Take your email spam filter. It may seem basic today, but it is based on machine learning. One if these AI tribes believes in knowledge composition. They assume all email is spam. They use rules to confirm this. Is Viagra in the title? then probably spam. is FREE in the title? then probably spam. Is a close friend's name in the message? then maybe not. It is all about probabilities. They make email worthwhile due to these helpful probability filters. Your email spam filter is using AI right now. It is already helping you to focus on what is important for you. It is here and operating in your business right now.
AI is all around us and programmers are trying to figure it out. Your human brain functions a certain way. Why not figure out how exactly? Why not reverse engineer it as a process? Why stop there? Humans have brains, but all animals have evolved, and the planet earth evolved, so why not try to better understand all of evolution? Would that not be a better bigger picture to figure out? This is just one of the many concepts that AI specialists struggle with when the choose what exactly, to focus on.
The Top 5 Takeaways from this book that impact any reader are based on the 5 main tribes within AI. These are just general overviews. Much deeper details are covered in the book. It is explained very well.
1) Symbolists: Try to focus on the problem of knowledge composition. They figure it out with inverse deduction. If 2+3=5, then what is 5-2=? by deducing similar data, you can figure out 3 as an answer. They focus on gaps in knowledge and is the most scientific in approach. The surprise is when algo figures out problems without a human. A robot called Eve discovered a new malaria drug by itself, without human guidance.
2) Connectionists: Try to focus on the problem of credit assignment. They figure it out with backpropagation. They focus on a more human, less logical world. Neural networks with newly discovered knowledge. When your brain learns, a synapes takes place between neurons. A large 1 billion network of inputs from cat videos on YouTube was the first algo used to recognize the cat content from the network.
3) Evolutionaries: Try to focus on the problem of structure discovery. They figure it out with genetic programming. They focus on genetic coding or genome. The best algos replicate to create child algos made from half male half female algos. New electronic discoveries have been made that could not have been made by humans alone. In fact some of these patents would never have been created by a human.
4) Bayesians: Try to focus on the problem of uncertainty. They figure it out with probability inference. They figure it out with comparisons. If type A people DO like X, and type A people do NOT like Y, then if one type A person DOES like X, another type A may NOT like Y as well, if the likelihood of these comparisons work well. Repeat with millions of cases and you can find this pattern out clearly. Spam filters come from this.
5) Analogizers: Try to focus on the problem of similarity. They figure it out with kernel machines (support vector machines). They figure it out with similar examples. Recommendations systems in e-commerce, where if you buy something, you are also asked to buy other things people with similar tastes also buy.
2) Connectionists: Try to focus on the problem of credit assignment. They figure it out with backpropagation. They focus on a more human, less logical world. Neural networks with newly discovered knowledge. When your brain learns, a synapes takes place between neurons. A large 1 billion network of inputs from cat videos on YouTube was the first algo used to recognize the cat content from the network.
3) Evolutionaries: Try to focus on the problem of structure discovery. They figure it out with genetic programming. They focus on genetic coding or genome. The best algos replicate to create child algos made from half male half female algos. New electronic discoveries have been made that could not have been made by humans alone. In fact some of these patents would never have been created by a human.
4) Bayesians: Try to focus on the problem of uncertainty. They figure it out with probability inference. They figure it out with comparisons. If type A people DO like X, and type A people do NOT like Y, then if one type A person DOES like X, another type A may NOT like Y as well, if the likelihood of these comparisons work well. Repeat with millions of cases and you can find this pattern out clearly. Spam filters come from this.
5) Analogizers: Try to focus on the problem of similarity. They figure it out with kernel machines (support vector machines). They figure it out with similar examples. Recommendations systems in e-commerce, where if you buy something, you are also asked to buy other things people with similar tastes also buy.
There are many amazing stories about how AI is changing our lives. Besides spam filters, another widely used concept is the recommendation engine. It may be the most financially successful use of algo yet. When Amazon suggests you buy another book, based on what other similar readers buy, you are using that algo. this is responsible for 33% of Amazon's revenue! Considering the total, that is a big umber. Netflix also uses one and it accounts for 75% of gross revenues. Again, this is a very large number and actively used by millions of customers. There are many ways to understand our brain and the world around us. AI is just the starting point that helps us figure out how exactly, that process can be better understood. Highly Recommended!
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