Artificial intelligence + generative AI 

Some of the best scenes in the television show Star Trek, the original version, are those involving the crew members — usually Mr. Spock — asking the computer a question and the computer spitting out the answer in the form of a conversation. When I was younger, I thought this was utterly amazing, and, of course, I wanted my own computer that would “answer” any question I cared to ask. This was around the time when typewriters and dinosaurs ruled the Earth, and the first calculators — addition, subtraction, multiplication, and division only — were coming on the market. So, needless to say, a computer that could talk and interact with people was just a notion in a science fiction story back then.

I also remember the first time you could access the internet, send an email, stream music, wirelessly connect to a printer, use Slack or Teams to collaborate, and the time when Zoom made video conferencing as easy as clicking a button. Before this, setting up a video conference call took several hours, several folks from IT, and several years of your life as the technology inevitably failed, wasting a ton of time and effort. There was so much friction in setting up a video conference in those days; I am surprised no one burst into flames trying.

Yes, I’m a bit creaky and have been around awhile, but I am pleased to say that I have never been a “get off my lawn” type of person regarding technology. On the contrary, I have always embraced it, believing that technology can help lawyers do more, do it better, and do it at a lower cost. Never has this been truer than it is now in 2023.

Tracking and predicting the impact of AI and legal tech

So why the technology history tour? In 2017, I wrote a series of articles for Thomson Reuters on artificial intelligence (AI) and its impact on lawyers, particularly in-house lawyers. I wrote the series after my first exposure to what was then the advanced artificial intelligence — the arrival of Amazon Alexa and Google Assistant, two devices that allowed you to ask questions and get an answer. From a computer.  Just like Star Trek! The advent of these home assistants brought back two important memories from the TV show: intelligent computers that can talk and answer questions are pretty cool, and being the landing-party crewman in the red shirt is not.    I have always lived by the latter. Back in 2017, it dawned on me that I could start to live by the former, too, thanks to this giant leap in technology. Granted, Apple’s ground-breaking “Siri” product came first in 2011, but it was not as fully functional for purposes of what I was after with my Star Trek fantasy. Apple now has Homepod to compete with Amazon and Google, utilizing a beefed-up version of Siri. Honestly, it feels like the home-assistant technology peaked here in 2023, but adding ChatGPT to the technology may change that.

So, I jotted down a number of things I thought relevant about this “wow” moment in artificial intelligence, and the good folks at Thomson Reuters put it up on the internet.

In 2017, AI was just coming into its own in terms of uses by lawyers. I predicted that over the next few years , we would find ourselves on the cusp of a revolution in the practice of law led by the adoption of AI computers — in particular by in-house lawyers. Much like email changed how we did business every day, I wrote back then that AI would become ubiquitous — an indispensable assistant to practically every lawyer. Those who did not adopt and embrace the change would get left behind. Those that did embrace it would ultimately find themselves freed up to do the two things there always seemed to be too little time for: thinking and advising.

So, how’d I do? Not bad, but not great. It's kind of on par with the guys on NFL Countdown predicting the winners of the day’s football games. In short, the promise of AI for lawyers over the past several years has left a lot to be desired. Still, there were a lot of gains during that time, particularly with machine learning. While improvements were made and many products came online using AI, we have simply lacked the “oh my God” AI moment — until now.

Many of you probably first heard of ChatGPT in late 2022 or early 2023. Unless you have been living on the moon for the past nine months or so, ChatGPT has come to dominate headlines. Not only in the business world generally with changing how people do their jobs but also in the world of legal services where, for perhaps the first time, people are starting to ask if lawyers can survive this technological tsunami. I think they can, but that is for a later post. Welcome to the first of a multi-part series on artificial intelligence, generative AI (GenAI), and how in-house legal departments will be affected by both — especially the latter — which is the technology behind ChatGPT and its imitators.

Over the course of this series, I will discuss what AI and GenAI are; how they are, or can be, used by legal departments ; ethical considerations ; and — most importantly — what you as an in-house lawyer should be doing next regarding both.

What is artificial intelligence?

The term “artificial intelligence” can be a bit misleading, at least when it comes to application in the legal field. We’re not talking about some type of walking and talking robot from the “Terminator” movies with a briefcase and bowtie — although that would be awesome. No, artificial intelligence is an umbrella term to describe technologies that rely on data to make decisions. For purposes of the legal work, a better description — and one that has caught on — is “cognitive computing.” 

Cognitive computing uses AI systems that simulate human thought to solve problems using neural networks, machine learning, deep learning, natural-language processing, speech and object recognition, and other technology. Cognitive tools are trained versus programmed — learning how to complete tasks traditionally done by people, where the focus is looking for patterns in data, testing the data, and finding and providing results. AI is basically automating tasks. Cognitive computing is a step ahead; it’s about augmenting human capabilities. I think of it as a “research assistant” who can sift through the dreck and tell you what it found.  Why is this important?

According to Fabio Duarte of Exploding Topics, 328.77 million terabytes of data are created daily. In case you’re not up to date on terabytes, that’s 328,770,000,000,000,000,000 bytes — every day. The ability of any human to review and comprehend that level of data without help is literally the definition of impossible. AI systems that augment our ability to digest such a vast amount of data are now a critical part of our workday, especially for lawyers — for example, a Google search or content search on Practical Law. ChatGPT and generative AI add a whole new and powerful method of doing this.

What’s going on, and why now?

In 2017, I wrote that the explosion in AI was due to a fundamental rule of technology: Moore’s Law. In 1965, Gordon Moore — a scientist at Intel — made a prediction based on his observation that the number of transistors per square inch on integrated circuits had doubled every year since their invention. His law predicts that this trend will continue and growth in computer power will double roughly every two years while the cost of that computing power goes down. Simply put, it means more computer power for less money. Coupled with the ever-lower cost of storing electronic data, you have the basis for the rapid rise in AI capabilities and availability. For ChatGPT and GenAI, the sudden explosion into our consciousness was caused by decades of scientific work that was finally matched with the right level of raw computer processing power to make it feasible to launch and use such GenAI technology — that is, in late 2022, the technology caught up with the thinking and now ChatGPT is everywhere.

The popularity of AI and generative AI tools is simple: substantial productivity gains and cost savings are available from freeing humans from routine tasks. Tasks that computers can handle faster and better, allowing people to focus on tasks that require human critical thinking and truly add value — things that computers really cannot do or do well. As we’ll see in Part II of this series, this rationale fits nicely into the legal world.

Legal departments need to embrace the use of AI

Legal departments  need to be ready for this change and adapt quickly to the use of AI and, more recently, GenAI. As business leaders and businesses become adept at using AI and GenAI, they will expect the other members of the C-suite — including the general counsel and the legal department — to follow suit. Thus, it is becoming critical that in-house lawyers get on board the AI and GenAI train or risk getting left at the station. In-house lawyers that embrace AI and GenAI will, simply put, become more valuable to the new generation of CEOs and CFOs who are far more comfortable with technology than their predecessors. This means that law schools must step up their game and incorporate AI and GenAI into their curriculum.

How AI, machine learning, and generative AI work

Most scientists consider the 1956 Dartmouth AI Conference to be the birthplace of AI. Despite this early start, AI did not take off until computing power increased and the cost of that computer power and data storage decreased, which is a fairly recent development. One estimate is that twenty years ago, a CIO pursuing AI would have spent almost 100% of their budget on the necessary computing power. Today, that same CIO would spend only 10% to 20% of their budget, expecting that cost to continue to decrease.

For our purposes, artificial intelligence has evolved in three stages, and understanding this evolution is critical to understanding the power of generative AI.

Stage 1 :

Artificial intelligence is a computer mimicking human intelligence. At this stage, it can recommend a song you might like, spot spam emails and move them out of your inbox, and even drive a car. AI is about programming machines — like computers) — that can learn and solve problems like humans do.

