University of Florida scientists want to assess livestock mobility faster and more accurately, ultimately helping farm animal health and production. To do so, they'll use artificial intelligence (AI) to analyze high-definition video of the animals as they move. Samantha Brooks, a UF/IFAS geneticist and associate professor of equine physiology – along with other UF researchers -- have been awarded a $49,713 grant from the Agricultural Genome to Phenome Initiative (AG2PI) for this research. The team will combine machine learning with gait analyses to speed their assessment of livestock mobility. Brooks cites an example of how this technology can help: In horses, one veterinarian can do a basic lameness exam in about 15 minutes.

Techniques for automatically generating abstractions of systems have been the linchpins of formal methods, playing crucial roles in extending the reach of formal methods to large hardware and software systems. To address the challenges of very high-dimensional hybrid-state spaces and input spaces for ML-based systems, we need to develop effective techniques to abstract ML models into simpler models that are more amenable to formal analysis. Some promising directions include using abstract interpretation to analyze DNNs (for example, Gehr et al.12), developing abstractions for falsifying cyber-physical systems with ML components,5 and devising novel representations for verification (for instance, star sets and other examples36).

Anti-surveillance makeup, used by people who do not want to be identified to fool facial recognition systems, is bold and striking, not exactly the stuff of cloak and daggers. While experts' opinions vary on the makeup's effectiveness to avoid detection, they agree that its use is not yet widespread. Anti-surveillance makeup relies heavily on machine learning and deep learning models to "break up the symmetry of a typical human face" with highly contrasted markings, says John Magee, an associate computer science professor at Clark University in Worcester, MA, who specializes in computer vision research. However, Magee adds that "If you go out [wearing] that makeup, you're going to draw attention to yourself." The effectiveness of anti-surveillance makeup has been debated because of racial justice protesters who do not want to be tracked, Magee notes.

Universities, and a growing number of companies, are producing brain-computer interfaces.

The intersection of learning to rank and choice modeling is an active area of research with applications in e-commerce, information retrieval and the social sciences. In some applications such as recommendation systems, the statistician is primarily interested in recovering the set of the top ranked items from a large pool of items as efficiently as possible using passively collected discrete choice data, i.e., the user picks one item from a set of multiple items. Motivated by this practical consideration, we propose the choice-based Borda count algorithm as a fast and accurate ranking algorithm for top $K$-recovery i.e., correctly identifying all of the top $K$ items. We show that the choice-based Borda count algorithm has optimal sample complexity for top-$K$ recovery under a broad class of random utility models. We prove that in the limit, the choice-based Borda count algorithm produces the same top-$K$ estimate as the commonly used Maximum Likelihood Estimate method but the former's speed and simplicity brings considerable advantages in practice. Experiments on both synthetic and real datasets show that the counting algorithm is competitive with commonly used ranking algorithms in terms of accuracy while being several orders of magnitude faster.

Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather than on the sample level. Units can be compared based on the distance between their means, however this ignores the within-unit distribution of samples. Here we develop an approach for exploratory analysis of hierarchical datasets using the Wasserstein distance metric that takes into account the shapes of within-unit distributions. We use t-SNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them. The distance matrix can be efficiently computed by approximating each unit with a Gaussian distribution, but we also provide a scalable method to compute exact Wasserstein distances. We use synthetic data to demonstrate the effectiveness of our Wasserstein t-SNE, and apply it to data from the 2017 German parliamentary election, considering polling stations as samples and voting districts as units.

Proponents of predictive technologies for consequential decision-making emphasize the seeming ability of statistical models to anticipate future outcomes. The ability to predict the future, so the argument goes, creates a rationale for adopting machine learning as policy: if a risk score charted the future trajectory of individuals, then intervening in a person's life on the basis of the score would be justified [KLMO15, OE16]. At the same time, critical scholars caution that predictive technologies reproduce historical patterns of injustice and social stratification. In this account, rather than predicting future outcomes, statistical risk assessment tools punish individuals for factors predating their own agency [Eub18, Ben19]. Does a statistical model predict the future or recite the past? The answer to the question is often not obvious. Consider the problem of loan default prediction, one of many tasks often framed as predicting future outcomes. A forward-looking predictor might identify individual behavior detrimental to loan repayment and adjust the predicted likelihood of default accordingly. Alternatively, a backward-looking predictor might take note of historical associations between repayment and demographic factors, then predict based solely on the historical factors.

Increasingly frequent wildfires significantly affect solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation to the earth. Atmospheric aerosols are measured by Aerosol Optical Depth (AOD), and AOD data streams can be retrieved and monitored by geostationary satellites. However, multi-source remote-sensing data streams often present heterogeneous characteristics, including different data missing rates, measurement errors, systematic biases, and so on. To accurately estimate and predict the underlying AOD propagation process, there exist practical needs and theoretical interests to propose a physics-informed statistical approach for modeling wildfire AOD propagation by simultaneously utilizing, or fusing, multi-source heterogeneous satellite remote-sensing data streams. Leveraging a spectral approach, the proposed approach integrates multi-source satellite data streams with a fundamental advection-diffusion equation that governs the AOD propagation process. A bias correction process is included in the statistical model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data streams obtained from the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model interpretability of the proposed approach. Computer code has been made available on GitHub.

The upcoming Czech Presidency shared a discussion paper with the other EU governments to gather their views on AI definition, high-risk systems, governance and national security. The paper, obtained by EURACTIV, will be the basis for the discussion in the Telecom Working Party on 5 July, with the view of providing an updated compromise text by 20 July. The member states will then be asked to provide written comments on the new compromise by 2 September. "The CZ Presidency has identified four high-level outstanding issues which require a more thorough discussion and where receiving directions from the member states would be crucial to moving the negotiations to the next level," the document reads. The document is the first of the Czech Presidency, which formally will only start in July. The draft indicates continuity with the direction taken by the French Presidency and provides the main topics where the Czechs will focus.

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