Filosofien og inteligensen

En artikel i Wired har en ret sjov indfaldsvinkel til udviklingen indenfor computere. Forfatterens pointer er på flere måder ret overbevisende. Han bemærker at det der blandt andet har været et vendepunkt i udviklingen er at mængden af data der bliver genereret digitalt gennem menneskers brug af søgemaskiner og socialemedier, og den mængde af materiale der er tilgængelig i alle mulige former og den måde som data tilgåes. Det er hele samlede menneskeheds interageren og afspejling i digital form, der nu har fået akkumuleret en kvalitet så det kan bruges som base for oplæring af neurale netværk. Man kan ikke lære en computer at genkende et ansigt på et billede hvis der ikke er millioner af billeder. Og det samme gælder alt andet som at forstå hvad der bliver sagt.
Som mange andre filosoffer er der dog også grund til at tage hans forudsigelser om fremtiden med betydelige mængder af forbehold. Der er ingen garanti for at det bliver Google, IBM eller Apple eller et andet stort firma der kommer til at få kontrol med kunstig inteligens. Og det er helt sikkert ikke nødvendigt med en computer eller en anden form for udenjordisk inteligens for at reflektere og forstå livet i al dens mangfoldighed.

Her er et super kort sammendrag af hovedtesen:

The Three Breakthroughs That Have Finally Unleashed AI on the World: parallel computation, bigger data, and deeplearning algorithms

1. Cheap parallel computation
In 2009, Andrew Ng and a team at Stanford realized that GPU chips could run neural networks in parallel.

That discovery unlocked new possibilities for neural networks, which can include hundreds of millions of connections between their nodes. Traditional processors required several weeks to calculate all the cascading possibilities in a 100 million-parameter neural net. Ng found that a cluster of GPUs could accomplish the same thing in a day. Today neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or, in the case of Netflix, to make reliable recommendations for its more than 50 million subscribers.

2. Big Data
Part of the AI breakthrough lies in the incredible avalanche of collected data about our world, which provides the schooling that AIs need. Massive databases, self-tracking, web cookies, online footprints, terabytes of storage, decades of search results, Wikipedia, and the entire digital universe became the teachers making AI smart.

3. Better algorithms
organize neural nets into stacked layers. Take the relatively simple task of recognizing that a face is a face. When a group of bits in a neural net are found to trigger a pattern—the image of an eye, for instance—that result is moved up to another level in the neural net for further parsing. The next level might group two eyes together and pass that meaningful chunk onto another level of hierarchical structure that associates it with the pattern of a nose. It can take many millions of these nodes (each one producing a calculation feeding others around it), stacked up to 15 levels high, to recognize a human face. In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers. Deep-learning algorithms accelerated enormously a few years later when they were ported to GPUs. The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBM’s Watson, Google’s search engine, and Facebook’s algorithms.

supplerende: http://www.newscientist.com/article/mg22429932.300-computers-are-learning-to-see-the-world-like-we-do.html

Reklamer

Om hubertnaur

Særlig interesse i fri og åben adgang til viden
Dette indlæg blev udgivet i Uncategorized. Bogmærk permalinket.

Skriv et svar

Udfyld dine oplysninger nedenfor eller klik på et ikon for at logge ind:

WordPress.com Logo

Du kommenterer med din WordPress.com konto. Log Out / Skift )

Twitter picture

Du kommenterer med din Twitter konto. Log Out / Skift )

Facebook photo

Du kommenterer med din Facebook konto. Log Out / Skift )

Google+ photo

Du kommenterer med din Google+ konto. Log Out / Skift )

Connecting to %s