Ai With Ai

In COVID-related AI news, MIT researchers have published a machine learning algorithm that can diagnose COVID-19 by the sounds of a person’s forced cough. And the US Veterans Affairs Department rolls out a machine learning tool to predict mortality rates of COVID-19 patients. In non-COVID news, the JAIC releases the Department of Defense’s AI Education Strategy, which contains a detailed description of requirements, required instruction, and competencies. DoD also releases a new electromagnetic spectrum strategy, which contains a number of machine-learning mentions. And Tesla began making available its “full self-driving beta” to a small number of “expert and careful drivers.” Research from MIT CSAIL have created a machine learning system that can reportedly decipher “lost” languages; they built it on several principles from insights into historical linguistics, such as the observation that languages generally only evolve in certain predictable ways (such as sound substitutions). In other language news, Faceb

Sam Bendett joins Andy and Dave to discuss the latest developments and happenings in Russia's research into artificial intelligence and autonomy capabilities. They discuss Russia's national strategy and the challenges that have occurred in programmatic implementation due to COVID impacts. They also discuss the status of higher education in Russia and the standing of various institutions, as well as their relationship and interaction with the global community of researchers. They cover a variety of other trends and topics, including the Army 2020 convention and some of the announcements made during that event; and they discuss CNA's Russia Program and its on-going series of newsletters dedicated to summarizing the latest in Russian advances and research in AI. Click here to visit our website and explore the links mentioned in the episode.   

In COVID-related AI news, Andy and Dave discuss the COVID-19 Grand Challenge from C3.ai. In non-COVID AI news, the Department of Defense releases its Data Strategy. The Defense Science Board publishes a report on Counter Autonomy. The National Security Commission on AI releases its 3rd Quarter interim report and recommendations. The Center for Security and Emerging Technology releases a report on Building Trust through Testing. And the US Patent and Trademark Office publishes the responses to its initial queries, in Public Views on AI and Intellectual Property Policy. Researchers from MIT and Berkeley explore the idea that children’s learning has analogy to hacking, by making code better through an open-ended set of goals and activities. Nathan Benaich and Ian Hogarth release the State of AI Report 202, which examines the latest developments in AI research across a variety of areas (such as observing that only 15% of papers publish their code). And Taylor and Dorin publish Rise of the Self-Replicators: Early

Andy and Dave have a chat with Chad Jenkins, Professor of Computer Science and Engineering at the University of Michigan, Director of the Laboratory for Perception, RObotics, and Grounded REasoning SystemS (PROGRESS), and newest member of CNA's Board of Trustees. They discuss Chad's background and his current research at Michigan, which includes interactive robot systems and human-robot interaction. And then they discuss a variety of topics ranging from movement primitives, neural networks and fat tails, the issue of reinvention, students' experiences with AI research and the role of historical research, the culture of research in AI, and much more. Click here to visit our website and explore the links mentioned in the episode.   

The bipartisan Future of Defense Task Force releases its 2020 report, which includes the suggestion of using the Manhattan Project as a model to develop AI technologies.  Facebook AI releases Dynabench as a way to dynamically benchmark the performance of machine learning algorithms. Amsterdam and Helsinki launch AI registers that explain how they use algorithms, in an effort to increase transparency. In research, the Allen Institute of AI, University of Washington, and University of North Carolina publish research on X-LXMERT (learning cross-modality encoder representations from transformers), which trains GPT-3 on both text and images, to then generate images from scratch by providing descriptions (e.g., a large clock tower in the middle of a town). Researchers at Swarthmore College and Los Alamos National Labs demonstrate the challenges that neural networks of various sizes have in learning Conway’s Game of Life. Maria Jeansson, Claudio Sanna, and Antoine Cully create a stunning visual infographic on the “a

In COVID-related AI news, Youyang Gu provides world- and county-level COVID-19 predictions using machine learning, along with a rolling examination of accuracy. In regular AI news, a military coalition of 13 countries meets to discuss the use of and ethics of AI. Orcan Intelligence provides a deeper look into Europeans’ concerns about AI technologies. Ben Lee and the Library of Congress unveil the full open version of the Newspaper Navigator, which provides access to 1.56 million photographs from newspapers. Research from Intel and Binghamton University uses the pulse of the beating heart to identify deep fake videos with a 97% accuracy. And Arthur Holland Michel publishes the Black Box, Unlocked: Predictability and Understandability in Military AI. Click here to visit our website and explore the links mentioned in the episode. 

Andy and Dave kick off Season 4.0 of AI with AI with a discussion on social media bots. CNA colleagues Meg McBride and Kasey Stricklin join to discuss the results of their recent research efforts, in which they explored the national security implications of social media bots. They describe the types of activities that social media bots engage in (distributing, amplifying, distorting, hijacking, flooding, and fracturing), how these activities might evolve in the near future, the legal frameworks (or lack thereof), and the implications for US special operations forces and the broader national security community. Click here to visit our website and explore the links mentioned in the episode.   

