The definitive weekly newsletter on A.I. and Deep Learning, published by Waikit Lau and Arthur Chan. Our background spans MIT, CMU, Bessemer Venture Partners, Nuance, BBN, etc. Every week, we curate and analyze the most relevant and impactful developments in A.I.
We also run Facebook’s most active A.I. group with 191,000+ members and host a weekly “office hour” on YouTube.
Editorial
Thoughts From Your Humble Curators
This week we cover Google Dataset Search Engine and other topics.
As always, if you find this newsletter useful, feel free to share it with your friends/colleagues.
This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook’s most active A.I. group with 172,000+ members and host an occasional “office hour” on YouTube. To help defray our publishing costs, you may donate via link. Or you can donate by sending Eth to this address: 0xEB44F762c58Da2200957b5cc2C04473F609eAA65.
Join our community for real-time discussions here – Expertify
News
NIPS sold out in 12 minutes.
Just wow. Just like NIPS 2017, some researchers lamented that they cannot get into this year conference. Is that what the organizers want? For VC who bought up all the seats, they also don’t have the benefit to listen to the thought of real researchers.
Deals
- Kira System’s $65M
- AI Chip Company ThinCI $65M
- Grow $52M – Notice the secretive nature of Groq. See Issue 11
- Atrium $64M
DARPA $1.5 Billion Investment in AI
This is perhaps the most important news recently. DARPA’s continual involvement in A.I. shows us that US will still be a presence in the space.
And this sentence says a lot about how DARPA projects are managed:
Still, “this isn’t a blue-sky place,” Highnam said. “Everything we do, even the fundamental work, we know why we’re doing it. We work for defense.”
Blog Posts
Google Dataset Search Engine
Google promised if a dataset is “[…] hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page”, you will be able to find them in GDSE. We typed in “cancer” as the key term and we got 20+ results, from sources such as NHIH or Kaggle. Sounds like a service similar to Google Scholar and arxiv. And perhaps that’s why we joked: From now on at AIDL, we should rephrase the answer “go Google” to “go Google Dataset Search”.”
How Do You Start With Machine Learning?
This is a well-written Quora answer on how you should learn machine learning. The part we like is that it mentioned Bishop’s PRML. PRML has attained a status of “CLRS of machine learning”. That is one of those books people said you should read it, but no one really does. But we can assure you, if you even just browse through the book, your skill and understanding of machine learning will get better.
Open Source
Ethereum Data
Google is sharing a dataset of ethereum smart contract which works with BigQuery. This new database presents new opportunity on research. e.g. One can query the statistics of ethereum transactions and search for interesting patterns.
Member’s Question
How come my post get so few clicks?
Lately we have been asked this question often:
“I have this super-duper posts with super-fine details on why deep neural network is going to revolutionize humanity. My post talks about loss function and back-propagation. There is NO MATH! So I think it is very good for the community. And I think it is the first time we see this kind of blog post on AIDL!”
“But How come I only get 4 likes? My post should go VIIIIRAAALLL!”
We rephrased of course. But we think viewership has a lot of to do with qualities of our content. So the question deserves a good answer.
The first answer perhaps is just “Well your post is not that interesting…..” But why is it the case? And in particular why is it the case at AIDL?
You should understand, by now, knowledge about deep learning is not entirely new. And AIDL has been there for almost 3 years. So most beginner topics you try to cover, has been covered more than once. For example, (again I rephrase),
- “Write your own neural network in less than 5 characters”
- “The detailed, simple, easy, beginner’s, expert’s, mathematical-but-tons-of-mistakes, mathematical-but-the-notation-was-inconsistent, mathematical-and-correct-but-boring guide of back propagation”
- “The very detailed guide of convolutional networks with a wrong notion of convolutions”
- “How do you start machine/deep learning and AI if you don’t only know how to add and subtract.”
- “Why AI is/isn’t an imminent danger for humanity (Disclaimer: I have no other statistics in other human calamities to compare. But I feel like writing one.)”
So my take is you should ask yourself, “am I really writing something new?” Now that’s a tough requirement. Writing something new means you have already read up a body of literature yourself. And after studying the nuances of important literature, you then write something which people never read before. Yet these new reading also has to be convincing and interesting…..
In a nutshell, technical blogging is not easy. Some statistics for you: out of 50 blog posts submitted to AIDL. we might accept approve around 20-30% of them. And for the published ones, a majority of them will not gather more than 10 likes. That has nothing to do with whether you hashtag or come up with a catchy title. It has to do with whether you are creating genuinely interesting content for AIDL members.
On the other side of the coin, when your content is valuable, you will get attention. Just check out Joey Adrian Rosebrock’s posts on computer vision? Or Raymond de Lacaze’s regular posts on different papers? Rosebrock’s posts usually include non-trivial implementation with working python code. And de Lacaze’s posts are explanations of recent papers. Both of them post useful links which we check out from time to time. That’s why their posts got clicks, not because they use any type of gimmicks.
One last thing: don’t give up – writing about machine learning, just like learning machine learning, takes a long time to master. Also as Adrian later commented: writing a post is really not about getting likes. Writing by itself, it’s a learning process. Once you master the knowledge, other things will naturally come.
About Us
This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook’s most active A.I. group with 172,000+ members and host an occasional “office hour” on YouTube. To help defray our publishing costs, you may donate via link. Or you can donate by sending Eth to this address: 0xEB44F762c58Da2200957b5cc2C04473F609eAA65.
Join our community for real-time discussions here: Expertify