Using arrest and victimization records from the Chicago PD, a machine learning model can predict the risk of being shot in the next 18 months. UMD College of Information Studies Assistant Professor Zubin Jelveh--alongside co-authors Sara B. Heller of the University of Michigan, Benjamin Jakubowski of the Courant Institute of Mathematical Sciences, and Max Kapustin of the Brooks School of Public Policy--recently published a paper on research that supports that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, the team trained a machine learning model to predict the risk of being shot in the next 18 months. They addressed central concerns about police data and algorithmic bias by predicting shooting victimization rather than arrest, which accurately captures risk differences across demographic groups despite bias in the predictors. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, 13 percent are shot within 18 months, a rate 130 times higher than the average Chicagoan.

Adobe and Meta, together with the University of Washington, have published an extensive criticism regarding what they claim to be the growing misuse and abuse of user studies in computer vision (CV) research. User studies were once typically limited to locals or students around the campus of one or more of the participating academic institutions, but have since migrated almost wholesale to online crowdsourcing platforms such as Amazon Mechanical Turk (AMT). Among a wide gamut of grievances, the new paper contends that research projects are being pressured to produce studies by paper reviewers; are often formulating the studies badly; are commissioning studies where the logic of the project doesn't support this approach; and are often'gamed' by cynical crowdworkers who'figure out' the desired answers instead of really thinking about the problem. The fifteen-page treatise (titled Towards Better User Studies in Computer Graphics and Vision) that comprises the central body of the new paper levels many other criticisms at the way that crowdsourced user studies may actually be impeding the advance of computer vision sub-sectors, such as image recognition and image synthesis. Though the paper addresses a much broader tranche of issues related to user studies, its strongest barbs are reserved for the way that output evaluation in user studies (i.e. when crowdsourced humans are paid in user studies to make value judgements on – for instance – the output of new image synthesis algorithms) may be negatively affecting the entire sector.

Reinforcement learning (RL) is a machine learning (ML) paradigm that is capable of optimizing sequential decisions. RL is interesting because it mimics how we, as humans, learn. We are instinctively capable of learning strategies that help us master complex tasks like riding a bike or taking a mathematics exam. RL attempts to copy this process by interacting with the environment to learn strategies. Recently, businesses have been applying ML algorithms to make one-shot decisions. These are trained upon data to make the best decision at the time.

Integration platform AI Squared announced today the closing of a $6 million seed round led by NEA with participation from Ridgeline Partners. Launched in 2021, AI Squared helps companies adopt artificial intelligence by using a low-code platform to integrate it into existing applications in a timely and straightforward manner. Its founder, Benjamin Harvey, was inspired to start the company after a decade at the U.S. National Security Agency, where he saw how it and other organizations struggled to adopt artificial intelligence into existing applications. The struggle came from what's known as the last-mile challenge, which refers to the costly and time-consuming process of implementing an AI model within an application used on a day-to-day basis, like Netflix's program recommender system, he told TechCrunch. AI Squared helps solve the last-mile problem, assisting companies in adopting AI by using a low-code platform to integrate it into existing applications.

Dr. Nisha Talagala is a world-renowned computer scientist and an expert in Artificial Intelligence and Machine Learning. The inspiration to write this book started with her experiences sharing the power of AI technology with her then 9 year old daughter. She found that there were not many resources available for kids to learn and interact with AIs in a way that is engaging and not intimidating. She found that, with the right tools and approach, kids can learn AI, become empowered, and create amazing innovations. Just like computer science and coding is an integral part of learning today, AI is required learning for all the professionals of tomorrow.

In this article, we will discuss how data processing is done in DALI and examine the foundational concepts of this capable package, namely, operations, data nodes, and pipelines, and discover how to build a PyTorch data loader with them. Without further ado, let's get coding! DALI's core is nvidia.dali.Pipeline, a class that defines the data processing procedure, for instance, reading image bytes, decoding them, and normalization, as illustrated below. Note that there are two classes of nodes in this figure; one would be operations that transform the data, portrayed by rectangles, and the other would be data nodes that are the inputs/outputs of the operations, denoted by circles. Reading bytes, decoding them, and normalization are therefore operations, and their inputs/outputs are data nodes.

