Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.
The main topic of this paper is a brief overview of the field of Artificial Intelligence. The core of this paper is a practical implementation of an algorithm for object detection and tracking. The ability to detect and track fast-moving objects is crucial for various applications of Artificial Intelligence like autonomous driving, ball tracking in sports, robotics or object counting. As part of this paper the Fully Convolutional Neural Network "CueNet" was developed. It detects and tracks the cueball on a labyrinth game robustly and reliably. While CueNet V1 has a single input image, the approach with CueNet V2 was to take three consecutive 240 x 180-pixel images as an input and transform them into a probability heatmap for the cueball's location. The network was tested with a separate video that contained all sorts of distractions to test its robustness. When confronted with our testing data, CueNet V1 predicted the correct cueball location in 99.6% of all frames, while CueNet V2 had 99.8% accuracy.
FIFTY YEARS ago investing was a distinctly human affair. "People would have to take each other out, and dealers would entertain fund managers, and no one would know what the prices were," says Ray Dalio, who worked on the trading floor of the New York Stock Exchange (NYSE) in the early 1970s before founding Bridgewater Associates, now the world's largest hedge fund. Kenneth Jacobs, the boss of Lazard, an investment bank, remembers using a pocket calculator to analyse figures gleaned from company reports. His older colleagues used slide rules. Even by the 1980s "reading the Wall Street Journal on your way into work, a television on the trading floor and a ticker tape" offered a significant information advantage, recalls one investor. Since then the role humans play in trading has diminished rapidly. In their place have come computers, algorithms and passive managers--institutions which offer an index fund that holds a basket of shares to match the return of the stockmarket, or sectors of it, rather than trying to beat it (see chart 1).
Some 9,000 people, about one-third of Goldman's staff, are computer engineers." Artificial Intelligence is causing massive paradigm shifts across many industries, but its biggest impacts is felt in financial services sector. Simply put, artificial intelligence provides unfair advantage in the financial markets. Nonetheless, AI has limited capability as exemplified in the chess game between Russian chess grandmaster and IBM Deep Blue computer. This piece provides insight into why algorithm trading won't necessarily render human traders useless on Wall Street. In recent years, technology has made it possible to'teach' computers how to trade. Yet day traders continue to remain an integral part of the stock trading markets globally. Apart from specialist niche trading sections where corporations engage in high frequency trading, day trading by robots fails time and time again. But the stock markets behavior constantly changes. Savvy traders can adjust themselves to changes, while adjusting algorithms is too expensive, and time consuming. For that reason and others, day traders still do a better job than any day trading algorithm. Computers are facilitating many of the trades happening on the floor of exchanges globally; yet, the actual task of the algorithms is often limited to analyzing and predicting market trends. The final decision to buy or sell an asset is still often determined by a human. In some instances, the human hits the buy/sell button and in most instances, the human instructs the algorithm to buy X when the price/profit/loss reaches a certain threshold or sell Y when certain parameters are met. Interestingly, Meir Barak, author of The Market Whisperer: A New Approach to Stock Tradingand founder of Tradenet observes that there will always be a place for human traders because the stock market is fluid and dynamic. The fact that humans don't consistently act rationally suggests that computers won't necessarily be adept in the face unexpected market performance. "Let's say a chess grandmaster plays against the best computer in the world.
This refers to the day machines are smarter than humans. Some experts put that date at 2029; we, however, think it could occur much faster. Machines are already dominating the trading world, and they are being made to think like their masters; this means that mass psychology will still be a force to reckon with; only in the years to come, the mass mindset will represent machines also instead of only humans. The British pound flash crash that occurred on the 7th of October was triggered by machines (computers). As machines move into the game the action will be more volatile as they can run 100 times faster than humans so it will create swings and reversals that are spectacular in nature; we are not quite there yet but expect volatility to continue rising.
Aussie investors have eagerly embraced Initial Public Offerings (IPO's) in the past few years, especially in companies operating in "hot" sectors like technology. Unfortunately, many of these stocks see significant drops in share price following a hot start. One stock breaking that trend dramatically is Appen Limited (ASX), debuting on the ASX on 7 January of 2015. The share price has been on a steady climb since and is now up over 800% from its first day of trading. The Appen website describes the company as a global leader in the development of high-quality, human-annotated datasets for machine learning and artificial intelligence.
Can technology build a better Buffett? Nevertheless, the world has yet to see anything like a Wall Street version of Deep Blue, the artificially intelligent machine that defeated chess grand master Gary Kasparov in 1997. Today those early adopters of AI, like Fidelity Investments and Batterymarch Financial, refuse to even talk about the technology. Still, artificial intelligence has steadily improved in sophistication and quietly made itself indispensable on Wall Street. According to Andrew Lo, director of the Laboratory for Financial Engineering at MIT, every investment firm embracing a math-driven strategy uses some form of AI in its research, and Lo expects the terminology to appear again soon in promotions for retail investments like mutual funds and privately managed accounts.