Orchestrate all the Things

SambaNova just added another offering under its umbrella of AI-as-a-service portfolio for enterprises: GPT language models.

As the company continues to execute on its vision, we caught up with CEO Rodrigo Liang to look both at the big picture and under the hood.

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A number of successive funding rounds have given Yugabyte unicorn status, while positioning the company to aim for a big piece of a growing pie in the shape-shifting database market

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Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb

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IOTA is unveiling its smart contracts, with a clear onboarding path and many interesting features for developers.

We discuss this release, as well as progress made since moving to the new network and other new features in the works with IOTA Foundation co-founder and CEO Dominik Schiener.

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It's this time of year again: reports on the state of AI for 2021 are out. 

In what is becoming a valued yearly tradition, we caught up with AI investors and authors of the State of AI report, Nathan Benaich and Ian Hogarth, to discuss the 2021 release.

Some of the topics we covered are lessons learned from operationalizing AI and MLOps, new concepts and datasets, language models, AI ethics, and AI-powered biotech and pharma.

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Appwrite, an open source platform that offers a slew of features to developers, aims to capitalize on its grass-roots popularity.

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A multi-trillion dollar business in crisis, upending incumbents, unfettered ambition, and pragmatic deep learning. FLYR's story has it all.

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Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market.

For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.

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Will Deep Learning really be able to do everything? We don't really know. 

But if it's going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.

This work goes by the name of Neural Algorithmic Reasoning. Join us as we discuss roots and first principles, the defining characteristics, similarities and differences of algorithms and Deep Learning models with the people who came up with this. 

We also cover the details of how Neural Algorithmic Reasoning works, as well as future directions and applications in areas such as path finding for Google Maps

Could this be the one algorithm to rule them all?

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Image: Getty

MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market

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