AI News, Machine Learning–What’s Next?

Machine Learning–What’s Next?

On top of that, the Federal government is increasing its focus on machine learning, with the MGT Act, Technology Modernization Fund, and the President’s Management Agenda all supporting transformation efforts.  And the promise hinges around turning the Federal government’s massive amounts of data into actionable intelligence–helping to improve operational efficiency, decision making, and service delivery.

“Data science and machine-learning approaches require increasingly accurate data to build models representative of the real world.” Using machine learning means managing massive amounts of data–agencies must make sure they can handle the new workloads.

“An important goal of machine learning work,” said Thomas Dietterich, founding president of the International Machine Learning Society, “is to make machine learning techniques usable by people with little or no formal training in machine learning.” Next-Gen Cybersecurity “Machine learning has the potential to empower a more intelligent approach to cybersecurity, one that can evolve quickly at the pace cybercriminals evolve their approaches,” Chehreh said.

“There are many agencies not using technologies available today that would considerably help reduce insider threat risk.” As AI approaches break out of the labs and into production environments, they’ll drag the shortcomings of geriatric infrastructure into the light of day.

The Next Federal Data Center Infrastructure Management Tool: Cognitive Computing?

Federal data center teams will have a new member suiting up this summer to help drive efficiency and optimization.

Nlyte Software has partnered with IBM’s Watson IoT group to develop a cognitive data center infrastructure management (DCIM) solution that can tap into the power of advanced analytics and artificial intelligence to make data centers more resilient and efficient.

Today’s data centers are powered by an ecosystem of power distribution devices, cooling technologies, data backup applications, security software, backup generators, and batteries.

“The scale, complexity, and optimization in modern data centers requires analytics,” for data center managers to make informed decisions, said Enzo Greco, chief strategy officer of Nlyte.

The future data center will be “always-available, always-healing,” and optimized for predictive maintenance where faults are detected before they happen, and workloads can be optimally placed based on informed data.

It is in the form of a cognitive solution that provides current analysis of total operations and also future insights into device failures,” Amy Bennett, a manager with IBM Watson IoT’s marketing team, wrote in IBM’s blog.

Other DCIM vendors are applying machine learning capabilities to help organizations achieve greater efficiency in data center operations, such as Vigilent, said Rhonda Ascierto, research director for data centers and critical infrastructure at 451 Research.

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