AI

Big Tech needs to generate $600 billion in annual revenue to justify AI hardware expenditure

The big picture: The tech industry is riding a new high amid a frenzy fueled by AI. Big Tech companies have been plowing huge sums to build out the necessary infrastructure to meet what they perceive demand will be for these products in the coming years. One analyst warns however that the industry needs to stop and consider whether the actual revenue generated by AI will be enough to support these investments.

Nvidia's RTX Remix goes open source, giving classic games an AI-powered remastering renaissance

OpenAI's internal discussion board about AI development was compromised by hackers

Apple AI home device could merge the HomePod and Apple TV

Cloudflare offers one-click solution to block AI bots

Why it matters: There is a growing consensus that generative AI has the potential to make the open web much worse than it was before. Currently all big tech corporations and AI startups rely on scraping all the original content they can off the web to train their AI models. The problem is that an overwhelming majority of websites isn't cool with that, nor have they given permission for such. But hey, just ask Microsoft AI CEO, who believes content on the open web is "freeware."

New technology enables GPUs to use PCIe-attached memory for expanded capacity

AI and Ukraine drone warfare are bringing us one step closer to killer robots

Even staunch fans are calling out Apple's less-than-transparent AI training data harvesting

Almost half of all web traffic is bots, and they are mostly malicious in nature

Google's greenhouse emissions are growing because of AI, and AI could help with that

Pixel 9 phones could ship with a Microsoft Recall-like feature called "Google AI"

Microsoft AI CEO: Content on the open web is "freeware" for AI training

Is there a sustainable model for AI factories?

Editor's take: Investors usually hate airlines – they're capital-intensive, have high operating expenses, limited suppliers, and no way to differentiate with customers. How are AI factories any different?

Bill Gates says we don't have to worry about AI energy use

Move over GPUs, with 1,536 cores the Thunderbird RISC-V CPU is ready to eat your lunch

Google Gemini is not as good at analysis as its hype says, other AI models also struggle

The big picture: If there's one thing that generative AI is supposed to be good at, it's analyzing the written word. However, two studies suggest that this ability may have been overhyped. One study demonstrates that Gen AI struggles with understanding long-form books, while another shows that these models find answering questions about videos challenging. This is something companies should consider as they augment their workforce with Gen AI.

Audi is adding ChatGPT into existing cars going back to 2021, more going forward

Intel says its optical interconnect chiplet technology is a milestone in high-speed data transmission

Google Translate adds support for 110 new languages using the power of AI

Google, Snap, Meta and many others are "quietly" changing privacy policies to allow for AI training

AMD approached to make world's fastest AI supercomputer powered by 1.2 million GPU

Forward-looking: It's no secret that Nvidia has been the dominant GPU supplier to data centers, but now there is a very real possibility that AMD might become a serious contender in this market as demand grows. AMD was recently approached by a client asking to create an AI training cluster consisting of a staggering 1.2 million GPUs. That would potentially make it 30x more powerful than Frontier, the current fastest supercomputer. AMD supplied less than 2% of data center GPUs in 2023.

The PC market is on the rebound, thanks to Windows 10's looming end-of-support date and AI PCs

Energy-efficient AI model could be a game changer, 50 times better efficiency with no performance hit

Cutting corners: Researchers from the University of California, Santa Cruz, have devised a way to run a billion-parameter-scale large language model using just 13 watts of power – about as much as a modern LED light bulb. For comparison, a data center-grade GPU used for LLM tasks requires around 700 watts.

Deepfaking it: GenAI is more likely to be misused to influence your vote than to steal your money

Google could launch AI chatbots based on celebs and influencers later this year