![]() ![]() I remember in 2012 Yann LeCun wrote an open letter to the academic community, the computer vision community, being like: You are not accepting my papers you guys don’t like us, the deep learning people, we’re going to persist. People use many of these things interchangeably - they use AI machine learning, deep learning, interchangeably. Nowadays, because a specific type of a machine learning technique is one that is almost everywhere. You could use machine learning techniques and natural language processing, you can use machine learning techniques in computer vision, etc. Machine learning would be a technique that many times people try to use for many of these various things. Then, machine learning is more of a technique. There’s robotics, where people are working on robotics. So at Stanford these are all under subsets of the AI Lab. Maybe you can do some statistics, maybe you can say: There’s a chair in that image there’s a hat, a person, house, whatever. It could be computer vision techniques, where you look at what’s in an image and analyze that image more so than just taking it. Or automated machine translation from A to B, from one language to another language, etc. This could mean natural language processing, where people are more interested in analyzing texts and speech, so then they can create things such as speech to text transcriptions in an automated way. I would say, my understanding of the field is that you try to create machines or things that can do more than what’s been programmed into them. The way I look at it, artificial intelligence, is the big tent, a big field, with sub-sets of things inside that field. There was an article by Emily Tucker, from Georgetown Law Center for Privacy and Security, titled “Artifice and Intelligence,” where she talks about how it’s a misnomer, we should not call it artificial intelligence. I remember about 10 years ago is when the hype started and everything has been rebranded as, “AI.” I first and foremost, think of it as a brand, as a rebranding, more of a marketing term. I just will start with that, so as a researcher I had never described myself as an “AI researcher,” until that became the mainstream term and super hyped up. I find myself asking the questions that you’re asking. I have my PhD in Electrical Engineering, but that lab is in Computer Science department. TG: So I want to start with a comment that I got my PhD from a lab called the AI Lab at Stanford. What is AI, artificial intelligence? What does that refer to? Is machine learning the same thing? Is that different? How should we understand these terms and what they actually mean? Previously, they’ve certainly come up, but I was hoping that you could give us a general understanding of what it actually means. As I said, we haven’t talked very much about AI, machine learning, on the show. It seems that AI is going to be one of these technologies that the industry is refocusing on and trying to build this hype around into the future. Especially with the failure or loss of interest in Web3 and crypto and metaverse. I’m really excited to talk to you today about artificial intelligence, all this hype that has been around lately, how we should actually be thinking about these technologies and what they might actually mean for us going forward and into the future. One upside to that whole situation, I guess. Admittedly, I didn’t know your name before you were fired from Google, but at least we got to know you, many more people did, at that time. A lot of people will know you and when you came to their attention. PM: I have obviously been following you and your work for a while as well. When you get around to it, though, you can certainly let me know what you thought of it. I get sent books all the time, and I’m like: where am I going to find the time for all these? So, no pressure. I have a million books on my reading list. But given all that’s on our plate, it’s really hard to figure out how to do things that we need to be doing like read books. I’m a huge fan of your work and am very much looking forward to reading your book. Paris Marx: Timnit, welcome to Tech Won’t Save Us!
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