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Po 33 letech náhle skončil herní časopis Game Informer
Arduino a čeština. I drobný mikročip umí psát háčky a čárky a zvládne samozřejmě i emoji
Limine 8.0.0
DARPA suggests turning old C code automatically into Rust – using AI, of course
To accelerate the transition to memory safe programming languages, the US Defense Advanced Research Projects Agency (DARPA) is driving the development of TRACTOR, a programmatic code conversion vehicle.…
DoJ and FTC Sue TikTok for Violating Children's Privacy Laws
DoJ and FTC Sue TikTok for Violating Children's Privacy Laws
Otisknete zadek do krabice a pak vám přijde sedlo na míru z 3D tiskárny. Český startup dobývá svět unikátní službou
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Hackers Exploit Misconfigured Jupyter Notebooks with Repurposed Minecraft DDoS Tool
Hackers Exploit Misconfigured Jupyter Notebooks with Repurposed Minecraft DDoS Tool
Chance of Nvidia losing antitrust probe unlikely, says analyst
A probe by the US Department of Justice (DOJ) into Nvidia’s selling practices will likely go nowhere, Scott Bickley, advisory practice lead at Info-Tech Research Group, predicted Friday.
On Thursday, Reuters first reported findings of an earlier published report that “DOJ investigators are looking at whether Nvidia pressured cloud providers to buy multiple products,” and in a follow-up article stated that 10 progressive groups, and Democratic Senator Elizabeth Warren, have also asked the DOJ to investigate the company, citing its “dominance of the market for the chips driving the artificial-intelligence boom.”
In a joint letter to Assistant Attorney General Jonathan Kanter, sent on Tuesday, the groups welcomed news of plans to open an antitrust investigation into Nvidia.
They stated that the company “has made it clear that it intends to ride the AI wave as long and as far as it can, and its astonishing dominance in GPU accelerator chips — it now holds an 80% overall global market share in GPU chips and a 98% share in the data center market — puts it in a position to crowd out competitors and set global pricing and the terms of trade. Given the integral part technology plays in contemporary life, this constitutes a dire danger to the open market, and well deserves DOJ scrutiny.”
Bickley said that the real reason for any investigation is the “battle of mega corporations currently going on. Basically, you have the hyperscalers who are the largest consumers of Nvidia’s GPUs. They’re also trying to develop their own version of GPUs. And no one in the world has been successful at displacing Nvidia in this marketplace. They have close to 90% market share. So Nvidia is pulling levers to drive their revenue and ancillary products and services like the CUDA platform, like their cabling, like their server racks.”
The company’s biggest customers, he said, “are fighting back and saying, ‘we want to do it our way, we need you to adapt, we want the number and quantity of chips we want.’ You have these battles going on.”
According to Bickley, anything related to an antitrust probe is “more hype than substance at this point, because, first, semiconductors are the most volatile sector in the stock market. [Nvidia CEO] Jensen Huang knows full well that this wave that they’re riding at the end of the day will crest, and it will crash, and it will happen violently when it does. They are squeezing everything they can out of the short runway and they are trying to elongate that runway as much as possible.”
As for the DOJ, he said, “if you have reputable names complaining, they are going to take a look at it, but under the Biden administration, the department has been extraordinarily unsuccessful in about every antitrust action they’ve decided to take on. Just because they’re looking at it doesn’t mean they’re going to do anything about it.”
“[By the time] they do something about it, and the time that it takes to resolve, this cycle will have already crested, in my opinion. The money will already be made, the damage will already be done … The reality is that we are talking about an anti-competition inquiry for a product that there is really no competition for.”
That is, he said, “the first thing that comes to my mind. The second is, is they (DOJ) may attack the CUDA platform, which only works for development on Nvidia GPUs. The EU has a practice of saying ‘you can’t do this; you have to make your platform open.’ I think that’s arguable. But why would a platform designed to support a specific piece of hardware have to support other people’s hardware?”
The DOJ could force them to do that, and what Nvidia then might do, said Bickley, is develop an alternative platform that will ultimately be less effective on non-Nvidia products in order to comply.
“And the reality is this, and I will use the Adobe example,” he said. “If I’m a graphic designer, and I go to school, and I’m trained on Photoshop, and it integrates with the other Adobe suite of products you can’t make me use Corel Painter to do what Photoshop does and think I’m not going to go find another job.”
It’s the same thing with developers in the GPU space, he said. “They only want to learn one platform; they don’t have the bandwidth to learn up on multiple platforms. The lock-in is real, and by the time these things [antitrust probes] come around to any sort of fruition, the ecosystem is going to be pretty much set in stone.”
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Meta Just Launched the Largest ‘Open’ AI Model in History. Here’s Why It Matters.
In the world of artificial intelligence, a battle is underway. On one side are companies that believe in keeping the datasets and algorithms behind their advanced software private and confidential. On the other are companies that believe in allowing the public to see what’s under the hood of their sophisticated AI models.
Think of this as the battle between open- and closed-source AI.
In recent weeks, Meta, the parent company of Facebook, took up the fight for open-source AI in a big way by releasing a new collection of large AI models. These include a model named Llama 3.1 405B, which Meta’s founder and chief executive, Mark Zuckerberg, says is “the first frontier-level open-source AI model.”
For anyone who cares about a future in which everybody can access the benefits of AI, this is good news.
The Danger of Closed-Source AI—and the Promise of Open-Source AIClosed-source AI refers to models, datasets, and algorithms that are proprietary and kept confidential. Examples include ChatGPT, Google’s Gemini, and Anthropic’s Claude.
Though anyone can use these products, there is no way to find out what dataset and source codes have been used to build the AI model or tool.
While this is a great way for companies to protect their intellectual property and profits, it risks undermining public trust and accountability. Making AI technology closed-source also slows down innovation and makes a company or other users dependent on a single platform for their AI needs. This is because the platform that owns the model controls changes, licensing, and updates.
There are a range of ethical frameworks that seek to improve the fairness, accountability, transparency, privacy, and human oversight of AI. However, these principles are often not fully achieved with closed-source AI due to the inherent lack of transparency and external accountability associated with proprietary systems.
In the case of ChatGPT, its parent company, OpenAI, releases neither the dataset nor code of its latest AI tools to the public. This makes it impossible for regulators to audit it. And while access to the service is free, concerns remain about how users’ data are stored and used for retraining models.
By contrast, the code and dataset behind open-source AI models is available for everyone to see.
This fosters rapid development through community collaboration and enables the involvement of smaller organizations and even individuals in AI development. It also makes a huge difference for small- and medium-size enterprises as the cost of training large AI models is colossal.
Perhaps most importantly, open-source AI allows for scrutiny and identification of potential biases and vulnerability.
However, open-source AI does create new risks and ethical concerns.
For example, quality control in open-source products is usually low. As hackers can also access the code and data, the models are also more prone to cyberattacks and can be tailored and customized for malicious purposes, such as retraining the model with data from the dark web.
An Open-Source AI PioneerAmong all leading AI companies, Meta has emerged as a pioneer of open-source AI. With its new suite of AI models, it is doing what OpenAI promised to do when it launched in December 2015—namely, advancing digital intelligence “in the way that is most likely to benefit humanity as a whole,” as OpenAI said back then.
Llama 3.1 405B is the largest open-source AI model in history. It is what’s known as a large language model, capable of generating human language text in multiple languages. It can be downloaded online but because of its huge size, users will need powerful hardware to run it.
While it does not outperform other models across all metrics, Llama 3.1 405B is considered highly competitive and does perform better than existing closed-source and commercial large language models in certain tasks, such as reasoning and coding tasks.
But the new model is not fully open because Meta hasn’t released the huge dataset used to train it. This is a significant “open” element that is currently missing.
Nonetheless, Meta’s Llama levels the playing field for researchers, small organizations, and startups because it can be leveraged without the immense resources required to train large language models from scratch.
Shaping the Future of AITo ensure AI is democratized, we need three key pillars:
- Governance: regulatory and ethical frameworks to ensure AI technology is being developed and used responsibly and ethically
- Accessibility: affordable computing resources and user-friendly tools to ensure a fair landscape for developers and users
- Openness: datasets and algorithms to train and build AI tools should be open source to ensure transparency.
Achieving these three pillars is a shared responsibility for government, industry, academia and the public. The public can play a vital role by advocating for ethical policies in AI, staying informed about AI developments, using AI responsibly, and supporting open-source AI initiatives.
But several questions remain about open-source AI. How can we balance protecting intellectual property and fostering innovation through open-source AI? How can we minimize ethical concerns around open-source AI? How can we safeguard open-source AI against potential misuse?
Properly addressing these questions will help us create a future where AI is an inclusive tool for all. Will we rise to the challenge and ensure AI serves the greater good? Or will we let it become another nasty tool for exclusion and control? The future is in our hands.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Google DeepMind / Unsplash
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