Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology'to mitigate' its risks. A class-action complaint filed Wednesday in the northern district of California alleges tech leaders OpenAI and Microsoft Corp. used "stolen and misappropriated" information from hundreds of millions of internet users without their knowledge to train and develop its artificial intelligence tech like chatbot ChatGPT. The 16 plaintiffs, who are represented by the Clarkson Law Firm and listed with initials, claimed the defendants "continue to unlawfully collect and feed additional personal data from millions" worldwide to that end and that they systematically scraped 300 billion words from the internet without consent. "Once trained on stolen data, defendants saw the immediate profit potential and rushed the products to market without implementing proper safeguards or controls to ensure that they would not produce or support harmful or malicious content and conduct that could further violate the law, infringe rights and endanger lives," Clarkson continued. "Without these safeguards, the products have already demonstrated their ability to harm humans, in real ways."

ChatGPT AI is often accused of leveraging "stolen" data from websites and artists to build its AI models, but this is the first time another AI firm has been accused of stealing from ChatGPT. ChatGPT is powering Bing Chat search features, owing to an exclusive contract between Microsoft and OpenAI. It's something of a major coup, given that Bing leap-frogged long-time search powerhouse Google in adding AI to its setup first, leading to a dip in Google's share price. Google has been desperate to catch up, reportedly calling a "code red" all-hands meeting a few months ago to respond. The result was Google's "Bard" chatbot, which has been the subject of some ridicule thus far for its odd answers and difficulty understanding context.

Operationalizing machine learning is a critical step in making AI-powered products and services successful. Let's discuss how MLOps can help businesses resolve issues efficiently. Operationalizing machine learning, or "MLOps", as it is now called, is the latest trend in many industries. Operating is something that businesses do every day; they operate their factories, their offices, their stores, and so on. But what does it mean to "operationalize machine learning"?

Artificial intelligence (AI) is not a new phenomenon. It is human-made technology, that simulates, replicates and can potentially replace or add to human intelligence. It's been under development for over 60 years. You and I have been using it for quite some time now -- be it the maps we use for navigation, chatbots we use, digital assistants, video gaming, and much more. AI is similarly used a lot in the financial services sector.

It's time for Fortune 1000 companies to rethink their investments in data, analytics, and AI. Of course, companies should be investing in these critical business capabilities and differentiators. What they need to take a hard look at is how they're investing, and whether these investments are leading to the kinds of gains and the levels of business value that companies are aspiring to achieve. Responses to a recently released survey of Fortune 1000 and global data and business leaders show that data, analytics, and AI efforts have stalled -- or even backslid. Since 2012, when I launched the survey to investigate organizations' investments in data initiatives, the survey has expanded into related topics such as analytics, AI and machine learning, the role of the Chief Data Officer, and data ethics.

High above the valley in California's wine country, Joanna Wells' vineyard is a challenging place to grow grapes. It's nearly 3,000 feet above sea level, atop a mountain ridge, "Forty minutes off a main road just to get there," Wells says. But it's these rocky hilltop terrains, with plenty of sunshine and maritime breezes, that Wells says, are perfect for the job. What's not idyllic, though, is California's extreme weather. "Every year tends to be climatically extreme now."

According to a report by New Media Consortium, adaptive learning (AL) and learning analytics are two crucial developments emerging in the educational technology market. Today, students pay more and more attention to individualized learning and instruction. If you are one of the higher ed institutions ramping up efforts to improve learning outcomes, implementing adaptive learning systems can be the potential solution. In this article at Hackernoon, Shannon Flynn explains how big data shapes AL. AL is an online educational system that focuses on understanding the student.

In recent years Alzheimer's disease has been on the rise throughout the world and is rarely diagnosed at an early stage when it can still be effectively controlled. Using artificial intelligence, KTU researchers conducted a study to identify whether human-computer interfaces could be adapted for people with memory impairments to recognize a visible object in front of them. Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering at Kaunas University of Technology (KTU), considers that the classification of information visible on the face is a daily human function: "While communicating, the face "tells" us the context of the conversation, especially from an emotional point of view, but can we identify visual stimuli based on brain signals?" The visual processing of the human face is complex. Information such as a person's identity or emotional state can be perceived by us, analyzing the faces.

Marco Iansiti and Karin Lakhani write in their book, Competing in the Age of AI, "the learning algorithms at the heart of new digital systems can be misused to tailor, optimize and amplify inaccurate and harmful information from targeting and shaping misleading ads to creating highly realistic fake social personas that are used to extract personal information from users." The question is what should CIOs and other data leaders do to protect enterprises and their key stakeholders? CIO Anthony McMahon of Target State Consulting suggests in a recent #CIOChat on Twitter that ethical and privacy issues are not unique to artificial intelligence (AI). "Every decision on how to use data, either in platform or offline, has an ethical consideration," he said. Other data leaders claim there are all sorts of unique ethical and privacy issues to AI. Privacy is particularly important when a piece of data becomes connected in a new and novel way.

Signifyd leads the world in bringing the insights, innovation and compassion required to foster fearless commerce in a time of increasing digital threats. Working with some of the industry's most recognizable retailers and brands, we are focused on using technology to enhance customer lifetime value and protect enterprises from fraud so they can focus on growing their business. We process billions in ecommerce transactions annually through our Commerce Network of thousands of merchants selling in more than 100 countries. We focus every day on harnessing machine learning and artificial intelligence in more powerful ways to maximize our customers' revenue and their security. None of that happens without the right people.