The promise of artificial intelligence is immense, but the roadmap to achieving those goals still remains unclear. Onstage at TechCrunch Disrupt SF, some of AI's leading minds shared their thoughts on current competition in the market, how to ensure algorithms don't perpetuate racism and the future of human-machine interaction. The meteoric rise in China's focus on AI has been well-documented and has become impossible to ignore these days. With mega companies like Alibaba and Tencent pouring hundreds of millions of dollars into home-grown businesses, American companies are finding less and less room to navigate and expand in China. AI investor and Sinnovation CEO Kai-Fu Lee described China as living in a "parallel universe" to the U.S. when it comes to AI development.

I've been thinking lately about why there is so much hype recently among lawyers and legal market commentators about artificial intelligence (AI). I was not ready to put hands to keyboard for a full explanation, so on Sunday I recorded a short video on Twitter stating my preliminary views. It's always hard to predict reactions to a single Tweet. This video generated a lot of discussion on Twitter and at least one direct reply blog post. I capture here some of that that commentary. By the way, I don't rehearse my videos and I don't have notes when I record them.

Recent innovations in artificial intelligence (AI) have given financial institutions (FIs) the ability to fight fraud and better serve their customers. As of late, FIs and law enforcement agencies have been using AI-enabled tools to tighten the noose around money laundering and putting an end to human rights abuses happening around the world. In the latest Digital Banking Tracker, PYMNTS explores the latest on FIs' use of AI to improve customer service, among other use cases. A growing number of players in the financial services space are working to use open banking data to offer new services, with the help of strategic partners. Micro-investing app Moneybox, for example, is collaborating with Santander on a new integration.

SAP SE (NYSE: SAP) today announced its guiding principles for artificial intelligence (AI) and its creation of an external AI ethics advisory panel – the first European technology company to do so. The panel, comprised of experts from academia, politics and industry, will ensure the adoption of the principles and further develop them in collaboration with the AI steering committee at SAP, a group of SAP executives from development, strategy and human resources. The new guidelines, the external panel and the internal committee aim to ensure that the AI capabilities supported by SAP Leonardo Machine Learning capabilities are used to maintain integrity and trust in all solutions. As the market leader in enterprise technology that touches 77 percent of the world's transaction revenue and serves more than 400,000 customers worldwide, SAP solutions and applications impact the lives of billions of people daily. "SAP considers the ethical use of data a core value," said Luka Mucic, chief financial officer and member of the Executive Board of SAP Se. "We want to create software that enables the intelligent enterprise and actually improves people's lives. Such principles will serve as the basis to make AI a technology that augments human talent."

This year, the regulatory agenda has been dominated by the ethics of data, from the controversy around Cambridge Analytica to calls for far tighter regulation of crypto currencies. Are these issues relevant to insurance? How can we avoid a similar loss of trust in the way we use data? Fortunately, we already have a tool with which we can build trust: professionalism. Professionalism can be broken down into three constituent parts: competence, integrity and care for the customer.

In this interview, Tamim Saleh cuts through the hype around artificial intelligence with guidance for executives about where and how to employ AI in their businesses. In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera.

Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, websites and devices in everyday, humanlike natural language via voice, text, touch or gesture input. For users it allows fast interaction using their own words and terminology. For enterprises it offers a way to build a closer connection with customers through personalized interaction and receive an unprecedented amount of vital business information in return. The last eighteen months have seen no abatement in the demand for conversational AI platforms and predictions from major analyst firms shows the trend is set to continue strongly in 2018. But throughout this, there is an underlying message; enterprises need to deploy conversational platforms that are capable of truly understanding the customer--however they phrase the question.

Society increasingly relies on machine learning models for automated decision making. Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality. Substantial work in algorithmic fairness has surged, focusing on either post-processing trained models, constraining learning processes, or pre-processing training data. Recent work has proposed optimal post-processing methods that randomize classification decisions on a fraction of individuals in order to achieve fairness measures related to parity in errors and calibration. These methods, however, have raised concern due to the information inefficiency, intra-group unfairness, and Pareto sub-optimality they entail. The present work proposes an alternative active framework for fair classification, where, in deployment, a decision-maker adaptively acquires information according to the needs of different groups or individuals, towards balancing disparities in classification performance. We propose two such methods, where information collection is adapted to group- and individual-level needs respectively. We show on real-world datasets that these can achieve: 1) calibration and single error parity (e.g., equal opportunity); and 2) parity in both false positive and false negative rates (i.e., equal odds). Moreover, we show that, by leveraging their additional degree of freedom, active approaches can outperform randomization-based classifiers previously considered optimal, while also avoiding limitations such as intra-group unfairness.

A former employee of Google has warned of the web giant's'disturbing' plans for a search engine in China which could help Beijing monitor its citizens online. Jack Poulson wrote in a letter to the US Senate's commerce committee that the proposed Dragonfly website was'tailored to the censorship and surveillance demands of the Chinese government'. In his letter he also claimed that discussion of the plans among Google employees had been'increasingly stifled'. Mr Poulson was a senior research scientist at Google until he resigned last month in protest at the Dragonfly proposals. A former employee of Google has warned of the web giant's'disturbing' plans for a search engine in China which could help Beijing monitor its citizens online While China is home to the world's largest number of internet users, a 2015 report by US think tank Freedom House found that the country had the most restrictive online use policies of 65 nations it studied, ranking below Iran and Syria.

Recently I had the opportunity to join the O'Reilly Foo Camp, where around 150 leading technologists, academics, economists, and business innovators come to talk about the future of technology. It was an inspiring and energizing few days, and one where almost every session seem to revolve around the ethics, responsibility, and potentially dangerous role of AI. This is an important topic and one which is being discussed all over the world. Intelligent algorithms will inform our doctors, our travel, our shopping, and of course our news and information. In the world of HR and business management, AI has incredible potential, as I detail some of the applications in AI in HR: A Killer App, but it brings risks as well.