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Veritone Wins 2021 Artificial Intelligence Excellence Award for Second Consecutive Year


DENVER--(BUSINESS WIRE)--Veritone, Inc. (NASDAQ: VERI), the creator of the world's first operating system for artificial intelligence, aiWARE, today announced that Business Intelligence Group has named Veritone as a winner in the 2021 Artificial Intelligence Excellence Awards for its patented suite of real-time AI-powered Veritone Energy Solutions. Launched in the fall of 2020, Veritone's energy solutions optimize smart grid energy distribution by continuously knowing how much of what type of energy to deliver where, providing grid resilience and autonomous microgrid management when portions of the grid fail, and optimal economic dispatch during normal operations. The solutions deliver supply/demand forecasting, energy smoothing and optimization, DER synchronization and predictive control, energy arbitrage, and smart grid simulation. The solutions collect current weather forecast data, energy demand, and pricing data, and detect the current state and capacity of all energy devices, to intelligently determine the ideal energy supply mix and pricing to meet grid demand, in real time. Utilities and developers can now deliver profitable renewable energy with unparalleled grid efficiency and resiliency.

10 Best Artificial Intelligence Stocks to Buy for 2021


In this article we will take a look at the 10 best artificial intelligence stocks for 2021. You can skip our detailed analysis of the AI industry's outlook for 2021 and some of the major growth catalysts for AI stocks and go directly to 5 Best Artificial Intelligence Stocks for 2021. Artificial intelligence is a buzzword increasingly being used by companies around the world that seek to project themselves at the forefront of cutting-edge research that promises to transform the lives of humans. As the word loses its meaning, it is important for investors to understand what artificial intelligence is and what companies stand to gain from breakthroughs in the new technology. Market estimates suggest that the artificial intelligence industry will witness a compound annual growth of more than 40% in the first half of this decade. Artificial intelligence, in the simplest words, uses data analytics to perform tasks that would otherwise be performed by humans.

Modular Machine Learning - Best Of Both Worlds?


A Webinar By Joseph Simonian Abstract: After reviewing some differences between traditional statistics & data science, we present a modular machine learning framework for model validation which blends the two paradigms. Model validation is set up as a sequence of procedures, in which the output from one procedure serves as the input to another procedure within a single validation framework. An econometric model is used in the first module to classify data in an economically intuitive way. Proceeding modules apply data science techniques to evaluate the predictive characteristics of the model components. We apply the framework to the fundamental law of active management, a well-known formal characterization of portfolio managers alpha generation process.

Why AI can help you beat the market


Humans have always welcomed other beings in finance: over twenty years ago, some of the best Wall Street traders were outsmarted by Raven, a chimpanzee who picked stocks by throwing darts. Her index, called MonkeyDex, became one of the biggest sensations at the turn of the century after delivering a 213% gain. Perhaps because animals are not so easy to fit in offices, people have turned to other kinds of brains to choose equities. Big institutions are resorting to artificial intelligence (AI) to analyse stocks collating all sorts of information coming from a plethora of sources. In fact, while investments could previously be assessed based on financial reports and share price movement – what is called structured data – markets have been heavily influenced by unstructured data over the past few years.

Machine learning: Economics and computer science converge


Today's digital economy is blurring the boundaries between computer science and economics -- in Silicon Valley, on Wall Street, and increasingly on university campuses. Yale undergraduates interested in both fields can pursue the Computer Science and Economics (CSEC) interdepartmental degree program, which launched in fall 2019, with coursework covering topics such as machine learning and computational finance. Philipp Strack, CSEC's inaugural director of undergraduate studies, is comfortable straddling multiple disciplines. With an academic background in economics and mathematics, his research reflects this broad and interdisciplinary outlook -- ranging from behavioral economics and neuroscience to auction design, market design, optimization, and pure probability theory. Strack, an associate professor of economics in the Faculty of Arts and Sciences, recently spoke to YaleNews about the real-world implications of this work, what the CSEC program offers students, and how it bridges these critical fields.

Global Machine Learning Infrastructure as a Service Market Top Manufacturers Analysis by 2026: Amazon Web Services (AWS), Google, Valohai, Microsoft, VMware etc. – The Market Eagle


Predicting Growth Scope: Global Machine Learning Infrastructure as a Service Market The Global Machine Learning Infrastructure as a Service Market research report is comprised of the thorough study of all the market associated dynamics. The research report is a complete guide to study all the dynamics related to global Machine Learning Infrastructure as a Service market. The comprehensive analysis of potential customer base, market values and future scope is included in the global Machine Learning Infrastructure as a Service market report. Along with that the research report on the global market holds all the vital information regarding the latest technologies and trends being adopted or followed by the vendors across the globe.The research report provides an in-depth examination of all the market risks and opportunities. The analysis covered in the report helps manufacturers in the industry in eliminating the risks offered by the global market.

How blockchain and machine learning can deliver the promise of omnichannel marketing


Researchers from University of Minnesota, New York University, University of Pennsylvania, BI Norwegian Business School, University of Michigan, National Bureau of Economic Research, and University of North Carolina published a new paper in the Journal of Marketing that examines how advances in machine learning (ML) and blockchain can address inherent frictions in omnichannel marketing and raises many questions for practice and research. The study, forthcoming in the Journal of Marketing, is titled "Informational Challenges in Omnichannel Marketing Remedies and Future Research" and is authored by Koen Pauwels, Haitao (Tony) Cui, Catherine Tucker, Raghu Iyengar, S. Sriram, Anindya Ghose, Sriraman Venkataraman, and Hanna Halaburda. In this new study in the Journal of Marketing, researchers define omnichannel marketing as the "synergistic management of all customer touch points and channels both internal and external to the firm that ensures that the customer experience across channels and firm-side marketing activity, including marketing-mix and marketing communication (owned, paid, and earned), is optimized." Often viewed as the panacea for one-to-one marketing, omnichannel experiences data, marketing attribution, and consumer privacy frictions. The research team demonstrates that advances in machine learning (ML) and blockchain can address these frictions.

The Success of AdaBoost and Its Application in Portfolio Management Machine Learning

Equal-weighted portfolios are one of the most important strategies in portfolio management. They are portfolios with weights equally distributed across the selected securities in the long and/or short positions. In academic research, numerous studies have suggested that equal-weighted portfolios have a better out-of-sample performance than other portfolios (e.g., Jobson and Korkie 1981; James 2003; DeMiguel et al. 2007). Michaud (1989) and DeMiguel et al. (2007) argued that, the equal-weighted strategies do not suffer from the estimation error of the covariance matrix, which is vulnerable to outliers (Tu and Zhou, 2011). In industry, equal-weighted portfolios are popular across portfolio management in practice, particularly in the hedge funds. The MSCI has issued many equal-weighted indexes, which are "some of the oldest and best-known factor strategies that have aimed to identify specific characteristics of stocks generating excess return"

What's Ahead for a Cooperative Regulatory Agenda on Artificial Intelligence?


In her first major speech to a U.S. audience after the U.S. presidential election, European Commission President Ursula von der Leyen laid out priority areas for transatlantic cooperation. She proposed building a new relationship between Europe and the United States, one that would encompass transatlantic coordination on digital technology issues, including working together on global standards for regulating artificial intelligence (AI) aligned with EU values. A reference to cooperation on standards for AI was included in the New Transatlantic Agenda for Global Change issued by the Commission on December 2, 2020. In remarks to Parliament on January 22, 2021, President von der Leyen called for "creating a digital economy rule book" with the United States that is "valid worldwide." Some would say Europe's new outreach on issues of tech governance and the suggestion of establishing an "EU-U.S. Trade and Technology Council" is incongruous to the current regulatory war being waged against ...

How machine learning can improve money management


Two disciplines familiar to econometricians, factor analysis of equities returns and machine learning, have grown up alongside each other. Used in tandem, these fields of study can build effective investment-management tools, according to City University of Hong Kong's Guanhao Feng (a graduate of Chicago Booth's PhD Program), Booth's Nicholas Polson, and Booth PhD candidate Jianeng Xu. The researchers set out to determine whether they could create a deep-learning model to automate the management of a portfolio built on buying stocks that are expected to rise and short selling those that are expected to fall, known as a long-short strategy. They created a machine-learning algorithm that built a long-short equity portfolio from the top and bottom 20 percent of a 3,000-stock universe. They ranked the equities using the five-factor model of Chicago Booth's Eugene F. Fama and Dartmouth's Kenneth R. French.