Few jobs look more ripe for robotization than those of the guide dogs used by blind and sight-impaired people. While the canny canines are great at helping their human owners navigate safely around people and obstacles in streets, buildings, and cities, the animals themselves are in extremely short supply, so few of those who need a guide dog actually get to own one. If their guidance task can be affordably and reliably automated, however, a clutch of robotics research labs around the world say many more people could get the help they need to lead more independent lives. There are several reasons for the dearth of guide dogs. First, each costs more than 50,000 to breed, raise, and train, according to the Guide Dog Foundation, a Smithtown, NY-based charity.

Understanding the nature of representation in neural networks is a goal shared by neuroscience and machine learning. It is therefore exciting that both fields converge not only on shared questions but also on similar approaches. A pressing question in these areas is understanding how the structure of the representation used by neural networks affects both their generalization, and robustness to perturbations. In this work, we investigate the latter by juxtaposing experimental results regarding the covariance spectrum of neural representations in the mouse V1 (Stringer et al) with artificial neural networks. We use adversarial robustness to probe Stringer et al's theory regarding the causal role of a 1/n covariance spectrum. We empirically investigate the benefits such a neural code confers in neural networks, and illuminate its role in multi-layer architectures. Our results show that imposing the experimentally observed structure on artificial neural networks makes them more robust to adversarial attacks. Moreover, our findings complement the existing theory relating wide neural networks to kernel methods, by showing the role of intermediate representations.

Artificial intelligence can help accurately map and track penguin colonies in Antarctica by analysing tourist photos. "Right now, everyone has a camera in their pocket, and so the sheer volume of data being collected around the world is incredible," says Heather Lynch at Stony Brook University in New York. Haoyu Wu at Stony Brook University and his colleagues, including Lynch, used an AI tool developed by Meta to highlight Adélie penguins in photographs taken by tourists or scientists on the ground. With guidance from a human expert, the AI tool was able to automatically identify and outline entire colonies in photos. This semi-automated method is much faster than doing everything manually because the AI tool takes just 5 to 10 seconds per image, compared with a person taking 1 to 2 minutes, says Wu. The team also created a 3D digital model of the Antarctic landscape using satellite imagery and terrain elevation data.

Development of a Novel Impedance-Controlled Quasi-Direct-Drive Robot Hand by Jay Best Master of Science in Mechanical Engineering Stony Brook University 2023 Most robotic hands and grippers rely on actuators with large gearboxes and force sensors for controlling gripping force. However, this might not be ideal for tasks which require the robot to interact with an unstructured and/or unknown environment. We propose a novel quasidirect-drive two-fingered robotic hand with variable impedance control in the joint space and Cartesian space. The hand has a total of four degrees of freedom, a backdrivable gear train, and four brushless direct current (BLDC) motors. Field-Oriented Control (FOC) with current sensing is used to control motor torques. Variable impedance control allows the hand to perform dexterous manipulation tasks while being safe during human-robot interaction. The quasidirect-drive actuators enable the fingers to handle contact with the environment without the need for complicated tactile or force sensors. A majority 3D printed assembly makes this a lowcost research platform built with affordable off-the-shelf components. The hand demonstrates grasping with force-closure and form-closure, stable grasps in response to disturbances, tasks exploiting contact with the environment, simple in-hand manipulation, and a light touch for handling fragile objects.

Panic rippled through the cybersecurity world in early December 2021 as word spread about a newly discovered vulnerability in a piece of open source software used by millions. A string of code called Log4J, which instructs programs written in Java to create a record of program activity, would allow attackers to insert malicious code into programs. The flaw led to risks in software used by government agencies, Web service providers such as Amazon Web Services and Apple iCloud, and even video games such as Minecraft. In fact, within days of the first announcement, attackers used the flaw to get into the computer of the Suffolk County, NY, clerk's office. Over the next few months, they stole files and passwords, installed malware and crypto-currency mining software, and gained access to other county networks, including the health and sheriff's departments.

