“…the story of Homo sapiens trying to stake a claim on shifting ground, flanked on both sides by beast and machine, pinned between meat and math.” (p.13)

No typo in the title, this is truly how this book by Brian Christian is called. It was kindly sent to me by my friends from BUY and I realised I could still write with my right hand when commenting on the margin. (I also found the most marvellous proof to a major theorem but the margin was just too small…)  “The most human human: What artificial intelligence teaches us about being alive” is about the Turing test, designed to test whether an unknown interlocutor is a human or a machine. And eventually doomed to fail.

“The final test, for me, was to give the most uniquely human performance I could in Brighton, to attempt a successful defense against the machines.” (p.15)

What I had not realised earlier is that there is a competition every year running this test against a few AIs and a small group of humans, the judges (blindly) giving votes for each entity and selecting as a result the most human computer. And also the most human … human! This competition is called the Loebner Prize and it was taking place in Brighton, this most English of English seaside towns, in 2008 when Brian Christian took part in it (as a human, obviously!).

“Though both [sides] have made progress, the `algorithmic’ side of the field [of computer science] has, from Turing on, completely dominated the more `statistical’ side. That is, until recently.” (p.65)

I enjoyed the book, much more for the questions it brought out than for the answers it proposed, as the latter sounded unnecessarily conflictual to me, i.e. adopting a “us vs.’em” posture and whining about humanity not fighting hard enough to keep ahead of AIs… I dislike this idea of the AIs being the ennemy and of “humanity lost” the year AIs would fool the judges. While I enjoy the sci’ fi’ literature where this antagonism is exacerbated, from Blade Runner to Hyperion, to Neuromancer, I do not extrapolate those fantasised settings to the real world. For one thing, AIs are designed by humans, so having them winning this test (or winning against chess grand-masters) is a celebration of the human spirit, not a defeat! For another thing, we are talking about a fairly limited aspect of “humanity”, namely the ability to sustain a limited discussion with a set of judges on a restricted number of topics. I would be more worried if a humanoid robot managed to fool me by chatting with me for a whole transatlantic flight. For yet another thing, I do not see how this could reflect on the human race as a whole and indicate that it is regressing in any way. At most, it shows the judges were not trying hard enough (the questions reported in The most human human were not that exciting!) and maybe the human competitors had not intended to be perceived as humans.

“Does this suggest, I wonder, that entropy may be fractal?” (p.239)

Another issue that irked me in the author’s perspective is that he trained and elaborated a complex strategy to win the prize (sorry for the mini-spoiler: in case you did  not know, Brian did finish as the most human human). I do not know if this worry to appear less human than an AI was genuine or if it provided a convenient canvas for writing the book around the philosophical question of what makes us human(s). But it mostly highlight the artificial nature of the test, namely that  one has to think in advance on the way conversations will be conducted, rather than engage into a genuine conversation with a stranger. This deserves the least human human label, in retrospect!

“So even if you’ve never heard of [Shanon entropy] beofre, something in your head intuits [it] every time you open your mouth.” (p.232)

The book spend a large amount of text/time on the victory of Deep Blue over Gary Kasparov (or, rather, on the defeat of Kasparov against Deep Blue), bemoaning the fact as the end of a golden age. I do not see the problem (and preferred the approach of Nate Silver‘s). The design of the Deep Blue software was a monument to the human mind, the victory did not diminish Kasparov who remains one of the greatest chess players ever, and I am not aware it changed chess playing (except when some players started cheating with the help of hidden computers!). The fact that players started learning more and more chess openings was a trend much before this competition. As noted in The most human human,  checkers had to change its rules once a complete analysis of the game had led to  a status-quo in the games. And this was before the computer era. In Glasgow, Scotland, in 1863. Just to draw another comparison: I like playing Sudoku and the fact that I designed a poor R code to solve Sudokus does not prevent me from playing, while my playing sometimes leads to improving the R code. The game of go could have been mentioned as well, since it proves harder to solve by AIs. But there is no reason this should not happen in a more or less near future…

“…we are ordering appetizers and saying something about Wikipedia, something about Thomas  Bayes, something about vegetarian dining…” (p.266)

While the author produces an interesting range of arguments about language, intelligence, humanity, he missed a part about the statistical modelling of languages, apart from a very brief mention of a Markov dependence. Which would have related to the AIs perspective. The overall flow is nice but somehow meandering and lacking in substance. Esp. in the last chapters. On a minor level, I also find that there are too many quotes from Hofstadter’ Gödel, Escher and Bach, as well as references to pop culture. I was surprised to find Thomas Bayes mentioned in the above quote, as it did not appear earlier, except in a back-note.

