Companies are starting to use emotional recognition technology for recruitment. But we know the results should not be accepted without further research. It simply isn’t fair for us to judge other people for what they’re feeling—especially if, as the science shows, we actually don’t know.
As the 2020 Coronavirus pandemic forces populations around the world to adopt social distancing strategies, many of the technologies that we considered “complementary” to our traditional day-to-day productivity have now become our primary modes of presence, collaboration, and productivity.
Microsoft Research AI ethics checklist is a set of published principles for designing ethics checklists that can be readily operationalized in AI DevOps processes. We commend Microsoft Research’s recent effort to catalyze consensus within the practitioner community for the purpose of developing clear principles for designing operationalizable AI ethics checklists.
Though the researchers don’t publish a one-size-fits-all operationalizable AI ethics checklist, they provide a useful discussion of the scenarios within which checklists can be helpful, and also within which they can be counterproductive or irrelevant to their intended users. This is important as the abstract nature of AI ethics principles makes them difficult for practitioners to operationalize. As befits the scope of this topic, AI applications and tool vendors are still trying to bring their ethics-assurance frameworks into coherent shape. Everybody—even the supposed experts—are groping for a consensus approach and practical tools to make ethics assurance a core component of AI DevOps governance.
Superwise.ai addresses a growing enterprise need for AI model assurance. That said, there are both challenges and opportunities ahead for this AI startup. Model assurance is the ability to determine whether an AI application’s machine learning (ML) models remain predictively fit for their assigned tasks. This is a critical feature of any operational AI DevOps platform, which is one of the reasons Superwise.ai caught my attention. I see both challenges and opportunities ahead for this AI startup.
AI and predictive analytics are still making an impact for businesses in terms of identifying customer trends, building customer profiles, and constructing tighter potential target audiences.
Google’s latest announcement lands in a fast-moving, but still immature, quantum computing marketplace. By extending the most popular open-source ML development framework, Google will almost certainly catalyze use of TensorFlow Quantum in a wide range of ML-related initiatives. The Google-developed quantum ML framework will find its way into a wide range of other solution providers’ quantum computing environments. So what will Google’s likely next move be in the Quantum ML space? Read on to see what I think.
