Peter Singer & David Pearce on Utilitarianism, Bliss & Suffering

Moral philosophers Peter Singer & David Pearce discuss some of the long term issues with various forms of utilitarianism, the future of predation and utilitronium shockwaves.

Topics Covered

Peter Singer

– long term impacts of various forms of utilitarianism
– Consciousness
– Artificial Intelligence
– Reducing suffering in the long run and in the short term
– Practical ethics
– Pre-implantation genetic screening to reduce disease and low mood
– Lives today are worth the same as lives in the future – though uncertainty must be brought to bear in deciding how one weighs up the importance of life
– The Hedonistic Imperative and how people react to it
– Correlation to high hedonic set points with productivity
existential risks and global catastrophic risks
– Closing factory farms

David Pearce

– Veganism and reducitarianism
– Red meat vs white meat – many more chickens are killed per ton of meat than beef
– Valence research
– Should one eliminate the suffering? And should we eliminate emotions of happiness?
– How can we answer the question of how far suffering is present in different life forms (like insects)?

Talk of moral progress can make one sound naive. But even the darkest cynic should salute the extraordinary work of Peter Singer to promote the interests of all sentient beings.David Pearce


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Is there a Meaningful Future for Non-Optimal Moral Agents?

In an interview last year, I had a discussion with John Danaher on the Hedonistic Imperative & Superintelligence – a concern he has with HI is that it denies or de-emphasises some kind of moral agency – in moral theory there is a distinction between moral agents (being a responsible actor able to make moral decisions, influence direction of moral progress, shapes its future, and owes duties to others) and moral patients who may be deemed to have limited or no grounds for moral agency/autonomy/responsibility – they are simply a recipient of moral benefits – in contrast to humans, animals could be classified as moral patients – (see Stanford writing on Grounds for Moral Status).

As time goes on, the notion of strong artificial intelligence leading to Superintelligence (which may herald in something like an Intelligence Explosion) and ideas like the hedonistic imperative becomes less sensational sci-fi concepts and more like visions of realizable eventualities. Thinking about moral endpoints comes to me a paradoxical feeling of triumph and disempowerment.

John’s concern is that ensuring the well-being of humans (conscious entities) is consistent with denying their moral agency – minimizing their capacity to act – that there is a danger that the outcome of HI or an Intelligence Explosion may result in sentient life being made very happy forever, but unable to make choices – with a focus on a future entirely based on bliss whilst ignoring other aspects of what makes for a valuable or worthwhile existence.

Artificial Heart chipsSo even if we have a future where a) we are made very happy and b) we are subject to a wide variety of novelty (which I argue for in Novelty Utilitarianism) without some kind of self-determination we may not be able to enjoy part of what arguably makes for a worthwhile existence.

If the argument for moral agency is completely toppled by the argument against free will then I can see why there would be no reason for it – and that bliss/novelty may be enough – though I personally haven’t been convinced that this is the case.

Also the idea that moral agency and novelty should be ranked as auxiliary aspects to the main imperative of reducing suffering/increasing bliss seems problematic – I get the sense that they (agency/novelty) could easily be swapped out for most non-optimal moral agents in the quest for -suffering/+bliss troublesome.
The idea that upon evaluating grounds for moral status, our ethical/moral quotient may not match or even come close to a potential ethical force of a superintelligence is also troubling. If we are serious about the best ethical outcomes, when the time comes, should we be committed to resigning all moral agency to agents that are more adept at producing peek moral outcomes?
ancillary-one-esk-glitchIs it really possible for non-optimal agents to have a meaningful moral input in a universe where they’ve been completely outperformed by moral machines? Is a life of novelty & bliss the most optimal outcome we can hope for?

There probably should be some more discussion on trade-offs between moral agency, peek experience and novelty.

Discussion in this video here starts at 24:02

Below is the whole interview with John Danaher:

The long-term future of AI (and what we can do about it) : Daniel Dewey at TEDxVienna

daniel deweyThis has been one of my favourite simple talks on AI Impacts – Simple, clear and straight to the point. Recommended as an introduction to the ideas (referred to in the title).

