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Generative AI: the big questions to ask before you invest

As a science team, we’ve been studying the rise of Generative Artificial Intelligence (Generative AI) models for some time now. Long enough, in fact, that when we started we weren’t even sure what to call them. Were they Foundation Models? Pre-Trained, or Large Language Models? Generative AI, as it obviously transpired, emerged as the winner.

While we’ve been studying Generative AI for a long time, though, it’s only in the last year that the subject has been thrown into the mainstream. Two notable events are behind this shift. Firstly, in November 2022, Open AI released ChatGPT to the public for the very first time. Six months later GPT-4 launched, redefining what the technology was capable of.

Since then, we’ve seen a great deal of excitement… and even more media hype. With all of that in mind, I’d like to share my take on the true potential of Generative AI.

 

AI vs Generative AI: what’s the difference?

 

Before we get too deep into things, let’s take a moment to remind ourselves of the difference between Generative AI, and AI as a whole.

AI is a broad term. It relates to the ability of a machine to perform tasks that typically require human intelligence. Some of the most common tasks in retail science – price optimisation, the recommendation of relevant products to customers, store clustering – all leverage machine learning algorithms. As a result, we can include all of them within that overarching category of AI.

Generative AI is a subset of this, and relates specifically to the creation of new content like summaries, images, songs, or code. To build this new content, we need models of a different scale; models that have been pre-trained on almost inconceivable amounts of data using the kind of compute that we couldn’t fathom just a few years back.

At a high level, these models are called Foundation Models, but there are further variations for specific types of content. If they are trained on text, for instance, then they are called Large Language Models. Large Image Models are trained on images, and so on.

Generating results? Or just more complexity?

Generative AI is moving at lighting speed. Almost every week we see a new model more powerful than the last, and we are all still learning about the limitations and applications of this emergent technology. Because of that, I think that there are three critical questions that any organisation needs to answer before making an investment into Generative AI:

  • Where will it add value to our business?
  • How do we access the Generative landscape?
  • What ethical responsibilities do we need to consider?

Let’s look at all three, and see what needs to be considered in each area.

 

To understand Generative AI’s value, we first need to understand its potential use cases. At dunnhumby, that’s something we’ve explored across four key angles.

  • Personalised experiences

Can we use Generative AI to create a more personalised experience for our customers? Could we minimise manual tasks by enabling them to use natural language to navigate software products? What if they could just ask our interface for a summary of drivers of growth or decline in a given category, for example?

  • A source of new data

With large language and image models trained using a vast amount of data, they can also serve as sources of information on products and retailers in their own right. This, of course, comes with the caveat that while that information might be extremely helpful, it may not be entirely reliable.

  • Productivity and efficiency

Tools like Microsoft Copilot are well known for wider use cases but the rise of Generative AI means there’s now a tool for just about everything, from creative design to people coaching and skills development. Can our people benefit by embedding those applications into our existing processes?

  • Better science

This is something that we’re particularly focused on in the natural language processing space. Improved summaries and descriptions make for greater precision and better prescriptive capabilities – things that we’re naturally keen to see.

Interestingly, as we’ve looked at each of those issues, we’ve learned that many of the things we’re trying to achieve can actually be accomplished without Generative AI. That’s not surprising: we’re all still learning what these models are and how they differ from  standard machine learning algorithms, after all. But it does show that it’s important not to rush into a Generative AI-heavy approach unnecessarily.

 

 

We’ve found it extremely useful to get an understanding of the different ways in which Generative AI models can be accessed, not least because the costs, privacy concerns, and enabling tasks differ for each.

Up until recently, the most talked about Generative AI models are what we term “industry models”. These include GPT-4, Google BARD, and Stability AI. Typically, these are all very powerful, but also come with two main drawbacks; data privacy concerns and cost.

Using external APIs, for instance, means that you share your data with the provider. Terms and conditions will vary on whether that data can then be used for retraining, but many companies are naturally deeply uncomfortable about that prospect, particularly when it comes to confidential or IP-related information.

The question then is how these large, externally trained models can be leveraged without a company’s own data becoming part of that dataset, which is prompting innovations in hosting, federated models, and firewalls to proceed at pace.

This brings us to open-source models like Falcon and Llama 2. The open-source community has played an enormous game of catchup in recent months, to the point at which these models can no longer be considered to be lagging in terms of quality.

The clear advantage with these models is that, if they can be hosted securely on a company’s internal systems, all of those data privacy concerns just dissolve away. The biggest challenge here, however is size: these systems tend to be prohibitively large.

To tackle that challenge, some organisations are opting to build their own Generative AI models. These leverage the underlying transformer infrastructure of a Foundation Model, but instead of training on external data, are trained on that company’s own data.

It’s unlikely that these home-grown model will compete with industry or open-source models for generic image or natural language tasks due to the expense involved in hardware and staffing. For tasks specific to an individual business, though, training on a smaller and more relevant dataset may actually be more useful than fine-tuning a larger one.

 

Regardless of whether we use industry, open-source, or proprietary models, any company employing Generative AI also has an ethical responsibility around how it is used and the outputs it creates. Some of the biggest considerations include:

  • Privacy and Security

While businesses set up policies to protect data and IP, those policies also need to be understood. Some of the tools and interfaces used to access Generative AI can look very benign, but actually present much wider security and privacy concerns. Educating employees on this new technology and creating a culture of strong privacy understanding is critically important.

