the direction of Donald Michie. FREDDY was able to recognize a variety of objects and could be instructed to assemble simple artifacts, such as a toy car, from a random heap of components.
Language
A language is a system of signs having meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a minilanguage, it being a matter of convention that ⚠ means “hazard ahead” in some countries. It is distinctive of languages that linguistic units possess meaning by convention, and linguistic meaning is very different from what is called natural meaning, exemplified in statements such as “Those clouds mean rain” and “The fall in pressure means the valve is malfunctioning.”
An important characteristic of full-fledged human languages—in contrast to birdcalls and traffic signs—is their productivity. A productive language can formulate an unlimited variety of sentences.
It is relatively easy to write computer programs that seem able, in severely restricted contexts, to respond fluently in a human language to questions and statements. Although none of these programs actually understands language, they may, in principle, reach the point where their command of a language is indistinguishable from that of a normal human. What, then, is involved in genuine understanding, if even a computer that uses language like a native human speaker is not acknowledged to understand? There is no universally agreed upon answer to this difficult question. According to one theory, whether or not one understands depends not only on one’s behaviour but also on one’s history: in order to be said to understand, one must have learned the language and have been trained to take one’s place in the linguistic community by means of interaction with other language users.
Methods and goals in AI
Symbolic vs. connectionist approaches
AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach. The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols—whence the symbolic label. The bottom-up approach, on the other hand, involves creating artificial neural networks in imitation of the brain’s structure—whence the connectionist label.
To illustrate the difference between these approaches, consider the task of building a system, equipped with an optical scanner, that recognizes the letters of the alphabet. A bottom-up approach typically involves training an artificial neural network by presenting letters to it one by one, gradually improving performance by “tuning” the network. (Tuning adjusts the responsiveness of different neural pathways to different stimuli.) In contrast, a top-down approach typically involves writing a computer program that compares each letter with geometric descriptions. Simply put, neural activities are the basis of the bottom-up approach, while symbolic descriptions are the basis of the top-down approach.
In The Fundamentals of Learning (1932), Edward Thorndike, a psychologist at Columbia University, New York City, first suggested that human learning consists of some unknown property of connections between neurons in the brain. In The Organization of Behavior (1949), Donald Hebb, a psychologist at McGill University, Montreal, Canada, suggested that learning specifically involves strengthening certain patterns of neural activity by increasing the probability (weight) of induced neuron firing between the associated connections. The notion of weighted connections is described in a later section, Connectionism.
In 1957 two vigorous advocates of symbolic AI—Allen Newell, a researcher at the RAND Corporation, Santa Monica, California, and Herbert Simon, a psychologist and computer scientist at Carnegie Mellon University, Pittsburgh, Pennsylvania—summed up the top-down approach in what they called the physical symbol system hypothesis. This hypothesis states that processing structures of symbols is sufficient, in principle, to produce artificial intelligence in a digital computer and that, moreover, human intelligence is the result of the same type of symbolic manipulations.
During the 1950s and ’60s the top-down and bottom-up approaches were pursued simultaneously, and both achieved noteworthy, if limited, results. During the 1970s, however, bottom-up AI was neglected, and it was not until the 1980s that this approach again became prominent. Nowadays both approaches are followed, and both are acknowledged as facing difficulties. Symbolic techniques work in simplified realms but typically break down when confronted with the real world; meanwhile, bottom-up researchers have been unable to replicate the nervous systems of even the simplest living things. Caenorhabditis elegans, a much-studied worm, has approximately 300 neurons whose pattern of interconnections is perfectly known. Yet connectionist models have failed to mimic even this worm. Evidently, the neurons of connectionist theory are gross oversimplifications of the real thing.
Strong AI, applied AI, and cognitive simulation
Employing the methods outlined above, AI research attempts to reach one of three goals: strong AI, applied AI, or cognitive simulation. Strong AI aims to build machines that think. (The term strong AI was introduced for this category of research in 1980 by the philosopher John Searle of the University of California at Berkeley.) The ultimate ambition of strong AI is to produce a machine whose overall intellectual ability is indistinguishable from that of a human being. As is described in the section Early milestones in AI, this goal generated great interest in the 1950s and ’60s, but such optimism has given way to an appreciation of the extreme difficulties involved. To date, progress has been meagre. Some critics doubt whether research will produce even a system with the overall intellectual ability of an ant in the foreseeable future. Indeed, some researchers working in AI’s other two branches view strong AI as not worth pursuing.
Applied AI, also known as advanced information processing, aims to produce commercially viable “smart” systems—for example, “expert” medical diagnosis systems and stock-trading systems. Applied AI has enjoyed considerable success, as described in the section Expert systems.
In cognitive simulation, computers are used to test theories about how the human mind works—for example, theories about how people recognize faces or recall memories. Cognitive simulation is already a powerful tool in both neuroscience and cognitive psychology.
Alan Turing and the beginning of AI
Theoretical work
The earliest substantial work in the field of artificial intelligence was done in the mid-20th century by the British logician and computer pioneer Alan Mathison Turing. In 1935 Turing described an abstract computing machine consisting of a limitless memory and a scanner that moves back and forth through the memory, symbol by symbol, reading what it finds and writing further symbols. The actions of the scanner are dictated by a program of instructions that also is stored in the memory in the form of symbols. This is Turing’s stored-program concept, and implicit in it is the possibility of the machine operating on, and so modifying or improving, its own program. Turing’s conception is now known simply as the universal Turing machine. All modern computers are in essence universal Turing machines.
