Data is the foundation on which AI models are built, and integration of enterprise data will be the key to generative AI applications. This episode of Utilizing Tech brings Nick Magnuson and Clive Bearman from Qlik to discuss the integration of data and AI with Frederic Van Haren and Stephen Foskett. Enterprises sometimes worry that their data will never be ready for AI or that they will feed models with too much low-quality data, and overcoming this issue is one of the first hurdles. Another application for machine learning is improving data quality, organizing and tagging unstructured data for applications. The concept of curated data is an interesting one, since it promises to elevate the value of enterprise data. But what if a flood of data causes the model to make the wrong connections? If data is to be a product it must be profiled, tagged, and organized, and ML can help make this happen. The trend of generative AI is driving budgets and priorities to make data more useful and organized, but even unstructured data streams can be valuable. The application of large language models to structured data is promising as well, since it enables people to query these data sets even if they lack the background and skills to construct queries.
In the two years since we focused on AI on this podcast, OpenAI added a simple conversational interface to their deep-learning model and AI has exploded on society. This season of Utilizing Tech focuses on practical applications for artificial intelligence and features co-hosts Frederic Van Haren and Mark Beccue along with Stephen Foskett. In this episode we return to AI with a look back at the incredible impact that generative AI has had on society. Humans traditionally interfaced with machines using keyboard and mouse, touch and gesture, but ChatGPT changed all that by enabling people to communicate with computers verbally. But this is just one of many potential AI model components that can be used to build business applications. The true power of generative AI will be realized when these other components appear, and when they are able to integrate custom data. We will also see innovation in the AI infrastructure stack, from GPUs to NPUs to CPUs, storage and data platforms, and even client devices.
Frederic Van Haren and Stephen Foskett look back on all the subjects covered during Season 3 of Utilizing AI.
How fast is your machine learning infrastructure, and how do you measure it? That’s the topic of this episode, featuring David Kanter of MLCommons, Frederic Van Haren, and Stephen Foskett.
The quality of an AI application depends on the quality of the data that feeds it
Machine learning is unlike any other enterprise application, demanding massive datasets from distributed sources
With so many AI tools available, it can be a challenge to integrate everything into a productive platform
As machine learning is used to market and sell, we must consider how biases in models and data can impact society
With AI technology changing so quickly, we often need to step back and take a big picture look at the market
Data is the most important element of artificial intelligence, but how is that data managed and stored? In this episode of Utilizing AI, Adi Gelvan of Speedb goes deep under the hood to take a look at the data engine along with Frederic Van Haren and Stephen Foskett