AI News, Cyberbotics' Robot Curriculum/What is Artificial Intelligence?
- On Thursday, October 4, 2018
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Cyberbotics' Robot Curriculum/What is Artificial Intelligence?
Artificial Intelligence (AI) is an interdisciplinary field of study that includes computer science, engineering, philosophy and psychology.
Early in the 17th century, René Descartes envisioned the bodies of animals as complex but reducible machines, thus formulating the mechanistic theory, also known as the 'clockwork paradigm'.
Wilhelm Schickard created the first mechanical digital calculating machine in 1623, followed by machines of Blaise Pascal (1643) and Gottfried Wilhelm von Leibniz (1671), who also invented the binary system.
In 1931 Kurt Gödel showed that sufficiently powerful consistent formal systems contain true theorems unprovable by any theorem-proving AI that is systematically deriving all possible theorems from the axioms.
During the 1960s and 1970s, Joel Moses demonstrated the power of symbolic reasoning for integration problems in the Macsyma program, the first successful knowledge-based program in mathematics.
Leonard Uhr and Charles Vossler published 'A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators' in 1963, which described one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of simple perceptrons of Rosenblatt.
Ted Shortliffe demonstrated the power of rule-based systems for knowledge representation and inference in medical diagnosis and therapy in what is sometimes called the first expert system.
In 1995, one of Ernst Dickmanns' robot cars drove more than 1000 miles in traffic at up to 110 mph, tracking and passing other cars (simultaneously Dean Pomerleau of Carnegie Mellon tested a semi-autonomous car with human-controlled throttle and brakes).
Hence, he will interact with the machine, for example by chatting using the keyboard and the screen to try to understand whether or not there is a human intelligence behind this machine writing the answers to his questions.
Hence he will want to ask very complicated questions and see what the machine answers and try to determine if the answers are generated by an AI program or if they come from a real human being.
Although the original Turing test is often described as a computer chat session (see picture), the interaction between the observer and the machine may take very various forms, including a chess game, playing a virtual reality video game, interacting with a mobile robot, etc.
Unlike adults who will generally say that the robots were programmed in some way to perform this behavior, possibly mentioning the sensors, actuators and micro-processor of the robot, the children will describe the behavior of the robots using the same words they would use to describe the behavior of a cat running after a mouse.
They will grant feelings to the robots like ”he is afraid of”, ”he is angry”, ”he is excited”, ”he is quiet”, ”he wants to...”, etc.
For example if a benchmark consists in playing chess against the Deep Blue program, some observers may think that this requires some intelligence and hence it is a cognitive benchmark, whereas some other observers may object that it doesn't require intelligence and hence it is not a cognitive benchmark.
They include IQ tests developed by psychologists as well as animal intelligence tests developed by biologists to evaluate for example how well rats remember the path to a food source in a maze, or how do monkeys learn to press a lever to get food.
The last chapter of this book will introduce you to a series of robotics cognitive benchmarks (especially the Rat's Life benchmark) for which you will be able to design your own intelligent systems and compare them to others.
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
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.'
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.
Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.
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.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding;
and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate any conceivable act of mathematical deduction.
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.
According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a 'sporadic usage' in 2012 to more than 2,700 projects.
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.
An AI's intended goal function can be simple ('1 if the AI wins a game of Go, 0 otherwise') or complex ('Do actions mathematically similar to the actions that got you rewards in the past').
this is similar to how animals evolved to innately desire certain goals such as finding food, or how dogs can be bred via artificial selection to possess desired traits.
Some of the 'learners' described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any function, including whatever combination of mathematical functions would best describe the entire world.
In practice, it is almost never possible to consider every possibility, because of the phenomenon of 'combinatorial explosion', where the amount of time needed to solve a problem grows exponentially.
The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: 'After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza'.
A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial 'neurons' that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to 'reinforce' connections that seemed to be useful.
Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.
Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.
A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.
instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects.
Humans also have a powerful mechanism of 'folk psychology' that helps them to interpret natural-language sentences such as 'The city councilmen refused the demonstrators a permit because they advocated violence'.
For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.
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 addition, some projects attempt to gather the 'commonsense knowledge' known to the average person into a database containing extensive knowledge about the world.
by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern).
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.
A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts.
Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well.
Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications.
is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world.
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.
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.
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'.
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.
Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.
These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI.
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.
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.
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.
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.
For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence).
