What is Generative AI – and How Can Government Use It?

Artificial intelligence has the potential to impact almost every area of life. In this article, we’ll delve into the technology behind generative AI, and explore how governments can harness its power.

You’ve probably heard about generative AI over the last year or so, since a free version of ChatGPT was made available to the general public. But what exactly is it? Generative AI (GenAI) is at the forefront of today’s focus, not merely as an advancement of artificial intelligence but as transformative technology that has the power to reshape how businesses, governments, and humans innovate, operate, create, and deliver value.

Generative AI is transforming the way we work

GenAI is an AI method that learns from real-world data to generate new content – and this could be text, images, audio, code, video, or tabular data, with similar characteristics of the data it is trained on. There are three primary applications of generative AI: large language models (or LLMs), synthetic data, and digital twins.

Large Language Models

A large language model (LLM) analyzes huge amounts of text – millions or billions of words – to train itself to know the relationships of words, and to then produce human-like text. For example, how would you finish this sentence, “After he messed up, the boy was in the dog-”? You said “doghouse” because that is a common phrase that you have heard over and over again. “He was in the doghouse” is commonly associated with making a mistake, messing up, acting badly.

The inventors of LLMs took the natural language processing concept of scanning each word in a sentence and translating it in a sequential process to instead reading an entire sentence at once, analyzing all its parts rather than the individual words. This provides better context.

So, LLMs track and learn relationships in sequential text – or text in which the arrangement matters – to respond to prompts using similar text. When we ask an LLM like ChatGPT or Copilot to write a sonnet about our favorite dog and his love of slippers, it seems like the system is being creative and generating completely new ideas. In reality, the system – using millions or billions of data – sequentially lines up the most probable next best word or groups of words. With unlimited computing power and tremendous speed, tasks that would take humans hours, weeks, or even years to perform can be done in seconds.

One of the most prevalent uses of LLMs is as an advanced search engine. Not only does it retrieve information from the far corners of the internet or an internal database, it often assembles the information in a relevant and consumable manner. But it is important to note that an LLM alone does not solve business tasks. The key is to integrate an LLM into a decisioning process. Combining an LLM with other computer or human systems – such as layered on top of other AI algorithms or part of an investigator’s inquiry process – accelerates value for an organization.

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Synthetic Data

Synthetic data is artificial data that accurately mimics real data. This on-demand, automated data is generated by algorithms or rules, as opposed to traditional data sets gathered from the real world. Synthetic data reproduces the same statistical properties, probabilities, patterns, and characteristics of the real-world data set from which the synthetic data is trained, and has been found to be as much as 99% statistically valid.

Governments can use synthetic data for various purposes, including research, testing, and analysis, without violating privacy regulations or exposing sensitive information. There are three primary reasons why governments will want to use synthetic data: to supplement their data set when there is not enough real-world data, to protect sensitive data, and to complete a data set when the real-world data is dirty or has gaps.

Synthetic data can revolutionize the way governments work

Digital Twins

A digital twin is a virtual model of a physical object or system from the real world. For example, a government might build a digital twin of a road network, a supply chain, or a financial system. A digital twin can be used to make predictions about the real-world impacts, such as that from an accident on a highway, a supply shortage, or an economic disaster. What-if analysis can be used to virtually test the effects that certain decisions might have in the real world.

Digital twins use a combination of different data as inputs such as historical, real-time data, synthetic data, and system feedback loop data as inputs. These inputs can be processed in batch or in real-time.

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The rise of AI: how you can use it

Many people ask why AI is taking off now if it has been in existence since the 1950s. It comes down to the maturity of three elements: computing power, data, and analytic models. For anyone who ever did programming, you recall how long it took to churn a program that used a lot of data. In the 1980s, it was common to start a program running before leaving work with the hope that it would run without errors overnight. Today, computers’ power to churn data is so great that those same programs run in seconds.

The computers have a whole lot more to churn now as we have lots of data – especially in the public sector. All of this data is the fuel that AI needs to produce results. And, finally, we have sophisticated analytic models that emulate tasks previously performed by humans.

Even with advancements in machine learning, deep learning, and generative AI, there is still a clear distinction between what humans and machines do well. Humans use common sense, intuition, creativity, empathy, and versatility. Machines take on tasks that humans could perform if we had all of the time in the world and didn’t get fatigued: processing large data sets – not only consuming, but learning from massive amounts of data, performing complex calculations, and automating tasks which can be performed without human intervention or assistance.

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In short, we expect machines to take on the work that is too cumbersome, too time-consuming – too boring – so that humans can optimize their use of time to improve outcomes.

AI technology is very powerful and will become even more so. But as with any great power, there is great responsibility. The advancements that we are seeing in AI technology have far-reaching effects and implications. Therefore, we must have a trustworthy and ethical approach as we set our strategy and guardrails for our use of AI and generative AI. We must place human-centricity, citizen interests, and doing the right thing first.

Consider these six principles as you adopt any AI or GenAI solution:

• Human centricity – remember that the AI solution is developed and run in order to promote wellbeing of people.
• Inclusivity – ensure that a system is created by and for people from a variety of backgrounds, with diverse perspectives and experiences.
• Accountability – be proactive in identifying and stemming adverse impacts.
• Transparency – provide a “clear box” rather than a “black box” – in other words – be open about how the AI system works, why it produces certain results, and what data it uses.
• Robustness (also referred to as stable AI or resilient AI) – implement an AI system that is able to function effectively even when operating in unexpected or changing environments.
• Privacy and security – protect the identities of the people who are subjects of the AI system.

These six principles can be difficult to navigate when working with AI and generative AI. It is critically important for humans to qualify the input, the prompts, and the output when AI and Generative AI is used. By keeping the “human in the loop,” governments will be able to take advantage of the strengths of AI while limiting the risks. Rather than diminishing the role of the human in government work, it will change and elevate the work that humans do.

The future of government work is changing

But, with the potential of AI to take on so much work, it is understandable that people are concerned about AI taking over their jobs. Is AI going to take your job? It is more likely that someone who knows how to use AI could take your job.