I couldn’t find the audio of this talk at TED – it has been added to


Daniel Dewey is a research fellow in the Oxford Martin Programme on the Impacts of Future Technology at the Future of Humanity Institute, University of Oxford. His research includes paths and timelines to machine superintelligence, the possibility of intelligence explosion, and the strategic and technical challenges arising from these possibilities. Previously, Daniel worked as a software engineer at Google, did research at Intel Research Pittsburgh, and studied computer science and philosophy at Carnegie Mellon University. He is also a research associate at the Machine Intelligence Research Institute.


Brian Greene on Artificial Intelligence, the Importance of Fundamental Physics, Alien Life, and the Possible Future of Our Civilization

March 14th was Albert Einstein’s birthday, and also PI day, so it was a fitting day to be interviewing well known theoretical physicist and string theorist Brian Greene – the author of a number of books including, The Elegant Universe, Icarus at the Edge of Time, The Fabric of the Cosmos, and The Hidden Reality!
Think-Inc-logo2Many thanks to Suzi and Desh at THINKINC for helping organize this interview & for bringing Brian Greene to Australia for a number of shows (March 16 in Perth, March 18 in Sydney and March 19 in Melbourne) – check out for more info!

Audio recording of the interview:

About the Interview with Brian Greene

Brian Greene discusses implications Artificial Intelligence and news of DeepMind AI (AlphaGo) beating the world grand champion in the board game Go.  He then discusses physics string theory, the territory of opinion on grand unifying theories of physics, the importance of supporting fundamental science, the possibility of alien life, the possible future of our space-faring civilization and of course gravitational waves!

In answer to the question on the importance of supporting fundamental research in science, Brain Greene said:

I tell them to wake up! Wake up and recognize that fundamental science has radically changed the way they live their lives today. If any of these individuals have a cell phone, or a personal computer, or perhaps they themselves or loved ones has been saved by an MRI machine.. I mean any of these devices rely on integrated circuits, which they themselves rely on quantum physics – so IF those folks who were in charge in the 1920s had have said, ‘hey you guys working on quantum physics, that doesn’t seem to be relevant to anything in the world around as so were going to cut your funding – well those people would have short circuited on of the greatest revolutions that our species has gone through – the information age, the technological age – so the bottom line is we need to support fundamental research because we know historically that when you gain a deep understanding of how things work – we can often leverage that to then manipulate the world around us in spectacular ways! And that needs to be where our fundamental focus remains – in science!


Layered art of Brian Greene, background and series titleBrian Randolph Greene is an American theoretical physicist and string theorist. He has been a professor at Columbia University since 1996 and chairman of the World Science Festival since co-founding it in 2008. Greene has worked on mirror symmetry, relating two different Calabi–Yau manifolds (concretely, relating the conifold to one of its orbifolds). He also described the flop transition, a mild form of topology change, showing that topology in string theory can change at the conifold point.

Greene has become known to a wider audience through his books for the general public, The Elegant Universe, Icarus at the Edge of Time, The Fabric of the Cosmos, The Hidden Reality, and related PBS television specials. He also appeared on The Big Bang Theory episode “The Herb Garden Germination“, as well as the films Frequency and The Last Mimzy. He is currently a member of the Board of Sponsors of the Bulletin of the Atomic Scientists.


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Can Intelligence Explode? – Marcus Hutter at Singularity Summit Australia 2012

Abstract: The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. After a short introduction to this intriguing potential future, I will elaborate on what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity.



Slides (pdf):
Slides (PowerPoint):
Paper: M.Hutter, Can Intelligence Explode, Journal of Consciousness Studies, Vol.19, Nr 1-2 (2012) pages 143–166.

Also see:

Marcus Hutter (born 1967) is a German computer scientist and professor at the Australian National University. Hutter was born and educated in Munich, where he studied physics and computer science at the Technical University of Munich. In 2000 he joined Jürgen Schmidhuber’s group at the Swiss Artificial Intelligence lab IDSIA, where he developed the first mathematical theory of optimal Universal Artificial Intelligence, based on Kolmogorov complexity and Ray Solomonoff’s theory of universal inductive inference. In 2006 he also accepted a professorship at the Australian National University in Canberra.

Hutter’s notion of universal AI describes the optimal strategy of an agent that wants to maximize its future expected reward in some unknown dynamic environment, up to some fixed future horizon. This is the general reinforcement learning problem. Solomonoff/Hutter’s only assumption is that the reactions of the environment in response to the agent’s actions follow some unknown but computable probability distribution.


