Nvidia shares tumble as China lockdown, Russia blamed for dent in outlook Nvidia exceeded market expectations and on Wednesday reported record first-quarter fiscal 2023 revenue of $8.29 billion, an increase of 46 percent from a year ago and eight percent from the previous quarter. Nonetheless the GPU goliath's stock slipped by more than nine percent in after-hours trading amid remarks by CFO Colette …
Nvidia exceeded market expectations and on Wednesday reported record first-quarter fiscal 2023 revenue of $8.29 billion, an increase of 46 percent from a year ago and eight percent from the previous quarter.
I expect the above to be amended, but 2023 revenues already. GO NVIDIA!
Interview 2023 is shaping up to become a big year for Arm-based server chips, and a significant part of this drive will come from Nvidia, which appears steadfast in its belief in the future of Arm, even if it can't own the company.
Several system vendors are expected to push out servers next year that will use Nvidia's new Arm-based chips. These consist of the Grace Superchip, which combines two of Nvidia's Grace CPUs, and the Grace-Hopper Superchip, which brings together one Grace CPU with one Hopper GPU.
The vendors lining up servers include American companies like Dell Technologies, HPE and Supermicro, as well Lenovo in Hong Kong, Inspur in China, plus ASUS, Foxconn, Gigabyte, and Wiwynn in Taiwan are also on board. The servers will target application areas where high performance is key: AI training and inference, high-performance computing, digital twins, and cloud gaming and graphics.
Siemens and Nvidia don’t want manufacturers to imagine what the future will hold – they want to build a fancy digital twin that helps them to make predictions about whatever comes next.
During a press conference this week, Siemens CEO Roland Busch painted a picture of a future in which manufacturers are besieged with productivity, labor, and supply chain disruptions.
"The answer to all of these challenges is technology and digitalization," he said. "The point is, we have to make the digital twin as realistic as possible and bring it as close as possible to the real world."
Analysis After re-establishing itself in the datacenter over the past few years, AMD is now hoping to become a big player in the AI compute space with an expanded portfolio of chips that cover everything from the edge to the cloud.
It's quite an ambitious goal, given Nvidia's dominance in the space with its GPUs and the CUDA programming model, plus the increasing competition from Intel and several other companies.
But as executives laid out during AMD's Financial Analyst Day 2022 event last week, the resurgent chip designer believes it has the right silicon and software coming into place to pursue the wider AI space.
Qualcomm knows that if it wants developers to build and optimize AI applications across its portfolio of silicon, the Snapdragon giant needs to make the experience simpler and, ideally, better than what its rivals have been cooking up in the software stack department.
That's why on Wednesday the fabless chip designer introduced what it's calling the Qualcomm AI Stack, which aims to, among other things, let developers take AI models they've developed for one device type, let's say smartphones, and easily adapt them for another, like PCs. This stack is only for devices powered by Qualcomm's system-on-chips, be they in laptops, cellphones, car entertainment, or something else.
While Qualcomm is best known for its mobile Arm-based Snapdragon chips that power many Android phones, the chip house is hoping to grow into other markets, such as personal computers, the Internet of Things, and automotive. This expansion means Qualcomm is competing with the likes of Apple, Intel, Nvidia, AMD, and others, on a much larger battlefield.
Microsoft has pledged to clamp down on access to AI tools designed to predict emotions, gender, and age from images, and will restrict the usage of its facial recognition and generative audio models in Azure.
The Windows giant made the promise on Tuesday while also sharing its so-called Responsible AI Standard, a document [PDF] in which the US corporation vowed to minimize any harm inflicted by its machine-learning software. This pledge included assurances that the biz will assess the impact of its technologies, document models' data and capabilities, and enforce stricter use guidelines.
This is needed because – and let's just check the notes here – there are apparently not enough laws yet regulating machine-learning technology use. Thus, in the absence of this legislation, Microsoft will just have to force itself to do the right thing.
GPUs are a powerful tool for machine-learning workloads, though they’re not necessarily the right tool for every AI job, according to Michael Bronstein, Twitter’s head of graph learning research.
His team recently showed Graphcore’s AI hardware offered an “order of magnitude speedup when comparing a single IPU processor to an Nvidia A100 GPU,” in temporal graph network (TGN) models.
“The choice of hardware for implementing Graph ML models is a crucial, yet often overlooked problem,” reads a joint article penned by Bronstein with Emanuele Rossi, an ML researcher at Twitter, and Daniel Justus, a researcher at Graphcore.
Comment More than 250 mass shootings have occurred in the US so far this year, and AI advocates think they have the solution. Not gun control, but better tech, unsurprisingly.
Machine-learning biz Kogniz announced on Tuesday it was adding a ready-to-deploy gun detection model to its computer-vision platform. The system, we're told, can detect guns seen by security cameras and send notifications to those at risk, notifying police, locking down buildings, and performing other security tasks.
In addition to spotting firearms, Kogniz uses its other computer-vision modules to notice unusual behavior, such as children sprinting down hallways or someone climbing in through a window, which could indicate an active shooter.
In Brief No, AI chatbots are not sentient.
Just as soon as the story on a Google engineer, who blew the whistle on what he claimed was a sentient language model, went viral, multiple publications stepped in to say he's wrong.
The debate on whether the company's LaMDA chatbot is conscious or has a soul or not isn't a very good one, just because it's too easy to shut down the side that believes it does. Like most large language models, LaMDA has billions of parameters and was trained on text scraped from the internet. The model learns the relationships between words, and which ones are more likely to appear next to each other.
In brief US hardware startup Cerebras claims to have trained the largest AI model on a single device powered by the world's largest Wafer Scale Engine 2 chip the size of a plate.
"Using the Cerebras Software Platform (CSoft), our customers can easily train state-of-the-art GPT language models (such as GPT-3 and GPT-J) with up to 20 billion parameters on a single CS-2 system," the company claimed this week. "Running on a single CS-2, these models take minutes to set up and users can quickly move between models with just a few keystrokes."
The CS-2 packs a whopping 850,000 cores, and has 40GB of on-chip memory capable of reaching 20 PB/sec memory bandwidth. The specs on other types of AI accelerators and GPUs pale in comparison, meaning machine learning engineers have to train huge AI models with billions of parameters across more servers.
Cisco has decided it's time to leave Russia and Belarus, almost four months after stopping operations in response to Russia's illegal invasion of Ukraine.
The networking giant announced it would halt operations in Russia and Belarus "for the foreseeable future" on March 3 this year.
A June 23 update suggests Cisco sees no future in either nation.
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