Predicting the Generative AI Revolution Requires Learning From Our Past

Having frequently worked with governments around the world over the course of my career, I’ve  had all kinds of discussions about the global impact of generative AI. Today, I’m publicly wading into those waters to deliver my perspective, and my opinion is that … it’s incredibly hard to predict the future. Done. Wrapped up this entire post in a single sentence.

All joking aside, there’s a great deal of hype around gen AI, with predictions that it will have a huge impact on office workers, our everyday way of life, wealth disparity, the future of work, education — you name it. For my part, I believe there certainly will be impacts, but I’m reluctant to make specific predictions. 

However, I think certain insights can help us prepare. A general framing of where this particular innovative technology might lead us can be helpful — especially for those developing data strategies, AI capabilities and technology transformations across government. Not only do government leaders need to consider how they evolve as mission-driven organizations, but also how their outsized effect on citizens needs to account for this revolution. 

Formulating a revolution

Let’s take a look at a macro-framework for how technology creates revolutions and then apply it to gen AI.

Here is the basic formula:

Infrastructure + Products = Revolution in X

A revolutionary innovation requires infrastructure that makes the underlying technology readily available. Products align the innovation to answer specific value requirements or use cases. These two aspects democratize the usage and make an innovation cost efficient enough to create a revolution. Another way to describe the equation is that it takes an ecosystem of specialized products on top of an expansive infrastructure for an innovation to change the world.

This is easier to see with examples from history. Here are two previous technology revolutions viewed through this framing:


[electric grid] + [electric consumer products] = better form of energy transfer (vs. coal or wood)

The electric grid plus electricity-based products such as lights or computers allowed for an innovative way to transfer energy to transform the world. 


[telco networks] + [software and hardware] = better form of data transfer (vs. paper or fax) 

A digital telecommunications network plus data-leveraging products allowed for an innovative way to transfer data to change the world. (In this case, the early infrastructure leveraged existing telco networks.)

This basic model can be applied to a number of revolutionary technologies such as the combustion engine, currency, the printing press and more.

So what would AI/ML look like in this model?

Infrastructure = data

Products = algorithms

If data is the infrastructure in our equation and algorithms the product, what then is the X factor? I think X in this equation would be a better form of functions (those that are more complex and accurate), which can be thought of as probabilistic models of reality. This isn’t something new — we’ve already modeled economies, financial trends, businesses, even golf. Physics is a mathematical model of reality. But what happens when we can do this easily and accurately with small sets of data? What happens when everyone can do this without taking graduate-level statistics and modeling? In a generation or less, dieticians could model ideal healthful diets for patients and society could model optimized learning pathways for students. On top of that, they’d be able to share individual functions and outcomes for an incredible network effect.

This algorithmic thinking, at scale and across society, will launch a revolution. Where do we use humans today to essentially perform a set of complex functions? Examples of work likely to be redefined and augmented by AI include the collecting of medical diagnostics, financial advising, and more. Now think about a society in which those functions are easy to create, customize and share. 

There is much to unpack when we frame the AI revolution in this way, but I’ll say this: I spend a lot of time working with governments and helping them adjust their perspective to see that data is infrastructure, on top of the traditional concept of infrastructure (cloud). We strategize together on the second- and third-order implications of this perspective, such as how this data infrastructure needs to be architected not just for the products we know about today, but also for those yet to be imagined. Crawl, walk, run.

Language is a reflection of ourselves

Disinformation attacks are only going to get worse as we head into key elections around the world. AI can be used to generate increasingly convincing fakes. We have more bad actors leveraging disinformation than ever, and this problem will only get worse because of large language models (LLMs). 

While I said I was unwilling to make a prediction on the future impact of AI, I’ll wager that a malicious nation-state somewhere out there is already researching how to use LLMs to make disinformation campaigns worse. And they’re not prompting the LLM for fake news; they’re using it for what it is: a probabilistic representation of society.

Let’s use GPT-4 as an example. It is a highly complex statistical model that represents the data it was trained on. Where does that data come from, you ask? It comes from the internet, textbooks, social media and many other sources. This model is fantastic at generating responses to our prompts because it so closely represents us as a society. I’m thinking of a quote from one of my favorite novels, Babel by fantasy writer R.F. Kuang: “Languages aren’t just made of words. They’re modes of looking at the world. They’re the keys to civilization.” Because they are based on language, LLMs are also “modes of looking at the world.”

There is a good amount of research in this area. We’ve seen researchers use LLMs in economics to simulate many individuals and the decisions they’d make. Others have used them to predict partisan reactions to various politically charged responses. One researcher fed an LLM a specific media diet to predict public opinion.

I talked earlier about the democratization of these functions, but let’s dive into the implications of what a complex function means in reality. An LLM trained on the data of a society represents a view of that society. What can that view be used for? What can it tell us about ourselves that we don’t know?

Opportunities and threats

When we think about LLMs, it shouldn’t be all doom and gloom. A strengths, weaknesses, opportunities and threats (SWOT) analysis rightfully places opportunities and threats together because they coexist. There’s a huge potential for LLMs to have a positive impact on the world. This simulation function means governments can pre-test domestic policies for societal impacts before they are implemented. New laws and government programs can be tested for unknown negative externalities. Our own intelligence agencies can use these models to help keep us safe.

GPT-4 cost $100 million to train. Would the U.S. intelligence community be willing to pay $100 million to have an accurate model of another country’s decision-making processes? How about a set of functions that model key nation-states and how they interact? 

As gen AI models become more ubiquitous, we also face the distinct risk of regression to the mean. This means extended AI usage gravitates around the averages in our models. So society ends up producing similar tasting products, similar sounding songs, similar style books and movies, and so on. Commercialism already drives some of this, but LLMs could accelerate regression to the mean. What we lose are the happy accidents and serendipity. We could lose the benefits of diversity. This is something that policy-makers across government should seriously consider.

Hopefully, the incredible insights that LLMs bring help us better understand each other. Despite the many risks, I believe we’ll find we’re much more alike than different, and there are many paths to cooperation across governments in the global community. 

Moving beyond LLMs

Gen AI has captured the imagination of people everywhere with its very human-like outputs of conversations, writing, music, images and more. These interactions with AI can feel amazingly natural and authentic, and occasionally surprising in delightful or humorous ways. However, gen AI is not only for human interaction — other models can be used effectively for  analytic and business applications that differ from LLMs. Let’s dig into some examples, all explained at an executive level, and how businesses might deploy these.

To understand how these gen AI models work, we need to understand how a generative algorithm works. The simplest explanation is that we enter a prompt, which is converted into a set of numbers (a “numeric input”), and that is entered into the function. The function then generates the output. It’s not that unlike sixth grade algebra, when we took a function and plugged in x to calculate y. The key difference is that in order to get the y output to be as detailed as a generated image, the function and inputs x must be extremely complex. 

Understanding GANs and VAEs

But how does the algorithm know how to convert our input into something we understand? This is where we get into how specific models are trained. Let’s look at two generative models called generative adversarial networks (GANs) and variational autoencoders (VAEs).

GANs work by making two models (neural networks) compete against each other, which is why it is called “adversarial.” The first model’s job is to generate an output that looks like real data. The second model (called a discriminator) tries to discern fake data from real data. The second model gets inputs of both real data and the fake (generated) data of the first model. Both models continue to train and get better at their job until the discriminator cannot tell fake from real data. At this point, your first model is trained to output very realistic data and can be used for generative AI.

VAEs also have two models but they do different things. The first model takes a lot of data and converts it into a simplified set of numbers (we call this encoding, which is where the “autoencoder” term comes from). Those numbers are then organized. The second model takes those simplified numbers and tries to generate the original data, or as close to it as possible. It’s sort of like dehydrating food and then reconstituting it — the goal is for the second model to reconstruct the first as closely as possible. When the second model gets really good at this, the training is completed. It becomes a generator. The trick is the simplified numbers in the middle of this training process were organized in a logical manner. The result of that organization means our inputs now generate logical outputs in the same way the original data was organized.

Using AI insights to solve real-world problems

Let’s look at this in practice. I had some fun building a GAN for profiles of whiskey. I scraped the web for various whiskey reviews, converted those into tasting profiles, and then trained the GAN on that data. Eventually, I could ask the generative model to output a 1,000 unique whiskey profiles a master distiller might realistically create.

So what did I do with that generated data? I analyzed it and used the insights to help my own home aging techniques. It’s like having a huge survey of master distiller’s advice on what profiles to develop.

Let’s apply this to problems faced by governments globally. Here are some questions that, with the right data and training, these models could help answer:

  • For banking, financial regulatory, and AML oversight: What might new forms of money laundering look like? Can we generate 10K synthetic financial statements that show us the risk of money laundering? What could we learn from that?
  • For military and transportation departments: What different logistics plans solve our needs but in unique ways? If we looked at a large sample of logistic routes that all met our mission, would we see trade-offs between decisions we never noticed before?
  • For central banks: What fiscal policies might help to reduce bank failures given our plans to change interest rate targets? If we could run a distribution of simulated bank outcomes to a monetary change, would we discover unforeseen effects and risks?
  • For counterintelligence: What unknown patterns of behavior might indicate intelligence gathering? Could we identify collection methods not in use or unknown to us? Could we identify sources we didn’t realize existed? 

AI is out of the barn

There is a whole world of generative options beyond LLMs. In this post we looked at a macro-framework to prepare us for the coming AI revolution, unpacked the depths of what an LLM can offer, and explored other generative AI models. I’d like to share a final example of a policy point that affects global governments and those they regulate.

We’re moving to a point in time when all decisions will require consulting an AI. Not because the AI will be right all the time, but because it will soon be irresponsible not to have weighed an AI input, a relevant statistical model of reality, during the decision-making process. There is no going back on innovation. In fact, ChatGPT gave me 14 idioms that convey this exact idea, including one I hadn’t heard before but which makes perfect sense: “The horse is out of the barn.”

The post Predicting the Generative AI Revolution Requires Learning From Our Past appeared first on Snowflake.


See Our Latest

Blog Posts

admin June 12th, 2024

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with […]

admin June 12th, 2024

Discovering and surfacing telemetry traditionally can be a tedious and challenging process, especially when it comes to pinpointing specific issues […]

admin June 12th, 2024

Today’s data-driven world requires an agile approach. Modern data teams are constantly under pressure to deliver innovative solutions faster than […]