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 →GraphQL for databases: A layer for universal database access?
GraphQL is a query language mostly used to streamline access to REST APIs. Now, a new breed of GraphQL implementations wants to build an abstraction layer for any database on top of GraphQL, and it seems to be catching up.
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 →Business analytics: The essentials of data-driven decision-making
Data shows that data-driven organizations perform better. But what does it take to get there?
Read More →NBA analytics and RDF graphs: Game, data, and metadata evolution, and Occam’s razor
Three-point shooting, Steph Curry, and coming up with stories. If you feel like doing your own analysis to investigate hypotheses or discover insights at any level, RDF graph's got your back. Case in point: The NBA.
Read More →AWS Neptune going GA: The good, the bad, and the ugly for graph database users and vendors
It's official: AWS has a production-ready graph database. What features are included today, and what will be included in the near future, what use cases are targeted, and what does AWS Neptune's release mean for users and graph database vendors?
Read More →GDPR in real life: Transparency, innovation, and adoption across borders and organizations
Part two: Auditing data on premise and in the cloud, spurring innovation in machine learning and interpretable AI, and influencing organizations, consumers, and legislation all over the world, GDPR is here to stay.
Read More →Human in the loop: Machine learning and AI for the people
HITL is a mix and match approach that may help make ML both more efficient and approachable.
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