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Be Greedy For The Most Good You Can Do – Kerry Vaughan – EA Global Melbourne 2015

Filmed at EA Global Melbourne 2015 Slides of talk are here
Kerry Vaughan discusses:
What is effective altruism? what is it’s history? what isn’t EA? and how to succeed at being an effective altruist.
Approaches to doing good include:
– Being Skeptical – using the case study of play pumps in africa – hoping to utilize the renewable energy of children playing – on the surface it looked like a good idea, but unfortunately it didn’t work – so be skeptical
– Changing your Mind – you can score social points in the EA movement by changing your mind – so yay! Moving beyond entrenched beliefs to better ways of thinking leads decision making – do change your mind, update your beliefs when there is evidence to support you doing so
– Do it! – when you find out better approaches to being altruistic, actually follow up and do it – without getting too involved in theorizing whether you have a moral obligation to solve the problem, just go solve it
– 3 strands to the history of EA – Peter Singer’s work, Holden Karlovsky and Elie Hassenfeld at Give Well, the rationalist movement (inc CFAR)
Kerry then discusses the growth of the EA movement.
Approaches to EA based on evidence (empiricism) and also strong philosophical arguments (esp in the absence of evidence – for instance with Existential Risks or far future scenarios)
How to succeed at EA Global: get help, and make radical life change.

Kerry Vaughan - Be Greedy for the Most Good - EA Global Melbourne 2015 - Effective Altruism 2

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Nietzsche, the Overhuman, and Transhumanism – Stefan Lorenz Sorgner

Did Nietzsche have something like Transhumanism in mind when he wrote about the Übermensch?


Abstract

Bostrom rejects Nietzsche as an ancestor of the transhumanist movement, as he claims that there were merely some “surface-level similarities with the Nietzschean vision” (Bostrom 2005a, 4). In contrast to Bostrom, I think that significant similarities between the posthuman and the overhuman can be found on a fundamental level. In addition, it seems to me that Nietzsche explained the relevance of the overhuman by referring to a dimension which seems to be lacking in transhumanism. In order to explain my position, I will progress as follows. First, I will compare the concept of the posthuman to that of Nietzsche’s overhuman, focusing more on their similarities than their differences. Second, I will contextualise the overhuman in Nietzsche’s general vision, so that I can point out which dimension seems to me to be lacking in transhumanist thought.”

Introduction

Nietz-wordsWhen I first became familiar with the transhumanist movement, I immediately thought that there were many fundamental similarities between transhumanism and Nietzsche’s philosophy, especially concerning the concept of the posthuman and that of Nietzsche’s overhuman. This is what I wish to show in this article. I am employing the term “overhuman instead of “overman,” because in German the term Übermensch can apply to both sexes, which the notion overhuman can, but overman cannot. I discovered, however, that Bostrom, a leading transhumanist, rejects Nietzsche as an ancestor of the transhumanist movement, as he claims that there are merely some “surface-level similarities with the Nietzschean vision” (Bostrom 2005a, 4).

In contrast to Bostrom, I think that significant similarities between the posthuman and the overhuman can be found on a fundamental level. Habermas agrees with me in that respect, as he has already referred to the similarities in these two ways of thinking. However, he seems to regard both of them as absurd. At least, he refers to transhumanists as a bunch of mad intellectuals who luckily have not managed to establish support for their elitist views from a bigger group of supporters (Habermas 2001, 43).1

In addition, it seems to me that Nietzsche explained the relevance of the overhuman by referring to a dimension which seems to be lacking in transhumanism. In order to explain my position, I will progress as follows. First, I will compare the concept of the posthuman to that of Nietzsche’s overhuman, focusing more on their similarities then on their differences. Second, I will contextualise the overhuman in Nietzsche’s general vision, so that I can point out which dimension seems to me to be lacking in transhumanist thought.
Nietzsche, the Overhuman, and Transhumanism – Journal of Evolution and Technology

Bio: Dr. Stefan Lorenz Sorgner is director and co-founder of the Beyond Humanism Network, Fellow at the Institute for Ethics and Emerging Technologies (IEET) and teaches philosophy at the University of Erfurt. He studied philosophy at King’s College/University of London (BA), the University of Durham (MA by thesis; examiners: David E. Cooper, Durham ; David Owen, Southampton), the University of Giessen and the University of Jena (Dr. phil.; examiners: Wolfgang Welsch, Jena; Gianni Vattimo, Turin). In recent years, he taught at the Universities of Jena (Germany), Erfurt (Germany), Klagenfurt (Austria) and Erlangen-Nürnberg (Germany). His main fields of research are Nietzsche, the philosophy of music, bioethics and meta-, post- and transhumanism.

 

sorgner

Also see David Pearce’s critique on whether Nietzsche was a transhumanist.
Various articles on transhumanism and Nietzsche at IEET.

Science, Technology & the Future

Meta: Overman / Übermensch, Will to Power & Transhumanism, The Last Man
2014 08 02 01 16 09

Was Friedrich Nietzsche a Transhumanist? A critique by David Pearce

Bioconservatives often quote a line from Nietzsche: “That which does not crush me makes me stronger.” But alas pain often does crush people: physically, emotionally, morally. Chronic, uncontrolled pain tends to make the victim tired, depressed and weaker. True, some people are relatively resistant to physical distress. For example, high testosterone function may make someone “tougher”, more “manly”, more resilient, and more able to deal with physically painful stimuli. But such strength doesn’t necessarily make the subject more empathetic or a better person. Indeed, if I may quote W. Somerset Maugham, “It is not true that suffering ennobles the character; happiness does that sometimes, but suffering, for the most part, makes men petty and vindictive.”

To those human beings who are of any concern to me I wish suffering, desolation, sickness, ill-treatment, indignities – I wish that they should not remain unfamiliar with profound self-contempt, the torture of self-mistrust, the wretchedness of the vanquished: I have no pity for them, because I wish them the only thing that can prove today whether one is worth anything or not – that one endures.Friedrich Nietzsche - The Will to Power, p 481
You want, if possible – and there is no more insane “if possible” – to abolish suffering. And we? It really seems that we would rather have it higher and worse than ever. Well-being as you understand it – that is no goal, that seems to us an end, a state that soon makes man ridiculous and contemptible – that makes his destruction desirable. The discipline of suffering, of great suffering – do you not know that only this discipline has created all enhancements of man so far?Friedrich Nietzsche - Beyond Good and Evil, p 225
“I do not point to the evil and pain of existence with the finger of reproach, but rather entertain the hope that life may one day become more evil and more full of suffering than it has ever been.Friedrich Nietzsche (1844-1900)

Of course, suffering doesn’t always enfeeble and embitter. By analogy, someone who is emotionally depressed may feel that despair is the only appropriate response to the horrors of the world. But the solution to the horrors of the world is not for us all to become depressed. Rather it’s to tackle the biology of depression. Likewise, the solution to the horrors of physical pain is not to flagellate ourselves in sympathy with the afflicted. Instead it’s to tackle the biological roots of suffering.

See also the article at IEET

i09 article on eliminating suffering

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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

Bayeswatch – The Pitfalls of Bayesian Reasoning – Chris Guest

Chris Guest - Headshot 1Bayesian inference is a useful tool in solving challenging problems in many fields of uncertainty. However, inferential arguments presented with a Bayesian formalism should be subject to the same critical scrutiny that we give to informal arguments. After an introduction to Bayes’ theorem, some examples of its misuse in history and theology will be discussed.

Chris is a software developer with an academic background in Philosophy, Mathematics and Machine Learning. He is also President of the Australian Skeptics Victorian Branch. Chris is interested in applying critical reasoning to boundary problems in skepticism and is involved in consumer complaints and skeptical advocacy.

 

Talk was held at the Philosophy of Science Conference in Melbourne 2014

Video can be found here.

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.

The Shaky Foundations of Science: An Overview of the Big Issues – James Fodor

James Fodor 2013Many people think about science in a fairly simplistic way: collect evidence, formulate a theory, test the theory. By this method, it is claimed, science can achieve objective, rational knowledge about the workings of reality. In this presentation I will question the validity of this understanding of science. I will consider some of the key controversies in philosophy of science, including the problem of induction, the theory-ladenness of observation, the nature of scientific explanation, theory choice, and scientific realism, giving an overview of some of the main questions and arguments from major thinkers like Popper, Quine, Kuhn, Hempel, and Feyerabend. I will argue that philosophy of science paints a much richer and messier picture of the relationship between science and truth than many people commonly imagine, and that a familiarity with the key issues in the philosophy of science is vital for a proper understanding of the power and limits of scientific thinking.

Slides to the presentation available here: