More media outlets are using AI to write articles, if not as aggressively as others. VentureBeat editorial director Michale Nuñez tells Bloomberg his publication is using Microsoft's Bing Chat to help edit and write stories. Reporters are encouraged to slip AI-written "sentences and fragments" into articles so long as they're accurate and independently verifiable. The OpenAI-powered tech is akin to having "another person on the team," Nuñez says. VentureBeat doesn't disclose the use of AI content provided it's limited and authentic, but also doesn't intend to create whole articles using the technology. Word surfaced in January that CNET had been using AI to produce entire financial explainer articles since November.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Earlier this week, a group of more than 1,800 artificial intelligence (AI) leaders and technologists ranging from Elon Musk to Steve Wozniak issued an open letter calling on all AI labs to immediately pause development for six months on AI systems more powerful than GPT-4 due to "profound risks to society and humanity." While a pause could serve to help better understand and regulate the societal risks created by generative AI, some argue that it's also an attempt for lagging competitors to catch up on AI research with leaders in the space like OpenAI. According to Gartner distinguished VP analyst Avivah Litan, who spoke with VentureBeat about the issue, "The six-month pause is a plea to stop the training of models more powerful than GPT-4. GPT 4.5 will soon be followed by GPT-5, which is expected to achieve AGI (artificial general intelligence). Once AGI arrives, it will likely be too late to institute safety controls that effectively guard human use of these systems."

The graph represents a network of 1,419 Twitter users whose recent tweets contained "iot bigdata", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/14/2023 5:00:36 PM. The network was obtained from Twitter on Wednesday, 15 March 2023 at 11:43 UTC. The tweets in the network were tweeted over the 2136-day, 23-hour, 8-minute period from Monday, 08 May 2017 at 00:51 UTC to Tuesday, 14 March 2023 at 23:59 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,692 Twitter users whose recent tweets contained "iot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/14/2023 5:00:36 PM. The network was obtained from Twitter on Wednesday, 15 March 2023 at 12:17 UTC. The tweets in the network were tweeted over the 2072-day, 12-hour, 58-minute period from Tuesday, 11 July 2017 at 11:00 UTC to Tuesday, 14 March 2023 at 23:59 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

This article is part of a VB special issue. To say that it's challenging to achieve AI at scale across the enterprise would be an understatement. An estimated 54% to 90% of machine learning (ML) models don't make it into production from initial pilots for reasons ranging from data and algorithm issues, to defining the business case, to getting executive buy-in, to change-management challenges. In fact, promoting an ML model into production is a significant accomplishment for even the most advanced enterprise that's staffed with ML and artificial intelligence (AI) specialists and data scientists. Enterprise DevOps and IT teams have tried modifying legacy IT workflows and tools to increase the odds that a model will be promoted into production, but have met limited success.

After months in preview, PyTorch 2.0 has been made generally available by the PyTorch Foundation. The open source PyTorch project is among the most widely used technologies for machine learning (ML) training. Originally started by Facebook (now Meta), PyTorch 1.0 came out in 2018 and benefitted from years of incremental improvements. Don't miss our special issue: The quest for Nirvana: Applying AI at scale. In September 2022, the PyTorch Foundation was created in a bid to enable more open governance and encourage more collaboration and contributions.

This article is part of a VB special issue. Enterprise companies have experimented with artificial intelligence (AI) for years -- a pilot here, a use case there. But company leaders have long dreamed of going bigger, better and faster when it comes to AI. That is, applying AI at scale. The goals of this quest may vary.

Data can be complicated to collect and it is often even more complex to understand in a way that brings a business value. Salesforce's Tableau business unit today announced the 2023.1 release of its enterprise platform known as Tableau Server, which can run on-premises or in an organization's own virtual private cloud deployment. Tableau is generally used as a data analytics technology that helps users get insights from data. The new 2023.1 update integrates enhanced features to help organizations connect to data including a data mapping feature that has been designed to make it easier to execute analytics on any data source. There is now also a deeper integration with Salesforce's Slack messaging application in a bid to help users benefit from data analytics directly within Slack.

As the search AI chatbot shindigs -- like Microsoft's Bing bot debut and Google's Bard launch -- wind down for now, who knew the hottest, trendiest party in generative AI would be … business productivity apps? After years of being relegated to nerdy, wallflower AI status while self-driving cars, robot dogs and the future of the AI-powered metaverse got the spotlight, generative AI's email-writing, blog-producing, copy-powering abilities are suddenly popular. And top companies from startups to Big Tech are developing tools to gain admittance to the generative AI bash. Follow VentureBeat's ongoing generative AI coverage Arriving fashionably late to this generative AI soiree is San Francisco-based Grammarly. The digital writing assistant with a browser extension is far from a newbie to the AI space, but today the company announced its GPT-powered, chatbot-style GrammarlyGo. The new offering will start rolling out to its 30 million daily customers in beta in early April, as well as 50,000 teams in Grammarly Business.

Data is the new oil, but raw data is no good in and of itself. Like oil, data assets have to be gathered entirely and accurately and sent through different refining processes to create value for end users. This is the general data lifecycle -- an area where artificial intelligence (AI) is going to play a major role for enterprises. Initially, managing the data lifecycle was a task small enough to be handled manually by a team of experts. The volume of information was not that much, the sources were just a handful and the possible applications were also limited.