It's been an incredible year for the'New Wave' of legal technology and Artificial Lawyer has hopefully been able to bring you some of the key moments in this evolutionary journey that is unfolding week by week all around the world. Now, as we head toward 2018, many of the leading players and commentators in the legal AI, legal bot and data analysis world have been asked to give their views on what has taken place and what will happen next. Plus, next year there will be predictions from the world of smart contracts and legal blockchain, (Hi, Clause, Integra Ledger and IBM's Cognitive Legal team, to name a few!) Artificial Lawyer asked an array of experts to name what was the biggest development for legal AI and the New Wave of legal technology this year, and what they expected to see happen in 2018. They were invited to respond with text or images to illustrate their views, and if they were up for it, a haiku or longer poem. Naturally, we couldn't fit everyone in – the legal tech world is just so massive now – but hopefully you'll find this collective wisdom both inspiring and thought-provoking – and fun – I know Artificial Lawyer did. Biggest development of 2017: 'I think the most significant thing was how mainstream legal AI became – mass adoption by firms and NewLaw, and more focus on integrations, grown up security requirements, APIs and the like.' Biggest development of 2018: 'I think we're going to see more news about wider ML applicability, not just NLP/ML for litigation document review, contract review in diligence and in-house contract review (the primary use cases to date).

Republicans argue that the lower taxes for corporations and wealthy individuals promised in the tax bill currently before Congress will result in new investment in businesses and more jobs. But in the age of artificial intelligence and automation, trickle-down economics won't create employment. What corporations and the US economy at large need most in this emerging era is not more free cash, but a new approach to machine-assisted human productivity and purpose. Olaf J. Groth (@olafgrothsf) is a professor of global strategy, innovation, and digital futures at Hult International Business School, as well as CEO of Cambrian.ai. With Mark Nitzberg he is the co-author of Solomon's Code: Humanity in a World of Thinking Machines, due in 2018.

Last Friday, the Northern District Court of California finally posted a long-awaited document, a letter written by the lawyer of an ex-Uber security employee. It was a doozy, a 37-page compendium of alleged criminal and unsavory activity witnessed by that employee, Ric Jacobs, while he worked at the company in 2016 and 2017. The letter came to light last week (after much legal tussling) as part of an ongoing lawsuit between Uber and Waymo, Alphabet's self-driving car spinoff. Waymo alleges Anthony Levandowski, a former employee, made off with trade secrets when when he left to found his own company, then brought those secrets to Uber when it acquired the startup. It's bad news for Uber in this legal fight, but the damage may not stop there.

From a raise of an eyebrow to a tilt of the head, we use several micro-movements when we're lying without even knowing it. Now, scientists have developed an artificial intelligence system that can detect these micro-expressions and detect if you're lying – and it's already'significantly better' than humans. The researchers hope their system could soon be used in courtrooms to tell if people on the stand are telling the truth. The researchers trained the AI to recognise five expressions known to indicate if someone is lying - frowning, eyebrows raising, lip corners turning up, lips protruded and head side turn. After watching 15 videos from courtrooms, DARE was then tested on whether it could tell if someone was lying in a final video.

Bias can creep in, but algorithms tend to increase fairness in the courts. In courtrooms across the country, judges turn to computer algorithms when deciding whether defendants awaiting trial must pay bail or can be released without payment. The increasing use of such algorithms has prompted warnings about the dangers of artificial intelligence. But research shows that algorithms are powerful tools for combating the capricious and biased nature of human decisions. Bail decisions have traditionally been made by judges relying on intuition and personal preference, in a hasty process that often lasts just a few minutes.

The experts realized the transformative power of A.I., and robotics, while it also seems to be willing to tackle ethical concerns, legal issues around these new technologies and their consequences on the labor market.

In less than five years, a 2012 academic breakthrough in artificial intelligence evolved into the technology responsible for making healthcare decisions, deciding whether prisoners should go free, and determining what we see on the internet. Machine learning is beginning to invisibly touch nearly every aspect of our lives; its ability to automate decision making challenges the future roles of experts and unskilled laborers alike. Hospitals might need fewer doctors, thanks to automated treatment planning, and truck drivers might not be required by 2030. Serious questions are starting to be raised about whether the decisions made by AI can be trusted. Research suggests that these algorithms are easily biased by the data from which they learn, meaning societal biases are reinforced and magnified in the code.

In April, it named a number of companies including Black & Decker, Bissell, iLife and Hoover in a complaint filed to the US International Trade Commission wherein it asked the commission to investigate their supposed patent violations and ban any products that it finds to incorporate any infringed upon intellectual property. Now, however, iRobot says it has reached an agreement with Black & Decker. Most of the settlement's contents are confidential, but iRobot says that Black & Decker has agreed to stop selling its robotic vacuums for a certain period of time once it works through its current inventory. In return, iRobot has removed its competitor from the pending US International Trade Commission investigation and US District Court of Massachusetts case. "This settlement represents another successful milestone in the enforcement effort iRobot initiated earlier this year," Glen Weinstein, chief legal officer at iRobot, said in a statement.

A pilot scheme will see machine learning taught how to grade the severity of the disturbing photos and footage, saving detectives from the distressing task. If successful, the trial could go into full service'within two to three years', according to the force behind its development. The approach is not without its drawbacks, including the legal ramifications of uploading such sensitive information. Police are granted legal permission from the courts to store criminal images. This protection would not apply to any cloud storage service providers.

Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers.