Virtually Human: the promise of digital immortality

Martine Rothblatt’s latest book, Virtually Human, the promise – and the peril – of digital immortality, recommended by none less than the likes of Craig Venter and Ray Kurzweil, is based on an interesting premise, which looks quite reasonable in principle.

Each one of us leaves behind such a large digital trace that it could be used, at least in principle, to teach a machine to behave like the person that generated the trace. In fact, if you put together all the pictures, videos, emails and messages that you generate in a lifetime, together with additional information like GPS coordinates, phone conversations, and social network info, there should be enough information for the right software to learn to behave just like you.

Rothblatt imagines that all this information will be stored in what she calls a mindfile and that such a mindfile could be used by software (mindware) to create mindclones, software systems that would think, behave and act like the original human that was used to create the mindfile. Other systems, similar to these, but not based on a copy of a human original, are called bemans, and raise similar questions. Would such systems have rights and responsibilities, just like humans? Rothblatt argues forcefully that society will have to recognize them as persons, sooner or later. Otherwise, we would assist to a return to situations that modern societies have already abandoned, like slavery, and other practices that disrespect basic human rights (in this case, mindclone and beman’s rights).

Most of the book is dedicated to the analysis of the social, ethical, and economic consequences of an environment where humans live with mindclones and bemans. This analysis is entertaining and comprehensive, ranging from subjects as diverse as the economy, human relations, families, psychology, and even religion.  If one assumes the technology to create mindclones will happen, thinking about the consequences of such a technology is interesting and entertaining.

However, the book falls short in that it does not provide any convincing evidence that the technology will come to exist, in any form similar to the one that is assumed so easily by the author. We do not know how to create mindware that could interpret a mindfile and use it to create a conscious, sentient, self-aware system that is indistinguishable, in its behavior, from the original. Nor are we likely to find out soon how such a mindware could be designed. And yet, Rothblatt seems to think that such a technology is just around the corner, maybe just a few decades away. All in all, it sounds more like (poor) science fiction than the shape of things to come.

Deepmind presents Artificial General Intelligence for board games

In a paper recently published in the journal Science, researchers from DeepMind describe Alpha Zero, a system that mastered three very complex games, Go, chess, and shogi, using only self-play and reinforcement learning. What is different in this system (a preliminary version was previously referred in this blog), when compared with previous ones, like AlphaGo Zero, is that the same learning architecture and hyperparameters were used to learn different games, without any specific customization for each different game.
Historically, the best programs for each game were heavily customized to use and exploit specific characteristics of that game. AlphaGo Zero, the most impressive previous result, used the spatial symmetries of Go and a number of other specific optimizations. Special purpose chess program like Stockfish took years to develop, use enormous amounts of field-specific knowledge and can, therefore, only play one specific game.
Alpha Zero is the closest thing to a general purpose board game player ever designed. Alpha Zero uses a deep neural network to estimate move probabilities and position values. It performs the search using a Monte Carlo tree search algorithm, which is general-purpose and not specifically tuned to any particular game. Overall, Alpha Zero gets as close as ever to the dream of artificial general intelligence, in this particular domain. As the authors say, in the conclusions, “These results bring us a step closer to fulfilling a longstanding ambition of Artificial Intelligence: a general game-playing system that can master any game.
While mastering these ancient games, AlphaZero also teaches us a few things we didn’t know about the games. For instance, that, in chess, white has a strong upper hand when playing the Ruy Lopez opening, or when playing against the French and Caro-Kann defenses. Sicilian defense, on the other hand, gives black much better chances. At least, that is what the function learned by the deep neural network obtains…
Actualization: The NY Times just published an interesting piece on this topic, with some additional information.