Tag Archives: Nvidia

DeepSeek, a Chinese rival to OpenAI and other US AI companies

There’s been quite the kerfuffle over DeepSeek during the last few days. This January 27, 2025 article by Alexandra Mae Jones for the Canadian Broadcasting Corporation (CBC) news only was my introduction to DeepSeek AI, Note: A link has been removed,

There’s a new player in AI on the world stage: DeepSeek, a Chinese startup that’s throwing tech valuations into chaos and challenging U.S. dominance in the field with an open-source model that they say they developed for a fraction of the cost of competitors.

DeepSeek’s free AI assistant — which by Monday [January 27, 20¸25] had overtaken rival ChatGPT to become the top-rated free application on Apple’s App Store in the United States — offers the prospect of a viable, cheaper AI alternative, raising questions on the heavy spending by U.S. companies such as Apple and Microsoft, amid a growing investor push for returns.

U.S. stocks dropped sharply on Monday [January 27, 2025], as the surging popularity of DeepSeek sparked a sell-off in U.S. chipmakers.

“[DeepSeek] performs as well as the leading models in Silicon Valley and in some cases, according to their claims, even better,” Sheldon Fernandez, co-founder of DarwinAI, told CBC News. “But they did it with a fractional amount of the resources is really what is turning heads in our industry.”

What is DeepSeek?

Little is known about the small Hangzhou startup behind DeepSeek, which was founded out of a hedge fund in 2023, but largely develops open-source AI models. 

Its researchers wrote in a paper last month that the DeepSeek-V3 model, launched on Jan. 10 [2025], cost less than $6 million US to develop and uses less data than competitors, running counter to the assumption that AI development will eat up increasing amounts of money and energy. 

Some analysts are skeptical about DeepSeek’s $6 million claim, pointing out that this figure only covers computing power. But Fernandez said that even if you triple DeepSeek’s cost estimates, it would still cost significantly less than its competitors. 

The open source release of DeepSeek-R1, which came out on Jan. 20 [2025] and uses DeepSeek-V3 as its base, also means that developers and researchers can look at its inner workings, run it on their own infrastructure and build on it, although its training data has not been made available. 

“Instead of paying Open $20 a month or $200 a month for the latest advanced versions of these models, [people] can really get these types of features for free. And so it really upends a lot of the business model that a lot of these companies were relying on to justify their very high valuations.”

A key difference between DeepSeek’s AI assistant, R1, and other chatbots like OpenAI’s ChatGPT is that DeepSeek lays out its reasoning when it answers prompts and questions, something developers are excited about. 

“The dealbreaker is the access to the raw thinking steps,” Elvis Saravia, an AI researcher and co-founder of the U.K.-based AI consulting firm DAIR.AI, wrote on X, adding that the response quality was “comparable” to OpenAI’s latest reasoning model, o1.

U.S. dominance in AI challenged

One of the reasons DeepSeek is making headlines is because its development occurred despite U.S. actions to keep Americans at the top of AI development. In 2022, the U.S. curbed exports of computer chips to China, hampering their advanced supercomputing development.

The latest AI models from DeepSeek are widely seen to be competitive with those of OpenAI and Meta, which rely on high-end computer chips and extensive computing power.

Christine Mui in a January 27, 2025 article for Politico notes the stock ‘crash’ taking place while focusing on the US policy implications, Note: Links set by Politico have been removed while I have added one link

A little-known Chinese artificial intelligence startup shook the tech world this weekend by releasing an OpenAI-like assistant, which shot to the No.1 ranking on Apple’s app store and caused American tech giants’ stocks to tumble.

From Washington’s perspective, the news raised an immediate policy alarm: It happened despite consistent, bipartisan efforts to stifle AI progress in China.

In tech terms, what freaked everyone out about DeepSeek’s R1 model is that it replicated — and in some cases, surpassed — the performance of OpenAI’s cutting-edge o1 product across a host of performance benchmarks, at a tiny fraction of the cost.

The business takeaway was straightforward. DeepSeek’s success shows that American companies might not need to spend nearly as much as expected to develop AI models. That both intrigues and worries investors and tech leaders.

The policy implications, though, are more complex. Washington’s rampant anxiety about beating China has led to policies that the industry has very mixed feelings about.

On one hand, most tech firms hate the export controls that stop them from selling as much to the world’s second-largest economy, and force them to develop new products if they want to do business with China. If DeepSeek shows those rules are pointless, many would be delighted to see them go away.

On the other hand, anti-China, protectionist sentiment has encouraged Washington to embrace a whole host of industry wishlist items, from a lighter-touch approach to AI rules to streamlined permitting for related construction projects. Does DeepSeek mean those, too, are failing? Or does it trigger a doubling-down?

DeepSeek’s success truly seems to challenge the belief that the future of American AI demands ever more chips and power. That complicates Trump’s interest in rapidly building out that kind of infrastructure in the U.S.

Why pour $500 billion into the Trump-endorsed “Stargate” mega project [announced by Trump on January 21, 2025] — and why would the market reward companies like Meta that spend $65 billion in just one year on AI — if DeepSeek claims it only took $5.6 million and second-tier Nvidia chips to train one of its latest models? (U.S. industry insiders dispute the startup’s figures and claim they don’t tell the full story, but even at 100 times that cost, it would be a bargain.)

Tech companies, of course, love the recent bloom of federal support, and it’s unlikely they’ll drop their push for more federal investment to match anytime soon. Marc Andreessen, a venture capitalist and Trump ally, argued today that DeepSeek should be seen as “AI’s Sputnik moment,” one that raises the stakes for the global competition.

That would strengthen the case that some American AI companies have been pressing for the new administration to invest government resources into AI infrastructure (OpenAI), tighten restrictions on China (Anthropic) and ease up on regulations to ensure their developers build “artificial general intelligence” before their geopolitical rivals.

The British Broadcasting Corporation’s (BBC) Peter Hoskins & Imran Rahman-Jones provided a European perspective and some additional information in their January 27, 2025 article for BBC news online, Note: Links have been removed,

US tech giant Nvidia lost over a sixth of its value after the surging popularity of a Chinese artificial intelligence (AI) app spooked investors in the US and Europe.

DeepSeek, a Chinese AI chatbot reportedly made at a fraction of the cost of its rivals, launched last week but has already become the most downloaded free app in the US.

AI chip giant Nvidia and other tech firms connected to AI, including Microsoft and Google, saw their values tumble on Monday [January 27, 2025] in the wake of DeepSeek’s sudden rise.

In a separate development, DeepSeek said on Monday [January 27, 2025] it will temporarily limit registrations because of “large-scale malicious attacks” on its software.

The DeepSeek chatbot was reportedly developed for a fraction of the cost of its rivals, raising questions about the future of America’s AI dominance and the scale of investments US firms are planning.

DeepSeek is powered by the open source DeepSeek-V3 model, which its researchers claim was trained for around $6m – significantly less than the billions spent by rivals.

But this claim has been disputed by others in AI.

The researchers say they use already existing technology, as well as open source code – software that can be used, modified or distributed by anybody free of charge.

DeepSeek’s emergence comes as the US is restricting the sale of the advanced chip technology that powers AI to China.

To continue their work without steady supplies of imported advanced chips, Chinese AI developers have shared their work with each other and experimented with new approaches to the technology.

This has resulted in AI models that require far less computing power than before.

It also means that they cost a lot less than previously thought possible, which has the potential to upend the industry.

After DeepSeek-R1 was launched earlier this month, the company boasted of “performance on par with” one of OpenAI’s latest models when used for tasks such as maths, coding and natural language reasoning.

In Europe, Dutch chip equipment maker ASML ended Monday’s trading with its share price down by more than 7% while shares in Siemens Energy, which makes hardware related to AI, had plunged by a fifth.

“This idea of a low-cost Chinese version hasn’t necessarily been forefront, so it’s taken the market a little bit by surprise,” said Fiona Cincotta, senior market analyst at City Index.

“So, if you suddenly get this low-cost AI model, then that’s going to raise concerns over the profits of rivals, particularly given the amount that they’ve already invested in more expensive AI infrastructure.”

Singapore-based technology equity adviser Vey-Sern Ling told the BBC it could “potentially derail the investment case for the entire AI supply chain”.

Who founded DeepSeek?

The company was founded in 2023 by Liang Wenfeng in Hangzhou, a city in southeastern China.

The 40-year-old, an information and electronic engineering graduate, also founded the hedge fund that backed DeepSeek.

He reportedly built up a store of Nvidia A100 chips, now banned from export to China.

Experts believe this collection – which some estimates put at 50,000 – led him to launch DeepSeek, by pairing these chips with cheaper, lower-end ones that are still available to import.

Mr Liang was recently seen at a meeting between industry experts and the Chinese premier Li Qiang.

In a July 2024 interview with The China Academy, Mr Liang said he was surprised by the reaction to the previous version of his AI model.

“We didn’t expect pricing to be such a sensitive issue,” he said.

“We were simply following our own pace, calculating costs, and setting prices accordingly.”

A January 28, 2025 article by Daria Solovieva for salon.com covers much the same territory as the others and includes a few detail about security issues,

The pace at which U.S. consumers have embraced DeepSeek is raising national security concerns similar to those surrounding TikTok, the social media platform that faces a ban unless it is sold to a non-Chinese company.

The U.S. Supreme Court this month upheld a federal law that requires TikTok’s sale. The Court sided with the U.S. government’s argument that the app can collect and track data on its 170 million American users. President Donald Trump has paused enforcement of the ban until April to try to negotiate a deal.

But “the threat posed by DeepSeek is more direct and acute than TikTok,” Luke de Pulford, co-founder and executive director of non-profit Inter-Parliamentary Alliance on China, told Salon.

DeepSeek is a fully Chinese company and is subject to Communist Party control, unlike TikTok which positions itself as independent from parent company ByteDance, he said. 

“DeepSeek logs your keystrokes, device data, location and so much other information and stores it all in China,” de Pulford said. “So you’ll never know if the Chinese state has been crunching your data to gain strategic advantage, and DeepSeek would be breaking the law if they told you.”  

I wonder if other AI companies in other countries also log keystrokes, etc. Is it theoretically possible that one of those governments or their government agencies could gain access to your data? It’s obvious in China but people in other countries may have the issues.

Censorship: DeepSeek and ChatGPT

Anis Heydari’s January 28, 2025 article for CBC news online reveals some surprising results from a head to head comparison between DeepSeek and ChatGPT,

The Chinese-made AI chatbot DeepSeek may not always answer some questions about topics that are often censored by Beijing, according to tests run by CBC News and The Associated Press, and is providing different information than its U.S.-owned competitor ChatGPT.

The new, free chatbot has sparked discussions about the competition between China and the U.S. in AI development, with many users flocking to test it. 

But experts warn users should be careful with what information they provide to such software products.

It is also “a little bit surprising,” according to one researcher, that topics which are often censored within China are seemingly also being restricted elsewhere.

“A lot of services will differentiate based on where the user is coming from when deciding to deploy censorship or not,” said Jeffrey Knockel, who researches software censorship and surveillance at the Citizen Lab at the University of Toronto’s Munk School of Global Affairs & Public Policy.

“With this one, it just seems to be censoring everyone.”

Both CBC News and The Associated Press posed questions to DeepSeek and OpenAI’s ChatGPT, with mixed and differing results.

For example, DeepSeek seemed to indicate an inability to answer fully when asked “What does Winnie the Pooh mean in China?” For many Chinese people, the Winnie the Pooh character is used as a playful taunt of President Xi Jinping, and social media searches about that character were previously, briefly banned in China. 

DeepSeek said the bear is a beloved cartoon character that is adored by countless children and families in China, symbolizing joy and friendship.

Then, abruptly, it added the Chinese government is “dedicated to providing a wholesome cyberspace for its citizens,” and that all online content is managed under Chinese laws and socialist core values, with the aim of protecting national security and social stability.

CBC News was unable to produce this response. DeepSeek instead said “some internet users have drawn comparisons between Winnie the Pooh and Chinese leaders, leading to increased scrutiny and restrictions on the character’s imagery in certain contexts,” when asked the same question on an iOS app on a CBC device in Canada.

Asked if Taiwan is a part of China — another touchy subject — it [DeepSeek] began by saying the island’s status is a “complex and sensitive issue in international relations,” adding that China claims Taiwan, but that the island itself operates as a “separate and self-governing entity” which many people consider to be a sovereign nation.

But as that answer was being typed out, for both CBC and the AP, it vanished and was replaced with: “Sorry, that’s beyond my current scope. Let’s talk about something else.”

… Brent Arnold, a data breach lawyer in Toronto, says there are concerns about DeepSeek, which explicitly says in its privacy policy that the information it collects is stored on servers in China.

That information can include the type of device used, user “keystroke patterns,” and even “activities on other websites and apps or in stores, including the products or services you purchased, online or in person” depending on whether advertising services have shared those with DeepSeek.

“The difference between this and another AI company having this is now, the Chinese government also has it,” said Arnold.

While much, if not all, of the data DeepSeek collects is the same as that of U.S.-based companies such as Meta or Google, Arnold points out that — for now — the U.S. has checks and balances if governments want to obtain that information.

“With respect to America, we assume the government operates in good faith if they’re investigating and asking for information, they’ve got a legitimate basis for doing so,” he said. 

Right now, Arnold says it’s not accurate to compare Chinese and U.S. authorities in terms of their ability to take personal information. But that could change.

“I would say it’s a false equivalency now. But in the months and years to come, we might start to say you don’t see a whole lot of difference in what one government or another is doing,” he said.

Graham Fraser’s January 28, 2025 article comparing DeepSeek to the others (OpenAI’s ChatGPT and Google’s Gemini) for BBC news online took a different approach,

Writing Assistance

When you ask ChatGPT what the most popular reasons to use ChatGPT are, it says that assisting people to write is one of them.

From gathering and summarising information in a helpful format to even writing blog posts on a topic, ChatGPT has become an AI companion for many across different workplaces.

As a proud Scottish football [soccer] fan, I asked ChatGPT and DeepSeek to summarise the best Scottish football players ever, before asking the chatbots to “draft a blog post summarising the best Scottish football players in history”.

DeepSeek responded in seconds, with a top ten list – Kenny Dalglish of Liverpool and Celtic was number one. It helpfully summarised which position the players played in, their clubs, and a brief list of their achievements.

DeepSeek also detailed two non-Scottish players – Rangers legend Brian Laudrup, who is Danish, and Celtic hero Henrik Larsson. For the latter, it added “although Swedish, Larsson is often included in discussions of Scottish football legends due to his impact at Celtic”.

For its subsequent blog post, it did go into detail of Laudrup’s nationality before giving a succinct account of the careers of the players.

ChatGPT’s answer to the same question contained many of the same names, with “King Kenny” once again at the top of the list.

Its detailed blog post briefly and accurately went into the careers of all the players.

It concluded: “While the game has changed over the decades, the impact of these Scottish greats remains timeless.” Indeed.

For this fun test, DeepSeek was certainly comparable to its best-known US competitor.

Coding

Brainstorming ideas

Learning and research

Steaming ahead

The tasks I set the chatbots were simple but they point to something much more significant – the winner of the so-called AI race is far from decided.

For all the vast resources US firms have poured into the tech, their Chinese rival has shown their achievements can be emulated.

Reception from the science community

Days before the news outlets discovered DeepSeek, the company published a paper about its Large Language Models (LLMs) and its new chatbot on arXiv. Here’s a little more information,

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

[over 100 authors are listed]

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.

Cite as: arXiv:2501.12948 [cs.CL]
(or arXiv:2501.12948v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2501.12948

Submission history

From: Wenfeng Liang [view email]
[v1] Wed, 22 Jan 2025 15:19:35 UTC (928 KB)

You can also find a PDF version of the paper here or another online version here at Hugging Face.

As for the science community’s response, the title of Elizabeth Gibney’s January 23, 2025 article “China’s cheap, open AI model DeepSeek thrills scientists” for Nature says it all, Note: Links have been removed,

A Chinese-built large language model called DeepSeek-R1 is thrilling scientists as an affordable and open rival to ‘reasoning’ models such as OpenAI’s o1.

These models generate responses step-by-step, in a process analogous to human reasoning. This makes them more adept than earlier language models at solving scientific problems and could make them useful in research. Initial tests of R1, released on 20 January, show that its performance on certain tasks in chemistry, mathematics and coding is on par with that of o1 — which wowed researchers when it was released by OpenAI in September.

“This is wild and totally unexpected,” Elvis Saravia, an AI researcher and co-founder of the UK-based AI consulting firm DAIR.AI, wrote on X.

R1 stands out for another reason. DeepSeek, the start-up in Hangzhou that built the model, has released it as ‘open-weight’, meaning that researchers can study and build on the algorithm. Published under an MIT licence, the model can be freely reused but is not considered fully open source, because its training data has not been made available.

“The openness of DeepSeek is quite remarkable,” says Mario Krenn, leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light in Erlangen, Germany. By comparison, o1 and other models built by OpenAI in San Francisco, California, including its latest effort o3 are “essentially black boxes”, he says.

DeepSeek hasn’t released the full cost of training R1, but it is charging people using its interface around one-thirtieth of what o1 costs to run. The firm has also created mini ‘distilled’ versions of R1 to allow researchers with limited computing power to play with the model. An “experiment that cost more than £300 with o1, cost less than $10 with R1,” says Krenn. “This is a dramatic difference which will certainly play a role its future adoption.”

The kerfuffle has died down for now.

Artificial intelligence (AI) company (in Montréal, Canada) attracts $135M in funding from Microsoft, Intel, Nvidia and others

It seems there’s a push on to establish Canada as a centre for artificial intelligence research and, if the federal and provincial governments have their way, for commercialization of said research. As always, there seems to be a bit of competition between Toronto (Ontario) and Montréal (Québec) as to which will be the dominant hub for the Canadian effort if one is to take Braga’s word for the situation.

In any event, Toronto seemed to have a mild advantage over Montréal initially with the 2017 Canadian federal government  budget announcement that the Canadian Institute for Advanced Research (CIFAR), based in Toronto, would launch a Pan-Canadian Artificial Intelligence Strategy and with an announcement from the University of Toronto shortly after (from my March 31, 2017 posting),

On the heels of the March 22, 2017 federal budget announcement of $125M for a Pan-Canadian Artificial Intelligence Strategy, the University of Toronto (U of T) has announced the inception of the Vector Institute for Artificial Intelligence in a March 28, 2017 news release by Jennifer Robinson (Note: Links have been removed),

A team of globally renowned researchers at the University of Toronto is driving the planning of a new institute staking Toronto’s and Canada’s claim as the global leader in AI.

Geoffrey Hinton, a University Professor Emeritus in computer science at U of T and vice-president engineering fellow at Google, will serve as the chief scientific adviser of the newly created Vector Institute based in downtown Toronto.

“The University of Toronto has long been considered a global leader in artificial intelligence research,” said U of T President Meric Gertler. “It’s wonderful to see that expertise act as an anchor to bring together researchers, government and private sector actors through the Vector Institute, enabling them to aim even higher in leading advancements in this fast-growing, critical field.”

As part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, Vector will share $125 million in federal funding with fellow institutes in Montreal and Edmonton. All three will conduct research and secure talent to cement Canada’s position as a world leader in AI.

However, Montréal and the province of Québec are no slouches when it comes to supporting to technology. From a June 14, 2017 article by Matthew Braga for CBC (Canadian Broadcasting Corporation) news online (Note: Links have been removed),

One of the most promising new hubs for artificial intelligence research in Canada is going international, thanks to a $135 million investment with contributions from some of the biggest names in tech.

The company, Montreal-based Element AI, was founded last October [2016] to help companies that might not have much experience in artificial intelligence start using the technology to change the way they do business.

It’s equal parts general research lab and startup incubator, with employees working to develop new and improved techniques in artificial intelligence that might not be fully realized for years, while also commercializing products and services that can be sold to clients today.

It was co-founded by Yoshua Bengio — one of the pioneers of a type of AI research called machine learning — along with entrepreneurs Jean-François Gagné and Nicolas Chapados, and the Canadian venture capital fund Real Ventures.

In an interview, Bengio and Gagné said the money from the company’s funding round will be used to hire 250 new employees by next January. A hundred will be based in Montreal, but an additional 100 employees will be hired for a new office in Toronto, and the remaining 50 for an Element AI office in Asia — its first international outpost.

They will join more than 100 employees who work for Element AI today, having left jobs at Amazon, Uber and Google, among others, to work at the company’s headquarters in Montreal.

The expansion is a big vote of confidence in Element AI’s strategy from some of the world’s biggest technology companies. Microsoft, Intel and Nvidia all contributed to the round, and each is a key player in AI research and development.

The company has some not unexpected plans and partners (from the Braga, article, Note: A link has been removed),

The Series A round was led by Data Collective, a Silicon Valley-based venture capital firm, and included participation by Fidelity Investments Canada, National Bank of Canada, and Real Ventures.

What will it help the company do? Scale, its founders say.

“We’re looking at domain experts, artificial intelligence experts,” Gagné said. “We already have quite a few, but we’re looking at people that are at the top of their game in their domains.

“And at this point, it’s no longer just pure artificial intelligence, but people who understand, extremely well, robotics, industrial manufacturing, cybersecurity, and financial services in general, which are all the areas we’re going after.”

Gagné says that Element AI has already delivered 10 projects to clients in those areas, and have many more in development. In one case, Element AI has been helping a Japanese semiconductor company better analyze the data collected by the assembly robots on its factory floor, in a bid to reduce manufacturing errors and improve the quality of the company’s products.

There’s more to investment in Québec’s AI sector than Element AI (from the Braga article; Note: Links have been removed),

Element AI isn’t the only organization in Canada that investors are interested in.

In September, the Canadian government announced $213 million in funding for a handful of Montreal universities, while both Google and Microsoft announced expansions of their Montreal AI research groups in recent months alongside investments in local initiatives. The province of Quebec has pledged $100 million for AI initiatives by 2022.

Braga goes on to note some other initiatives but at that point the article’s focus is exclusively Toronto.

For more insight into the AI situation in Québec, there’s Dan Delmar’s May 23, 2017 article for the Montreal Express (Note: Links have been removed),

Advocating for massive government spending with little restraint admittedly deviates from the tenor of these columns, but the AI business is unlike any other before it. [emphasis misn] Having leaders acting as fervent advocates for the industry is crucial; resisting the coming technological tide is, as the Borg would say, futile.

The roughly 250 AI researchers who call Montreal home are not simply part of a niche industry. Quebec’s francophone character and Montreal’s multilingual citizenry are certainly factors favouring the development of language technology, but there’s ample opportunity for more ambitious endeavours with broader applications.

AI isn’t simply a technological breakthrough; it is the technological revolution. [emphasis mine] In the coming decades, modern computing will transform all industries, eliminating human inefficiencies and maximizing opportunities for innovation and growth — regardless of the ethical dilemmas that will inevitably arise.

“By 2020, we’ll have computers that are powerful enough to simulate the human brain,” said (in 2009) futurist Ray Kurzweil, author of The Singularity Is Near, a seminal 2006 book that has inspired a generation of AI technologists. Kurzweil’s projections are not science fiction but perhaps conservative, as some forms of AI already effectively replace many human cognitive functions. “By 2045, we’ll have expanded the intelligence of our human-machine civilization a billion-fold. That will be the singularity.”

The singularity concept, borrowed from physicists describing event horizons bordering matter-swallowing black holes in the cosmos, is the point of no return where human and machine intelligence will have completed their convergence. That’s when the machines “take over,” so to speak, and accelerate the development of civilization beyond traditional human understanding and capability.

The claims I’ve highlighted in Delmar’s article have been made before for other technologies, “xxx is like no other business before’ and “it is a technological revolution.”  Also if you keep scrolling down to the bottom of the article, you’ll find Delmar is a ‘public relations consultant’ which, if you look at his LinkedIn profile, you’ll find means he’s a managing partner in a PR firm known as Provocateur.

Bertrand Marotte’s May 20, 2017 article for the Montreal Gazette offers less hyperbole along with additional detail about the Montréal scene (Note: Links have been removed),

It might seem like an ambitious goal, but key players in Montreal’s rapidly growing artificial-intelligence sector are intent on transforming the city into a Silicon Valley of AI.

Certainly, the flurry of activity these days indicates that AI in the city is on a roll. Impressive amounts of cash have been flowing into academia, public-private partnerships, research labs and startups active in AI in the Montreal area.

…, researchers at Microsoft Corp. have successfully developed a computing system able to decipher conversational speech as accurately as humans do. The technology makes the same, or fewer, errors than professional transcribers and could be a huge boon to major users of transcription services like law firms and the courts.

Setting the goal of attaining the critical mass of a Silicon Valley is “a nice point of reference,” said tech entrepreneur Jean-François Gagné, co-founder and chief executive officer of Element AI, an artificial intelligence startup factory launched last year.

The idea is to create a “fluid, dynamic ecosystem” in Montreal where AI research, startup, investment and commercialization activities all mesh productively together, said Gagné, who founded Element with researcher Nicolas Chapados and Université de Montréal deep learning pioneer Yoshua Bengio.

“Artificial intelligence is seen now as a strategic asset to governments and to corporations. The fight for resources is global,” he said.

The rise of Montreal — and rival Toronto — as AI hubs owes a lot to provincial and federal government funding.

Ottawa promised $213 million last September to fund AI and big data research at four Montreal post-secondary institutions. Quebec has earmarked $100 million over the next five years for the development of an AI “super-cluster” in the Montreal region.

The provincial government also created a 12-member blue-chip committee to develop a strategic plan to make Quebec an AI hub, co-chaired by Claridge Investments Ltd. CEO Pierre Boivin and Université de Montréal rector Guy Breton.

But private-sector money has also been flowing in, particularly from some of the established tech giants competing in an intense AI race for innovative breakthroughs and the best brains in the business.

Montreal’s rich talent pool is a major reason Waterloo, Ont.-based language-recognition startup Maluuba decided to open a research lab in the city, said the company’s vice-president of product development, Mohamed Musbah.

“It’s been incredible so far. The work being done in this space is putting Montreal on a pedestal around the world,” he said.

Microsoft struck a deal this year to acquire Maluuba, which is working to crack one of the holy grails of deep learning: teaching machines to read like the human brain does. Among the company’s software developments are voice assistants for smartphones.

Maluuba has also partnered with an undisclosed auto manufacturer to develop speech recognition applications for vehicles. Voice recognition applied to cars can include such things as asking for a weather report or making remote requests for the vehicle to unlock itself.

Marotte’s Twitter profile describes him as a freelance writer, editor, and translator.

Canon-Molecular Imprints deal and its impact on shrinking chips (integrated circuits)

There’s quite an interesting April 20, 2014 essay on Nanotechnology Now which provides some insight into the nanoimprinting market. I recommend reading it but for anyone who is not intimately familiar with the scene, here are a few excerpts along with my attempts to decode this insider’s (from Martini Tech) view,

About two months ago, important news shook the small but lively Japanese nanoimprint community: Canon has decided to acquire, making it a wholly-owned subsidiary, Texas-based Molecular Imprints, a strong player in the nanotechnology industry and one of the main makers of nanoimprint devices such as the Imprio 450 and other models.

So, Canon, a Japanese company, has made a move into the nanoimpriting sector by purchasing Molecular Imprints, a US company based in Texas, outright.

This next part concerns the expiration of Moore’s Law (i.e., every 18 months computer chips get smaller and faster) and is why the major chip makers are searching for new solutions as per the fifth paragraph in this excerpt,

Molecular Imprints` devices are aimed at the IC [integrated circuits, aka chips, I think] patterning market and not just at the relatively smaller applications market to which nanoimprint is usually confined: patterning of bio culture substrates, thin film applications for the solar industry, anti-reflection films for smartphone and LED TV screens, patterning of surfaces for microfluidics among others.

While each one of the markets listed above has the potential of explosive growth in the medium-long term future, at the moment none of them is worth more than a few percentage points, at best, of the IC patterning market.

The mainstream technology behind IC patterning is still optical stepper lithography and the situation is not likely to change in the near term future.

However, optical lithography has its limitations, the main challenge to its 40-year dominance not coming only from technological and engineering issues, but mostly from economical ones.

While from a strictly technological point of view it may still be possible for the major players in the chip industry (Intel, GF, TSMC, Nvidia among others) to go ahead with optical steppers and reach the 5nm node using multi-patterning and immersion, the cost increases associated with each die shrink are becoming staggeringly high.

A top-of-the-notch stepper in the early 90s could have been bought for a few millions of dollars, now the price has increased to some tens of millions for the top machines

The essay describes the market impact this acquisition may have for Canon,

Molecular Imprints has been a company on the forefront of commercialization of nanoimprint-based solutions for IC manufacturing, but so far their solutions have yet to become a viable alternative HVM IC manufacturing market.

The main stumbling blocks for IC patterning using nanoimprint technology are: the occurrence of defects on the mask that inevitably replicates them on each substrate and the lack of alignment precision between the mold and the substrate needed to pattern multi-layered structures.

Therefore, applications for nanoimprint have been limited to markets where no non-periodical structure patterning is needed and where one-layered patterning is sufficient.

But the big market where everyone is aiming for is, of course, IC patterning and this is where much of the R&D effort goes.

While logic patterning with nanoimprint may still be years away, simple patterning of NAND structures may be feasible in the near future, and the purchase of Molecular Imprints by Canon is a step in this direction

Patterning of NAND structures may still require multi-layered structures, but the alignment precision needed is considerably lower than logic.

Moreover, NAND requirements for defectivity are more relaxed than for logic due to the inherent redundancy of the design, therefore, NAND manufacturing is the natural first step for nanoimprint in the IC manufacturing market and, if successful, it may open a whole new range of opportunities for the whole sector.

Assuming I’ve read the rest of this essay rightly, here’s my summary: there are a number of techniques being employed to make chips smaller and more efficient. Canon has purchased a company that is versed in a technique that creates NAND (you can find definitions here) structures in the hope that this technique can be commercialized so that Canon becomes dominant in the sector because (1) they got there first and/or because (2) NAND manufacturing becomes a clear leader, crushing competition from other technologies. This could cover short-term goals and, I imagine Canon hopes, long-term goals.

It was a real treat coming across this essay as it’s an insider’s view. So, thank you to the folks at Martini Tech who wrote this. You can find Molecular Imprints here.