The Federal Reserve Board, the CFPB, the FDIC, the National Credit Union Administration and the OCC (the "agencies") solicited comment on financial institution use of artificial intelligence ("AI") and machine learning. The agencies are seeking information on operational purposes, governance and cybersecurity, risk management, credit decisions, and controls over AI, as well as whether the agencies can provide guidance regarding a financial institution's use of AI in a safe and sound manner. Comments on the request for information must be submitted within 60 days of its publication in the Federal Register.
From the business closure to economic turmoil, the year 2020 has disrupted every aspect of our lives. Many events, conferences and other meetings in technology have been cancelled or postponed. However, as many economic activities are coming to the new normal, many conferences are starting to take place both online and in person. Analytics Insight has listed here the top 10 upcoming AI and ML conferences that will help you decide which one to attend and which one suits you. With artificial intelligence and machine learning presenting new possibilities, AI and ML conferences are gaining much popularity.
In Season 4 of the show Silicon Valley, Jian-Yang creates an app called SeeFood that uses an AI algorithm to identify any food it sees--but since the algorithm has only been trained on images of hot dogs, every food winds up being labeled "hot dog" or "not hot dog." While Jian-Yang's creation may seem absurd, in fact his app displays an intelligence that most AI models in use today do not: it only gives an answer that it knows is 100% accurate. In real life, when you ask most machine learning algorithms a question, they are programmed to give you an answer, even when they are somewhat or entirely unqualified to do so. The data on which these models are trained may have nothing to do with the specific question being asked, but the model delivers an answer anyway -- and as a result, that answer is often wrong. It's as if SeeFood tried to identify every food based only on a knowledge of hot dogs. This issue, known as "model overconfidence," is a key reason why many AI deployments fail to meet their business objectives.
AI models not only take time to build and train, but also to deploy in an organization's workflow. That's where MLOps (machine learning operations) companies come in, helping clients scale their AI technology. InfuseAI, a MLOps startup based in Taiwan, announced today it has raised a $4.3 million Series A, led by original design manufacturer Wistron Corporation, with participation from Hive Ventures, Top Taiwan Venture Capital Group and Silicon Valley Taiwan Investments. Founded in 2018, InfuseAI says the market for MLOps solutions is worth $30 million a year in Taiwan, with the global market expected to reach about $4 billion by 2025, according to research firm Cognilytica. Its clients include E.SUN, one of Taiwan's largest banks, SinoPac Holdings and Chimei. InfuseAI helps companies deploy and manage machine learning models with turnkey solutions like PrimeHub, a platform that includes a model training environment, cloud or on-premise cluster computing (including container orchestration with Kubernetes) and collaboration tools for teams.
Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It's the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand. According to Indeed.com, the average IT salary -- the keyword is "artificial intelligence engineer" -- in the San Francisco area ranges from approximately $134,135 per year for "software engineer" to $169,930 per year for "machine learning engineer." Check out our editorial recommendations on the best machine learning books. However, it can go much higher if you have the credentials firms need.
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.
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.
The AI Investor recently caught up with Jeremiah Lowin, founder of Prefect, an exciting AI startup with offices in Washington, DC and San Francisco. Jeremiah has a Finance/Risk Management background. The company is setting the standard in dataflow automation used to build, run, and monitor millions of data workflows and pipelines. While his father is a value investor and entrepreneur, Jeremiah likes to dabble in side projects that catch his interest, but having started a business a decade ago, being a founder again wasn't something he was looking for. Constantly experimenting, Jeremiah discovered people wanted to pay him for what he was building.
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.
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.