Microsoft, pursuing its strategy of integrating OpenAI's generative AI technology within enterprise application platforms, on Monday introduced Power Virtual Agents conversation booster and AI Builder model with content generation. Power Virtual Agents enables developers to create AI-powered chatbots for different scenarios, such as a bot for answering complex questions or one to engage with customers in multiple languages. AI Builder is a Microsoft Power Platform feature that provides AI models that help automate business processes. Power Virtual Agents conversation booster equips enterprises' chatbots with capabilities from GPT -- the large language model from the tech giant's partner, OpenAI -- to answer questions when connected to company-specific resources such as a public website and internal knowledge base. In addition, AI Builder now includes Azure OpenAI services in its interface, giving users access to new low-code generative AI models and templates in Power Automate and Power Apps.

As AI makes dramatic inroads in enterprises, the U.S. government has quietly started to regulate the use of AI in the consumer credit industry and other areas by banning the use of biased and unexplainable algorithms in decisions that affect consumers. In recent years, the Federal Trade Commission has tried to regulate AI in lending with laws that are already in place, chief among them the Fair Credit Reporting Act (FCRA). The federal agency has also included AI regulation under the FTC Act and the Equal Credit Opportunity Act (ECOA). While the federal laws don't contain explicit language regulating AI, the FTC, which enforces the laws, has issued guidance over the last two years stipulating that under the FCRA, lenders can't use biased or unexplainable algorithms for not only consumer credit, but also employment, housing, insurance and other benefits. The FTC has also clarified that the sale or use of racially biased algorithms, for example, is a deceptive practice banned by the FTC Act.

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.

The enterprise is taking a hard look at machine learning (ML) as a means to bring network infrastructure into the 21st century, and many leading organizations have already implemented the technology in production environments. But is ML the panacea vendors make it out to be? And can it produce the kind of autonomous, intent-based environments that currently populate the hype cycle? The good news about machine learning is that it does not necessarily require a huge upfront investment. Leading cloud providers are rolling out ML-as-a-Service (MLaaS) options.

Software-defined networks give you flexibility, but to make them really effective at scale we need to take humans out of the loop and use automation to respond more quickly – like taking an optical link down for maintenance and moving the traffic over to another line automatically as the latency rises. Plus, we need to do that before the speed drops enough to cause problems for the workloads relying on that connection. That kind of automation will create something more like a "self-driving" network, Juniper platform systems CTO Kireeti Kompella told Data Center Knowledge; but just as with self-driving cars, the prospect is exciting but also raises some long term concerns. This is about creating adaptive, self-customizing services built on the flexibility of SDNs and Network Function Virtualization which means that instead of being a monolithic device, network hardware exposes APIs and functions. But even though we have what Kompella calls "power sharing between equipment makers and the people who deploy networks, who want more of a say in how systems are being built," the problem is that it can also end up like parents fighting, forgetting about the children caught in the middle.

The European Telecommunications Standards Institute (ETSI), which wrote the original specification for network functions virtualisation (NFV), has set up its experiential network intelligence industry specification group to handle the task. He sees many parallels between the evolution of cars and where networks can get to. "Years ago, cars were painfully manual, but we've made it much more convenient and 14 years ago we started looking at self-driving cars, which is an absolute disruptive change." In order for self-driving networks to become a reality, vendors and providers must fully collaborate, he says. Interestingly, ETSI's AI initiative, which has an initial two-year work programme, is coming from vendors, ETSI director general Luis Jorge Romero told GTB at Mobile World Congress.