Javed Qadrud-Din was an Insight Fellow in Fall 2017. He is currently a machine learning engineer at Casetext where he works on natural language processing for the legal industry. Prior to Insight, he was at IBM Watson. BERT can be pre-trained on a massive corpus of unlabeled data, and then fine-tuned to a task for which you have a limited amount of data. This allows BERT to provide significantly higher performance than models that are only able to leverage a small task-specific dataset.

UPDATE: Check out below where Lexis outlines some issues they have with the quality of the results. The language around the technology has softened from the height of its hype cycle, but there's still a sense out there that AI is this "thing." As one legal tech leader put it to me last year, "a lot of lawyers act like'we need to get some AI' without trying to figure out how AI solutions might be different." To some extent, that still holds sway. It's a conclusion that's not entirely off base because some solutions use the same underlying AI algorithms.

Often when we hear about artificial intelligence in legal it's addressed from a high-level, philosophical perspective that sometimes ignores the immediate use cases for practicing lawyers. Flying in the face of this, AI in legal took a major step forward on June 20 at the University of Chicago's Gleacher Center where leaders from law firms, legal technology providers, law schools and in-house legal departments gathered to examine AI's convergence within specific areas of legal, namely: e-discovery, contract review, contract analysis, litigation, and of course, legal research. There are roughly 900 legal tech startups in the legal ecosystem all attempting to improve how law is practice from solo shops to the biggest firms in the world. Among these tech providers, AI-based tools are becoming more widely accepted and better understood among lawyers. In an encouraging sign, more corporate clients are demanding their outside counsel use these technologies to be more accurate, more innovative and more efficient.

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The rampant spread of artificial intelligence (AI) across all industries is happening so fast that our MBAs can barely keep up with it, making us wonder – when can we just replace our MBAs with artificial intelligence and never have to hear about Porter's 5 Forces ever again? Just about every stakeholder out there who has an economic interest tied to a firm is asking the same question – no, not about Porter – about how can we use AI to create more efficiencies and outperform our competitors. Some industries just scream out their suitability for an AI take over, like the legal industry for example. According to the American Bar Association, there are 1.1 million lawyers practicing in the U.S., a number which represents 80% of the world's lawyers. While some of these lawyers do good, many do not.

Artificial intelligence companies are sprouting all over the world. With rapid advancements ranging from virtual assistants to fraud detection, AI has come a long way recently. Almost every organization, whether it is fashion, banking or retail, is using AI in a way possible to innovate and make their services better. Technology giants like Apple, Google, Intel, Samsung and Salesforce are competing in the race to acquire private AI companies. More than 200 small companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in the first quarter of 2017 alone.

Robot lawyers have been getting so much attention lately that AI-and-law thought leaders believe we have reached peak hype. Journalists have responded by toning down their headlines to better manage expectations. For example, last month the New York Times ran an article titled, "A.I. But It Won't Replace Lawyers, Yet," and the ABA Journal gently warned, "The robot lawyers are coming (to help, not to take your jobs)." The Times article explains that automation generally happens task by task.