Twitter said Wednesday it was launching an initiative on "responsible machine learning" that will include reviews of algorithmic fairness on the social media platform. The California messaging service said the plan aims to offer more transparency in its artificial intelligence and tackle "the potential harmful effects of algorithmic decisions." The move comes amid heightened concerns over algorithms used by online services, which some say can promote violence or extremist content or reinforce racial or gender bias. "Responsible technological use includes studying the effects it can have over time," said a blog post by Jutta Williams and Rumman Chowdhury of Twitter's ethics and transparency team. "When Twitter uses (machine learning), it can impact hundreds of millions of tweets per day and sometimes, the way a system was designed to help could start to behave differently than was intended."
Albert Einstein once said that "wisdom is not a product of schooling, but the lifelong attempt to acquire it." Centuries of human progress have been built on our brains' ability to continually acquire, fine-tune and transfer knowledge and skills. Such continual learning however remains a long-standing challenge in machine learning (ML), where the ongoing acquisition of incrementally available information from non-stationary data often leads to catastrophic forgetting problems. Gradient-based deep architectures have spurred the development of continual learning in recent years, but continual learning algorithms are often designed and implemented from scratch with different assumptions, settings, and benchmarks, making them difficult to compare, port, or reproduce. Now, a research and development team from ContinualAI with researchers from KU Leuven, ByteDance AI Lab, University of California, New York University and other institutions has proposed Avalanche, an end-to-end library for continual learning based on PyTorch.
April 6, 2021 – Korn Ferry has named Tammy Wang as vice president of data science and machine learning for the firm's digital business. She is based in San Francisco. "Tammy has incredible experience working as a leader in the data science and machine learning space. Her breadth of industry and technical knowledge is going to be an asset to our team," said Satish Gannu, chief technology officer for Korn Ferry's digital unit. "She specializes in building teams to deliver robust, scalable platforms to fuel the fast growth of various businesses including online search auction, content monetization and recruiting technology . We're thrilled to have her on board."
Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel's Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture. The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today: Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise.
This post contains a list of the AI-related seminars that are scheduled to take place between 14 April and 31 May 2021. All events detailed here are free and open for anyone to attend virtually. Machine learning for medical image analysis and why clinicians are not using it Speaker: Christian Baumgartner (Tuebingen University) Organised by: Tuebingen University Zoom link is here. Real-time Distributed Decision Making in Networked Systems Speaker: Na Li (Harvard) Organised by: Control Meets Learning Join the Google group to find out how to register. The limits of Shapley values as a method for explaining the predictions of an ML system Speaker: Suresh Venkatasubramanian (University of Utah) Organised by: Trustworthy ML Join the mailing list for instructions on how to sign up, or check the website a few days beforehand for the Zoom link.
A pioneer in machine learning has argued that the technology is best placed to augment human intelligence and bemoaned'confusion' over the meaning of artificial intelligence (AI). Michael I. Jordan, a professor in the department of electrical engineering and computer science, and department of statistics, at the University of California, Berkeley, told the IEEE that while science-fiction discussions around AI were'fun', they were also a'distraction.' "There's not been enough focus on the real problem, which is building planetary-scale machine learning-based systems that actually work, deliver value to humans, and do not amplify inequities," said Jordan, in an article from IEEE Spectrum author Kathy Pretz. Jordan, whose awards include the IEEE John von Neumann Medal, awarded last year for his contributions to machine learning and data science, wrote an article entitled'Artificial Intelligence: The Revolution Hasn't Happened Yet', first published in July 2019 but last updated at the start of this year. With various contributors thanked at the foot of the article – including one Jeff Bezos – Jordan outlined the rationale for caution.
Can a machine powered by artificial intelligence (AI) successfully persuade an audience in debate with a human? Researchers at IBM Research in Haifa, Israel, think so. They describe the results of an experiment in which a machine engaged in live debate with a person. Audiences rated the quality of the speeches they heard, and ranked the automated debater's performance as being very close to that of humans. Such an achievement is a striking demonstration of how far AI has come in mimicking human-level language use (N.
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.
Machine learning MLSys 2021: Bridging the divide between machine learning and systems Amazon distinguished scientist and conference general chair Alex Smola on what makes MLSys unique -- both thematically and culturally. Email Alex Smola, Amazon vice president and distinguished scientist The Conference on Machine Learning and Systems ( MLSys), which starts next week, is only four years old, but Amazon scientists already have a rich history of involvement with it. Amazon Scholar Michael I. Jordan is on the steering committee; vice president and distinguished scientist Inderjit Dhillon is on the board and was general chair last year; and vice president and distinguished scientist Alex Smola, who is also on the steering committee, is this year's general chair. As the deep-learning revolution spread, MLSys was founded to bridge two communities that had much to offer each other but that were often working independently: machine learning researchers and system developers. Registration for the conference is still open, with the very low fees of $25 for students and $100 for academics and professionals. "If you look at the big machine learning conferences, they mostly focus on, 'Okay, here's a cool algorithm, and here are the amazing things that it can do. And by the way, it now recognizes cats even better than before,'" Smola says. "They're conferences where people mostly show an increase in capability. At the same time, there are systems conferences, and they mostly care about file systems, databases, high availability, fault tolerance, and all of that. "Now, why do you need something in-between? Well, because quite often in machine learning, approximate is good enough. You don't necessarily need such good guarantees from your systems.