- Alphabet, Amazon, and Microsoft have all discovered that the artificial intelligence they use to make their own products better can be turned into a service and sold to corporate customers as a value-added service on top of their booming cloud-computing businesses.
- Once Google decided to get more serious about its cloud computing business and serving enterprise customers—Google Cloud storage officially launched in 2010—it has found more ways to take its AI investment and acumen and use it to serve others.
- Both groups have worked on applying AI in healthcare, for example, which then allows Google Cloud to better serve businesses in that field.
- Although most of the value in Google’s AI accrues to its own products and services, the company has stated that Google Cloud is one of its fastest-growing business units.
- Amazon has a much more natural synergy between its AI efforts and how it can sell those initiatives to others via its industry-leading cloud computing service.
The tech giants with cloud computing businesses are using artificial intelligence offerings to distinguish themselves and win business.
Continue reading “How Amazon, Google, Microsoft, And IBM Sell AI As A Service”
- Google Talks About the Machine Learning Technology Behind Gboard
Google has been heavily investing in machine learning and neural network technology for a few years now.
- Once it was good enough to be announced, we started seeing Google apply various machine learning techniques to many of their applications and services.
- So as Google’s machine learning technology gets better, we will continue to see them figure out ways to implement it into their products.
- In a new post over on the Google Research Blog, Google spoke about how they’re using machine learning to improve the user experience of people using Gboard.
- With Neural Spatial Models, Google is able to address the ‘fat finger’ issue that some people experience when typing on a smartphone keyboard.
Google has been heavily investing in machine learning and neural network technology for a few years now. Once it was good enough to be announced, we started seeing Google apply various machine learning techniques to many of their applications and services.
Continue reading “Google Talks About the Machine Learning Technology Behind Gboard”
- You can program these TPUs with TensorFlow, the most popular open-source machine learning framework on GitHub, and we’re introducing high-level APIs, which will make it easier to train machine learning models on CPUs, GPUs or Cloud TPUs with only minimal code changes.With Cloud TPUs, you have the opportunity to integrate state-of-the-art ML accelerators directly into your production infrastructure and benefit from on-demand, accelerated computing power without any up-front capital expenses.
- Since fast ML accelerators place extraordinary demands on surrounding storage systems and networks, we’re making optimizations throughout our Cloud infrastructure to help ensure that you can train powerful ML models quickly using real production data.Our goal is to help you build the best possible machine learning systems from top to bottom.
- For example, Shazam recently announced that they successfully migrated major portions of their music recognition workloads to NVIDIA GPUs on Google Cloud and saved money while gaining flexibility.Introducing the TensorFlow Research CloudMuch of the recent progress in machine learning has been driven by unprecedentedly open collaboration among researchers around the world across both industry and academia.
- To help as many researchers as we can and further accelerate the pace of open machine learning research, we’ll make 1,000 Cloud TPUs available at no cost to ML researchers via the TensorFlow Research Cloud.Sign up to learn moreIf you’re interested in accelerating training of machine learning models, accelerating batch processing of gigantic datasets, or processing live requests in production using more powerful ML models than ever before, please sign up today to learn more about our upcoming Cloud TPU Alpha program.
- If you’re a researcher expanding the frontier of machine learning and willing to share your findings with the world, please sign up to learn more about the TensorFlow Research Cloud program.
Announcing that our second-generation Tensor Processing Units (TPUs) will soon be available for Google Cloud customers who want to accelerate machine learning workloads.
Continue reading “Build and train machine learning models on our new Google Cloud TPUs”
- Geoffrey Hinton, who has worked for Google primarily in California since 2013, will soon be working permanently out of the tech giant’s Toronto offices and will be the Vector Institute’s chief scientific adviser.
- (Noah Berger / Associated Press file photo) By Kate AllenScience and Technology reporterTues., March 28, 2017Toronto will host a new institute devoted to artificial intelligence, a major gambit to bolster a field of research pioneered in Canada but consistently drained of talent by major U.S. technology companies like Google, Facebook and Microsoft.The Vector Institute, an independent non-profit affiliated with the University of Toronto, will hire about 25 new faculty and research scientists.
- More than two dozen companies have committed millions more over 10 years, including $5 million each from sponsors including Google, Air Canada, Loblaws, and Canada’s five biggest banks.The mode of artificial intelligence that the Vector Institute will focus on, deep learning, has seen remarkable results in recent years, particularly in image and speech recognition.
- Geoffrey Hinton, considered the “godfather” of deep learning for the breakthroughs he made while a professor at U of T, has worked for Google since 2013 in California and Toronto.Hinton will move back to Canada to lead a research team based at the tech giant’s Toronto offices and act as chief scientific adviser of the new institute.
- Academic institutions and startups in Toronto, Waterloo, Montreal and Edmonton boast leaders in the field, but other researchers have left for U.S. universities and corporate labs.The goals of the Vector Institute are to retain, repatriate and attract AI talent, to create more trained experts, and to feed that expertise into existing Canadian companies and startups.
Large companies including Google and Air Canada are sponsoring the Vector Institute, which intends to retain and repatriate the AI talent Canada is already producing.
Continue reading “New institute aims to make Toronto an ‘intellectual centre’ of AI capability”
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- 2016 has been a breakthrough year for deep learning, especially for Google and DeepMind.
- Today we are featuring the year’s most interesting breakthroughs in deep learning that we have been fawning over at Grakn Labs.
- (For those of you who are interested in a crash course in deep learning, ‘s a great video by Andrew Ng at Stanford.)
- Although not the most sophisticated use of deep learning that we’ve seen, we must hand it to him for originality and capturing the zeitgeist.
Today we are featuring the year’s most interesting breakthroughs in deep learning that we have been fawning over at Grakn Labs. (For those of you who are inter…
Continue reading “Year in Review: Deep Learning Breakthoughts 2016”
- Google says its GNMT system is even approaching human-level translation accuracy.
- SpaceX CEO Elon Musk has unveiled the design for the giant rocket booster he wants to build in order to colonize Mars.
- Elon Musk has a lot to prove at today’s Mars colonization announcement
- Google’s AI translation system is approaching human-level accuracy
- Google is one of the leading providers of artificial intelligence-assisted language translation, and the company now says a new technique for doing so is vastly improving the results.
Google is one of the leading providers of artificial intelligence-assisted language translation, and the company now says a new technique for doing so is vastly improving the results. The company’s…
Continue reading “Google’s AI translation system is approaching human-level accuracy”