In COVID-related AI news, another concerning report, this time in Nature Medicine, found “serious concerns” with 20,000 studies on AI systems in clinical trials, with many reporting only the best-case scenarios; in response, an international consortium has developed CONSORT-AI, reporting guidelines for clinical trials involving AI. In Nature, an open dataset provides a collection and overview of governmental interventions in response to COVID-19. In regular AI news, the DoD wraps up its 2020 AI Symposium. And the White House nominates USMC Maj. Gen. Groen to lead the JAIC. The latest report from the NIST shows that facial recognition technology still struggles to identify people of color. Portland, Oregon passes the toughest ban on facial recognition technology in the US. And The Guardian uses GPT-3 to generate some hype. In research, OpenAI demonstrates the ability to apply transformer-based language models to the task of automated theorem proving. Research from Berkeley, Columbia, and Chicago proposes a new

In COVID-related AI news, a report from Cambridge University and University of Manchester examines recent studies on using chest x-rays and CTs scans to detect and diagnose COVID, and finds that only 29 of 168 studies had reproducible results; the report further found that all of the studies had high or unclear risk of bias, such that none of the studies had value for use in clinics. CSET provides an overview of how China has used AI in its COVID-19 response. In non-COVID AI news, a GAO report finds systemic problems with facial recognition technology at U.S. airports. The University College of London provides an overview of AI’s use in crime, with deepfakes ranked as the most concerning. Researchers at the University of Warwick and the Alan Turing Institute develop a machine learning algorithm to identify potential planets from astronomy data. And NASA uses an algorithm to predict more accurately when hurricanes will rapidly intensify. In research, MIT, MIT-IBM Watson AI Lab, and Columbia University present

In COVID-related AI news, Andy and Dave discuss an effort from Google and Harvard to provide county-level forecasts on COVID-19 for hospitals and first responders. The National Library of Medicine, National Center of Biotechnology Information, and NIH provide COVID-19 literature analysis with interesting data analytic and visualization tools. In regular AI news, Elon Musk demonstrates the latest iteration of Neuralink, complete with pig implantees. The UK attempted a prediction system for Most Serious Violence, but found that it had serious flaws. Amazon awards a $500k “Alexa Prize” to Emory University students for their Emora chatbot, which scored a 3.81 average rating across categories. The Bipartisan Policy Center releases two reports on AI. And Russell Kirsch, inventor of the pixel and other groundbreaking technology, passed away on 11 August at the age of 91. In research, three papers tackle the problem of reconstructing 3D (in some cases, 4D) models of locations based on tourist photos taking from diffe

CNA’s Center for Autonomy and Artificial Intelligence kicks off its first panel for 2019 with a live recording of AI with AI! Andy and Dave take a step back and look at the broader trends of research and announcements involving AI and machine learning, including: a summary of historical events and issues; the myths and hype, looking at expectations, buzzwords, and reality; hits and misses (and more hype!), and some of the many challenges of why AI is far from a panacea. Click here to visit our website and explore the links mentioned in the episode.   

With Season 3 drawing to a close, Andy and Dave decided to focus this discussion entirely on the latest results from DARPA’s Air Combat Evolution (ACE) program. On 20 August, DARPA held a contest between 8 competitors, and pitted their AI agents in simulated combat against each other, and against a human pilot (who used a VR system). Heron Systems won the event, beating out the other AI agents, and also not allowing the human pilot to attain a valid targeting solution. Andy and Dave discuss the results, the limitations, and the broader context of these results in light of other research and announcements. Click here to visit our website and explore the links mentioned in the episode. 

In COVID-related AI news, Andy and Dave discuss survey from Amazon Web Surveys that examines the current status of Internet of Things applications related to COVID-19, include scenarios that might help to reduce the severity of an outbreak. MIT publishes an combinatorial machine learning method to maximize the coverage of a COVID-19 vaccine. In “quick takes” on research, Andy and Dave discuss research from Microsoft, University of Washington, and UC Irvine, which provides a checklist to help identify bugs in natural language processing algorithms. A paper from Element AI and Stanford examines whether benchmarks for natural language systems actually correspond to how we use those systems. University of Illinois at Urbana-Champaign, Columbia University, and US Army Research Lab introduce GAIA, which processes unstructured and heterogeneous multimedia data and creates a coherent knowledge base, and allows for text queries. Research published in Nature Neuroscience examines the brain connectivity of 130 mammalian

In COVID-related AI news, Andy and Dave discuss an article from Wired that describes how COVID confounded most predictive models (such as finance). And NIST investigates the effect of face masks on facial recognition software. In regular-AI news, CSET and the Bipartisan Policy Center release a report on “AI and National Security,” the first of four “meant to be a roadmap for Washington’s future efforts on AI.” The Intelligence Community releases its AI Ethics Principles and AI Ethics Framework. Researchers from the University of Chicago announce “Fawkes,” a way to “cloak” images and befuddle facial recognition software. In research, OpenAI demonstrates that GPT-2, a generator designed for text, can also generate pixels (instead of words) to fill out 2D pictures. Researchers at Texas A&M, University of S&T of China, and MIT-IBM Watson AI Lab create a 3D adversarial logo to cloak people from facial recognition. And other research explores how the brain rewires when given an additional thumb. CSET publis

