The amount of waste involved in retail returns is staggering, yet it doesn't have to be that way. Sustainable retail returns can be a viable part of the circular economy - early adopters are proving why.
Live AI systems, already operating at scale, can have dangerous consequences - and concerning downsides. In theory, the AI ethics movement should provide a corrective force. Yet so far, it has failed to do so. What is AI ethics getting wrong?
By now, the world knows about Southwest's massive holiday logistical meltdown, which stranded millions of passengers. Southwest blamed the system-wide breakdown on winter weather, but that doesn't hold up. A deeper look at Southwest's predicament surfaces harsh lessons on the problem of technical debt.
The US healthcare narrative is about "patient outcomes" and personalized medicine, supported by permissioned data. But the data on the state of rural hospitals tells a very different story. Unless we reckon with this, healthcare transformation is off the table.
UK-based Graphcore has announced that it will deliver, by 2024, the world's first "ultra-intelligence AI computer" - called the Good Computer. Here's a deeper look at what they're planning, and the implications.
The international space station has been orbiting for 21 years - but change is afoot. Here's why enterprises should care about the emerging space economy.
Digital twins are not always properly defined, but their use cases are intriguing. The limitations of digital twins become clear in high stakes scenarios - and healthcare is one.
Agriculture isn't a monolith. In the US, there are a sizable amount of smaller farms. Can AI give farmers a leg up, even those with limited resources? The use cases deserve a closer look.
Despite all our tech-for-good efforts, food insecurity remains a global problem. Meanwhile, the stats on food waste in the US alone are eye-opening. Can AI startups like Wasteless help to close this gap?
Semantic layers have been around for a long time - but with underwhelming results. Is the concept outdated? No, but something it missing. In the big data era, we need to raise our data abstraction game.
Data overload is a real problem, but we keep piling it on. After all, AI and machine learning require massive data sets. But are we framing the problem correctly? Causal emergence makes a different argument - and could impact the "big data" obsession.
After five years of AI ethics, it's time to get real. We need better projects - and a better approach to the pitfalls of data and talent. Here's why I find the academic approach to AI ethics limiting.
Foundational models address a fundamental flaw in bespoke AI. But foundational and large language models have limitations. GPT-3, BERT, and DALL·E 2 garnered gushing headlines, but models like these deserve scrutiny.
