AIDL Issue 80 - Alright..... We are Going to Talk About the AI News Anchor.....

Editorial

Thoughts From Your Humble Curators

This week we cover two topics: first off, why Facebook fails to build their own speech recognizer. It is surprising to see an organization like Facebook to have so much troubles to build and grow one machine learning technology. What are the reasons behind? We will quote the Forbes' article as well as give out some of our own opinions.

Also.... since we got around 10 post submission on the much hyped AI news anchor, we are going to talk about it too. What is it really? Is it "intelligent"? And what is the true impact of such technology?

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 178,000+ 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.

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News

Deals

Correction: Early version of this item states that Precision got $20M Series B, the correct company name should be Taranis.

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Factchecking

Blog Posts


Google AI Blogs Last Two Weeks

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 179,000+ 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.

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AIDL Weekly Issue #79 - The Man from La Famille de Belamy

Editorial

Thoughts From Your Humble Curators

We brought you "Edmond de Belamy, from La Famille de Belamy", the AI-generated artwork which was sold for 10 times its estimated price; We point you to several technical blogs including Ruder's "Neural History of NLP", and the very entertaining Google AI's post on curiosity vs procrastination in robotic agents.

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 178,000+ 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.

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 178,000+ 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

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AIDL Weekly #78 - Uber vs Waymo: A Revisit

Editorial

Thoughts From Your Humble Curators

This week we revisit the Uber vs Waymo case, and share several resources, including the second edition of Sutton and Barto's book: "Reinforcement Learning: An Introduction".

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 177,000+ 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.

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 177,000+ 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

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AIDL Weekly Issue #77 - Keras vs Tensorflow by Adrian Joey Rosebrock Stanford NLP 2012

Editorial

Thoughts From Your Humble Curators

This week we cover two recent Google's results: one on metastatic breast cancer detection, the other on their recent ActiveQA system.

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 177,000+ 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

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 177,000+ 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

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AIDL Weekly Issue #76 - fast.ai pytorch library, MS's infer.NET Oct 8th 2018

Editorial

Thoughts From Your Humble Curators

This week we cover two new open source frameworks: the fast.ai Pytorch library and Microsoft infer.NET.

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 176,000+ 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

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 176,000+ 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

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AIDL Weekly #75 - Inside Google Dataset Search

Editorial

Thoughts From Your Humble Curators

We cover Paige.ai and what's inside Google Dataset Search this issue.

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 175,000+ 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

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 175,000+ 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

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AIDL Weekly Issue #74 - Facebook vs Fake News (or AI vs Fake News?)

Editorial

Thoughts From Your Humble Curators

This week we look into the details of Facebook's usage of machine learning in detecting misinformation. What is the purpose of their latest Rosetta system?. And how prepared is Facebook against faked news?

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 173,000+ 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.

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News


Deals and Acquisitions

Microsoft Acquires Lobe

Also take a look at the trends in AI in healthcare fundings.

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About Us

This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 173,000+ 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

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AIDL Weekly Issue 70 - Nvidia Turing, TF 2.0

Editorial

Thoughts From Your Humble Curators

Hey Hey! We are back. This issue we brings you two interesting stories:

  • The Nvidia Turing architecture - how much would it affect deep learning?
  • Tensorflow 2.0 - what is the major change? How would that affect you?

As always, if you like our newsletter, share 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 168,000+ 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.

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News

Deals

Also this from Techcrunch: Artificial Intelligence Continues Its Fundraising Tear In 2018

For a counter view, check out this piece on Quanergy Systems and how they lost its ways.

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Open Source


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Member's Question

"You know more than Silicon Valley Engineers"

(Original Link) Question: (Excerpt and rewritten) At the end of the lecture 3 of Ng's Machine Learning Coursera Course, Andrew says that "if you understood what you have done so far in the course, you know much more than many of the Silicon Valley engineers that are having a lot of success" . Is it actually true?

Answer: (By Arthur) You might be around 5 years ago - that was the time machine learning was more an esoteric topic. At then, it is true that general programmers and engineers lack of basic understanding of ML concepts such as under/over-fitting, metric-driven development.

Translate to now though, you should be aware that machine learning became a mainstream topic and general CS major knows quite well ML works. You are competing with many young bright minds on your knowledge of machine learning now.

So I would probably say, given what you know so far until Lecture 3, "you have good basic understanding of machine learning". You have a good start, but my guess you still have things to learn.

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This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 168,000+ 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.

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AIDL Weekly Issue 69 - A Dexterous Robotic Hand

Editorial

Thoughts From Your Humble Curators

This week we look deep into latest OpenAI's work on dexterous robotic hand, and ask if feed-forward networks are just as good as the recurrent ones.

As always, if you like our newsletter, feel free to share it with your friends and colleages.


This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 165,000+ 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.

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Member's Question

AIDL Admin's Feedback on the New Pre-approval System.

An answer from Arthur: Zubair Ahmed posted a thread on getting everyone's feedback about our now 2-month-old pre-approval system. So far, most feedbacks we got are positive. I just want to give you my take on the new system.

First off, our system (or think of it as post-approval) was meant to give members freedom to post what they like.

Unfortunately, self-inspection from all members couldn't filter out all malicious postings such as porns, religious and political messages which have nothing to do with AI, etc. Plus there are too many complaints about basic questions such as "How do I learn AI?" was asked repetitively.

So here comes our new pre-approval system. How does it really work in practice? Let me just give you a sample of my day, and how we processed different posts and decide if they should appear in the feed. I am not the only approver, but we have fairly consistent standard across admins/mods. So you will have a good feel of our work.

Daily, we receive 50-70 posts required to be approved. In my timezone, I will process around 40-50 of them. Here is a rough breakdown of them:

  • 10%: selling irrelevant products such as rolexes, web hosting. What I do: delete the post.
  • 20%: technology-related but has nothing to do with AI. What I do: delete the post.
  • 30%: AI-related news which comes from unreliable sources, or from a Page which just reposts a piece. Or sensational opinion about AI-related technology. What I do: I usually delete the post unless it reflect a certain zeitegeist in AI development.
  • 10%: Members questions which are unclear. Usually these posts are poorly formatted and not proofread. These posts usually solicit angry responses from impatient AIDL members. What I do: sometimes I let them in, but comment on the quality of the questions. If they are "How do I learn AI?" I would just delete them.

Members questions which I have no idea the meanings are. Usually they are the results from poor formatting and poor or no proof-reading from the posters. They are usually gone because the post will only solicit angry response from the slightly more knowledgeable but impatient AIDL members. What I do: sometimes I let them in, but comment on the quality of the questions. But I don't mind to delete them. If they are "How do I learn AI?" Sorry, I would just delete them.

So the rest is what you see in the feed. That accounts for ~10-15 posts. If they are posts, they are original form the authors, if they are code, they share from the programmers. If they are questions, they are usually non-trivial. And their answers are good for everybody knows.

One questions members often asked is how does the pre-approval affect our workload as admin? I'll say : at the moment, it lighten up our load. The reason is we pretty much just used similar curation criteria before the pre-approval system. But now we see fewer group-wide outrages of poor quality posts. Spams such as porn, while infrequent, they are disruptive to our members', and thus our life.

There are some members just completely disagree with any pre-approval system. I'll say this: If you just look at the post breakdown, you should quickly notice that 60% of pending posts are inappropriate for the group. So we have always been removing them even before the system. We really tried to get the old system working, but it's too hard.

I'll also say we admins realize that we are just humans and can be biased and make mistakes. So let's say we keep an open-minded and feel free to give us feedbacks.

On a lighter note: me and Zubair Ahmed found that there are always someone suggest that ML should be used to replace us admins/mods. Of course, we also repeatedly pointed out that this is a cliche idea. But let's see how often they appear? 🙂

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This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 165,000+ 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.

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AIDL Weekly Issue #61 - Project Maven

Editorial

Thoughts From Your Humble Curators

This week, we take a closer look at Google's involvement in project Maven, and how it ends abruptly after complaints from Google's employees.

As always, if you like our newsletter, feel free to subscribe and forward it to your colleagues.


This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 145,000+ 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 with this iOS app here: https://itunes.apple.com/us/app/expertify/id969850760

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I realized ML is just Statistics, should I feel demotivated?

Question: Anyone had the feeling where you feel very motivated and eager to learn machine learning, and once you actually start, you realize it's just stats which is something you really don't like, and become completely demotivated?

Answer: (By Arthur) There are two parts of your frustrations. First, it's that you equate machine learning and statistics. Second it is you feel that statistics is boring. So let's address the second part first. Then I will come back to the first part.

Is statistics boring? I guess many people who learn statistics usually learn Math first. If that's your route, then perhaps one of the reasons why statistics is boring is that it is empirical and deal with imperfect phenomenon of the world. So unlike Euclidean geometry, or solving quadratic or cubic, you can't quite come up with an exact solution.

To many people's dissatisfaction though, the world is better to be described to be uncertain, rather than certain. Unfortunately, only statistics can teach us more in the realm of uncertainty. So statistics is actually a rescue, and I personally feel grateful for the subject.

Can statistics be fun? You ask. It all depends on how you look at it. e.g. It took me a while to find a good proof of how a full covariance matrix can be estimated through maximum likelihood. In particular, in matrix form, the math is quite interesting. I end-up bought a book by Abadir and Magnus called "Matrix Algebra" and browse it from time to time. I am pretty sure it is boring to some, but it's a lot of fun to me. Btw, Matrix Algebra can be quite mathematical too. But you may say it is more technical type of Math.

My conclusion of the second part is: are you sure you see everything in statistics and machine learning? There are many deep topics in both subjects. But then you might miss nuance in your first glance. So that's that. Of course, your frustration might come from your personal philosophy. Perhaps you don't like uncertainty? Perhaps you don't like the time-consuming process of collecting data? No one can blame you for that. You just have to be honest to yourself.

Let’s go back to the first part on whether machine learning is just statistics. And this is slightly controversial. Let me just quote one prominent person. Say if you ask Prof. Nil Nilsson, he once said machine learning just the subject of "machine to learn". But statistics is clearly more focused on the data and its observation. So if the fancy image of an intelligent robot is doing things was what attracts you, yeah, ML is the subject to learn. It's just that modern theory of ML has found that statistics is very important.

So why is that the case? Oh well, it's not like people love to be statistical, it has to do with nature is better described by uncertain rules. So say speech recognition? People would love to create several rules of phonetics and do speech recognition. In fact they were tried by PhD students in 60s. So in 70s, people start to realize that's not the way to go. Ha tada, here comes HMM. Boring it is. But it is the basis of many previous generation ASR before seq2seq NN models. In fact, there are still many HMM-based system.

So, to summarize, if you are disappointed by ML. Ask whether reality is better described by certainty or uncertainty. It will help you to have a closure.

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Paper/Thesis Review

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This newsletter is published by Waikit Lau and Arthur Chan. We also run Facebook's most active A.I. group with 145,000+ 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 with this iOS app here: https://itunes.apple.com/us/app/expertify/id969850760

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