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) …
AI is making strides at many levels in the world of investment management. Investors may already be riding the wave of artificial intelligence, unaware of the many ways they've been integrated. There are three main levels where AI is making a mark, says Amit Gupta, a managing director in Accenture's capital market industry group. At the first level, firms are using AI in back-office administrative tasks like net asset value calculations, reconciliation, settlement operations. At the second level, they use it in front-office tasks like client targeting and management, profiling of clients, personalization of service.
We are all familiar with the dictum that "correlation does not imply causation". Furthermore, given a data file with samples of two variables x and z, we all know how to calculate the correlation between x and z. But it's only an elite minority, the few, the proud, the Bayesian Network aficionados, that know how to calculate the causal connection between x and z. Neural Net aficionados are incapable of doing this. Their Neural nets are just too wimpy to cut it.
I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.
Classification has been a go-to approach for various problems for many years now. However, with problems becoming more and more specific a simple classification model can't be the solution for all of them. Rather than having one label/class for an instance, it's more appropriate to assign a subset of labels for an instance. This is exactly how multi-label classification is different from multi-class classification. For example, Classifying if a piece of audio file is a music file or not is a classification problem while classifying all the genres of forms in a piece of fusion music is a multi-label classification.
Artificial intelligence (AI) gained unprecedented attention within the hedge fund community in recent years. However, AI is not some new kid on the block. In fact, its roots go as far back as the 1940s when Warren McCulloch and Walter Pitts first introduced the neural network. Today, it finds widespread use in applications from identifying images, speech, natural language processing to robotics and more. Similarly, the use of AI techniques for trading or investment is not a new idea either. But it was not successful in any big way in the earlier attempts. So why is everyone so excited about using AI for investments again? From my own lens, I attribute this to a confluence of technology advances and changing market dynamics. Our technology have improved by leaps and bounds over the years. My first encounter with a PC was an 8-bit Apple machine with a monochrome CRT monitor running on MS DOS. Then came machines with more powerful Intel processors.
All Models are wrong, but some are useful. Mainstream AI discourse stresses the need for unbiased data and algorithms to ensure fair representation, but overlooks the intrinsic limits of any statistical technique. Machine learning is a statistical model of the world and we should question the way it operates, also statistically, in world-making. The statistical models of machine learning have silently become a new ubiquitous Kulturtechnik through which the perception of the world is increasingly mediated and jobs are automated. From face recognition and self-driving cars to automated decision making, AI constructs, fosters and controls statistical models of society.
The Artificial Intelligence, according to a recent and interesting work, "Artificial Intelligence for Cybersecurity", realized with four hands by Matteo E. Bonfanti and Kevin Kohler, promises to change the panorama of cybersecurity in the coming years, and launches a warning to the various Governments, to seek and adopt adequate regulatory frameworks, in order to face the growing future cybernetic threats. Artificial Intelligence comes from a subset of machine learning, deep learning, which through layering of layers and artificial neurons, produces certain results. The applicability of the phenomenon, which extends to various areas, here, in this pamphlet, is analyzed in relation to cyber security, and the related security needs. The AI development community, has always had an open approach, at least in principle, and therefore has always been inclined to share, not only the results of the studies carried out, but also source codes, tutorials and data sets. The advent of "cloud computing" on demand has done the rest, making accessible, to many, a computational power, previously exclusive to States and government structures.
As COVID-19 continues to affect millions of lives and livelihoods, it is delivering perhaps the most significant shock to industries--from education to healthcare to food supply--in almost a century. Mineral processing companies also have to grapple with profound uncertainty and volatility. Before COVID-19, some were already taking steps to build their capabilities to cope with fluctuations inherent in commodities markets. But recent events triggering challenges in workforce availability, supply chains, and demand created a need for higher levels of operational resilience in a short period of time. Here is where recent advances in artificial intelligence (AI) helped.
Correlation is the most misunderstood term in the history of Statistics, Machine Learning, and Data Science despite being the fact that it is one of the simplest concepts of statistics. The correlation also denoted as'r' is a measure of association of two variables. It is a statistical technique that helps us determine whether there exists a relationship between two variables and if yes then to what degree (high/ medium/ low). Where x and y are the data points of datasets and x and y stand for means of X and Y. The method of calculating the Kendall correlation is quite similar to the ρ.