Day: March 19, 2020

Hybrid AI systems are quietly solving the problems of deep learning

Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.

This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three “godfathers of deep learning,”

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Coronavirus challenges remote networking | ITworld

As the coronavirus spreads, many companies are requiring employees to work from home, putting unanticipated stress on remote networking technologies and causing bandwidth and security concerns.

Businesses have facilitated brisk growth of teleworkers over the past decades to an estimated 4 million-plus. The meteoric rise in new remote users expected to come online as a result of the novel coronavirus calls for stepped-up capacity.

Research by VPN vendor Atlas shows that VPN usage in the U.S. grew by 53% between March 9 and 15, and it could grow faster. VPN usage in Italy, where the virus outbreak is about two

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Explaining machine learning models to the business

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal — or operators to override — inevitable wrong decisions.

Of course all that sounds great, but explainable machine learning is not yet a perfect science. The reality is there are two major issues with explainable machine learning to keep in mind:

  1. Some “black-box” machine
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