As evidenced by the example of ChatGPT, artificial intelligence is advancing in unprecedented directions to solve exciting new problems. But, as AI is being pointed toward critical cybersecurity operations, do the gains outweigh the potential risks and concerns? "You should be worried," said Andy Thurai (pictured), vice president and principal analyst at Constellation Research Inc. "The problem people don't realize is that ChatGPT, being a new, shiny object, it's all the craze that's about. But the problem is that most of the content that's produced either by ChatGPT or others are assets with no warranties, accountability or whatsoever. If it is content, it's OK. But if it is something like code that you use, then it's mostly not."

Putting data in the hands of every business user has become fundamental in today's enterprise ecosystem. As an end-to-end data science and machine learning platform, Dataiku enables any professional to be a data analyst for better decision-making through democratization, according to Jed Dougherty (pictured), vice president of platform strategy at Dataiku. "I don't know if data analysts are going away; I think everybody's just going to have to become a bit more of one," Dougherty said. "Just like how Excel taught everybody how to use the spreadsheet, in the future, in the next five, 10 years, the democratization of AI means that tools like Dataiku and other data science tools are going to teach everybody how to analyze data." Dougherty spoke with theCUBE industry analyst Lisa Martin at the recent AWS re:Invent conference, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how Dataiku facilitates data democratization so that anyone can be a data analyst.

Amazon Web Services Inc. is not only infusing its own tools with machine learning capabilities, the cloud giant is also providing technology for developers, scientists and engineers to run and manage thousands of complex machine learning workloads at the same time. An example can be found in how Ampersand Inc., a data-driven TV advertising sales and technology firm, uses AWS Batch to run 50,000 concurrent machine learning models in less than one day. A look at how AWS enables enterprises to put data to work with artificial intelligence and machine learning-powered low-code and automation initiatives will be the focus for theCUBE, SiliconANGLE Media's livestreaming studio, during its coverage of the AWS Summit San Francisco event, airing April 21. Ampersand's business model delivers data-driven insights to assist TV advertisers in planning and measuring campaigns for apps and networks in all U.S. markets. It is complicated, data-heavy work that requires the creation of machine learning models at scale to produce viewer insights.

Data privacy measures such as the General Data Protection Regulation and the California Consumer Privacy Act are expanding the definition and protection of private sensitive data. Anonymization efforts, though valiant, can only go so far. "You can only manage what you measure, right?" said Hannah Sperling (pictured), business process intelligence, academic and research alliances at SAP SE. "But if everybody is afraid to touch sensitive data, we might not get to where we want to be. I've been getting into data anonymization procedures, because if we could render more workforce data usable, especially when it comes to increasing diversity in STEM or in technology jobs, we should really be letting the data speak." Sperling spoke with Lisa Martin, host of theCUBE, SiliconANGLE Media's livestreaming studio, during the Women in Data Science (WiDS) event.

Computers have been taught to use data to establish patterns where possible. And while the delegation of these activities to machines has helped mankind in many ways, bias still exists in technologies such as artificial intelligence. For instance, there are biases in facial recognition systems, according to Alex Hanna (pictured), director of research at The Distributed AI Research Institute. "The fact remains that facial recognition is used and is disproportionally deployed on marginalized populations," she said. "So in the U.S., that means black and brown communities. That's where facial recognition is used disproportionately."

Ride-sharing apps like Uber and Lyft have made the daily commute a less frustrating part of the day for many. Now a crowd-sourced fleet of 14-passenger vehicles acquired by Ford is promising more. "What Chariot and Ford is looking forward to doing in the next couple of years is to actually make [your commute], believe it or not, the best part of your day," said Ali Vahabzadeh, founder and chief executive officer of Chariot Transit Inc., a Y Combinator-backed startup. At South by SouthWest in Austin, TX, Vahabzadeh claimed that Chariot is reinventing mass-transit by crowd-sourcing new routes in undeserved or overcrowded areas. Vahabzadeh told John Furrier (@furrier), host of theCUBE, SiliconANGLE Media's mobile live streaming studio, that Chariot is using Artificial Intelligence and commuter data to finetune rider experiences on-board.

Black box "magic" isn't acceptable now that companies are moving from the "fun" phase of playing with AI's capabilities to implementing the technology in essential business workflows. "Every company in the world is looking for the opportunity to take advantage of AI to improve their business processes, whether it's to improve their revenue, lower their cost profile, or lower their risk," said Nenshad Bardoliwalla (pictured), chief product officer at DataRobot Inc. They discussed DataRobot's AI Cloud and self-service AI. (* Disclosure below.) The danger of AI is that algorithms aren't infallible. Trust is provided through transparency and explainability, which together allow data scientists to see into the workings of the AI's "mind" to understand how it arrived at insights.

Data is at the core of today's digital revolution, but its refinement processes have become the real points of differentiation for enterprises. As companies convert raw data into actionable insights, artificial intelligence signals a burgeoning ecosystem of management models. The current wave of AI startup investment has received $16.5 billion in capital over the three months of the fourth quarter 2020, according to financial analysis from PitchBook Data Inc. The boom is continuing, with the latest figures from International Data Corp. showing a 16.4% year over year growth for the overall AI and machine learning market in 2021, with the AI software platforms market coming in strongest at a predicted compound annual growth rate of 32.7% between 2020 and 2024. AI has already become an essential part of many enterprise technology solutions. The McKinsey & Company Inc. 2020 "The State of AI in 2020″ survey found that 50% of companies had incorporated AI into at least one process, with marketing and sales the most common functions.

As artificial intelligence, robotic process automation and machine learning become more prominent, there is a lot of misinformation and fear about what these technologies actually do and if they will replace people at work. But instead of taking away people's jobs, according to some, AI can help people do their work better. And this is a huge issue -- since more and more people are less engaged at work and turnover rates are peaking in ways never experienced before. Shelly Kramer (pictured), founder and chief executive officer of V3 Broadsuite, president of Broadsuite Media Group and founder and principal analyst at Futurum Research, often writes and speaks about this subject of the relationship between automation and people. "Technology is fueling our world, our personal lives, our business world," Kramer said.

As the use of machine learning grows and gains complexity, the cost and time of training models can be challenging for enterprises. Amazon Web Services Inc. announced that its EC2 will be powered by Gaudi accelerators from Habana Labs, an Intel company, to increase the efficiency of this process, according to Chetan Kapoor (pictured, left), senior manager of product management, EC2, at AWS. "As we all know, training of deep learning models is a really computationally extensive task; oftentimes, it takes too long and costs too much," he said. "We expect for [these instances] to provide up to 40% better price performance than top-of-the-line GPU instances." Kapoor and Eitan Medina (pictured, right), chief business officer at Habana Labs Ltd., spoke with John Furrier, host of theCUBE, SiliconANGLE Media's livestreaming studio, during AWS re:Invent. They discussed the main characteristics of the partnership between AWS and Habana, the benefits of the solution for enterprises, and important use cases.