If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The report, the Worldwide Artificial Intelligence Spending Guide, said that in 2019, worldwide spending on AI is expected to be $37.5 billion, but that this amount will almost treble by 2023, hitting just a smidgeon less than $100 billion -- $97.9 billion. Not only does the IDC report present good news for AI, it also refers to the machine learning phrase -- or ML, a nod to those who work in the business and are often quite frustrated by the over use of the AI acronym with all its connotations with hype. David Schubmehl, research director at Cognitive/Artificial Intelligence Systems at IDC said: "The use of artificial intelligence and machine learning (ML) is occurring in a wide range of solutions and applications from ERP and manufacturing software to content management, collaboration, and user productivity. Artificial intelligence and machine learning are top of mind for most organisations today, and IDC expects that AI will be the disrupting influence changing entire industries over the next decade." The IDC report said that investment in AI will be led by retail and banking industries.
It connects top AI experts, enterprises, government representatives, data scientists, technology leaders, startups, investors, researchers, academicians, and global AI innovators - to discuss the impact of AI on commercial applications and the revolutionary ways it can transform businesses and government functions.
Abstract: Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcement learning (RL) based market maker agent. We use the simulator to train an RL-based market maker agent with different competitive scenarios, reward formulations and market price trends (drifts). We show that the reinforcement learning agent is able to learn about its competitor's pricing policy; it also learns to manage inventory by smartly selecting asymmetric prices on the buy and sell sides (skewing), and maintaining a positive (or negative) inventory depending on whether the market price drift is positive (or negative). Finally, we propose and test reward formulations for creating risk averse RL-based market maker agents.
Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical. In this work, we design an adaptively-growing prediction market with a bounded financial subsidy, while achieving privacy, incentives to produce accurate predictions, and precision in the sense that market prices are not heavily impacted by the added privacy-preserving noise. We briefly discuss how our mechanism can extend to the data-purchasing setting, and its relationship to traditional learning algorithms. Papers published at the Neural Information Processing Systems Conference.
AI for Longevity has more potential to increase healthy Longevity in the short term than any other sector. The application of AI for Longevity will bring the greatest real-world benefits and will be the main driver of progress in the widespread extension of healthy Longevity. The global spending power of people aged 60 and over is anticipated to reach $15 trillion annually by 2020. The Longevity industry will dwarf all other industries in both size and market capitalization, reshape the global financial system, and disrupt the business models of pension funds, insurance companies, investment banks, and entire national economies. Longevity has become a recurring topic in analytical reports from leading financial institutions such as CitiBank, UBS Group, Julius Baer, and Barclays.
At a two-hour hearing in Washington, D.C. on Friday, lawmakers questioned experts on bias in artificial intelligence, the struggle to attract skilled workers, and how to navigate and regulate an increasingly data-driven financial market, Bloomberg reports. Why it matters, per Bloomberg: "The use of algorithms in electronic markets has automated the jobs of tens of thousands of execution traders worldwide, and it's also displaced people who model prices and risk or build investment portfolios," the former head of machine learning at AQR Capital Management LLC Marcos Lopez de Prado said.
December 07, 2019: According to the AI-based prediction from'DHL Global Trade Barometer', India is the only country with a positive trade outlook for the running quarter out of the world's seven largest economies. Thanks to the strong maritime exports and Imports that will maintain India's trade growth over the three-month period ending in January 2020. The DHL Global Trade Barometer, an indicator of global trade developments calculated using artificial intelligence and big data, predict mildly positive growth for Indian trade with the country's Index rising five points to 54. The positive outlook is driven primarily by an uptake in ocean imports of basic & industrial raw materials and chemicals & products, coupled with a gradual revival in air exports of consumer fashion goods. In total, ocean trade grew by 10 points, maintaining India's positive outlook even as air trade forecasts experience relative weakness.
Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn't just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential. Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score -- typically between 0 and 1 -- to indicate the likelihood that it's fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not -- typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable. False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed.
By now, many people have heard of the impending "fourth industrial revolution," and there's more than a little trepidation surrounding the subject. Just as mechanization and the steam engine changed the landscape of manufacturing, the arrival of interconnected machine learning systems will inevitably transform the way products are made and sold. The fourth industrial revolution may spark the fear that jobs will disappear. Emerging technologies will have a far-reaching impact, affecting almost every industry and economy on our globalized planet. However, artificial intelligence will serve in large part to augment – not replace – the jobs humans perform in the workplace.