The Confederation of British Industry is calling on the Government to establish a joint commission tasked with examining the impact of Artificial Intelligence on people and jobs across all sectors of the UK economy. Based on research it conducted into the way that technology is changing the way we live and work, the CBI said on Friday that it had identified three technologies -- AI, Blockchain and the Internet of Things – that are set to move from the fringes to the mainstream within the next five years. It also found, however, that only a third of businesses currently have the skills and capabilities needed to adopt AI technologies, and that more therefore needs to be done to help prepare those companies for the future. The aim of the commission, the CBI said, would be to examine the impact of AI on people and jobs, and to subsequently set out plans for action that will "raise productivity, spread prosperity and open up new paths to economic growth". "The UK must lead the way in adopting these technologies but we must also prepare for their impacts," said Josh Hardie, deputy director-general of the CBI.
Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson's adage applies well to AI adoption: The future is already here, it's just not evenly distributed. The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities.
The young man at the center of all the commotion smiled calmly on Wednesday, the day before the first meaningful game of his NBA career. Lonzo Ball is rarely any other way. "It is going to be a lot of fun," Ball said. On Thursday night the Lakers will host the Clippers in the season opener for both teams. The organization has goals that go beyond this season, but when it comes to the team itself, its coaches and players, their goal is simple.
The RICS has launched an insight paper which explores the impact of using artificial intelligence (AI) in the built environment, and the urgent need for industry professionals to understand how it will influence their role, as the future will rely less on human labour and more on technology. Authored by Chris Hoar, Brian Atkin and Katie King of AIinFM, the report examines the potential impact of AI on the built environment, with a focus on facilities management. One sector that the Artificial Intelligence: What it means for the built environment highlights as facing a significant impact of AI is facilities management (FM), due to the labour-intensive and repetitive nature of many FM jobs, making it an ideal place for automation of previously human-dominated tasks. However, the report weighs up the positives and negatives of such changes and how companies should deal with them. FM will always have a vital role to play within the built environment, and even though many operational roles will become more technology-led, the sector could benefit hugely from AI at a strategic level.
A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success from having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. In order to train a machine learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept.
There's no doubt in my mind that machine learning (ML) as part of a data science strategy can help revolutionize many aspects of everyday life. Below I highlight a few examples of how different industries are able to leverage machine learning for competitive differentiation and customer benefit. There are tens of thousands of daily published journals and papers across the world. It is impractical for every clinician to read and absorb these. ML can help identify patterns and correlations that humans alone would otherwise miss -- possibly resulting in diagnosis and treatment plans that are suboptimal.
There's growing excitement – admittedly, at times, borderline hype – about what artificial intelligence can, and will, do for businesses. While speculation abounds among pundits, journalists, and'thought leaders' surrounding the impact that AI will have on jobs (CBInsights predicts 10 million jobs are at risk in the next 5-10 years), there's relatively little analysis of the tangible effect AI will have on marketer's day-to-day work, and the opportunity to'upskill' us all. Writing exclusively for ExchangeWire, Gareth Davies (pictured below), founder and CEO, Adbrain, explains why and how artificial intelligence can realise tangible benefits for marketers. Today's marketers will benefit by navigating an increasingly AI-centric (and AI-literate) world where bots, intelligent software and machine learning play an increased role in the marketing function. To help you cut through the noise, here are some tangible examples of where AI is likely to become a relevant part of the modern marketers' workflow, as well as ideas on how to better understand and qualify the impact that AI can have on your business.
We are looking for a Machine Learning Researcher with a specialised focus on Reinforcement and Active Learning. The candidate will have a sound understanding of modern machine learning, deep learning, probabilistic modelling techniques and expertise in Reinforcement and Active Learning and their applications in real-world problems. You will have the opportunity to contribute to this high performing team who seek to apply their knowledge in the high impact field of improving human's capability in drug discovery. If this challenge and opportunity excites you, please email your CV and a covering letter to email@example.com
A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success in having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. In order to train a machine-learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept. Ritter explained: "I was really trying to answer the question, does machine learning have any application to trading at all, or no application; sort of a binary question.
CLOSING THE GAP BETWEEN AMBITION AND ACTION...... Disruption from artificial intelligence (AI) is here, but many company leaders aren't sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company's AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business. Executive Summary 1. Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts.