▲ image source Pexels

In the earnings report, Microsoft said, “The growth of our data center business is dependent on buildable land permits and predictable energy, network and server supply, including the availability of graphics processing units (GPUs) and other components.”

That’s one of three paragraphs in the earnings report that mentions GPUs, but Microsoft didn’t mention them once in last year’s report. similar statements haven’t appeared in recent earnings reports from other major tech companies, either.

As part of a complex partnership, OpenAI relies on Microsoft’s Azure cloud service to run ChatGPT and various AI model calculations. Microsoft has also begun using OpenAI’s models to augment existing products such as Outlook, Word, and Bing with generative AI.

These efforts and interest in ChatGPT have led Microsoft to seek out more GPUs than it had anticipated, and it has begun to seek out more GPU support outside of its own data centers after signing a deal with NVIDIA-powered CoreWeave, which rents out GPUs as a cloud service to third-party developers.

Meanwhile, Microsoft has spent years building its own custom AI chips, and as it was previously reported, the focus on ChatGPT has prompted Microsoft to accelerate the deployment of its chip business, with Google, Amazon, and Meta all releasing their own AI chips.

Microsoft CFO Amy Hood previously told analysts on a conference call that Microsoft expects to continue to increase capital expenditures this quarter to pay for data centers, networking hardware, CPUs and GPUs.

The post Microsoft warns data center services could be disrupted if it can’t get enough AI chips appeared first on TechGoing.

Sam Altman argued that we are approaching the limits of LLM scale, and that larger scale does not necessarily mean a better model, but may simply be the pursuit of a number. the scale of LLM is no longer an important indicator of model quality, and there will be more ways to improve the capability and utility of models in the future. He drew an analogy between the scale of LLM and the race for chip speed in the past, noting that today we are more concerned with whether chips can do the job than how fast they are. He said that OpenAI’s goal is to provide the world with the most capable, useful, and safe models, not to be self-absorbed over the number of parameters.

Sam Altman also responded to an open letter asking OpenAI to suspend the development of more powerful AI for six months, saying he agrees that safety standards need to improve as capabilities get more powerful, but that he believes the letter lacks technical detail and accuracy. He said OpenAI had spent more than six months working on security issues before releasing GPT-4 and had invited external audits and “red teams” to test the models. “I also agree that as capabilities become more robust, security standards must improve. But I think this letter is missing most of the technical nuances about what we need to pause — an earlier version of this letter claimed we were training for GPT-5. we’re not, and won’t be for some time, so in that sense, it’s kind of silly. But there are other things that we’re doing on top of GPT-4 that I think have various security issues that need to be addressed that are not mentioned in the letter at all. So I think it’s very important to be cautious and to be increasingly rigorous about security issues. I don’t think the recommendations (in the letter) are the ultimate solution to this problem,” he said.

OpenAI has been working in the field for seven years and has put a lot of effort into it, which most people are not willing to do, Ault said. He said he is willing to openly discuss security issues and the limitations of the model because it is the right thing to do. He admitted that sometimes he and other company representatives say “dumb things” that turn out to be wrong, but he’s willing to take the risk because the technology requires dialogue.

The post OpenAI CEO: Large language models are nearing their limits in scale, not bigger is better appeared first on TechGoing.

According to the report, ChatGPT was interviewed for a coding position and was hired as an L3 engineer, which is a junior software engineer position at Google, usually for new college graduates and first-time coders, with an annual salary of about $180,000 (currently about RMB 1.222 million).

And when asked whether ChatGPT will replace programmers, ChatGPT gave a negative answer, “No, ChatGPT will not replace software engineers. ChatGPT is a tool that can assist with certain tasks, but it cannot completely replace the creativity, problem-solving skills, and critical thinking skills of human software engineers. In addition, ChatGPT requires human oversight and guidance to function effectively.”

So will it be able to replace it in 20 years, or are lower-level positions likely to be at greater risk, ChatGPT acknowledges that it may affect lower-level positions. But it reiterated that it will never be a complete replacement, but rather a tool to assist human software engineers.

ChatGPT also passed four law course exams at the University of Minnesota and another at the Wharton School of the University of Pennsylvania, according to professors at the University of Minnesota and the Wharton School of the University of Pennsylvania.

The post ChatGPT Passes Google L3 Engineer Onboarding Test, $180K per year appeared first on TechGoing.