 A couple of years ago, if you said you were into AI, most people simply roll their eyes and think you're some weird tech freak. Nowadays, chat GPT is a household name, and even your grandma has heard of AI. As data scientists, we're pretty close to AI. It's part of our job. However, the rate of research and funding is going makes it quite hard to keep up. It seems like every week, some big tech companies got a new fancy transform model, which they're claiming trumps their competition. Let's be honest, none of us have time to read several research papers a week. Even if we did, a lot of times the language is quite complex and very difficult to understand, which makes for quite a dry read. In this video, I want to explain the ways I keep up to date with all the latest on-goings in AI as a data scientist. Obviously, everyone has their preferences, but I've found these methods quite frictionless and very easy to get my daily AI dose without being too overwhelmed. The easiest way to keep up with all the latest in AI is to follow some of the big names on Twitter or X. For example, I follow people such as Dimus Asabasis, Andrew Erng, Andrew Kapafi and Sam Altman. Obviously, you don't have to follow all these people, but follow a handful that are the big leaders and you should be up to date with most things going on. If anything big happens in the industry, you best believe these people will probably tweeting about it, so you find it very hard to miss. You can also utilize platforms such as X or Twitter for more educational content. For example, I followed Data Science Fact and it does exactly what it says on the tin. It basically tweets a couple of tweets a day on some interesting things in data science and statistics and it's quite refreshing to log on to an app and learn something new that can be applied in your day job. Many of the big tech companies have tech blogs where they publish work which is a bit more industrial and less research focused and it's also in a more digestible manner. The ones I mainly follow are Spotify, Meta, DeepMind and Airbnb. Obviously, there's many others, but I find these ones have the most applicable blogs to my job and they're also posted most frequently. The one I like the most was DeepMind's AlphaTensor blog. This blog delves into their reinforcement learning algorithm which discovered new ways to multiply matrices. The blog post pretty much covered all the main bits and all the main theory I needed to know. Obviously, it missed some of the fine-grained details but to be honest, I'm not a researcher so I just want to get the main insight how they did the problem, how they solved it and not worry too much about all the intricate mathematical details that the research paper may show. Tech blogs are useful but I don't really tell you what's going on with all the business scandals inside the AI. For that, I really like to use newsletters because they literally deliver the news of AI to you. My go-to newsletter is a rundown AI. You get one newsletter Monday to Friday with two to three of the biggest AI stories happening that day or that week. It takes a maximum of me five minutes to read per day and it really keeps me up to date with everything going on, particularly on that business side, like I said. There are also several other newsletters such as TLDR AI, Morning Tech Broome and The Batch. Obviously, just find one you like and make sure you read it. I find this best to have one newsletter that you read often than having 10 which don't open a tool and simply clog your inbox. YouTube is probably the one of the best sources online for any form of educational or entertainment content that you're after. However, I will recommend four channels to you that in my opinion will cover all your bases from tech tutorials to the breaking news. Like I said, you don't have to follow every single channel. These four suffice. The first one is Two Minute Papers. It basically says where it doesn't attend. It publishes two videos a week which are basically breakdowns of big research papers that were released that week. I really like the animations and it's very clear explanation. The second one is Fireship. Fireship is kind of like a staple in the YouTube tech community and it does videos from tech tutorials to breaking news. What I really like about it is that it throws in a lot of humor in there so it makes for a quite entertaining watch. If you are understanding research papers and all the mathematics and theory behind it, then Neonik killed your channels for you. He breaks down papers and all these new research topics in such a digestible way using old school style or pen and paper. So I definitely recommend if you're looking for a deep dive into journal papers and what they really mean and what the results are really impacting in the industry. And finally, needless to say, most of you have probably heard of this channel and that is Lex Freeman's channel slash podcast. This podcast originally started off as the artificial intelligence podcast, nowadays it's a bit more diverse than it gets on but at as high it's still very much a computer science and tech driven podcast and most of the guests come from that industry. To really learn about all this cutting edge technology, you really need to apply it and one way to do that is take courses. For example, LLMs and GPTs are all the rage right now and I want to learn more about them, particularly the theoretical side and how the algorithm actually generates predictions. To do this I took Andrew Kapafi's neural networks zero to hero course which basically we went from understanding how neural nets work to building our own chat GPT ride the end or GPT transformer. Finally, the last way to keep up the date of all the latest technology and research and arguably the hardest way is to document your learning. It is often said that you only remember 10% of what you read but 95% of what you teach. This is the main reason why I started my blog. It wasn't necessarily to teach others but to become a better data scientist myself and find gaps in my knowledge which I don't know existed. I can't tell you the number of times where I started a blog and I realized I'm missing this bit or I don't understand this bit quite right and then I delve into these small intricacies and small nuances inside these theories to really get a better understanding and that really slenderifies everything I know about data science and see how things hang together. Obviously, like I said, there's quite a lot of effort and more effort than simply reading some tweets but it's by far the most beneficial way of learning and also keeping up to date and being a real expert in the field. Being a data scientist means continual learning so it's very important to stay up to date with all the latest goings on in the industry to ensure that your tech stack is sharp. It's very difficult to keep up with everything but you don't have to. Just being aware of the lay of the land is sufficient and then you can dive deeper into the topics you're interested in. If you enjoyed this video and want to see more videos like this on this channel then make sure you click the like and subscribe button and I'll see you in the next one.