By now, it's a truism that automation will replace certain careers even as leaving others intact. Experts agree with the maximum susceptible are jobs that require routine, rote tasks: a bookkeeper, a secretary, or a manufacturing facility worker. Each of those contains notably repetitive and predictable obligations effortlessly taught to machines. The rise of machine learning and self-replicating artificial intelligence (AI) has jeopardized many other professions, significantly programmers. Ironically, a number of their first-class work can be their downfall: As builders make ever-more effective and shrewd algorithms, they threaten coding themselves into obsolescence.
Pegasystems launches X-ray Vision, a tool that allows Robotic Process Automation (RPA) bots to repair themselves without human intervention. X-ray Vision aims to address the problem of bot failures. This service detects when a bot is malfunctioning and then repairs it immediately. Defective bots occur when the user interfaces and processes change. X-ray Vision's AI model is constantly modified using machine learning to improve the detection of these defective bots.
LOS ANGELES – VMware is integrating Cellwize's automation and orchestration technology into its Smart Assurance Suite to provide network operators with a full-loop view into their operations. Gabriele Di Piazza, VP of VMware's Telco Products and Solutions business, said the Cellwize integration allows for deeper monitoring and network management. This includes end-to-end visibility of the radio access network (RAN) topology along with the virtual and physical elements of the network; an automated root cause analysis that uses machine learning and impact correlation to provide more control over the network; and the use of self-optimization and self-healing to optimize the RAN and core network power management. "We can now improve this process and correlate this information with all the other parts of the network," Di Piazza told SDxCentral during this week's MWC Los Angeles 2019 event. The integration will build on Smart Assurance Suite's existing capabilities.
Artificial intelligence promises to enable machines or bots to take on the heavy-duty work of many parts of enterprises. Now, there are increasingly more initiatives, as well as vendor products, that will autonomously take on the heavy-duty work of information technology departments as well. The automation of IT functions has been evolving for decades, of course -- from job-scheduling systems in the 1990s to self-healing systems introduced more than a decade ago. These days, IT automation goes by many names -- such as autonomous systems, self-driving systems or bots. Lately, more of it is falling under the moniker of AIOps, joining the parade of xOps methodologies, promising to apply AI and machine learning to mechanize, standardize and automate the delivery of IT services.
In my opening blog post this year I wrote about the many issues the market seemed to indicate would be important in 2018. Obviously, I mentioned SD-WAN technology, 5G and Big Data, and I also talked about Artificial Intelligence (AI) and a few others. However, there is one area that I did not mention, which is intent-based networking (IBN). Perhaps it may be early to call 2018 the year of intent-based networking, but some important analysts have said that this is one of the next big things to come!
The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.