Climate change is the most important crisis the planet is facing today. Millions of people from all over the world took to the streets recently demanding urgent governmental action to help control the ongoing catastrophe and reverse the negative impact of climate change. We will need to marshal all our resources, including Artificial Intelligence to save our planet from peril. Some of the foremost minds in machine learning and artificial intelligence recently published a study where they outlined 13 crucial areas where machine learning can be used to mitigate the adverse effects of climate change. The recommendations they made were divided into three major categories – high leverage solutions, where machine learning can make a noticeable impact, long term solutions that will take at least a couple of decades to pay off, and finally, high risk pursuits, where the technology is either not mature enough or we don't know enough to effectively predict the consequences.

AI systems intended to help people make tough choices -- like prescribing the right drug or setting the length of a prison sentence -- can instead end up effectively making those choices for them, thanks to human faith in machines. How it works: These programs generally offer new information or a few options meant to help a human decision-maker choose more wisely. Why it matters: Over-reliance on potentially faulty AI can harm the people whose lives are shaped by critical decisions about employment, health care, legal proceedings and more. The big picture: This phenomenon is called automation bias. Early studies focused on autopilot for airplanes -- but as automation technology becomes more complex, the problem could get much worse with more dangerous consequences.

Europe a century ago was a global powerhouse of innovation, but it has started to lose its edge: today, despite some notable exceptions, many innovative companies are found elsewhere. Europe is falling behind in growing sectors as well as in areas of innovation such as genomics, quantum computing, and artificial intelligence, where it is being outpaced by the United States and China. A discussion paper from the McKinsey Global Institute (MGI), suggests five paths that could help the continent regain its competitive edge. The paper, Innovation in Europe: Changing the game to regain a competitive edge (PDF--395KB), focuses on ways that Europe could seek to build on its strengths rather than trying to play catch-up, given that it is hindered by fragmentation and lack of scale. This article is a condensed version of the original paper, which draws from MGI research as well as from a recent collaboration with the World Economic Forum. Given Europe's relatively high wage costs and low reliance on natural resources, innovation remains of fundamental importance for the continent's economic and social system. European companies still account for one-quarter of total industrial R&D in the world, but over the past ten years US companies have continued to increase their share, reinforcing their leadership position.

If you thought taking a few machine learning courses on Udemy might be enough to inure you against future unemployment then yesterday's report on machine learning in financial services from the Bank of England and Financial Conduct Authority (FCA) will come as a bit of a shock. The report is based on a survey of 106 banks and finance firms in London. It turns out that, yes, machine learning is being used in banks. But, no, it's not hard to find anyone to fill the roles and that this is the least of the worries as machine learning is rolled out across the finance sector. The charts below, from the report, show where machine learning (ML) is already most in use in the banking sector (defined as building societies, international banks, retail banks, UK deposit takers, and wholesale banks) and in the investments and capital markets sector (defined as alternatives, corporate finance firms, fund managers, principal trading firms, wealth managers and stockbrokers, and wholesale brokers.)

Can patents be issued for technology or algorithms derived by artificial intelligence? That's a problem that lawyers and researchers are now grappling with. Law firm Baker McKenzie predicts that "patentability of AI-created inventions, liability for infringement by AI, and patent subject-matter eligibility of AI technologies are the top three areas of patent law that will be disrupted by AI." Kay Firth-Butterfield, Head of AI and ML at the World Economic Forum Center, said that "we are about to witness a collision between artificial intelligence and various aspects of patent law. This technology is going to change the game for many sectors, and will impact numerous regulations and legal fields." Currently patents are awarded to individuals.

Take advantage of our discount rooms at the conference hotel. We are beginning to announce speakers for 2020. Want to join us as a speaker? Check out our proposals page. Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers.

Disclaimer This press release contains forward-looking statements within the meaning of the United States Private Securities Litigation Reform Act of 1995 that can generally be identified by words such as "to transform," "multiyear," "commitment," "to found," "aims," "vision," "potential," "can," "will," "plan," "expect," "anticipate," "committed," or similar terms, or regarding the development or adoption of potentially transformational technologies and business models and the collaboration with Microsoft; or by express or implied discussions regarding potential marketing approvals, new indications or labeling for the healthcare products described in this press release, or regarding potential future revenues from collaboration with Microsoft or such products. You should not place undue reliance on these statements. Such forward-looking statements are based on our current beliefs and expectations regarding future events, and are subject to significant known and unknown risks and uncertainties. Should one or more of these risks or uncertainties materialize, or should underlying assumptions prove incorrect, actual results may vary materially from those set forth in the forward-looking statements. There can be no guarantee that the collaboration with Microsoft will achieve any or all of its intended goals or objectives, or in any particular time frame.

Another study is saying facial recognition software is proving unreliable when dealing with people of color. The American Civil Liberties Union of California recently released their study of Amazon's Rekognition software marketed to law enforcement authorities.

Around 20% of the attorney's time is consumed by their legal research on previous judgments, case files and recordings. Especially in Patent application filing where the prior art search involves manual keyword-based searching for related patents. A good part of the attorney's time is spent on such mundane and repetitive work that they don't have time to invest in the more innovative and creative aspects of patent applications. We are working on building a semantic analysis tool that will do a semantic search on all the documents to pre-select existing closely related to the new cases for the attorney to review. The ability to search within a defined boundary and also across the web would help attorneys to increase their efficiency.

Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it's getting far more important to make sure they're properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions. Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight. Ironically, these regulations also make it more difficult for companies to gather enough data to train an AI system or to prove how their AI reaches its decisions (an impossible task for many deep learning systems).