I recently had the opportunity to record a segment for the CES Tech Talk podcast focusing on how AI is shaping the future of business. We touched on some of the biggest discussion points and trends in the field today, from concerns over workforce impact, to solving the ‘black box’ issue of AI. The podcast offered an opportunity to separate the reality from the hype of where AI is today–and to cover real-world examples that showcase the latest AI breakthroughs and their impact on business.
Among the most critical takeaways is that AI platforms and solutions like Watson require a partnership between human and machine to be valuable. AI is there to augment the work we do in countless industries–helping companies reimagine their business workflows, redefine how they uncover deep insights from data, and augment human creativity and productivity.
Equally as important is the reality that people need to trust the recommendations and outcomes from their AI systems. The way to achieve that trust is by ensuring fairness in AI models, which will lead to more explainable, transparent outcomes. Detecting and mitigating bias is certainly part of that equation, but even more immediately, our clients need to better understand and explain the output of their models, foster transparency in these systems and the recommendations they make, and ensure the underlying models and frameworks are secure.
In this podcast, you’ll hear a few examples of companies using AI to solve some of their biggest business challenges –from reimagining the customer experience to managing complex contracts and mitigating risk.
We’re at a pivotal moment in the evolution of AI systems and tools, and we’re excited to see so many IBM clients leading the way. I hope you’ll take a few minutes to listen to this episode.
View original content: Here