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.
EO 13800 mandates that agencies are to maintain reliable, redundant, and resilient systems, assess and address their electricity disruption incident response capabilities, identify and resolve known vulnerabilities in operating systems and hardware, and adhere to the NIST Cybersecurity Framework.
Nlyte delivers an agentless solution that provides agencies a repository including all hardware, software, configuration, services, and circuit information and extends its discovery and asset repository beyond the data center including all attached devices to the IP network (Cameras, IoT devices, Printers, Copiers, Fax, etc.). While many DCIM vendors only focus on the IT asset management needs of data center operators, Nlyte provides a single solution that bridges the IT and Facilities teams who manage the data center environment.
Machine-Learning Technologies Help Agencies Develop Highly Intelligent Security Postures
In fact, as malicious actors become more insidious, federal network security managers are finding the reaction time between identifying and mitigating potential threats has gone from minutes to milliseconds.
Once used within the Defense Department primarily for real-world target recognition, machine-learning technologies have evolved to become very effective at quickly detecting and responding to potential cyber threats.
Through analytics and predetermined risk factors established by cyber operators, these highly intelligent and adaptable systems can evolve to “learn” about threats as they happen and apply that knowledge to better fortify the network in anticipation of future threats.
They can also be used in combination with other network security solutions, including firewalls and edge and core routing and switching infrastructures, to fend off attacks and isolate infected hosts.
It should be noted the treasure trove of real-time network monitoring data and analytics federal organizations have at their disposal can be an effective cybersecurity resource when used in conjunction with machine-learning tools.
Machine learning can have a positive impact beyond enhanced security and decreased risk of hostile attacks because it can be used to create a more efficient and automated security apparatus that reduces operator workloads.
The combination of machine learning with other automated network technologies, such as software-defined networking and cloud solutions, can allow operators to do more with less and free up time to pursue other mission-critical activities.
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.
Network Automation Brings Federal Agencies Closer to AI
It has become increasingly evident that artificial intelligence (AI) and machine learning (ML) are poised to impact government technology.
Additionally, without pervasive visibility, companies eventually run the risk of automating into trouble by taking action using insufficient or inaccurate information and expending already constrained resources.
To better illustrate how agencies can benefit from this technology, consider a distributed application in a data center where an operator gets a call from an end user who shares that an application isn’t running or is slow.
The network operator would log the ticket and determine the origin of the problem, whether it is the application, insufficient or overloaded compute resources, a misconfigured overlay or a faulty network connection.
Today, the operator must gather data from different places in the network with multiple tools and process that data before taking action, which relies mostly on manual effort to complete the process.
Alternatively, a federal agency using an automated tool that offers the right level of information at the right time and location enables action on an individual switch or router, and can send the data to an external tool.
Classical programming approaches are based on the premise that programs are developed to produce data. ML reverses that relationship—data now produce programs. The accuracy and reliability of ML is completely dependent on the data that it trains or learns from.
By identifying strategic areas in the network where automation and visibility can be injected, agencies can begin cutting costs and creating opportunities to implement administrative efficiencies as they work to meet their mission.
As federal agencies work to counter expanding cybersecurity threats, some are finding solutions in machine learning.
Applying automated machine learning can help detect threats that have previously circumvented traditional security systems.
Security teams can develop predictive models to detect intrusions and malware attacks, analyze and adapt to new threats, and identify risky or malicious behavior During
Your Computer As federal agencies work to counter expanding cybersecurity threats, some are finding solutions in machine learning.
Security teams can develop predictive models to detect intrusions and malware attacks, analyze and adapt to new threats, and identify risky or malicious behavior During this live webcast, Leonel Garciga, CTO for the Joint Improvised Threat Defeat Organization, will discuss what needs to be in place for agencies to benefit from using machine learning in cybersecurity.
- On Tuesday, January 15, 2019
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