When reading in Nature about two deep learning algorithms winning at a version of poker within a few weeks of difference, I came back to my “usual” wonder about poker, as I cannot understand it as a game. (Although I can see the point, albeit dubious, in playing to win money.) And [definitely] correlatively do not understand the difficulty in building an AI that plays the game. [I know, I know nothing!]
Archive for deep learning
the incomprehensible challenge of poker
Posted in Statistics with tags /Pages/SIMAccueil.aspx, artificial intelligence, bills, deep learning, Denmark, game theory, Nature, poker, statistics and sports on April 6, 2017 by xi'anmachines learning but not teaching…
Posted in Books, pictures with tags AIs, artificial intelligence, black box, brain imaging, deep learning, machine learning, Nature, neural network on October 28, 2016 by xi'an
A few weeks after the editorial “Algorithms and Blues“, Nature offers another (general public) entry on AIs and their impact on society, entitled “The Black Box of AI“. The call is less on open source AIs and more on accountability, namely the fact that decisions produced by AIS and impacting people one way or another should be accountable. Rather than excused by the way out “the computer said so”. What the article exposes is how (close to) impossible this is when the algorithms are based on black-box structures like neural networks and other deep-learning algorithms. While optimised to predict as accurately as possible one outcome given a vector of inputs, hence learning in that way how the inputs impact this output [in the same range of values], these methods do not learn in a more profound way in that they very rarely explain why the output occurs given the inputs. Hence, given a neural network that predicts go moves or operates a self-driving car, there is a priori no knowledge to be gathered from this network about the general rules of how humans play go or drive cars. This rather obvious feature means that algorithms that determine the severity of a sentence cannot be argued as being rational and hence should not be used per se (or that the judicial system exploiting them should be sued). The article is not particularly deep (learning), but it mentions a few machine-learning players like Pierre Baldi, Zoubin Ghahramani and Stéphane Mallat, who comments on the distance existing between those networks and true (and transparent) explanations. And on the fact that the human brain itself goes mostly unexplained. [I did not know I could include such dynamic images on WordPress!]
To predict and serve?
Posted in Books, pictures, Statistics with tags AIs, algorithms, crime prediction, deep learning, drug users, Nature, Oakland, Philip K. DIck, Significance, social determinism, software, The Minority Report on October 25, 2016 by xi'anKristian Lum and William Isaac published a paper in Significance last week [with the above title] about predictive policing systems used in the USA and presumably in other countries to predict future crimes [and therefore prevent them]. This sounds like a good idea for a science fiction plot, à la Philip K Dick [in 
his short story, The Minority Report], but that it is used in real life definitely sounds frightening, especially when the civil rights of the targeted individuals are impacted. (Although some politicians in different democratic countries increasingly show increasing contempt for keeping everyone’ rights equal…) I also feel terrified by the social determinism behind the very concept of predicting crime from socio-economic data (and possibly genetic characteristics in a near future, bringing us back to the dark days of physiognomy!)
“…crimes that occur in locations frequented by police are more likely to appear in the database simply because that is where the police are patrolling.”
Kristian and William examine in this paper one statistical aspect of the police forces relying on crime prediction software, namely the bias in the data exploited by the software and in the resulting policing. (While the accountability of the police actions when induced by such software is not explored, this is obviously related to the Nature editorial of last week, “Algorithm and blues“, which [in short] 
calls for watchdogs on AIs and decision algorithms.) When the data is gathered from police and justice records, any bias in checks, arrests, and condemnations will be reproduced in the data and hence will repeat the bias in targeting potential criminals. As aptly put by the authors, the resulting machine learning algorithm will be “predicting future policing, not future crime.” Worse, by having no reservation about over-fitting [the more predicted crimes the better], it will increase the bias in the same direction. In the Oakland drug-user example analysed in the article, the police concentrates almost uniquely on a few grid squares of the city, resulting into the above self-predicting fallacy. However, I do not see much hope in using other surveys and datasets towards eliminating this bias, as they also carry their own shortcomings. Even without biases, predicting crimes at the individual level just seems a bad idea, for statistical and ethical reasons.
Matlab goes deep [learning]
Posted in Books, pictures, R, Statistics, University life with tags deep learning, Le Monde, Matlab on September 5, 2016 by xi'an
A most interesting link I got when reading Le Monde, about MatLab proposing deep learning tools…
go, go, go…deeper!
Posted in pictures, Statistics with tags Deep Blue, deep learning, Go, Google, IBM, machine learning, Monte Carlo algorithm, neural network, reinforced learning, superintelligence, supervised learning on February 19, 2016 by xi'an
While visiting Warwick, last week, I came across the very issue of Nature with the highly advertised paper of David Silver and co-authors from DeepMind detailing how they designed their Go player algorithm that bested a European Go master five games in a row last September. Which is a rather unexpected and definitely brilliant feat given the state of the art! And compares (in terms of importance, if not of approach) with the victory of IBM Deep Blue over Gary Kasparov 20 years ago… (Another deep algorithm, showing that the attraction of programmers for this label has not died off over the years!)
This paper is not the easiest to read (especially over breakfast), with (obviously) missing details, but I gathered interesting titbits from this cursory read. One being the reinforced learning step where the predictor is improved by being applied against earlier versions. While this can lead to overfitting, the authors used randomisation to reduce this feature. This made me wonder if a similar step could be on predictors like random forests. E.g., by weighting the trees or the probability of including a predictor or another.
Another feature of major interest is their parallel use of two neural networks in the decision-making, a first one estimating a 
probability distribution over moves learned from millions of human Go games and a second one returning a utility or value for each possible move. The first network is used for tree exploration with Monte Carlo steps, while the second leads to the final decision.
This is a fairly good commercial argument for machine learning techniques (and for DeepMind as well), but I do not agree with the doom-sayers predicting the rise of the machines and our soon to be annihilation! (Which is the major theme of Superintelligence.) This result shows that, with enough learning data and sufficiently high optimising power and skills, it is possible to produce an excellent predictor of the set of Go moves leading to a victory. Without the brute force strategy of Deep Blue that simply explored the tree of possible games to a much more remote future than a human player could do (along with the perfect memory of a lot of games). I actually wonder if DeepMind has also designed a chess algorithm on the same principles: there is no reason why it should no work. However, this success does not predict the soon to come emergence of AI’s able to deal with vaguer and broader scopes: in that sense, automated drivers are much more of an advance (unless they start bumping into other cars and pedestrians on a regular basis!).
