What to expect from artificial intelligence in 2024

2023 has been a wild year for the artificial intelligence industry. The year that began with ChatGPT becoming the fastest-growing app of all time ended with the emergence of Gemini, Google's answer to OpenAI's phenomenally successful AI model. During this time, AI has transformed virtually every aspect of the technology industry and raised fears of existential doom. What will happen in 2024, the publication said Business Insider.

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Experts said that in 2024 the pace of AI development is unlikely to slow down: next year it will become an even more integral part of our lives, and everyone from Google to Elon Musk will lay claim to the OpenAI crown.

Here are the key forecasts for the next 12 months

1. AI will be everywhere

Google ended the year by launching Gemini, an artificial intelligence model it says can match OpenAI GPT-4, and plans to release more advanced versions in the next few months.

Not to be outdone, OpenAI plans to launch a GPT store in early 2024, which will allow users to create and sell their own versions of ChatGPT.

It's part of a trend that AI experts say will see the technology become a much larger part of our daily lives as tech companies integrate it into as many of their products as possible and AI adoption becomes ubiquitous.

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“I think 2024 will be the year where we really start to see widespread adoption of all these AI tools,” said Charles Higgins, co-founder of artificial intelligence training startup Tromero. He is a PhD candidate in AI safety.

“For a model like the Gemini, accessibility is an important part. It is already integrated into the products you are used to and use. Therefore, the adoption of an AI toolkit will become the norm rather than the exception,” Higgins said.

Another trend to watch in 2024 is open source models. Unlike closed systems such as GPT-4 and Gemini, these models are freely available and anyone can use and modify them.

Meta has made a big bet on this form of AI by making its Llama 2 model widely available and forming an "open science" alliance with other technology companies such as IBM.

However, the high cost of training AI models means that truly open AI alternatives developed by big tech companies are unlikely to become popular any time soon.

“Training models is very, very expensive,” stressed Sofia Kalanowska, co-founder of Tromero and Ph.D. “So the open source community still depends on large companies like Meta to release their models.”

2. OpenAI won't be easy

OpenAI has been riding the ChatGPT wave since its incredible success in late 2022, but in recent weeks there have been signs that the chatbot is having some problems.

Users have complained that ChatGPT's performance has deteriorated and it even refuses to complete some tasks. OpenAI said it was looking into reports that the chatbot was becoming "lazier."

“I think ChatGPT has performed quite poorly over the last three weeks. Network errors are constantly appearing, and the answers have become much shorter,” commented Kalanowska.

The chatbot's strange behavior is another illustration that much is still unknown about how large language models work, but it has increased pressure on OpenAI after weeks of chaos at the company.

The dramatic departure and reappointment of Sam Altman as CEO has left the company's leadership in the AI ​​arms race looking shaky, with the startup's customers shifting to competitors and Microsoft introducing its own AI systems to reduce its reliance on OpenAI.

With the advent of Gemini and other competing models such as Elon Musk's Grok, the next year could be even more difficult for OpenAI as the AI ​​industry becomes increasingly crowded.

"Whatever the drama, there's a chink in their armor now," Higgins said. “It's rocked the boat and I think the other big players are certainly looking to step up and take advantage of that.”

3. AI companies face a looming copyright battle.

There's a huge legal question mark hanging over the entire AI industry right now.

Cases such as Getty Images' lawsuit against Stability AI, due to be heard in the UK next year, and the lawsuit brought by comedian Sarah Silverman and others against OpenAI in the US, revolve around a simple, unanswerable question: is it legal to train models? artificial intelligence on data containing copyrighted content?

“In most countries this remains an open question,” explained Dr Andres Guadamus, professor of intellectual property law at the University of Sussex. “I think we will have potentially one or two decisions in 2024 that will help clear things up, but it will take a long time, probably four to five years, to get it all sorted out.”

These legal disputes pose an existential threat to the AI ​​industry. Major tech companies have acknowledged that having to pay for the massive amounts of copyrighted data used to train AI models will likely make it impossible to train models as large and complex as GPT-4.

“If these cases were decided tomorrow and all the AI ​​companies lost, they would face serious consequences because they would likely have to pay out huge amounts,” Guadamuz suggested.

In his view, while a series of disastrous legal defeats for AI companies in the US and UK will set the AI ​​revolution back significantly, it will likely not stop it entirely.

AI developments will likely simply move to countries with looser regulations, Guadamuza said.

4. Regulation is urgently needed

American politicians have famously failed to pass new laws to curb the influence of social media, and now it looks like history may repeat itself with AI.

This month, after torturous negotiations, the EU finally agreed on a set of controls for generative AI tools, but despite a series of congressional hearings on AI, the US appears no closer to settling the cutting-edge technology.

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Experts say this is set to change in 2024, as AI is already disrupting a wide range of professions and concerns grow about the real impact of AI-generated content.

“2024 is the year when we will need to make decisions in the field of AI regulation,” says Vincent Konitzer, a professor of computer science at Carnegie Mellon University. “There are a lot of regulatory initiatives that seem reasonable enough at a high level, but the actual implementation details matter a lot and they are still missing and untested.”

“Finding out these details is a difficult task because regulation is slow and AI is now an extremely fast-moving target,” he said.

Guadamuza agreed, adding that regulators likely need to intervene now rather than wait for complex AI issues to be resolved by the courts.

“The law will always lag behind technology. So we need regulators to intervene rather than wait for case law to decide,” he concluded.

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