Businesses are locked in an expensive AI ‘arms race’

Businesses are locked in an expensive AI ‘arms race’

There is no doubt we are in an AI arms race says Jon Collins.

He has worked in IT for 35 years in various roles, including as a software programmer, system manager and chief technology officer.

He is currently an industry analyst for research firm Gigaom.

The current arms race is fueled by the launch of ChatGPT in late 2022, Mr Collins said.

Since then, many such generative AI systems have appeared, and millions of people use them every day to create works of art, text or video.

For business leaders, the stakes are high. Generative AI systems are incredibly powerful tools that can digest more data in minutes than humans can in several lifetimes.

Suddenly company leaders are realizing what AI can allow them, and their competition, to achieve, Mr Collins explained.

“Fear and greed drive it,” he said. “And that creates an avalanche momentum.”

With the right training, a custom AI system can allow a company to leap ahead of its competitors with research findings, or by reducing costs by automating work that humans are currently doing.

In the pharmaceutical sector, firms are adapting AI to help them discover new compounds to treat diseases. But it is an expensive process.

“You need data scientists, and you need model engineers,” Mr. Collins explained.

Those scientists and engineers need to understand, at least to some extent, the pharmaceutical field in which AI will work.

And it doesn’t stop there. “You need infrastructure engineers who can build your AI platform,” he continued.

Highly skilled workers like this are not easy to come by.

Not enough people “understand how to make these systems, how to make them really perform, and how to solve some of the challenges going forward,” said Andrew Rogoyski, director of innovation at the Surrey Institute for People-Centred AI at the University of Surrey.

The pay for those who can take on this challenge has reached “ridiculous” levels, he added, because it’s so important.

“We could produce hundreds of AI PhDs, if we had the capacity, because people would give them jobs.”

Beyond the skills shortage, just getting access to the physical infrastructure needed for large-scale AI can be a challenge.

The type of computer system needed to run AI for cancer drug research typically requires between two and three thousand state-of-the-art computer chips.

The cost of such computer hardware alone can easily reach $60m (£48m), even before the costs for other requirements such as data storage and networking.

Part of the problem for businesses is that this type of AI has appeared out of nowhere. Earlier technologies, such as the advent of the internet, were built more slowly.

A large bank, pharmaceutical firm or manufacturer may have the resources to buy in the technology needed to take advantage of the latest AI, but what about smaller firms?

Italian startup Restworld is a recruitment website for catering staff, with a database of 100,000 employees.

Chief technology officer Edoardo Conte is keen to see if AI can benefit businesses.

The firm is considering building an AI-driven chatbot to communicate with users of the service.

But Mr. Conte said that, across thousands of users, “The cost goes up tremendously.”

Instead, it looks at a narrower problem – the issue that candidates don’t always present their experience in the best possible way.

For example, a candidate may not list waiting as a skill. But the algorithm Mr Conte developed made it easier to reveal additional information, including whether they had applied for and got a waiting role in the past.

“The AI can deduce that they’re a server, or they might be interested in another server’s job offer,” he said.

One hurdle in hospitality recruitment is getting candidates to the interview stage.

So, Mr Conte’s next challenge is to use AI to automate and customize the interview process for his candidates.

AI may also conduct “conversations” with candidates and produce summaries to present to recruiters.

It might speed up the whole process, which can currently take days, where a waiter or chef might have found another job.

Meanwhile, larger firms will continue to pour cash into AI projects, although it’s not always clear what they might achieve.

As Mr. Rogoyski said, the use of AI is in a “Darwinian experimental phase,” and it’s hard to see what the consequences will be.

“That’s where it gets interesting. But I think we have to move on,” he said, before adding “I’m not sure we’ve got a choice.”

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