  • Bias

Bias is an ethical consideration for any model build, not just AI. Basic predictive models in healthcare or the insurance industry may easily be found to have a bias against certain genders or income brackets, for instance. When it comes to large language and image models, though, the bias can be harder to explain. Regardless, any company utilising those tools has a responsibility for spotting and mitigating that problem.

  • Disinformation

Some Generative AI outputs can sound very official and authoritative, but that doesn’t guarantee that what they output is actually real. Just ask the lawyers who used ChatGPT-to generate citations, only to find out that some were completely fake. Remember that these models are designed to create new content, not necessarily real content – so their reliability always needs to be checked.

  • 1. Where can generative AI truly add value to our business?

    To understand Generative AI’s value, we first need to understand its potential use cases. At dunnhumby, that’s something we’ve explored across four key angles.

    • Personalised experiences

    Can we use Generative AI to create a more personalised experience for our customers? Could we minimise manual tasks by enabling them to use natural language to navigate software products? What if they could just ask our interface for a summary of drivers of growth or decline in a given category, for example?

    • A source of new data

    With large language and image models trained using a vast amount of data, they can also serve as sources of information on products and retailers in their own right. This, of course, comes with the caveat that while that information might be extremely helpful, it may not be entirely reliable.

    • Productivity and efficiency

    Tools like Microsoft Copilot are well known for wider use cases but the rise of Generative AI means there’s now a tool for just about everything, from creative design to people coaching and skills development. Can our people benefit by embedding those applications into our existing processes?

    • Better science

    This is something that we’re particularly focused on in the natural language processing space. Improved summaries and descriptions make for greater precision and better prescriptive capabilities – things that we’re naturally keen to see.

    Interestingly, as we’ve looked at each of those issues, we’ve learned that many of the things we’re trying to achieve can actually be accomplished without Generative AI. That’s not surprising: we’re all still learning what these models are and how they differ from  standard machine learning algorithms, after all. But it does show that it’s important not to rush into a Generative AI-heavy approach unnecessarily.

  • 2. What’s the best way to access the Generative landscape?

    We’ve found it extremely useful to get an understanding of the different ways in which Generative AI models can be accessed, not least because the costs, privacy concerns, and enabling tasks differ for each.

    Up until recently, the most talked about Generative AI models are what we term “industry models”. These include GPT-4, Google BARD, and Stability AI. Typically, these are all very powerful, but also come with two main drawbacks; data privacy concerns and cost.

    Using external APIs, for instance, means that you share your data with the provider. Terms and conditions will vary on whether that data can then be used for retraining, but many companies are naturally deeply uncomfortable about that prospect, particularly when it comes to confidential or IP-related information.

    The question then is how these large, externally trained models can be leveraged without a company’s own data becoming part of that dataset, which is prompting innovations in hosting, federated models, and firewalls to proceed at pace.

    This brings us to open-source models like Falcon and Llama 2. The open-source community has played an enormous game of catchup in recent months, to the point at which these models can no longer be considered to be lagging in terms of quality.

    The clear advantage with these models is that, if they can be hosted securely on a company’s internal systems, all of those data privacy concerns just dissolve away. The biggest challenge here, however is size: these systems tend to be prohibitively large.

    To tackle that challenge, some organisations are opting to build their own Generative AI models. These leverage the underlying transformer infrastructure of a Foundation Model, but instead of training on external data, are trained on that company’s own data.

    It’s unlikely that these home-grown model will compete with industry or open-source models for generic image or natural language tasks due to the expense involved in hardware and staffing. For tasks specific to an individual business, though, training on a smaller and more relevant dataset may actually be more useful than fine-tuning a larger one.

  • 3. What are our ethical responsibilities?

    Regardless of whether we use industry, open-source, or proprietary models, any company employing Generative AI also has an ethical responsibility around how it is used and the outputs it creates. Some of the biggest considerations include:

    • Privacy and Security

    While businesses set up policies to protect data and IP, those policies also need to be understood. Some of the tools and interfaces used to access Generative AI can look very benign, but actually present much wider security and privacy concerns. Educating employees on this new technology and creating a culture of strong privacy understanding is critically important.

    • Bias

    Bias is an ethical consideration for any model build, not just AI. Basic predictive models in healthcare or the insurance industry may easily be found to have a bias against certain genders or income brackets, for instance. When it comes to large language and image models, though, the bias can be harder to explain. Regardless, any company utilising those tools has a responsibility for spotting and mitigating that problem.

    • Disinformation

    Some Generative AI outputs can sound very official and authoritative, but that doesn’t guarantee that what they output is actually real. Just ask the lawyers who used ChatGPT-to generate citations, only to find out that some were completely fake. Remember that these models are designed to create new content, not necessarily real content – so their reliability always needs to be checked.

No matter what your personal view about the promise of Generative AI, one thing is very clear: we’re only at the start of what looks set to be a fascinating, if complex, era. I’m sure we’ll have more to say on the subject in the coming months.

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