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AI is learning when it should and shouldn't defer to a human

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Is a doctor or an AI system better at diagnosing a particular patient? This machine learning tool makes the call.


Can Artificial Intelligence Help Increase Diversity in IT? - InformationWeek

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Technology companies are poster children for diversity problems in the workforce. Although they far surpass the national average when hiring Asian Americans, Brookings found African Americans and Latinos were employed in tech at half the rate as they were in all other professions. There is no shortage of theories as to why these gaps persist, but no solution to date has made a significant dent in the industries' problem. Is it time to look at artificial intelligence to eradicate bias from our hiring process? First, we have to deal with the elephant in the room.


Conservation machine learning

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Ensemble techniques--wherein a model is composed of multiple (possibly) weaker models--are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence--e.g., AdaBoost [2]--the latter in a parallel manner--e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier.


Author Correction: Analysis of the Human Protein Atlas Image Classification competition

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.


Combining Spatial Computing & IoT Can Unleash Data's Full Potential

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The enterprise has been talking about Digital Transformation and Industry 4.0 for years. We have seen transformation accelerate and the adoption of artificial intelligence, connected devices, and even virtual reality speed-up over the last few months due to the pandemic. As enterprise digitization continues to be top of mind and data becomes even more critical in this process, we need to look at how all the data created can be better visualized to generate better business outcomes. The Internet of Things (IoT) allows devices to talk to each other through connected sensors - producing real-time data. Companies had to learn how to process large amounts of data from IoT devices.


AI is learning when it should and shouldn't defer to a human

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The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision. The research: Researchers at MIT's Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator.


Developing Artificial Intelligence in Russia: Objectives and Reality

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Russia's leaders have been paying close attention to artificial intelligence (AI) technologies for several years now. President Vladimir Putin has said on numerous occasions that the leader in the field of AI would become "the master of the world." Until recently, however, Russia remained virtually the only large country without its own AI development strategy. That changed in October 2019, when the country adopted a long-discussed National Strategy for the Development of Artificial Intelligence Through 2030. One of the driving forces behind the strategy was Sberbank president German Gref. The state-owned bank has also developed a road map for developing AI in Russia and coordinated the creation of Russia's AI development strategy, which is largely corporate, involving the internet giants Yandex and Mail.ru


India's Bharti Airtel Partners With Amazon to Grow Cloud Business

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Bharti Airtel Ltd and Amazon Web Services (AWS) will join forces to develop the Indian telecoms firm's cloud business, allowing it to offer a wider range of products to its enterprise clients, the two companies said on Wednesday. Airtel Cloud currently provides data centre and cloud services to companies and governments via different partnerships. "AWS with the depth and breadth of our platform, and Airtel with its deep reach and expertise and focus, I think together we can build a set of really differentiated cloud products and go serve customers at scale in India," Puneet Chandok, President, India and South Asia at Amazon Internet Services told a virtual news conference. New products could include data analytics, artificial intelligence and machine learning, and security services among others, the two companies said.


Simplifying the design of soft robotic actuators – Advanced Science News – IAM Network

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A new layer-by-layer fabrication process allows researchers to create new and improved soft robot actuators with variable degrees of stiffness. Over the past decade, there has been a growing interest in developing soft robots that mimic nature to make them safer and more compliant with the physical world. Soft robots offer the promise of being able to interact more effectively with unknown objects and surroundings while operating with variable degrees of freedom. However, soft robots' inherent compliance often makes it difficult for them to exert forces on surrounding surfaces or withstand mechanical loading. To circumvent this problem, researchers are investigating and developing new technologies to control and tune the stiffness of soft robotics applications. Nowadays, these technologies are widely implemented to enhance the grasping capabilities of soft actuators or to provide a physical feedback in wearable devices.


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The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A I before anyone else, make sure to subscribe using the form at the bottom of this page. Five projects have received $29 million in funding from Scale AI and a number of companies to support the implementation of artificial intelligence. In these unprecedented times, entrepreneurs need all the help they can get. BetaKit has teamed up with Microsoft for Startups on a new series called Just One Thing, where startup founders and tech leaders share the'one thing' they want the next generation of entrepreneurs to learn. Instrumental, a startup that uses vision-powered AI to detect manufacturing anomalies, announced that it has closed a $20 million Series B led by Canaan Partners.