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Investors share their predictions for AI and machine learning in 2018

#artificialintelligence

Over the past three years, building intelligent apps -- apps powered by machine learning that continuously improve as they ingest new data -- has become easier and easier. Given the continued rise of machine learning, where are venture capitalists looking for the next set of investment opportunities? Generally, we see the core machine learning tools and building block services maturing, and now we are most interested in companies that are "moving up" the stack toward vertical applications, "moving down" the stack toward purpose-built hardware, and "moving out" of the data center toward intelligence at the edge. Here are four categories which we have been investing in and tracking closely because we believe they will play meaningful parts in the future of intelligent apps. As machine learning becomes more prevalent, cloud providers have raced to offer the latest GPUs for training machine learning models.


Amazon shifts some Alexa and Rekognition computing to its own Inferentia chip

#artificialintelligence

Amazon.com on Thursday said it shifted part of the computing for its Alexa voice assistant to its own custom-designed chips, aiming to make the work faster and cheaper while moving it away from chips supplied by Nvidia. When users of devices such as Amazon's Echo line of smart speakers ask the voice assistant a question, the query is sent to one of Amazon's data centers for several steps of processing. When Amazon's computers spit out an answer, that reply is in a text format that must be translated into audible speech for the voice assistant. Amazon previously handled that computing using chips from Nvidia but now the "majority" of it will happen using its own Inferentia computing chip. First announced in 2018, the Amazon chip is custom designed to speed up large volumes of machine learning tasks such as translating text to speech or recognizing images.


Deep learning projects: Cloud-based AI or dedicated hardware?

@machinelearnbot

Chip and system vendors are developing -- and rapidly innovating -- new AI processors designed for deep learning projects that use neural networks, the computing systems designed to approximate how human brains work.


Xnor shrinks AI to fit on a solar-powered chip, opening up big frontiers on the edge

#artificialintelligence

It was a big deal two and a half years ago when researchers shrunk down an image-recognition program to fit onto a $5 computer the size of a candy bar -- and now it's an even bigger deal for Xnor.ai to re-engineer its artificial intelligence software to fit onto a solar-powered computer chip. "To us, this is as big as when somebody invented a light bulb," Xnor.ai's co-founder, Ali Farhadi, said at the company's Seattle headquarters. Like the candy-bar-sized, Raspberry Pi-powered contraption, the camera-equipped chip flashes a signal when it sees a person standing in front of it. The point is that Xnor.ai has figured out how to blend stand-alone, solar-powered hardware and edge-based AI to turn its vision of "artificial intelligence at your fingertips" into a reality. "This is a key technology milestone, not a product," Farhadi explained.


Five Fast-Growing British Businesses To Watch In 2017

Forbes - Tech

Even the biggest businesses start out small. The sale last month of Skyscanner, the travel industry start-up, is just another example of British entrepreneurial success, built over a number of years – and defies those who bemoan the country's failure to produce big winners. But which are the businesses to watch in 2017? Well, while picking winners from smaller companies is fraught with difficulties, here are five young British companies tipped for big things over the year ahead. Captify is an insights-driven advertising technology company founded in 2011 that, via its purpose-built Search Intelligence platform, analyses more than 15 billion online searches each month.