Jensen Huang getting carried away about an emerging technology is nothing new. This time last year, the charismatic and excitable co-founder and CEO of chip design giant Nvidia was telling anyone who'd listen about the potential of the metaverse (or the Omniverse, as Nvidia's marketing department prefers to call it). Since then, the metaverse bubble has suffered a slow puncture, and Huang is back to evangelising about one of his favourite topics: artificial intelligence. Describing the growth in power of generative AI systems like GPT-4 – the model that powers OpenAI's tools such as ChatGPT – as a "new era of computing", Huang told investors on his company's most recent earnings call that AI was at an "inflection point", stating that businesses have "an urgency to develop and deploy new AI strategies". However, Huang added that he believes many companies face "an insurmountable obstacle" in getting access to the resources and skills needed to make AI work, which is why, he says, Nvidia is getting into the services business.

Obviously, it's artificial intelligence, but it is an intelligence we can use to help us determine what's useful in all of the big data being collected through the Internet of Things (IoT). And we can teach it to tell us what's important to investigate there, too. AI won't replace humans, but it will give us a huge boost. Paul Muller, vice president of strategic marketing at Hewlett Packard Enterprise, has a conversation with technology analyst Theo Priestley about AI, including what it is, how to use it, fears about it, and how it can help us long term. They talk about the myths around AI--such as it's a human replacement vehicle rather than a way to augment our work, or it's just software rather than hardware and software--and how artificial intelligence doesn't mean it's not intelligent and doesn't practice self-preservation.

One way of simplifying neural networks so they generalize better is to add an extra t.erm

One way of simplifying neural networks so they generalize better is to add an extra t.erm

One way of simplifying neural networks so they generalize better is to add an extra t.erm