During World War II, Turing was a leading cryptanalyst at the Government Code and Cypher School in Bletchley Park, Buckinghamshire, England. Turing could not turn to the project of building a stored-program electronic computing machine until the cessation of hostilities in Europe in 1945. Nevertheless, during the war he gave considerable thought to the issue of machine intelligence. One of Turing’s colleagues at Bletchley Park, Donald Michie (who later founded the Department of Machine Intelligence and Perception at the University of Edinburgh), later recalled that Turing often discussed how computers could learn from experience as well as solve new problems through the use of guiding principles—a process now known as heuristic problem solving.
Turing gave quite possibly the earliest public lecture (London, 1947) to mention computer intelligence, saying, “What we want is a machine that can learn from experience,” and that the “possibility of letting the machine alter its own instructions provides the mechanism for this.” In 1948 he introduced many of the central concepts of AI in a report entitled “Intelligent Machinery.” However, Turing did not publish this paper, and many of his ideas were later reinvented by others. For instance, one of Turing’s original ideas was to train a network of artificial neurons to perform specific tasks, an approach described in the section Connectionism.
Chess
At Bletchley Park, Turing illustrated his ideas on machine intelligence by reference to chess—a useful source of challenging and clearly defined problems against which proposed methods for problem solving could be tested. In principle, a chess-playing computer could play by searching exhaustively through all the available moves, but in practice this is impossible because it would involve examining an astronomically large number of moves. Heuristics are necessary to guide a narrower, more discriminative search. Although Turing experimented with designing chess programs, he had to content himself with theory in the absence of a computer to run his chess program. The first true AI programs had to await the arrival of stored-program electronic digital computers.
In 1945 Turing predicted that computers would one day play very good chess, and just over 50 years later, in 1997, Deep Blue, a chess computer built by the International Business Machines Corporation (IBM), beat the reigning world champion, Garry Kasparov, in a six-game match. While Turing’s prediction came true, his expectation that chess programming would contribute to the understanding of how human beings think did not. The huge improvement in computer chess since Turing’s day is attributable to advances in computer engineering rather than advances in AI—Deep Blue’s 256 parallel processors enabled it to examine 200 million possible moves per second and to look ahead as many as 14 turns of play. Many agree with Noam Chomsky, a linguist at the Massachusetts Institute of Technology (MIT), who opined that a computer beating a grandmaster at chess is about as interesting as a bulldozer winning an Olympicweightlifting competition.
The Turing test
In 1950 Turing sidestepped the traditional debate concerning the definition of intelligence, introducing a practical test for computer intelligence that is now known simply as the Turing test. The Turing test involves three participants: a computer, a human interrogator, and a human foil. The interrogator attempts to determine, by asking questions of the other two participants, which is the computer. All communication is via keyboard and display screen. The interrogator may ask questions as penetrating and wide-ranging as he or she likes, and the computer is permitted to do everything possible to force a wrong identification. (For instance, the computer might answer, “No,” in response to, “Are you a computer?” and might follow a request to multiply one large number by another with a long pause and an incorrect answer.) The foil must help the interrogator to make a correct identification. A number of different people play the roles of interrogator and foil, and, if a sufficient proportion of the interrogators are unable to distinguish the computer from the human being, then (according to proponents of Turing’s test) the computer is considered an intelligent, thinking entity.
In 1991 the American philanthropist Hugh Loebner started the annual Loebner Prize competition, promising a $100,000 payout to the first computer to pass the Turing test and awarding $2,000 each year to the best effort. However, no AI program has come close to passing an undiluted Turing test.
Early milestones in AI
The first AI programs
The earliest successful AI program was written in 1951 by Christopher Strachey, later director of the Programming Research Group at the University of Oxford. Strachey’s checkers (draughts) program ran on the Ferranti Mark I computer at the University of Manchester, England. By the summer of 1952 this program could play a complete game of checkers at a reasonable speed.
Information about the earliest successful demonstration of machine learning was published in 1952. Shopper, written by Anthony Oettinger at the University of Cambridge, ran on the EDSAC computer. Shopper’s simulated world was a mall of eight shops. When instructed to purchase an item, Shopper would search for it, visiting shops at random until the item was found. While searching, Shopper would memorize a few of the items stocked in each shop visited (just as a human shopper might). The next time Shopper was sent out for the same item, or for some other item that it had already located, it would go to the right shop straight away. This simple form of learning, as is pointed out in the introductory section What is intelligence?, is called rote learning.
The first AI program to run in the United States also was a checkers program, written in 1952 by Arthur Samuel for the prototype of the IBM 701. Samuel took over the essentials of Strachey’s checkers program and over a period of years considerably extended it. In 1955 he added features that enabled the program to learn from experience. Samuel included mechanisms for both rote learning and generalization, enhancements that eventually led to his program’s winning one game against a former Connecticut checkers champion in 1962.
Evolutionary computing
Samuel’s checkers program was also notable for being one of the first efforts at evolutionary computing. (His program “evolved” by pitting a modified copy against the current best version of his program, with the winner becoming the new standard.) Evolutionary computing typically involves the use of some automatic method of generating and evaluating successive “generations” of a program, until a highly proficient solution evolves.
A leading proponent of evolutionary computing, John Holland, also wrote test software for the prototype of the IBM 701 computer. In particular, he helped design a neural-network “virtual” rat that could be trained to navigate through a maze. This work convinced Holland of the efficacy of the bottom-up approach. While continuing to consult for IBM, Holland moved to the University of Michigan in 1952 to pursue a doctorate in mathematics. He soon switched, however, to a new interdisciplinary program in computers and information processing (later known as communications science) created by Arthur Burks, one of the builders of ENIAC and its successor EDVAC. In his 1959 dissertation, for most likely the world’s first computer science Ph.D., Holland proposed a new type of computer—a multiprocessor computer—that would assign each artificial neuron in a network to a separate processor. (In 1985 Daniel Hillis solved the engineering difficulties to build the first such computer, the 65,536-processor Thinking Machines Corporation supercomputer.)
Holland joined the faculty at Michigan after graduation and over the next four decades directed much of the research into methods of automating evolutionary computing, a process now known by the term genetic algorithms. Systems implemented in Holland’s laboratory included a chess program, models of single-cell biological organisms, and a classifier system for controlling a simulated gas-pipeline network. Genetic algorithms are no longer restricted to “academic” demonstrations, however; in one important practical application, a genetic algorithm cooperates with a witness to a crime in order to generate a portrait of the criminal.
Logical reasoning and problem solving
The ability to reason logically is an important aspect of intelligence and has always been a major focus of AI research. An important landmark in this area was a theorem-proving program written in 1955–56 by Allen Newell and J. Clifford Shaw of the RAND Corporation and Herbert Simon of the Carnegie Mellon University. The Logic Theorist, as the program became known, was designed to prove theorems from Principia Mathematica (1910–13), a three-volume work by the British philosopher-mathematicians Alfred North Whitehead and Bertrand Russell. In one instance, a proof devised by the program was more elegant than the proof given in the books.
Newell, Simon, and Shaw went on to write a more powerful program, the General Problem Solver, or GPS. The first version of GPS ran in 1957, and work continued on the project for about a decade. GPS could solve an impressive variety of puzzles using a trial and error approach. However, one criticism of GPS, and similar programs that lack any learning capability, is that the program’s intelligence is entirely secondhand, coming from whatever information the programmer explicitly includes.
English dialogue
Two of the best-known early AI programs, Eliza and Parry, gave an eerie semblance of intelligent conversation. (Details of both were first published in 1966.) Eliza, written by Joseph Weizenbaum of MIT’s AI Laboratory, simulated a human therapist. Parry, written by Stanford University psychiatrist Kenneth Colby, simulated a human paranoiac. Psychiatrists who were asked to decide whether they were communicating with Parry or a human paranoiac were often unable to tell. Nevertheless, neither Parry nor Eliza could reasonably be described as intelligent. Parry’s contributions to the conversation were canned—constructed in advance by the programmer and stored away in the computer’s memory. Eliza, too, relied on canned sentences and simple programming tricks.
AI programming languages
In the course of their work on the Logic Theorist and GPS, Newell, Simon, and Shaw developed their Information Processing Language (IPL), a computer language tailored for AI programming. At the heart of IPL was a highly flexible data structure that they called a list. A list is simply an ordered sequence of items of data. Some or all of the items in a list may themselves be lists. This scheme leads to richly branching structures.
In 1960 John McCarthy combined elements of IPL with the lambda calculus (a formal mathematical-logical system) to produce the programming language LISP (List Processor), which remains the principal language for AI work in the United States. (The lambda calculus itself was invented in 1936 by the Princeton logician Alonzo Church while he was investigating the abstract Entscheidungsproblem, or “decision problem,” for predicate logic—the same problem that Turing had been attacking when he invented the universal Turing machine.)
The logic programming language PROLOG (Programmation en Logique) was conceived by Alain Colmerauer at the University of Aix-Marseille, France, where the language was first implemented in 1973. PROLOG was further developed by the logician Robert Kowalski, a member of the AI group at the University of Edinburgh. This language makes use of a powerful theorem-proving technique known as resolution, invented in 1963 at the U.S. Atomic Energy Commission’sArgonne National Laboratory in Illinois by the British logician Alan Robinson. PROLOG can determine whether or not a given statement follows logically from other given statements. For example, given the statements “All logicians are rational” and “Robinson is a logician,” a PROLOG program responds in the affirmative to the query “Robinson is rational?” PROLOG is widely used for AI work, especially in Europe and Japan.
Researchers at the Institute for New Generation Computer Technology in Tokyo have used PROLOG as the basis for sophisticated logic programming languages. Known as fifth-generation languages, these are in use on nonnumerical parallel computers developed at the Institute.
Other recent work includes the development of languages for reasoning about time-dependent data such as “the account was paid yesterday.” These languages are based on tense logic, which permits statements to be located in the flow of time. (Tense logic was invented in 1953 by the philosopher Arthur Prior at the University of Canterbury, Christchurch, New Zealand.)
Microworld programs
To cope with the bewildering complexity of the real world, scientists often ignore less relevant details; for instance, physicists often ignore friction and elasticity in their models. In 1970 Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that likewise AI research should focus on developing programs capable of intelligent behaviour in simpler artificial environments known as microworlds. Much research has focused on the so-called blocks world, which consists of coloured blocks of various shapes and sizes arrayed on a flat surface.
An early success of the microworld approach was SHRDLU, written by Terry Winograd of MIT. (Details of the program were published in 1972.) SHRDLU controlled a robot arm that operated above a flat surface strewn with play blocks. Both the arm and the blocks were virtual. SHRDLU would respond to commands typed in natural English, such as “Will you please stack up both of the red blocks and either a green cube or a pyramid.” The program could also answer questions about its own actions.Although SHRDLU was initially hailed as a major breakthrough, Winograd soon announced that the program was, in fact, a dead end. The techniques pioneered in the program proved unsuitable for application in wider, more interesting worlds. Moreover, the appearance that SHRDLU gave of understanding the blocks microworld, and English statements concerning it, was in fact an illusion. SHRDLU had no idea what a green block was.
