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Ben Zhao is the Neubauer Professor of Computer Science at the University of Chicago. He completed his PhD from Berkeley (2004) and his BS from Yale (1997). He is an ACM distinguished scientist, and recipient of the NSF CAREER award, MIT Technology Review’s TR-35 Award (Young Innovators Under 35), ComputerWorld Magazine’s Top 40 Tech Innovators award, Google Faculty award, and IEEE ITC Early Career Award. His work has been covered by media outlets such as Scientific American, New York Times, Boston Globe, LA Times, Wall Street Journal, MIT Tech Review, and Slashdot. He has published more than 160 publications in areas of security and privacy, machine learning, networked systems, Internet measurements and HCI. He served as Program (co)chair for the World Wide Web Conference (WWW 2016) and the ACM Internet Measurement Conference (IMC 2018), and General Co-Chair for ACM HotNets 2020. Over the years, Ben followed his own interests in pursuing research problems that he finds intellectually interesting and meaningful. That’s led him to work on a sequence of areas from P2P networks, online social networks, SDR/open spectrum systems, graph mining and modeling, user behavior analysis, to adversarial machine learning. Since 2016, he mostly worked on security and privacy problems in machine learning and mobile systems.
Today we kick off our annual coverage of the CVPR conference joined by Fatih Porikli, Senior Director of Engineering at Qualcomm AI Research. In our conversation with Fatih, we explore a trio of CVPR-accepted papers, as well as a pair of upcoming workshops at the event. The first paper, Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation, presents a novel framework to integrate semantic and instance contexts for panoptic segmentation. Next up, we discuss Imposing Consistency for Optical Flow Estimation, a paper that introduces novel and effective consistency strategies for optical flow estimation. The final paper we discuss is IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes, which proposes a transformer architecture to simultaneously estimate depths, normals, spatially-varying albedo, roughness, and lighting from a single image of an indoor scene. For each paper, we explore the motivations and challenges and get concrete examples to demonstrate each problem and solution presented.
There are few things I love more than cuddling up with an exciting new book. There are always more things I want to learn than time I have in the day, and I think books are such a fun, long-form way of engaging (one where I won’t be tempted to check Twitter partway through). This book roundup is a selection from the last few years of TWIML guests, counting only the ones related to ML/AI published in the past 10 years. We hope that some of their insights are useful to you! If you liked their book or want to hear more about them before taking the leap into longform writing, check out the accompanying podcast episode (linked on the guest’s name). (Note: These links are affiliate links, which means that ordering through them helps support our show!) Adversarial ML Generative Adversarial Learning: Architectures and Applications (2022), Jürgen Schmidhuber AI Ethics Sex, Race, and Robots: How to Be Human in the Age of AI (2019), Ayanna Howard Ethics and Data Science (2018), Hilary Mason AI Sci-Fi AI 2041: Ten Visions for Our Future (2021), Kai-Fu Lee AI Analysis AI Superpowers: China, Silicon Valley, And The New World Order (2018), Kai-Fu Lee Rebooting AI: Building Artificial Intelligence We Can Trust (2019), Gary Marcus Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) (2019), Meredith Broussard Complexity: A Guided Tour (2011), Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans (2019), Melanie Mitchell Career Insights My Journey into AI (2018), Kai-Fu Lee Build a Career in Data Science (2020), Jacqueline Nolis Computational Neuroscience The Computational Brain (2016), Terrence Sejnowski Computer Vision Large-Scale Visual Geo-Localization (Advances in Computer Vision and Pattern Recognition) (2016), Amir Zamir Image Understanding using Sparse Representations (2014), Pavan Turaga Visual Attributes (Advances in Computer Vision and Pattern Recognition) (2017), Devi Parikh Crowdsourcing in Computer Vision (Foundations and Trends(r) in Computer Graphics and Vision) (2016), Adriana Kovashka Riemannian Computing in Computer Vision (2015), Pavan Turaga Databases Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (2021), Xin Luna Dong Big Data Integration (Synthesis Lectures on Data Management) (2015), Xin Luna Dong Deep Learning The Deep Learning Revolution (2016), Terrence Sejnowski Dive into Deep Learning (2021), Zachary Lipton Introduction to Machine Learning A Course in Machine Learning (2020), Hal Daume III Approaching (Almost) Any Machine Learning Problem (2020), Abhishek Thakur Building Machine Learning Powered Applications: Going from Idea to Product (2020), Emmanuel Ameisen ML Organization Data Driven (2015), Hilary Mason The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019), David Carmona MLOps Effective Data Science Infrastructure: How to make data scientists productive (2022), Ville Tuulos Model Specifics An Introduction to Variational Autoencoders (Foundations and Trends(r) in Machine Learning) (2019), Max Welling NLP Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics (2013), Emily M. Bender Robotics What to Expect When You’re Expecting Robots (2021), Julie Shah The New Breed: What Our History with Animals Reveals about Our Future with Robots (2021), Kate Darling Software How To Kernel-based Approximation Methods Using Matlab (2015), Michael McCourt
