Pre-2023 people would roll their eyes if you referred to AI and just thought you were some crazy tech freak. However, it is now a different story. ChatGPT is a household name and everyone knows who OpenAI are.
Obviously, as Data Scientists we are close to AI, it’s part of the job. However, the rate of research and funding going into this field is going through the roof. Every week it seems some big tech company is announcing their new fancy transformer model, which they claim will trump the competition (pointing no fingers).
Let’s be honest: no one has time to read several research papers per week. Even if we did, the language is often esoteric and complex, which makes for a pretty hard and dry read.
This is why I want to explain the methods I use to get my weekly AI dose and keep up with everything happening in the industry. Of course, people have their preferences. What works for me may not work for you. But, I believe everything in this article is low friction and won’t take significant time out of your day.
Follow Researchers On Social Media
Many AI leaders are on X (formerly Twitter), so you best believe that if anything cosmic happens in the field, they will be talking about it. There are so many AI experts on X, but you don’t need to follow all of them.
If you follow say, 5, then your “for you” page should show you post recommendations effectively at suitable times. There is such a thing as “too much information,” so you don’t need to go on a crazy follow spree to anyone who has “AI” or “ML” in their bio.
The people I follow, and recommend you follow, are Demis Hassabis, Sam Altman, Yann LeCun, Andrej Karpathy, Andrew Ng, and Lex Fridman. I find this is suitable for me to get all the information I need from X.
You can also utilize X by following more educational content. A golden example is Data Science Fact. It basically does what it says in the tin, and it’s refreshing to log on and learn something new!
However, it wouldn’t be a proper social media app if there weren’t funny memes involved.
Tech Blogs
All the big tech companies have their own Data Science and machine learning blog. The ones I regularly read are Meta, Netflix, DeepMind, Spotify and AirBnb. There are many others, but these are big players in tech and post quite frequently.
Their posts are often way more digestible than a research paper from arXiv and contain more industry application rather than a completely theoretical approach. This is useful for most practicing Data Scientists, as most of us work in industry rather than research labs.
For example, this blog post from DeepMind on their improved matrix multiplication AlphaTensor algorithm is a lot easier to read than their published research paper. There will be fine-grain details missing, but the blog post will encapsulate all the most useful information you need to know.
I like Netflix’s ‘Day in the Life’ series, where employees walk us through a normal work day. It may not be technical, but it offers insights and tips on how top Data Science companies and people function.
Subscribe To Newsletters
Tech Blogs are great, but they don’t inform you of all the business scandals happening in the industry. Newsletters are arguably the best way to stay up-to-date with the actual “news” in AI and the latest on the big tech giants.
My go-to is The Rundown AI.
You receive one email Monday to Friday with 2–3 of the recent biggest AI stories. It’s unbelievably easy to read and scan over. It will take 5 minutes max per day, covering all your AI need-to-know bases.
There are several other newsletters, such as TLDR AI, Tech Brew, and The Batch.
I like to stick to one newsletter, to avoid clutter in my inbox. This is a personal preference.
These are just some examples, but there are so many useful newsletters. See here for a full list if you want to do thorough research.
Find one you like and try to make it a habit to read every email. It’s better to have one newsletter that you often read than ten you barely open and clog your inbox.
YouTube
YouTube is truly a goldmine for data science, Machine Learning, and AI content. There are literally multiple tutorials for any niche topic you want to learn. This is both good and bad, as it can be overwhelming to find the right channels that suit your needs without having an influx of information.
These are the four channels I highly recommend you follow as they will cover all your biases from theoretical/research topics to the business side of AI.
Two Minute Papers
This channel explains every big research paper and its findings in less than 10 minutes. The explanations are super clear with useful animations to help you as a viewer.
One of my favorites is their “AI Boxing” video, but every single one is quality.
Fireship
A mix of news and tutorials, with a touch of humor, fireship is a staple in the tech YouTube niche. Every video is short and packed with information. Just what we need as busy Data Scientists!
This one is the best and probably the funniest:
Yannic Kilcher
If you are interested in really understanding research papers, then this channel is for you. Yannic breaks down papers the old-school way with (digital) pen and pencil, and his explanations are always crystal clear. He is also an AI researcher himself, so he knows what he is talking about.
My personal favorite is his explanation of Retentive Networks:
Lex Fridman
Needless to say, Lex’s podcast is one of the best and biggest in tech. Originally, it started as a purely AI podcast, but he has now broadened the scope of the guests he gets on.
The old episodes particularly have so much wisdom in AI and computer science, that they are a treasure trove of knowledge. My favorite was his conversation with Demis Hassabis, the founder of DeepMind.
Join Online Communities
Many online groups within social media apps have built-in communities around certain topics. For example, I am part of the data science community group on X, where you can interact with other Data Scientists and discuss the latest goings on.
Facebook arguably has the most data science groups and is another great place to network. I am personally not on Facebook that much, but I know others who find it a great place to hear all the latest. See here for a list of the top 18 groups you could join.
Reddit is another fantastic place with a great community of AI enthusiasts. I frequently browse r/MachineLearning, r/artificial, and r/datascience to read the latest gossip. People on Reddit are normally quite candid with their opinions and not afraid to voice them.
Attend Journal Clubs & Online Conferences
Most companies run a lunch & learn or journal club, where people can present on interesting topics or learnings.
I run my organization’s journal club, which is a lot of fun. The idea is for people to present a research paper or a blog they have read. It encourages discussion and also gives you a reason to actually read a recent research paper, which is quite a good skill to have. Not to mention it can help spark some ideas for your day-to-day work and keep your organisation aware of the current cutting-edge algorithms.
Many companies have conferences and webinars that you can attend for free. If you are into cloud computing, then one of the AWS webinars would be ideal to attend, or if you are into learning about LLMs, then maybe one of the Databricks conferences is for you.
Feel free to do your research on local meet-ups and online sessions that have what you are after. If you live in a major city, chances there will be monthly in-person events that you can attend.
Take Courses
To really learn the latest technology you need to apply it yourself. The best way to do this is to take a course in the cutting-edge area you are most interested in.
LLMs and GPTs are all the rage right now, so I wanted to learn more about these language models and what drives them. So, I took the Neural Networks: Zero to Hero course by Andrej Karpathy on YouTube, where we made our GPT from scratch! I highly recommend this to anyone who wants to understand how ChatGPT really works.
Websites I recommend for online courses are Coursera and Udemy. However, like everything else in this article, there are so many options, that it’s best to find the one that suits your needs the most.
Document Your Learning
If you want to take your understanding to an even higher level, you can document what you learn by writing online. Teaching others is the best way to learn.
As Edgar Dale’s cone of experience says, you remember 10% of what you read, but “95% of what we teach others.”
This is the main reason I started this blog. It was never to formally teach others, but to learn something and write about it. During the writing process, I often find gaps in my knowledge that I didn’t realize were there, which forces me to dig deeper and consolidate my understanding.
It doesn’t necessarily need to be writing, you can enter Kaggle competitions or build your projects using the latest tools from research. For example, I tried to create my own ChatGPT using Meta’s open-source Llama 2 model. Unfortunately, the RAM on my computer wasn’t sufficient, but I learned a lot in the process.
However, this approach is the most time-consuming out of everything I have discussed in this post, but by far the most effective. Reading tweets is a lot less effort!
Summary
Being a Data Scientist means continual learning, so it’s important to stay in the loop with all the latest advancements and tools to keep your tech stack sharp. It’s very difficult to keep up with everything, but you don’t have to. Just having an overview of the lay of the land is sufficient, and the methods I have discussed in this post will enable that.
Another Thing!
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