A problem like machine translation is considered 'AI-complete', because all of these problems need to be solved simultaneously in order to reach human-level machine performance.
When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation.
in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science.
Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems.
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.
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.
When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.
By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.
This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
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.).
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.
Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results.
However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures.
The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d]
Compared with GOFAI, new 'statistical learning' techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets.
The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models;
In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.
These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top.
Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).
Fuzzy set theory assigns a 'degree of truth' (between 0 and 1) to vague statements such as 'Alice is old' (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false.
Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as 'if you are close to the destination station and moving fast, increase the train's brake pressure';
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).
Complicated graphs with diamonds or other 'loops' (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities.
Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as 'naive Bayes' on most practical data sets.
A simple 'neuron' N accepts input from multiple other neurons, each of which, when activated (or 'fired'), cast a weighted 'vote' for or against whether neuron N should itself activate.
one simple algorithm (dubbed 'fire together, wire together') is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another.
In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending;
The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events).
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.
However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches.
For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a 'credit assignment path' (CAP) depth of seven.
Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.
In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.
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.
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.
The main areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.
The 'imitation game' (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.
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
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,
In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.
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.
The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.
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.
Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.
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.
Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.
report by the Guardian newspaper in the UK in 2018 found that online gambling companies were using AI to predict the behavior of customers in order to target them with personalized promotions.
Developers of commercial AI platforms are also beginning to appeal more directly to casino operators, offering a range of existing and potential services to help them boost their profits and expand their customer base.
He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down.
If this AI's goals do not reflect humanity's – one example is an AI told to compute as many digits of pi as possible – it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.
For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.
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.
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.
In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines.
Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence.
Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).
I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence.'
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.'
Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization.
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.
Invisible, this auxiliary lobe answers your questions with information beyond the realm of your own memory, suggests plausible courses of action, and asks questions that help bring out relevant facts.
In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later 'the Gynoids' book followed that was used by or influenced movie makers including George Lucas and other creatives.
Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.
Max Tegmark: ‘Machines taking control doesn’t have to be a bad thing’
Once we understood how muscles worked we built much better muscles in the form of machines, and maybe when we understand how our brains work we’ll build much better brains and become utterly obsolete.” Tegmark’s melancholy insight was not some idle hypothesis, but instead an intellectual challenge to himself at the dawn of the age of artificial intelligence.
We're in a situation where something truly dramatic might happen within decades – that’s a good time to start preparing With his friend the Skype co-founder Jaan Tallinn, and funding from the tech billionaire Elon Musk, he set up the Future of Life Institute, which researches the existential risks facing humanity.
Tegmark has tried to address this image problem by carefully unpacking the ideas involved or associated with AI – intelligence, memory, learning, consciousness – and then explaining them in demystifying fashion.
Humans, even the very young, possess a general intelligence across a broad range of abilities, whereas, for all their processing power, computers are confined to prescribed tasks.
We design our own software – our ability to “walk, read, write, calculate, sing and tell jokes” – but our biological hardware (the nature of our brains and bodies) is subject to evolution and necessarily restricted.
But if trends continue apace, then it’s not unreasonable to assume that at some point – 30 years’ time, 50 years, 200 years?
This is the argument that Bostrom laid out in his 2014 book Superintelligence, and the result of this massive expansion in intelligence – or the ability to accomplish complex goals – is indeed superintelligence, a singularity that we can only guess at.
The computer was completely clueless about human goals, even though we have the technology today to build airplanes that whenever the pilot tries to fly into something, it goes into safe mode, locks the cockpit and lands at the nearest airport.
And world governments should include this as a major part of computer science research.” Preventing the rise of a superintelligence by abandoning research in artificial intelligence is not, he believes, a credible approach.
Here, there’s a huge opportunity to make everyone better off if the government can redistribute some of this great wealth that machines can produce to benefit everybody.” In this respect Tegmark believes the UK, with its belief in the free market and history of the NHS and welfare state, could play a leading role in harnessing corporate innovation for national benefit.
The problem with that analysis that, aside from the fact that much AI research is led by authoritarian regimes in Russia and China, the lion’s share of advances are coming from America or American companies – and as a society the US has not been traditionally over-concerned with issues of inequality.
In the book, Tegmark hails Google’s Larry Page, one of the wealthiest men on Earth, as someone who might turn out to be the most influential human who has ever lived: “My guess is that if super intelligent digital life engulfs our universe in my lifetime, it will be because of Larry’s decisions.” He describes Page as he describes Musk – as thoughtful and sincerely concerned about humanity’s plight.