The use of machine learning in business, government, and other settings that require users to understand the model’s predictions has exploded in recent years. This growth, combined with the increased popularity of opaque ML models like deep learning, has led to the development of a thriving field of model explainability research and practice. In this panel discussion, we bring together experts and researchers to explore the current state of explainability and some of the key emerging ideas shaping the field. Each guest will share their unique perspective and contributions to thinking about model explainability in a practical way. Join us as we explore concepts like stakeholder-driven explainability, adversarial attacks on explainability methods, counterfactual explanations, legal and policy implications, and more. We round out the session with an audience Q&A! Check out the list of resources below! https://www.youtube.com/embed/B2QBnVnbt7A Panelists: Rayid Ghani - Carnegie Mellon University Solon Barokas - Cornell, Microsoft Kush R. Varshney - IBM Alessya Labzhinova - Stealth Hima Lakkaraju - Harvard  Thank you to IBM for their support in helping to make this panel possible! IBM is committed to educating and supporting data scientists, and bringing them together to overcome technical, societal and career challenges. Through the IBM Data Science Community site, which has over 10,000 members, they provide a place for data scientists to collaborate, share knowledge, and support one another. IBM’s Data Science Community site is a great place to connect with other data scientists and to find information and resources to support your career. Join and get a free month of select IBM Programs on Coursera. Resources Rayid Ghani, Carnegie Mellon University - Professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy Topic: Explainability Use Cases in Public Policy and Beyond Twitter: @rayidghani TWIML AI Podcast - #283 - Real World Model Explainability Solon Barocas, Cornell University - Assistant Professor, Department of Information Science, Principal Researcher at Microsoft Research Topic: Hidden Assumptions Behind Counterfactual Explanations Twitter: @s010n TWIML AI Podcast - #219 - Legal and Policy Implications of Model Interpretability: Resources: : The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons: Published at 2020 ACM Conference on Fairness, Accountability, and Transparency: Shorter version for the 2020 Workshop on Human Interpretability in Machine Learning (WHI) Additional References: Roles for Computing in Social Change. Published at the 2020 ACM Conference on Fairness, Accountability, and Transparency Textbook on Fairness and Machine Learning. Published by MIT Press. The Intuitive Appeal of Explainable Machines Kush R. Varshney, IBM, Distinguished Research Staff Member and Manager at IBM Thomas J. Watson Research Center Topic: Model Explainability as a Communications Challenge Twitter: @krvarshney Resources: IBM AI Fairness 360 IBM AI Explainability 360 IBM Adversarial Robustness 360 IBM AI FactSheets 360 Paper: On Mismatched Detection and Safe, Trustworthy Machine Learning Democast: Mitigating Discrimination and Bias with AI Fairness 360 Alessya Labzhinova, CEO of a stealth startup and former CTO in residence AI2 Topic: Stakeholder-Driven Explainability Resources: Explainable Machine Learning in Deployment, Bhatt et al. You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods, Dimanov et al. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods, Slack et al. - Causability and eExplainability of Artificial Intelligence in Medicine, Holzinger et al Getting a CLUE: A Method for Explaining Uncertainty Estimates, Antorán et al Hima Lakkaraju, Harvard  University Assistant Professor with appointments in Business School and Department of Computer Science Topic: Adversarial Attacks, Misleading Explanations, and Solutions Twitter: @hima_lakkaraju  TWIML AI Podcast - #387 - AI for High Stakes Decision Making Resources: Presentation Brief Slide Deck The slides also have references to these papers: Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Robust and Stable Black Box Explanations
Renowned 19th-century biologist Louis Pasteur once said, "Science knows no country, because knowledge belongs to humanity, and is the torch which illuminates the world." Yet, we can learn much about the evolution of science by exploring its local origins. And with the help of AI, we can use this local knowledge to predict its future. Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach, along with co-authors including former TWIML AI Podcast guest Bruno Gonçalves. "The idea is essentially to look under the microscope of how science works, meaning for example, how it evolves over time, how collaboration occurs between different scientists, in between different fields. How scientists pick their research problems, how they, for example, move across different institutions, how nations develop expertise in different fields of research and so on." In addition to predicting the trajectory of physics research, Matteo is also active in the computational epidemiology field. His work in that area involves building simulators that can model the spread of diseases like Zika or the seasonal flu at a global scale. Science of Science Matteo's background in economics and his interest in human behavior sparked his desire to explore the "science of science." Physics was the natural starting point since he already worked with many individuals in the field. To build his models, Matteo uses a core data set of papers published in the journals of The American Physical Society. This dataset was chosen in part because of the robustness of its classification scheme, the Physics and Astronomy Classification Scheme (PACS), which provides references to affiliated topics, authors and publications for each of the papers in the archive. PACS also provides a consistent set of keywords for each of the papers. These keywords are used to relate the various physics researchers to one another using an embedding model. In Matteo's case, the model they use is StarSpace, developed by Facebook AI Research. As Matteo puts it, "We are treating each author as a bag of topics, a bag of research fields in which that author has worked. Then we use this bag of topics to infer the embeddings for each specific research sub-area." Having created an embedding that relates the various research topics to one another, Matteo and his co-authors then use it to create what they call the Research Space Network (RSN). The RSN is a "mapping of the research space [created] by essentially looking at the expertise of authors to guide us on what it means for two topics to be similar to each other." Principle of Relatedness One of the main findings from the research so far is what Matteo refers to as a "fingerprint" of the scientific production of cities. The work is based on the idea of The Principle of Relatedness, an economics term that aims to measure the relationship between a nation's overall production, exports, expertise, and trade partners to predict what items the country should export next. In applying this idea to their research, Matteo would look at all of the scientific publications from a city and use the embedding space to measure the level of relatedness, and predict the direction of the city's scientific knowledge. You can use a network to visually show the interactions between different vectors (science topics) and rank the probability that a city will enter a specific field. That ranking becomes your "classifier" and allows you to determine where that field will or will not be developed next. If you were to plot out the topics of existing research in a city, you could see where the "knowledge density" collects, and note where the density is high, to predict the trajectory of research. If a country is in an intermediate stage of development, there's a higher chance of "jumping" to a different space. Focus and Limitations The focus, for now, is to find the best way of creating embeddings for a very specific problem, not for a variety of tasks. For example, there is no weighting of a researcher's volume of work or its relative importance--the associations include anything they've been active in. Likewise, for some analyses, you might want to identify where the scientist is most active and remove any side projects or abandoned subjects. None of these are considered in this paper. Rather, Matteo approaches the problem from the simplest possible scenario, effectively asking "What if we are blind?" "We...get a big pile of papers from an author. We just list all the topics in which he has worked on and train on that." They want to prove that you do not need to perform manual checks and optimizations to get useful results. Performance Metrics Matteo tested the results using a couple of different validations: One approach was to visualize the RSN and regional fingerprints for assessment. This made it easy to see the macro areas where the PACS classification distinguishes the different subfields of physics. This hierarchy was not used at training time and the algorithm was able to determine the right classification. The second method was to measure the predictive power of the algorithm by looking at each city at a given time period and listing the topics where they had a competitive advantage. Then they compared them using a standard metric like an ROC curve to see if the model was performing better than a random model. What's Next? While the goal is to eventually expand and apply these techniques to entire papers (vs just the PACS keywords), having a predetermined taxonomy and hierarchical structure laid out gives them a benchmark to validate their own observations. Scaling this approach to other fields is something they are starting to work on. They've made some progress using the Microsoft Academic Graph which includes all the different fields in science. As of now, they can't replicate the results they get when they apply the algorithm to physics, but the potential for the embedding space can be evolved for tracking things like the semantics of a term over time, or how authors tend to move in this space. There's also the possibility of finding gaps in the science and making connections that the field might not know to make.
Kamran Khan, founder of BlueDot, recently found his company the subject of attention for being among the first to publicly warn about the coronavirus (COVID-19) that initially appeared in the Chinese city of Wuhan. How did the company's system of data gathering techniques and algorithms help flag the potential dangers of the disease? In this interview, Kamran shares how they use a variety of machine learning techniques to track, analyze and predict infectious disease outbreaks. As a practicing physician based in Toronto, Kamran was directly impacted by the SARS outbreak in 2003. "We saw our hospitals completely overwhelmed. They went into lockdown. All elective procedures were canceled...even the city took on a different feel...there were billions of financial losses...and Toronto was just one of dozens." In the wake of that crisis, governments have been slow to act. Efforts like the International Health Regulations Treaty (2005), which aims to standardize communication about diseases, help but are not well enforced. It doesn't help that these nations are often unaware of the severity of an outbreak, or are hesitant to report a threat because of potential economic consequences. Ultimately, his experience with the SARS crisis led Kamran to explore the role technology might play in anticipating outbreaks and predicting how they might spread. Kamran's insight ultimately lead to the creation of BlueDot, which applies machine learning to four main challenges in infectious disease tracking: Surveillance, Dispersion, Impact, and Communication. Surveillance The BlueDot engine gathers data on over 150 diseases and syndromes around the world, looking at over 100,000 online articles each day spanning 65 languages, searching every 15 minutes, 24 hours a day. This includes official data from organizations like the Center for Disease Control or the World Health Organization, but also counts on less structured, local information from journalists and healthcare workers. BlueDot's epidemiologists and physicians manually classified the data and developed a taxonomy so relevant keywords could be scanned efficiently. They later applied ML and NLP to train the system. Kamran points out that the algorithms in place perform "relatively low-complexity tasks, but they're incredibly high volume and there's an enormous amount of them, so we can simply train a machine to replicate our judgment [for classifying]". As a result of their system's algorithms, only a handful of cases are flagged for human experts to analyze. In the case of COVOID-19, the system highlighted articles in Chinese that reported 27 pneumonia cases associated with a market that had seafood and live animals in Wuhan. Dispersion Recognizing the role that travel plays in disease dispersion—especially in the age of air travel—BlueDot uses geographic information system (GIS) data and flight ticket sales to create a dispersion graph for each disease based on the airports connected to a city and where passengers are likely to fly. Not everyone travels by air, so they also use anonymized location data from 400 million mobile devices to track flows from outbreak epicenters to other parts of the region or world. The locations receiving the highest volume of travelers are identified and diligently evaluated for what the impact of the disease could be in the area. For COVOID-19, BlueDot applied this methodology to identify many of the cities among the first to receive the coronavirus, including Tokyo, Bangkok, Hong Kong, Seoul, and Taipei. Impact Once a virus leaves its region of origin, a wide variety of factors determine whether it will ultimately die out or grow into a full-fledged outbreak: A region may have better or worse public health infrastructure, hospitable or inhospitable climates, or varying economic resources. BlueData's systems consider factors such as these to predict the potential impact on an identified area. For example, if a virus is being spread by ticks, and Vancouver is in the middle of winter snow, the likelihood of an outbreak is very low because ticks would not survive that climate. However, the same virus might thrive in a humid environment like Florida, making the region at-risk for an outbreak. Communication If an area is determined to be at-risk, the focus shifts to providing early warnings to health officials, hospitals, airlines, and government agencies in public health, national defense, national security and even agriculture. Kamran reiterates the importance of providing only the most relevant information to those who need it, referencing the ideas Clay Shirky and his 2008 talk], "It's Not Information Overload. It's Filter Failure. BlueDot first became aware of the pneumonia