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.

In the current pandemic, AI models are experiencing unique levels of traffic and interest. The coronavirus pandemic has brought many industries to a halt, with businesses reducing hours, sacking employees, and halting new projects. Artificial intelligence projects could definitely fall into the dispensable category, but that's not the case according to a recent report from FICO and the market intelligence firm Corinium. The Building AI-Driven Enterprises in a Disrupted Environment report surveyed more than 100 c-level data and analytic executives and conducted in-depth interviews to understand how organizations are developing and deploying AI capabilities. It found AI demand has risen during the pandemic for a majority of businesses surveyed.

There's no denying that artificial intelligence is having a huge impact on our lives. According to PwC, AI will add $16 trillion to the world's economy over the next 10 years as automated decision-making spreads widely. Despite this incredible impact, AI doesn't bring much value for some problems, like predicting a viral pandemic, forecasting the winner of the presidential election, or servicing clients with diverse needs, experts say. Data is, of course, the rootstock for all forms of AI, whether it takes the form of a basic search engine or a self-driving car. But it turns out that some data are quite hard to come by, even for some of the most high-impact events.

[It's] an impossible dream because privacy, reliability, bias, human rights and transparency are subject to both technology limitations and differing cultural definitions,

AWS launched a host of new tools and capabilities for Amazon SageMaker, AWS' cloud platform for creating and deploying machine learning models; drawing the most notice was Amazon SageMaker Studio, a web-based integrated development platform (IDE) . In addition to SageMaker Studio, the IDE for platform for building, using, and monitoring machine learning models, the other new AWS products aim to make it easier for non-expert developers to create models and to make them more explainable. During a keynote presentation at the AWS re:Invent 2019 conference here Tuesday, AWS CEO Andy Jassy described five other new SageMaker tools: Experiments, Model Monitor, Autopilot, Notebooks, and Debugger. "SageMaker Studio along with SageMaker Experiments, SageMaker Model Monitor, SageMaker Autopilot, and Sagemaker Debugger collectively add lots more lifecycle capabilities for the full ML (machine learning) lifecycle and to support teams," said Mike Gualtieri, an analyst at Forrester. SageMaker Studio, Jassy claimed, is a "fully-integrated development environment for machine learning." The new platform pulls together all of SageMaker's capabilities, along with code, notebooks, and datasets, into one environment.

Data science platforms are gunning toward automation. With the widespread release and popularity of Google Cloud AutoML, DataRobot Inc. tools and other automated machine learning platforms, analysts, businesses and users are beginning to tap into the technology and the rapidity of automation. In this Q&A, Mike Gualtieri, vice president and principal analyst at Forrester Research, outlines the state of automated machine learning platforms and their use cases. Editor's note: The following has been edited for clarity and brevity. What are some key capabilities of automated machine learning platforms?

Several machine learning frameworks have emerged to streamline the development and deployment of AI applications. These frameworks help abstract away the grunt work of testing and configuring AI workloads for experimentation, optimization and production. However, developers need to make some hard choices when it comes to picking the right framework. Some may want to focus on ease of use when training a new AI algorithm, while others may prioritize parameter optimization and production deployment. Different frameworks have different strengths and weaknesses in these diverse areas.

It doesn't really matter if you use Google, Amazon or Microsoft's AI algorithms, according to experts. The real differentiator is not whether you use Lex, Azure AI or Google's machine learning tools, but the quality of your own data, speakers at OpenText's Enterprise World 2018 conference said yesterday. "The algorithms are nothing without the data," Mike Gualtieri, principal analyst at Forrester, said. "Algorithms get all the press, but the competitive advantage, the accuracy of those models, the decisions that the models can make, it all comes from the data, and the data that enterprises have is very specific to them and their customers." While Google and the other tech giants investing fortunes into machine learning models have trained their tools on billions of images, pieces of text and other media, none of these AI systems have the specific data that companies have to hand, Gualtieri explained.

Microsoft has spent the past few years coming up with ways to use artificial intelligence internally. Now it will let customers take advantage of some of these tools while aiming to lure business from Amazon and Google. The company will let customers use a chip system it built to process AI queries cheaper and faster, called Project Brainwave. The first Brainwave service will speed up image recognition so it's almost instantaneous, said Doug Burger, a distinguished engineer in Microsoft Research, who works on the company's chip development strategy for the cloud. Microsoft, starting next year, also will sell an AI-sensor device based on the technology it its motion-controlled Kinect gaming sensor.

Machine learning (ML) based data analytics is rewriting the rules for how enterprises handle data. Research into machine learning and analytics is already yielding success in turning vast amounts of data--shaped with the help of data scientists--into analytical rules that can spot things that would escape human analysis in the past--whether it be in pursuit of pushing forward genome research or predicting problems with complex machinery. Now machine learning is beginning to move into the business world. But most organizations haven't truly grasped how machine learning will change the way they do business--or how it will change the shape of their organizations in the process. Companies are looking to ML to automate processes or to augment humans by assisting them in data-driven tasks.