#NVIDIA AI for industries

[logiclitz]

Top 7 Companies Leading Industrial Computing in 2024

Industrial computing has revolutionized sectors such as manufacturing, healthcare, energy, and transportation. These systems, designed for robust and efficient performance in industrial environments, play a pivotal role in enabling automation, data processing, and connectivity at scale. As industries embrace the Industrial Internet of Things (IIoT) and edge computing, several companies stand out…

[el-ffej]

Regarding the DeepSeek AI Hysteria:

Text of posting:

To people who see the performance of DeepSeek and think: "'China is surpassing the US in AI." You are reading this wrong. The correct reading is: "Open source models are surpassing proprietary ones." DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta). They came up with new ideas and built them on top of other people's work. Because their work is published and open source, everyone can profit from it. That is the power of open research and open source.

Also recommended reading:

[l-in-c-future]

DeepSeek spells the end of the dominance of Big Data and Big AI, not the end of Nvidia. Its focus on efficiency jump-starts the race for small AI models based on lean data, consuming slender computing resources. The probable impact of DeepSeek’s low-cost and free state-of-the-art AI model will be the reorientation of U.S. Big Tech away from relying exclusively on their “bigger is better” competitive orientation and the accelerated proliferation of AI startups focused on “small is beautiful.”

Wall Street share market reacted in the wrong way for the wrong thing.

Without Nvidia, Deepseek can't survive because they use specific Nvidia chips that are currently not under US ban.

[meelsport]

Perplexity AI Stock and AI Market Growth: Top Insights for Investors

Artificial intelligence (AI) is at the forefront of technological innovation, driving significant changes across industries. Companies like Perplexity AI are leading the charge with groundbreaking AI-driven tools, although Perplexity AI itself isn’t publicly traded at this time. However, the broader AI market growth offers numerous opportunities for investors. This article delves into the market…

[kamalkafir-blog]

Nvidia CEO Jensen Huang calls US ban on H20 AI chip ‘deeply painful’

[ASIA] Nvidia CEO Jensen Huang said Washington’s plan to stymie China’s artificial intelligence (AI) capabilities by restricting access to its H20 graphics processing units (GPUs) was “deeply uninformed”, as the US semiconductor giant continues to navigate through a deepening tech rivalry between the world’s two largest economies. In an interview with tech site Stratechery following his keynote…

[nocknocknock]

Nvidia Corp.: Innovations in the World of Artificial Intelligence

In recent years, the world has been witnessing a rapid development of technologies, and one of the most notable companies in this field is Nvidia Corp. Renowned for its graphics processors and data processing chips, Nvidia has recently announced steps that could significantly alter the landscape of the artificial intelligence (AI) industry. Amidst the growing demand for AI solutions, the unveiling of new chips, software, and services underscores Nvidia’s confidence in its leadership in this domain.

New Technologies to Accelerate AI Adoption

During the presentation, Nvidia’s CEO emphasized that this is not just about new products, but about creating a comprehensive ecosystem for developers and enterprises, enabling quicker and easier integration of AI into existing processes. The new chips have been designed with a focus on performance and energy efficiency. They are capable of processing massive volumes of data in real time, making them ideal for applications in finance, healthcare, and security technology.

The company also announced new tools for training AI models that allow developers to more rapidly create advanced AI solutions. This opens up new horizons for startups and large enterprises seeking to maximize the capabilities of deep learning and machine learning.

Expanding Possibilities: From Cars to Robots

One of the most impressive aspects of the new announcement is the focus on expanding the application of AI across various life sectors. Nvidia aims to integrate its technologies into autonomous driving systems. Given the rapid growth of the electric vehicle market, implementing such solutions will be crucial for enhancing road safety. Vehicles equipped with Nvidia’s new chips will process information about their surroundings more quickly and accurately, ensuring safer and more efficient transportation.

Additionally, Nvidia is actively exploring the potential of AI in robotics. The company’s new products will facilitate the creation of smarter and more adaptive robots capable of interacting with humans and performing complex tasks across various industries, from manufacturing to household services. This will not only streamline routine tasks but also significantly improve business efficiency.

Conclusion

Thus, Nvidia Corp. continues to actively develop technologies that have the potential to revolutionize various industries. The launch of the new line of chips and software solutions opens up unique opportunities for both developers and end-users. With each new announcement, Nvidia strengthens its reputation as a pioneer in the world of artificial intelligence.

Against the backdrop of increased interest in AI from businesses and consumers alike, Nvidia is poised to be one of the primary drivers of progress. Investors and analysts are eagerly awaiting the groundbreaking solutions and technologies that the company will introduce in the coming years.

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[techverse1]

Nvidia Stock Dips Amid China’s Anti-Monopoly Investigation

Nvidia’s stock fell by 3% on Monday following news that Chinese regulators have opened an antitrust investigation into the company. The probe reportedly centers on Nvidia's $6.9 billion acquisition of network and data transmission firm Mellanox Technologies in 2019.

Market Reaction and Nvidia's Growth

Despite Monday’s dip, Nvidia shares remain up 179% year-to-date, fueled by soaring demand for artificial intelligence (AI) technologies. The California-based company has become a bellwether for the AI industry, with its GPUs driving the training and deployment of AI systems for tech giants worldwide.

Nvidia’s revenue surged to $35.08 billion in its most recent earnings report, up 94% from $18.12 billion a year ago. Quarterly profits more than doubled to $19.31 billion, highlighting the company's dominance in the AI and data center sectors. About 16% of Nvidia's revenue comes from China, making the country its second-largest market after the U.S.

China's Antitrust Focus

China’s antitrust investigation follows similar scrutiny from the U.S. Department of Justice, which reportedly began investigating Nvidia this summer for alleged abuse of market dominance. Reports suggest Nvidia may have threatened to penalize customers for purchasing products from both itself and competitors.

David Bieri, an international finance expert at Virginia Tech, suggests that China's investigation is more than a regulatory move. "This is a signal to the U.S.," Bieri said, adding that China is leveraging Nvidia's significant business ties to the country as a warning in the face of geopolitical tensions.

Nvidia’s Response

A spokesperson for Nvidia stated the company is “happy to answer any questions regulators may have about our business.”

The investigation highlights the growing complexity of doing business in China, with Bieri noting that Nvidia will likely need to revise its strategy to manage the risks of operating in the region.

Nvidia’s Position in the Tech Landscape

Nvidia's invention of GPUs in 1999 revolutionized the PC gaming market and computer graphics. Its recent focus on AI technologies has made it one of the most valuable companies in the world, with a market value briefly surpassing $3.5 trillion and overtaking Apple.

Last month, Nvidia replaced Intel on the Dow Jones Industrial Average, ending Intel's 25-year tenure on the index. Unlike Intel, Nvidia designs but does not manufacture its own chips, instead relying on Taiwan Semiconductor Manufacturing Co. for production.

As the investigation unfolds, Nvidia faces heightened uncertainty in one of its most critical markets, underscoring the growing intersection of geopolitical tensions and global technology supply chains

[ujusttry]

Shocking 3 Reasons Why Nvidia Lost Its Crown as World's Most Valuable Company

Why Nvidia lost its crown as the world’s most valuable company is a story of rapid stock movement and market volatility. The semiconductor giant’s stock plummeted by 6.7% over two days, resulting in a $200 billion market value loss and positioning Apple and Microsoft ahead in market capitalization. Shocking 3 Reasons Why Nvidia Lost Its Crown1. Nvidia’s Meteoric Rise Made It Vulnerable to…

[techtoio]

Emerging Tech Trends in the Internet of Things (IoT)

Introduction

The Internet of Things (IoT) is transforming our world by connecting devices and enabling smarter, more efficient interactions. In everything from smart homes to industrial automation, the IoT is leading a revolution in our living and working environments. In this article, TechtoIO explores the emerging tech trends in IoT, highlighting the innovations and advancements that are shaping the future. Read to continue link

[akwyz]

The Convergence of Siemens and NVIDIA: Driving the Industrial Metaverse Forward

"Dive into the future of tech with our insights on the Omniverse and simulation tech at Nvidia & Siemens' digital partnership! 🌐💡 #Innovation #TechTrends"

The world of technology is rapidly evolving, and at the forefront of this transformation are two industry giants: Siemens and NVIDIA. In a recent conversation at a technology fair, Roland Busch, CEO and President of Siemens, and Rev Lebaredian from NVIDIA discussed their partnership and the impact of AI and simulation technology on various industries. The Next Level of Technology As day one of…

[roadmaperp-software]

Google launches ever most capable AI

Gemini is the outcome of large-scale collaborative efforts by teams across Google and Google DeepMind 🧠, who built from the ground up to be multimodal.

