A Quote by Ramez Naam
I'm a geek through and through. My last job at Microsoft was leading much of the search engine relevance work on Bing. There we got to play with huge amounts of data, with neural networks and other AI techniques, with massive server farms.
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Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.
Emotion AI uses massive amounts of data. In fact, Affectiva has built the world's largest emotion data repository.
A lot of environmental and biological science depends on technology to progress. Partly I'm talking about massive server farms that help people crunch genetic data - or atmospheric data. But I also mean the scientific collaborations that the Internet makes possible, where scientists in India and Africa can work with people in Europe and the Americas to come up with solutions to what are, after all, global problems.
Meat production is one of the leading causes of climate change because of the destruction of the rainforest for grazing lands, the massive amounts of methane produced by farm animals and the huge amounts of water, grain and other resources required to feed animals.
I've always believed that human learning is the result of relatively simple rules combined with massive amounts of hardware and massive amounts of data.
Google or other search engines are examples of AI, and relatively simple AI, but they're still AI. That plus an awful lot of hardware to make it work fast enough.
Health care - the ability of neural networks to ingest lots of data and make predictions is very well suited to this area, and potentially will have a huge societal impact.
My central thesis is that combining increased temporal and spatial resolution in MRI techniques with increasingly powerful data correlation techniques will allow the derivation of interpreted meanings from neural signals. I observed, further, that the techniques that exist already allow some correlations.
I see the mycelium as the Earth's natural Internet, a consciousness with which we might be able to communicate. Through cross-species interfacing, we may one day exchange information with these sentient cellular networks. Because these externalized neurological nets sense any impression upon them, from footsteps to falling tree branches, they could relay enormous amounts of data regarding the movements of all organisms through the landscape.
Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
In spite of my own reservations about Bing's ability to convert Google users, I have to admit that the search engine does offer a genuine alternative to Google-style browsing, a more coherently organized selection of links, and a more advertiser-friendly environment through which to sell space and links.
I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
Search is the means through which we navigate the Web. If your business is not visible in search it is difficult for it to be found by your customers. Search, above all else, is marketing, and it is undergoing a massive change.
Bing Crosby and I weren't the types to go around kissing each other. We always had a light jab for each other. One of our stock lines used to be "There's nothing I wouldn't do for Bing, and there's nothing he wouldn't do for me." And that's the way we go through life - doing nothing for each other!
Relevance is a search engine's holy grail. People want results that are closely connected to their queries.
Listening to the data is important... but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?
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