Dn_Ab

As others have said, F# interesting language features are computation expressions, active patterns, units and type-providers. The library, platforms and ecosystem benefits are gravy. Though subjective, the syntax is clean too, being somewhere between an ML and Python.

Something that no one has mentioned yet is that F# is now among the fastest functional first programming languages. At least according to (take with a grain of salt) benchmarks like [1] and https://www.techempower.com/benchmarks/

[1] https://benchmarksgame-team.pages.debian.net/benchmarksgame/...

I did not downvote (and you're clear now) but your post is not a relevant argument. The determinism that the SFWT is arguing against is that of certain hidden variable theories of quantum mechanics. It states that if the humans are free to choose particular configurations for an experiment measuring this or that spin, then bounded by relativity and experimentally verified aspects of quantum mechanics, the behaviors of the particles cannot be dependent on the past history of the universe. The main characters are the particles, people are incidental.

"Our argument combines the well-known consequence of relativity theory, that the time order of space-like separated events is not absolute, with the EPR paradox discovered by Einstein, Podolsky, and Rosen in 1935, and the Kochen-Specker Paradox of 1967"

So as far as I can tell, it takes for granted the humans' ability to choose the configurations freely, which though suspect in of itself doesn't matter so much to their argument as it's not really an argument for free will, it's a discussion of how inherent to quantum mechanics non-determinism is.

"To be precise, we mean that the choice an experimenter makes is not a function of the past."

"We have supposed that the experimenters’ choices of directions from the Peres configuration are totally free and independent."

"It is the experimenters’ free will that allows the free and independent choices of x, y, z, and w ."

It is actually, if anything, in favor of no distinction between humans and computers (more precisely, it is not dependent on humans, only a "free chooser") as they argue that though the humans can be replaced by pseudo random number generators, the generators need to be chosen by something with "free choice" so as to escape objections by pendants that the PRNG's path was set at the beginning of time.

The humans who choose x, y, z, and w may of course be replaced by a computer program containing a pseudo-random number generator.

"However, as we remark in [1], free will would still be needed to choose the random number generator, since a determined determinist could maintain that this choice was fixed from the dawn of time."

There is nothing whatsoever in the paper that stops an AI from having whatever ability to choose freely humans have. The way you're using determinism is more akin to precision and reliability—the human brain has tolerances but it too requires some amount of reliability to function correctly, even if not as much as computers do. In performing its tasks, though the brain is tolerant to noise and stochasticity, it still requires that those tasks happen in a very specific way. Asides, the paper is not an argument for randomness or stochasticity.

” In the present state of knowledge, it is certainly beyond our capabilities to understand the connection between the free decisions of particles and humans, but the free will of neither of these is accounted for by mere randomness."

Wow, your idea is heading incredibly towards the same general direction as https://en.wikipedia.org/wiki/Sparse_distributed_memory described there. Excellent!

One can view RNNs as a sort of generalization to markov chains. RNNs have the advantage of a memory, context tracking and are not limited to learning patterns of some specific length. RNNs can apply these advantages to learn subtleties of grammars, balance parenthesis, the proper use of punctuation and other things that a markov chain might never learn (and certainly not memory efficiently). For any given piece of text, RNNs can be said to have gotten closer to understanding what was consumed.

The other question is, are those difficult to learn things truly worth the cost of training and running an RNN? If a fast and simple markov chain serves, as is likely the case in practical settings, then it is better to go with the markov chain. The RNN will still make obvious mistakes, all while correctly using subtle rules that trouble even humans. Unfortunately, this combination is exactly the kind of thing that will leave observers less than impressed: "Yes I know it rambles insensibly but look, it uses punctuation far better than your average forum dweller!" Alas, anyone who has gone through the trouble of making a gourad shaded triangle spin in Mode X and proudly showing their childhood friends, can explain just what sort of reaction to expect.

Eh, so, the moral here is pay attention to cost effectiveness and don't make things any more complicated than they need to be.

Yoav Goldberg treats much the same thing as this blog post but with far more detail and attention to subtlety here: http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139

It's not a report, it's an almost 400 page book. He doesn't compare random energies, instead he looks at what, under reasonably generous conditions, the daily power budget each energy source could provide per person. The very generous 20kWh/d of power per person is the key point. 6 m/sec is already high, with few places reaching such speeds consistently. And almost no one will reach double that, so you can look at an optimal 17 W/m^2 for wind. http://web.stanford.edu/group/efmh/winds/global_winds.html

In chapter 25, he acknowledges that while the cost of photovoltaics will fall, he does not see it doing so in a timeline that will be useful in terms of getting everything deployed for a ~2050 deadline. Economically speaking, carpeting deserts with concentrating collectors will be the cheaper of the solar options. The book is careful about doing all the math, citing all its sources and carefully explaining the scenarios it models. It is a very good book[+].

But cost is not the only issue—even as prices fall, there is still the problem of land use area. Efficiencies aren't going to pass 30% (without going to much more expensive materials) and for mass production, we can halve that; cheap as panels may someday become, places with high pop densities (on top of seasonal variations/not being near the equator) are going to have trouble meeting their needs. Especially if they don't want to get rid of their curling irons, hair/clothes dryers, toasters and electric stove/kettles. But panels/turbines aren't the whole picture.

Already today, panels take up only a fraction of the cost of solar. You ideally, want an MPPT controller. You might need voltage regulators, you'll need a rack for the panel and batteries, an appropriately sized inverter, wiring and installation. Batteries—to save more money long term—you want to oversize them so you rarely hit a low depth of discharge. But more batteries means more panels. You also want enough batteries such that you can wait out ~4 days of low light (speaking from experience, on cloudy days you can go the entire day at ~13% typical amp output). Even those at the equator will only get ~6 good hours of sunlight (~8 hours for an appreciable amount), so even for the best case scenario, 12 hours of storage per person is not going to cut it. Solar is great but it's no panacea. And the math doesn't work out for chemical energy storage. Molten salt storage, compressed air look to be more logical at the grid level but even they won't be sufficient.

That said, Mr Theil is also incorrect to place Nuclear in opposition to renewables. Renewables will be in addition to Nuclear [-]. As well as looking into more DC appliances, more HVDC and working out circuit breakers for them, optimal manufacturing layouts such that 'waste output' can be redirected to where it is needed. More energy efficient devices, energy routing algorithms (and a global grid of superconducting HVDC while we're at it—seems far fetched but still at a much higher technological readiness level when compared to fusion), better city planning, climate control with geothermal heatpumps, more material reclamation and recycling, nuclear waste as fuel, carbon capture, extracting CO2 from the ocean for fuel and a cultural move away from an over consuming disposable society.

[+] I am biased in that I'd already known the author for one of the best free books on information theory and machine learning. Anyone interested in the link between learning, energy and thermodynamics should see this book as a starting point. http://www.inference.phy.cam.ac.uk/itprnn/book.pdf

[-] Ch. 24 of sewtha.pdf goes into numeric data backed detail on why most build out, waste, cost arguments against nuclear are weak. Personally, I think at best, we only have a couple hundred more years where we can all be justifiably irrationally paranoid over Nuclear. We should have DNA repair down by then.

Ah, I was confused for a second—I'd thought Markov was a library, but you meant the markov assumption—the topics are actually loosely related and orthogonal. Your excellent looking library deals with reinforcement learning agents that model environment/agent interactions as a (PO)Markov Decision Process, where as the Alchemy library combines FOL with network representations of particular (satisfying certain markov properties) probability distributions to perform inference.

More pertinent to your post, Sutton's working on an updated RL book here: http://people.inf.elte.hu/lorincz/Files/RL_2006/SuttonBook.p...

If anyou have the time, Chapter 15 (pdf pg 273) of the above link is a fascinating read. In particular, TD-Gammon had already achieved impressive results using NNs in the early 90s; reaching world class levels in Backgammon with zero specialized knowledge.

This is a medical case study however, not a journalistic piece and you two are being at least a little bit unfair, I think. Since seizures and sudoku are not a common combination, upon seeing the title, I assumed it was something conditional. This sort of Crucifix Glitch—ahem, environmental epilepsy—is also very uncommon and usually genetic, which makes this all the more interesting.

Here, it seems inhibitory circuits in a section of the right parietal lobe were damaged; without dampening, as with any feedback system, the system quickly goes out of whack. What's interesting here is that in this patient, the only activity that seems to generate a pattern resulting in such over-excitation is playing sudoku. But surely that's not the only Visuospatial task he partakes in, so why? All we're left with is: "Our patient stopped solving sudoku puzzles and has been seizure free for more than 5 years".

I agree that this wasn't done by the computer (did computers uncover the Higgs Boson?) but I also do not believe humans can take most of the credit: this was the result of a Man Machine System team up—trying to disentangle credit assignment is not a worthwhile activity. Roughly and from a quick reading of a paper thickly frosted with jargon I am unfamiliar with, the method works by creating networks—which highlight key relationships—for visualization by searching for stable clusters in a reduced dimensionality space of the variables.

Humans are there to explore the visualizations, interpret the network structures and understand the clusters and variables. The machines are intelligent too; they do the heavy work of comparing large numbers of points in a high dimensional space, factorization and searching for a way to express the data in a manner that makes it easier to uncover promising research directions and hypotheses.

Scanning this, it seems the most valuable contribution are their network visualization and exploratory tools. I think they should be proud of those and see no need to stretch so mightily to connect this to Stronger AI. As Vinge notes, "I am suggesting that we recognize that in network and interface research there is something as profound (and potential wild) as Artificial Intelligence."

http://www.nature.com/ncomms/2015/151014/ncomms9581/full/nco...

All the things you mentioned (plus e.g. bayesian networks and Restricted Boltzmann Machines) are examples of Graphical Models. You can roughly think of (linear chain) CRFs as being to HMMs as logistic regression is to Naive Bayes. HMMs and Naive bayes learn a joint probability distribution on the data while Log Reg and CRFs fit conditional probabilities.

If none of that makes sense then, basically, in general and with more data, the CRF (or discriminative classifier) will tend to make better predictors because they don't try to directly model complicated things that don't really matter for prediction anyways. Because of this they can use richer features without having to worry about how such and such relates to this or that. All this ends up making discriminative classifiers more robust when model assumptions are violated because they don't sacrifice as much to remain tractable (or rather, the trade off/sacrifice they make tends to end up not mattering as much when prediction accuracy is your main concern).

So in short, you use a HMM instead of a Markov Chain when the sequence you're trying to predict is not visible. Like say when you want to predict the parts of speech but only have access to words, you'll use the relationship between the visible sequence of words to learn the hidden sequence of Parts of speech labels. You use CRFs instead of HMMs because they tend to make better predictors while remaining tractable. The downside is discriminative classifiers will not necessarily learn the most meaningful decision boundaries, this starts to matter when you want to move beyond just prediction.

Time reversibility exists in quantum mechanics because observables are self adjoint operators. Closed systems evolve unitarily. In simpler terms, you can think of it as the requirement that maps preserve distances and are easily invertible. We need this so that the information describing a system (which we can still talk about in terms of traces), remains invariant with time. In the classical sense, the corresponding violation leads to probabilities not summing to 1! We clearly can't have information shrink and for pure systems, dropping distance preserving maps leads to a really awesome universe (I believe this also ends up highly recommending L2). We literally go from a universe that is almost certainly near the bottom end of the Slow Zone of Thought to the Upper Beyond (https://en.wikipedia.org/wiki/A_Fire_Upon_the_Deep#Setting). We gain non-locality, causality violations and powerful computational ability.

In practice, our confusion about a system does increase with time as classical systems become ever more correlated, losing distinguishability, aka decoherence.

In nature, decentralized networks are more the norm. You find them in neural, gene, protein and metabolic networks. You find them in mycorrhizal networks of forests and in various self-organizing systems. Food webs, IIRC are even more random, as far from centralized as you can get.

These networks with small world properties strike a highly pragmatic balance. They are far more robust to insult when compared to centralized networks (though not so much as random) while having much more efficient propogation of information than in more random networks.

The short version is, if this is correct (which is exceedingly unlikely) then either quantum mechanics is wrong or whether P=NP has just become irrelevant. This is because, as Scott Aaronson is often forced to tirelessly point out, an inability to build a Quantum Computer must also show Quantum mechanics to somehow be in error. If instead, we can build a Quantum computer and this result is true then the universe has suddenly become an incredibly more interesting place (for one example, building this would also solve AI).

The result also has consequences for quantum computation specifically, in that it also takes care of the negative sign problem, which arises when simulating certain kinds of important quantum many body systems.