Another product of the microworld approach was Shakey, a mobile robot developed at the Stanford Research Institute by Bertram Raphael, Nils Nilsson, and others during the period 1968–72. The robot occupied a specially built microworld consisting of walls, doorways, and a few simply shaped wooden blocks. Each wall had a carefully painted baseboard to enable the robot to “see” where the wall met the floor (a simplification of reality that is typical of the microworld approach). Shakey had about a dozen basic abilities, such as TURN, PUSH, and CLIMB-RAMP.
Critics pointed out the highly simplified nature of Shakey’s environment and emphasized that, despite these simplifications, Shakey operated excruciatingly slowly; a series of actions that a human could plan out and execute in minutes took Shakey days.
The greatest success of the microworld approach is a type of program known as an expert system, described in the next section.
Expert systems
Expert systems occupy a type of microworld—for example, a model of a ship’s hold and its cargo—that is self-contained and relatively uncomplicated. For such AI systems every effort is made to incorporate all the information about some narrow field that an expert (or group of experts) would know, so that a good expert system can often outperform any single human expert. There are many commercial expert systems, including programs for medical diagnosis, chemical analysis, credit authorization, financial management, corporate planning, financial document routing, oil and mineral prospecting, genetic engineering, automobile design and manufacture, camera lens design, computer installation design, airline scheduling, cargo placement, and automatic help services for home computer owners.
Knowledge and inference
The basic components of an expert system are a knowledge base, or KB, and an inference engine. The information to be stored in the KB is obtained by interviewing people who are expert in the area in question. The interviewer, or knowledge engineer, organizes the information elicited from the experts into a collection of rules, typically of an “if-then” structure. Rules of this type are called production rules. The inference engine enables the expert system to draw deductions from the rules in the KB. For example, if the KB contains the production rules “if x, then y” and “if y, then z,” the inference engine is able to deduce “if x, then z.” The expert system might then query its user, “Is x true in the situation that we are considering?” If the answer is affirmative, the system will proceed to infer z.
Some expert systems use fuzzy logic. In standard logic there are only two truth values, true and false. This absolute precision makes vague attributes or situations difficult to characterize. (When, precisely, does a thinning head of hair become a bald head?) Often the rules that human experts use contain vague expressions, and so it is useful for an expert system’s inference engine to employ fuzzy logic.
DENDRAL
In 1965 the AI researcher Edward Feigenbaum and the geneticist Joshua Lederberg, both of Stanford University, began work on Heuristic DENDRAL (later shortened to DENDRAL), a chemical-analysis expert system. The substance to be analyzed might, for example, be a complicated compound of carbon, hydrogen, and nitrogen. Starting from spectrographic data obtained from the substance, DENDRAL would hypothesize the substance’s molecular structure. DENDRAL’s performance rivaled that of chemists expert at this task, and the program was used in industry and in academia.
MYCIN
Work on MYCIN, an expert system for treating blood infections, began at Stanford University in 1972. MYCIN would attempt to diagnose patients based on reported symptoms and medical test results. The program could request further information concerning the patient, as well as suggest additional laboratory tests, to arrive at a probable diagnosis, after which it would recommend a course of treatment. If requested, MYCIN would explain the reasoning that led to its diagnosis and recommendation. Using about 500 production rules, MYCIN operated at roughly the same level of competence as human specialists in blood infections and rather better than general practitioners.
Nevertheless, expert systems have no common sense or understanding of the limits of their expertise. For instance, if MYCIN were told that a patient who had received a gunshot wound was bleeding to death, the program would attempt to diagnose a bacterial cause for the patient’s symptoms. Expert systems can also act on absurd clerical errors, such as prescribing an obviously incorrect dosage of a drug for a patient whose weight and age data were accidentally transposed.
The CYC project
CYC is a large experiment in symbolic AI. The project began in 1984 under the auspices of the Microelectronics and Computer Technology Corporation, a consortium of computer, semiconductor, and electronics manufacturers. In 1995 Douglas Lenat, the CYC project director, spun off the project as Cycorp, Inc., based in Austin, Texas. The most ambitious goal of Cycorp was to build a KB containing a significant percentage of the commonsense knowledge of a human being. Millions of commonsense assertions, or rules, were coded into CYC. The expectation was that this “critical mass” would allow the system itself to extract further rules directly from ordinary prose and eventually serve as the foundation for future generations of expert systems.
With only a fraction of its commonsense KB compiled, CYC could draw inferences that would defeat simpler systems. For example, CYC could infer, “Garcia is wet,” from the statement, “Garcia is finishing a marathon run,” by employing its rules that running a marathon entails high exertion, that people sweat at high levels of exertion, and that when something sweats it is wet. Among the outstanding remaining problems are issues in searching and problem solving—for example, how to search the KB automatically for information that is relevant to a given problem. AI researchers call the problem of updating, searching, and otherwise manipulating a large structure of symbols in realistic amounts of time the frame problem. Some critics of symbolic AI believe that the frame problem is largely unsolvable and so maintain that the symbolic approach will never yield genuinely intelligent systems. It is possible that CYC, for example, will succumb to the frame problem long before the system achieves human levels of knowledge.
Connectionism
Connectionism, or neuronlike computing, developed out of attempts to understand how the human brain works at the neural level and, in particular, how people learn and remember. In 1943 the neurophysiologist Warren McCulloch of the University of Illinois and the mathematician Walter Pitts of the University of Chicago published an influential treatise on neural nets and automatons, according to which each neuron in the brain is a simple digital processor and the brain as a whole is a form of computing machine. As McCulloch put it subsequently, “What we thought we were doing (and I think we succeeded fairly well) was treating the brain as a Turing machine.”
