AI News, The People Who Would Teach Machines toLearn
The People Who Would Teach Machines toLearn
In this little riposte, I’ll present my case that the machine learning research community is doing work that is just as high-minded as Hofstadter’s when viewed from the proper angle.
All I contend here is that his approach to the problem he’s trying to solve isn’t the only one, and there are reasons to believe the machine learning approach is also valid.
in artificial intelligence, trying to replicate mind-level cognition in computers, and have his work largely ignored by the rest of the AI community.
And machine learning has turned raw data into useful systems for speech recognition, autonomous vehicle navigation, medical diagnosis, and on and on.
We have used techniques inspired by cognition to craft some nice solutions, but we’ve fallen short of the original goal: To make a system that thinks.
So Dr. Hofstadter, unimpressed, continues to work on the big questions in relative solitude, while the rest of AI research, lured by the siren’s song of easy success, attacks lesser problems.
It’s a nice, clean story: The eccentric but brilliant loner versus the successful but small-minded establishment, and I suppose if you’re reading a magazine you’d prefer such a story to a messy reality.
A system (evolution) did its best to produce a solution to the following optimization problem: Out of this (this!) came a solution that, as a side effect, produced things like Brahms’
With this in mind, it doesn’t seem unreasonable to think that a really marvelous solution to problems as general and useful as those in machine learning might lead to a system that exhibits some awfully interesting ancillary behaviors, one of which might even be something like creativity.
The question is whether the set of problems that machine learning researchers tackle are interesting enough to inspire solutions with mind-level complexity.
After much algorithmic toying and feature engineering, we reached a point where I said, “Well, it works, but this is totally unrelated to the way people do it”.
There’s a fundamental problem in trying to model human cognition by observing cognitive processes, which is that the observations themselves are results of the process.
Isn’t it at least possible that the inner workings of such an algorithm bears some relationship to human cognition, regardless of whether that relationship is easily observed?
Hofstadter, while working on the big questions, generally works on them in toy domains, like word games, trying to make his machines solve them like humans might.
After all, the systems we’ve built so far can translate between languages, recognize your speech, face, and handwriting, pronounce words it has never seen, and do it all by learning from examples.
If that sounds like a cop-out, like an excuse not to look for big answers, consider the path followed by children’s cancer research: Science has yet to find a single wonder drug or treatment that cures all childhood cancers, yet decades of piecemeal research on the parts of the problem has driven cure rates from about 10% to nearly 90%.
The Dark Secret at the Heart of AI
Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey.
The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence.
Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems.
The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation.
There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.
But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users.
But banks, the military, employers, and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable.
“Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.” There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a fundamental legal right.
The resulting program, which the researchers named Deep Patient, was trained using data from about 700,000 individuals, and when tested on new records, it proved incredibly good at predicting disease.
Without any expert instruction, Deep Patient had discovered patterns hidden in the hospital data that seemed to indicate when people were on the way to a wide range of ailments, including cancer of the liver.
If something like Deep Patient is actually going to help doctors, it will ideally give them the rationale for its prediction, to reassure them that it is accurate and to justify, say, a change in the drugs someone is being prescribed.
Many thought it made the most sense to build machines that reasoned according to rules and logic, making their inner workings transparent to anyone who cared to examine some code.
But it was not until the start of this decade, after several clever tweaks and refinements, that very large—or “deep”—neural networks demonstrated dramatic improvements in automated perception.
It has given computers extraordinary powers, like the ability to recognize spoken words almost as well as a person could, a skill too complex to code into the machine by hand.
The same approach can be applied, roughly speaking, to other inputs that lead a machine to teach itself: the sounds that make up words in speech, the letters and words that create sentences in text, or the steering-wheel movements required for driving.
The resulting images, produced by a project known as Deep Dream, showed grotesque, alien-like animals emerging from clouds and plants, and hallucinatory pagodas blooming across forests and mountain ranges.
In 2015, Clune’s group showed how certain images could fool such a network into perceiving things that aren’t there, because the images exploit the low-level patterns the system searches for.
The images that turn up are abstract (imagine an impressionistic take on a flamingo or a school bus), highlighting the mysterious nature of the machine’s perceptual abilities.
It is the interplay of calculations inside a deep neural network that is crucial to higher-level pattern recognition and complex decision-making, but those calculations are a quagmire of mathematical functions and variables.
“But once it becomes very large, and it has thousands of units per layer and maybe hundreds of layers, then it becomes quite un-understandable.” In the office next to Jaakkola is Regina Barzilay, an MIT professor who is determined to apply machine learning to medicine.
The diagnosis was shocking in itself, but Barzilay was also dismayed that cutting-edge statistical and machine-learning methods were not being used to help with oncological research or to guide patient treatment.
She envisions using more of the raw data that she says is currently underutilized: “imaging data, pathology data, all this information.” How well can we get along with machines that are
After she finished cancer treatment last year, Barzilay and her students began working with doctors at Massachusetts General Hospital to develop a system capable of mining pathology reports to identify patients with specific clinical characteristics that researchers might want to study.
Barzilay and her students are also developing a deep-learning algorithm capable of finding early signs of breast cancer in mammogram images, and they aim to give this system some ability to explain its reasoning, too.
The U.S. military is pouring billions into projects that will use machine learning to pilot vehicles and aircraft, identify targets, and help analysts sift through huge piles of intelligence data.
A silver-haired veteran of the agency who previously oversaw the DARPA project that eventually led to the creation of Siri, Gunning says automation is creeping into countless areas of the military.
But soldiers probably won’t feel comfortable in a robotic tank that doesn’t explain itself to them, and analysts will be reluctant to act on information without some reasoning.
A chapter of Dennett’s latest book, From Bacteria to Bach and Back, an encyclopedic treatise on consciousness, suggests that a natural part of the evolution of intelligence itself is the creation of systems capable of performing tasks their creators do not know how to do.
But since there may be no perfect answer, we should be as cautious of AI explanations as we are of each other’s—no matter how clever a machine seems.“If it can’t do better than us at explaining what it’s doing,” he says, “then don’t trustit.”
Human-Centered Machine Learning
That’s fine for pure exploration or seeing what a technology can do, and often inspires new product thinking.
This is all the ethnography, contextual inquiries, interviews, deep hanging out, surveys, reading customer support tickets, logs analysis, and getting proximate to people to figure out if you’re solving a problem or addressing an unstated need people have.
Here are three example exercises we have teams walk through and answer about the use cases they are trying to address with ML: Spending just a few minutes answering each of these questions reveals the automatic assumptions people will bring to an ML-powered product.
After these exercises and some additional sketching and storyboarding of specific products and features, we then plot out all of the team’s product ideas in a handy 2x2: This allows us to separate impactful ideas from less impactful ones as well as see which ideas depend on ML vs.
If the whole value of your product is that it uses unique user data to tailor an experience to her, you can’t just prototype that up real quick and have it feel anywhere near authentic.
Also, if you wait to have a fully built ML system in place to test the design, it will likely be too late to change it in any meaningful way after testing.
Having a teammate imitate an ML system’s actions like chat responses, suggesting people the participant should call, or movies suggestions can simulate interacting with an “intelligent” system.
These interactions are essential to guiding the design because when participants can earnestly engage with what they perceive to be an AI, they will naturally tend to form a mental model of the system and adjust their behavior according to those models.
It doesn’t understand that people using the system may be much more offended being accidentally labeled a troll compared to trolls accidentally being labeled as people.
That is, you need to decide if it is more important to include all of the right answers even if it means letting in more wrong ones (optimizing for recall), or minimizing the number of wrong answers at the cost of leaving out some of the right ones (optimizing for precision).
The Facebook ads machine learning team has developed a series of videos to help engineers and new researchers learn to apply their machine learning skills to real-world problems. The Facebook Field Guide to Machine Learning series breaks down the machine learning process into six steps: 1.
How the right set up is often more important than the choice of algorithm, and why a few hours spent at this stage in the process can save many weeks work further downstream, preventing you from solving the wrong problem.
MIT's automated machine learning works 100x faster than human data scientists
A new automated machine learning system can analyze data and come up with a solution 100x faster than humans, according to a new paper from MIT and Michigan State University.
The issue is, there are hundreds of techniques to choose from, including neural networks and support vector machines, and choosing the best one could potentially mean the difference between millions of dollars in ad revenue or none, or catching a flaw in a medical device or not.
SEE: Research: Companies lack skills to implement and support AI and machine learning (Tech Pro Research) ATM uses cloud-based, on-demand computing to perform a high-throughput search, and find the best possible modeling technique for a given problem, according to an article from MIT News.
ATM also worked much more quickly than the humans could: It took human open-ml users an average of 200 days to deliver a solution, while it took ATM less than a day to create a better-performing model.
'If a data scientist chose support vector machines as a modeling technique, the question of whether she should have chosen a neural network to get better accuracy instead is always lingering in her mind.'
Special report: How to implement AI and machine learning (free PDF) (TechRepublic) The great data science hope: Machine learning can cure your terrible data hygiene (ZDNet) Machine learning: The smart person's guide (TechRepublic) How to build a data science team (ZDNet) 5
In computer science AI research is defined as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term 'artificial intelligence' is applied when a machine mimics 'cognitive' functions that humans associate with other human minds, such as 'learning' and 'problem solving'. The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring 'intelligence' are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, 'AI is whatever hasn't been done yet.' For instance, optical character recognition is frequently excluded from 'artificial intelligence', having become a routine technology. Capabilities generally classified as AI as of 2017[update] include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations.
'robotics' or 'machine learning'), the use of particular tools ('logic' or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers). The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI.
The field was founded on the claim that human intelligence 'can be so precisely described that a machine can be made to simulate it'. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding;
Turing proposed that 'if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent'. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete 'artificial neurons'. The field of AI research was born at a workshop at Dartmouth College in 1956. Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as 'astonishing': computers were learning checkers strategies (c.
At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began. In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997. In 2011, a Jeopardy!
data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One use algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No.
Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011. He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. In a 2017 survey, one in five companies reported they had 'incorporated AI in some offerings or processes'. A
The traits described below have received the most attention. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a 'combinatorial explosion': they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model.
Such formal knowledge representations can be used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining 'interesting' and actionable inferences from large databases), and other areas. Among the most difficult problems in knowledge representation are: Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or 'value') of available choices. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty.
a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its 'object model' to assess that fifty-meter pedestrians do not exist. AI is heavily used in robotics. Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage. A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment;
the paradox is named after Hans Moravec, who stated in 1988 that 'it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility'. This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years. Moravec's paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject. In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent.
Nowadays, the vast majority of current AI researchers work instead on tractable 'narrow AI' applications (such as medical diagnosis or automobile navigation). Many researchers predict that such 'narrow AI' work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance.
One high-profile example is that DeepMind in the 2010s developed a 'generalized artificial intelligence' that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to 'slurp up' a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, 'Master Algorithm' could lead to AGI. Finally, a few 'emergent' approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges. Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do.
This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s. Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming. Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior.
Roger Schank described their 'anti-logic' approaches as 'scruffy' (as opposed to the 'neat' paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of 'scruffy' AI, since they must be built by hand, one complicated concept at a time. When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This 'knowledge revolution' led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.
Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.). Interest in neural networks and 'connectionism' was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.
For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.
AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics. Bayesian networks are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm),[f] planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive.
Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design. The simplest AI applications can be divided into two types: classifiers ('if shiny then diamond') and controllers ('if shiny then pick up').
The decision tree is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network, k-nearest neighbor algorithm,[g] kernel methods such as the support vector machine (SVM),[h] Gaussian mixture model, the extremely popular naive Bayes classifier[i] and improved version of decision tree - decision stream. Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, the dimensionality, and the level of noise.
Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ('fire together, wire together'), GMDH or competitive learning. Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa, and was introduced to neural networks by Paul Werbos. Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. In short, most neural networks use some form of gradient descent on a hand-created neural topology.
Many deep learning systems need to be able to learn chains ten or more causal links in length. Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others. According to one overview, the expression 'Deep Learning' was introduced to the Machine Learning community by Rina Dechter in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V.
Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture.
In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US. Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions. CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's 'AlphaGo Lee', the program that beat a top Go champion in 2016. Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) which are in theory Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.
thus, an RNN is an example of deep learning. RNNs can be trained by gradient descent but suffer from the vanishing gradient problem. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems. Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter &
There is no consensus on how to characterize which tasks AI tends to excel at. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a 'highly imperfect rule of thumb', that 'almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.' Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well. Games provide a well-publicized benchmark for assessing rates of progress.
this phenomenon is described as the AI effect. High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, prediction of judicial decisions and targeting online advertisements. With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. Artificial intelligence is breaking into the healthcare industry by assisting doctors.
Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot.
However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions. Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger.
AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies. The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing.
This concern has recently gained attention after mentions by celebrities including the late Stephen Hawking, Bill Gates, and Elon Musk. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1billion to OpenAI a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. In his book Superintelligence, Nick Bostrom provides an argument that artificial intelligence will pose a threat to mankind.
for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at 'high risk' of potential automation, while an OECD report classifies only 9% of U.S. jobs as 'high risk'. Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Author Martin Ford and others go further and argue that a large number of jobs are routine, repetitive and (to an AI) predictable;
This issue was addressed by Wendell Wallach in his book titled Moral Machines in which he introduced the concept of artificial moral agents (AMA). For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as 'Does Humanity Want Computers Making Moral Decisions' and 'Can (Ro)bots Really Be Moral'. For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs. The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines.
The philosophical position that John Searle has named 'strong AI' states: 'The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.' Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the 'mind' might be. Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel?
Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045. You awake one morning to find your brain has another lobe functioning.
- On Sunday, January 20, 2019
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