However, these machines aren't conscious or aware like us — they don't have feelings. They just use mathematical rules and large amounts of data to simulate human intelligence. So, when you talk to Alexa, for example, it's not understanding you the way a person would. It's just analyzing your words quickly and choosing the best response from its vast programming resources.

Stage 2 :

On top of general AI came machine learning, a branch of artificial intelligence that allows a computer to learn from data without being specifically programmed. It's like teaching a computer to play chess. At first, the computer doesn't know how to play, but it gets better over time as it gets more and more information or experience. For example, you have a big pile of photographs and want to sort them by whether they show a dog or not. Before AI, you'd need to sort through all of them yourself.

But with the advent of machine learning, computers can learn to do this job. In essence, you show the computer thousands of pictures, telling it whether each picture has a dog or doesn't have a dog. Over time, the computer learns how to recognize a dog. Later, when it sees a new picture, it can predict whether a dog is in it. “Predict” is key because the computer isn’t learning like people do; it’s using math and algorithms, hence the ability to predict versus know.

Stage 3 :

Now comes generative AI, which is like Picasso in the artificial intelligence world. Instead of just learning patterns and making decisions like other versions of AI, GenAI can create new stuff. It can write songs, paint pictures, design graphics, or even write stories — like a computer dreaming up its own ideas. For example, imagine you teach the computer by showing it hundreds of pictures of cats. A typical AI might learn to tell you if a new picture is of a cat or not, but GenAI goes further. Much further.

After learning what cats look like, it can create a new image of a cat that doesn't exist, drawing on what it's learned from all the cat pictures it's seen. The same is true for music — and, for lawyers, drafting and writing. Generative AI is about creating new content or generating new ideas based on patterns learned from millions, if not billions, of examples. This ability to create something new is where the giant leap has occurred with GenAI, all made possible by recent breakthroughs in computer processing power.

Natural-language processing

On top  of these three phases comes the interface — that is, how do people and the machine interact? For years, the most common way has been to type information or queries into a computer, press “enter,” and wait for the answer. These types of searches have historically run on Boolean logic, or keyword searches, meaning each search is linear and bears no relationship to past or future inquiries. With AI and GenAI, that changes as each search becomes part of the learning process, and each search and answer — and correction, if necessary — makes the machine that much better for the next task.

Like my Star Trek example above, most people want to interact now via language, or talking to the machine. This is called natural-language processing (NLP). For example, “find me [x],” where [x] is the question you need answered. We’ll discuss this later in the series when we discuss prompts, the primary way you interact with generative AI today. Unlike AI from 2017, interacting via prompts is a new ball game that allows for incredibly realistic interactions with GenAI that were not possible until now.

Generative AI use cases for legal tech

With  generative AI, you have the basis for the next great leap forward in using AI by legal departments. Where initially it was the ability of machines to learn tasks that previously were done by lawyers — coupled with the ability of lawyers to extract pertinent information by either typing a query directly or by asking the machine to perform a task — with GenAI, the game is truly afoot. Lawyers can ask AI to create things instead of merely retrieving them. AI brought the ability to search for concepts, like contract review and analysis for due diligence; to identify changes in the tone of email communications, including looking for code words used to try to disguise the true nature of the conversation; and even crude drafting, that is, the computer understands what needs to be drafted and prepares the document.

GenAI takes all of this to another level. As we will see, reviewing and responding to redlines; preparing negotiation books, including anticipating the arguments the other side will bring to the table; and preparing summaries of meetings and documents — and doing so via different personas you ask the GenAI to adopt — are already here. Plus, those are just the tip of the generative AI iceberg!

In summary

C-suite executives are becoming excited about the potential of GenAI to stimulate change, enhance productivity, and drive cost savings to the business. The unspoken — or perhaps even spoken — expectation is that their legal department will follow suit. The capabilities of GenAI take the early promise of AI from the theoretical to the practical, allowing in-house lawyers to truly deliver better, faster, and cheaper legal services to the company.

We have an early look at the potential impact of this new, powerful technology on the legal industry, but how do you use it, what can it do, and does it mean an army of robo-lawyers will take over the profession? Tune in to Part II of this series to find out!