A List of Chip/IP for Deep Learning（updated）
Machine Learning, especially Deep Learning technology is driving the evolution of artificial intelligence (AI). In the beginning, deep learning has primarily been a software play. Start from the year 2016, the need for more efficient hardware acceleration of AI/ML/DL was recognized in academia and industry. This year, we saw more and more players, including world’s top semiconductor companies as well as a number of startups, even tech giants Google, have jumped into the race.
Artificial Intelligence — The Revolution Hasn’t Happened Yet
Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us — enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.
Why tech companies are racing each other to make their own custom A.I. chips
Earlier this week Alibaba said will make its own chip available for access through its cloud. Google has developed chips for AI, and Facebook has a nascent chip effort.
Facebook Is Forming a Team to Design Its Own Chips
Facebook Inc. is building a team to design its own semiconductors, adding to a trend among technology companies to supply themselves and lower their dependence on chipmakers such as Intel Corp. and Qualcomm Inc., according to job listings and people familiar with the matter.
What Human Teams Can Learn From Machine Learning Marketing Algorithms
Opinion: If you can’t beat it, copy it
Why Deep Learning is perfect for NLP (Natural Language Processing)
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
Is Open Source The AI Nirvana for Intel?
In the AI space, Intel recently announced that its nGraph code for managing AI graph APIs has also been opened to the community. After opening it up last month, Intel has been followed up on the initial work on MXNet with further improvements to TensorFlow.
How machine learning allowed one company to detect Meltdown and Spectre before Intel went public
In-memory attacks are part of the next generation of exploits that cybersecurity experts need to guard against, and AI and machine learning can help.
Frontier AI: How far are we from artificial “general” intelligence, really?
Some call it “strong” AI, others “real” AI, “true” AI or artificial “general” intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human — possibly even at a superhuman intelligence level, with unpredictable, uncontrollable consequences.
Text Embedding Models Contain Bias. Here’s Why That Matters.
Neural network models can be quite powerful, effectively helping to identify patterns and uncover structure in a variety of different tasks, from language translation to pathology to playing games. At the same time, neural models (as well as other kinds of machine learning models) can contain problematic biases in many forms.
How Music Generated by Artificial Intelligence Is Reshaping — Not Destroying — The Industry
There is an enduring fear in the music industry that artificial intelligence will replace the artists we love, and end creativity as we know it.
Welcome to GLUE.
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
