Artificial intelligence is the new beast companies globally are attempting to tame, and data science is the life-giving blood pumping through its veins.
As the foundation of AI, data scientists need to strategize and maintain this data to ensure an efficient and powerful infrastructure to support AI. Domino Data Lab Inc. seeks to accelerate deployment and research, offering scaling and simplicity in one platform, the Domino Enterprise AI Platform, according to Kjell Carlsson (pictured), head of AI strategy at Domino Data Lab.
“It is an enterprise AI platform that provides all of the tools and infrastructure necessary to be able to complete the end-to-end AI lifecycle,” Carlsson said. “Being able to access the data that you want, being able to go in and share version code, being able to access the ID that you want to use, the infrastructure, the distributed compute framework enables you to go all the way from that data to deploying a model and then being able to monitor that model.”
Carlsson spoke with theCUBE host Paul Gillin at the “Supercloud 6: AI Innovators” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. In addition to Domino’s enterprise AI platform, they also discussed how data science empowers AI, how AI is shaking up the business leadership world and how to regulate AI.
Ethics and expertise: The dual pillars of data science in the age of AI
Data science has a critical role in steering the development and application of AI through uncertain waters. Also important is understanding ethical concerns, the expertise required to navigate the AI revolution responsibly, and the importance of education in expanding the talent pool to meet the demands of this transformative era.
ChatGPT introduced some of the potential of AI to the general public, showcasing just a few of the features AI and large-language models can offer. With the public now aware of AI and what it can do, companies everywhere are scrambling to figure out how to implement the technology into their own services, according to Carlsson.
“You’re getting the investment, the willingness to change and do all of these things, but it’s also the scariest time to be one of those leaders, because there are so hyped expectations around this that you’re running headlong into that trough of disillusionment,” he said. “Six months to a year, folks are going to be really asking, ‘OK, well, so what have we been doing with AI? What have we achieved with all of this?’ It’s not fair on the data science leaders and the AI leaders.”
Conversations around AI frequently revolve around the ethical concerns it brings with it. With such fantastic power, it’s easy for the technology to serve nefarious purposes if presented to the wrong hands.
“For example, when we’re talking about election interference or fraud for cybercrime, absolutely these models do enable that. But, unfortunately, so much of the regulation has been kind of knee-jerk and been very disconnected from the technology itself,” Carlsson said.
One solution to these emerging problems would be more data scientists employed across the board, but a shortage of data scientist talent has crippled the industry for years, he added.
“There are ways in which we can shortcut this and we can focus on the education about how you leverage these technology components, how you work with generative AI models, how you get LLMs to behave in a way that this is what the data scientist does,” Carlsson said. “Today the data scientist is the person who’s best set up to do these, but we can make that education far faster and more effectively.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the “Supercloud 6: AI Innovators” event:
Photo: SiliconANGLE
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