Technology will drive efficiency in terms of hours, but to get to that, you must invest and develop a tech platform. It would be folly to think it will not have an impact. If your organization hasn't integrated Artificial Intelligence (AI) into its M&A practice, you're behind the curve. In fact, this quote from Deloitte is not from this year, but 2016. Now, as we approach 2020, it is clear to see that AI is essential to an advanced, efficient M&A practice.

Registration is open for our highly successful Winter Academy on Artificial Intelligence and International Law. In the second edition of this unique and innovative training, you will get the latest insights into current and future issues raised by AI from the perspective of international law. The Winter Academy offers you foundational knowledge on key issues at the interface of international law and artificial intelligence, and provides a platform for critical debate and engagement on emerging questions. The programme is structured along five themes: Understanding AI, AI for good, AI and armed conflict, AI and responsibility, and AI governance. The Winter Academy includes lectures that address both technical and legal aspects of artificial intelligence, and will alternate theoretical and more practical perspectives.

Is my organisation a member? The Whitehall & Industry Group's AI Collaboration Forum will bring together a wide audience from our 230 members, spanning the private, public and not-for-profit sectors, as well as academic institutions. Supported by the Office for Artificial Intelligence and kindly hosted by EY. The agenda will explore the vital role of cross-sector collaboration to ensure the endless possibilities of AI are harnessed and regulated effectively, generating maximum positive economic and societal impacts for the UK. Holding a BSc in Computer Science and an MBA from the Massachusetts Institute of Technology, Sana Khareghani has over 20 years' experience in technology and business across the private and public sectors.

The value of investments and the income from them may go down as well as up and you may not get back the amount you originally invested. You must read this before proceeding, as it explains both the legal and regulatory restrictions which apply to the information contained and investment products referred to within this section of the Website. The performance information contained on the site contains information which accords with UK performance standards. Past performance is not a guide to future performance. Investors should note that changes in rates of exchange may have an adverse effect on the value, price or income of investments.

From speech recognition, big data analysis and machine learning to bold new healthcare advances, financial and business advice, talking computers, robots and self-driving cars, the rapid development and adoption of artificial intelligence ("AI") is becoming widespread in more and more areas of daily life. While unleashing opportunities for businesses and communities across the world, AI technologies have also brought about a host of new risks. The implications of AI for companies' legal and ethical responsibilities are now being discussed among governments and a variety of business and non-governmental groups worldwide. A raft of new voluntary ethical codes and even some legislative proposals relating to AI have also started to appear. The trend is that while companies and other organizations that develop, implement or use AI systems are and will be expected to comply with existing data privacy and other legal norms, they will soon have to adhere to a number of new transparency and other ethical expectations as well--the outlines of which are beginning to converge.

The court was established in 2017 in the eastern city of Hangzhou to deal with legal disputes with a digital aspect. In a demonstration, authorities revealed how the Hangzhou Internet Court operates, featuring an online interface with litigants appearing by video chat as an AI judge – complete with on-screen avatar – prompts them to present their cases. A black-robed virtual judge sitting under China's national emblem was heard asking in a pre-trial meeting: "Does the defendant have any objection to the nature of the judicial blockchain evidence submitted by the plaintiff?" READ MORE: AI-manipulated media will be'WEAPONISED' to trick military

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. For more information see the IDTechEx report on Digital Health 2019: Trends, Opportunities and Outlook. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML.

At IGF 2019, several policy initiatives, reports, and publications were launched or used as background material for discussions.

Such is the ideal scenario for every manager everywhere, right? Just assign that task to a trusted colleague and watch as the magic happens. Well, maybe that's a reality best expressed in a famous sci-fi TV series, but ... Science fiction often predicts the future quite accurately; and the 1990s future is, well, today! In other words, the vision espoused by Star Trek: Next Generation, in many ways can now be qualified as realistic, at least with respect to the dynamic use of data. Let's face it: Data drives the Information Economy, especially when modern technologies such as artificial intelligence and machine learning are trained on it.

This post will be the first post on the series. The content is based on: the tutorial on fairness given by Solon Bacrocas and Moritz Hardt at NIPS2017, day1 and day4 from CS 294: Fairness in Machine Learning taught by Moritz Hardt at UC Berkeley and my own understanding of fairness literatures. I highly encourage interested readers to check out the linked NIPS tutorial and the course website. Fairness is becoming one of the most popular topics in machine learning in recent years. Publications explode in this field (see Fig1). The research community has invested a large amount of effort in this field.