Joy Zhang, 7 real-world applications of reinforcement learning, gocoder, February 17, 2022
1. Autonomous driving with Wayve
2. Personalizing your Netflix recommendations
3. Optimizing inventory levels for Walmart
4. Improving search engine results with search.io
5. Improving language models with OpenAI's WebGPT
6. Trading on the financial markets with IBM's DSX platform
7. Robotics with the University of California, Berkeley
蔡淇華，台灣有多少人才，正被當蠢才教？，翻轉教育，2021-01-18
原來學不會，不是「永遠學不會」，是難度、是教法、更是時機的問題。一天學生科翰打電話給我，帶來了這樣的一個消息，更提醒了我這件事，不禁也讓我回想起身邊的幾個例子 ……
Mordechai Rorvig, Computer Scientists Prove Why Bigger Neural Networks Do Better, Quanta Magazine, February 10, 2022.
Interpolation
An old mathematical result says that to fit n data points with a curve, you need a function with n parameters. (In the previous example, the two points were described by a curve with two parameters.) When neural networks first emerged as a force in the 1980s, it made sense to think the same thing. They should only need n parameters to fit n data points — regardless of the dimension of the data.
“This is no longer what’s happening,” said Alex Dimakis of the University of Texas, Austin. “Right now, we are routinely creating neural networks that have a number of parameters more than the number of training samples. This says that the books have to be rewritten.”
Jeff Dean, Google Research: Themes from 2021 and Beyond, Google, January 11, 2022.
· Trend 1: More Capable, General-Purpose ML Models
· Trend 2: Continued Efficiency Improvements for ML
· Trend 3: ML Is Becoming More Personally and Communally Beneficial
· Trend 4: Growing Benefits of ML in Science, Health and Sustainability
· Trend 5: Deeper and Broader Understanding of ML
JEREMY KAHN, YouTube video streaming now using A.I. that mastered chess and Go, Fortune, February 11, 2022.
The artificial intelligence algorithm, called MuZero, was developed by YouTube’s London-based sister company within Alphabet, DeepMind, which is dedicated to advanced A.I. research. When applied to YouTube videos, the system has resulted in a 4% reduction on average in the amount of data the video-sharing service needs to stream to users, with no noticeable loss in video quality.
ELIZA STRICKLAND, Andrew Ng: Unbiggen AI The AI pioneer says it’s time for smart-sized, “data-centric” solutions to big issues, IEEE Spectrum, 09 FEB 2022.
Q: You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?
Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.