Generative artificial intelligence (AI) – defined by the World Economic Forum as “a category of AI algorithms that generate new outputs based on the data they have been trained on” – has created a great deal of hype about its potential. Commentary on generative AI varies from those who claim it will transform creativity and efficiency, to those who claim it will cause problems, such as replacing people’s jobs and uncertainties about the consequences of handing more tasks over to the technology.

This report provides practical insight for organizations looking to leverage generative AI to drive innovation and growth with insights from industry leaders such as Volvo and Unilever amongst others.

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#GenAISummit is the first event to join business communities to discuss how Generative AI being used for enterprise transformation. 

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An overview of Generative AI Week 2023 in the US to bring the business community together to discuss what this means for the enterprise.

Generative AI Week saw 300+ industry leaders descend on Atlanta to discuss what this new tech means for the future of the enterprise as the event provided practical steps for implementing and scaling generative AI.

For a snapshot of who attended, what was discussed, and the key focus areas download the Post Show Report here.

When someone utters the phrase ‘generative AI’, you’ll often hear the word ‘potential’ used in the same breath. For example, it’s often said that generative AI is a potential game-changer for business.

However, there are two sides to the coin here. From one perspective, the use of the word ‘potential’ conjures a world of possibilities. But it also signals that transformation won’t occur automatically – potential needs to be unlocked. So accessing the treasure trove of opportunity within the realm of generative AI is possible for any business – as long as it has the right set of keys.

Fortunately, there are innovators in the field who have made it their mission to supply enterprises with tools designed to unleash the power of generative AI and extract its full value in the relevant business context. One of them is May Habib

There’s no doubt that generative AI has gone mainstream; a cursory glance at recent – often sensationalist – headlines is all it takes to confirm that fact. But despite all the recent excitement caused by the likes of ChatGPT, the true experts in the field have been aware of generative AI’s power for quite some time. In fact, they have spent years – away from the glare of the media – working to find practical ways to harness the technology’s potential.

Many of these future-shaping thought leaders will be gathering for the Generative AI Summit on 16 and 17 May at the Hilton Syon Park in London. Among the specialists sharing their insights and expertise on the diverse applications of machine learning will be Lukasz Szpruch, programme director for finance and economics at The Alan Turing Institute. 

He gave an exclusive interview discussing how generative AI is shaping the future already, download it now >>>

For all the media attention generative AI is attracting right now, few commentators are examining the game-changing advances it is enabling in the financial services sector.

But those looking for a deep dive into this area need look no further than the Generative AI Summit, taking place at Hilton Syon Park on 16 and 17 May 2023. Among the experts gathering to share their knowledge and experience is Debasmita Das, manager – AI research & product development at Mastercard.

Das will be speaking at the summit in a session on how generative AI can increase innovation, efficiency and agility in financial services.

Through her work at Mastercard, Das has witnessed first hand the ways in which generative AI is shaping the future of financial services and the specific areas it is impacting most. And she believes one of the most important applications of generative AI in the banking industry is tracking down fraudulent transactions.

“AI labs in financial service institutions have been exploring this area for quite some time using the Generative Adversarial Network,” Das explains.

The Generative Adversarial Network is a type of machine learning model that involves a generator and a discriminator. The generator takes random noise as input and creates fake data samples, while the discriminator tries to distinguish between the generated data and real data from the training set. In this way, the two networks learn together in a game-like scenario where the generator tries different ways to fool the discriminator, while the discriminator gets better at spotting the fake data among the real data.

According to Das, this method of distinguishing between legitimate and illicit activities has reaped “promising results”.

Another aspect of financial services where Das predicts generative AI will have a marked impact is investment and wealth-planning consultancy. “Financial advisors will be able to make situation-specific financial guidance by using generative AI to model diverse customer exigencies considering all types of economic scenarios,” she explains.

Das believes generative AI will also see extensive future use in compliance, algorithmic trading, the creation of personalized offers and automatic chatbots.

But for an example of how the technology has already been used to increase innovation, efficiency and agility in the financial service industry, Das points to the regulated use of synthetic data – artificially generated data that mimics the characteristics of real-world data. She believes this has the potential to solve many of the problems the banking sector is now experiencing, particularly with regards to data protection.

“Customer data that cannot be shared owing to privacy concerns and data protection rules can be replaced with shareable data created using synthetic data,” she explains.

“We can get rid of the conventional compliance obstacles and silos that come with working with sensitive data by using financial synthetic data.”

There are other applications for synthetic data, such as testing out uncertainties like market collapses or software failures. “We don’t always have the data from these circumstances,” says Das. “These gaps can be filled with synthetic data generations, which can also assist organizations in creating plans of action for situations of this nature.”

It’s certainly a brave new world, and one full of opportunities. And although generative AI has the ability to automate many tasks, Das plays down the chances of the technology replacing people in the workforce, insisting it should be seen more as an “associate”.

She explains: “Current advances in AI closely resemble human intelligence but human minds pick up knowledge through reasoning, learning, experience and sense of understanding. AI-based algorithms are quicker, more precise, can handle the ever-increasing volume of data, but they still lack intuition, emotion, or cultural sensitivity.”

What’s more, it’s imperative that the humans interacting with AI are aware of the risks, Das adds.

“The financial service company should be aware that they will be fully responsible and accountable for their use of generative AI and the content generated by the algorithms, which should not lead to any legal violations,” she warns. “Companies should be careful of how their employees are using prompts while interacting with open-source generative AI algorithms so that they do not end up sharing personal or sensitive company-related information.”

Das also highlights some of the other challenges associated with the technology. “Generative AI algorithms are trained on huge pools of data – the sources of which are in many cases unverifiable and also may be out of date. The training data might also include bias and various discriminations – which may lead to generation of output that would amplify the bias.”

So while the power of generative AI is formidable, that power comes with great responsibility.

Find out more about maximizing the value and navigating the risks of the technology at the Generative AI Summit.

The term ‘wow factor’ could have been invented for generative AI – because ‘wow’ is usually the first thing people say when they see what it’s capable of.

Anyone with experience of generative AI will remember the first time they became aware of its power. But how can that power be harnessed and maximized?

This and other big questions surrounding machine learning are set to be discussed at the Generative AI Summit, taking place at London’s Hilton Syon Park on 16 and 17 May, where expert speakers will be sharing their knowledge and insights and exploring the wide range of areas that generative AI is transforming.

Among those experts is Dan Dixon,Head of Data & AI, Global Functions Innovation, HSBC, who will be taking part in a panel session examining the ways in which enabling intelligent automation can improve back office efficiency.

According to Dixon, it’s an area that’s ripe with potential. “We’re still exploring the opportunities and finding appropriate use cases,” he says. But the value of generative AI in the back office is becoming increasingly apparent.

“What we’re seeing with this technology is lower barriers to entry, quicker time to market and the ability to let subject matter experts verify some outputs of generative AI and move them further up the value chain,” Dixon explains.

We are trying to strike the right balance between letting people understand and explore these technologies, while putting in place the appropriate guardrails – and we’re continuously learning what ‘appropriate’ means to different people in different scenarios.

“As we collate the different ideas and proof of concepts across the group, some common themes are beginning to emerge. For example, there are several businesses and back office functions that require large amounts of research on publicly available information.”

According to Dixon, often that time consists of analysts copying and pasting information and moving numbers around.

“But what we’ve explored,” he says, “is whether generative AI models can accurately summarize big PDFs, press releases and news articles to produce a story that can help articulate the bigger picture.

“So, the research activities don’t go away, but the automation piece means that analysts spend less time retrieving data or processing documents. They get a base-level summary, ready for them to review, verify and edit, and those time savings allows them to do more value-add tasks.”

This example, he says, shows that generative AI isn’t about “robots coming for all our jobs”, as the scaremongers would have it. “It’s about moving people up the value chain and enabling them to do more interesting, meaningful work.”

It’s also important to remember that human input is still very much necessary in addressing issues around accuracy and explainability.

“We’re very conscious of those challenges and for every use case I’ve come across so far, there is 100% a need for the human in the loop for the foreseeable future,” Dixon insists.