“A girl on the stairs listen to her father / Beat up her mother” C.D. Wright,  Tours

As a side note to Andrew, there was no mention made of Alan Turing’s chess rules in the book, even though both Turing and chess were central themes. I actually wondered if a Turing test could apply to AIs playing Turing’s chess: they would have to be carried by a small enough computer so that the robot could run around the house in a reasonable time. (I do not think chess-boxing should be considered in this case!)

The convergence results in the paper indeed assume a uniform minorisation condition on all proposal densities: although this sounded restrictive at first (but allows for straightforward proofs), I realised this could be implemented by adding a specific component to the mixture as in Corollary 3. (I checked the proof to realise that the minorisation on the proposal extends to the minorisation on the Metropolis-Hastings transition kernel.) A reversible kernel is defined as satisfying the detailed balance condition, which means that a single Gibbs step is reversible even though the Gibbs sampler as a whole is not. If a reversible Markov kernel with stationary distribution ζ is used, the acceptance probability in the Metropolis-Hastings transition is

α(x,z) = min{1,π(z)ζ(x)/π(x)ζ(z)}

(a result I thought was already known). The sweet deal is that the transition kernel involves Dirac masses, but the acceptance probability bypasses the difficulty. The way mixtures of t distributions can be reversible follows from Pitt & Walker (2006) construction, with  ζ  a specific mixture of t distributions. This target is estimated by variational Bayes. The paper further bypasses my classical objection to the use of normal, t or mixtures thereof, distributions:  this modelling assumes a sort of common Euclidean space for all components, which is (a) highly restrictive and (b) very inefficient in terms of acceptance rate. Instead, Tran & al. resort to Metropolis-within-Gibbs by constructing a partition of the components into subgroups.

“The tone of his talks, he said, was “Let’s not talk about the plumbing, the nuts and bolts — that’s for plumbers, for statisticians.””

As I got a tablet last week and immediately subscribed to the New York Times, I started reading papers from recent editions and got to this long article of April 26, by Yudhijit Bhattacharjee on Diederik Stapel, the Dutch professor of psychology who used fake data in dozens of papers and PhD theses.

“In his early years of research — when he supposedly collected real experimental data — Stapel wrote papers laying out complicated and messy relationships between multiple variables. He soon realized that journal editors preferred simplicity.”

This article is rather puzzling in its presentation of the facts. While Stapel acknowledges making up the data that conveniently supported his theses, the journalist’s analysis is fairly ambivalent, for instance considering that faking data is a “lesser threat to the integrity of science than the massaging of data and selective reporting of experiments”. At the beginning of the article, Stapel is shown going back to places where his experiments were supposed to have taken place, but he “could not find a location that matched the conditions described in his experiment”, making it sound as if he had forgotten…

“Science is of course about discovery, about digging to discover the truth. But it is also communication, persuasion, marketing (…) People are on the road with their talk. With the same talk. It’s like a circus (…) They give a talk in Berlin, two days later they give the same talk in Amsterdam, then they go to London. They are traveling salesmen selling their story.”

The above quote from Stapel is even more puzzling, as if giving the same talk in different places is an unacceptable academic behaviour, in par with faking data and plagiarism… I do give the same talk in several conferences and seminars, mostly to different people and I do not see a problem with this. If I persist in this behaviour, it will get boring to people who see the same talk over and over, and it should lead to me not being invited to conferences or seminars any longer, but there is nothing unethical or a-scientific in this. Another illustration of the ambivalence of both the character and the article. I frankly dislike this approach to fraud, a kind of “50 shades of lies”, where all academics get under suspicion that one way or another they also acted un-ethically and in their own interest rather than towards the advancement of Science…