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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

Aubrey de Grey – Engaging the Disengaged

There is likely a lot of mileage in engaging the disengaged in untapped support for more efficient progress in regenerative medicine. We need to talk about the familiar and positive aspects of rejuvenation medicine!

Aging issues have appeared in the media a lot recently – all to often the narrative is skewed in the direction of sci-fi sounding future scenarios, and are embedded in sensationalized media stunts, to the effect that for many the ideas ‘go out one ear and out the other’ – the people whom are currently disengaged forget about rejuvenation medicine and loose interest when they hear about the latest patch for their iphone.

There are a lot more people out there in the world besides transhumanists who have resources and energy to transform into meaningful progress in the science of rejuvenation biotechnology.
People also get fixated on long term Malthusian visions or the pseuodoscientific and religious connotations of words like ‘immortality’ and loose sight of the fact that SENS and others are working on _health_.

History shows a bleak picture, but the further back we go, the worse it seems. It seems civilization is getting better at healthy living into older age – now it really is a priority to get better at getting better – effective aging, so to speak.
There is so much in the world to do – many people grow old and unable to do the things they want to do before they have finished doing much of what they want to do. Live is precious – it’s an imperative that we focus on giving people extra healthy life-time for them to do more of the things they love to do.

Aubrey-de-Grey-Engaging-the-Disengaged

The main thing that people misunderstanding is the actual relationship between aging and the diseases of old age – and this is largely the fault of gerontologists….people would go out and say, all the time, ‘Aging is not a disease’ – that’s not useful. Ultimately it’s very counter productive. What happened was people would think to themselves ‘well ok then, aging is this natural thing that’s never going to be amenable to medical intervention, because it’s not a disease – and also because it’s not a disease, then why should we care about it?’ – so it was absolutely the wrong thing to be saying… it’s even more the wrong thing to be saying because it’s not even true. Aubrey de Grey

Aubrey de Grey is the chief science officer of the SENS Research Foundation, which is a 501(c)(3) public charity that is transforming the way the world researches and treats age-related disease.

The research SENS funds at universities around the world and at SENS own Research Center uses regenerative medicine to repair the damage underlying the diseases of aging. The goal of SENS is to help build the industry that will cure these diseases.


Aubrey de Grey was interviewed by Adam Ford in 2012.

Here is a playlist of all the interview sections:

Peter Singer – Ethics, Utilitarianism & Effective Altruism

Peter Singer at UMMS - Ethics Utilitarianism Effective Altruism
Peter Singer discusses Effective Altruism, including Utilitarianism as a branch of Ethics. Talk was held as a joint event between the University of Melbourne Secular Society and Melbourne University Philosophy Community.

Is philosophy, as a grounds to help decide how good an action is, something you spend time thinking about?

Audio of Peter’s talk can be found here at the Internet Archive.

In his 2009 book ‘The Life You Can Save’, Singer presented the thought experiment of a child drowning in a pond before our eyes, something we would all readily intervene to prevent, even if it meant ruining an expensive pair of shoes we were wearing. He argued that, in fact, we are in a very similar ethical situation with respect to many people in the developing world: there are life-saving interventions, such as vaccinations and clean water, that can be provided at only a relatively small cost to ourselves. Given this, Singer argues that we in the west should give up some of our luxuries to help those in the world who are most in need.

If you want to do good, and want to be effective at doing good, how do you go about getting better at it?

UMMS - James Fodor - Peter Singer

Nick, James, and Peter Singer during Q&A

Around this central idea a new movement has emerged over the past few years known as Effective Altruism, which seeks to use the best evidence available in order to help the most people and do the most good with the limited resources that we have available. Associated with this movement are organisations such as GiveWell, which evaluates the relative effectiveness of different charities, and Giving What We Can, which encourages members to pledge to donate 10% or more of their income to effective poverty relief programs.

Peter-Singer--Adam-Ford-1I was happy to get a photo with Peter Singer on the day – we organised to do an interview, and for Peter to come and speak at the Effective Altruism Global conference later in 2015.
Here you can find number of videos I have taken at various events where Peter Singer has addressed Effective Altruism and associated philosophical angles.

