Posts tagged DeepMind
Over the last week, a number of forum threads have popped up to discuss this mystery debutante who has been thrashing the world’s best players. Given its unbeaten record and some very “non-human” moves, most onlookers were certain that Master and Magister were being played by an AI—they just weren’t certain if it was AlphaGo, or perhaps another AI out of China or Japan. It is somewhat unclear, but it seems that DeepMind didn’t warn the opponents that they were playing against AlphaGo. Perhaps they were told after their games had concluded, though. Ali Jabarin, a professional Go player, apparently bumped into Ke Jie after he’d been beaten by the AI: “He [was] a bit shocked… just repeating ‘it’s too strong.’” Gu Li, as quoted by Hassabis, was a lot more philosophical about his loss to the new version of AlphaGo: “Together, humans and AI will soon uncover the deeper mysteries of Go.” Gu Li is referring to the fact that AlphaGo plays Go quite differently from humans, placing stones that completely confound human players at first—but upon further analysis these strategies become a “divine move.” While there’s almost no chance that a human will ever beat AlphaGo again, human players can still learn a lot about the game itself by watching the AI play. If you want to watch the new AlphaGo in action, a German website has the first 41 games from the 51-game streak, including victories against many of the world’s best human players. At this point it isn’t clear how this new version of AlphaGo differs from the one we saw last year, though some Go observers suggest that this version is making more “non-human” moves than before, indicating that the deep neural network might’ve been trained in a different way.
Natürlich gibt es Gerüchte, dass es hinter Master(P) niemand anderes als das noch stärker gewordene AlphaGo stecken muss, dass vor einem Wettkampf im ersten Quartal 2017 mal eben noch zeigen wollte, wie hoch der Hammer mittlerweile hängt. Andere Kandidaten wären das koreanische DolBaram-Projekt, das von der Korean Amateur Baduk Association (KABA) und der koreanischen Regierung unterstützt wird, und ein chinesisches Projekt, das Gerüchten zufolge bereits längere Zeit auf AlphaGo-Niveau spielen können soll. DeepZen, das unlängst gegen Cho Chikun 9p angetreten war, scheint es zumindest nicht zu sein, denn das spielte parallel auch recht erfolgreich auf Tygem – aktuell mit einem Score von 159:18, zumeist gegen spielstarke 9d-Spieler mit oder ohne (P)-Zusatz. Aja Huang vom AlphaGo-Projekt kommentierte Spekulationen um die Identität von Mater(P) und AlphaGo auf jeden Fall nur mit einem vielsagenden “interesting”.
In recent months, the Alphabet Inc. unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, “which is a huge saving in terms of cost but, also, great for the environment,” he said. The savings translate into a 15 percent improvement in power usage efficiency, or PUE, Google said in a statement. PUE measures how much electricity Google uses for its computers, versus the supporting infrastructure like cooling systems.
So let’s address our children as though they are our children, and let us revel in the fact they are playing and painting and creating; using their first box of crayons, and us proud parents are putting every masterpiece on the fridge. Even if we are calling them all %E2%80%9Cnightmarish%E2%80%9D–a word I really wish we could stop using in this context; DeepMind sees very differently than we do, but it still seeks pattern and meaning. It just doesn’t know context, yet. But that means we need to teach these children, and nurture them. Code for a recognition of emotions, and context, and even emotional context. There’s been some fantastic advancements in emotional recognition, lately, so let’s continue to capitalize on that; not just to make better automated menu assistants, but to actually make a machine that can understand and seek to address human emotionality. Let’s plan on things like showing AGI human concepts like love and possessiveness and then also showing the deep difference between the two.