Anders Sandberg -The Technological Singularity

Anders Sandberg.00_23_53_16.Still031Anders gives a short tutorial on the Singularity – clearing up confusion and highlighting important aspects of the Technological Singularity and related ideas, such as accelerating change, horizons of predictability, self-improving artificial intelligence, and the intelligence explosion.

Tutorial Video:

Points covered in the tutorial:

  • The Mathematical Singularity
  • The Technological Singularity: A Horizon of predictability
  • Confusion Around The Technological Singularity
  • Drivers of Accelerated Growth
  • Technology Feedback Loops
  • A History of Coordination
  • Technological Inflection Points
  • Difficult of seeing what happens after an Inflection Point
  • The Intelligence Explosion
  • An Optimisation Power Applied To Itself
  • Group Minds
  • The HIVE Singularity: A Networked Global Mind
  • The Biointelligence explosion
  • Humans are difficult to optimise

An Overview of Models of the Technological Singularity

anders-sandberg-technology-feedback-loopsSee Anders’ paper ‘An overview of models of technological singularity
This paper reviews different definitions and models of technological singularity. The models range from conceptual sketches to detailed endogenous growth models, as well as attempts to fit empirical data to quantitative models. Such models are useful for examining the dynamics of the world-system and possible types of future crisis points where fundamental transitions are likely to occur. Current models suggest that, generically, even small increasing returns tends to produce radical growth. If mental capital becomes copyable (such as would be the case for AI or brain emulation) extremely rapid growth would also become likely.
http://agi-conf.org/2010/wp-content/uploads/2009/06/agi10singmodels2.pdf

[The] Technological singularity is of increasing interest among futurists both as a predicted possibility in the midterm future and as subject for methodological debate. The concept is used in a variety of contexts, and has acquired an unfortunately large number of meanings. Some versions stress the role of artificial intelligence, others refer to more general technological change. These multiple meanings can overlap, and many writers use combinations of meanings: even Vernor Vinge’s seminal essay that coined the term uses several meanings. Some of these meanings may imply each other but often there is a conflation of different elements that likely (but not necessarily) occur in parallel. This causes confusion and misunderstanding to the extent that some critics argue that the term should be avoided altogether. At the very least the term ‘singularity’ has led to many unfortunate assumptions that technological singularity involves some form of mathematical singularity and can hence be ignored as unphysical.Anders Sandberg

A list of models described in the paper:

A. Accelerating change

Exponential or superexponential technological growth (with linked economical growth and social change) (Ray Kurzweil (Kur05), John Smart (Smang))

B. Self improving technology

Better technology allows faster development of new and better technology. (Flake (Fla06))

C. Intelligence explosion

Smarter systems can improve themselves, producing even more intelligence in a strong feedback loop. (I.J. Good (Goo65), Eliezer Yudkowsky)

D. Emergence of superintelligence

(Singularity Institute) 1

E. Prediction horizon

Rapid change or the emergence of superhuman intelligence makes the future impossible to predict from our current limited knowledge and experience. (Vinge, (Vin93))

F. Phase transition

The singularity represents a shift to new forms of organisation. This could be a fundamental difference in kind such as humanity being succeeded by posthuman or artificial intelligences,
a punctuated equilibrium transition or the emergence of a new meta-system level. (Teilhard de Chardin, Valentin Turchin (Tur77), Heylighen (Hey07))

G. Complexity disaster

Increasing complexity and interconnectedness causes increasing payoffs, but increases instability. Eventually this produces a crisis, beyond which point the dynamics must be different.
(Sornette (JS01), West (BLH+07))

H. Inflexion point

Large-scale growth of technology or economy follows a logistic growth curve. The singularity represents the inflexion point where change shifts from acceleration to de-acceleration. (Extropian
FAQ, T. Modis (Mod02))

I. Infinite progress

The rate of progress in some domain goes to infinity in nite time. (Few, if any, hold this to be plausible 2 )

anders-sandberg-the-technological-singularity-predictability-horizon

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Juergen Schmidhuber on DeepMind, AlphaGo & Progress in AI

In asking AI researcher Juergen Schmidhuber about his thoughts on progress at DeepMind and about the AlphaGo vs Lee Sedol Go tournament – provided some initial comments. I will be updating this post with further interview.

juergen288x466genova1Juergen Schmidhuber: First of all, I am happy about DeepMind’s success, also because the company is heavily influenced by my former students: 2 of DeepMind’s first 4 members and their first PhDs in AI came from my lab, one of them co-founder, one of them first employee. (Other ex-PhD students of mine joined DeepMind later, including a co-author of our first paper on Atari-Go in 2010.)

