Should Politicians be Replaced by Artificial Intelligence?

The U.S. Food and Drug Administration (FDA) has just approved the first medical robot from Auris Surgical, a stealthy startup led by the co-founder of industry leader Intuitive Surgical, makers of the widely-used da Vinci robot. The teleoperated ARES robot (the acronym stands for Auris Robotic Endoscopy System), was cleared by the FDA at the end of May, and could now be used for diagnosing and treating patients. Auris, which describes itself only as a "technology company based in Silicon Valley," was previously thought to be working on a robotic microsurgical system designed to remove cataracts, and the company has in fact filed several patent applications along those lines. However, an investigation by IEEE Spectrum suggests that the company has greater ambitions, including, according to current and former employees, "building the next generation of surgical robots… capable of expanding the applicability of robotics to a broad spectrum of medical procedures." A close reading of recent patent applications filed by Auris scientists shows that the company is focusing on so-called endolumenal (or endoluminal) surgery.

This invention relates generally to image processing and, more particularly, to object detection using machine learning. Face detection systems perform image processing on digital images or video frames to automatically identify people. In one approach, face detection systems classify images into positive images that contain faces and negative images without any faces. Face detection systems may train neural network for detecting faces and separating the faces from backgrounds. By separating faces from backgrounds, face detection systems may determine whether images contain faces. A good face detection system should have a low rate of false positive detection (i.e., erroneously detecting a negative image as a positive image) and a high rate of true positive detection (i.e. Face detection remains challenging because the number of positive images and negative images available for training typically are not balanced. For example, there may be many more negative images than positive images, and the neural network may be trained in a biased manner with too many negative images. As a result, the neural network trained with the imbalance number of positive and negative samples may suffer from low accuracy in face detection with high false positive detection rate or low true positive detection rate. Face detection also remains challenging because facial appearance may be irregular with large variance. For example, faces may be deformed because of subjects having varying poses or expressions. In addition, faces may be deformed by external settings such as lighting conditions, occlusions, etc. As a result, neural network may fail to distinguish faces from backgrounds and cause a high false positive detection rate. Thus, there is a need for good approaches to accurate face detection and detection of other objects.

Star Trek turned 50 in 2016. In its half-century of existence -- on TV, on the big screen, and in the worldwide community of its fans -- Star Trek has become an integral part of our everyday lives. Even casual viewers know the pointed ears, the Vulcan salute, and the meaning of "beam me up, Scotty." Yet, Star Trek does not owe its enduring popularity and its place in our collective imagination to its aliens or to its technological speculations. What makes it so unique, and so exciting, is its radical optimism about humanity's future as a society: in other words, utopia.

The definition of civility typically revolves around the rules, mores and assumptions for how we deal with each other. The previous talks in this series have focused on that kind of civility in a variety of human activities including sports, education, business and law enforcement. But I'm going to be talking about something that is not human--the increasingly clever computing technology that surrounds us. And how we think about, relate to and interact with this technology. For the title of this talk, I chose the most evocative term, artificial intelligence, or AI for short. It was "cooked up," as its author the mathematician John McCarthy once told me, for a grant proposal he wrote in 1955. He was seeking funds for a conference the following summer at Dartmouth College. It was a brainy marketing pitch.

In a promotional video for Amazon's Echo virtual assistant device, a young girl no older than 12 asks excitedly: "Is it for me?". The voice-controlled speaker can search the web for information, answer questions and even tell kids' jokes. An investigation by the Guardian has found that despite Amazon marketing the Echo to families with young children, the device is likely to contravene the US Children's Online Privacy Protection Act (COPPA), set up to regulate the collection and use of personal information from anyone younger than 13. Along with Google, Apple and others promoting voice-activated artificial intelligence systems to young children, the company could now face multimillion-dollar fines. "This is part of the initial wave of marketing to children using the internet of things," says Jeff Chester, executive director of the Center for Digital Democracy, a privacy advocacy group that helped write the law.

Artificial Intelligence has permeated almost every industry, either in word or deed, in the last couple of years. From financial institutions to ride-hailing services such as Uber, companies are clambering over one another to take advantage of this technology to stay ahead of the competition. However, one area which Artificial Intelligence has been unable to find a platform in, until very recently, has been the legal sector. There is a belief that the legal sector, particularly when it comes to the courtroom environment, is reserved exclusively for sharp-suited lawyers, who have trained for years to be able to build and present a case in order to persuade a jury of their peers of the validity of their argument. However, the tide might now be turning.

Why do you think technologies such as AI and predictive coding are apparently gaining momentum in term of their uptake in the UK legal market? First and foremost, the legal market isn't an isolated bubble. There is so much happening right now in the world at large about AI and cognitive computing that the legal market simply can't be immune. Our behaviours in the workplace are driven (and increasingly so) by our experiences in the rest of our lives, so when there's a constant stream of mainstream news about AI, it's inevitable that it must impact the legal market. Add into the mix a number of other legal market factors – the increasing price sensitivity of clients, the explosion in data, the long-standing feeling that there must be a better way to do repetitive knowledge tasks than simply adding more junior lawyers – and you've got the perfect conditions for these technologies to take hold.

In an interview with iTnews, Philip Scorgie, who joined Allens last month from Chicago-based Mayer Brown, said that he expects large Australian law firms to begin adopting cloud-based cognitive computing systems within the next year. But he believes predictions that'robot lawyers' will replace humans are overstated. He instead views cognitive computing as augmenting, rather than replacing, human capabilities, for example by assisting lawyers to handle large data volumes to produce structured documents. The CIO also said that nervousness around the access foreign governments might have to firms' sensitive client data has meant the legal sector has been hesitant to adopt cloud technologies, particularly in Australia and Europe. But he added that doing so carried a risk that in-house legal teams would do so and handle more work themselves.