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Emergent // Future – Google Doodles, Canada’s AI Hub, TPUs vs GPUs, and more

Issue 48
This week we check out how Google
taught computers to draw, what AI innovation means to Canada, how the TPU stacks up against the GPU, and more. Plus our favorite reads and some projects to try at home

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Google Doodles

Google taught an AI to draw by using doodles. Last year, Google released Quick, Draw!, a game where you had to draw an image of a word or phrase in less than 20-seconds.

Well, they used some 70,000 Quick, Draw! doodles as training data for Sketch-RNN, a recurrent neural network that can draw stroke-based drawings of objects.

Interestingly, Quick, Draw! recorded not only the final image, but also the order and direction of every pen stroke used to make it. The result was more data about how humans draw.

Check out Sketch-RNN here.


CanadA.I.

Toronto is one of the world’s largest innovation hubs, but a growing number of A.I start-ups often head to California, where venture capital is abundant.

The Canadian government has made keeping A.I. talent at home a top priority for companies, universities, and technologists. The goal is to build a business environment around the country’s expertise and to keep the experts in the country.

Prime Minister Justin Trudeau has pledged $93 million ($125 million Canadian) to support A.I. research centers in Toronto, Montreal and Edmonton.


What We’re Reading books

  • AI Drives the Rise of Accelerated Computing in Data Centers. How the Google TPU compares to current NVIDIA technology. (NVIDIA)
  • Google’s Dueling Neural Networks Spar to Get Smarter, No Humans Required. When Ian Goodfellow explains the research he’s doing, he’s talking about the machines: “What an AI cannot create, it does not understand.” (Wired)
  • The first decade of augmented reality. Augmented reality is somewhere between great demos and a mass-market commercial  product, yet. (Benedict Evans)
  • The Boundaries of Artificial Emotional Intelligence. It’s apparently time to consider what kind of work only humans can do, and frantically reorient ourselves toward those roles. (How We Get To Next)
  • How Word2Vec Conquered NLP.  Word embeddings are one of the main drivers behind the success of deep learning in Natural Language Processing. Even technical people outside of NLP have often heard of word2vec and its uncanny ability to model the semantic relationship between a noun and its gender or the names of countries and their capitals. (Yves Peirsman)
  • The Dark Secret at the Heart of AI. No one really knows how the most advanced algorithms do what they do. That could be a problem. (MIT Technology Review)

Things To Try At Home 🛠


Emergent // Future is a weekly, hand-curated dispatch exploring technology through the lens of artificial intelligence, data science, and the shape of things to come. 

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Lovingly curated for you by Algorithmia

Product manager at Algorithmia helping to give developers super powers.

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