Monthly Archives: July 2024

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Sundar Pichai and Daniel Sank, right, with one of Google's Quantum Computers in the Santa Barbara lab, in California.Handout ./Reuters

Christian Weedbrook is founder of the quantum technologies company Xanadu.

Data centres are the backbone of all of our digital lives. They are used behind the scenes by most companies to store and process information for streaming services, e-commerce, search, social media, and more recently artificial intelligence (AI) tools such as ChatGPT.

In essence, a data centre is a collection of computer server racks housed in a physically secure facility requiring large amounts of electricity and water. The server racks are connected to each other by fibre optic cables, shuttling information between them.

Its a domain we dont often think of, even if we all rely on services that originate from it. And in this domain, a crisis is coming. The growth of what the industry calls compute another way of describing the processing power of the data centres is unsustainable as it demands increasingly vast amounts of energy and resources and exacerbates environmental challenges.

Along with this extreme usage comes significant problems. Data centres currently count for 1.5 per cent of the worlds energy consumption and this is projected to increase. All of this is leading to concerns both here in Canada and abroad that we dont have enough energy for our future data centre ambitions. One of the biggest drivers of this increase in data centre usage is AI; by 2027, it is estimated that AI applications will account for 20 per cent to 25 per cent of all such usage.

A number of solutions are being considered. These include powering data centres using nuclear, sun-chasing initiatives and the more traditional approaches of wind and solar power. But none of these solutions comes close.

Most forms of renewable energy are intermittent and can only be built on specific sites. Nuclear energy has a dangerous reputation, making widespread implementation difficult. And these solutions do nothing to address the underlying efficiency of the computations happening in these data centres, which are bounded by the domain of classical physics.

There is only one real solution: Quantum computing goes beyond this and enables exponential improvements in efficiency, allowing far more to be done per kilowatt-hour (kWh) of energy on certain applications.

A quantum computer is a computer that can perform certain important problems exponentially faster than normal computers by leveraging the properties of quantum physics. Small quantum computers exist today, but none exist at the scale of a data centre (scaling up while keeping their quantum-ness is hard).

Once built, a data centre containing quantum server racks will be half a football field in size, and networked using fibre optics. A single quantum data centre will have energy consumption similar to a single traditional data centre. But one quantum data centre, for key applications, will be equivalent to hundreds or thousands of standard data centres.

But by far the biggest energy savings will come from the innovation and discoveries of the quantum data centres helping to find more efficient ways of doing things. Such discoveries and others like it would perhaps take a century to achieve using traditional tools. A quantum data centre could deliver them much sooner.

It is only these data-centre-sized quantum computers that will be able to solve problems such as developing novel catalysts for the synthesis of synthetic hydrocarbons, new carbon capture and sequestration solutions, discovering new materials to create next-generation batteries.

The idea of quantum computing as the future of data centres has only recently begun to pick up steam. There have been significant investments by the Australian government to build a quantum data centre in Brisbane, and by the U.S. state of Illinois for a cryogenics facility (a key component of a quantum data centre) among other infrastructure in Chicago.

It is imperative that Canada follows suit; otherwise, from an economic-independence, national-security and energy point of view, it will be left behind.

Fortunately the foundation has been set. Canada has a long history of creating and supporting the talent in quantum computing with $1-billion being invested in quantum science between 2012 and 2022. Furthermore, in the 2021 federal budget, $360-million was announced for Canadas National Quantum Strategy, and the Council of Canadian Academies estimates that quantum technologies could account for 3 per cent of Canadas GDP by 2045. In its April budget, the Canadian government announced it will invest $2.4-billion in AI infrastructure to catch up with other countries a field in which we were originally the leaders.

Lets not make the same mistakes for quantum and invest in quantum data centres early.

Link:

Opinion: As artificial intelligence rises, data-centre costs spiral. Quantum is the solution - The Globe and Mail

After 28 years spent literally or figuratively in Silicon Valley, Ive grown increasingly concerned about AIs potential to cause widespread, permanent unemployment. So I clambered out of the valley and into the Ivory Tower to share my fears with Israels leading economists. Turns out, the view from the Tower is wildly different from the Valley. Not a good thing.

The view from the Valley is that AI will achieve human-level intelligence within a few years, leading to rising unemployment.

Leopold Aschenbrenner, a former superintelligence researcher at OpenAI, says: We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word... That doesnt require believing in sci-fi; it just requires believing in straight lines on a graph.

Avital Balwit, chief of staff at Anthropic, says: I am 25. These next three years might be the last few years that I work. I stand at the edge of a technological development that seems likely, should it arrive, to end employment as I know it.

Consequently, tech rivals like Sam Altman, Mark Zuckerberg, and Elon Musk agree on the need to prepare for permanent mass unemployment with universal basic income.

Whos right? I hope its the economists, but Id wager on the technologists for three reasons:

The Great Crash of 1929 saw 90% of the stock markets value vanish. A few days prior, the prominent economist, Irving Fisher, pronounced that stock prices have reached what looks like a permanently high plateau. Economists have missed every crash since, prompting the IMF to conclude that economists are notoriously poor at spotting a crisis coming, and that there is little evidence that forecasts at horizons of two to five years contain much predictive content.

Add technology to the mix, and the economists record goes from notoriously poor to comical. McKinsey, for example, pronounced that mobile phones will never be a mass market, while Nobel-winning economist Paul Krugman predicted the Internets impact would be no greater than the fax machines. In retrospect, Krugman conceded that most macroeconomics of the last 30 years was spectacularly useless at best and positively harmful at worst.

In contrast, technologists have an impressive record in predicting key milestones decades in advance. Writing in the 80s and 90s, computer scientist Ray Kurzweil accurately forecasted to within a couple of years the arrival of the Internet, smartphones, voice recognition, self-driving cars, and virtual reality. In 1990, he predicted that human-level AI would arrive in 2029, a prognostication that has since catapulted from preposterous to prescient.

The difference? The economy is governed by the butterfly effect, while technology is governed by Moores Law, which posits that computational power doubles every two years.

Kurzweil calculated roughly how much compute is needed for each milestone he envisaged, and predicted their realization at the point where these needs intersect with the exponential progression of Moores Law. His track record isnt perfect, but no economist can hold a candle to it.

The second reason is that I find the economists arguments unconvincing. One explanation they offer for their equanimity is that, despite the rise of AI, unemployment has not risen at all. Yet nobody expected generative AI to move the macroeconomic needle so quickly; and in smaller, bellwether sectors, the needle is buried in the red. Freelance job boards, for example, have seen massive drops in demand for writers, web developers, graphic designers, and engineers.

More importantly, when a macroeconomic signal does emerge, I expect it to show that AI augments people rather than making them dispensable right up to the point where it dispenses with them. By way of analogy, consider the story of Bob, a mediocre manager. In Act 1, Bob hires Sam, a wunderkind, who boosts the quality and quantity of Bobs deliverables. The big boss is happy. In Act 2, Sam has learned the ropes and is able to fly solo. Bob looks expensive and incompetent by comparison. In Act 3, Bob gets canned. The End.

The second explanation offered for their poise is that weve seen this movie before and it has a happy ending. Theres full employment today even though 99% of the pre-industrial jobs have vanished. Stay calm and carry on.

But, unlike the industrial revolution, where machines replaced our brawn and we found jobs using our brains, todays machines are set to outperform our brains. What part of our being will we use to earn a living once that happens?!

Oh, and the industrial revolution triggered a century of catastrophic hardship, including mass displacement of skilled workers, and a rush for raw materials that fueled colonialism and wars that claimed tens of millions of lives. Not a movie we want to take our kids to.

AI is approaching human-level performance across the spectrum of intellectual endeavors. At its current rate of progress, AI will soon project onto your screen a talking-head that will shape-shift to be your lawyer, graphic designer, doctor, software engineer you name it. As a rule of thumb, therefore, we should assume that any job that can be done over Zoom today can be done by an AI tomorrow. Ive encountered no credible counterargument to this.

Which leads to my second rule of thumb: employers will replace humans with AI whenever theres a buck to be made. That is the true lesson from the industrial revolution. Ive heard no credible counterargument to this either.

Silicon Valley has tunnel vision. Taken together, you see why, on balance, Id wager on Silicon Valleys predictions for what AI will soon do. But Id never trust the Valley to tell society how to adapt or prepare. When it comes to the societal implications of its technologies, Silicon Valley is spectacularly useless at best and positively harmful at worst. Indeed, recent years have seen devastating unintended consequences of the Valleys innovations, from skyrocketing teen suicides to the radicalization of our society.

Tech titans casually toss out slogans like universal basic income as though UBI is a panacea for the coming age. I favor UBI, but they seem oblivious to the monumental challenges it entails, including staggering costs, elusive sources, and complex second-order effects. Moreover, the societal problems born of mass unemployment wont end with any universal income, let alone a basic one. We need the full engagement by the Ivory Tower, leveraging the expertise of economists, political scientists, and sociologists to navigate these intricate issues.

In the coming years, AI is likely to achieve superhuman intelligence and drive rising unemployment. To my knowledge, no one has articulated a convincing case for how such AI can coexist with full employment, and so we must prepare accordingly. Yet those who see the gathering storm are ill-equipped to prepare for it, while those who know how to prepare dont see it coming.

Aesops Fable tells of two men, one blind, the other lame. Alone they cant survive, so the lame man climbs onto the blind mans back, and united they can navigate safely.

The moral is clear: we cant rely solely on economists predictions or Silicon Valleys optimism. We need technologists who understand AIs potential, economists who can model its impacts, and policymakers who can implement solutions.

The clock is ticking. Our choices today will shape whether AI becomes humanitys greatest achievement or, like the golem of Jewish lore, a force that turns on its master.

The writer is CEO and co-founder of Lemonade (NYSE: LMND), and chairman of the MOSAIC Policy Institute, whose mission it is to ensure that Artificial Intelligence benefits all of Israels society.

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Are Elon Musk's concerns of AI-surged unemployment a real concern? - The Jerusalem Post

As tech giants worldwide scramble to dominate the artificial intelligence (AI) chip market, South Koreas SK Hynix is making a $74.6 billion wager that could redefine the semiconductor industry and perhaps the future of computing itself.

The company reportedly said it will spend the money through 2028 to strengthen its chips business, with a focus on AI.

But SK Hynix isnt alone in this high-stakes gamble. Across the globe, tech giants and upstarts are pouring unprecedented sums into AI chip development, sparking what some industry insiders call a modern-day gold rush.

The rise of AI-specific chips is poised to revolutionize commerce across sectors. These specialized processors, optimized for machine learning tasks, promise to dramatically accelerate AI applications in everything from autonomous vehicles to personalized marketing. As businesses increasingly rely on AI to drive decision-making and enhance customer experiences, the demand for powerful, efficient AI chips is expected to surge, potentially reshaping supply chains and creating new economic powerhouses.

The AI boom has sparked an unexpected consequence: a global scramble for specialized chips. Nvidia, long known for gaming hardware, has become the unlikely kingmaker of AI development. Its advanced GPUs now power the most sophisticated AI models, propelling the company to a multitrillion-dollar valuation. But demand sometimes outstrips supply.

This scarcity is reshaping the tech landscape. Giants like Microsoft, Meta and Google are now developing proprietary AI processors, seeking to reduce their reliance on Nvidia. Meanwhile, chipmakers AMD and Intel are pouring resources into competing products.

As AI applications proliferate across industries, from healthcare to finance, control of this critical hardware has become a strategic imperative. With billions in investments and potential market dominance at stake, the AI chip race is rapidly becoming the next frontier in computing.

The numbers are eye-popping. In the U.S., Nvidias market cap has skyrocketed past $3 trillion on the strength of its AI-focused GPUs. Apple has reportedly been working on developing chips designed to run AI software in data centers.

Meta recently launched a new version of its custom AI chips, which perform better than the previous generation and help power ads on Facebook and Instagram, the company said. Even cloud computing behemoths like Google and Amazon are designing their own custom AI chips to gain an edge in the race for faster, more efficient machine learning.

This frenzy of investment comes as nations jockey for position in what many see as a critical technology for the future. Feeling pressure from rivals like Taiwan and the United States, South Korea recently unveiled a 26 trillion won ($19 billion) support package for its domestic chip industry.

For SK Group, the parent company of SK Hynix, the AI push is part of a broader strategy to revitalize its fortunes after a bruising period in the memory chip market. The conglomerate is streamlining its sprawling empire of over 175 subsidiaries while focusing on what it calls the AI value chain from high-bandwidth memory chips to AI data centers and services.

While chip fabrication plants rise from former farmland and R&D budgets swell to historic highs, one thing is clear: AI is reshaping the silicon landscape. Whether this bet pays off in the long run remains to be seen, but for now, the industry mantra seems to be AI or bust.

The impact of AI chips on commerce will likely be profound and far-reaching. As these specialized processors become more powerful and energy-efficient, they will enable new AI applications that were previously impractical or impossible. As AI-powered solutions become increasingly sophisticated and ubiquitous, this could lead to significant disruptions in industries ranging from healthcare to finance.

However, the AI chip boom raises important questions about market concentration and technological dependence. As a handful of companies emerge as leaders in AI chip design and manufacturing, concerns about the potential for monopolistic practices and the vulnerability of global supply chains arise. Policymakers and business leaders must grapple with these challenges as they navigate the rapidly evolving landscape of AI chip technology and its impact on the global economy.

For all PYMNTS AI coverage, subscribe to the daily AINewsletter.

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How Many Chips Could You Buy With $74 Billion? - PYMNTS.com

Todays increasingly digitized business-to-business (B2B) tech stack has transformed the way companies do business. At the same time, it is also transforming the way companies get business.

Thats because, with the news last week (June 27) that accounts payable (AP) management firmMedius has introduced a pair of artificial intelligence (AI)-powered offerings, B2B firms are beginning to explore where else AI can be applied across their operations.

And companies across sectors are finding that from lead generation and automated sales, to personalized outreach and forecasting AI can create more effective and efficient outbound strategies, driving higher conversion rates and better customer relationships while saving on resource and labor costs.

AI offers powerful tools for analyzing data, predicting trends and automating tasks, streamlining operations and providing deeper insights into customer behavior and preferences. This allows for the development of more sophisticated and personalized marketing strategies.

Within the confines of an uncertain macro backdrop, staying ahead of the competition for B2B businesses requires embracing technology, and with a generational shift in B2B with younger buyers and Generation Z decision-makers, providing the frictionless and personalized digital experiences theyve come to expect is crucial.

But that doesnt mean that an automated B2B marketing program will be as easy as 1, 2, 3.

Read more: AIs Essential Use Cases Across B2B Operations

To capture the benefits of AI, businesses must integrate AI seamlessly into their existing processes. This includes determining what the company wants to achieve with its AI-driven marketing, whether its improving lead generation, increasing conversion rates, enhancing customer personalization, or optimizing campaign performance.

Recent PYMNTS Intelligence in the June report SMBs Race to Critical Mass on AI Usage found that 96% of small to medium-sized businesses (SMBs) that have tried AI tools see it as an effective method to streamline tasks.

As Andre Machicao, senior vice president atVisa Acceptance Solutions, andJosh Scheer, president and owner ofWhite Lotus Travel Design, explained to PYMNTS in a discussion posted June 28, SMBs can use AI to create marketing plans and product descriptions in minutes without a big marketing team to do it.

And by using automated AI solutions, B2B firms are able to personalize outreach efforts at scale. AI technology is able to optimize the content of the emails and manage the timing and frequency of outreach, maximizing the potential for successful connections.

Read more:The Power of Precision: Driving Revenue From B2B Customer Data

Automation is another area where AI is making an impact, streamlining various sales processes and freeing up valuable time for sales teams. AI tools can uncover trends, patterns and correlations within massive datasets that might be missed by human analysts.

Plus, while traditionally lead generation is labor intensive, reliant on manual research and broad outreach, AI is revolutionizing this domain by leveraging advanced algorithms to analyze vast amounts of data and identify potential leads with a high probability of conversion.

These insights enable companies to refine their targeting and messaging strategies, ensuring that their outreach efforts are based on solid data rather than intuition.

The moment you slice the world through the lens ofhistorical transactional behavior, you can then leverage a predictive GenAI framework and say something about the likelihood of those future transactions,Pecan CEO and Co-Founder Zohar Bronfman told PYMNTS. Its evolutionary in terms of how businesses can operate.

Still, it is crucial for firms to ensure that the AI tools they select can scale with their business and integrate seamlessly with existing customer relationship management platforms, marketing automation platforms, and data management systems.

AI has the potential to really move the needle for so much of the industry in terms of the ability to be more proactive with their buyers or suppliers, Nick Izquierdo, executive vice president of payments atBilltrust, told PYMNTS. And alongside that, the productivity AI brings is really driving more success satisfaction out of both sides of the equation.

For all PYMNTS AI coverage, subscribe to the daily AINewsletter.

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AI's Impact on B2B Marketing Spotlights Value of Automation - PYMNTS.com