August 7, 2023 |
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Contact UsMachines Learning: The Rise of Generative AI in Business, Research and Education
August 7, 2023 | 8 Minute Read
Lou D'AmbrosioGlobal Head of the GS Value Accelerator platform Lou D'Ambrosio |
David FerrucciCEO of Elemental Cognition David Ferrucci |
Key Takeaways
- It may seem that large language models arrived almost overnight, but they are the result of decades of research and more powerful models are still being developed.
- The value chain is likely to shift, with employees focusing on where they can add most value rather than on mundane tasks that a computer can fulfill.
- Humans are still training machines, but we believe in ten years machines may be training people—providing personalized, interactive education in a way that has not previously been possible.
There has been a strong focus on the capabilities of generative artificial intelligence (GAI) recently, with people astounded by the ability of large language models such as ChatGPT to perform tasks including writing a novel, suggesting a recipe or summarizing complex research. And there’s more to come: these models only represent the tip of the iceberg, with much more powerful tools being developed. But there are concerns, too—will they result in wide-scale unemployment, and could machines eventually take over the world?
Lou D’Ambrosio, Head of Goldman Sachs’ Value Accelerator, recently sat down with Dave Ferrucci, an award-winning AI researcher. Between 2007 and 2011 Dr. Ferrucci led a team of IBM experts and academics in the development of the Watson computer system, which defeated the best contestants of all time from the television quiz show Jeopardy! In 2015 he founded Elemental Cognition, an AI company focused on deep natural language understanding.
Where did the language models that everyone is talking about suddenly come from?
Ferrucci: They’re a big step for artificial intelligence, to be sure, but I don’t think everybody realizes that this has been a long haul—they haven’t appeared out of nowhere. People have been working on AI for decades, but what we’re seeing now is a watershed moment. That’s because the ability to master language is one of the things that’s so closely associated with human intelligence, and these large language models can do just that. We’re now seeing machines formulate coherent, fluent language like the best of us.
How do these models work?
Ferrucci: Large language models are an application of deep learning. Working in a predictive way, they look at word patterns in large bodies of training data to compute the probabilities that certain words would follow a particular sequence of other words. They do it so well because of the vast amount of data they’re trained on. This creates incredible fluency that is consistent not just with the language in the training data, but also with the prompt the user specifies. Then of course you can respond to their output, and you get a dialogue. They’re not thinking like humans do but, nonetheless, their effect is dramatic and significant. The result is human-to-machine communication.
What are the implications for companies?
Ferrucci: Language is vital in the acquisition of knowledge. The communication of ideas from human to human is through language. As a result, language models could impact fundamental business functions around summarizing, synthesis, explanation, and delivery of key data and insights. The reporting role of many middle managers who act as go-betweens could be affected. The value chain starts to shift because the decision-maker can get the synthesis, the summary, the aggregation and the delivery of information from a machine and at a much lower cost. The company’s customers now have access to that knowledge much more readily and with less human expertise required.
“Language models could impact fundamental business functions around summarizing, synthesis, explanation, and delivery of key data and insights.”
Could a company develop its own large language model to take advantage of all the capabilities, but retain some control of the training data?
Ferrucci: I think the expectation is that we’ll see language models that specialize in a particular area and that are trained within a company based on that company’s content, potentially using what are called foundational language models to train them on how general language is structured. But then you add a layer on top of that to train them with the specific knowledge within a company. You can also train them to be domain-specific, only focusing on certain subjects.
How should companies embrace the potential offered by language models?
Ferrucci: I think they can’t afford to ignore what’s going on. The specific solution depends on the nature of their business, and experimentation will be required. A key question is whether the product or service being offered is something that AI can directly replicate. Beyond that fundamental question, companies may need to consider their workflows and processes to identify low-risk experiments to identify viable opportunities. I think the main risk is that they ignore AI or don’t consider all the various ways it could impact their business processes.
What kind of roles will be affected?
Ferrucci: Everyone has been talking about the impact on creative roles. Imagine the role of writers in sales and marketing roles—much of what they do may be repetitive and formulaic. These models can take on these tasks at an extremely cheap cost, so writers can shift their focus to the 20% of their role that involves coming up with new ideas to really add value.
Existing databases and computer systems already have powerful, effective and reliable computational techniques, but they require an intermediary between the decision-maker and the use of that technology. Large language models can now help decision-makers communicate fluently with these systems to conduct analysis and reach conclusions. Take logistics, where complex issues in travel, healthcare plans, finance or design previously required multiple human interactions. Now, a machine can solve these problems and interact fluently with someone who doesn’t necessarily understand all the processes. Call centers, for example, are now using machines to deal with 90-95% of incoming calls, up from 40-50% in the past.
How is AI affecting areas like research and biopharma?
Ferrucci: Companies are combining large language models with search and semantic analysis, and that’s dramatically reducing the need for desktop research. Producing well-evidenced, high-quality research in areas like investments or drug discovery that previously took months can now be done in potentially days, hours or even minutes.
Another area is protein folding. The way proteins fold follows a series of predictable patterns, which is essentially like a language in itself. Large language models use powerful deep learning techniques to learn these sequences of patterns, so they can help us understand proteins and how they fold. This means they have a big role to play in discovering new drugs and understanding disease.
Language is about more than words. Language involves sequences and patterns that provide meaning. The structure of a protein is based on language, and language models can help us learn the typical patterns behind them much more efficiently.
What’s the role of AI in education?
Ferrucci: You can get educated on any topic at present, but the main problem is it’s not personalized to the individual student. But imagine that you’re able to take all the content out there and make it dynamic, interactive and personalize it to every student. Doing so will result in huge improvements to education in both academia and corporate settings.
What do we need to be careful about?
Ferrucci: We do need to be careful about how these tools could be abused. There are concerns about security, intellectual property, and the impact of these tools on the economy. We still need to figure out all the possible implications.
I think the people who best understand these models have legitimate concerns about privacy, security, reliability and trust. The technology is already easy to use and pervasive, so I don’t know how practical it is to pause current progress and development, as some people are suggesting.
It’s important to acknowledge these concerns, be aware of them and step back and think them through. I think many of them will be worked through. But we need to remember these are generative systems—they’re generating new things. They could pick up information that is not true and develop things based on this false information. It’s not the same as search.
Where do you think we’ll be 10 years from now?
Ferrucci: I’m generally optimistic, although there are lots of caveats we need to consider. Today, we’re still in the phase in which humans are training machines. But these models are going to be so fluent and capable of synthesizing language that machines may eventually be training humans. Machines could be teaching us new skills, educating us on a whole range of topics. The machine may be providing personalized, interactive access to educational material; at the same time, regular people may be able to program computers to perform a wide variety of tasks just by talking to them.
In fact, I don’t see a future for humanity without artificial intelligence. In my view, it’s the most fundamental tool for the advancement of the human species. That’s not to say that it won’t be a bumpy ride, or that we don’t have a lot to learn along the way. People are going to have to experiment, we have to be careful, and it needs to be regulated carefully. But it’s powerful technology, and I see it as our destiny to work it through.
“Machines could be teaching us new skills, educating us on a whole range of topics.”
How much more can large language models improve?
Ferrucci: I think we will reach a point of diminishing returns, and I think the value will come from how effectively we adapt models to solve specific problems. We can train the models on a huge amount of human language, but how do we use them effectively in business? How do we integrate them with other techniques and other technologies to be effective? That’s where a lot of the excitement will likely lie in the future.
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