The sudden accessibility of generative AI has produced huge excitement but also concern. Ian Taylor reports the debate at Travel Weekly’s Future of Travel Conference
Technology experts insist the huge excitement around generative artificial intelligence (AI) is justified despite the considerable hype since the launch of ChatGPT 3.5 last November.
Deloitte director and digital and analytics consultant Ellena Ronca-Thompson told the Travel Weekly Future of Travel conference: “I’ve been working in data for 20 years and this was the first time that people wanted to talk about data.”
She noted ChatGPT “is not actually new technology” but argued: “Suddenly, we could all get our hands dirty with it, without any need for special skills.
“You can use your own language and speak normally to start to experience how these technologies work. So suddenly everyone was talking about it.”
Google industry head Jay Chauhan agreed, saying: “This technology wasn’t accessible in the past. It’s now becoming accessible to everyone. It’s no longer in the data science teams. You don’t need an IT degree to run these models. It exists now for anyone to use the technology, to use natural language to query it and to set up projects. Anyone can do it.
“The other development is, AI was around 10 years ago but more analytical and predictive. The big thing with ChatGPT is the breakthrough in large language models and in the computing power available to drive these, because it takes a hell of a lot of computer power to drive them. It wasn’t as accessible or as powerful before as today.”
He described AI as the “third big shift” after the internet and mobile, or “a computer in our pocket”. Chauhan argued: “It’s going to change the way we live, the way we work and the way we experience life.”
Ronca-Thompson said: “Certainly, there is a lot of hype. But I’m excited at the potential for this technology to be in everybody’s hands. You no longer need to be a data scientist to understand deep patterns or make predictions. The possibilities opening are so vast, and in the power of everyone.”
There remains controversy about whether generative AI is ‘intelligent’ or simply mimicking existing patterns and data.
Chauhan argued: “It’s mimicking intelligence – learning from a dataset, picking out patterns and predicting what the next answer or proposition should be.”
‘We need to get it right’
Yet there is a need for caution among businesses developing uses for generative AI tools such as ChatGPT, according to Chauhan.
He told the Future of Travel conference: “This is an incredibly powerful technology that we need to roll out and scale responsibly. We need to take a cautious approach to generative AI.
“When we launch something, we need to get it right. We need to do it responsibly.”
He suggested that when Google launches something “it makes a huge difference. We want to make sure it’s safe [that] we’re observing consumers’ privacy, [that] we’re getting rid of any bias. It’s not a race to be first, it’s a race to get it right.”
Ronca-Thompson noted: “We’ve developed a safety framework [at Deloitte]. We’re evaluating use cases and, for every application we decide to put into production, we’re clear on the limitations at the outset and what could be some of the implications.”
For example, she said: “What if a hacker tries to interact with a chatbot to access private company information? How do you put safeguards in at the beginning to make sure you don’t have to claw back a product or suffer reputational risk.”
Simon Powell, chief executive of travel technology firm Inspiretec, dismissed a suggestion that launching ChatGPT was irresponsible, insisting: “It’s hard to control or to stop this type of technology. There will be debates forever and a day about whether it is a good thing. [But] there was no way to stop this coming out. It was always going to happen.”
However, Powell acknowledged the need for human ‘curation’ of ChatGPT results, arguing the content produced requires “a human to critique it”. He said: “Text needs to be looked at to make sure it’s relevant, that it’s correct and you’re happy to send it to a client.”
Ronca-Thompson argued: “Just because you can, doesn’t mean you should. My car can be driven faster than 70 miles an hour, but that doesn’t mean I should. I can add bells and whistles to an email, but how is that going to deliver anything to the bottom line?”
‘We’re still having to train the models’
The need for caution in using generative AI stems from the underlying structure.
Ronca-Thompson explained: “You have the computer power which has made it possible, the data underlying it and the large language models [LLMs] consuming the data. Issues come up where the data underlying the model is inadequate or the model generates content that doesn’t exist.
“What is not well understood is that these models are probabilistic” – meaning they incorporate randomness.
She said: “If I ask, ‘What was my revenue yesterday’ and ask the same question again in two minutes, the answer should be the same. But when generating content in a more commercial style, it can be different every time.
“We’re still having to train the models to get more deterministic answers” – meaning answers determined by the parameters and values set by those operating the model.
Ronca-Thompson argued: “These technologies are now in the hands of everybody. Analytics teams are no longer a bottleneck to the technology’s potential and how it can be applied. But how do we know the technology is going to be used as intended?
“You want to make sure you’re not getting model creep [or] data creep and you’re not introducing bias.”
Business applications of generative AI
The business applications of generative AI will differ from consumer applications in important ways.
Chauhan explained: “When applying this to a business, you’re going to be using an ‘enterprise’ version of AI, trained on your data and not any other data unless you want it to be.
“It’s in a walled garden, not being shared outside. It’s secure. You can play with different models, use different models for different use cases and ask, ‘How creative do I want it to be?’
“As an enterprise version, it’s safe in your own environment. That is very different to what we’re seeing in consumer and public use.”
Chauhan advised: “Start with whatever your business problem is, [such as] manual tasks that are taking ages, and how data can help. Then use the models to train against that data.”
He said the biggest areas of use will be in “marketing, customer service and operational tasks”.
Ronca-Thompson suggested travel companies consider partnering with bigger businesses to develop generative AI uses, saying: “Attracting and retaining skilled people in technological fields is difficult across the consumer sector.
“Having a partnership, working with people who understand the technology, makes it easier to retain the people you have.”
‘You can’t just push AI into production and forget it’
It appears self-evident that generative AI should bring improvements in productivity, but that could depend on how it is used because, despite all the digital developments of the last 20 years, official data suggests UK productivity has stagnated.
Ronca-Thompson insisted: “We can have productivity gains if we apply generative AI with discipline.”
She suggested AI tools such as ChatGPT could “radically reduce the time required to generate reports” and said: “In my team, we use that extra time on research and development. When you give people time back and use it more productively, you start to realise productivity gains.”
Rather than eliminate jobs, Ronca-Thompson suggested generative AI-use would require new skills and new jobs. She noted: “The calculator was introduced decades ago. It still needs a skilled operator.
“It’s important having a human in the loop when these models are generating tonnes of content. You can’t just push it into production and forget about it.
“A skill set needs to come in now around critical thinking, knowing the right questions to ask, understanding and being sceptical about an answer, being able to evaluate whatever answer you get.”