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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

For decades, enterprises have jury-rigged software designed for structured data when trying to solve unstructured, text-based data problems. Although these solutions performed poorly, there was nothing else. Recently, though, machine learning (ML) has improved significantly at understanding natural language.

Unsurprisingly, Silicon Valley is in a mad dash to build market-leading offerings for this new opportunity. Khosla Ventures thinks natural language processing (NLP) is the most important technology trend of the next five years. If the 2000s were about becoming a big data-enabled enterprise, and the 2010s were about becoming a data science-enabled enterprise — then the 2020s are about becoming a natural language-enabled enterprise.

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Over the last 10 years, neural networks have taken a giant leap from recognizing simple visual objects to creating coherent texts and photorealistic 3D renders. As computer graphics get more sophisticated, neural networks help automate a significant part of the workflow. The market demands new, efficient solutions for creating 3D images to fill the hyper-realistic space of the metaverse.

But what technologies will we use to construct this space, and will artificial intelligence help us?

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Enterprises are seeking ways to modernize their operations. Emerging as a critical element in the digital transformation tech stack are cloud databases.

Having the right cloud database can help address the range of applications companies depend on and also those that they build, from the cloud to mobile and edge. For companies aspiring to provide better and more personalized customer experiences, implementing a DBaaS (database as a service) should be a key consideration.

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The future of neural network computing could be a little soggier than we were expecting.

A team of physicists has successfully developed an ionic circuit – a processor based on the movements of charged atoms and molecules in an aqueous solution, rather than electrons in a solid semiconductor.

Since this is closer to the way the brain transports information, they say, their device could be the next step forward in brain-like computing.

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Physicists at the Princeton Plasma Physics Laboratory (PPPL) have proposed that the formation of “hills and valleys” in magnetic field lines could be the source of sudden collapses of heat ahead of disruptions that can damage doughnut-shaped tokamak fusion facilities. Their discovery could help overcome a critical challenge facing such facilities.

The research, published in a Physics of Plasmas paper in July, traced the collapse to the 3D disordering of the strong magnetic fields used to contain the hot, charged plasma gas. “We proposed a novel way to understand the [disordered] field lines, which was usually ignored or poorly modelled in the previous studies,” said Min-Gu Yoo, a post-doctoral researcher at PPPL and lead author of the paper.

Fusion is the process that powers the Sun and stars as hydrogen atoms fuse together to form helium, and matter is converted into energy. Capturing the process on Earth could create a clean, carbon-free and almost inexhaustible source of power to generate electricity, but comes with many engineering challenges: in stars, massive gravitational forces create the right conditions for fusion. On Earth those conditions are much harder to achieve.

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Juncal Arbelaiz Mugica is a native of Spain, where octopus is a common menu item. However, Arbelaiz appreciates octopus and similar creatures in a different way, with her research into soft-robotics theory.

More than half of an octopus’ nerves are distributed through its eight arms, each of which has some degree of autonomy. This distributed sensing and information processing system intrigued Arbelaiz, who is researching how to design decentralized intelligence for human-made systems with embedded sensing and computation. At MIT, Arbelaiz is an applied math student who is working on the fundamentals of optimal distributed control and estimation in the final weeks before completing her PhD this fall.

She finds inspiration in the biological intelligence of invertebrates such as octopus and jellyfish, with the ultimate goal of designing novel control strategies for flexible “soft” robots that could be used in tight or delicate surroundings, such as a surgical tool or for search-and-rescue missions.

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