Creating an artificial neural network
It was not until 1954, however, that Belmont Farley and Wesley Clark of MIT succeeded in running the first artificial neural network—albeit limited by computer memory to no more than 128 neurons. They were able to train their networks to recognize simple patterns. In addition, they discovered that the random destruction of up to 10 percent of the neurons in a trained network did not affect the network’s performance—a feature that is reminiscent of the brain’s ability to tolerate limited damage inflicted by surgery, accident, or disease.
The simple neural network depicted in the figure illustrates the central ideas of connectionism. Four of the network’s five neurons are for input, and the fifth—to which each of the others is connected—is for output. Each of the neurons is either firing (1) or not firing (0). Each connection leading to N, the output neuron, has a “weight.” What is called the total weighted input into N is calculated by adding up the weights of all the connections leading to N from neurons that are firing. For example, suppose that only two of the input neurons, X and Y, are firing. Since the weight of the connection from X to N is 1.5 and the weight of the connection from Y to N is 2, it follows that the total weighted input to N is 3.5. As shown in the figure, N has a firing threshold of 4. That is to say, if N’s total weighted input equals or exceeds 4, then N fires; otherwise, N does not fire. So, for example, N does not fire if the only input neurons to fire are X and Y, but N does fire if X, Y, and Z all fire.
Training the network involves two steps. First, the external agent inputs a pattern and observes the behaviour of N. Second, the agent adjusts the connection weights in accordance with the rules:
- If the actual output is 0 and the desired output is 1, increase by a small fixed amount the weight of each connection leading to N from neurons that are firing (thus making it more likely that N will fire the next time the network is given the same pattern);
- If the actual output is 1 and the desired output is 0, decrease by that same small amount the weight of each connection leading to the output neuron from neurons that are firing (thus making it less likely that the output neuron will fire the next time the network is given that pattern as input).
The external agent—actually a computer program—goes through this two-step procedure with each pattern in a training sample, which is then repeated a number of times. During these many repetitions, a pattern of connection weights is forged that enables the network to respond correctly to each pattern. The striking thing is that the learning process is entirely mechanical and requires no human intervention or adjustment. The connection weights are increased or decreased automatically by a constant amount, and exactly the same learning procedure applies to different tasks.
Perceptrons
In 1957 Frank Rosenblatt of the Cornell Aeronautical Laboratory at Cornell University in Ithaca, New York, began investigating artificial neural networks that he called perceptrons. He made major contributions to the field of AI, both through experimental investigations of the properties of neural networks (using computer simulations) and through detailed mathematical analysis. Rosenblatt was a charismatic communicator, and there were soon many research groups in the United States studying perceptrons. Rosenblatt and his followers called their approach connectionist to emphasize the importance in learning of the creation and modification of connections between neurons. Modern researchers have adopted this term.
One of Rosenblatt’s contributions was to generalize the training procedure that Farley and Clark had applied to only two-layer networks so that the procedure could be applied to multilayer networks. Rosenblatt used the phrase “back-propagating error correction” to describe his method. The method, with substantial improvements and extensions by numerous scientists, and the term back-propagation are now in everyday use in connectionism.
Conjugating verbs
In one famous connectionist experiment conducted at the University of California at San Diego (published in 1986), David Rumelhart and James McClelland trained a network of 920 artificial neurons, arranged in two layers of 460 neurons, to form the past tenses of English verbs. Root forms of verbs—such as come, look, and sleep—were presented to one layer of neurons, the input layer. A supervisory computer program observed the difference between the actual response at the layer of output neurons and the desired response--came, say—and then mechanically adjusted the connections throughout the network in accordance with the procedure described above to give the network a slight push in the direction of the correct response. About 400 different verbs were presented one by one to the network, and the connections were adjusted after each presentation. This whole procedure was repeated about 200 times using the same verbs, after which the network could correctly form the past tense of many unfamiliar verbs as well as of the original verbs. For example, when presented for the first time with guard, the network responded guarded; with weep, wept; with cling, clung; and with drip, dripped (complete with double p). This is a striking example of learning involving generalization. (Sometimes, though, the peculiarities of English were too much for the network, and it formed squawked from squat, shipped from shape, and membled from mail.)
Another name for connectionism is parallel distributed processing, which emphasizes two important features. First, a large number of relatively simple processors—the neurons—operate in parallel. Second, neural networks store information in a distributed fashion, with each individual connection participating in the storage of many different items of information. The know-how that enabled the past-tense network to form wept from weep, for example, was not stored in one specific location in the network but was spread throughout the entire pattern of connection weights that was forged during training. The human brain also appears to store information in a distributed fashion, and connectionist research is contributing to attempts to understand how it does so.
Other neural networks
Other work on neuronlike computing includes the following:
- Visual perception. Networks can recognize faces and other objects from visual data. A neural network designed by John Hummel and Irving Biederman at the University of Minnesota can identify about 10 objects from simple line drawings. The network is able to recognize the objects—which include a mug and a frying pan—even when they are drawn from different angles. Networks investigated by Tomaso Poggio of MIT are able to recognize bent-wire shapes drawn from different angles, faces photographed from different angles and showing different expressions, and objects from cartoon drawings with gray-scale shading indicating depth and orientation.
- Language processing. Neural networks are able to convert handwritten and typewritten material to electronic text. The U.S. Internal Revenue Service has commissioned a neuronlike system that will automatically read tax returns and correspondence. Neural networks also convert speech to printed text and printed text to speech.
