We all have them. Subjects you’re somewhat familiar with – enough to pull your weight 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, offer a compulsory 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. 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-to-you matter is artificial intelligence (AI), it might not seem like something you need to invest a lot of time or energy into wholly understanding. After all, it’s not impacting your livelihood, right?
And then, suddenly, it does.
When the AI conversation infiltrates your firm
Your colleagues are talking more and more about AI technology for legal professionals that is supposed to be revolutionary. Machine learning, they say. Structured data, they say.
When it comes to artificial intelligence, you understand that Amazon’s Alexa and Apple’s Siri are essentially robots who 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 health care industry.
Beyond that, things get a little fuzzy. AI is a technical subject, and not every law firm leader comes from a technical background. What does all of this AI stuff mean and how can it make your life – and your firm – better?
This is what you need to know about artificial intelligence
The aim of this list is to help you participate in the AI discussion. To provide meaningful contributions in the same way you do most everything else. Some of it will be familiar concepts classified as more complex terms. We will break it down for you, question by question. And no one will know that, up until today, you knew relatively little about AI.
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 required 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 phone knows you.
Your cellphone 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 analyzing 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 from 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 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 [insert 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 focuses on understanding human language – both spoken and written – not robotic speech or restrictive text. Natural language processing applies algorithms to extract and analyze language data in a way that computers can understand.
For machines to be able 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?
You don’t have to be good at Boolean searches to get good Google results.
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 inquiry doesn’t have to be all inclusive (gym and studio and fitness and yoga and CrossFit and health and club) to get all-inclusive results. You get to type like a human, not a robot. And with a little help from machine learnings, it’s going to keep your results local.
It also means that when you’re searching through court decisions, dockets 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 recognize anomalies within the data at a scale that would be impossible for humans. This analysis helps predict outcomes, finds potential wrongdoings, and notices questionable trends, and that 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 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 organized 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 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. And it’s the bulk of what most organizations deal with daily. It includes most text-heavy data, such as reports, Microsoft Word documents, emails, and webpages.
What does 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 organized and objective, you can be sure that the results you are shown are the most accurate. Earlier, we talked about your favorite restaurant’s operating hours. Structured data is what tells a search engine that “Mo-Fr 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 means 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 is 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 in the mail due to outdated addresses can tell you, 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 clean data to know which auto parts store has a replacement battery in stock, or whether the witness you’re counting on has a clean criminal record, the quality of the data is paramount.
What does AI mean for your firm?
Most of these examples were specifically chosen to place AI technology in a familiar, real-world context. But the same applies for 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 both read and understand the massive volume of information contained within the law. Armed with a meaningful amount of clean, organized data, attorneys and their highest-tech tools can deliver services with a speed and confidence far beyond what has been possible before.
That’s the promise of AI, and the key takeaway you need to know. Pair that idea with the terms addressed above, and you’re ready for your next conversation about AI.