cases in Wuhan on December 31st, and in addition to notifying their clients and government stakeholders directly, they publicly released their findings in the Journal of Travel Medicine on January 14th. Criticism and Limitations These are incredibly difficult predictions to make, and the science behind the transmission of infectious diseases is complex and evolving every day. So, what is the proper role of technology? Kamran asserts that "by no means would [they] claim that AI has got this problem solved. It's just one of the tools in the toolbox." In some cases, Kamran and his team may lack sufficient observations to develop a machine learning model for a particular disease. For this and other reasons, the company relies on a combination of approaches and a diverse team of specialists in their work. With coronavirus already in full swing, BlueDot is looking more heavily at analyzing location data from mobile devices to provide a real-time understanding of how people are moving around. However, Kamran compares this to predicting the weather—the further ahead you're looking, the less accurate your prediction. Despite the limitations, Kamran reinforces the value of the work by acknowledging that "Manually, it would take a hundred people around the clock [to process the data], and we have four people and a machine."
How does LinkedIn allow its data scientists to access aggregate user data for exploratory analytics while maintaining its users' privacy? That was the question at the heart of our recent conversation with Ryan Rogers, a senior software engineer in data science at the company. The answer, it turns out, is through differential privacy, a topic we've covered here on the show quite extensively over the years. Differential privacy is a system for publicly sharing information about a dataset by describing patterns of groups within the dataset, the catch is you have to do this without revealing information about individuals in the dataset (privacy). Ryan currently applies differential privacy at LinkedIn, but he has worked in the field, and on the related topic of federated learning, for quite some time. He was introduced to the subject as a PhD student at the University of Pennsylvania, where he worked closely with Aaron Roth, who we had the pleasure of interviewing back in 2018. Ryan later worked at Apple, where he focused on the local model of differential privacy, meaning differential privacy is performed on individual users' local devices before being collected for analysis. (Apple uses this, for example, to better understand our favorite emojis 🤯 👍👏). Not surprisingly, they do things a bit differently at LinkedIn. They utilize a central model, where the user's actual data is stored in a central database, with differential privacy applied before the data is made available for analysis. (Another interesting use case that Ryan mentioned in the interview: the U.S. Census Bureau has announced plans to publish 2020 census data using differential privacy.) Ryan recently put together a research paper with his LinkedIn colleague, David Durfee, that they presented as a spotlight talk at NeurIPS in Vancouver. The title of the paper is a bit daunting, but we break it down in the interview. You can check out the paper here: Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. There are two major components to the paper. First, they wanted to offer practical algorithms that you can layer on top of existing systems to achieve differential privacy for a very common type of query: the "Top-k" query, which means helping answer questions like "what are the top 10 articles that members are engaging with across LinkedIn?" Secondly, because privacy is reduced when users are allowed to make multiple queries of a differentially private system, Ryan's team developed an innovative way to ensure that their systems accurately account for the information the system returns to users over the course of a session. It's called Pay-what-you-get Composition. One of the big innovations of the paper is discovering the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise, which is commonly used in machine learning. One of the really nice connections that we made in our paper was that actually the exponential mechanism can be implemented by adding something called Gumbel noise, rather than Laplace noise. Gumbel noise actually pops up in machine learning. It's something that you would do to report the category that has the highest weight, [using what is] called the Gumbel Max Noise Trick. It turned out that we could use that with the exponential mechanism to get a differentially private algorithm. [...] Typically, to solve top-k, you would use the exponential mechanism k different times⁠ —you can now do this in one shot by just adding Gumbel noise to [existing algorithms] and report the k values that are in the the top […]which made it a lot more efficient and practical. When asked what he was most excited about for the future of differential privacy Ryan cited the progress in open source projects. This is the future of private data analytics. It's really important to be transparent with how you're doing things, otherwise if you're just touting that you're private and you're not revealing what it is, then is it really private? He pointed out the open-source collaboration between Microsoft and Harvard's Institute for Quantitative Social Sciences. The project aims to create an open-source platform that allows researchers to share datasets containing personal information while preserving the privacy of individuals. Ryan expects such efforts to bring more people to the field, encouraging applications of differential privacy that work in practice and at scale. Listen to the interview with Ryan to get the full scope! And if you want to go deeper into differential privacy check out our series of interviews on the topic from 2018. Thanks to LinkedIn for sponsoring today's show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn's highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Bits & Bytes Google scraps controversial AI ethics council days after it was announced. Google has shuttered its new artificial intelligence ethics council, a little more than a week after announcing it, in large part due to employee reactions to the appointment of a conservative think tank leader to the group. WIPO launches AI-based image search tool for brands. The World Intellectual Property Organization (WIPO) has launched a new AI-powered image search technology that uses deep learning to identify combinations of concepts within an image, thus making it faster and easier to establish a trademark’s distinctiveness. Tim Apple poaches Ian GANfellow. Apple has poached another of Google’s top AI researchers. This time it’s Ian Goodfellow, best known as the creator of GANs, or generative adversarial networks, who has joined Apple in a director role. FDA Proposes Regulatory Framework for AI- and Machine Learning-Driven SaMD. US Food and Drug Administration (FDA) requested feedback on a new discussion paper that proposes applying a “focused review” approach to premarket assessments of software as a medical device (SaMD) technologies that are powered by AI and ML. [Paper] Qualcomm Reveals “Cloud AI 100” AI Inference Accelerator. Qualcomm announced their first discrete dedicated AI processors, the Qualcomm Cloud AI 100 family. The chip, which it expects to begin producing in 2020, is designed for use in datacenters to meet increasing demand for AI inference processing. Google launches an end-to-end AI platform. At Google Next ’19, Google announced the beta version of its end-to-end AI platform, which aims to offer developers and data scientists a one-stop shop for building, testing and deploying models. At Next, the company made several additional announcements as well, including updates to its suite of Cloud AI Solutions, AutoML, BigQuery ML, its pre-trained ML APIs, and more. Petuum Unveils Industrial AI Product for Complex Manufacturing Operations. Petuum announced the Petuum Industrial AI Autopilot product, which enables optimization of complex manufacturing operations with modules that continuously learn and adapt. Dollars & Sense Intel Capital announced investments in five AI companies at its annual Global Summit. IntelCapital led a $13M round in AI chip startup Untether AI and a $150M round in AI systems company SambaNova Systems. In addition, the company invested undisclosed amounts in Andrew Ng’s Landing AI and China-based CloudPick and Zhuhai Eeasy Technology. Run.AI, a startup building a new virtualization and acceleration platform for deep learning, announced that it has raised $13M Enlitic, a San Francisco, CA-based company leveraging AI for medical imaging raised $15M in Series B financing Boston Dynamics announced that it is acquiring California-based startup Kinema Systems, which makes computer-vision and ML systems for warehouse robots Evolve IP, announced that it has acquired Jog.ai, a speech analytics and natural language technology firm based in Austin, Texas Rasa, San Francisco-based an open source company that enables developers to build contextual AI assistants, has secured $13M in Series A funding Labelbox, a collaborative training data platform for machine learning applications, has raised a $10M Series A funding Observe.AI has secured $8M in a Series A funding To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.