Generative artificial intelligence is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. In the early 2020s, advances in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. These include large language model chatbots such as ChatGPT, Bing Chat, Bard, and LLaMA, and text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E.
Generative AI has uses across a wide range of industries, including art, writing, software development, product design, healthcare, finance, gaming, marketing, and fashion. Investment in generative AI surged during the early 2020s, with large companies such as Microsoft, Google, and Baidu as well as numerous smaller firms developing generative AI models. However, there are also concerns about the potential misuse of generative AI, including cybercrime or creating fake news or deepfakes which can be used to deceive or manipulate people.
Transparency
Generative artificial intelligence (AI) has been billed as an everyday technology. And while consumers may already be reaping the technology’s benefits, there is a gap between consumers’ confidence in their knowledge of AI and the ongoing realities of its almost invisible integration into their daily lives.
“What is key for most consumers is knowing that [AI] goes well beyond just a large language model [LLM], it goes well beyond what you’re sort of seeing at the surface, and it’s been touching and permeating a lot of parts of your daily lives for years,” Shaunt Sarkissian, founder and CEO of AI-ID, told PYMNTS CEO Karen Webster. Still, when you ask consumers the role AI’s capabilities play in powering their lives today, many struggle to explain where the technology sits and what it impacts.
Then again, who among us can speak to the intricate payment networks at play that spring into action for routine and simple on-the-surface tasks like buying a coffee or shopping online? “The same way that the cloud is something new, how digital payment networks are interconnected beneath the surface, that’s how consumers’ understanding of AI relates to a lot of other technologies,” said Sarkissian. “But consumers are smart, they can handle the truth as long as you tell them what’s going on.” That is why it is so important for AI firms to be transparent and tell them.
Trust
While consumers generally think that AI can improve their daily lives, there exists an undercurrent of the unknown to their perception of the innovative technology — and that undercurrent is centred around doubts that AI will provide them with the right information 100% of the time. There’s also the uncertainty over whether the information AI platforms provide is safe and reliable, particularly when it comes to sensitive areas like banking and healthcare.
“The food industry was the first sector to really start adopting things like disclosure of ingredients and nutrition labels, providing consumers transparency and knowledge of what’s in their products — and with AI, it’s much of the same. Companies need to say, ‘Look, this was AI-generated, but this other piece was not,” Sarkissian said. “Get into the calorie count, if you will. As long as you can tell consumers what the content is made of, they can then choose to make decisions around that information based on what they see. But if you don’t give that to them, then it’s that shielding and blackboxing that the industry needs to be careful with, and where regulators can step in more aggressively if the industry fails to be proactive,” he said. “It is going to be critical for areas like intellectual property.”
Particularly as AI continues to evolve and integrate into various industries, it is crucial for consumers to have a clear understanding of its capabilities and potential risks. Transparency and disclosure will play a vital role in building trust and ensuring that consumers can make informed decisions in an increasingly AI-driven world, as well as help maintain a productive balance between public oversight and private innovation.
Already, AI is showing its ability to outperform humans in certain tasks when measured on a scale and speed basis. “If it is a data-intensive industry with a large data set, AI will perform very well. And you just need to be aware of, and wary of, that,” explained Sarkissian, noting that AI generated outputs may not perform as well when tasked with taste-based prompts, such as being asked to “make a painting that would sell in a gallery.”
“AI is all based on the training, so if you feed it a very specialized field of data, the chance of it becoming an expert or being the best in that particular category is very high,” he said. “And what might occur over time is that one large AI model might source information for multiple, specialized sub-models.”
Call it GPT Orchestration. But despite the promise, training an AI model to an expert-level degree of specialization is incredibly expensive, laborious and time-consuming. “There really needs to be commercial utility, it has to be something like recognizing a tumour in an image,” said Sarkissian. And he views healthcare as a promising opportunity area for AI applications to enhance care delivery by making doctors and provider systems more effective and efficient at scale. “An AI-enabled diagnostic tool can be better at identifying things earlier in the prognosis around cancer than the human eye can, and those things are saving lives and saving money as well,” Sarkissian said.
