Artificial Intelligence: An Historic Perspective

We’re seeing three trends emerging around AI in legal tech.

We’ve discussed artificial intelligence (AI) quite a bit in this column thus far — and with good reason. AI is currently THE topic in legal tech (although Blockchain is certainly running a close second), and it’s almost impossible to carry on an in-depth discussion on the future of the legal industry without mentioning AI. Legal professionals, librarians, and analysts alike have speculated on the rise of the robo-lawyer, the role that increasingly sophisticated machines will play in the practice of law — and even whether lawyers will cease to exist at some point in the future.

Given the way in which AI has penetrated the conversation around legal technology, I think it makes sense to examine AI’s larger history. To quote from one of my favorite musicians, Bob Marley: “In this great future, we can’t forget our past.”

While AI is all the current rage, the term AI is not so recent in origin. The basic concept has actually existed for centuries, and the term itself was coined at a conference in 1956 at Dartmouth College. This session, known as the Dartmouth Summer Research Project on Artificial Intelligence, consisted of  prominent mathematicians and scientists who gathered to clarify and develop concepts around “thinking machines.”  Significant optimism around AI followed, as did funding, rooted in the belief that it was possible to create a machine with the intelligence of a human being. In a 1970 issue of Life magazine, Marvin Minsky (co-founder of MIT’s AI lab) predicted that this machine would exist in three to eight years’ time.

But as it became clear that computing limitations would prevent true advancement in AI, government funding and interest in the field came to a halt. This period, from 1974 to 1980, was the first “AI winter” — an apocalyptic term built on the concept of a nuclear winter where all life would be extinguished following a nuclear conflict, and was so called due to the stark fall-off in interest in AI. Nonetheless, interest in the field was revived in the 1980s with the rise of expert systems — computer systems that are modeled on “if-then” logic rules to simulate the decision-making ability of a human. Many of these systems did not deliver quite as successfully as hoped, leading to a second AI winter.

It seems that the third time for AI was indeed the charm. Interest in the field picked up again around the time of the internet boom. In 1997, IBM’s Deep Blue defeated World Chess Champion Gary Kasparov, and in 2011 we saw IBM’s Watson win “Jeopardy.”  As computing power and the availability of data continued to increase, we started to see a change in thinking around AI. The field has moved toward leveraging a machine’s ability to identify patterns in large sets of data, and in essence, AI has become less focused on machine thinking and more focused on machine processing.

So what changed? One important factor is the exponential increase in machines’ processing power. In 1965, Intel co-founder Gordon Moore famously predicted the doubling of processing power every two years — known as Moore’s Law — that has essentially been proven true for more than a century. The other crucial factor is data. Simply put, there’s more data being produced than ever before, and it has become increasingly cheap (and easy) to store. For perspective, it is predicted that more data will be produced in the year 2017 than has been created in all of recorded history.

What does this mean for AI in legal tech? We have the power to process data, store it so we can track it, and we have a tremendous amount of it.  Broadly speaking, when one has a data set — as we have discussed a bit before – one can apply AI to achieve two core goals: perform certain tasks faster, and provide a better outcome for clients. If law firms can accomplish both goals, there is a tremendous opportunity to improve overall profitability and growth for attorneys. We’re seeing three trends emerging around AI in legal tech.

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  • Law firms are investing in AI directly: Some law firms are bypassing the third-party vendor and partnering directly with providers like IBM Watson and Ross Intelligence. Law firms are also hiring their own data scientists to build their own analytics around areas such as billing and contracts.
  • Legal tech startups are emerging: A plethora of legal tech startups have come to the fore in recent years, leveraging AI tools to perform specific tasks such as research, prediction, document review and eDiscovery. Companies such as these have identified core tasks that law firms of all types and sizes can leverage.
  • Large, traditional publishers are investing in AI: We are seeing traditional publishers make significant moves to invest in AI, from Thomson Reuters’s agreement with IBM Watson, to Bloomberg BNA’s development of its own litigation analytics, to our own partnerships with several legal tech startups at Wolters Kluwer.

As we see AI’s potential in legal tech, we should approach with some caution based on what we have learned from AI’s life cycle thus far. Gartner developed a concept called the “hype cycle,” which states that new technologies are often accompanied by heightened expectations. When the technology falls short, interest decreases and the hype fades. Eventually, the technology moves to a productive use.

The disillusionment phase can sometimes tarnish the reputation of the technology on the whole — and in this case, could lead to another AI winter. But it’s possible to avoid that if we keep the following ideas in mind:

Avoid making AI a solution in search of a problem.

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Technology — irrespective of which technology — works to serve the business and not the other way around. No technology (including AI) is a catch-all for a multitude of problems, and therefore should not be treated as such.

Find legitimate use cases.

A bit of a corollary from the previous statement (and hopefully obvious), but technology projects do well when addressing a specific use case or problem. A useful technique in formulating the same is to ask yourself, “What is the user trying to do?” and in particular, “Why?” or “Why does that matter?”

Partner with like-minded individuals and organizations where possible.

Finding partners who are seeking to solve the same problem as you can be a highly productive method of leveraging resources, energy and time to benefit all parties.

The all-knowing computer that the Dartmouth Summer Research Project attendees dreamt of did not become a reality — but for lawyers, today’s version of AI application offers something better — a tool that can optimize one’s own expertise and provide better outcomes for clients.


May Goren Photography

Dean Sonderegger is Vice President & General Manager, Legal Markets and Innovation at Wolters Kluwer Legal & Regulatory U.S., a leading provider of information, business intelligence, regulatory and legal workflow solutions. Dean has more than two decades of experience at the cutting edge of technology across industries. He can be reached at Dean.Sonderegger@wolterskluwer.com.

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