And last but certainly not least, all those press releases breathlessly proclaiming Quantum computers work by trying a gazillion possibilities at once (with no worries about the plausibility of actually reading out the correct answer with a non-negligible probability) weren't so far off after all.

All in all, either there is a mistake hidden somewhere in the preprint or this is the greatest scientific achievement since Evolution invented Human Level intelligence.

See here for intuition on why NP-complete problems if at all solvable without brute force, should also be efficiently solvable: http://windowsontheory.org/2013/05/06/reasons-to-care-in-hon...

I must very strongly disagree with you. ScienceDaily is as good an aggregator as you'll find. Sometimes, I'll even purposely seek out their write-ups on a topic because of one thing they almost always do, while almost everyone else doesn't: make it easy to find the source paper.

I rarely ever stop to read their replicated press releases—read the press release and read the article. They're basically the same thing. ScienceDaily just regurgitates source articles; any grievance one has with the article should instead be taken with the issuing University. It's why there's such high variance in the quality of ScienceDaily articles.

They provide a link here: http://www.eurekalert.org/pub_releases/2015-05/uoc--dpu05121...

And provide a title: Journal Reference:

    David Schaffer et al. Systemic attenuation of the TGF-β pathway by a single drug simultaneously rejuvenates hippocampal neurogenesis and myogenesis in the same old mammal. Oncotarget, May 201

SVMs were invented by a couple statisticians/mathematicians in the 60s. k-means also harkens back to the 60s, by mathematicians and control theorists. Decision Trees and Random forests were invented by a famous statistician, with the latter related to bootstrapping, a statstical technique. PCA and factor analysis, forms of or closely related to low rank matrix approximation, were pioneered in the early 1900s, by some of the most famous statisticians ever.

> Would, say, a Ruby or node.js-oriented web developer find the F#/.NET community lacking, incomplete, unfriendly, uncool? Is there even a community to speak of?

The answer in my opinion is yes. This may change, given recent efforts by Microsoft, however evolution will be slow because the whole ecosystem has to change, not just Microsoft.

A suspicion gnaws at me. I try to rid myself of it but alas, it holds fast and unyielding. That suspicion being: you know very little about the F# community. What statements you make, they seem descendents of generalizations from your experience with the .Net community. Happily, F# is not like the stereotypical .NET community (I must be so specific, in order that I not offend the subset to whom your claims do not apply). There is a strong culture of embracing open tools and code; Microsoft is not looked at for direction beyond what basic support they provide.

The community is neither lacking nor is it unfriendly. It is small, yes. Uncool? Opposite of! But opinions run with tastes. Incomplete is a tricky matter. Certainly it's not going to have as many libraries as Python or the JVM, but neither is it some kind of backwater.

There are excellent build tools like FAKE and paket. Awesome ideas like—why, take a look at how fantastic the HTML type provider is, and it works in real life too! Most of the time =) [http://fsharp.github.io/FSharp.Data/library/HtmlProvider.htm...]

There are tons of cool libraries available, most of them open source, with generous licensing terms. Stuff with Haskell heritage like fparsec, fscheck, the blazing fspickler combinators (serializer) or more computation oriented tools that let you target and run on the GPU or Here: Automatic Differentiation (after which, loss minimizing Machine Learning algorithms are made much easier) [http://gbaydin.github.io/DiffSharp/]. Or the DataFrames library: http://bluemountaincapital.github.io/Deedle/

Type providers also allow easy use of UI builders. Web frameworks and also, js targeting can be reached here: http://fsharp.org/guides/web/. You can target Unity3D and apparently, also the Unreal Engine.

There are interesting projects looking at distributed computation (https://github.com/fsprojects/Cricket and ilk) or light weight concurrency, in Hopac's take on the Pi Calculus.

F# was one of the earlier languages with light weight threads, a solid async story and first class events (pre-reactive trend). Active Patterns (not unique to F# but more common in) take us close to Predicate Dispatch (http://c2.com/cgi/wiki?PredicateDispatching). There is much more I could list and hopefully, I have piqued some interest.

But it's not perfect. Adding Functor support would be very useful. A while ago, in an early Active Patterns paper, there were hints that generalized algebraic datatypes might soon be introduced. Nothing came of that. Higher kindedness is nice but not as much missed—it is my suspicion that, the gains from each level of types that can be parameterized over, quickly saturates.

There are lots of cool projects going on in F#, and while, as a language it's definitely not as powerful as say Scala or Haskell; the tools, libraries and environment, alternate ideas as well as breezy syntax, do make up. Having used them all, I wouldn't say it is any less expressive, it just...prioritizes differently beyond the core functional ideas (REPL, sum/product types, currying, closures, point free application where possible, Hindley Milner inference, immutability by default, pattern matching and deconstruction, etc., etc.).

It strikes a lot of middle ground across many planes, in terms of pragmatism vs functional purity—that is its ML heritage (but perhaps even more so, bargained in exchange for the .NET ecosystem). Most functional languages focus on types in terms of algebra, F# does too but only basically, instead it focuses more on the easy bridging of types with unruly data.

There are actually 2 dates,the 24th and the 25th, in the data sample.

You can do this in a manner that's both fairly comprehensible and succint, for arbitrary number of dates, using a Json TypeProvider in F#.

  #r @"../Fsharp.Data.dll"  
  open FSharp.Data  
  open System

  type PersonsData = JsonProvider<"../data.sample.json">

  let dateTriple (d:PersonsData.Root) = d.Timestamp.Year, d.Timestamp.Month,d.Timestamp.Day 
  
  let info = PersonsData.Load ("./data.json")
  
  let uniqueDates = info |> Array.map dateTriple |> set
  
  let createCsv (d : PersonsData.Root seq) =    
    d |> Seq.filter (fun p -> Option.isSome p.Creditcard)
      |> Seq.map (fun p -> sprintf "%s,%s" p.Name p.Creditcard.Value )   
      |> String.concat "\n"

  info |> Seq.groupBy dateTriple
       |> Seq.iter (fun ((y,m,d), data) ->
         IO.File.WriteAllText (sprintf "%d%02d%02d.csv" y m d, createCsv data))

    [{"name":"Quincy Gerhold","email":"laron.cremin@macejkovic.info","city":"Port Tiabury","mac":"64:d2:17:ff:28:13","timestamp":"2015-04-25 15:57:12 +0700","creditcard":null},{"name":"Lolita Hudson","email":"tracy.goodwin@schmidt.com","city":"Port Brookefurt","mac":"2d:20:78:41:8e:35","timestamp":"2015-04-25 23:20:21 +0700","creditcard":"1211-1221-1234-2201"}]

Other excellent stories in this genre: Blood Music by Greg Bear, Permutation City and Crystal Nights (http://ttapress.com/553/crystal-nights-by-greg-egan/) by Greg Egan. Marooned in Realtime by Vinge had this a bit too—probably closest thematically to the GP's comment. A bunch of people wake up to a world that's been empty of humans for many thousands of years; the book spends some time talking about how some animals evolved.

I might be mistaken, but it seems to me that the predictor should also be able to solve the halting problem. Additionally, if the entity to which the source-code is a descriptor for is so simple that they can be predicted without full simulation, can we really say they had free will?

It depends, some people distinguish economic rent from economic profit by restricting it to economic profits that cannot be reduced to normal by competition in the long run. Some additionally include profits without any opportunity costs.

Economic rents run counter to properly functioning markets, e.g. by raising artificial barriers such as patents or counterproductive regulation.

The key tenet of perfect competition is that in the long run, only normal profits exist. Since economic rent stands contrary to this, it cannot be a fundamental law of capitalism. In fact, economic rent is a sign of market failure. But this is reality and markets can't be perfect, so we try for good tax policies to redress this imbalance. Look at the section on rents here: http://www.economist.com/economics-a-to-z/r#node-21529784

As an aside, people think Thiel's idea on monopoly is controversial, but really it's just a catchy way of portraying the observation that, since in well functioning markets, competition leads to zero economic profit, you should always be seeking advantages (innovating) and situations which allow you to (temporarily) extract economic profit.

This uses a particular form of a fundamentally simple yet surprisingly powerful class of learning algorithms called regret minimization. CFR is interesting in an of itself as it specializes regret minization to play extensive form games. There are also CFR algorithms to play multiplayer and no-limit games and though the guarantees of optimality are no longer there, the players are still strong (but for now, far away from experts).

The article states that this algorithm is weak to bad players but that's more an artifact of resources and training method; one advantage of minimizing regret on games instead of using linear programming is that online learning versions can adapt to exploit poor play with payoff larger than the game's value.

I've also posted here before that RM solves 2 player Zero sum game more efficiently than linear programming and how it's related to boosting, portfolio optimization and as an abstraction of natural selection.

http://www.pnas.org/content/111/29/10620.full

I do not think the manner in which you've apportioned blame is correct. The article is less technical than the press release yes, but the core of both remains the same. They both talk about fasting leading to a depletion of white blood cells, triggering "stem cell-based regeneration" of new immune cells and how this may prove useful when treating cancer patients.

The main difference in the two is that the press release does not use the word 'entire' in its headline and the regeneration aspect was only specified for mice. But, and this is important, the press release does not make particularly clear whether the regenerative aspect was also looked for in humans (was it? the quote makes it seem so). I do not blame the likely time starved writer for not bothering with nuance if the press release itself doesn't take the time to be clear. The news article is also better for its attempt at sampling more skeptical views, most 'reporting' does not go that far.

A lot of (most?) science reporting is just regurgitating press releases, if science reporting is poor then press releases can explain the bulk of that shoddy reporting. The game of telephone aspect of paper to blogs can't be avoided so it pays to take a much more sober approach when writing press releases:

40% (95% confidence interval 33% to 46%) of the press releases contained exaggerated advice, 33% (26% to 40%) contained exaggerated causal claims, and 36% (28% to 46%) contained exaggerated inference to humans from animal research. When press releases contained such exaggeration, 58% (95% confidence interval 48% to 68%), 81% (70% to 93%), and 86% (77% to 95%) of news stories, respectively, contained similar exaggeration, compared with exaggeration rates of 17% (10% to 24%), 18% (9% to 27%), and 10% (0% to 19%) in news when the press releases were not exaggerated

-- http://www.bmj.com/content/349/bmj.g7015

Good science reporting on: http://phenomena.nationalgeographic.com/2014/12/09/the-power...

I'd characterize this differently and also, as a lot more interesting than that. understanding.pdf can be viewed as a sort of dual to this paper but they're not covering the same thing. In Szegedy et al., they constructed invisibly perturbed images that resulted in the misclassification of previously correctly classified images. Here, the results of a search were images whose classification have little to no visual similarity to typical members of that class.

In a way this is interesting because it's a sort of visualization of what the network views as important in discriminating between different objects. It's also interesting as a display of how alien the learned model's view of the world is.

Take optical illusions...optical illusions are remotely similar to this sort of exploit, although the sort of scene modeling we do is a lot more complex than recognition or decomposition. Anyways, illusions exploit cues that result in distorted recognition but not drastically so, unlike the case for these networks. My guess is that this is due to animal vision using a lot more high level cues -- cues that are also useful in a natural setting -- depending on things like size, color, shade, lines, context and so on. Visual systems are also a lot more proactive, filtering out things that don't make sense, fudging color at the edges of vision, smoothing out shades and generally making inferences and deductions about what it should be seeing and how things are "supposed" to be. In fact, a good number of illusions exploit those aspects of vision.

In the case of these networks, the cues are incomprehensible, having no natural counterpart, so we see most of them as noise. But sometimes they make a kind of sense, as in the starfish, baseball and sunglasses examples. Based on the observations in the paper, I would guess only a handful activations strongly associated to each feature are responsible for each susceptibility.

With animal brains the distortions usually end up in a slightly transformed space, a different scaling or something. It's useful to match a bit overzealously and get something like pareidolia but it also makes sense to have the conflations actually be like something you might run into. The ANNs have no such incentive.

Their paper also wonders about whether this is unique to discriminative classifiers. Would a generative classifier, with access to a proper distribution, be so susceptible? That'd be very interesting to see.

They also mention some real world consequences, some of which I disagree with. Neural Networks are good at interpolating between examples, so if your training has good coverage over what is to be expected then it'll work very well. And in the era of big data this isn't really a problem (that they don't generalize as we do might explain some of why they have trouble with abstract images) so I'm skeptical an image search solution would be thrown off by textures.

There is, however, a better example of facial or speaker recognition. For example, you could train a network to distinguish between faces or voices and then evolve a pattern against it. This could then be used in such a way as to be randomly matched to an individual on a target database. Not good. Driverless cars are also mentioned but those are typically augmented beyond just vision. Personally, I'd add medical scans to the list of things to be careful with.

Finally, it's worth mentioning that some of the evolved images are inspired works of art. And a few of the images optimized (not evolved) with an L2 penalization are recognizable without the label and a few more where you can see why it gave the label it did.

Your offhanded dismissal was unwarranted IMO.

Excellent observation! In this video, Dr Christos Papadimitriou lays out a compelling argument for why genetic algorithms are poor at optimizing individual fitness when compared to simulated annealing. The video is about asexual vs sexual evolution and how it connects to multiplicative update/weighted majorty type algorithms. https://www.youtube.com/watch?v=cWv-s6KuDlM

In other settings, a simple approximation of what Evolution does will balance your portfolio and find Nash Equilibriums for zero sum games much more quickly than linear programming (query: multiplicative update evolution).

Having to wait for things to die can be restrictive (but genomes are declarative, what does that buy?). Roughly, with what amounts to the assumption that Lamarckian evolution is more or less not true, you can characterize evolution's learning power as almost as good as PAC (query: statistical query learning evolution).

See alsos:

http://vserver1.cscs.lsa.umich.edu/~crshalizi/weblog/601.htm...

http://arxiv.org/abs/1211.4037: Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory

Dynamic dispatch is a special case of predicate dispatch. In particular, captainmuon pointed out that:

Pattern matching in functional languages solves this in a different way.

captainmuon wanted a more flexible way of dispatching on types similar to what can be done with Pattern matching (which is also a special case of predicate dispatch).

In the post I linked to above this example is given:

Basically, instead of basing the dispatch on an "is_a" check, you check whether a general predicate is valid on the argument. So, in imaginary syntax instead of writing

  int foo( int a, int b):
  if a > 0 
	return a
  else
	return b-a

  int foo ( gt_zero? a, int b): return a
  int foo ( int a, int b): return b

Polymorphic dispatch (is that the term?) to replace if blocks.

It looks like you're talking about Predicate Dispatch. http://c2.com/cgi-bin/wiki?PredicateDispatching

There are some doubting the veracity of the article, and while it is onesided in how it paints Newton, the main conclusion, how Newton's childhood might have contributed to his penchant for isolation is a solid one.

Newton was in his time known as vindictive, secretive, paranoid, sensitive (especially to criticism, he would have made many enemies here on HN) and fairly querulous. Throughout his life he had few friends, wasted no time on idling and had no time for art or music. He was puritanical and deeply religious. In his early notebooks he recorded sins such as: Squirting water on Thy day' and 'Making pies on Sunday night'

He was also prone to rages, as recorded in that same notebook:

'Striking many'; 'Punching my sister'; 'Wishing death and hoping it to some'.

In [1] it is stated: Given the rage that Newton had shown throughout his life when criticised, it is not surprising that he flew into an irrational temper directed against Leibniz and that Newton's assistant Whiston had seen his rage at first hand. He wrote:- 'Newton was of the most fearful, cautious and suspicious temper that I ever knew.'

However, while these aspects of Newton's personality have recently been the topics of focus in various articles and books, what is often omitted is that Newton was only so vindictive if he felt slighted. Granted, this was not hard to do, but there were other aspects to his character[2]:

He has usually been found to have been an unsmiling and humourless, puritanical man with a countenance that was ‘ordinarily melancholy and thoughtfull’, but which, as Henry More FRS (1614–87) described during a discussion about biblical prophecy, could sometimes become ‘mighty lightsome and chearfull, and in a maner transported.’20

He always kept Close to his Studyes, very rarely went a visiting, & had as few Visitors, ...excepting 2 or 3 Persons… in whose Company he took much Delight and Pleasure at an Evening...I never knew him take any Recreation or Pastime, either in Riding out to take ye Air, Walking, bowling, or any other Exercise whatever, Thinking all Hours lost, yt was not spent in his Studyes

Newton grew up with what seems to be have been a rather disturbed childhood. Isolated, with indication of having been distant to his mother (at least early on) and mistreated by his stepfather; when coupled with how far ahead he was when compared to his peers†, seems to have resulted in an emotionally stunted, insecure and sensitive individual. Taken together with his obsession with righteousness, it is not hard to see how he might have been difficult to get along with, in turn feeding back to poor social ability and bolstering his inclinations towards being alone.

Newton seemed to have suffered from depression and poor self image throughout his life, exacerbated no doubt, to paranoid delusions later on by consumption of heavy metals as mercury, arsenic and lead.

Certainly Newton was not an easy person to deal with; his brilliance, lack of interests and religiosity coupled with insecurity and pensive nature made for a difficult combination to not somehow run afoul of. He was likely not one to suffer fools lightly and was probably very good at holding a grudge. There are signs that he would have been pedantic and insufferable, the servants at his home certainly had no kind words to lay on him. Yet in the context of his early abandonment, the antagonism of his household against his bookishness, the hostility of his stepfather and the isolated childhood from having grown up with a mind so blazingly sharp, it is difficult to fault him for ending up as one so prone to churlishness. But despite a tendency towards a dour disposition, Stukeley wrote of him in his Memoir:

"according to my own observation, tho'. Sr. Isaac was of a very serious, & compos'd frame of mind yet I have often seen him laugh, & that upon moderate occasions. he had in his disposition, a natural pleasantness of temper, & much good nature, very distant from moroseness, attended neither with gayety nor levity. he usd a good many [shrewd] sayings, bordering on joke, & wit. in company he behavd very agreably; courteous, affable, he was easily made to smile, if not to laugh."

†Stukeley later wrote: one reason why Sr. Isaac did not play much with his schoolfellows, was, that generally, they were not very affectionate toward him. he was commonly too cunning for them in every thing. they were sensible, that he had more ingenuity than they

[1] http://www-history.mcs.st-and.ac.uk/Biographies/Newton.html

[2] http://rsnr.royalsocietypublishing.org/content/62/3/289.full

http://www.newtonproject.sussex.ac.uk/prism.php?id=40

Your code is not doing the same thing. A few minor bits; env has no 'lines' in its definition and String.sub does not allocate objects: it's better to think of String.sub, String.length, etc. as functions packaged in a module of helpful combinators.

OCaml is a language with currying and higher order functions, all that power is wasted if instead of composition you `.Objected` into everything. The advantages of piping, currying and composition on first class functions simply cannot be overstated.

Okay, so the next thing, from String.sub line 0 3 it's clear that you need to know the string length, hence the > 4 can't be avoided. You could pack it away into a function but why waste time if you're not going to write this 3 or more times?

The function `start` takes an env, a string list and returns unit. It's a procedure. The function looks for a string that starts with "+++" and either fails to find and returning () else passes to `modified` (also a procedure, tail recursive) which goes through the list (line :: lines returns the head and the tail of the list) applying modifications to env by tracking which lines were modified. `Lines` is an integer list and `result` is an integer pair list, so hopefully it's clearer now why your code is doing something completely different.

OCaml doesn't so much make monads invisible (except for a technical yet handwavy note on semicolons just as true for C) as make them unnecessary due to being more pragmatic. It's strict and allows mutability when necessary. Of course explicitly recognizing when you're using a monad is extemely powerful and allows elegant formulations of lots of wonderful things like LogicT, probabilistic computation and continuations.

Specific instances of monads are definable in most languages in use today, it's when you want to generalize over monads that Haskell stands out. For that you want higher kindedness, Ocaml can simulate them using the module system but it's rather cumbersome. There is a work around described here: https://ocamllabs.github.io/higher/lightweight-higher-kinded.... The work around is also applicable to the cousin, F#: https://github.com/palladin/Higher/tree/master/src/Higher.Co...

As others have mentioned, when the assumptions of conditional independence are met, Gaussian NaiveBayes and MaxEnt will asymptotically learn the same classifier. Except NB will learn it in O(log dimensions) instead of O(dimensions) examples. So in those cases and where you have fewer examples you'll want to use NB.

Even when the independence assumptions are not met, NB will often produce a good classifier if you don't care about the accuracy of the probabilities. NB is also online, resistant to the curse of dimensionality and for categorical data will learn polynomial decision boundaries. So the gain in the trade-off is in ease of implementation (you can write one in a dozen lines or less) that you can throw something close to your raw data at and often get good enough results without any ahead of time training, kernels or regularization.

You are right though that MaxEnt (some sparsity capturing algorithms are as powerful as SVMs) will in general outperform naive bayes and that the averaged perceptron probably has, on average, the best performance/ease of implementation ratio. But for many implementors/problems, where clarity is paramount, those distinctions will not be worth the cost.

Also, you have a wonderful website.