Traders and financial professionals work at the opening bell on the floor of the New York Stock Exchange (NYSE). It's no secret on Wall Street that a "sell" recommendation in sell-side research reports is exceedingly rare, and it can't be chalked up to today's bull market recently surpassing its ninth birthday. According to FactSet data, only 6% of analyst recommendations on S&P 500 companies are "sell" ratings or the equivalent, lending credence to the notion that conflicts of interest persist despite reform efforts to make recommendations more objective in nature. Put simply, negative recommendations can place an analyst in the virtual penalty box when it comes to getting access to companies, and the effects are clear in a business where access is king. So, is there still use to looking through research reports to figure out which stocks are worth buying and selling?
HyperScience delivers machine learning solutions for the enterprise, working with Fortune 500 companies. The HyperScience team is guided by the belief that AI is destined to be the biggest event in the history of human labor since the industrial revolution. HyperScience offers leading global businesses the tools to take advantage of this new technology and create innovative solutions ranging from predictions, automated classifications and anomaly detection in any domain. There are many examples of AI currently applied to everyday life, ranging from self-driving cars to medical software that diagnoses patients. The company already counts a number of businesses in the Fortune 500 as customers and their engagements start at the C-suite, solving these large businesses-- most challenging problems.
It was winter in New York City and Asaf Jacobi's Harley-Davidson dealership was selling one or two motorcycles a week. Jacobi went for a long walk in Riverside Park and happened to bump into Or Shani, CEO of an AI firm, Adgorithms. After discussing Jacobi's sales woes, Shani, suggested he try out Albert, Adgorithm's AI-driven marketing platform. It works across digital channels, like Facebook and Google, to measure, and then autonomously optimize, the outcomes of marketing campaigns. Jacobi decided he'd give Albert a one-weekend audition.
Each of these areas already features a significant level of complexity, so the following description of data mining and artificial intelligence applications has necessarily been restricted to an overview. Vehicle development has become a largely virtual process that is now the accepted state of the art for all manufacturers. CAD models and simulations (typically of physical processes, such as mechanics, flow, acoustics, vibration, etc., on the basis of finite element models) are used extensively in all stages of the development process. The subject of optimization (often with the use of evolution strategies or genetic algorithms and related methods) is usually less well covered, even though it is precisely here in the development process that it can frequently yield impressive results. Multi-disciplinary optimization, in which multiple development disciplines (such as occupant safety and noise, vibration, and harshness (NVH)) are combined and optimized simultaneously, is still rarely used in many cases due to supposedly excessive computation time requirements.
FANUC, the world's largest maker of industrial robots, plans to start connecting 400,000 of their installed systems by the end of this year. The goal is to collect data about their operations and, through the use of deep learning, improve performance. Similarly, Kuka is building a deep-learning AI network for their industrial robots. FANUC is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs.