"Algorithms without data are often worthless. Similarly, algorithms with 'standard' data from typical data sources, such as exchanges or data service providers like Refi‐ nitiv or Bloomberg, might only be of limited value. This is due to the fact that such data is intensively analyzed by many, if not all, relevant players in the market, making it hard or even impossible to identify alpha-generating opportunities or similar competitive advantage." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"All in all, it seems questionable whether a superintelligence can be properly and systematically controlled when it has reached that level. After all, its superpowers can at least in principle be used to overcome any human-designed control mechanism." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Although the normality assumption is a good approximation for many real-world phenomena, such as in physics, it is not appropri‐ ate and can even be dangerous when it comes to financial returns." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Almost no financial return sample data set passes statistical normality tests. Beyond the fact that it has proven useful in other domains, a major reason why this assumption is found in so many financial models is that it leads to elegant and relatively simple mathematical models, calculations, and proofs." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Even if markets are weakly efficient on an end-of-day basis, they can nevertheless be weakly inefficient intraday. Such statistical ineffi‐ ciencies might result from temporary imbalances, buy or sell pres‐ sures, market overreactions, technically driven buy or sell orders, and so on. The central question is whether such statistical ineffi‐ ciencies, once discovered, can be exploited profitably via specific trading strategies." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Even strong proponents of a utopian future based on advance‐ments in AI must agree that a dystopian future after a technological singularity cannot be fully excluded. Since the consequences might be catastrophic, dystopian outcomes must play a role in broader discussions about AI and superintelligence." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"In the definition of learning through an algorithm or computer program, it is important to note the difference between the task of learning and the tasks to be learned. Learning means to learn how to (best) execute a certain task, such as estimation or classification." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"It is to be assumed that any form of superintelligence will have instrumental goals that are independent of its main goal. This might lead to a number of unintended consequences, such as the insatiable quest to acquire ever more resources with any means that seem promising." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Of all the possible paths to superintelligence, AI seems to be the most promising one. Recent successes in the field based on reinforcement learning and neural networks have led to another AI spring, after a number of AI winters. Many even now believe that a superintelligence might not be as far away as we thought even a few years ago. The field currently is characterized by much faster advancements than originally predicted by experts only a short while ago." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"The first and second moment of a probability distribution only describe a normal distribution completely. There are infinitely many other distributions that might share the first two moments with a normal distribution while being completely different." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"The randomized population of training, validation, and test data sets is a common and useful technique for data sets that are neither sequence-like nor temporal in nature. However, when one is dealing, say, with a financial time series, shuffling the data is generally to be avoided because it breaks up temporal structures and sneaks foresight bias into the process by using, for example, later samples for training and implementing the testing on earlier samples." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
"Whereas in supervised learning the training, validation, and test data sets are assumed to exist before the training begins, in RL the agent generates its data itself by interacting with the environment. In many contexts, such as in games, this is a huge simplification. Consider the game of chess: instead of loading thousands of histor‐ical human-played chess games into a computer, an RL agent can generate thousands or millions of games itself by playing against another chess engine or another version of itself, for instance." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)
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