AI could solve all our thorny problems and help humanity flourish like never before.” But wouldn’t that radically alter humanity’s sense of itself, looking to superior agents to take care of us?
We humans are much better off if we can be humble and say maybe there can be beings much smarter than us, but that’s OK, we get our self-worth from other things: having really profound relationships with our fellow humans and wonderfully inspiring experiences.” At such moments Tegmark can sound less like a hardcore materialist physicist than some trippy new-age professor who’s spent too long contemplating the cosmos.
But surely, I say, the modernist project that has built these machines was fuelled by a belief that God was an invention we no longer required – wouldn’t it be a bitter historical irony if we ended up inventing new gods to supplant the old one?
We keep gloating about being the smartest on the planet precisely because we’re able to build all this fancy technology which is on track to make us not be the smartest on the planet!” Having researched and written this book, Tegmark is much more optimistic than he was in that lachrymose moment in South Kensington.
People and governments alike, he says, must turn their attention to the oncoming future, prepare appropriate safety engineering, and think deeply about the kind of world we want to create.
“Fund AI safety research, ban lethal autonomous weapons, and expand social services so that wealth created by AI makes everybody well off.” As ever, the road ahead will be filled with the unforeseen consequences of today’s action or lack of it, but adopting that three-point plan seems like a firm step in the direction of making the future that much less worrying.
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.”
2029: the year when robots will have the power to outsmart their makers
The entrepreneur and futurologist has predicted that in 15 years' time computers will be more intelligent than we are and will be able to understand what we say, learn from experience, make jokes, tell stories and even flirt.
Kurzweil, 66, who is considered by some to be the world's leading artificial intelligence (AI) visionary, is recognised by technologists for popularising the idea of 'the singularity' – the moment in the future when men and machines will supposedly converge.
This month it bought the cutting-edge British artificial intelligence startup DeepMind for £242m and hired Geoffrey Hinton, a British computer scientist and the world's leading expert on neural networks.
In 1990 he predicted that a computer would defeat a world chess champion by 1998 (in 1997, IBM's Deep Blue defeated Garry Kasparov), and he predicted the future prominence of the world wide web at a time when it was only an obscure system that was used by a few academics.
We'll give you the independence you've had with your own company, but you'll have these Google-scale resources.'' In 2009 Kurzweil co-founded the Singularity University, partly funded by Google, an unaccredited graduate school devoted to his ideas and the aim of exploring exponential technologies.
- On Thursday, June 27, 2019
Two robots talking to each other. Gone wrong
Part 2 -
Amazing! Conversation Between Robots - The Hunt for AI - BBC
Marcus Du Sautoy meets robots that learn about their own body from their reflection and begin to communicate, a step closer to artificial intelligence? Taken from ...
How Machines Learn
How do all the algorithms around us learn to do their jobs? Bot Wallpapers on Patreon: Discuss this video: ..
What Machines Can't Do - David Chalmers, Kate Devlin and Hilary Lawson
The late Alan Turing has been joined by Stephen Hawking and others in claiming that computers could overtake humanity. Will machines soon match their ...
Amir Husain: "The Sentient Machine: The Coming Age of Artificial Intelligence" | Talks at Google
The Sentient Machine addresses broad existential questions surrounding the coming of AI: Why are we valuable? What can we create in this world? How are we ...
The Hugh Thompson Show: Artificial Intelligence
Hugh Thompson, RSA Conference Program Chair Dr. Dawn Song, Professor of Computer Science, UC Berkeley, MacArthur Fellow, and Serial Entrepreneur Dr.
Machine Learning Control: Overview
This lecture provides an overview of how to use machine learning optimization directly to design control laws, without the need for a model of the dynamics.
The astounding athletic power of quadcopters | Raffaello D'Andrea
In a robot lab at TEDGlobal, Raffaello D'Andrea demos his flying quadcopters: robots that think like athletes, solving physical problems with algorithms that help ...
The Dawn of Killer Robots (Full Length)
Subscribe to Motherboard Radio today! In INHUMAN KIND, Motherboard gains exclusive access to a small fleet of US Army bomb ..
ML in Unity3D: robot learns to navigate using NEAT
The software and the physical agent was created from scratch using Unity3D to build the learning environment and Arduino + Raspberry Pi to control the robot ...