Daily Artificial Intelligence News Roundup #131

1. 5 Best Machine Learning GitHub Repositories & Reddit Discussions (November 2018)


Coding is among one of the best things about being a data scientist. There are often days when I find myself immersed in programming something from scratch. We need to be aware of all the latest developments in the community, what other machine learning professionals and thought leaders are talking about, what are the moral implications of working on a controversial project, etc. Read More

2. Google’s DeepMind predicts 3D shapes of proteins

Having laid waste to the Atari classics and reached superhuman performance in chess and the Chinese board game, Go, Google’s DeepMind outfit has turned its artificial intelligence on one of the toughest problems in science. At an international conference in Cancun on Sunday, organisers announced that DeepMind’s latest AI program, AlphaFold, had beaten all-comers at a particularly fiendish task: predicting the 3D shapes of proteins, the fundamental molecules of life. Each one is a chain of amino acids, of which there are 20 different types. Read More

3. AI software can dream up an entire digital world from a simple sketch

The latest blockbuster video game, Red Dead Redemption 2, for example, took a team of around 1000 developers more than 8 years to create—occasionally working 100-hour weeks. It could also be used to auto-generate virtual environments for virtual reality or for teaching self-driving cars and robots about the world. ”

Nvidia’s researchers used a standard machine learning approach to identify different objects in a video scene: cars, trees, buildings, and so forth. Read More

4. [1811.12627] Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders

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5.  — Issue #62 –


Artificial Intelligence, China And The U. Is Losing The Technology War

Where is technology leading us to, what are the major powers doing. The author explores these questions with some views of their own. Read More

6. AlphaFold: Using AI for scientific discovery

Why is protein folding important.
The ability to predict a protein’s shape is useful to scientists because it is fundamental to understanding its role within the body, as well as diagnosing and treating diseases believed to be caused by misfolded proteins, such as Alzheimer’s, Parkinson’s, Huntington’s and cystic fibrosis.

We are especially excited about how it might improve our understanding of the body and how it works, enabling scientists to design new, effective cures for diseases more efficiently. Read More

7. The UK FCA could become the world’s first AI robo-regulator –

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8. 8-Bit Precision for Training Deep Learning Systems

The state-of-the-art hardware platforms for training deep neural networks (DNNs) are moving from traditional single precision (32-bit) computations towards 16-bit precision, in large part due to the high energy efficiency and smaller bit storage associated with using reduced-precision representations. In the Conference on Neural Information Processing Systems (NeurIPS) paper “Training Deep Neural Networks with 8-bit Floating Point Numbers” (authors: Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan) we demonstrate, for the first time, the successful training of DNNs using 8-bit floating point numbers (FP8) while fully maintaining the accuracy on a spectrum of deep learning models and datasets. First introduced in 2015 at the International Conference on Machine Learning (ICML), we demonstrated that the precision of deep learning training systems could be reduced from 32 bits to 16 bits and still fully preserve model accuracy. Read More

9. Turning to AI to save endangered languages

Group of Yugambeh Aboriginal warriors dance.

As languages are becoming extinct at an alarming rate, speakers of endangered languages are turning to technology in a race against time to pass on their unique languages and cultures to the next generation.

The United Nations has declared 2019 as the International Year of Indigenous Languages in an effort to promote awareness of the plight of languages that are in danger of disappearing. Read More

10. The countries where robot adoption is happening faster than expected

There’s perhaps no more telling metric for our time than the number of robots in a country per every 10,000 manufacturing workers.  But researchers believe the metric may not be the best way to measure countries’ openness to a bot-filled future. Not surprisingly, rich countries—like South Korea, Germany, and the United States—have some of the highest rates of adoption. Read More

11. How China is pulling ahead on AI and biotech

China is poised to take the lead in innovations at the intersection of AI and biotech, with clinical applications of gene-editing and cell therapies, as well as blood-based cancer diagnostics.

Why it matters: China and Silicon Valley are competing for proprietary access to the genetic data of entire populations, which can be analyzed using machine learning to drastically advance genomic and medical research. Breakthroughs and overall leadership in these fields will have repercussions for the global economy. Read More

12. Walmart leads the way … in floor scrubbing robots?

Walmart is set to put a bunch of floor scrubbing robots to work in its stores by the end of Jan 2019. It’s another foray into automation for the brick-and-mortar megastore, which has been deploying cutting edge automation and AI solutions in a bid to lean its operations and cut into Amazon’s efficiency edge.

The automated floor scrubbers are powered by an OS built by Brain Corp. Read More

13. I tested an AI sales assistant that’s trained to push you into spending more

Even the persuasive skills of a sales clerk are not beyond the creep of automation.

First impressions: I tried out the company’s avatar, called Millie, at NeurIPS, a major AI conference in Montreal. Her sales pitch was nothing if not direct: “are you a model?” the software gushed. Read More

14. AI has a culturally-biased worldview that Google has a plan to change

Google has launched an Inclusive Images Competition, an effort to expand the cultural fluency of image-recognition software. Recent leaps in image recognition have coincided with the release of large, publicly available datasets, including ImageNet and Open Images.

The challenge: One way to mitigate this issue is to build more diverse and representative image datasets. Read More

15. Nvidia has created the first video game demo using AI-generated graphics

The recent boom in artificial intelligence has produced impressive results in a somewhat surprising realm: the world of image and video generation. The latest example comes from chip designer Nvidia, which today published research showing how AI-generated visuals can be combined with a traditional video game engine. The result is a hybrid graphics system that could one day be used in video games, movies, and virtual reality. Read More