Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) is best known for its market-leading search engine, Google. But even though this tool has been widely adopted across the world, most people probably don’t consider the technology under the hood. Google search uses artificial intelligence to understand the language and provide more accurate results. In other words, it tries to interpret what you mean, not just the type in the search bar.
However, Alphabet is not limited to Google and it is always at the forefront of new use cases of artificial intelligence in its various activities. In this Backstage Pass video, which was broadcast September 27, 2021Motley Fool contributor John Bromels discusses some ways Alphabet uses artificial intelligence.
Jean Bromels: Speaking of artificial intelligence, the first name that comes to mind is Alphabet. The stock symbol is GOOG and GOOGL. He’s a little handyman. By the way, that’s what I look like when I’m not projected on a screen, that yellow guy over there. We believe that Google is a search engine, that it has professional suites and a lot of other things. But Google is actually doing a lot behind the scenes, Google in particular and Alphabet.
The other companies that are part of Alphabet in general are actually doing a lot behind the scenes, especially in artificial intelligence. To set the stage for a lot of modern and contemporary artificial intelligence work, you actually have to go back to 1997, when IBMDeep Blue first defeated international grandmaster Gary Kasparov in chess. It was a big deal considering that, and people don’t remember, the year before, Deep Blue had actually failed to do that. He had played Gary Kasparov and lost. He had a rematch in 97 and was able to win. Of course, Deep Blue is IBM’s thing. IBM also shocked the world in 2011 when Watson defeated endangered Ken Jennings and Brad Rutter. But Google made the headlines a few years later in 2017, and that was the grand prize. It was the AlphaGo computer that beat a Chinese Go Master three games in a row in 2017.
Go is so much more complex than failures in terms of the number of possible moves and the number of possible iterations that Google didn’t do what IBM did with Deep Blue. In 1997, Deep Blue, literally, programmers asked Deep Blue to look at every possible move and examine every possibility, then go back and choose the one that had the most possible combinations of that branch than it did. had configured, because Go is thousands and thousands of times more complex with so much more movement possible. I mean, I believe there are something like billions of possible moves or possible sequences in any given Go game. What AlphaGo, which was Google’s project, did, they learned by playing it on its own. Play against games to determine what were the best practices and the best strategy. From that process, in fact, the Chess and the Go stuff started Google and Alphabet’s interest in solving these AI and machine learning issues. It would take a long time to go through all the things Google and Alphabet are trying to do in AI and machine learning because they have so many plans. But I just wanted to highlight one.
DeepMind is the name of the subsidiary of Alphabet, just like Google is a subsidiary of Alphabet, DeepMind is the subsidiary of Alphabet which specifically focuses on AI and machine learning. Google turns this to the issue of protein folding. This is a very specific process in biotechnology. When a protein forms from a bunch of amino acids, it takes those amino acids, which are all shaped like strings of wool, and bends them to form them into this type of 3D structure. Kind of like almost like holding a piece of paper inside an origami bird or some other animal. The point is, it uses many of the same immunological acids, it can form them in those structures, but each structure determines what that protein can do. Think of it like you could take the same piece of paper and fold it into many Origami animals. It is the same with proteins.
However, if your protein is a bit shifted, if it bends slightly badly, it can cause a whole host of genetic issues, including things like cystic fibrosis. It is the result of a protein that is just formed slightly differently. It can take years and tons and tons of money and research to try to analyze the structure and folding of a single protein and be able to predict how it’s going to work in the lab.
Alphabet’s DeepMind decided to try to solve this problem in 2016, they start working on AI in 2016. In 2018 they have this program called AlphaFold, it barely scores a victory in the biennial cash competition, which is a biennial competition to see if we can predict how proteins are going to form based on the amino acids that basically make them up by replicating by prediction those years and years of lab work and all that money.
Then, last year in 2020, DeepMind came back with AlphaFold 2, two years later, and basically announced that this issue was fixed because AlphaFold 2 was able to replicate itself through simple prediction modeling. Has been able to replicate essentially what it takes years and years for scientists to do in the lab, by examining the actual protein. He was able to predict, “Yeah, that’s what it’s going to look like.” This has incredible implications for biotechnology and for the work of genetics and other areas of treatment of diseases and conditions. This is literally one thing that Google and Alphabet are doing among the dozens of things they are doing in this area.
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