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来源:LM317 Electronics Components编辑:TriQuint时间:2021-06-14 04:28:13

What area of tech development might present itself that is not yet on society’s radar? Spreading his arms for emphasis, Gerrold answered: The computer and all the allied technology that we’ve developed in the last half-century have made it possible to manage large data sets. This lets us create a much more accurate understanding of everything from whole economies to whole ecologies. We are moving from anecdata toward empirical evidence as source material.

As with all design trade-offs, costs can be greatly minimized by making decisions early in the design cycle, during initial requirements gathering, feasibility studies and proof-of-concept work. Business stakeholders need to work closely with the engineering design team to understand the technical implications of digital security and map them to potential business risks.

With some additional planning and understanding of potential trade-offs, the digital security market will better be prepared to get ahead of any attacks to make IoT devices safer. 

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— Jake Sprouse is director of software engineering ay Synapse

DeepMind, the division of the Alphabet conglomerate that is devoted to artificial intelligence, recently announced that its Go-playing AI, called Alpha Go, had evolved into a new iteration it calls AlphaGo Zero. The reason for the zero is that the new version is capable of teaching itself how to win the game from scratch.

Zero is even more powerful and is arguably the strongest Go player in history,” according to the DeepMind announcement. The AI not only can beat the best human players but can even defeat the previously published champion-defeating version of AlphaGo by 100 games to 0.”

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The key difference between this version and earlier AlphaGo iterations is that Zero’s predecessors were taught to play Go, having been trained on thousands of human” experiences in the game. In contrast, AlphaGo Zero learns to play simply by playing games against itself, starting from completely random play,” DeepMind states.

By doing that through a million games, the AI consistently improved. A gif that graphs AlphaGo Zero’s learning progress shows its game improvement over time as it fine-tuned its ability to predict moves. This self-teaching is called reinforcement learning, and it could hold great potential for AI applications.

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To gain some expert insight into what this means for future AI applications, I spoke with Bob Rogers, Intel’s chief data scientist. Rogers explained the possibilities that emerge as AI progresses beyond standard machine learning with reinforcement learning, as well as the limitations that are still in place.

Machine learning uses data to create a model,” Rogers noted, but the data has to be selected and fed in by humans who determine the parameters and rules for classification. That requires people with complete domain expertise as well as programming ability — resources that are not on hand for every organization.

Morgan said the increase in spending on smaller geometries would let the early adopters gain market share as the cost equation swings in favour of manufacturers who use the combination of smaller geometries and 300mm systems, at the expense of those working on older processes.

There is a lot of pressure, particularly for those working at larger chip sizes, to go to 300mm. Below 0.25µm, you get fewer benefits from shrinkage alone.”

He said the larger number of dice per wafer on 300mm would give volume users cheaper chips.

However, Morgan said the investment in 300mm was taking second place to development of deep-submicron processes.

They will go to tighter geometries. Volumes will determine whether they go to 300mm.

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