Indirectly – by forming the Partnership on AI – Facebook, Amazon, Google, IBM and Microsoft to promote “best practices” in Artificial Intelligence. I just hope that includes not wanting to wipe out humans!
The full name of the new partnership is: The Partnership on Artificial Intelligence to Benefit People and Society. That’s a worthy goal. Here’s hoping they go a measure toward achieving it.
Over recent years, I’ve read a lot on Apple* but only during the flight back did I start reading anything on Google: In the Plex by Steven Levy. While I’m not yet finished it struck me the fundamental difference between Google and Apple is “who’s in control”.
With Google, engineers rule. Data rules. Everyone else is in the service of the engineers.
At Apple, designers rule. (Design in the full sense of how something operates and feels, not just how it looks).
And right there is the difference between the two companies. All else leads from that fundamental focus.
*Becoming Steve Jobs Brent Schlender & Rick Tetzeli
Design Crazy Max Chafkin
Insanely Simple Ken Segall
Inside Apple Adam Lashinsky
Steve Jobs Walter Isaacson
It seems the smartest way to make money right now is to have a startup in speech recognition, machine learning, neural networks or other Artificial Intelligence related startup.
TechCrunch reported late last week the Apple had acquired another machine learning company:
After buying Perceptio at the end of 2015 and Turi just a few months ago, Apple has now acquired an India/US-based machine learning team, Tuplejump.
Presumably to beef up its efforts in AI and machine learning across the company.
Not to be left behind, Google:
…said that it’s acquired API.ai, a startup with tools for speech recognition and natural language understanding….
In addition to its developers tools, Api.ai offers a conversational assistant app with more than 20 million users.
I would expect the purchase is to beef up their speech recognition in its AI assistant Google Now.
Google have open sourced it’s Show and Tell model for automatically captioning images. This is an excellent example of how neural networks work: train the model with examples – in this case human captioned images – and then let it loose on new images. From the Venture Beat article:
Google trains Show and Tell by letting it take a look at images and captions that people wrote for those images. Sometimes, if the model thinks it sees something going on in a new image that’s exactly like a previous image it has seen, it falls back on the caption for the caption for that previous image. But at other times, Show and Tell is able to come up with original captions. “Moreover,” Shallue wrote, “it learns how to express that knowledge in natural-sounding English phrases despite receiving no additional language training other than reading the human captions.”
As the article points out, there are many more players looking to do the same thing. Imagine how much easier life would be in editorial if all the B-roll came in organized like this.
In this latest episode of The Terence and Philip Show Terry and I discuss metadata, my citizenship, smart APIs, Artificial Intelligence and more.
Karl Soule has been with Adobe for over 10 years, focused on the Pro Video and Broadcast market. Karl traveled worldwide as an Adobe Video Evangelist, inspiring video professionals on five continents. For the last 5 years, Karl has been living in Singapore and working in Asia, helping to grow the broadcast business and drive awareness of the video tools for that growing market.Currently based in Los Angeles, Karl is supporting the Hollywood and Broadcast markets for the US West Coast.
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