Go is a board game where the Markov assumption holds: in principle, the current input (the board state) conveys all the information needed to determine an optimal next move (no need to consider the history of previous states). That is, the game can be tackled by traditional reinforcement learning (RL), a bit like 2 decades ago, when Tesauro used RL to learn from scratch a backgammon player on the level of the human world champion (1994). Today, however, we are greatly profiting from the fact that computers are at least 10,000 times faster per dollar.

In the last few years, automatic Go players have greatly improved. To learn a good Go player, DeepMind’s system combines several traditional methods such as supervised learning (from human experts) and RL based on Monte Carlo Tree Search. It will be very interesting to see the system play against the best human Go player Lee Sedol in the near future.

Unfortunately, however, the Markov condition does not hold in realistic real world scenarios. That’s why games such as football are much harder for machines than Go, and why Artificial General Intelligence (AGI) for RL robots living in partially observable environments will need more sophisticated learning algorithms, e.g., RL for recurrent neural networks.

For a comprehensive history of deep RL, see Section 6 of my survey with 888 references:
http://people.idsia.ch/~juergen/deep-learning-overview.html

Also worth seeing Juergen’s AMA here.

Juergen Schmidhuber’s website.

The Simpsons and Their Mathematical Secrets with Simon Singh

You may have watched hundreds of episodes of The Simpsons (and its sister show Futurama) without ever realizing that cleverly embedded in many plots are subtle references to mathematics, ranging from well-known equations to cutting-edge theorems and conjectures. That they exist, Simon Singh reveals, underscores the brilliance of the shows’ writers, many of whom have advanced degrees in mathematics in addition to their unparalleled sense of humor.

A mathematician is a machine for turning coffee into theorems. Simon Singh, The Simpsons and Their Mathematical Secrets

The Simpsons and their Mathematical SecretsWhile recounting memorable episodes such as “Bart the Genius” and “Homer3,” Singh weaves in mathematical stories that explore everything from p to Mersenne primes, Euler’s equation to the unsolved riddle of P v. NP; from perfect numbers to narcissistic numbers, infinity to even bigger infinities, and much more. Along the way, Singh meets members of The Simpsons’ brilliant writing team—among them David X. Cohen, Al Jean, Jeff Westbrook, and Mike Reiss—whose love of arcane mathematics becomes clear as they reveal the stories behind the episodes.
With wit and clarity, displaying a true fan’s zeal, and replete with images from the shows, photographs of the writers, and diagrams and proofs, The Simpsons and Their Mathematical Secrets offers an entirely new insight into the most successful show in television history.

Buy the book on amazon

An astronomer, a physicist, and a mathematician (it is said) were holidaying in Scotland. Glancing from a train window, they observed a black sheep in the middle of a field. “How interesting,” observed the astronomer, “all Scottish sheep are black!” To which the physicist responded, “No, no! Some Scottish sheep are black!” The mathematician gazed heavenward in supplication, and then intoned, “In Scotland there exists at least one field, containing at least one sheep, at least one side of which is black. Simon Singh, The Simpsons and Their Mathematical Secrets

 

 

Simon Singh is a British author who has specialised in writing about mathematical and scientific topics in an accessible manner. His written works include Fermat’s Last Theorem (in the United States titled Fermat’s Enigma: The Epic Quest to Solve the World’s Greatest Mathematical Problem),The Code Book (about cryptography and its history), Big Bang (about the Big Bang theory and the origins of the universe), Trick or Treatment? Alternative Medicine on Trial[6] (about complementary and alternative medicine) and The Simpsons and Their Mathematical Secrets (about mathematical ideas and theorems hidden in episodes of The Simpsons and Futurama).

Singh has also produced documentaries and works for television to accompany his books, is a trustee of NESTA, the National Museum of Science and Industry and co-founded the Undergraduate Ambassadors Scheme.

