TensorFlow

Tachyum CEO Discusses Architecture High-Performance Symbolic and General AI at Machine Learning & AI DevCon

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星期一, 六月 4, 2018
Computational neuroscience, Cybernetics, Technology, Artificial intelligence, Computing, Coprocessors, Computer engineering, Nvidia, Graphics processing unit, Artificial general intelligence, Machine learning, TensorFlow

"Computationally order of magnitude efficient approaches, like symbolic and general AI, require very different chips than Intel, GPUs from nVidia or TPUs from Google," said Danilak.

Key Points: 
  • "Computationally order of magnitude efficient approaches, like symbolic and general AI, require very different chips than Intel, GPUs from nVidia or TPUs from Google," said Danilak.
  • The Machine Learning & AI Developers Conference is designed specifically for developers, engineers and managers developing machine learning and artificial intelligence technologies and applications.
  • The conference offers a unique blend of market insights, technology forecasts and practical assessments of the challenges and opportunities of machine learning and artificial intelligence in embedded systems.
  • In addition, the Machine Learning Developers Conference features an exhibition showcasing demos and technologies focused on machine learning and artificial intelligence implementations.Additional information about the conference is available at https://mldevcon.com/

AI Frameworks Move Toward Interoperability in Order to Compete with TensorFlow

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星期四, 五月 17, 2018
Artificial intelligence, Free software, Software, Deep learning, Free statistical software, TensorFlow, Framework, Software framework

One of the central features of AI development is the various software frameworks needed to make it possible.

Key Points: 
  • One of the central features of AI development is the various software frameworks needed to make it possible.
  • Although TensorFlow currently leads the pack, several other frameworks are emerging as potential contenders likely to shape specific use cases and applications.
  • These challenger frameworks currently lack the size and scale of developer community interaction but are undertaking intense rationalization around interoperability, in a significant move against the dominance of TensorFlow.
  • A multiplicity of interoperable frameworks will enable developers to better research and productize AI," says Jack Vernon, Industry Analyst at ABI Research.