The AI chip unicorn that’s about to revolutionize everything has computational Graph at its Core
AI is the most disruptive technology of our lifetimes, and AI chips are the most disruptive infrastructure for AI. By that measure, the impact of what Graphcore is about to massively unleash in the world is beyond description. Here is how pushing the boundaries of Moore's Law with IPUs works, and how it compares to today's state of the art on the hardware and software level. Should incumbent Nvidia worry, and users rejoice?
Read More →Just another Cyber Monday: Amazing Amazon and the best deal ever
When you get something at 80% off on Amazon, who do you think wins — you or Amazon? If you think that’s a strange question, you ain’t seen nothing yet. Maybe it’s time we re:Invent some things. But, how can possibly getting a huge discount be bad? It’s not, if you actually need what you’re buying, and […]
Read More →From big data to AI: Where are we now, and what is the road forward?
It took AI just a couple of years to go from undercurrent to mainstream. But despite rapid progress on many fronts, AI still is something few understand and fewer yet can master. Here are some pointers on how to make it work for you, regardless of where you are in your AI journey.
Read More →Artifictional Intelligence: is the Singularity or the Surrender the real threat to humanity?
Artificial intelligence is one of those things: overhyped and yet mystical, the realm of experts and yet something everyone is inclined to have an opinion on. Harry Collins is no AI expert, and yet he seems to get it in a way we could only wish more experts did. Collins is a sociologist. In his book “Artifictional […]
Read More →AI chips for big data and machine learning: Hard choices in the cloud and on-premise
How can GPUs and FPGAs help with data-intensive tasks such as operations, analytics, and machine learning, and what are the options?
Read More →Zen and the art of data structures: From self-tuning to self-designing data systems
Designing data systems is something few people understand, and it's very hard and costly. But that, too, could be automated, says new research from Harvard, and we're about to start seeing it in real life.
Read More →The best programming language for data science and machine learning
Hint: There is no easy answer, and no consensus either.
Read More →Moving fast without breaking data: Governance for managing risk in machine learning and beyond
How do you resolve the tension between the need to build and deploy accurate machine learning models fast, and the need to understand how those models work, what data they touch upon, and what are the implications? Immuta says data governance is the answer.
Read More →Wolfram Research goes for Software 2.0, releases neural net repository
Wolfram, having been into AI before it was cool, now gets a piece of the deep learning hype, in its sui generis way. Where does it stand compared to the competition, and how easy is it to use and integrate Wolfram with the rest of the world?
Read More →AI-powered DevOps is how CA wants to reinvent software development and itself
How does non-deterministic software sound? Making data-driven software to help make data-driven software may seem like catch 22, but that's what CA wants to do. Here's why and how.
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