This is great. Ask people at random to sketch a men’s bicycle. Realise that most people can’t and make serious renderings of the results.
It does feel that the rational voices speaking up against the war on drugs are starting to be heard. This is excellent news for all of us especially the usually minority groups that become the target of the war.
Legalise, regulate and tax is the only sensible solution to our current known crop and any futureÂ synthetic stimulants we create.
It doesn’t matter whether the government targets whiskey or cocaine; a ban forces legal businesses out of the market â€” and armed criminal gangs take it over. They then go to war to control the trade. But once the prohibition ends, so does the violence. (Ask yourself: Where are the violent alcohol dealers today?).
More on AlphaGo. Such respect to Sedol for choosing to play the last match against what he views as the stronger incarnation of the machine. It didn’t help, of course.
Fan Hui, the 3-time European championÂ has improved from number 600-ish in the world to be in the 300sÂ while playing AlphaGo as part of its training. It’s been emotional, mostly sad, but he has found some beautiful moments.
Fabulous long Facebook reflections from Eliezer Yudkowsky. My take-aways (but it’s all worth reading).
More on how DeepMind works. We should all start understanding this, as best we can. Suleyman thinks it is too early to be talking about AGI rights: many people “know how difficult it is to get these things to anything.”
So muchÂ sci-fi coming to earthÂ this last week.
AlphaGo beat Lee Sedol and convincingly. It’s important because the game was thought to be too “big” for traditional computerÂ strategiesÂ based onÂ simulating playing the game forward a few rounds and seeing what happens (like chess programs often do). Go hasÂ 10^761 possible games compared to the estimated 10^120 for chess.
For me the most astonishing thing about AlphaGo is that it was not designed to play Go. It is a generic learning engine that was trained on 30m Go positionsÂ from public databases and then played itself across 50 computers to reinforce its learnings. It has improved steadily over time and now plays “a little strange, but a very strong player, a real person”. Lots more in this Nature article.Â Lee Sedol won a remarkable game four where a stunning move from the 9p seemed to break the computer’s learning and caused it to play weak move after weak move leading to an eventual resignation from AlphaGo.
Just imagine what a pattern engine could do when applied to other endeavours for example the law (pattern: winning vs losing cases) and risk management (pattern: successful vs failed contracts), medical image diagnosisÂ (pattern: life-years saved vs lost). Sadly, the Go learning can’t easily be transferred to another field of endeavour: AlphaGo is now a Go specialist and nothing else.
Even so, the speed at which these artificial general intelligences learn outpaces humans by orders of magnitude. We’re toast.
Separately, scientists have shown that rat cyborgs are better at solving mazes than rats left toÂ their own devices. Maybe that’s our way back in?
Intriguing thought. I stronglyÂ dislike and avoid advertising, but maybe free self-driving transportation is my price?
I had never seen a view of opera scheduling across seasons like this. Fascinating how often the ABC+T get performed.
My assumption is that ROH and Glyndebourne would be more varied, but that’s just gut feel.
Everyone expected computer domination of Go to be “ten years” away. It looks strongly like it’s nearly with us. A European master was defeated 5-0 and a match up with a world champion match is due in March.
Once again, the human player described the computer as “a wall”:Â it doesn’t make mistakes and doesn’t spend too much time on particular moves. It’s widely thought that Kasparov cracked under the pressure Deep Blue exerted in the rematch that he lost.
I wonder if humans will be able to find a winning strategy against this kind of pressure: it’s certainly possible. It seems as though human chess players haven’tÂ quite given up yet.
Usable water isn’t scarce, it’s in the wrong buckets. How will technology and trading improvements in bucket transfer affect adjacent industries? Could Enron be a model?
Lots to think about here.
Two articles covering different aspects of the fact thatÂ policy always has to follow developments in the world and is always rushing to keep up. This often leads to terrible law-making.
On privacy, Â aÂ postulated axiom:
the defense of privacy follows, and never precedes, the emergence of new technologies for the exposure of secrets
Lots of interest in the article: the discussion as to whether e.g. wire-tapping is enforced testimony is one I haven’t encounteredÂ before.Â I wonder if we should we all have some version of canary out there (“I have never been asked to provide my clients’ data to law enforcement or other government agencies”)?
On genetic engineering: we’ve been doing it for so long usingÂ more-or-less test-tubey methods that these hopefully discrete edits (hornless Holsteins, fast-growing salmon, non-malaria-carrying mosquitoes) shouldn’t be a cause for concern. Yes, we need to monitor very closely how they develop to see if there are any unexpected consequences, but we shouldn’t ban them up front.
Weâ€™re going to see a stream of edited animals coming through because itâ€™s so easy