While the discovery rules generally allow for liberal discovery, when it comes to eDiscovery, and a robot or A.I. would clearly fall under that hub, courts are less inclined to allow unrestricted access to an adversary's system, especially if there are no reasons to believe they've been hiding anything. It's almost always going to be a question of proportionality. One of the unanswered questions that a court will soon be faced with is whether a robot can be deposed, or actually take the witness stand. While clearly there is another way to extract data from a robot and present that data, when the merger of A.I. and robotics actually produces functional, Westworld-quality robots (or even just close to it), how will the courts deal with robot discovery? How will the court, and parties, ensure robots are telling the truth when queried? Rule 601 presumes that witnesses are persons.

Artificial intelligence, commonly referred to as AI, has great potential to increase efficiency, accuracy, and cost savings within the legal industry. But the ability to make decisions autonomously without any human involvement has caused concern in some legal circles as to the ethical implications. Specifically, a literal artificial decision does not apply the same critical thinking, intuition, and professional judgement traditionally practiced by a seasoned lawyer. While the point is valid, it is important to first consider how AI is transforming routine tasks in legal firms. AI in a law practice would use rules based logic developed with attorneys to abbreviate labor intensive tasks, such as contract review, where items of concern would be brought to the attention of a practicing lawyer for review.

When you mention robots, most people think of Star Wars or those car-manufacturing assembly lines where giant, agile machines move heavy parts, make spot welds or secure bolt-on chassis. Robots have developed to the point where robotic process automation (RPA) is now coming to the world of corporate finance. In finance, we've automated by implementing general ledger and enterprise resource planning (ERP) systems, as well as spreadsheet programs like Microsoft Excel. Yet many corporate financial processes are still stuck in the 20th century. Corporate finance teams spend about 80% of their time manually gathering, verifying and consolidating data, leaving only about 20% for higher-level tasks like analysis and decision making.

Taxi app company Uber has applied for a patent to use artificial intelligence to determine how drunk potential passengers might be. The app used to summon rides could also feed other information to the driver, including a passenger's location, how accurately they are typing and even the angle they are holding their phone at. It could help drivers who do not want to pick up inebriated riders. But critics said it could also be used to identify vulnerable passengers. According to the application to the US patent office, the system would spot "uncharacteristic user activity".

There's no doubt that artificial intelligence (AI) can do wonderful things for humanity. From communicating with other machines to help us build a more comfortable living environment to supporting the autonomous vehicles that will transform what mobility means for the human race, AI can do it all. However, it's also a technology that can do a lot of harm. Whether it is to take away existing jobs by automating repetitive tasks to sending unmanned drones into war zones and target a certain individual or group with precision, AI is also making people fear it. Elon Musk, one of today's leading technologists, says that "AI is the biggest risk we face as a civilization" Google, however, recognizes that the technology, though powerful, must be developed – for the good of people and society.

Amazon has agreed to review its labor practices at Foxconn plant in China where its popular Echo Dot smart speakers are assembled. SAN FRANCISCO -- The Chinese plant where Amazon's popular Echo Dot smart speakers are assembled underpaid workers, some of whom worked as many as 14 consecutive days and more than 100 overtime hours per month, according to a U.S.-based labor rights group. Amazon says it knew of problems at the plant and has requested corrective action. The report by China Labor Watch found that the Foxconn plant in Hengyang in China broke multiple Chinese labor laws, underpaying workers and subjecting them to verbal abuse. More than 40% of the staff there were temporary employees, while China only allows 10% of any workforce to be temps.

Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good performance with minimal analyst intervention, making them well suited to a high volume analytic environment, but the majority are black box tools that provide very limited insight or interpretability into key drivers of model performance or predicted model output values. This paper presents a methodology that blends one of the most popular predictive statistical modeling methods for binary classification with a core model enhancement strategy found in machine learning. The resulting prediction methodology provides solid performance, from minimal analyst effort, while providing the interpretability and rationality required in regulated industries, as well as in other environments where interpretation of model parameters is required (e.g. businesses that require interpretation of models, to take action on them).

Rosenstein said during a speech in Montreal the United States is "enhancing its commitment to international law enforcement coordination," through personal relationships, policy changes and additional resources, citing examples of recent collaboration between Canadian and U.S. law enforcement.

Artificial intelligence and Big Data get lots of ink (and electrons) these days about their awesome promise for the future. But here are two things of real and immediate importance that AI could do today to improve people's lives and strengthen our economy. Big Data is a Big Deal because we are being already overwhelmed with information, and it's going to get a lot more overwhelming very, very soon. The research firm IDC estimates that within two years we will have be awash in an astonishing 40 zettabytes of data (a zettabyte is one sextillion bytes), or 50 times the data that existed just in 2010. This will be the equivalent of 5,200 gigabytes of data for every man, woman, and child on the planet.

Artificial Intelligence (AI) is changing how our society operates. AI now helps make judiciary decisions, medical diagnoses, and drives cars. The use of AI in our society also has important environmental implications. AI can help improve resource use, improve energy efficiency, predict extreme weather events, and aid in scientific research. But while AI has the potential to improve human interaction with the environment, AI can also exacerbate existing environmental issues. Some form of governance is needed to ensure that AI is deployed in a manner that is beneficial for our environment.