Professor Marcus Hutter

Research interests:

Artificial intelligence, Bayesian statistics, theoretical computer science, machine learning, sequential decision theory, universal forecasting, algorithmic information theory, adaptive control, MDL, image processing, particle physics, philosophy of science.


Marcus Hutter is Professor in the RSCS at the Australian National University in Canberra, Australia. He received his PhD and BSc in physics from the LMU in Munich and a Habilitation, MSc, and BSc in informatics from the TU Munich. Since 2000, his research at IDSIA and now ANU is centered around the information-theoretic foundations of inductive reasoning and reinforcement learning, which has resulted in 100+ publications and several awards. His book “Universal Artificial Intelligence” (Springer, EATCS, 2005) develops the first sound and complete theory of AI. He also runs the Human Knowledge Compression Contest (50’000€ H-prize).

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.

7th Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS2015)

November 23 – 24, 2015: Pre-Conference Workshop
November 25 – 26, 2015: Conference

[Official Website Here]

Location: Monash University, Caulfield, Melbourne (Australia)
Promo vid | Contact:

Keynote Speakers: The conference organisers are pleased to announce that Dr Bruce Marcot of the US Forest Service, Dan Ababei from Lighttwist Software, Netherlands and Assoc Prof Jonathan Keith from Monash University will deliver the keynote address.

You will be able to register for the tutorials and the conference separately or together.

Bayesian Intelligence blog about the conf

– Dr. Kevin B. Korb is a Director and co-founder of Bayesian Intelligence, and a reader at Monash University. He specializes in the theory and practice of causal discovery of Bayesian networks (aka data mining with BNs), machine learning, evaluation theory, the philosophy of scientific method and informal logic. Email: kevin.korb (at)

Seventh Annual Conference of the Australasian Bayesian Network Modelling Society - Ann E Nicholson– Prof. Ann E. Nicholson is a Director and co-founder of Bayesian Intelligence and a professor at Monash University who specializes in Bayesian network modelling. She is an expert in dynamic Bayesian networks (BNs), planning under uncertainty, user modelling, Bayesian inference methods and knowledge engineering BNs. Email: ann (dot) nicholson (at) bayesian-intelligence (dot) com

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Vernor Vinge on the Technological Singularity

What is the Singularity? Vernor Vinge speaks about technological change, offloading cognition from minds into the environment, and the potential of Strong Artificial Intelligence.

Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended.” – “The Coming Technological SingularityVernor Vinge 1993

Vernor Vinge popularised and coined the term “Technological Singularity” in his 1993 essay “The Coming Technological Singularity“, in which he argues that the creation of superhuman artificial intelligence will mark the point at which “the human era will be ended,” such that no current models of reality are sufficient to predict beyond it.

courtesy of the Imaginary Foundation

courtesy of the Imaginary Foundation

Vinge published his first short story, “Bookworm, Run!”, in the March 1966 issue of Analog Science Fiction, then edited by John W. Campbell. The story explores the theme of artificially augmented intelligence by connecting the brain directly to computerised data sources. He became a moderately prolific contributor to SF magazines in the 1960s and early 1970s. In 1969, he expanded two related stories, (“The Barbarian Princess”, Analog, 1966 and “Grimm’s Story”, Orbit 4, 1968) into his first novel, Grimm’s World. His second novel, The Witling, was published in 1975.

Vinge came to prominence in 1981 with his novella True Names, perhaps the first story to present a fully fleshed-out concept of cyberspace, which would later be central to cyberpunk stories by William Gibson, Neal Stephenson and others.


Vernor Vinge

Image Courtesy – Long Now Foundation

Automating Science: Panel – Stephen Ames, John Wilkins, Greg Restall, Kevin Korb

A discussion among philosophers, mathematicians and AI experts on whether science can be automated, what it means to automate science, and the implications of automating science – including discussion on the technological singularity.

– implementing science in a computer – Bayesian methods – most promising normative standard for doing inductive inference
– vehicle : causal Bayesian networks – probability distributions over random variables showing causal relationships
– probabilifying relationships – tests whose evidence can raise the probability

05:23 does Bayesianism misrepresent the majority of what people do in science?

07:05 How to automate the generation of new hypotheses?
– Is there a clean dividing line between discovery and justification? (Popper’s view on the difference between the context of discovery and context of justification) Sure we discuss the difference between the concepts – but what is the difference between the implementation?

08:42 Automation of Science from beginning to end: concept formation, discovery of hypotheses, developing experiments, testing hypotheses, making inferences … hypotheses testing has been done – through concept formation is an interestingly difficult problem

Panel---Automating-Science-and-Artificial-Intelligence---Kevin-Korb,-Greg-Restall,-John-Wilkins,-Stephen-Ames-1920x10839:38 – does everyone on the panel agree that automation of science is possible? Stephen Ames: not yet, but the goal is imminent, until it’s done it’s an open question – Kevin/John: logically possible, question is will we do it – Greg Restall: Don’t know, can there be one formal system that can generate anything classed as science? A degree of open-endedness may be required, the system will need to represent itself etc (Godel!=mysticism, automation!=representing something in a formal deductive theory)

13:04 There is a Godel theorem that applies to a formal representation for automating science – that means that the formal representation can’t do everything – therefore what’s the scope of a formal system that can automate science? What will the formal representation and automated science implementation look like?

14:20 Going beyond formal representations to automate science (John Searle objects to AI on the basis of formal representations not being universal problem solvers)

15:45 Abductive inference (inference to the best explanation) – & Popper’s pessimism about a logic of discovery has no foundation – where does it come from? Calling it logic (if logic means deduction) is misleading perhaps – abduction is not deductive, but it can be formalised.

17:10 Some classified systems fall out of neural networks or clustering programs – Google’s concept of a cat is not deductive (IFAIK)

19:29 Map & territory – Turing Test – ‘if you can’t tell the difference between the model and the real system – then in practice there is no difference’ – the behavioural test is probably a pretty good one for intelligence

22:03 Discussion on IBM Watson on Jeopardy – a lot of natural language processing but not natural language generation

24:09 Bayesianism – in mathematics and in humans reasoning probabilistically – it introduced the concept of not seeing everything in black and white. People get statistical problems wrong often when they are asked to answer intuitively. Is the technology likely to have a broad impact?

26:26 Human thinking, subjective statistical reasoning – and the mismatch between the public communicative act often sounding like Boolean logic – a mismatch between our internal representation and the tools we have for externally representing likelihoods
29:08 Low hanging fruit in human communication probabilistic reasoning – Bayesian nets and argument maps (Bayesian nets strengths between premises and conclusions)

29:41 Human inquiry, wondering and asking questions – how do we automate asking questions (as distinct from making statements)? Scientific abduction is connected to asking questions – there is no reason why asking questions can’t be automated – there is contrasted explanations and conceptual space theory where you can characterise a question – causal explanation using causal Bayesian networks (and when proposing an explanation it must be supported some explanatory context)

32:29 Automating Philosophy – if you can automate science you can automate philosophy –

34:02 Stanford Computational Metaphysics project (colleagues with Greg Restall) – Stanford Computational Metaphysics project – formalization of representations of relationships between concepts – going back to Leibniz – complex notions can be boiled down to simpler primitive notions and grinding out these primitive notions computationally – they are making genuine discoveries
Weak Reading: can some philosophy be automated – yes
Strong Reading of q: can All of philosophy be automated? – there seem to be some things that count as philosophy that don’t look like they will be automated in the next 10 years

35:41 If what we’re is interested in is to represent and automate the production of reasoning formally (not only to evaluate), as long as the domain is such that we are making claims and we are interested in the inferential connections between the claims, then a lot of the properties of reasoning are subject matter agnostic.

36:46 (Rohan McLeod) Regarding Creationism is it better to think of it as a poor hypothesis or non-science? – not an exclusive disjunct, can start as a poor hypothesis and later become not-science or science – it depends on the stage at the time – science rules things out of contention – and at some point creationism had not been ruled out

38:16 (Rohan McLeod) Is economics a science or does it have the potential to be (or is it intrinsically not possible for it to be a science) and why?
Are there value judgements in science? And if there are how do you falsify a hypothesis that conveys a value judgement? physicists make value judgements on hypothesis “h1 is good, h2 is bad” – economics may have reducible normative components but physics doesn’t (electrons aren’t the kinds of things that economies are) – Michael ??? paper on value judgements – “there is no such thing as a factual judgement that does not involve value” – while there are normative components to economics, it is studied from at least one remove – problem is economists try to make normative judgements like “a good economy/market/corporation will do X”

42:22 Problems with economics – incredibly complex, it’s hard to model, without a model exists a vacuum that gets filled with ideology – (are ideologies normative?)