On June 16, 2022, the Federal Trade Commission issued a report to Congress titled Combatting Online Harms Through Innovation (the "Report") that urges policymakers and other stakeholders to exercise "great caution" about relying on artificial intelligence ("AI") to combat harmful online content. The Report comes after Congress in the 2021 Appropriations Act directed the FTC to examine ways that AI may be used to address a wide variety of specified harmful online content such as scams, deepfakes, fake reviews, opioid sales, child sexual exploitation, revenge pornography, harassment, hate crimes, incitement of violence, misleading or exploitative interfaces, terrorist and violent extremist abuse of digital platforms, election-related disinformation and counterfeit product sales. The Report expresses concerns that AI tools can be inaccurate, biased and incentivize reliance on invasive forms of commercial surveillance. The Report finds that, given that major tech platforms and others are already using AI tools to address online harms, lawmakers should consider focusing on developing legal frameworks that would ensure that AI tools do not themselves cause harm. The FTC voted 4-1 to send the Report to Congress and released separate statements on the Report from Chair Lina M. Khan, Commissioner Rebecca Kelly Slaughter, Commissioner Alvaro M. Bedoya, Commissioner Christine S. Wilson (concurring) and Commissioner Noah Joshua Phillips (dissenting).

Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary computation applied to numerical optimization. While several handcrafted adaptation policies have been proposed over the years to address this problem, only few attempts have been done so far at apply machine learning to learn such policies. Here, we introduce a general-purpose framework for performing parameter adaptation in continuous-domain metaheuristics based on state-of-the-art reinforcement learning algorithms. We demonstrate the applicability of this framework on two algorithms, namely Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential Evolution (DE), for which we learn, respectively, adaptation policies for the step-size (for CMA-ES), and the scale factor and crossover rate (for DE). We train these policies on a set of 46 benchmark functions at different dimensionalities, with various inputs to the policies, in two settings: one policy per function, and one global policy for all functions.

Matterworks, Inc. is building the world's most powerful metabolomics platform, which accelerates the discovery, development, and manufacturing of biologics and gene therapies. A venture-backed startup, our mission is to democratize metabolomics and catalyze its widespread adoption and integration into the life sciences. The field of metabolomics sits today where genomics was positioned in the early 2000's: ripe for a breakthrough that drives explosive adoption of metabolomics as an essential tool for the life sciences. To realize this opportunity, our platform combines mass spectrometry with advanced AI to systematize, scale, and accelerate metabolomics workflows. Matterworks is seeking a machine learning engineer who will bring our core machine learning models to life in our microservices-based cloud platform.

In this two-part tutorial we will learn how to build a speech controlled robot using Tensil open source machine learning (ML) acceleration framework and Digilent Arty A7-100T FPGA board. At the heart of this robot we will use the ML model for speech recognition. We will learn how Tensil framework enables ML inference to be tightly integrated with digital signal processing in a resource constrained environment of a mid-range Xilinx Artix-7 FPGA. Part I will focus on recognizing speech commands through a microphone. Part II will focus on translating commands into robot behavior and integrating with the mechanical platform. Let's start by specifying what commands we want the robot to understand. To keep the mechanical platform simple (and inexpensive) we will build on a wheeled chassis with two engines. The robot will recognize directives to move forward in a straight line (go!), turn in-place clockwise (right!) and counterclockwise (left!), and turn the engines off (stop!). Now that we know what robot we want to build, let's define its high-level system architecture. This architecture will revolve around the Arty board that will provide the "brains" for our robot. In order for the robot to "hear" we need a microphone. The Arty board provides native connectivity with the PMOD ecosystem and there is MIC3 PMOD from Digilent that combines a microphone with ADCS7476 analog-to-digital converter. And in order to control motors we need two HB3 PMOD drivers, also from Digilent, that will convert digital signals to voltage level and polarity to drive the motors.