New episodes with Yannic Kilcher and swyx! PLUS: NIPS survey paper on production ML, protein folding solved, the GPT-3 debate continued...
Hope you had a great Thanksgiving (or great Thursday if you're not in the US) last week!
Featured in this newsletter:
- New podcast episodes with Yannic Kilcher and Shawn “swyx” Wang
- NIPS survey paper on challenges in deploying ML models
- DeepMind solves the protein folding grand challenge
- Continuing the great GPT-3 debate
Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field
In last week’s episode, I interviewed the creator of the best ML paper-explainer Youtube videos on the internet, Yannic Kilcher
He talked about starting his popular paper-explainer Youtube channel, how he reads for understanding, why the peer review process is broken, and where he thinks the AI field is going.
I wrote out the best quotes and takeaways from the episode in this Twitter thread. Check it out and like/re-tweet if you found it helpful!
Click here to listen to the episode, or find it in your podcast player of choice: https://www.mlengineered.com/listen
Coding Career Tactics - Salary Negotiation, Public Speaking, and Creating Your Own Luck
In this week’s episode, Shawn “swyx” Wang joined me as the podcast’s first return guest!
We discussed why and how you should negotiate your salary, getting started in public speaking, creating your own luck, learning in public, and much, much more!
Click here to listen to the episode, or find it in your podcast player of choice: https://www.mlengineered.com/listen
NIPS 2020 Survey Paper: Challenges in Deploying Machine Learning
This survey paper from Cambridge University is an absolute goldmine highlighting all the different problems that we face every day as ML practitioners. I have no doubt there will be multiple billion dollar companies that arise from addressing these in the near future.
Once my new website is up, I’ll be posting a comprehensive summary of it. But for now, you can read it on arXiv here.
ICYMI: DeepMind solves protein folding
Putting this here just in case you haven’t heard about it–Google’s PR department went on overdrive for this one.
Although the official paper hasn’t been released yet (forthcoming in Nature), Yannic lived up to his nickname “Lightspeed” and released a video explaining their previous solution and speculates what the advances in this one might be. Check it out here.
If you want to know more about uses for machine learning in scientific fields, episode four of the podcast featured Charles Yang, who writes the excellent ML4Science newsletter. Check it out here.
The "Is DL the solution to AI" debate continues...
Tim, Yannic and Keith of ML Street Talk released a mega-episode all about GPT-3 after they got access to it and ran all sorts of interesting experiments.
They also talked to previous GPT skeptics Walid Saba and Gary Marcus afterwards, who seemed to be more impressed with the system than before, but still mostly maintained their positions that it is only pattern matching and not understanding or reasoning.
Connor Leahy stood firm on his view that DL is the way and commented that “there’s a 30% chance that simply scaling up the GPT architecture will result in AGI.” He elaborated with some extremely interesting notes on parallels between the structure of the brain and GPT as well as positing that the reason NNs work so well is because the universe itself is a matrix program.
Like I said in the last newsletter, I have no idea who is right, but by golly is it fascinating to listen to. Check it out here.
Machine Learning Engineered
I mentioned last week that I had to skip an episode release because of guest reschedulings. Since then, I’ve recorded five episodes, which was fun but extremely tiring. Rest assured, I won’t be missing another release! Also, in case you missed it, I wrote up a study guide for aspiring ML engineers that lays out a clear starting path and contains a list of resources that I and my friends have learned from. Read the Study Guide In this week's edition: My Interview on the MLOps Community podcast...
There’s no podcast episode this week due to an unfortunate coincidence of multiple guests needing to reschedule. My apologies, I’ll be doing my best in the future to not let this happen again. That doesn’t mean I don’t have any new content for this week, though! Today I’m releasing an article that answers one of the most common questions I get: “I want to learn machine learning, where do I start / what do I do?” When I was first getting started in ML, it was pretty straightforward: there was...
After a month off from releasing original interviews on the podcast feed, I’m so excited to be sharing this episode with all of you! Aether Biomachines is one of the most interesting machine learning startups I’ve ever come across and I was thrilled to interview the founder, Pavle Jeremic. Building a Post-Scarcity Future using Machine Learning “How can we make sure that the economy is so productive that the desperation that leads people to commit atrocities never happens?” In this episode,...