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As a society, we rightly adore our great musicians and novelists, yet we seldom hear any mention of the humble mathematician. It is clear that mathematics is not considered part of our culture. Instead, mathematics is generally feared and mathematicians are often mocked. Simon Singh, The Simpsons and Their Mathematical Secrets

Science, Technology & the Future

Julian Savulescu – Government & Surveillance

julian savulescu - surveilanceIf you increase the altruistic motivation of people, you decrease the risk that they will negligently fail to consider the possible harmful effects of their behaviour on their fellow-beings. Being concerned about avoiding such risks is part of what having altruistic concern for these beings consists in. Moreover, the advance of technology will in all probability bring along more effective mechanisms of surveillance, and it is easier for these to pick up people who are negligent rather than evil-doers who are intent on beating them.

“The nutshell: Human societies have grown larger, more diverse, and more technologically complex, and as a result, our moral compasses are no longer up to the task of guiding us, argue Oxford University’s Persson (a philosopher) and Savulescu (an ethicist)—and we’re in danger of destroying ourselves. The severity of the problem demands an equally severe solution: biomedical moral enhancement and increased government surveillance of citizens.” – Slate

julian savulescu white shirtJulian Savulescu (born December 22, 1963) is an Australian philosopher and bioethicist. He is Uehiro Professor of Practical Ethics at the University of Oxford, Fellow of St Cross College, Oxford, Director of the Oxford Uehiro Centre for Practical Ethics, Sir Louis Matheson Distinguished Visiting Professor at Monash University, and Head of the Melbourne–Oxford Stem Cell Collaboration, which is devoted to examining the ethical implications of cloning and embryonic stem cell research. He is the editor of the Journal of Medical Ethics, which is ranked as the #1 journal in bioethics worldwide by Google Scholar Metrics as of 2013. In addition to his background in applied ethics and philosophy, he also has a background in medicine and completed his MBBS (Hons) at Monash University. He completed his PhD at Monash University, under the supervision of renowned bioethicist Peter Singer. Published Jan 30, 2014.

Science, Technology & the Future

Metamorphogenesis – How a Planet can produce Minds, Mathematics and Music – Aaron Sloman

The universe is made up of matter, energy and information, interacting with each other and producing new kinds of matter, energy, information and interaction.
How? How did all this come out of a cloud of dust?
In order to find explanations we first need much better descriptions of what needs to be explained.

By Aaron Sloman
Abstract – and more info – Held at Winter Intelligence Oxford – Organized by the Future of Humanity Institute

Aaron Sloman

Aaron Sloman

This is a multi-disciplinary project attempting to describe and explain the variety of biological information-processing mechanisms involved in the production of new biological information-processing mechanisms, on many time scales, between the earliest days of the planet with no life, only physical and chemical structures, including volcanic eruptions, asteroid impacts, solar and stellar radiation, and many other physical/chemical processes (or perhaps starting even earlier, when there was only a dust cloud in this part of the solar system?).

Evolution can be thought of as a (blind) Theorem Prover (or theorem discoverer).
– Proving (discovering) theorems about what is possible (possible types of information, possible types of information-processing, possible uses of information-processing)
– Proving (discovering) many theorems in parallel (including especially theorems about new types of information and new useful types of information-processing)
– Sharing partial results among proofs of different things (Very different biological phenomena may share origins, mechanisms, information, …)
Combining separately derived old theorems in constructions of new proofs (One way of thinking about symbiogenesis.)
– Delegating some theorem-discovery to neonates and toddlers (epigenesis/ontogenesis). (Including individuals too under-developed to know what they are discovering.)
– Delegating some theorem-discovery to social/cultural developments. (Including memes and other discoveries shared unwittingly within and between communities.)
– Using older products to speed up discovery of new ones (Using old and new kinds of architectures, sensori-motor morphologies, types of information, types of processing mechanism, types of control & decision making, types of testing.)

The “proofs” of discovered possibilities are implicit in evolutionary and/or developmental trajectories.

They demonstrate the possibility of development of new forms of development, evolution of new types of evolution learning new ways to learn evolution of new types of learning (including mathematical learning: by working things out without requiring empirical evidence) evolution of new forms of development of new forms of learning (why can’t a toddler learn quantum mechanics?) – how new forms of learning support new forms of evolution amd how new forms of development support new forms of evolution (e.g. postponing sexual maturity until mate-selection mating and nurturing can be influenced by much learning)
….
…. and ways in which social cultural evolution add to the mix

These processes produce new forms of representation, new ontologies and information contents, new information-processing mechanisms, new sensory-motor
morphologies, new forms of control, new forms of social interaction, new forms of creativity, … and more. Some may even accelerate evolution.

