Everything you wanted to know about AI but were afraid to ask
Thomson Reuters' Legal Insights provides an overall guide to AI and how it will affect the legal profession.
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. And most of the time, that’s good enough.
When that same topic starts to become more and more commonplace, you may start 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 then, suddenly, it does.
When AI terms and conversation infiltrate your workplace, make sure you can be a part of the conversation.
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. Machine learning, they say. Structured data, they say.
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. And 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, and not every lawyer comes from a technical background. What does all of this AI stuff mean and how can it make your life – and your job – better?
Common questions about AI
The aim of this list of common questions about AI is to help you participate in the AI discussion. To provide meaningful contributions in the same way 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 in order to enable them to execute tasks that otherwise require human intelligence. AI enables machines 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 focusing on the study of 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 stands in contrast to traditional computers which 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 is able to predict how long it’s going to take you to get home by analysing factors such as time of day and actual movement of traffic on the day in question. 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 personalised recommendations, face and voice recognition, and a host of other applications, including some listed below.
What is generative AI?
Generative AI performs creative processes based on prompts it is 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 modelling, and deep learning algorithms.
What is ChatGPT?
ChatGPT is a generative AI model. It is a variant of GPT, which stands for ‘’Generative Pre-Trained Transformer.’’ ChatGPT is a language model developed by OpenAI and it is 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 lawyers?
A Thomson Reuters report discusses the evolving attitudes towards the use of 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 can free up both lawyers 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.
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 favourite restaurant opens.
Your inquiry – “Alexa, what time does [Favourite 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 focuses on understanding human language – both spoken and written – not robotic speech or restrictive text. Natural language processing applies algorithms to extract and analyse language data in a way that computers can process.
For machines to be able to process enormous amounts of data – to be able to mine it and organise it and ultimately, to understand and translate it – is imperative.
What does natural language processing mean to you?
Natural language processing means that when you’re searching for a new gym by searching for “gym,” your results will include most places that are focused on fitness, regardless of whether the name of the business actually includes the word “gym.” From a traditional gym, a CrossFit or yoga studio, Google understands that “gym” and “studio” in this instance have a similar meaning.
Your search query doesn’t have to be all-inclusive (i.e. you don’t have to type in “gym” and “studio” and “fitness” and “yoga” and “CrossFit” and “health” and “club”) to get comprehensive results. You get to type like a human, not a robot. And with a little help from machine learning, it’s going to keep your results local.
It also means that when you’re 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 a process of looking for relationships, correlations, and patterns within large data sets. Technology systems scour data and recognise 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 analysing 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 that 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?
To put it simply, structured data is organised data. It may be referred to as quantitative data. It is objective and easy to export to and store in Microsoft Excel. The way it is organised is consistent and easily identifiable, which makes data mining better. Structured data is also less complicated to analyse and distil.
On the other hand, unstructured data isn’t so easily exported, stored, or organised. And it’s the bulk of what most organisations deal with daily. It 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 for you to complete searches and inquiries for decades.
And because the data is organised and objective, you can be sure that the results you are shown are the most accurate. Earlier, we talked about your favourite restaurant’s operating hours. Structured data is what tells a search engine that “Mon-Fri 6am to 10pm” 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. In other words, 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 endeavour. 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 be the result of 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?
You can be confident that you are relying on the best data 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. If that decision is where to go for dinner, the stakes are fairly low. 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?
Most of these examples were specifically chosen 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 lawyers rely on daily. The best AI-based legal tech needs to both read and understand the massive volume of information contained within the law. Armed with a meaningful amount of clean, organised data, legal professionals and their high-tech tools can deliver services with a speed and confidence far beyond what has been possible before.
That’s the promise of AI, and it’s the key 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.
This article was provided by Thomson Reuters Legal Insights Europe, which regularly shares news, insights and best practice on important trends impacting the legal industry.