Gemini is now available in Gemini Nano and Gemini Pro sizes on Google products- Google Pixel 8 and Google Bard respectively.

Unlike other generative AI multimodal models, Google's Gemini appears to be more product-focused, which is either integrated into the company's ecosystem or better plans to be.

[ayeforscotland]

It’s often been posited that NVIDIA would be the ‘canary in the coal mine’ when it comes to Generative AI.

Toys R Us recently launched an Gen AI advert produced via Sora, and it is an absolutely worthless pile of shit. The cars at the start, the kid changing appearance the whole way through, the horrible GenAI sheen that covers it.

It looks dreadful, it makes Toys R Us look cheap and inauthentic. It’s a genuinely embarrassing marketing blunder, and it doesn’t just make Toys R Us look bad, it makes the whole paper tiger GenAI industry look abysmal.

[mostlysignssomeportents]

“Humans in the loop” must detect the hardest-to-spot errors, at superhuman speed

I'm touring my new, nationally bestselling novel The Bezzle! Catch me SATURDAY (Apr 27) in MARIN COUNTY, then Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

If AI has a future (a big if), it will have to be economically viable. An industry can't spend 1,700% more on Nvidia chips than it earns indefinitely – not even with Nvidia being a principle investor in its largest customers:

https://news.ycombinator.com/item?id=39883571

A company that pays 0.36-1 cents/query for electricity and (scarce, fresh) water can't indefinitely give those queries away by the millions to people who are expected to revise those queries dozens of times before eliciting the perfect botshit rendition of "instructions for removing a grilled cheese sandwich from a VCR in the style of the King James Bible":

https://www.semianalysis.com/p/the-inference-cost-of-search-disruption

Eventually, the industry will have to uncover some mix of applications that will cover its operating costs, if only to keep the lights on in the face of investor disillusionment (this isn't optional – investor disillusionment is an inevitable part of every bubble).

Now, there are lots of low-stakes applications for AI that can run just fine on the current AI technology, despite its many – and seemingly inescapable - errors ("hallucinations"). People who use AI to generate illustrations of their D&D characters engaged in epic adventures from their previous gaming session don't care about the odd extra finger. If the chatbot powering a tourist's automatic text-to-translation-to-speech phone tool gets a few words wrong, it's still much better than the alternative of speaking slowly and loudly in your own language while making emphatic hand-gestures.

There are lots of these applications, and many of the people who benefit from them would doubtless pay something for them. The problem – from an AI company's perspective – is that these aren't just low-stakes, they're also low-value. Their users would pay something for them, but not very much.

For AI to keep its servers on through the coming trough of disillusionment, it will have to locate high-value applications, too. Economically speaking, the function of low-value applications is to soak up excess capacity and produce value at the margins after the high-value applications pay the bills. Low-value applications are a side-dish, like the coach seats on an airplane whose total operating expenses are paid by the business class passengers up front. Without the principle income from high-value applications, the servers shut down, and the low-value applications disappear:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Now, there are lots of high-value applications the AI industry has identified for its products. Broadly speaking, these high-value applications share the same problem: they are all high-stakes, which means they are very sensitive to errors. Mistakes made by apps that produce code, drive cars, or identify cancerous masses on chest X-rays are extremely consequential.

Some businesses may be insensitive to those consequences. Air Canada replaced its human customer service staff with chatbots that just lied to passengers, stealing hundreds of dollars from them in the process. But the process for getting your money back after you are defrauded by Air Canada's chatbot is so onerous that only one passenger has bothered to go through it, spending ten weeks exhausting all of Air Canada's internal review mechanisms before fighting his case for weeks more at the regulator:

https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454

There's never just one ant. If this guy was defrauded by an AC chatbot, so were hundreds or thousands of other fliers. Air Canada doesn't have to pay them back. Air Canada is tacitly asserting that, as the country's flagship carrier and near-monopolist, it is too big to fail and too big to jail, which means it's too big to care.

Air Canada shows that for some business customers, AI doesn't need to be able to do a worker's job in order to be a smart purchase: a chatbot can replace a worker, fail to their worker's job, and still save the company money on balance.

I can't predict whether the world's sociopathic monopolists are numerous and powerful enough to keep the lights on for AI companies through leases for automation systems that let them commit consequence-free free fraud by replacing workers with chatbots that serve as moral crumple-zones for furious customers:

https://www.sciencedirect.com/science/article/abs/pii/S0747563219304029

But even stipulating that this is sufficient, it's intrinsically unstable. Anything that can't go on forever eventually stops, and the mass replacement of humans with high-speed fraud software seems likely to stoke the already blazing furnace of modern antitrust:

https://www.eff.org/de/deeplinks/2021/08/party-its-1979-og-antitrust-back-baby

Of course, the AI companies have their own answer to this conundrum. A high-stakes/high-value customer can still fire workers and replace them with AI – they just need to hire fewer, cheaper workers to supervise the AI and monitor it for "hallucinations." This is called the "human in the loop" solution.

The human in the loop story has some glaring holes. From a worker's perspective, serving as the human in the loop in a scheme that cuts wage bills through AI is a nightmare – the worst possible kind of automation.

Let's pause for a little detour through automation theory here. Automation can augment a worker. We can call this a "centaur" – the worker offloads a repetitive task, or one that requires a high degree of vigilance, or (worst of all) both. They're a human head on a robot body (hence "centaur"). Think of the sensor/vision system in your car that beeps if you activate your turn-signal while a car is in your blind spot. You're in charge, but you're getting a second opinion from the robot.

Likewise, consider an AI tool that double-checks a radiologist's diagnosis of your chest X-ray and suggests a second look when its assessment doesn't match the radiologist's. Again, the human is in charge, but the robot is serving as a backstop and helpmeet, using its inexhaustible robotic vigilance to augment human skill.

That's centaurs. They're the good automation. Then there's the bad automation: the reverse-centaur, when the human is used to augment the robot.

Amazon warehouse pickers stand in one place while robotic shelving units trundle up to them at speed; then, the haptic bracelets shackled around their wrists buzz at them, directing them pick up specific items and move them to a basket, while a third automation system penalizes them for taking toilet breaks or even just walking around and shaking out their limbs to avoid a repetitive strain injury. This is a robotic head using a human body – and destroying it in the process.

An AI-assisted radiologist processes fewer chest X-rays every day, costing their employer more, on top of the cost of the AI. That's not what AI companies are selling. They're offering hospitals the power to create reverse centaurs: radiologist-assisted AIs. That's what "human in the loop" means.

This is a problem for workers, but it's also a problem for their bosses (assuming those bosses actually care about correcting AI hallucinations, rather than providing a figleaf that lets them commit fraud or kill people and shift the blame to an unpunishable AI).

Humans are good at a lot of things, but they're not good at eternal, perfect vigilance. Writing code is hard, but performing code-review (where you check someone else's code for errors) is much harder – and it gets even harder if the code you're reviewing is usually fine, because this requires that you maintain your vigilance for something that only occurs at rare and unpredictable intervals:

https://twitter.com/qntm/status/1773779967521780169

But for a coding shop to make the cost of an AI pencil out, the human in the loop needs to be able to process a lot of AI-generated code. Replacing a human with an AI doesn't produce any savings if you need to hire two more humans to take turns doing close reads of the AI's code.

This is the fatal flaw in robo-taxi schemes. The "human in the loop" who is supposed to keep the murderbot from smashing into other cars, steering into oncoming traffic, or running down pedestrians isn't a driver, they're a driving instructor. This is a much harder job than being a driver, even when the student driver you're monitoring is a human, making human mistakes at human speed. It's even harder when the student driver is a robot, making errors at computer speed:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

This is why the doomed robo-taxi company Cruise had to deploy 1.5 skilled, high-paid human monitors to oversee each of its murderbots, while traditional taxis operate at a fraction of the cost with a single, precaratized, low-paid human driver:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

The vigilance problem is pretty fatal for the human-in-the-loop gambit, but there's another problem that is, if anything, even more fatal: the kinds of errors that AIs make.

Foundationally, AI is applied statistics. An AI company trains its AI by feeding it a lot of data about the real world. The program processes this data, looking for statistical correlations in that data, and makes a model of the world based on those correlations. A chatbot is a next-word-guessing program, and an AI "art" generator is a next-pixel-guessing program. They're drawing on billions of documents to find the most statistically likely way of finishing a sentence or a line of pixels in a bitmap:

https://dl.acm.org/doi/10.1145/3442188.3445922

This means that AI doesn't just make errors – it makes subtle errors, the kinds of errors that are the hardest for a human in the loop to spot, because they are the most statistically probable ways of being wrong. Sure, we notice the gross errors in AI output, like confidently claiming that a living human is dead:

https://www.tomsguide.com/opinion/according-to-chatgpt-im-dead

But the most common errors that AIs make are the ones we don't notice, because they're perfectly camouflaged as the truth. Think of the recurring AI programming error that inserts a call to a nonexistent library called "huggingface-cli," which is what the library would be called if developers reliably followed naming conventions. But due to a human inconsistency, the real library has a slightly different name. The fact that AIs repeatedly inserted references to the nonexistent library opened up a vulnerability – a security researcher created a (inert) malicious library with that name and tricked numerous companies into compiling it into their code because their human reviewers missed the chatbot's (statistically indistinguishable from the the truth) lie:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

For a driving instructor or a code reviewer overseeing a human subject, the majority of errors are comparatively easy to spot, because they're the kinds of errors that lead to inconsistent library naming – places where a human behaved erratically or irregularly. But when reality is irregular or erratic, the AI will make errors by presuming that things are statistically normal.

These are the hardest kinds of errors to spot. They couldn't be harder for a human to detect if they were specifically designed to go undetected. The human in the loop isn't just being asked to spot mistakes – they're being actively deceived. The AI isn't merely wrong, it's constructing a subtle "what's wrong with this picture"-style puzzle. Not just one such puzzle, either: millions of them, at speed, which must be solved by the human in the loop, who must remain perfectly vigilant for things that are, by definition, almost totally unnoticeable.

This is a special new torment for reverse centaurs – and a significant problem for AI companies hoping to accumulate and keep enough high-value, high-stakes customers on their books to weather the coming trough of disillusionment.

This is pretty grim, but it gets grimmer. AI companies have argued that they have a third line of business, a way to make money for their customers beyond automation's gifts to their payrolls: they claim that they can perform difficult scientific tasks at superhuman speed, producing billion-dollar insights (new materials, new drugs, new proteins) at unimaginable speed.

However, these claims – credulously amplified by the non-technical press – keep on shattering when they are tested by experts who understand the esoteric domains in which AI is said to have an unbeatable advantage. For example, Google claimed that its Deepmind AI had discovered "millions of new materials," "equivalent to nearly 800 years’ worth of knowledge," constituting "an order-of-magnitude expansion in stable materials known to humanity":

https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/

It was a hoax. When independent material scientists reviewed representative samples of these "new materials," they concluded that "no new materials have been discovered" and that not one of these materials was "credible, useful and novel":

https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/

As Brian Merchant writes, AI claims are eerily similar to "smoke and mirrors" – the dazzling reality-distortion field thrown up by 17th century magic lantern technology, which millions of people ascribed wild capabilities to, thanks to the outlandish claims of the technology's promoters:

https://www.bloodinthemachine.com/p/ai-really-is-smoke-and-mirrors

The fact that we have a four-hundred-year-old name for this phenomenon, and yet we're still falling prey to it is frankly a little depressing. And, unlucky for us, it turns out that AI therapybots can't help us with this – rather, they're apt to literally convince us to kill ourselves:

https://www.vice.com/en/article/pkadgm/man-dies-by-suicide-after-talking-with-ai-chatbot-widow-says

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[probablyasocialecologist]

It’s hard to talk about 21st-century economic history without discussing the “China shock”. That is the term often used to describe China’s entrance into the global market, a change that brought rich countries an abundance of cheap goods, but left entire industries and workforces mothballed. DeepSeek may provide a sequel. A little-known Chinese hedge fund has thrown a grenade into the world of artificial intelligence with a large language model that, in effect, matches the market leader, Sam Altman’s OpenAI, at a fraction of the cost. And while OpenAI treats its models’ workings as proprietary, DeepSeek’s R1 wears its technical innards on the outside, making it attractive for developers to use and build on. Things move faster in the AI age; terrifyingly so. Five of the biggest technology stocks geared to AI — chipmaker Nvidia and so-called hyperscalers Alphabet, Amazon, Microsoft and Meta Platforms — collectively shed almost $750bn of market value before US markets opened on Monday. It could be particularly grim for Nvidia if it proves true that DeepSeek won without the use of its shiniest chips.

27 January 2025