It isn’t just medicine where specialized AI can have a game-changing impact. “A lot of the commoditized part of business law, transactional law, even real estate law is going be automated in my mind, where it can process documents and give lawyers back hours and days of their time, making them hyper-efficient,” Sarkissian said.
Is AI the future?
The EU should move on from ‘outdated’ debates over front-of-pack labelling and instead focus on tailor-made technological solutions for the future of nutrition, experts told a recent panel – but critics say that this privileges only the richest in society. The European Commission is expected to put forward its proposal for an EU-wide nutritional labelling system in early 2023.
The current front runner is the Nutriscore, a controversial colour-coded system championed by France which ranks food from A to E. The score has proven divisive, especially among stakeholders in Europe’s South, who argue the score penalises the Mediterranean diet. But, for Pietro Paganini, co-founder of the EU think-tank Competere, the debate is redundant. Instead, efforts should focus on technological developments and personalised diets rather than ‘old outdated system[s]’ such as front-of-pack nutritional labelling. “We keep saying the problem is food, [but] the problem is how we eat. That’s why we need more education, and we need systems that are actually considering our lifestyle,” he stressed during a panel discussion on Tuesday (29 November).
These devices can give suggestions for food choices that are tailor-made for each individual based on, for example, age or gender, or even offer recommendations based on what the person has already eaten that day. “Artificial intelligence knows exactly what you have done, how many calories you have consumed, how much you are going to consume,” he added. For example, a smartwatch may be able to tell you your glycaemic level, he explained. Stressing that genetic and scientific research in this area is ‘moving fast’ towards personalised diets, Paganini added a warning that the EU risks missing the boat.
“Either we take this challenge, or others will – and will invade Europe with software’s apps, technologies that will use our own health data,” he warned, citing the likes of China, the US, or Still, AI isn’t perfect — and today’s models still have a strong tendency to hallucinate and present end-users with misinformation or wrong results. That’s why, as mentioned above, transparency and accountability are crucial for the go-forward scalability of the still-nascent industry. “Technology is here, and technology will tell us exactly what we’re missing during the day, and what we should eat,” he said, giving examples of wearable devices such as smartwatches.
Israel. Likewise, Ramon Estruch Riba, associate professor at the department of medicine at Barcelona University, urged the EU to go further in ambitions on personalised nutrition. “Each one has to choose [their] own nutrition and has to be educated, this is the key,” he said.
But not everyone is sold on the idea of artificial intelligence as the solution to Europe’s nutritional crisis. “You are talking about some kinds of fiction which are not interesting,” MEP Veronique Trillet-Lenoir said, likening the use of technology to a ‘big brother’-type situation. The MEP lambasted the fact that artificial intelligence and connected objects are a “subject of wealthy people”, arguing that instead, the debate should focus on the health of “real people in the supermarket.”
“Disadvantaged people, uneducated people will not have access,” she warned, staunchly defending the need for a harmonised, mandatory nutritional label in Europe. Likewise, Green MEP Michele Rivasi backed the Nutriscore, arguing that research suggests such scores are easily understood by consumers and also lead to positive outcomes, such as reduced obesity and cardiovascular disease. “We need a label to allow consumers to make a fast choice in the supermarket,” said the French MEP, arguing that people do not have time to read nutritional labels.
Meanwhile, Camille Perrin from consumer organisation BEUC also reserved criticisms for the idea. “Interesting to see all those screaming [that] Nutriscore is too directive and paternalistic [are] liking a tweet that basically suggests an AI-powered connected fridge should tell me what I can eat or not,” she tweeted.
Still, it is one thing to have a good idea — it is another thing entirely to go and execute it successfully, which is something that still requires a decidedly human touch. That’s why, said Sarkissian, AI models are already being specialized to the degree that they can generate “good ideas” at scale for specific industries that are then able to be passed off to capable experts for execution.
