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The architecture behind A.I.

Talking about hardware is like comparing a bicycle with a 4×4. When you are in the city a bike will do just fine, but when you need to get across some rough terrain you’ll prefer a 4×4. The same goes for A.I. It requires a different set-up than the your ordinary web application. Let’s look at the two most promising trends.

1. CPU, GPU, NPU and TPU?
Today’s computers can be traced back at least to Blaise Pascal’s 1642 mechanical calculator. It’s descendant is the traditional Central Processing Unit (CPU) that does most of the computing in your laptops, desktop pc’s and your phone, but it wasn’t designed to deal with large datasets as needed with for example machine learning.

Now let’s look at the Graphics Processing Units. The GPU’s advanced capabilities were originally used primarily for 3D game rendering. But now those capabilities are being harnessed more broadly to accelerate computational workloads in areas such as financial modeling, cutting-edge scientific research and oil and gas exploration. Read More

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K’ching for Megvii and Dashbot

Venture Capital is finding its way to AI. According to Singularity University dealflow went up 7 fold in 2015 and also in 2016 we’ve seen substantial funding rounds. Here are two deals that caught our attention.

Dashbot labels itself as the Google Analytics for bots. The company has
raised a $2 million seed round led by ff Venture Capital. The investment will be used to bring in more talent and improve the company’s product. Dashbot now supports more than 1,100 bots on its platform, processing more than 230 million messages. Read More

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The brains behind A.I.

Remember when every software company showed up for the “race to the cloud”? Something similar is happening with AI. The Big Five (Google, Amazon, Facebook, IBM and Microsoft) are investing heavily and not just with R&D budgets. Meet the brains behind the race.

Google: Fei-Fei Li
Fei-Fei Li is an Associate Professor at the Computer Science Department at Stanford University. At Google she will apply her experience to democratize machine learning to the enterprise. Her task: Study the problems that machine learning could solve in a wide variety of industries and enable enterprises to adopt machine learning. Read More

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