A draft growing list of transitions in types of biological information-processing.

An attempt to identify a major type of mathematical reasoning with precursors in perception and reasoning about affordances, not yet replicated in AI systems.

Even in microbes I suspect there’s much still to be learnt about the varying challenges and opportunities faced by microbes at various stages in their evolution, including new challenges produced by environmental changes and new opportunities (e.g. for control) produced by previous evolved features and competences — and the mechanisms that evolved in response to those challenges and opportunities.

Example: which organisms were first able to learn about an enduring spatial configuration of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any one time?
What changes occurred to meet that need?

Use of “external memories” (e.g. stigmergy)
Use of “internal memories” (various kinds of “cognitive maps”)

More examples to be collected here.

Blockbuster Science! Tech investors reward ‘Breakthough Science’

Blockbuster Science! Its an awesome approach to incentivizing scientists – it’s great that people are applauding for stuff that really matters! People cheer at most ridiculous and inconsequential things – why not funnel this energy into science?

Next step, create high production shorts for real world advances in science (with a tinge of flair) – much like they do to promote blockbuster movies. NY Times stated : “Scientists don’t have the power of celebrities in American society. The Breakthrough Prize tries to change that”

Anne Wojcicki

Biologist Anne Wojcicki attends the 2016 Breakthrough Prize Ceremony

Yuri Milner

Entrepreneur and Investor Yuri Milner

“Yuri Milner, the Russian billionaire, and his high-tech Silicon Valley friends have awarded $29.5 million to seven scientists, a high school student, and a huge team of physics researchers for their varied science achievements.

Milner’s third annual Breakthrough Prizes were financed by his foundation with contributions from Sergey Brin of Google and his wife, 23&Me founder Anne Wojcicki; Mark Zuckerberg of Facebook; and Jack Ma of China’s e-commerce giant Alibaba.

Other prizes went to Ed Boyden, now at MIT, who was Deisseroth’s partner at Stanford developing optogenetics; Helen Hobbs, a University of Texas physician who discovered the roles that variant genes play in cholesterol and lipid levels leading to heart disease; John Hardy, a neuroscientist at University College in London, who discovered genetic mutations in the amyloid genes causing Alzheimer’s disease; and Svante Pääbo, the famed anthropologist at Germany’s Max Planck Institute, who sequenced the genes of Neanderthals and discovered traces of the vanished humans called Denisovans.” said David Perlman at SF Gate.

Yuri Milner did an inspiring interview with New Scientist on the positively huge impacts of fundamental research in science on society. ” If you go far enough into the future, a fundamental discovery leads to some new technology.”, said Yuri Milner.

Ed Boyden develops new strategies for analyzing and engineering brain circuits, using synthetic biology, nanotechnology, chemistry, electrical engineering, and optics to develop broadly applicable methodologies that reveal fundamental mechanisms of complex brain processes. A major goal of his current work is the development of technologies for controlling nerve cells using light – a powerful new technology known as optogenetics that is opening the door to new treatments for conditions such as epilepsy, Parkinson’s disease, and mood disorders.

Ed Boyden develops new strategies for analyzing and engineering brain circuits, using synthetic biology, nanotechnology, chemistry, electrical engineering, and optics to develop broadly applicable methodologies that reveal fundamental mechanisms of complex brain processes. A major goal of his current work is the development of technologies for controlling nerve cells using light – a powerful new technology known as optogenetics that is opening the door to new treatments for conditions such as epilepsy, Parkinson’s disease, and mood disorders.

Ed Boyden is on the closing Breakthrough Prize Panel Discussion hosted by Yuri Milner.

Will the breakthrough accomplishments in science one day outshine a season winning slam dunk?

Athletic heroes loom large in our imagination – though how often do we stop to think about brilliant scientists and the wonderful things they have achieved that make positive tractable difference in our lives and the world around us?

Elon Musk founder of Tesla and SpaceX said: “It is important to celebrate science and to create role models for science that kids want to emulate.. For the benefit of humanity, we want breakthroughs in science that help us improve standards of living, cure disease, make life better… I’d rather a super-smart, creative kid went into developing breakthrough technologies that improve the world rather than, say, went to Wall Street.”

I see this as a positive sign of a general warming to Enlightenment values and the idea that significant civilizational progress in improving the human condition through science.

