All robovacs use short-range infrared or laser sensors to detect and avoid obstacles, but iRobot added a camera, new sensors and software to Roombas in 2015 to give them the ability to map while they clean. So far investors have cheered Angle's plans, sending iRobot stock soaring to $102 in mid-June from $35 a year ago, giving it a market value of nearly $2.5 billion on 2016 revenue of $660 million. So far investors have cheered Angle's plans, sending iRobot stock soaring to $102 in mid-June from $35 a year ago, giving it a market value of nearly $2.5 billion on 2016 revenue of $660 million But there are headwinds for iRobot's approach, ranging from privacy concerns to a rising group of mostly cheaper competitors - such as the $300 Bissell SmartClean and the $270 Hoover Quest 600 - which are threatening to turn a once-futuristic product into a commoditized home appliance. All robovacs use short-range infrared or laser sensors to detect and avoid obstacles, but iRobot in 2015 added a camera, new sensors and software to its flagship 900-series Roomba that gave it the ability to build a map while keeping track of their own location within it.
Ant Financial Services Group, a subsidiary of e-commerce giant Alibaba, last month introduced an automated system to assess car damage by scanning accident-scene images and calculating payouts on insurance claims. If there it's consolation to human, most pilot projects to date are tapping the capability of machines largely for assistant roles involving repetitive, high-volume and rule-bound tasks. Andy Gillard, Asia Pacific digital operation leader at EY, one of the world's "big four" accounting firms, has looked at the wide-ranging applications of Robotic Process Automation across the securities, banking and insurance sectors. Ant Financial's car damage assessment system builds upon the second level of artificial intelligence called "machine learning."
SHANGHAI--A Chinese startup that sells facial recognition systems to police forces secured venture-capital funding that values it at more than $1.5 billion, underscoring the sector's emergence as one of technology's hottest areas of interest. Beijing-based SenseTime Co., which provides surveillance systems using facial recognition to Chinese law enforcement agencies, said Tuesday it raised $410 million in new funding from investors, lifting it to so-called unicorn status with a value of more than $1 billion. Using artificial intelligence, facial recognition systems from SenseTime and others can identify people in a crowd by matching their faces against those on file in image databases. SenseTime investors include Chinese private-equity fund CDH Investments and Sailing Capital, a VC fund linked to the Shanghai government.
The robots are coming for Wall Street's jobs, and McKinsey & Co has an idea of exactly what jobs they are coming for. The consultancy has published a report, titled Cognitive Technologies in Capital Markets, examining the areas of corporate and investment banking that are ripe for automation via machine learning, artificial intelligence, and natural language processing tools. "This "The application of cognitive technologies to capital markets functions can reduce budgets and free up capacity for teams to focus on higher-value activities such as research, idea generation and client relationship management," the report said. The report features a chart that breaks down the degree to which five cognitive technologies will impact business in the middle and back office.
Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be forgotten by people, although in the global market data scientist is still a major role spreading data science including both statistics and machine learning across industries. Although I did not explicitly mention in the post, I guess that Japanese people may think that data scientist is a professional for statistical analysis although artificial intelligence engineer is one for machine learning or artificial intelligence as a misleading technology. A Google Trends above clearly shows that a growing trend of "人工知能" (AI in Japanese) is steeper than that of "artificial intelligence" in English. Now it's an era of "AI", dominated by machine learning engineers, not data scientists, as Japanese people think.
Deep learning can screen social media behaviour on Twitter, Facebook and additional news stories to connect data points and make predictions. To figure this out, in 2014 the NASA, the Universities Space Research Association and Google joint the Quantum Artificial Intelligence Lab. Eurekahedge, an independent data provider and alternative investment research firm that specialises in hedge fund databases, stated that their own Eurekahedge AI/Machine Learning Hedge Fund Index has outperformed both traditional quant and more generalized hedge funds since 2010. The Guardian: Google's DeepMind makes AI program that can learn like a human
This boost is powered by the growing capabilities of machine learning and artificial intelligence. Before answering this question, it is important to highlight the difference between automation and real artificial intelligence, although both terms are used interchangeably sometimes. AI for banks and other financial institutions is expected to trigger similar outcomes to those recorded in e-commerce, which includes better customization of the experience, more efficiency, increased productivity and overall cost reduction. These include personalized financial advice, fraud detection mechanisms, investment decisions and blockchain.
Yann LeCun, arguably the father of modern machine learning, has described Generative Adversarial Networks (GANs) as the most interesting idea in deep learning in the last 10 years (and there have been a lot of interesting ideas in Machine Learning over the past 10 years). You train the discriminator on real data to classify, say, an image as either a real photo or a non-photographic image. Given that the central problem of using Deep Learning models in business applications is lack of training data, this is a really big deal. This technology could, and probably should, form a pillar of next generation (big data and machine learning) risk management.
According to a study by Preqin, a market research firm, some 1,360 quantitative funds use computer models to trade. New machine learning techniques, especially deep learning, have allowed computers to recognise materials such as images, text, and audio. Many tech companies, quantitative funds and financial firms are exploring whether methods like deep learning lend themselves to finance. Systems like Aidyia create a large number of random digital stock traders and test their performance with historical stock data.
I create virtual environments and evolve digital creatures and their brains to solve increasingly complex tasks. Even my own job could be done faster, by a large number of machines tirelessly researching how to make even smarter machines. There is one last fear, embodied by HAL 9000, the Terminator and any number of other fictional superintelligences: If AI keeps improving until it surpasses human intelligence, will a superintelligence system (or more than one of them) find it no longer needs humans? This first appeared on The Conversation -- What an artificial intelligence researcher fears about AI.