Nvidia had one of the more interesting demos; they're using multiple AI models to handle specific tasks with existing research and data. One example: their software took a set of medical research notes and built a scale model of a protein. IBM showed off a system that updates outdated code to work on modern platforms. Datadog is using AI to boost how it collects and organizes data from websites. Other companies are also finding creative ways to use AI for software protection, data analysis, and development.
Takeaway:
With AI’s rapid development, it’s exciting to imagine the breakthroughs it could bring across every field of research. It’s quickly becoming a powerful tool for building advanced models, running tests, and analyzing data more effectively, opening up new possibilities for the problems we might solve in the coming decades.
There’s a lot to be excited about with AI, but there are also real reasons to be cautious. Many of the speakers at the expo made it clear that there’s still a strong human element guiding AI at this stage—but will that always be the case? As AI continues to evolve, we need to be intentional about how we shape its development. It has the power to push human progress forward, but if we’re not careful, it could also create serious problems.
One of my biggest concerns is around security and privacy. AI systems need massive amounts of data to learn, and that data often comes from users. So, is our information truly staying anonymous and protected? It’s not always clear. As impressive as AI can be in solving complex challenges, there's also the risk of it being used more for corporate gain than for the greater good. We’re at a point where we need to ask tough questions, stay informed, and make sure that AI grows in a way that benefits everyone, not just a few.