With barriers to entry now much lower, education regarding what generative AI models can and can’t do is also essential. “ChatGPT means anyone can just start typing in and getting an output, so we have to open up the conversations about ethics, fairness, bias, governance and explainability to a very different audience from the data scientists, model reviewers and compliance officers who have been discussing this for the last 10 years.”

As for future developments, possibilities are opening up all the time. “I keep thinking we’re at the peak of the hype curve but then along comes something else,” Dixon observes. “This year, with things like synthetic voice and more accessible text to video tools, that will move us even further up the hype curve.”

Find out more about cutting-edge developments and the potential of their power at the Generative AI Summit.

The media crescendo surrounding generative AI has reached such a pitch in recent months that it’s difficult to ascertain genuine insight from all the noise. This means it’s more important than ever to go directly to the experts working at the cutting edge to discover the true significance of the latest developments.

Well, the expert opinion is in: believe the hype, because “there’s a big change coming”.

That’s the view of Chris Booth, generative and conversational AI consultant and product owner for NatWest Group’s artificially intelligent agent: Cora.

So where are we now with generative AI, and where are we heading? When assessing the potential impact of generative AI and the disruption that could be coming down the line, Booth says it’s helpful to think in terms of where we are on the ‘sigma curve’.

“What I mean by that is, if we’re at the top, then most of the impact has already happened and we won’t see much change going forward,” he explains. “If we’re in the middle of the curve, then we can still expect to see generative AI applied to other technologies in the future. Or are we at the start of the sigma curve, with big changes to come?

“Overall,” he says, “that’s where I am – I think there’s a big change coming.”

Booth is one of the experts speaking at the Generative AI Summit, taking place at Hilton Syon Park, London, on 16th and 17th May 2023. He’ll be addressing the topic of what generative AI means for chatbots, drawing on his direct experience of working with NatWest’s Cora chatbot.

Powered by IBM Watson, Cora operates in the closed domain, which is where chatbots primarily exist – especially in large organisations – responding to action- or task-oriented questions such as, ‘Can you change my address?’

“It’s basically a large logic tree,” Booth explains. “This means we dictate what buttons are presented and then what button you click obviously changes your path. So that’s closed domain, and it works really well.”

However, closed domain and logic trees can have limitations, says Booth. “Multiple trees are brittle. They can become very difficult to manage and maintain as they grow at an exponential rate. And the larger the tree, the more links you have to manage and it’s a mess.”

But this is where generative AI has the potential to change things massively, Booth insists. While he admits that generative AI is “nothing new” in the natural language processing (NLP) space – and that it’s only in the last few years that models have become good enough to make generative AI “a contender” – Booth is excited by the potential for changes it could bring for the “opposite end” – the open domain.

He explains: “The open domain deals with questions like, ‘What did Obama do before he was president?’ – an open-ended question that can be difficult to answer. And that’s where logic trees really struggle to capture the potential scope and possibilities of how you can answer that question.

“So that’s where generative AI has huge potential for expansion, with the potential of opening new use cases for businesses to approach.”

However, there are challenges and possible drawbacks. Among them are transparency and explainability.

“Generative AI is usually powered by language models – deep AI machine learning,” Booth explains. “And these deep neural networks have billions and billions of parameters, which makes it difficult to distinguish and understand how the AI has come to its decisioning.”

Also, the language models can be prone to ‘hallucinations’ – which Booth describes as “a fancy word for outputting nonsensical and incorrect answers”. From a language model perspective, these can be very difficult to control, he says. And added to these issues are obstacles surrounding cost, privacy and data security.

But despite the challenges, Booth believes everyone will be putting generative AI to practical use at some point. “There’s going to be varying degrees of how quickly it happens. There are already plenty of startups based on ChatGPT and GPT-3. And there are small businesses in marketing, for example, that are going absolutely nuts with ways of slowly automating things.”

What’s more, opportunity is ripe for breakthroughs in the development of generative AI. “There’s the potential to make a massive impact,” Booth insists. And he’s hoping to realise that potential himself. He reveals: “I’ve got a project right now I can’t talk about in detail, but we’re trying to find ways to cover the gaps and weaknesses of language models. If we do, the implications are pretty large.”

Find out more about the big changes on the way at the Generative AI Summit.