In COVID-related AI news, Tencent AI Labs publishes a “machine learning” model that can predict the risk of a coronavirus patient developing severe illness. Unsupervised machine learning on data from the U.K.’s COVID Symptom Tracker, which has more than 4 million users, suggests patients cluster into roughly 6 different symptom types. Amazon Web Services releases its version of a scientific literature search on COVID-19. Aminer.org offers an open access knowledge graph of COVID-19. And “Digital Contact Tracing for Pandemic Response” takes a look at global approaches and results with implementing contact tracing. In regular AI news, the National Security Commission on AI releases its latest quarterly report, with 35 recommendations. The latest Congressional Research Service Report covers Emerging Military Technologies, including AI and LAWS. Facebook rolls out a “bot army” to simulate “bad behavior” on a parallel version of its platform, in an effort to understand and combat online abuse. In research, DeepMind

In COVID-related AI news, Andy and Dave discuss research that provides a comprehensive survey on applications of AI in fighting COVID-19. The Stanford Institute for Human-Centered AI and the AI Initiative at the Future Society launch a global alliance: Collective and Augmented Intelligence against COVID-19 (CAIAC). MIT and the IBM Watson AI Lab publish a paper that suggests a computational limit to progress in deep learning. The Atlas of Surveillance provides an open-source look at technologies that law enforcement are using across the US, to include facial recognition and drones. Similarly, Surfshark has compiled information on the status of facial recognition technology around the globe, along with additional useful information. MIT finds systematic shortcomings in the ImageNet dataset, with an observation that the crowdsourcing data collection pipeline can cause “misalignments.” Research from Google Brain shows that “self-attention” can allow agents to identify task-critical visual hints, and ignore task-i

Andy and Dave start with COVID-related AI news, and efforts from the Roche Data Science Coalition for UNCOVER (the United Network for COVID-19 Data Exploration and Research), which includes a dataset of a curated collection of over 200 publicly available COVID-19 related datasets; efforts from Akai Kaeru are included. The Biomedical Engineering Society publishes an overview of emerging technologies to combat COVID-19. Zetane Systems uses machine learning to search the DrugVirus database and information from the National Center for Biotechnology to identify existing drugs that might be effective against COVID. And researchers at the Walter Reed Army Institute of Research are using machine learning to narrow down a space of 41 million compounds to identify candidates for further testing. And the IEEE hosted a conference on 9 July, “Does your COVID-19 tracing app follow you forever?” In non-COVID-related AI news, MIT takes offline the TinyImages dataset, due to its inclusion of derogatory terms and images. The s

In COVID-related AI news, Purdue University has built a website that tracks global response to social distancing, by pulling live footage and images from over 30,000 cameras in 100 countries. Simon Fong, Nilanjan Dey, and Jyotismita Chaki have published Artificial Intelligence for Coronavirus Outbreak, which examines AI’s contribution to combating COVID-19. Researchers at Harvard and Boston Children’s Hospital use a “regular” Bayesian model to identify COVID-19 hotspots over 14 days before they occur. In non-COVID AI news, the acting director of the JAIC announces a shift to enabling joint warfighting operations. The DoD Inspector General releases an Audit of Governance and Protection of DoD AI Data and Technology, which reveals a variety of gaps and weaknesses in AI governance across DoD. Detroit Police Chief James Craig reveals that the police department’s experience with facial recognition technology resulted in misidentified people about 96% of the time. Over 1400 mathematicians sign and deliver a letter

It’s a week of huge announcements! But first, in COVID-related AI news, Andy and Dave discuss a review paper in Chaos, Solitons, and Fractals that provides a more international focus on the role of AI and ML in COVID research. CSAIL teams with Ava Robotics to design a robot that maneuver between waypoints and disinfect surfaces of warehouses with UV-C light. C3.ai Digital Transformation Institute awards $5.4M to 26 AI researchers for projects related to COVID-19. In non-COVID news, the Association for Computing Machinery calls for the immediate suspension of facial recognition technologies until more mature and reliable. US lawmakers have introduced a bill that would ban police use of facial recognition, while separate bills seek to increase the AI talent available for the Department of Defense, and work to realign and rewire the JAIC within DoD. Over 2300 researchers sign a petition to Springer Nature to reject a publication from Harrisburg University, which developed facial recognition software to predict 

For COVID-related AI news, Andy and Dave discuss the Stanford Social Innovation Review report on the problem with COVD-19 AI solutions (e.g., data gaps, inconsistency, etc), and how to fix them. The National Endowment for Science Technology and the Arts (NESTA) provides a thorough report on AI and COVID-19, whose findings generally suggest that barriers might exist for the employment of AI in tackling COVID-19. In regular AI news, the US has its first known case of an erroneous arrest due to facial recognition technology, with the arrest of Robert Williams in Detroit in January 2020 (and disclosed on 24 June). The European Commission white paper on AI gets two more responses, from Facebook and from the Center for Data Innovation. Sergei Ivanov provides a breakdown of contributors for the upcoming International Conference on Machine Learning. Researchers have identified a new threat vector against neural networks, one that increases energy consumption and latency. And a follow-up with the Pulse upsampling tool