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Unleashing the power of machine learning models in banking through explainable artificial intelligence (XAI)

#artificialintelligence

The "black-box" conundrum is one of the biggest roadblocks preventing banks from executing their artificial intelligence (AI) strategies. It's easy to see why: Picture a large bank known for its technology prowess designing a new neural network model that predicts creditworthiness among the underserved community more accurately than any other algorithm in the marketplace. This model processes dozens of variables as inputs, including never-before-used alternative data. The developers are thrilled, senior management is happy that they can expand their services to the underserved market, and business executives believe they now have a competitive differentiator. But there is one pesky problem: The developers who built the model cannot explain how it arrives at the credit outcomes, let alone identify which factors had the biggest influence on them.


Google's DeepMind says it is close to achieving 'human-level' artificial intelligence

Daily Mail - Science & tech

DeepMind, a British company owned by Google, may be on the verge of achieving human-level artificial intelligence (AI). Nando de Freitas, a research scientist at DeepMind and machine learning professor at Oxford University, has said'the game is over' in regards to solving the hardest challenges in the race to achieve artificial general intelligence (AGI). AGI refers to a machine or program that has the ability to understand or learn any intellectual task that a human being can, and do so without training. According to De Freitas, the quest for scientists is now scaling up AI programs, such as with more data and computing power, to create an AGI. Earlier this week, DeepMind unveiled a new AI'agent' called Gato that can complete 604 different tasks'across a wide range of environments'. Gato uses a single neural network – a computing system with interconnected nodes that works like nerve cells in the human brain.


Modern Computing: A Short History, 1945-2022

#artificialintelligence

Inspired by A New History of Modern Computing by Thomas Haigh and Paul E. Ceruzzi. But the selection of key events in the journey from ENIAC to Tesla, from Data Processing to Big Data, is mine. This was the first computer made by Apple Computers Inc, which became one of the fastest growing ... [ ] companies in history, launching a number of innovative and influential computer hardware and software products. Most home computer users in the 1970s were hobbyists who designed and assembled their own machines. The Apple I, devised in a bedroom by Steve Wozniak, Steven Jobs and Ron Wayne, was a basic circuit board to which enthusiasts would add display units and keyboards. April 1945 John von Neumann's "First Draft of a Report on the EDVAC," often called the founding document of modern computing, defines "the stored program concept." July 1945 Vannevar Bush publishes "As We May Think," in which he envisions the "Memex," a memory extension device serving as a large personal repository of information that could be instantly retrieved through associative links.


The More You Write, the Better You Are at Explaining Your Work

#artificialintelligence

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we're thrilled to share our conversation with Dr. Varshita Sher. Dr. Sher is currently working as a data scientist at the Alan Turing Institute's Applied Research Centre, leveraging deep-learning technology to solve problems in the NLP and Computer Vision domains. She has a Master's degree in Computer Science from the University of Oxford and a Ph.D. in Learning Analytics from Simon Fraser University. Her work in the last eight years has focused on the intersection of research and implementation of AI/ML algorithms in myriad sectors, including Edtech, Fintech, and Healthcare.


Deep Learning Poised to 'Blow Up' Famed Fluid Equations

#artificialintelligence

For more than 250 years, mathematicians have been trying to "blow up" some of the most important equations in physics: those that describe how fluids flow. If they succeed, then they will have discovered a scenario in which those equations break down -- a vortex that spins infinitely fast, perhaps, or a current that abruptly stops and starts, or a particle that whips past its neighbors infinitely quickly. Beyond that point of blowup -- the "singularity" -- the equations will no longer have solutions. They will fail to describe even an idealized version of the world we live in, and mathematicians will have reason to wonder just how universally dependable they are as models of fluid behavior. But singularities can be as slippery as the fluids they're meant to describe.


La veille de la cybersécurité

#artificialintelligence

Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. The announcement of DALL-E 2 was accompanied by a social media campaign by OpenAI's engineers and its CEO, Sam Altman, who shared wonderful photos created by the generative machine learning model on Twitter. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits. It also provides an outlook of how generative deep learning models might finally unlock new creative applications for everyone to use.


AI & Deep Learning Predictions for Finance, Insurance and RegTech in 2022

#artificialintelligence

Recent developments in AI and deep learning have the potential to transform the way that banks and financial services firms do business, including but not limited to customer service, portfolio management, and fraud detection. But will 2022 be the year that advanced AI and machine learning techniques take off in the world of financial services? As we approach RE•WORK's upcoming London AI Finance Summit on 17-18 March, we asked some of our expert speakers what they predict for AI and deep learning in Finance, Insurance and RegTech in the coming year. Question: Which trend associated with deep learning and AI are you most interested in or passionate about? Why do you think this is so relevant today?


The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models - Science and Engineering Ethics

#artificialintelligence

Recent developments in artificial intelligence (AI) and machine learning, such as deep learning, has the potential to make medical decision-making more efficient and accurate. Deep learning technologies can improve how medical doctors gather and analyze patient data as a part of diagnostic procedures, prognoses and predictions, treatments, and prevention of disease (Becker, 2019; Ienca & Ignatiadis, 2020; Topol, 2019a, 2019b). However, applied artificial intelligence raises numerous ethical problems, such as the severe risk of error and bias (Ienca & Ignatiadis, 2020, p. 82; Marcus & Davis, 2019), lack of transparency (Müller, 2020), and disruption of accountability (De Laat, 2018). Describing the ethical challenges and concerns has so far been the main focus of the increasing research literature in general AI ethics (Müller, 2020) and ethics of medical AI (e.g., Char et al., 2018, 2020; Grote & Berens, 2019; McDougall, 2019; Vayena et al., 2018). Furthermore, if clinicians' decisions are to be substantially assisted, or even replaced by AI and machine learning, shared decision-making--a central ethical ideal in medicine that protects patient autonomy by letting patients make informed choices about their healthcare in line with their values--is challenged.


THE AGE OF AI -- BOOK REVIEW

#artificialintelligence

The book The Age of AI and Our Human Future is a graduate school level text. The Age of AI is the future, and it's coming way too fast. The human race has never been more challenged. We are all about to make some huge decisions. It is almost a magisterium for human life in the Fourth Industrial Revolution age. It is written by thought leaders of the highest-level, each in their respective fields. The first author is Henry Kissinger the former Secretary of State and NSC advisor to two US presidents, a philosopher and Nobel Peace Prize Laureate. At age 98 he has seen it all and done it, and remains an international counselor to politicians and business magnates. The second author, Eric Schmidt consolidated Google into the cutting edge technology giant that it is today. In this role he is a sought out counselor and business mogul. The third author is Daniel Huttenlocher -- the inaugural Dean of the MIT College of Computing. It is the place where AI is reinvented and recreated on self-teaching algorithm development and data aggregation from the global network platforms and the internet that occur 24/7 at a neck breaking pace. This compendium though incomplete, has more authors, contributors and editors. Meredith Potter is a contributor who augments Kissinger's intellectual pursuits she drafted, edited the texts and made the chapters flowing clearly and seamless. These and other editors made this textbook intellectually rich, informative, and easy to read. The Age of AI introduces the reader to the occurring changes we experienced in our society today. You are about to encounter many topics that involve the future in its continuing evolution. Every high school student is adapting to the new classroom intellectual reality. Here are two points to consider. First, the technology that this text discusses is not available in your community college courses or on other educational websites.


La veille de la cybersécurité

#artificialintelligence

Microsoft's Azure and Research teams are working together to build a new AI infrastructure service, codenamed « Singularity. A group of those working on the project have published a paper entitled « Singularity: Planet-Scale, Preemptible and Elastic Scheduling of AI Workloads, » which provides technical details about the Singularity effort. The Singularity service is about providing data scientists and AI practitioners with a way to build, scale, experiment and iterate on their models on a Microsoft-provided distributed infrastructure service built specifically for AI. Authors listed on the newly published paper include Azure Chief Technical Officer Mark Russinovich; Partner Architect Rimma Nehme, who worked on Azure Cosmos DB until moving to Azure to work on AI and deep learning in 2019; and Technical Fellow Dharma Shukla. Microsoft officials previously have discussed plans to make FPGAs, or field-programmable gate arrays, available to customers as a service.