AI Articles and Analysis about Data management

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.

In the last year, interest in so-called “autoML” has risen greatly in part due to its promise of bringing artificial intelligence to businesses that have been blocked from accessing it due to its serious time, talent, and budget requirements. Although machine learning may still be widely unavailable to small businesses, medium-sized businesses may find that autoML allows them to make use of it in the coming years.

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for.

Episode Summary: There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising. 

Even during a time when the buzz around AI and digital data storage is prominent, there are still some companies with large amounts of backlogged data that are taking up space in their databases, but not providing enough value. Large stores of enterprise and customer data can be valuable to insurance companies for optimizing their business operations and gaining analytical insights on how their business decisions affect company growth.

IT services firms are doing their best to keep up with the changing landscape as AI begins to dominate the tech conversation. Many IT services firms have recently started branding themselves as AI companies without having the requisite talent to back up their claims. In reality, many of the older IT services companies are struggling to hire PhD graduates in machine learning who would rather use their skillset at global AI firms like Google and Amazon.

This week we interview a leader at Facebook. Jason Sundram is the lead of World.ai at Facebook, which is one of their efforts to work with public data around roads and population and other projects of that kind. But Sundram is also highly involved in the Boston office here, where Facebook will soon have around 650 employees. Many of them focus on data science and artificial intelligence.

Militaries around the world are starting to make increasing investments in artificial intelligence and machine learning capabilities. The top military defense contractors in the US, Europe, and Israel are all working on AI software to sell into the defense sector. That said, the military adoption of AI is as of right now slow in comparison to that of contractors.