BlockBuster-Science---Yuri-Milner

What is the Philosophy of Science All About?

Slides [here], See this post by John Wilkins at Evolving Thoughts, the video is a talk John gave at the Philosophy of Science conference in Melbourne 2014.

Every so often, somebody will attack the worth, role or relevance of philosophy on the internets, as I have discussed before. Occasionally it will be a scientist, who usually conflates philosophy with theology. This is as bad as someone assuming that because I do some philosophy I must have the Meaning of Life (the answer is, variously, 12 year old Scotch, good chocolate, or dental hygiene).

But it raises an interesting question or two: what is the reason to do philosophy in relation to science? being the most obvious (and thus set up the context in which you can answer questions like: are there other ways to find truth than science?). So I thought I would briefly give my reasons for that.

When philosophy began around 500BCE, there was no distinction between science and philosophy, nor, for that matter, between religion and philosophy. Arguably, science began when the pre-Socratics started to ask what the natures of things were that made them behave as they did, and equally arguably the first actual empirical scientist was Aristotle (and, I suspect, his graduate students).

wilkins_picBut a distinction between science and philosophy began with the separation between natural philosophy (roughly what we now call science) and moral philosophy, which dealt with things to do with human life and included what we should believe about the world, including moral, theological and metaphysical beliefs. The natural kind was involved in considering the natures or things. A lot gets packed into that simple word, nature: it literally means “in-born” (natus) and the Greek physikos means much the same. Of course, something can be in-born only if it is born that way (yes, folks, she’s playing on some old tropes here!), and most physical things aren’t born at all, but the idea was passed from living to nonliving things, and so natural philosophy was born. That way.

In the period after Francis Bacon, natural philosophy was something that depended crucially on observation, and so the Empiricists arose: Locke, Berkeley, Hobbes, and later Hume. That these names are famous in philosophy suggests something: philosophy does best when it is trying to elucidate science itself. And when William Whewell in 1833 coined the term scientist to denote those who sought scientia or knowledge, science had begun its separation from the rest of philosophy.

Or imperfectly, anyway. For a start the very best scientists of the day, including Babbage, Buckland and Whewell himself wrote philosophical tomes alongside theologians and philosophers. And the tradition continues until now, such as the recent book by Stephen Hawking in which he declares the philosophical enterprise is dead, a decidedly philosophical claim to make. Many scientists seem to find the doing of philosophy inevitable.

So why do I do philosophy of science? Simply because it is where the epistemic action is: science is where we do get knowledge, and I wish to understand how and why, and the limitations. All else flows from this for me. Others I know (and respect) do straight metaphysics and philosophy of language, but I do not. It only has a bite if it gives some clarity to science. I think this is also true of metaphysics, ethics and such matters as philosophy of religion.

Philosoophy of Science 2014

Philosophy of Science – What & Why?

Interview with John Wilkins:

John-Wilkins---Phil-Sci-IntroEvery so often, somebody will attack the worth, role or relevance of philosophy on the internets, as I have discussed before. Occasionally it will be a scientist, who usually conflates philosophy with theology. This is as bad as someone assuming that because I do some philosophy I must have the Meaning of Life (the answer is, variously, 12 year old Scotch, good chocolate, or dental hygiene).

But it raises an interesting question or two: what is the reason to do philosophy in relation to science? being the most obvious (and thus set up the context in which you can answer questions like: are there other ways to find truth than science?). So I thought I would briefly give my reasons for that.

When philosophy began around 500BCE, there was no distinction between science and philosophy, nor, for that matter, between religion and philosophy. Arguably, science began when the pre-Socratics started to ask what the natures of things were that made them behave as they did, and equally arguably the first actual empirical scientist was Aristotle (and, I suspect, his graduate students).

But a distinction between science and philosophy began with the separation between natural philosophy (roughly what we now call science) and moral philosophy, which dealt with things to do with human life and included what we should believe about the world, including moral, theological and metaphysical beliefs. The natural kind was involved in considering the natures or things. A lot gets packed into that simple word, nature: it literally means “in-born” (natus) and the Greek physikos means much the same. Of course, something can be in-born only if it is born that way (yes, folks, she’s playing on some old tropes here!), and most physical things aren’t born at all, but the idea was passed from living to nonliving things, and so natural philosophy was born. That way.

In the period after Francis Bacon, natural philosophy was something that depended crucially on observation, and so the Empiricists arose: Locke, Berkeley, Hobbes, and later Hume. That these names are famous in philosophy suggests something: philosophy does best when it is trying to elucidate science itself. And when William Whewell in 1833 coined the term scientist to denote those who sought scientia or knowledge, science had begun its separation from the rest of philosophy.

Or imperfectly, anyway. For a start the very best scientists of the day, including Babbage, Buckland and Whewell himself wrote philosophical tomes alongside theologians and philosophers. And the tradition continues until now, such as the recent book by Stephen Hawking in which he declares the philosophical enterprise is dead, a decidedly philosophical claim to make. Many scientists seem to find the doing of philosophy inevitable.

So why do I do philosophy of science? Simply because it is where the epistemic action is: science is where we do get knowledge, and I wish to understand how and why, and the limitations. All else flows from this for me. Others I know (and respect) do straight metaphysics and philosophy of language, but I do not. It only has a bite if it gives some clarity to science. I think this is also true of metaphysics, ethics and such matters as philosophy of religion.

Now there are those who think that science effectively exhausts our knowledge-gathering. This, too, is a philosophical position, which has to be defended, and elaborated (thus causing more philosophy to be done). I don’t object to that view, but for me, it is better to be positive (say that science gives us knowledge even if other activities may do) than to be negative (deny that anything but science gives us knowledge). It may be that we get to the latter position after considering the former; if so, that would be a philosophical result.

I am fascinated by science. It allows us to do things no ancient Greek (or West Semitic) thinker would have been even able to conceive of. It means we make fewer mistakes. Philosophy is, and ought only to be, in the service of knowledge (I’m sure somebody has said that before). Science is a good first approximation of that.

But scientists who reject philosophy, as if that very rejection is not a philosophical stance (probably taken unreflectively or on the basis of half-digested emotive appeals), them I have no time for as philosophers. They should perhaps stick to their last and not make fools of themselves.

Not, of course, that every philosopher is worth reading. Sturgeon’s Law (90% of everything is crap) applies here too. But lest any scientist get too smug, recall that 99% of all scientific papers are never cited again many scientific papers are uncited . In philosophy, that ratio is perhaps lower… probably almost down to the Sturgeon limit.

See this post by John Wilkins at Evolving Thoughts: http://evolvingthoughts.net/2011/07/why-do-philosophy-of-science.

Ashley Barnett

Abstract: Skepticism and the Psychology of Magic – Ashley Barnett

Ashley BarnettOur brain’s simulation of the external world, our conscious experience, is often wrong. Optical illusions demonstrate how our perception of objects can be mistaken. Analogously, magic tricks are cognitive illusions that vividly illustrate how our perception and understanding of events can go awry.  Thanks to recent work by neuroscientists and psychologists we know under what circumstances magic tricks are effective and how we can get better at working out how they are done.  The psychological principles at work are general ones, so understanding them can help us be appropriately skeptical of our observations and to reduce error.

 

 

Bio

Ashley Barnett is a philosophy PhD candidate at the University of Melbourne. He teaches critical thinking and researchers how people can improve their critical thinking skills. Most recently he worked on an experimental course for IARPA, the main research body of the US intelligence community. His online course is available at www.improvingreasoning.com .  He also performs as a stage magician – see www.yourmindonmagic.com.

Abstract – Science v Pseudoscience: What’s the Difference? – Kevin Korb

Science has a certain common core, especially a reliance on empirical methods of assessing hypotheses. Pseudosciences have little in common but their negation: they are not science. They reject meaningful empirical assessment in some way or another. Popper proposed a clear demarcation criterion for Science v Rubbish: Falsifiability. However, his criterion has not stood the test of time. There are no definitive arguments
against any pseudoscience, any more than against extreme skepticism in general, but there are clear indicators of phoniness.

Slides can be found here:

 

Kevin KorbMy research is in: machine learning, artificial intelligence, philosophy of science, scientific method, Bayesian inference and reasoning, Bayesian networks, artificial life, computer simulation, epistemology, evaluation theory.

See http://www.csse.monash.edu.au/~korb/ The page is out of date, but accurate as far as it goes.

http://theconversation.com/is-passing-a-turing-test-a-true-measure-of-artificial-intelligence-27801

 

http://theconversation.com/profiles/kevin-korb-115721