Algorithmic trading has revolutionised the stock market and its surrounding industry. Over 70% of all trades happening in the US right now are being handled by bots[1]. Gone are the days of the packed stock exchange with suited people waving sheets of paper shouting into telephones. This got me thinking of how I could develop my own algorithm for trading stocks, or at least try to accurately predict them. I've learned a lot about neural networks and machine learning over the summer and one of the most recent and applicable ML technologies I learnt about is the LSTM cell [2]. Long Short Term Memory cells are like mini neural networks designed to allow for memory in a larger neural network.

From opening a bank account to applying for a mortgage, millions of people submit applications to thousands of banks around the world every day. Processing these applications and ensuring that the people applying are legitimate customers is a daunting challenge – a challenge that is compounded by the rapid digitalization of bank services. Mobile banking has all but replaced in-branch visits, and applying for a loan now only takes a few clicks. In this era of unprecedented digital access, banks must rapidly innovate to meet the current and future demands of their customers. As such, banks have prioritized rolling out slick online experiences that outshine their competitors.

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.)

Animating characters is a difficult task when it comes to interacting with objects and the environment. What if we used computer brains instead? In this research, we present the Neural State Machine, a data-driven deep learning framework that can handle such animations. The system is able to learn character-scene interactions from motion capture data, and produces high-quality animations from simple control commands. The framework can be used for creating natural animations in games and films, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.

By 2030 the total gross domestic product of the world will be 14% higher because of one thing: more use of artificial intelligence or AI. That's the conclusion of PwC, a professional services firm based in London. If such forecasts are right these sophisticated computer programs will be doing tasks such as driving vehicles, planning and waging wars, and advising humans on how to handle both their health and wealth. One observer writing in the Journal of the American Medical Association has declared that the "hype and fear" surrounding AI "may be greater than that which accompanied the discovery of the structure of DNA or the whole genome." Yet despite the possibility of colossal impacts from AI, the U.S. government has been doing little to study its ethical implications.

The Trump administration, as part of its strategy on artificial intelligence, has spent a considerable amount of time identifying jobs that become obsolete with the rise of automation. As part of that effort, agencies have also looked at predicting what new career paths automation might create in the years ahead. But now some officials say fear over automation-related job security might have gone too far. Federal Chief Information Officer Suzette Kent, who has overseen some of the administration's reskilling pilots, like the Federal Cyber Reskilling Academy, said some of these anxieties about automation aren't new. "This is not a story that we haven't heard before in our nation: Something comes along that radically changes the way that we work, the way that we live, and creating fear about that is not the best path forward," Kent said during a panel hosted by the Bipartisan Policy Center on Wednesday.

Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It's the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand. According to Indeed.com, the average IT salary -- the keyword is "artificial intelligence engineer" -- in the San Francisco area ranges from approximately $134,135 per year for "software engineer" to $169,930 per year for "machine learning engineer." However, it can go much higher if you have the credentials firms need. One tenured professor was offered triple his $180,000 salary to join Google, which he declined for a different teaching position.

Do-it-yourself security systems will cause some shifts in the residential security market as more than two million broadband homes will have a self-monitored system by year's end. According to research by Parks Associates, 23% of the self-monitored systems are fee-based with an average cost this year of $9 per month while 22% also include home control. By 2024, the average monthly fees will slide down to $8.50 while 34% will have home control add-on services. Home security systems seemed cutting edge when they were first introduced, but now the market is flooded with options that are available from the likes of ADT, AT&T, Ring, Comcast, and even chain hardware stores. In order to better cash-in on home security services, Parks said the industry needs to find new drivers for the homes that currently don't have such services.

Artificial intelligence (AI) isn't magic, it's just really complicated math, said Greg Mulholland, CEO and founder of Citrine Informatics (Redwood City, CA), at a press roundtable hosted by Lanxess (Cologne, Germany) at K 2019. But Mulholland's hosts seemed quite bedazzled by his AI-enabled platform, nonetheless. Lanxess is the first company to adopt Citrine's technology at scale, and Dr. Markus Eckert, Senior Vice President, Head of Business Unit Urethane Systems at Lanxess was eager to explain what it means for customers. Citrine is a Silicon Valley startup that couldn't be more niche: It has developed a platform that leverages data and AI specifically to accelerate the development of materials and chemicals. Citrine has been recognized for technology innovation by the World Economic Forum as a Tech Pioneer, and collaborates with world-class academic institutions such as Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

Intel's third quarter earnings report comes out on Oct. 24, but some industry experts have set their sights on years down the line to when investments in artificial intelligence could begin to pay off. "In three-plus years, the real competition will be in AI and machine learning-related systems," said Jack Gold, an analyst at J. Gold Associates, in an email to FierceElectronics. "That means if you're a semi company, it's where you need to have viable products going forward." Some analysts have focused heavily on Intel's competition with AMD and Nvidia for performance in the graphics space, often on a PC or workstation. AMD is growing much faster than Intel, but still has less than one-tenth the revenues.