Today, Intel launched the Movidius™ Neural Compute Stick, the world’s first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge. Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor.
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- We studied carefully the 2 most advanced organizations that embody this principle, Google and Facebook, to understand how they are structured.
- Whatever the company you belong to, there are a few lessons to be drawn from these 2 exemplars to design your own AI-first organization: AI-first organizations are complex!
- AI-first organizations are embodied through various means: not only teams, but also internal software platforms, open-source side projects, and the final services delivered.
- In AI-first organizations, the most technical experts are not only tasked with creating breakthrough research projects or new technologies; they must also lead internal training efforts.
- AI-first organizations are inherently agile: in the aggregate, their goal is to maximize both the number of experiments leveraging AI, as well as the speed of deployment and capacity of scaling the successful ones.
At the last Google I/O conference, Google’s CEO Sundar Pichai emphasized the ongoing shift from a mobile-first to an AI-first world. What does it mean in
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- Autonomous cars are going to need to send and receive mountains of data, and the current 4G wireless networks simply won’t cut it.
- Super-fast transfer speeds will be required by self-driving cars to enable them to communicate with a wide range of systems such as navigation services, traffic signals as part of connecting to smart city infrastructure, car-to-car communication and even to close-proximity mobile phone users – all in the interest of pilotless vehicle safety.
- Using 5G wireless networks will enable driverless cars to avoid hitting pedestrians by a direct connection between the car and the person’s handheld device as they approach an intersection.
- While 5G standards are currently being worked out, expect to see everything up and running by 2020, when Volkswagen has promised to bring its first semi-autonomous electric car to market.
- That said, electric car company Tesla will have fully-autonomous vehicles ready by 2018, Toyota, General Motors and Volkswagen by 2020, while Ford and BMW claim they will have autonomous cars on the road by 2021.
Autonomous cars are going to need to send and receive mountains of data, and the current 4G wireless networks simply won’t cut it….
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- Our goal is to ensure that the most promising researchers in the world have access to enough compute power to imagine, implement, and publish the next wave of ML breakthroughs.
- We’re setting up a program to accept applications for access to the TensorFlow Research Cloud and will evaluate applications on a rolling basis.
- The program will be highly selective since demand for ML compute is overwhelming, but we specifically encourage individuals with a wide range of backgrounds, affiliations, and interests to apply.
- The program will start small and scale up.
Researchers need enormous computational resources to train the machine learning models that have delivered
recent advances in medical imaging, speech recognition, game playing, and many other domains. The TensorFlow
Research Cloud is a cluster of 1,000 Cloud TPUs that provides the machine learning research community with
a total of 180 petaflops of raw compute power — at no charge — to support the next wave of breakthroughs.
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- 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.
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- According to Accenture, AI will define future customer experience .
- “The power of AI is the power to make better decisions.
- Reese shared a historical example of AI and whether AI should be used to make decision.
- If AI has the power to make better decisions, then any business that has to make decisions, will be able to make more informed and fast decisions.
- Reese notes that AI in business is really heating up.
Artificial intelligence (AI) is the new UI, according to Accenture’s Technology Vision 2017 report, identifying trends that are essential to business suc…
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- Eben Upton, founder of the Raspberry Pi Foundation, told the BBC: “It’s fantastic to see Google getting closer to the maker community.”
- Google has set up its very own survey and is asking makers what smart tools would be the “most helpful”.
- Google set to bring AI to Raspberry Pi computers
- Google is on its way to bringing artificial intelligence and machine learning tools to computer Raspberry Pi.
- “I’m particularly excited about the prospect of connecting Raspberry Pi to some of the machine learning work coming out of Google DeepMind in London, allowing us to build smart devices that interact in the real world.”
Take a look at this interesting tech…
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