42:56 One of the problems with economics is it gets treated like a natural system (in physics or chemistry) which hides all the values which are getting smuggled in – commitments and values which are operative and contribute to the configuration of the system – a contention is whether economics should be a science (Kevin: Yes, Stephen: No) – perhaps economics could be called a nascent science (in the process of being born)

44:28 (James Fodor) Well known scientists have thought that their theories were implicit in nature before they found them – what’s the role of intuition in automating science & philosophy? – need intuitions to drive things forward – intuition in the abduction area – to drive inspiration for generating hypothesis – though a lot of what get’s called intuition is really the unconscious processing of a trained mind (an experienced driver doesn’t have to process how to drive a car) – Louis Pasteur’s prepared mind – trained prior probabilities

46:55 The Singularity – disagreement? John Wilkins suspects it’s not physically possible – Where does Moore’s Law (or its equivalents in other hardware paradigms) peter out? The software problem could be solved near or far. Kevin agrees with I.J. Good – recursively improving abilities without (obvious) end (within thermodynamic limits). Kevin Korb explains the intelligence explosion.

50:31 Stephen Ames discusses his view of the singularity – but disagrees with uploading on the grounds of needing to commit to philosophical naturalism

51:52 Greg Restall mistrusts IT corporations to get uploading right – Kevin expresses concerns about using star-trek transporters – the lack of physical continuity. Greg discusses theories of intelligence – planes fly as do birds, but planes are not birds – they are differing

54:07 John Wilkins – way too much emphasis is put on propositional knowledge and communication in describing intelligence – each human has roughly the same amount of processing power – too much rests on academic pretense and conceit.

54:57 The Harvard Rule – under conditions of consistent lighting, feeding etc – the organism will do as it damn well pleases. But biology will defeat simple models.. Also Hulls rule – no matter what the law in biology is there is an exception (inc Hull’s law) – so simulated biology may be difficult. We won’t simulate an entire organism – we can’t simulate a cell. Kevin objects

58:30 Greg R. says simulations and models do give us useful information – even if we isolate certain properties in simulation that are not isolated in the real world – John Wilkins suggests that there will be a point where it works until it doesn’t

1:00:08 One of the biggest differences between humans and mice is 40 million years of evolution in both directions – the problem is in evo biol is your inductive projectability – we’ve observed it in these cases, therefore we expect it in this – it fades out relatively rapidly in direct disproportion to the degree of relatedness

1:01:35 Colin Kline – PSYCHE – and other AI programs making discoveries – David Chalmers have proposed the Hard Problem of Consciousness – pZombies – but we are all pZombies, so we will develop systems that are conscious because there is to such thing as consciousness. Kevin is with Dennet – info processing functioning is what consciousness supervenes upon
Greg – concept formation in systems like PSYCHE – but this milestone might be very early in the development of what we think of as agency – if the machine is worried about being turned off or complains about getting board, then we are onto something

On Artificial Intelligence – Tim Josling

Tim Josling discusses AI, the Singularity, the way the public might react, whether they would be prepared, John Searle’s Chinese Room thought experiment, and consciousness.

Filmed in the majestic Blue Mountains a couple of hours out of Sydney in Australia. Here are some photos I took while I was there.

Also see Tim’s talk at H+ @Melbourne 2012

Tim’s Bio

Tim Josling - On Artificial IntelligenceTim Josling studied Law, Anthopology, Philosophy and Mathematics before switching to Computer Science at the dawn of the computer era. He worked on implementing some of the first transactional systems in Australia, later worked on the first ATM networks and was the chief architect for one of the first Internet Banking applications in Australia, and designed an early message switching (“middleware”) application in the USA. During his career he specialised in making large scale applications reliable and fast, saving several major projects from being cancelled due to poor performance and excessive running costs. This led to an interest in the progress of computer hardware and in Moore’s Law, which states that the power of computers grows roughly 10-fold every 5 years. In his spare time he contributed to various open source projects such as the GNU Compiler Collection. After attending the first Singularity Summit in Australia, he decided to retire so he could devote himself full-time to researching Artificial Intelligence, the Technological Singularity and Trans-humanism. He is currently working on applying AI techniques to financial and investment applications.
Talk: The Surprising Rate of Progress in Artificial Intelligence Research

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