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New York state has required companies to disclose if "technological innovation or automation" was the cause of job loss for nearly a year. Over 160 companies in New York state have filed notices of mass layoffs since last March. None--in a group that includes Amazon, Goldman Sachs, and other employers that are adopting AI tools --attributed their workforce cuts in those filings to "technological innovation or automation." That option was added 11 months ago to a required question on paperwork that businesses with 50 or more employees must file with the state to notify of sizable job losses. New York's Department of Labor told WIRED that, as of the end of January, no employer had marked tech as the reason for their workforce reduction.

New York governor Kathy Hochul says she will propose a new law allowing limited autonomous vehicle pilots in smaller cities. Full-blown services could be next. As self-driving car services from Alphabet's Waymo, Amazon's Zoox, and Tesla have slowly, quietly expanded across the US, one big, important state has mostly stayed mum: New York . The union's fourth most populous state has some of the tightest laws governing autonomous vehicles, requiring companies approved to test in the state to only do so with a driver behind the wheel. There's no current path for companies to operate the sort of commercial robotaxi services like the sort seen in San Francisco or Las Vegas.

This study analyzes tract-level real estate ownership patterns in New York State (NYS) and New York City (NYC) to uncover racial disparities. We use an advanced race/ethnicity imputation model (LSTM+Geo with XGBoost filtering, validated at 89.2% accuracy) to compare the predicted racial composition of property owners to the resident population from census data. We examine both a Full Model (statewide) and a Name-Only LSTM Model (NYC) to assess how incorporating geospatial context affects our predictions and disparity estimates. The results reveal significant inequities: White individuals hold a disproportionate share of properties and property value relative to their population, while Black, Hispanic, and Asian communities are underrepresented as property owners. These disparities are most pronounced in minority-majority neighborhoods, where ownership is predominantly White despite a predominantly non-White population. Corporate ownership (LLCs, trusts, etc.) exacerbates these gaps by reducing owner-occupied opportunities in urban minority communities. We provide a breakdown of ownership vs. population by race for majority-White, -Black, -Hispanic, and -Asian tracts, identify those with extreme ownership disparities, and compare patterns in urban, suburban, and rural contexts. The findings underscore persistent racial inequity in property ownership, reflecting broader historical and socio-economic forces, and highlight the importance of data-driven approaches to address these issues.

The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases. However, most empirical work in this area has focused on the image domain with artificial benchmarks constructed to measure individual aspects of generalization. We present BIRB, a complex benchmark centered on the retrieval of bird vocalizations from passively-recorded datasets given focal recordings from a large citizen science corpus available for training. We propose a baseline system for this collection of tasks using representation learning and a nearest-centroid search. Our thorough empirical evaluation and analysis surfaces open research directions, suggesting that BIRB fills the need for a more realistic and complex benchmark to drive progress on robustness to distribution shifts and generalization of ML models.

Temperature sampling is a conventional approach to diversify large language model predictions. As temperature increases, the prediction becomes diverse but also vulnerable to hallucinations -- generating tokens that are sensible but not factual. One common approach to mitigate hallucinations is to provide source/grounding documents and the model is trained to produce predictions that bind to and are attributable to the provided source. It appears that there is a trade-off between diversity and attribution. To mitigate any such trade-off, we propose to relax the constraint of having a fixed temperature over decoding steps, and a mechanism to guide the dynamic temperature according to its relevance to the source through KL-divergence. Our experiments justifies the trade-off, and shows that our sampling algorithm outperforms the conventional top-k and top-p algorithms in conversational question-answering and summarization tasks.

Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally-segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC=0.893, real-world AUC=0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas.

As part of a major sustainability effort, New York State Gov. Kathy Hochul has issued an executive order mandating that NY state agencies use the NY Power Authority's NY Energy Manager application, a system developed and deployed with the leading enterprise AI software application company. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." "We are pleased to receive such broad recognition and confidence in our enterprise AI energy management solution," said Ed Abbo, President and CTO of C3 AI. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." Announces Leadership Promotions to Drive Next Stage of Company Growth Among the many other goals spelled out in Executive Order 22, enabled by C3 AI, is a mandate for state operations to run on 100% clean electricity by 2030. The NY Energy Manager application, built on C3 AI Energy Management, has already been deployed to about 1,000 customers, including communities, businesses, municipalities, and electricity providers in New York. It will now serve as the system of record for all energy data from all state agencies.

In 2019, Donald Trump signed an executive order ordering hospitals to make the costs of common medical services publicly available. Yet, as of March 2021, many hospitals have been non compliant, making it difficult for patients to properly consider the effect of health services on their finances. This article details my creation of a ML XGBoost model to supplement the efforts of the executive order, as well as unexpected findings. Using user-entered values for Length of Stay, Disease Severity, and other variables, the model is capable of predicting hospital charges for three common infections: pneumonia, septicemia, and skin infections/cellulitis. The model is currently only applicable to New York State.

For a few fleeting moments during the New York governor Andrew Cuomo's daily coronavirus briefing on Wednesday 6 May, the sombre grimace that has filled our screens for weeks was briefly replaced by something resembling a smile. "We are ready, we're all-in," the governor gushed. "We are New Yorkers, so we're aggressive about it, we're ambitious about it … We realise that change is not only imminent, but it can actually be a friend if done the right way." The inspiration for these uncharacteristically good vibes was a video visit from the former Google CEO Eric Schmidt, who joined the governor's briefing to announce that he will be heading up a panel to reimagine New York state's post-Covid reality, with an emphasis on permanently integrating technology into every aspect of civic life. "The first priorities of what we're trying to do," Schmidt said, "are focused on telehealth, remote learning, and broadband … We need to look for solutions that can be presented now, and accelerated, and use technology to make things better." Lest there be any doubt that the former Google chair's goals were purely benevolent, his video background featured a framed pair of golden angel wings.