Is AI in finance a pot of gold at the end of the rainbow? Zoho's Raju Vegesna does some myth-busting and explores the the middle ground where AI has a useful place amid the hype.
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.
This week - retailers get a pretty decent Black Friday, but questions linger. AI projects get a reality check, as data on ROI and employee buy-in rolls in. DevOps isn't easy, and neither are open source language models (just ask Meta). As always, your weekly whiffs.
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?
KPMG provides advice on how to keep customers happy in the risk-filled world of artificial intelligence
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 Is the concept outdated?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.
Some cautionary tales from the AI sector, which we may be trusting too deeply or too quickly for our own good.
At SAP TechEd, we heard plenty of useful talk on SAP automation, but mostly from a low-code/business user angle. What about ERP systems automaton? Avantra customer Scotts Miracle-Gro shares its ERP automation lessons.
Fast responses and resolutions to their issues are customers’ top priority - AI can make an immediate impact on the burden and complexity for customers and businesses. Zendesk's Cristina Fonseca points to three ways to get the most from AI in retail.
This week - Walmart and Target issue mixed earning news - where does consumer sentiment lie? Amazon's pending layoffs are another wake up call, and Alexa teams are impacted. Flexible work data is complicated, as is low-code adoption. Your whiffs include Web3 vinegar from yours truly.
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.
This week - responsible AI gets an Intel boost, but where are we with ethics versus adoption? Crypto grabs tech headlines for the wrong reasons - bad times at FTX. Event season rolls on - diginomica has your news and views. Whiffs are easy thanks to Twitter.
AI technology is a bigger challenge than leaders realise. The solution? Data-centric AI.
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.
Responsibility inside - Intel explains its framework for ethical AI development
Marketers reckon they're more valuable in their contribution to their organizations than ever.
