There are a lot of misconceptions concerning artificial intelligence circulating in public discourse. Classical works of science-fiction have taught us to think about A.I. either in terms of killer robots, god-like computers, and sly androids, or as the saviors of mankind in the form of automated workers, benevolent star-ship operators, or friendly house servants. The truth is, at least in the present moment, that contemporary artificial intelligence systems are much less proficient at things we thought they would be good at according to works of fiction. However, they are simultaneously pretty skilled and efficient at performing other kinds of tasks, albeit ones which are not as immediately awe-inspiring and spectacular. In this article, we will examine how artificial intelligence is being introduced into the realm of everyday business operations.

As Harvard University economist Jason Furman said when he was Chairman of the Council of Economic Advisors, our biggest worry about AI should be that there might not be enough of it. The United States needs to develop this powerful new technology to its fullest to maintain our economic and technological leadership in the face of increasingly sophisticated competition from China, which has made AI-development a strategic priority. Of course, AI is fraught with ethical challenges. In the course on AI and Ethics I teach at Georgetown University, I find the students concerned that AI will be a biased, unaccountable force in their lives and that it will be deployed to create joblessness and exacerbate social and economic inequality. The trade association I work for, the Software & Information Industry Association, addressed these challenges in its recent publication on ethical principles for developers and users of AI.

From driverless cars to Amazon's Alexa, artificial intelligence is growing at a phenomenal speed and is set to disrupt the corporate legal landscape through mining documents, creating contracts and performing due diligence. In a legal landscape defined by change, uncertainty, and an increasingly complex market, these developments will change the calculus of how business is conducted, alter solicitor-client relationships and engender new legal challenges which will, in turn, demand new solutions. Technological advancement is changing the face of business by translating into new demands and new market pressures in the context of unfamiliar market behaviours. For instance, blockchain raises countless implications about the very nature of transactions and contract formation; due to its ability to produce outputs autonomously, once a transaction is set in motion the contract's performance is taken out of the hands of the contracting parties, decreasing the extent of trust and due diligence required and making the contract immutable and cryptographically secure. In such a world, where contracts are executed by machines and written in computer code by software engineers, a myriad of potential benefits relating to speed, certainty and transparency arise, paired, however, with a corresponding array of risks related to error and the potential of hacking.

RoboReview, made by Turbo Patent, helps patent practitioners review and analyze patent applications. Like a spell-checker, the program reads the applications, suggests changes based on patent eligibility, novelty and other matters, and spit out a report that normally would get prepared by paralegals or other patent attorneys. EVA made by ROSS Intelligence is an amazing legal research tool that allows litigators and researchers to simply drop a brief, or pleading, or document, into the program and get back a hyperlinked list of every cited case. In addition to returning every cited cases, the tool also provides information as to whether those cases have received positive or negative treatment. Setting EVA apart from nearly every other AI tool for lawyers, ROSS Intelligence is offering EVA to everyone for free (and as of now, there is no catch, no credit card required, nor trial period; but email sign up is required).

For part one of the series, in which we explore the benefits and dangers of the EU's GDPR legislation, check this article.

An amazing video of a robot dog fighting off a human as it tries to open a door is not only creepy, but it also has raised the question: Why are we teaching a robot to fight back against humans? The "dog" in question is the SpotMini, a 66-lb. In the video, the dog is shown attempting to open a door--when a human comes with a hockey stick and shoves the robot's grasping arm away from the door knob. The robot manages to open the door anyway, and even continues standing when a human tries to pull "him" away from the door using a huge leash. It turns out, any successful robot assistant for the home needs to be good at dealing with "disturbances," according to the company -- and that may sometimes include pesky humans.

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose a transparent model distillation approach to audit such models. Model distillation was first introduced to transfer knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. To this we add a third criterion - transparency. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by the teacher. Moreover, we use side information in the form of the actual outcomes the teacher scoring model was intended to predict in the first place. By training a second transparent model on the outcomes, we can compare the two models to each other. When comparing models trained on risk scores to models trained on outcomes, we show that it is necessary to calibrate the risk-scoring model's predictions to remove distortion that may have been added to the black-box risk-scoring model during or after its training process. We also show how to compute confidence intervals for the particular class of transparent student models we use - tree-based additive models with pairwise interactions (GA2Ms) - to support comparison of the two transparent models. We demonstrate the methods on four public datasets: COMPAS, Lending Club, Stop-and-Frisk, and Chicago Police.

GraphGrail Ai, the world's first AI-based natural language processing platform with a DApps marketplace is passing through its TGE stage and has already garnered the acclaim of several prominent rating agencies with high rankings. Such results were made possible thanks to the extensive experience that the GraphGrail Ai development team has in implementing AI solutions for businesses and government agencies. To demonstrate and consolidate the experience of the GraphGrail Ai team for our followers, we have compiled a list of prominent cases that the development team had participated in over the last few years prior to undertaking the implementation of their solutions on blockchain systems. The GraphGrail Ai team was involved in the development and launch of a service that searches for extremist statements based on the legislation of the Russian Federation for the Rostov Center for Forensic Expertise. The system was used extensively and had successfully detected dangerous publications.

When considering threats posed by artificial intelligence (AI), the focus usually rests on two. Some foresee a terrifying Skynet dystopia, with sentient computers identifying mankind as their greatest enemy and turning the world into a killbot hellscape. But the most likely threats come from bias and failures in trust. Read more: The narrative is shifting – it's now time to rekindle trust in business AI and machine learning should result in fairer, more evidence-based decision-making, since machines are supposedly free from human biases. But as machine learning is reliant on data input, bias at this stage is not only reflected in the machine's model, but becomes ingrained.

The next generation of AI is poisoned with bias against dark skin, Joy Buolamwini says. Artificial intelligence is increasingly affecting our lives in ways most of us haven't even thought about. Even if we don't have emotional androids plotting revenge on humankind (yet), we're surrounded more and more by computers trained to look us over and make life-changing decisions about us. Some of the brightest minds in technology--including a hive of them clustered around Boston--are tinkering with machines designed to decide what kinds of ads we see, whether we get flagged by the police, whether we get a job, or even how long we spend behind bars. But they have a very big problem: Many of these systems don't work properly, or at all, for people with dark skin.