Where Is AI Headed in 2018?
13 predictions by researchers and experts from around the world.
China embraces AI: A Close Look and A Long View
China is poised to dominate multiple sectors of AI (including consumer applications, autonomous vehicles and perception/vision) while lagging the US on business applications, argue Kai-Fu Lee and Ian Bremmer. Chinese firms are also moving from copycats to leapfrogging American competitors, to support the State’s vision of $1.5trn in AI-related revenues by 2030.
China’s big brother: how artificial intelligence is catching criminals and advancing health care
Zhu Long, co-founder of pioneering Yitu Technologies, whose facial-recognition algorithms have logged 1.8 billion faces and caught criminals across China, says AI will change the world more than the industrial revolution
NASA uses Google machine learning for exoplanet detection
Neural networks have thrown up lots of false positives, but also previously undetected exoplanets.
Revitalizing manufacturing through AI
Andrew Ng’s announcement of Landing.ai, his new company. They are attempting to be an “A.I. partner” and provide fuller stack solutions to manufacturing plants.
AI 100: The Artificial Intelligence Startups Redefining Industries
The 100 startups on our list have raised $11.7B in aggregate funding across 367 deals since 2012.
Practical applications of reinforcement learning in industry
An overview of commercial and industrial applications of reinforcement learning.
Deep Learning for NLP, advancements and trends in 2017
“In this article I will go through some advancements for NLP in 2017 that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most this year. I think 2017 has been a great year for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop.”
Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)
An excellent tutorial on the building blocks of today’s Deep Learning systems. The tutorial covers Convolutional Models, Autoregressive Models, Domain Alignment, Meta Learning, Graph Networks, and more.
A List of Chip/IP for Deep Learning (keep updating)
Machine Learning, especially Deep Learning technology is driving the evolution of artificial intelligence (AI). At the beginning, deep learning has primarily been a software play. Start from the year 2016, the need for more efficient hardware acceleration of AI/ML/DL was recognized in academia and industry. This year, we saw more and more players, including world’s top semiconductor companies as well as a number of startups, even tech giants Google, have jumped into the race. I believe that it could be very interesting to look at them together. So, I build this list of AI/ML/DL ICs and IPs on Github and keep updating. If you have any suggestion or new information, please let me know.