- Financial analysis. Neural networks are being used increasingly for loan risk assessment, real estate valuation, bankruptcy prediction, share price prediction, and other business applications.
- Medicine. Medical applications include detecting lung nodules and heartarrhythmias and predicting adverse drug reactions.
- Telecommunications. Telecommunications applications of neural networks include control of telephone switching networks and echo cancellation in modems and on satellite links.
Nouvelle AI
New foundations
The approach now known as nouvelle AI was pioneered at the MIT AI Laboratory by the Australian Rodney Brooks during the latter half of the 1980s. Nouvelle AI distances itself from strong AI, with its emphasis on human-level performance, in favour of the relatively modest aim of insect-level performance. At a very fundamental level, nouvelle AI rejects symbolic AI’s reliance upon constructing internal models of reality, such as those described in the section Microworld programs. Practitioners of nouvelle AI assert that true intelligence involves the ability to function in a real-world environment.
A central idea of nouvelle AI is that intelligence, as expressed by complex behaviour, “emerges” from the interaction of a few simple behaviours. For example, a robot whose simple behaviours include collision avoidance and motion toward a moving object will appear to stalk the object, pausing whenever it gets too close.
three stages of mobile robot development for the Mars Rover Research Project
One famous example of nouvelle AI is Brooks’s robot Herbert (named after Herbert Simon), whose environment is the busy offices of the MIT AI Laboratory. Herbert searches desks and tables for empty soda cans, which it picks up and carries away. The robot’s seemingly goal-directed behaviour emerges from the interaction of about 15 simple behaviours. More recently, Brooks has constructed prototypes of mobile robots for exploring the surface of Mars. (See the photographs and an interview with Rodney Brooks.)
Nouvelle AI sidesteps the frame problem discussed in the section The CYC project. Nouvelle systems do not contain a complicated symbolic model of their environment. Instead, information is left “out in the world” until such time as the system needs it. A nouvelle system refers continuously to its sensors rather than to an internal model of the world: it “reads off” the external world whatever information it needs at precisely the time it needs it. (As Brooks insisted, the world is its own best model—always exactly up-to-date and complete in every detail.)
The situated approach
Traditional AI has by and large attempted to build disembodied intelligences whose only interaction with the world has been indirect (CYC, for example). Nouvelle AI, on the other hand, attempts to build embodied intelligences situated in the real world—a method that has come to be known as the situated approach. Brooks quoted approvingly from the brief sketches that Turing gave in 1948 and 1950 of the situated approach. By equipping a machine “with the best sense organs that money can buy,” Turing wrote, the machine might be taught “to understand and speak English” by a process that would “follow the normal teaching of a child.” Turing contrasted this with the approach to AI that focuses on abstract activities, such as the playing of chess. He advocated that both approaches be pursued, but until recently little attention has been paid to the situated approach.
The situated approach was also anticipated in the writings of the philosopher Bert Dreyfus of the University of California at Berkeley. Beginning in the early 1960s, Dreyfus opposed the physical symbol system hypothesis, arguing that intelligent behaviour cannot be completely captured by symbolic descriptions. As an alternative, Dreyfus advocated a view of intelligence that stressed the need for a body that could move about, interacting directly with tangible physical objects. Once reviled by advocates of AI, Dreyfus is now regarded as a prophet of the situated approach.
Critics of nouvelle AI point out the failure to produce a system exhibiting anything like the complexity of behaviour found in real insects. Suggestions by researchers that their nouvelle systems may soon be conscious and possess language seem entirely premature.
Is strong AI possible?
The ongoing success of applied AI and of cognitive simulation, as described in the preceding sections of this article, seems assured. However, strong AI—that is, artificial intelligence that aims to duplicate human intellectual abilities—remains controversial. Exaggerated claims of success, in professional journals as well as the popular press, have damaged its reputation. At the present time even an embodied system displaying the overall intelligence of a cockroach is proving elusive, let alone a system that can rival a human being. The difficulty of scaling up AI’s modest achievements cannot be overstated. Five decades of research in symbolic AI have failed to produce any firm evidence that a symbol system can manifest human levels of general intelligence; connectionists are unable to model the nervous systems of even the simplest invertebrates; and critics of nouvelle AI regard as simply mystical the view that high-level behaviours involving language understanding, planning, and reasoning will somehow emerge from the interaction of basic behaviours such as obstacle avoidance, gaze control, and object manipulation.
However, this lack of substantial progress may simply be testimony to the difficulty of strong AI, not to its impossibility. Let us turn to the very idea of strong artificial intelligence. Can a computer possibly think? Noam Chomsky suggests that debating this question is pointless, for it is an essentially arbitrary decision whether to extend common usage of the word think to include machines. There is, Chomsky claims, no factual question as to whether any such decision is right or wrong—just as there is no question as to whether our decision to say that airplanes fly is right, or our decision not to say that ships swim is wrong. However, this seems to oversimplify matters. The important question is, Could it ever be appropriate to say that computers think, and, if so, what conditions must a computer satisfy in order to be so described?
Some authors offer the Turing test as a definition of intelligence. However, Turing himself pointed out that a computer that ought to be described as intelligent might nevertheless fail his test if it were incapable of successfully imitating a human being. For example, why should an intelligent robot designed to oversee mining on the Moon necessarily be able to pass itself off in conversation as a human being? If an intelligent entity can fail the test, then the test cannot function as a definition of intelligence. It is even questionable whether passing the test would actually show that a computer is intelligent, as the information theorist Claude Shannon and the AI pioneer John McCarthy pointed out in 1956. Shannon and McCarthy argued that it is possible, in principle, to design a machine containing a complete set of canned responses to all the questions that an interrogator could possibly ask during the fixed time span of the test. Like Parry, this machine would produce answers to the interviewer’s questions by looking up appropriate responses in a giant table. This objection seems to show that in principle a system with no intelligence at all could pass the Turing test.
In fact, AI has no real definition of intelligence to offer, not even in the subhuman case. Rats are intelligent, but what exactly must an artificial intelligence achieve before researchers can claim this level of success? In the absence of a reasonably precise criterion for when an artificial system counts as intelligent, there is no objective way of telling whether an AI research program has succeeded or failed. One result of AI’s failure to produce a satisfactory criterion of intelligence is that, whenever researchers achieve one of AI’s goals—for example, a program that can summarize newspaper articles or beat the world chess champion—critics are able to say “That’s not intelligence!” Marvin Minsky’s response to the problem of defining intelligence is to maintain—like Turing before him—that intelligence is simply our name for any problem-solving mental process that we do not yet understand. Minsky likens intelligence to the concept “unexplored regions of Africa”: it disappears as soon as we discover it.
B.J. Copeland Artificial-intelligence/Is-strong-AI-possible
DEEP LEARNING VS. MACHINE LEARNING
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.
Deep learning is actually comprised of neural networks. “Deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. This is generally represented using the following diagram:
The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.
"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.
Artificial intelligence and IBM Cloud
IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:
- Collect: Simplifying data collection and accessibility.
- Organize: Creating a business-ready analytics foundation.
- Analyze: Building scalable and trustworthy AI-driven systems.
- Infuse: Integrating and optimizing systems across an entire business framework.
- Modernize: Bringing your AI applications and systems to the cloud.
IBM Watson gives enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore the IBM portfolio of managed services and solutions
What-is-artificial-intelligence
FAQs
1. Where is AI used?
Artificial intelligence is frequently utilized to present individuals with personalized suggestions based on their prior searches and purchases and other online behavior. AI is extremely crucial in commerce, such as product optimization, inventory planning, and logistics. Machine learning, cybersecurity, customer relationship management, internet searches, and personal assistants are some of the most common applications of AI. Voice assistants, picture recognition for face unlocking in cellphones, and ML-based financial fraud detection are all examples of AI software that is now in use.
2. What are the social benefits of AI?
Artificial intelligence has the potential to significantly increase workplace efficiency and supplement the job that people can undertake. When AI takes over monotonous or risky duties, it frees up the human workforce to focus on tasks that need creativity and empathy, among other things. AI reduces the time required to complete a task. AI allows for the performance of previously complicated activities at a low cost. AI functions continuously and without interruption, with no downtime. AI improves the capacities of people with disabilities.
3. What Are the 4 Types of AI?
The current categorization system categorizes AI into four basic categories: reactive, theory of mind, limited memory, and self-aware.
4. How Is AI Used Today?
Machines today can learn from experience, adapt to new inputs, and even perform human-like tasks with help from artificial intelligence (AI). Artificial intelligence examples today, from chess-playing computers to self-driving cars, are heavily based on deep learning and natural language processing. There are several examples of AI software in use in daily life, including voice assistants, face recognition for unlocking mobile phones and machine learning-based financial fraud detection. AI software is typically obtained by downloading AI-capable software from an internet marketplace, with no additional hardware required.
5. How Is AI Used in Healthcare?
NLP tools that can comprehend and categorize clinical documents are frequent use by artificial intelligence in healthcare. NLP systems can evaluate unstructured clinical notes on patients, providing remarkable insight into quality understanding, improved methodologies, and better patient outcomes.
6. How is AI helping in our life?
AI and ML-powered software and gadgets mimic human brain processes to assist society in advancing with the digital revolution. AI systems perceive their environment, deal with what they observe, resolve difficulties, and take action to help with duties to make daily living easier. People check their social media accounts on a frequent basis, including Facebook, Twitter, Instagram, and other sites. AI is not only customizing your feeds behind the scenes, but it is also recognizing and deleting bogus news. So, AI is assisting you in your daily life.
7. Why is AI needed?
As a result of artificial intelligence technology, the software is capable of performing human functions, such as planning, reasoning, communication, and perception, more effectively, efficiently, and at a lower cost. Artificial intelligence speeds up, improves precision, and increases the efficacy of human endeavors. To predict fraudulent transactions, implement rapid and accurate credit scoring, and automate labor-intensive tasks in data administration, financial institutions can use artificial intelligence approaches.
8. What is artificial intelligence with examples?
Artificial intelligence involves replicating human intellectual processes through machines, especially computers. There are many applications of AI, such as expert systems, natural language processing, speech recognition, and machine vision.
9. Is AI dangerous?
Aside from planning for a future with super-intelligent computers, artificial intelligence in its current state might already offer problems.
10. What are the advantages of AI?
The advantages of AI include reducing the time it takes to complete a task, reducing the cost of previously done activities, continuously and without interruption, with no downtime, and improving the capacities of people with disabilities.
11. Is artificial intelligence the future?
Artificial intelligence plays a significant role in virtually every field of human endeavor. It is already the primary driver of developing technologies such as big data, robots, and the Internet of Things, and it will continue to be a technical pioneer in the foreseeable future.
References
Videos
- Max Tegmark: How to get empowered, not overpowered, by AI
- Stuart Russell: 3 principles for creating safer AI
- Sam Harris: Can we build AI without losing control over it?
- Talks from the Beneficial AI 2017 conference in Asilomar, CA
- Stuart Russell – The Long-Term Future of (Artificial) Intelligence
- Humans Need Not Apply
- Nick Bostrom: What happens when computers get smarter than we are?
- Value Alignment – Stuart Russell: Berkeley IdeasLab Debate Presentation at the World Economic Forum
- Social Technology and AI: World Economic Forum Annual Meeting 2015
- Stuart Russell, Eric Horvitz, Max Tegmark – The Future of Artificial Intelligence
- Jaan Tallinn on Steering Artificial Intelligence
Media Articles
- Concerns of an Artificial Intelligence Pioneer
- Transcending Complacency on Superintelligent Machines
- Why We Should Think About the Threat of Artificial Intelligence
- Stephen Hawking Is Worried About Artificial Intelligence Wiping Out Humanity
- Artificial Intelligence could kill us all. Meet the man who takes that risk seriously
- Artificial Intelligence Poses ‘Extinction Risk’ To Humanity Says Oxford University’s Stuart Armstrong
- What Happens When Artificial Intelligence Turns On Us?
- Can we build an artificial superintelligence that won’t kill us?
- Artificial intelligence: Our final invention?
- Artificial intelligence: Can we keep it in the box?
- Science Friday: Christof Koch and Stuart Russell on Machine Intelligence (transcript)
- Transcendence: An AI Researcher Enjoys Watching His Own Execution
- Science Goes to the Movies: ‘Transcendence’
- Our Fear of Artificial Intelligence
Essays by AI Researchers
- Stuart Russell: What do you Think About Machines that Think?
- Stuart Russell: Of Myths and Moonshine
- Jacob Steinhardt: Long-Term and Short-Term Challenges to Ensuring the Safety of AI Systems
- Eliezer Yudkowsky: Why value-aligned AI is a hard engineering problem
- Eliezer Yudkowsky: There’s No Fire Alarm for Artificial General Intelligence
- Open Letter: Research Priorities for Robust and Beneficial Artificial Intelligence
Research Articles
- Intelligence Explosion: Evidence and Import (MIRI)
- Intelligence Explosion and Machine Ethics (Luke Muehlhauser, MIRI)
- Artificial Intelligence as a Positive and Negative Factor in Global Risk (MIRI)
- Basic AI drives
- Racing to the Precipice: a Model of Artificial Intelligence Development
- The Ethics of Artificial Intelligence
- The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents
- Wireheading in mortal universal agents
- AGI Safety Literature Review
Research Collections
- Bruce Schneier – Resources on Existential Risk, p. 110
- Aligning Superintelligence with Human Interests: A Technical Research Agenda (MIRI)
- MIRI publications
- Stanford One Hundred Year Study on Artificial Intelligence (AI100)
- Preparing for the Future of Intelligence: White House report that discusses the current state of AI and future applications, as well as recommendations for the government’s role in supporting AI development.
- Artificial Intelligence, Automation, and the Economy: White House report that discusses AI’s potential impact on jobs and the economy, and strategies for increasing the benefits of this transition.
- IEEE Special Report: Artificial Intelligence: Report that explains deep learning, in which neural networks teach themselves and make decisions on their own.
Case Studies
- The Asilomar Conference: A Case Study in Risk Mitigation (Katja Grace, MIRI)
- Pre-Competitive Collaboration in Pharma Industry (Eric Gastfriend and Bryan Lee, FLI): A case study of pre-competitive collaboration on safety in industry.
Blog posts and talks
- AI control
- AI Impacts
- No time like the present for AI safety work
- AI Risk and Opportunity: A Strategic Analysis
- Where We’re At – Progress of AI and Related Technologies: An introduction to the progress of research institutions developing new AI technologies.
- AI safety
- Wait But Why on Artificial Intelligence
- Response to Wait But Why by Luke Muehlhauser
- Slate Star Codex on why AI-risk research is not that controversial
- Less Wrong: A toy model of the AI control problem
- What Should the Average EA Do About AI Alignment?
- Waking Up Podcast #116 – AI: Racing Toward the Brink with Eliezer Yudkowsky
Books
- Superintelligence: Paths, Dangers, Strategies
- Life 3.0: Being Human in the Age of Artificial Intelligence
- Our Final Invention: Artificial Intelligence and the End of the Human Era
- Facing the Intelligence Explosion
- E-book about the AI risk (including a “Terminator” scenario that’s more plausible than the movie version)
Organizations
- Machine Intelligence Research Institute: A non-profit organization whose mission is to ensure that the creation of smarter-than-human intelligence has a positive impact.
- Centre for the Study of Existential Risk (CSER): A multidisciplinary research center dedicated to the study and mitigation of risks that could lead to human extinction.
- Future of Humanity Institute: A multidisciplinary research institute bringing the tools of mathematics, philosophy, and science to bear on big-picture questions about humanity and its prospects.
- Partnership on AI: Established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.
- Global Catastrophic Risk Institute: A think tank leading research, education, and professional networking on global catastrophic risk.
- Organizations Focusing on Existential Risks: A brief introduction to some of the organizations working on existential risks.
- 80,000 Hours: A career guide for AI safety researchers.
Many of the organizations listed on this page and their descriptions are from a list compiled by the Global Catastrophic Risk institute; we are most grateful for the efforts that they have put into compiling it. These organizations above all work on computer technology issues, though many cover other topics as well. This list is undoubtedly incomplete.
RELATED LINKS
- A.I. Rising
- AI control problem
- Artificial intelligence arms race
- Behavior selection algorithm
- Business process automation
- Case-based reasoning
- Emergent algorithm
- Female gendering of AI technologies
- Glossary of artificial intelligence
- Robotic process automation
- Synthetic intelligence
- Universal basic income
- Weak AI
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