What are currently the most important projects or trends in machine learning research that we should be closely following?
As the decades go by, schools of machine learning are ascending or descending. Right now, the connectionists are in descending, while the fastest progress right now is in deep learning. You always read about new research in deep learning, in things like speech recognition or learning from YouTube videos. We’ll see how far that gets — those doing the research think it will get us all the way, while others are skeptical.
A lot of the high-tech companies working in this field are competing to develop both an algorithm that can learn from all the data you produce, as well as the model of you that comes out of that data using those algorithms. It’s an arms race, and we’re going to be seeing more of that. Apple has Siri, Google has Google Now, Microsoft has Cortana, and Facebook has M. What they’re all trying to do is learn about all the data we produce — every last bit of data we put out. We’re gong to see a lot of results from this as we move forward.
Another thing to keep an eye on centers around the symbolist school in machine learning. Those really believe in learning by accumulating knowledge, like by reading text and web. Google’s Knowledge Graph is probably the most famous example of this, but there are a lot of others in most industry and academia, like Tom Mitchell’s NELL (Never Ending Language Learning) project, which continuously learns by reading the web.
- More Here on the new book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
by Pedro Domingos
As the decades go by, schools of machine learning are ascending or descending. Right now, the connectionists are in descending, while the fastest progress right now is in deep learning. You always read about new research in deep learning, in things like speech recognition or learning from YouTube videos. We’ll see how far that gets — those doing the research think it will get us all the way, while others are skeptical.
A lot of the high-tech companies working in this field are competing to develop both an algorithm that can learn from all the data you produce, as well as the model of you that comes out of that data using those algorithms. It’s an arms race, and we’re going to be seeing more of that. Apple has Siri, Google has Google Now, Microsoft has Cortana, and Facebook has M. What they’re all trying to do is learn about all the data we produce — every last bit of data we put out. We’re gong to see a lot of results from this as we move forward.
Another thing to keep an eye on centers around the symbolist school in machine learning. Those really believe in learning by accumulating knowledge, like by reading text and web. Google’s Knowledge Graph is probably the most famous example of this, but there are a lot of others in most industry and academia, like Tom Mitchell’s NELL (Never Ending Language Learning) project, which continuously learns by reading the web.
- More Here on the new book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
by Pedro Domingos
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