OpenAI has announced a major upgrade to the technology that underpins ChatGPT, the seemingly magical online tool that professionals have been using to draft e-mails, write blog posts and more. If you think of ChatGPT as a car, the new language model known as GPT-4 adds a more powerful engine.
The old ChatGPT could only read text. The new ChatGPT can look at a photograph of the contents of your fridge and suggest a dinner recipe. The old ChatGPT scored in the 10th percentile on the bar exam. The new one was in the 90th.
In the hours since its release, people have used it to create a Web site from a hand-drawn sketch or look through a dating site for an ideal partner.
However, this is the fun part of unleashing a powerful language model to the public — the honeymoon period. What are the long-term consequences?
OpenAI, once again, has not disclosed the datasets it used to train GPT-4, so that means researchers cannot scrutinize the model to determine how it might inadvertently manipulate or misinform people.
More broadly, though, it ushers in a new era of hyper-efficiency in which professionals must work smarter and faster — or perish.
There is no better example of this than Morgan Stanley, which has been using GPT-4 since last year. Morgan Stanley on Tuesday said it trained GPT-4 on thousands of papers published by its analysts on capital markets, asset classes, industry analysis and more, to create a chatbot for its own wealth advisers, adding that about 200 staff at the bank have been using it daily.
“Think of it as having our Chief Investment Strategist, Chief Global Economist, Global Equities Strategist and every other analyst around the globe on call for every adviser, every day,” Morgan Stanley analytics chief Jeff McMillan said in a statement.
Here was the line that really stood out from OpenAI’s own write-up of the case study: “McMillan says the effort will also further enrich the relationship between Morgan Stanley advisers and their clients by enabling them to assist more people more quickly.”
How much more quickly? A spokesperson for Morgan Stanley said its advisers can now do in seconds what they used to do in half an hour, such as looking at an analyst’s note to advise a client on the performance of certain companies and their shares.
Powerful artificial intelligence systems such as GPT-4 are not going to replace large swaths of professional workers, as many have instinctively feared.
However, they are likely to put them under greater pressure to be more productive and faster at what they do. The systems are raising the bar on what is considered acceptable output as they usher in an era of ultra-efficiency unlike anything the world has seen before.
That is what partly happened to professional translators and interpreters. As artificial intelligence tools such as Google Translate and DeepL grew in popularity among business customers, many translators feared they would be replaced. Instead, they were expected to increase their output.
Before the advent of translation tools, a professional would be expected to translate between 1,000 and 2,000 words a day, said to Nuria Llanderas, who has been a professional interpreter for more than 20 years.
“Now they are expected to manage 7,000,” she said.
Her industry peers have predicted more artificial intelligence systems are about to start supporting them on simultaneous translation, but that could also mean more work for the human translators in practice, checking that the machine’s output is not wrong.
It could also raise the bar on human performance.
“With the extra help, you have no excuses to leave anything out,” she added.
Much of this is typical of the march of technology. Smartphones allowed us to be connected to work at all times. Slack allowed us to communicate with more people inside a company, more seamlessly.
However, such tools also kept us further chained to work, squeezing out minutes in the day that workers might have used in the past for contemplation, strategic thinking or just taking a breather.
GPT-4 clearly has the potential to wring more value out of human workers, but it might well come at the cost of our mental energy. However brilliant these models become, watch out for how they might take you a tiny step closer to burnout.
Parmy Olson is a Bloomberg Opinion columnist covering technology and former reporter for the Wall Street Journal and Forbes.
This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.
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