China, Salesforce, Hololens, Amazon robot picking, Hyper-networks, NLP Best practices, ML infrastructure at Stripe and more
ARTIFICIAL INTELLIGENCE NEWS #66 August 3rd 2017
In the News
The country laid out a development plan to become the world leader in A..I. by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 billion.
"Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go."
Two years ago, a band of artificial-intelligence acolytes within Salesforce escaped the towering headquarters with the goal of crazily multiplying the impact of the machine learning models—by automating the creation of those models.
New HoloLens processor will let mixed reality goggles recognize speech and images.
Also in the news...
- Vicarious AI raises $50M for its work on AI applied to robotics. More
- Graphcore raises $30M for its AI chips. More
- The UK government launches a £23M grant for autonomous vehicles. More
Amazon wants to automate the human picking process at its warehouses. This is hard, robots nead to pick up and place arbitrary objects from a potential pool of millions, so Amazon has been running a competition for three years on the subject. Smart way to motivate great teams to work on this.
This post describes an approach where a so-called "hyper-network" is used to generate the weights of another network. Thereby improving this network. Hyper-networks provide an abstraction that is similar to what is found in nature: the relationship between a genotype - the hyper-network - and a phenotype - the main network.
Great post by Sebastian Ruder, with best practices on many aspects of NLP: word embeddings, network construction, multi-task learning, attention and more.
Machine learning at Stripe has a foundation built on Python and the PyData stack, with a production system written in Scala. This talk covers the ML Infra team’s work to bridge the serialization and scoring gap between Python and the JVM, as well as how ML Engineers ship models to production.
It's been almost 10 years since the creation of Imagenet, the set of images that jumpstarted the Deep Learning revolution. Time to look back at those few years!
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