New Book ‘The Point of View of the Universe – Sidgwick and Contemporary Ethics‘ – by Katarzyna de Lazari-Radek and Peter Singer

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My students often ask me if I think their parents did wrong to pay the $44,000 per year that it costs to send them to Princeton. I respond that paying that much for a place at an elite university is not justified unless it is seen as an investment in the future that will benefit not only one’s child, but others as well. An outstanding education provides students with the skills, qualifications, and understanding to do more for the world than would otherwise be the case. It is good for the world as a whole if there are more people with these qualities. Even if going to Princeton does no more than open doors to jobs with higher salaries, that, too, is a benefit that can be spread to others, as long as after graduating you remain firm in the resolve to contribute a percentage of that salary to organizations working for the poor, and spread this idea among your highly paid colleagues. The danger, of course, is that your colleagues will instead persuade you that you can’t possibly drive anything less expensive than a BMW and that you absolutely must live in an impressively large apartment in one of the most expensive parts of town.Peter Singer, The Life You Can Save: Acting Now to End World Poverty, London, 2009, pp. 138-139

 

Playlist of video interviews and talks by Peter Singer:

 

Science, Technology & the Future

 

Understanding the New Statistics

Geoff discusses statistics, confidence intervals, Bayesian approaches, meta-analysis, and problems with the use of ‘P’ values in significance testing.

Geoff Cumming v2.00_00_19_07.Still003Discussion points:
– Describe your background and involvement in statistics.
– How have orthodox statistics helped psychology (& science)? How has it harmed the science?
– What methods, models and tools do you commonly use in data analysis and why do you choose them?
– What is the dance of the p values? How do you cope with dancing p’s?
– What is meta-analysis & how is it done? How have meta-analysts coped with the bias in publishing data and results? What has the profession done about it?
– Confidence intervals help compared to p’s, by providing info about variation. Do they help enough? Why not credible intervals? Do you see a role for Bayesian statistics in day-to-day science?
– Where is statistical inference heading? Is there a next big thing and, if so, what is it?
– Does every student need to learn computer programming (“coding”) nowadays?

Interviewed by Kevin Korb and Adam Ford at Monash University Clayton.

Geoff’s YouTube Channel can be found here.
About the book:
Cumming, G. (2012). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge

–    Explains estimation, with many examples.
–    Designed for any discipline that uses statistical significance testing.
–    For advanced undergraduate and graduate students, and researchers.
–    Comes with free ESCI software.
–    May be the first evidence-based statistics textbook.
–    Assumes only prior completion of any intro statistics course.
–    See the dance of the confidence intervals, and many other intriguing things.

The main message of the book is summarised in two short magazine articles, in The Conversation, and InPsych.
Here is an interview on ABC Radio.

Buy ‘Understanding the New Statistics’ from Amazon

his is the first book to introduce the new statistics – effect sizes, confidence intervals, and meta-analysis – in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics – which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines.

Geoff Cumming - The New StatisticsAccompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI’s simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.

The book’s pedagogical program, built on cognitive science principles, reinforces learning:

  • Boxes provide “evidence-based” advice on the most effective statistical techniques.
  • Numerous examples reinforce learning, and show that many disciplines are using the new statistics.
  • Graphs are tied in with ESCI to make important concepts vividly clear and memorable.
  • Opening overviews and end of chapter take-home messages summarize key points.
  • Exercises encourage exploration, deep understanding, and practical applications.

This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed.

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Simulating for Computational Biology – Arun Konagurthu

Arun Konagurthu - Simulating for Computational Biology v2Arun Konagurthu is a Senior Lecturer at the Clayton School of Computer Science and Information Technology, Faculty of Information Technology, Monash University. Between 2011-2013, Arun was additionally a Larkins Fellow at this faculty.

Arun leads a small research group that researches mainly in computational biology and bioinformatics. His other research interests include data structures and algorithms, computational modeling and simulation, combinatorial optimization, and, since joining Monash in 2011, statistical learning using Minimum Message Length inference.

Points of discussion:
– What’s your overall research problem? If you solved it, how would things change?
– What is ‘stringology’ and how is it relevant to your research problem?
– Describe your use of simulation methods in bioinformatics. What problems do they overcome and how?
– Why do you prefer Bayesian statistics? What difference does it make?
– How do simulation and scoring work together? What kind of scores do you use?
– What’s been the impact of simulation on bioinformatics generally?
– What’s the future of sampling in data science? What’s coming around the corner?

#bayesian #artificialintelligence #datascience

 

 

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Arun Konagurthu Simulating for Computational Biology v1

Aubrey de Grey – SENS Therapy Delivery

In this interview Aubrey discusses some of the various approaches that SENS therapy will likely be delivered. Mostly covering gene therapy. Also see this interview where Aubrey de Grey discusses using artificial organs and synthetic devices as replacement parts to aid in defeating aging.

Ex-Vivo Gene Therapy

Ex Vivo Gene Therapy

ref: yolasite.com (click for larger image)

Some things that people are already looking at, for instance introducing new blood stem cells into AIDS patients that contain an AIDS resistant gene named CCR5. A very small portion of people have a natural variant of that gene, called Delta32, which confers very strong resistance to HIV. If you could give this variant of CCR5 this could be a very powerful therapy – luckily the cells that need to have that variant are blood cells – blood cells come from stem cells – so bone marrow transplants with this appropriately modified version of this gene would be very powerful, and that is already being worked on.
There may be very many other cases of inherited diseases (especially) which could be modified and indeed perhaps cured by using genetic modification of stem cells for stem cell therapy.
Now in the case of ageing, this may also be a good way of delivering certain of the SENS therapies – the one that’s most obvious is LysoSENS – the Lyso Enhancement idea for getting rid of the molecular garbage inside of cells – because here we have to introduce new enzymes into these cells (enzymes that are not encoded into the normal human genome) and in some cases it may make sense to actually introduce the enzymes by injecting the enzymes into the circulation in the same way we already treat certain inherited diseases of Lysosomal function (called Lysosomal storage diseases). But in other cases it may be actually be preferable to make genetic modifications to stem cells so that the blood cells or the other cells that are created from those stem cells are able to have that genetic modification and thereby not to accumulate the molecular garbage that we are talking about – perhaps even to eliminate the molecular garbage that had already accumulated.

Somatic Gene Therapy

In_vivo_gene_therapy

ref: yolasite.com (click for larger image)

Some of what were going to need to do in genetic modification of people so as to implement SENS will not, or almost certain not be able to be implemented using ex-vivo gene therapy – the genetic modification of cells outside the body that are then introduced into the body. Some of it is going to have to be done by genetically modifying cells in the body itself. That is what is called ‘Somatic Gene Therapy’ – the way it’s normally done is by engineering a virus contain the engineered DNA that we are interested in and not to contain the DNA that the virus naturally has that makes it bad for us*. And of course gene therapy as an idea has been around for quite a long time – and in fact the first clinical trials of gene therapy happened about 20 years ago. But it’s had a pretty rocky ride because in fact there is an awful lot of risks involved in gene therapy and it doesn’t really work very well yet.
There are certain diseases with a very low hit rate – that is getting a suitable genetic modification to a very small number of cells is enough to be able to actually cure the disease. But in most cases you have to hit quite a lot of cells and we really just don’t know how to do that yet. We at SENS foundation are very interested in helping to address that problem and there is one particular approach to improving very substantially the ability to very safely introduce new DNA into a lot of cells into the body which we are just starting a project to explore. **

* Note this interview was done shortly before CRISPR was discovered.
** This project is called?? Note I will follow up with Aubrey de Grey on this point – but my feeling is that CRISPR may have solved the problem, at least partially

Adeno-Associated Virus

Adeno-associated_virus_serotype_AAV2One of the biggest dangers in somatic gene therapy and also it’s a danger for ex-vivo gene therapy (where you genetically modify stem cells and then you introduce them) is that on occasion the engineered gene may go into the genome in the wrong place – into a place that causes damage in the form of disrupting the DNA that was already there – in a way that you don’t want.  In general that disruption is harmless, but very occasionally it may not be harmless – it may actually make the cell cancerous (and there have been genuine cases of this in clinical trials for particular gene therapies).  So, people are very interested in ways to stop that from happening.  The most obvious way to stop that from happening is to develop a gene therapy vector (a type of virus) that preferentially goes into a particular harmless place in the genome and not go into any of the potentially harmful places – now it turns out that there are some viruses that naturally do this – there is something called AAV (Adeno-Associated Virus) which preferentially go into one particular site of chromosome 19 and people have been very interested in that virus for quite a long time for exactly that reason.  However it turns out that its quite complicated to make that really work and the hit-rate is not good enough – it still has random integration at an unacceptably high level.   So people will want to find other ways to go about this – and there really are lot’s of very creative technologies out there that are being explored to do exactly that.  I’m very optimistic that quite soon we will have gene therapy that very robustly does not disrupt DNA that it would be dangerous to disrupt.

RNA Interference

I believe there are other types of manipulation of gene expression other than gene therapy are also potentially valuable in treatment of ageing and of course medicine in general.  A lot of interest these days is in RNA Interference (RNAi) which is a method for inhibiting expression of particular proteins by introducing short RNA molecules that interfere with that process.  And that’s got a lot of potential – people are looking into it in a variety of different applications – one area that people have been trying to look into it for is cancer.  So see if one can close down cells that are over -expressing when they shouldn’t be over-expressing (for example).  Personally I’m not very optimistic about this application for cancer because it’s just too easy for cancers to mutate into a form that makes the RNAi in-effective – so the short RNA does not work anymore.  But in other applications it might be useful.

Neuro-Regeneration

So the brain is of course arguably the most essential to repair from the damage of ageing – there’s not much point in rebuilding the rest of the body if you are demented – how hard is that?  In particular is it significantly harder (to repair) than the rest of the body?  I believe it’s not significantly harder than the rest of the body – ultimately the brain is certainly vastly more complicated than any other organ, and we are vastly more ignorant about how it works than we are about any other organ – but the thing about SENS, the thing about the whole preventative maintenance approach to combating ageing is that we don’t need to understand how the organ works in order to restore its function or we need to do is understand what its made of, and more specifically how what it’s made of changes throughout life so that we can reverse these changes – repair those changes – and put structure and composition of the organ back to how it was at an earlier stage in early adulthood and thereby restore its function irrespective of our ignorance of how that function arises from that structure – that’s just as true for the brain and any other organ.  So one example of this is the fact that brain cells (neurons) don’t divide, and in most cases don’t have per-cursor cells that don’t divide either – there are a couple of areas of the brain that do exhibit the creation of new neurons throughout adulthood – the rest of the brain doesn’t luckily the rest of the brain exhibits a very very very slow rate of cell death – so it’s not really a problem – and the problems we need to fix are the problems of accumulation of garbage inside neurons for example, or outside of neurons that make those neurons not work so well even while those neurons are still alive.

 

Aubrey-de-Grey---SNES-Therapy-Delivery


Aubrey de Grey is the chief science officer of the SENS Research Foundation, which is a 501(c)(3) public charity that is transforming the way the world researches and treats age-related disease.

The research SENS funds at universities around the world and at SENS own Research Center uses regenerative medicine to repair the damage underlying the diseases of aging. The goal of SENS is to help build the industry that will cure these diseases.


Aubrey de Grey was interviewed by Adam Ford in 2012.

Here is a playlist of all the interview sections:

The Revolutions of Scientific Structure – Colin Hales

colin hales orange bg“The Revolutions of Scientific Structure” reveals an empirically measured discovery, by science, about the natural world that is the human scientist. The book’s analysis places science at the cusp of a major developmental transformation caused by science targeting the impossible: the science of consciousness, which was started in the late 1980s by a science practice that cannot, in principle, ever succeed. This impossible science must fail, not because it is malformed, but because it cannot deliver to engineers what is needed to build artificial consciousness.

The book formally reveals how fully expressed scientific behaviour actually has two faces, like the Roman god Janus. Currently we only use one face, the ‘Appearance-Aspect’ and it is measured and properly documented by the book for the first time. Where some scientists accidentally use the other, the two faces are shown to be confused as one. There are actually two fundamental kinds of ‘laws of nature’ that jointly account for the one underlying natural world. The recognition and addition of the second kind, the ‘Structure-Aspect’, is the book’s proposed transformation of science.

The upgraded framework is called ‘Dual Aspect Science’ and is posited as the adult form of science that had to wait for computers before it could emerge a fully formed butterfly from its millennial larval form that is single (appearance)-aspect science. Only ‘Structure-Aspect’ computation can scientifically reveal the principles underlying the nature of consciousness — in the form of the consciousness that is/underlies scientific observation. While this outcome ultimately affects all scientists, initially only neuroscience and physics are those that, together, have the responsibility for the empirical work needed for the introduction of Dual-Aspect science. This is not philosophy. This is empirical science.

More information on this title can be found at: http://www.worldscientific.com/worldscibooks/10.1142/9211#t=aboutBook .

Document of presentation available here:

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.

Maria Entraigues on Anti-Aging and the SENS Research Foundation

Interview conducted in 2012 with Maria Entraigues at the eXtreme Futurist Festival in Los Angeles 2012.
Maria Entraigues is the Global Outreach Coordinator for SENS Research Foundation. As the outreach coordinator for the SENS Research Foundation, Entraigues has represented the Foundation internationally at conferences and in the media, and has explained and promoted the Foundation’s goals of eradicating the diseases and disabilities of aging through innovative biotechnologies, including presentations at conferences internationally. Entraigues is also one of “The 300 Members of Methuselah Foundation”, a group of people committed to help the advancement of technologies to eradicate the needless suffering of age-related disease and extend healthy human life.

The SENS Foundation (Strategies for Engineered Negligible Senescence Foundation) is a 501(c)(3) non-profit organization co-founded by Michael Kope, Aubrey de Grey, Jeff Hall, Sarah Marr and Kevin Perrott, which is based in Mountain View, California, United States. Its activities include SENS-based research programs and public relations work for the acceptance of and interest in scientific rejuvenation research. Before the Foundation was launched in March 2009, the SENS research program was mainly pursued by the Methuselah Foundation, co-founded by Aubrey de Grey and David Gobel.