In the blockbuster movie “Terminator,” Arnold Schwarzenegger plays a time-traveling robot and weapon of “Skynet,” a computer system which nearly wipes out humanity in the future. The movie raises questions such as, will machine learning one day decide to turn against humans? Is AI going to replace my job? How does machine learning benefit us?
Machine learning technology is saving law firms thousands of dollars and improvements are arriving. The future of the tech world is speculative but with the recent media focus on automation and replacement of workers by machines, it is important to examine what machine learning truly is.
What is Machine Learning and the Future of AI?
First and foremost, machine learning is an area of artificial intelligence which was created and defined by computer scientist Tom M. Mitchell who describes machine learning as,
“the study of computer algorithms that allow computer programs to automatically improve through experience.”
Unlike traditional computer programming, machine learning is programmed to teach itself by examining and making sense of data-sets. This technology can analyze, make connections and notice trends at lightning speed which makes becomes more accurate with the more data it uses.
A Gartner report predicts by the year 2020, machine learning will eliminate 1.8 million jobs and, in that time, create 2.3 million jobs.
The report claims the leading cause for the massive job gains is that humans are required to design and manage machine learning systems. Machine learning and AI systems will always need to maintain the highest ideals and ethics to prevent future “Skynet-like” scenarios from happening.
There are those like Elon Musk and Stephen Hawking who raise concern that machine learning could one day run amuck and become a danger to humans. However, those like Eric Schmidt, a former Google CEO, believes that Elon Musk is wrong and artificial intelligence is “fundamentally good for humanity.” Schmidt suggests that the government should fund research and education to unveil the negative stigma and unveil the positive benefits of such technology.
As Alvin Lindsay, partner at Hogan Lovells recently noted,
“[W]e are not yet at the point where one device, product, or ‘robot’ can do everything (or even many of the things) a lawyer can do. We’re also not at the point where there is a lot of self-learning or teaching AI.”
In fact, the general consensus stands that machine learning presents more of a benefit than a cause for fear. This consensus becomes clear and understandable when examining why and how people are benefiting by using machine learning today.
The World Health Organization (WHO) estimates that 2.2 million people in 2010 were infected with dengue fever with a dramatic include jumping to 3.34 million people in 2016. There is currently no treatment for the mosquito-borne virus. WHO also claims that half of the world’s population, approximately 3.9 billing people are at risk of getting infected.
Digital technology organizations like DrivenData are working to tackle such issues. DrivenData is an organization of scientists and engineers that hold competitions to crowdsource solutions to some of the world’s biggest social challenges.
One competition currently underway is called “DengueAI: Predicting Disease Spread.” The competition challenge summary outlines its objective for scientists to use data collected from various U.S. federal government agencies to predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru. The challenge competition goals include to:
- Help increase research initiatives
- Improve awareness
- Resource allocation
Through this competition, thousands of data scientists have made notable progress using machine learning to confront and discover solutions to the dengue fever problem.
The top of the leaderboard boasts well over a dozen contestants with a Mean Absolute Error (MAE) of under 14. MAE is a way to measure the difference between the predictions and actual reported cases. A lower score is better and at 13.0144 the current leader is a team from the Pennsylvania Institute of Health and Technology.
As another example, Microsoft recently announced plans to “solve cancer” in 10 years and as part of this effort, Microsoft researchers are using machine learning to better identify and track tumors.
It’s not just global challenges that machine learning is solving. The existence and adoption of AI and machine learning technology bring forth opportunities in business and productivity.
CEO of Adinton, Rafa Jimenez believes that "[m]achine learning and other cutting-edge technologies have opened new opportunities for investing their marketing budget smarter." For marketers, machine learning reduces cost by predicting customer reaction, behavior, and habits at a fraction of the cost of focus groups.
Without doubt research such as this is leading the way to save lives and challenges the way we face and solve major world challenges. In the business world, such research and information related to saving time and money—and law firms are no exceptions.
Computer-Assisted Billing Is Available Now
A large amount of data is required to “train” machine learning systems to think on their feet. However, in business verticals where such data is inexpensive and readily available, AI makes the information much more valuable.
One such business vertical is law firms, where billing information and other data abound.
Machine learning can significantly help make life easier for attorneys. AI and machine learning technology have been in the making for decades and the time has arrived to be useful to help lawyers improve the legal billing review and procedure.
Attorneys face the monthly challenge of billing their clients. Billing partners or other attorneys responsible for sending out bills to clients typically spend a considerable amount of un-billable time reviewing and documenting time and expense entries before the final bills are submitted for payment.
“Historically, a billing partner reviews and prepares bills for a client, manually making sure that no charges are duplicative and clients are not getting overcharged,” notes a billing partner at a law firm in Chicago.
An application called BillerAssist was created to solve this problem using AI and machine learning technology. BillerAssist increases a law firm’s realization and collection rates while decreasing write-offs with the power of automation.
Through artificial intelligence and machine learning, BillerAssist color codes time and expense entries based on a likelihood of being paid. The color-coding feature offers transparency and predictability from the timekeeper’s perspective. Timekeeper users can view if and what a client is likely to pay for and what will likely be written off as the billing entries are getting inputted in real time.
In other words, associates and other timekeepers are able to view in real-time if they are spending too much time on tasks for a client who is unlikely to pay by following simple color codes. The same color-coding helps billing partners review time and expense entries by flagging problematic charges before a final bill is approved for submission.
In real-time usage by law firms, this method substantially reduced the cost of bill review and processing.
Because BillerAssist uses AI and machine learning technology, the application software learns from your changes the more you use it. BillerAssist comes with a data set for lawyers to get started immediately. The application also allows users to easily provide their own billing data to be used, updates itself over time and adapts to the preferences of its law firm users or as the application is trained for specific clients.
“With BillerAssist LEDES Edition, we’re at a much higher realization rate now, and partners can much more easily predict whether the client will pay for charges and expenses. It solved significant realization and collection rate issues we used to have at our firm,” adds a Chicago billing partner and long-time user of the application.
Early Adopters are Winning and So Can You
Many law firms are seeking out ways to use machine learning to boost revenues and improve efficiency. Law firms have been using AI technology to stay efficient such as those like Perkins Coie. Geoffrey Vance, the law firm’s firmwide Chair of E-Discovery Services and Strategy Practice, noted that:
“I’m optimistic that AI will soon permeate law firm cultures, which will in turn provoke new companies and new investments to improve and increase law firm AI solutions so that every lawyer in every law firm will use and benefit from AI.”
It is no surprise to find law firms are losing out on revenue due to outdated technology. Old tools also contribute to low morale and overall low productivity. Those who stay on top of the latest technology trends already know the importance of using legal automation and artificial intelligence technology to increase revenue.
AI and machine learning are providing lawyers with the opportunity to implement time-saving features into their daily management systems and sharpen legal operations.
Machine learning and AI are not just buzzwords or hype. Nor are they a threat to lawyers or law firms. Quite the contrary, law firms can start to saving time and money by learning about these legal technological advancements and applying them in various appropriate parts of their practice.