- At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too.
- At Google I/O today, the search giant announced a new initiative to help its AI make this leap down to earth: a mobile-optimized version of its machine learning framework named TensorFlowLite.
- The newly announced version, TensorFlowLite, will build on this, helping users slim down their machine learning algorithms to work on-device.
- The company also announced that an API for making machine learning work better with phone chips would be coming sometime in the future — a clear sign that Google thinks your next phone will have an AI-optimized chip in it.
- TensorFlowLite should help Google (and the wider AI research community) bring even more interesting functions like this to our most-used and most-important devices.
At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too. At Google I/O today, the search giant announced a…
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- After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
- I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
- Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
- But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
- The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!
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- ‘Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey.
- The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence.
- The car didn’t follow a single instruction provided by an engineer or programmer.
- Getting a car to drive this way was an impressive feat.
- But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions.
‘Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.
Continue reading “The Dark Secret at the Heart of AI”
- Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature that detects when a payment is being discussed in a conversation on the social media platform and responds with a suggestion designed to help the user complete that payment.
- “M offers suggestions by popping into an open conversation to suggest relevant content and capabilities to enrich the way people communicate and get things done,” the social media giant says.
- M may make a suggestion in a conversation relevant to one of the core actions, and then the M logo and suggestion will appear — it’s that simple.”
- Facebook began testing payments through its Messenger service in July 2016.
- The social media giant also updated its Messenger chatbot platform to enable bots to accept payments without having to send shoppers to external sites to complete the checkout process in September 2016.
Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature.
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- People figured that if they could find a way to codify instructions to a machine to tell it what steps to take, any manual operation could be eliminated saving any business time and money.
- Algorithms, on the other hand, are a series of steps that describe a way of solving a problem that meets the criteria of both being correct and ability to be terminated if need be.
- Instead of writing code to search our data given a set of parameters of the certain pattern as traditional coding focuses on, with big data we look for the pattern that matches the data.
- Now another step’s been added to the equation that finds patterns humans don’t see, such as the certain wavelength of light, or data over a certain volume.
- So, this new algorithmic step now successfully searches for patterns and will also create the code needed to do it.
We are all now in what’s called the “big data era,” and we’ve been here for quite some time. Once upon a time we were only just starting to piece together
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- Ultimately, the approach could allow non-coders to simply describe an idea for a program and let the system build it, says Marc Brockschmidt, one of DeepCoder’s creators at Microsoft Research in Cambridge, UK.
- DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software – just like a programmer might.
- “It could allow non-coders to simply describe an idea for a program and let the system build it”
One advantage of letting an AI loose in this way is that it can search more thoroughly and widely than a human coder, so could piece together source code in a way humans may not have thought of.
- DeepCoder created working programs in fractions of a second, whereas older systems take minutes to trial many different combinations of lines of code before piecing together something that can do the job.
- Brockschmidt says that future versions could make it very easy to build routine programs that scrape information from websites, or automatically categorise Facebook photos, for example, without human coders having to lift a finger
“The potential for automation that this kind of technology offers could really signify an enormous [reduction] in the amount of effort it takes to develop code,” says Solar-Lezama.
Software called DeepCoder has solved simple programming challenges by piecing together bits of borrowed code
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- Which is why it makes sense that Apple has announced it’s expanding its offices in Seattle, where much of its AI and machine learning work is done.
- Last August, Apple even bought a Seattle-based machine learning and artificial intelligence startup named Turi for an estimated $200 million, and the team is said to be moving into Apple’s offices at Two Union Square as part of the expansion.
- Carlos Guestrin, a University of Washington professor, former Turi CEO, and now director of machine learning at Apple, told GeekWire: “There’s a great opportunity for AI in Seattle.”
- He added: “We’re trying to find the best people who are excited about AI and machine learning — excited about research and thinking long term but also bringing those ideas into products that impact and delight our customers.”
- As part of the news, the University of Washington also announced a $1 million endowed professorship in AI and machine learning named after Guesterin.
In many ways, the tech world’s AI arms race is really a fight for talent. Skilled engineers are in short supply, and Silicon Valley’s biggest companies are competing to nab the best minds from…
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