We all have them: subjects you’re somewhat familiar with — enough to take part in a short chat but not quite enough to add much depth. When your colleagues bring up one of those topics in casual conversation, you simply nod and offer a vague response, and the dialogue is generally over. Most of the time, that’s good enough.
When that topic becomes more commonplace, you may begin to worry that you should know more about it. Is this something everyone else already clearly understands? How can you catch up without drawing too much attention to the fact that you aren’t fully versed in the topic?
If that elusive matter is artificial intelligence (AI), it might not seem like something you need to invest a lot of time or energy into understanding fully. After all, it’s not impacting your work — and suddenly, it does.
When AI terms and conversation infiltrate your workplace
Your colleagues are talking more and more about AI technologies for legal professionals and how it is supposed to be revolutionary. They say things like “machine learning” and “structured data.”
When it comes to artificial intelligence, you may think that Amazon’s Alexa and Apple’s Siri are essentially robots that answer your questions. You know that many big-name companies employ robotic technology in warehouses, and you’ve read as many articles as the next person about how AI is changing the healthcare industry. More recently, you may have heard that OpenAI released its ChatGPT generative AI tool for public use.
Beyond that, things get a little fuzzy. AI is a technical subject; not every attorney comes from a technical background. What does all this AI stuff mean, and how can it make your life — and your job — better?
Common questions about AI
This list of common questions about AI aims to help you participate in the AI discussion — to provide meaningful contributions like you do with almost everything else. Some of it will be familiar concepts classified as more complex terms. We will break it down for you, question by question, so that, after today, you’ll know enough to hold your own in conversations about AI.
What is AI?
Artificial intelligence is the field of simulating human intelligence in machines to enable them to execute tasks that otherwise require human intelligence. AI enables devices to learn using previous experience, adjust to new inputs, and perform tasks, exhibiting aspects of human cognition such as perception, reasoning, learning, and problem-solving.
What is machine learning?
Machine learning is a foundational aspect of AI that focuses on studying algorithms. It allows computers to understand data and relationships, which enables them to perform certain tasks. Using multiple data points to identify patterns over time, machine learning powers technology to eventually make decisions or recommendations. This ability contrasts traditional computers that require explicit instruction for every aspect of a task. For decades, machines had to be taught everything. With AI, they learn.
What does machine learning mean to you?
Your smartphone knows you.
Your mobile learns that you always head home around 5:15 pm. Based on that information, your phone can predict how long it will take you to get home by analyzing factors such as time of day and actual traffic movement that day. It is learning from a combination of historical traffic patterns and real-time data about that day’s traffic. Machine learning also shows up in personalized recommendations, face and voice recognition, and a host of other applications, including some listed below.
What is generative AI?
Generative AI (GenAI) performs creative processes based on prompts given by people using it. For instance, you can ask it to generate new, sophisticated content, including complex text such as discursive and informative essays and articles — as well as images, audio, and video — just by giving it some clear parameters. The techniques it uses to generate these outputs include neural networks, probabilistic modeling, and deep-learning algorithms.
What is ChatGPT?
ChatGPT is a generative AI model. It is a variant of “generative pre-trained transformer” (GPT). ChatGPT is a language model developed by OpenAI, specifically designed for generating human-like responses in conversational contexts. It is perhaps the best-known generative AI model of its type currently available, but there are plenty of others, such as Microsoft’s Bing AI and Google Bard.
What do generative AI and ChatGPT mean for attorneys?
A new Thomson Reuters report discusses the evolving attitudes towards using generative AI and ChatGPT within law firms, even as concern persists.
Do you wonder if AI will put you out of a job? Your answer should be a firm no.
What AI can and will do is make completing tasks more efficient and less tedious. For instance, AI technology can go through and mark up hundreds of pages of documents much faster than doing it by hand. This time-saving technology can free up attorneys and support staff for more interesting and profitable work, such as building caseloads. It can also allow less-experienced legal professionals to begin delivering more value more quickly.
Steven Assie provided some advice in a recent interview with Canadian Lawyer: “One thing that lawyers can do today is to start experimenting and getting familiar with large language models like ChatGPT,” he said. “Not with confidential client work, but with routine questions as they go about their day. This will help them start to learn the basics of crafting a good prompt. It will also help them understand the current limitations of these types of tools.”
What is information retrieval?
Information retrieval uses stored data to help you find what you want when you want it. A web search engine like Google is a commonly used information-retrieval system. Stored information is searched based on the words or phrases used in the query and matched to the existing index of websites, data, content, and metadata that populates the web. It’s a lot to search through, but AI makes information retrieval fast and feasible for users across the globe.
What does information retrieval mean to you?
Alexa can tell you what time your favorite restaurant opens.
Your inquiry — “Alexa, what time does [favorite restaurant] open?” — is the first step in the retrieval process. Alexa, with a little help from the search engine Bing, combs through information stored on the internet to find your specified restaurant, specifically the one located close to your current location, and assess what piece of data on their business listing are the operating hours. The device must also be aware of the current day and time to ensure the information retrieved is a valid answer to your question.
What is natural-language processing?
Natural-language processing (NLP) focuses on understanding spoken and written human language, not robotic speech or restrictive text. Natural-language processing applies algorithms to extract and analyze language data in a way computers can process.
For machines to process enormous amounts of data — to be able to mine it and organize it and, ultimately, to understand and translate it — is imperative.
What does natural-language processing mean to you?
Natural-language processing means that when searching for a new gym by searching for “gym,” your results will include most places focused on fitness, regardless of whether the name of the business contains the word “gym.” From a traditional gym, CrossFit, or yoga studio, Google understands that “gym” and “studio” have a similar meaning.
Your search query doesn’t have to be all-inclusive — that is, you don’t have to type in “gym,” “studio,” “fitness,” “yoga,” “CrossFit,” “health,” and “club” — to get comprehensive results. You get to type like a human, not a robot. With a bit of help from machine learning, it’s going to keep your results local.
It also means that when searching through court decisions and briefs, you don’t always have to use exact-match language to find precisely what you’re looking for.
What is data mining?
Data mining is the process of looking for relationships, correlations, and patterns within large data sets. Technology systems scour data and recognize anomalies within the data at a scale that would be impossible for humans. This analysis helps predict outcomes, finds potential errors, and notices questionable trends. The information derived can be useful in a variety of ways.
What does data mining mean to you?
Your recommendations hopefully keep getting better.
By analyzing the patterns of people who also buy or are interested in the same products as you, a store can make relevant suggestions based on that data. This same concept plays out in Netflix recommendations or the seemingly endless stream of targeted advertisements on Facebook and other social media channels.
Another way data mining impacts your life is during document review. Rather than poring over page after page, hour after hour, you simply hit “enter” and the work is done. Imagine the confidence that comes from knowing something wasn’t missed because, unlike humans, computers don’t suffer from eye strain or fatigue from staring at documents and screens all day.
What is the difference between structured and unstructured data?
Simply put, structured data is organized data, sometimes referred to as quantitative data. It is objective and easy to export to and store in Microsoft Excel. The way it is organized is consistent and easily identifiable, which makes data mining better. Structured data is also less complicated to analyze and distill.
On the other hand, unstructured data isn’t so easily exported, stored, or organized. It’s the bulk of what most organizations deal with daily and includes most text-heavy data, such as reports, Microsoft Word documents, emails, and webpages.
What do structured and unstructured data mean to you?
Structured data has made it easy to complete searches and inquiries for decades.
Because the data is organized and objective, you can be sure the results shown are the most accurate. Earlier, we talked about your favorite restaurant’s operating hours. Structured data is what tells a search engine that “Monday – Friday, 6 am – 10 pm” is a meaningful record of store hours, not a nonsensical code to be ignored.
Until recently, unstructured data hasn’t been as effortlessly searchable. But advances in AI technology mean that there are now analytics tools that can gain insights from unstructured data.
What is clean data?
All data is not created equal. Clean data is properly maintained; incorrect, incomplete, or otherwise “bad” data has been modified or removed. Redundant data, whether it’s unstructured or because the data is being pulled from too many sources, can also skew results. Taking a proactive approach to data cleansing can be a time-consuming, expensive endeavor. But anyone who has spent hours sending out holiday cards only to receive dozens back in the mail due to outdated addresses can tell you that it is a critical component to creating trustworthy data.
Taking it a step further, clean data can also result from changing the processes that go into creating data in the first place. That discipline of managing data processes and ensuring data hygiene is called data governance.
What does clean data mean to you?
With clean data, you can be confident that you are relying on the best information to make the best decisions.
Making important decisions based on incorrect information never ends well. Tools that use a high volume of clean, structured data will produce the most trusted, high-quality results. The stakes are pretty low if that decision is where to go for dinner. But if you’re counting on data to know which auto parts store has a replacement battery in stock or whether the key witness has a criminal record, the quality of the data is paramount.
What does AI mean for your work?
We specifically chose most of these examples to place AI technology in a familiar, real-world context. But the same applies to AI-empowered legal technology. So many concepts and factors go into the search results, data analysis, and feedback that attorneys rely on daily. The best AI-based legal tech needs to read and understand the massive volume of information contained within the law. Armed with a meaningful amount of clean, organized data, legal professionals and their high-tech tools can deliver services with speed and confidence far beyond what has been possible before.
That’s the promise of AI and the vital takeaway you need to know. Pair that idea with the answers to some of the most common questions about AI, and you’re ready for your next conversation about artificial intelligence.
Watch this on-demand, free webinar hosted by Thomson Reuters to hear a discussion on the connections between artificial intelligence and legal work