Machine Learning for Probabilistic Prediction Quantitative Finance Webinar, Stony Brook University (11/11/2022) Valery Manokhin, PhD, MBA, CFQ Speaker Bio • PhD in Machine Learning (2022) from Royal Holloway, University of London • During PhD conducted research and published papers in probabilistic and conformal prediction. PhD supervised by Prof. Vladimir Vovk, the creator of Conformal Prediction (Prof. Vladimir Vovk is the last PhD student of Andrey Kolmogorov) • Dr. Valery Manokhin holds a number of advanced MSc degrees including from the Moscow Institute of Physics and Technology (Physics/Math), UCL (Computational Statistics and Machine Learning), University of Sussex (Quant Finance) and an MBA from the University of Warwick • Published in the leading machine learning journals, including'Neurocomputing', 'Journal of Machine Learning Research' and'Machine Learning Journal', also in the industry journals including'Frontiers in Energy Research' • Created'Awesome Conformal Prediction' - the most comprehensive professionally curated resource on Conformal Prediction (over 900 stars on GitHub). 'Awesome Conformal Prediction' has been featured at the leading conferences such as ICML and in Kevin Murphy's bestselling book'Probabilistic Machine Learning: An Introduction' Outline of this webinar Introduction to Probabilistic Prediction Probability Calibration Introduction to Conformal Prediction Conformal Prediction for Classification Conformal Prediction for Regression Conclusion 3 Why Probabilistic Prediction? Machine Learning is primarily concerned with producing functions mapping objects onto predicted labels Classical statistical techniques - for small scale, low-dimensional data High-dimensional data does not necessarily follow well-known distributions and hence required new approaches (e.g.

LOS ANGELES/WASHINGTON, Nov 15 (Thomson Reuters Foundation) - When the sheriff in Suffolk County, New York, requested $700,000 from the U.S. government for an artificial intelligence system to eavesdrop on prison phone conversations, his office called it a key tool in fighting gang-related and violent crime. But the county jail ended up listening to calls involving a much wider range of subjects - scanning as many as 600,000 minutes per month, according to public records from the county obtained by the Thomson Reuters Foundation. Beginning in 2019, Suffolk County was an early pilot site for the Verus AI-scanning system sold by California-based LEO Technologies, which uses Amazon speech-to-text technology to transcribe phone calls flagged by key word searches. The company and law enforcement officials say it is a crucial tool to keep prisons and jails safe, and fight crime, but critics say such systems trample the privacy rights of prisoners and other people, like family members, on the outside. "The ability to surveil and listen at scale in this rapid way - it is incredibly scary and chilling," said Julie Mao, deputy director at Just Futures Law, an immigration legal group.

When the sheriff in Suffolk County, New York, requested $700,000 from the U.S. government for an artificial intelligence system to eavesdrop on prison phone conversations, his office called it a key tool in fighting gang-related and violent crime. But the county jail ended up listening to calls involving a much wider range of subjects -- scanning as many as 600,000 minutes per month, according to public records from the county obtained by the Thomson Reuters Foundation. Beginning in 2019, Suffolk County was an early pilot site for the Verus AI-scanning system sold by California-based LEO Technologies, which uses Amazon speech-to-text technology to transcribe phone calls flagged by keyword searches. The company and law enforcement officials say it is a crucial tool to keep prisons and jails safe, and to fight crime, but critics say such systems trample the privacy rights of prisoners and other people, like family members, on the outside. "T he ability to surveil and listen at scale in this rapid way -- it is incredibly scary and chilling," said Julie Mao, deputy director at Just Futures Law, an immigration legal group.

For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video--specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. "Machines will be able to help us in our daily lives only if they really understand the world through our eyes," says Kristen Grauman at FAIR, who leads the project. Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. "The video in this data set is much closer to how humans observe the world," says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.

The coronavirus disease (COVID-19) has rapidly spread throughout the world and while pregnant women present the same adverse outcome rates, they are underrepresented in clinical research. We collected clinical data of 155 test-positive COVID-19 pregnant women at Stony Brook University Hospital. Many of these collected data are of multivariate categorical type, where the number of possible outcomes grows exponentially as the dimension of data increases. We modeled the data within the unsupervised Bayesian framework and mapped them into a lower-dimensional space using latent Gaussian processes. The latent features in the lower dimensional space were further used for predicting if a pregnant woman would be admitted to a hospital due to COVID-19 or would remain with mild symptoms. We compared the prediction accuracy with the dummy/one-hot encoding of categorical data and found that the latent Gaussian process had better accuracy.