Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution among many others. The models designed to tackle next-location prediction, however, require a significant amount of individual-level information to be trained effectively. Such data may be scarce or even unavailable in some geographic regions or peculiar scenarios (e.g., cold-start in recommendation systems). Moreover, the design of a next-location predictor able to generalize or geographically transfer knowledge is still an open research challenge. Recent advances in natural language processing have led to a rapid diffusion of Large Language Models (LLMs) which have shown good generalization and reasoning capabilities. These insights, coupled with the recent findings that LLMs are rich in geographical knowledge, allowed us to believe that these models can act as zero-shot next-location predictors. This paper evaluates the capabilities of many popular LLMs in this role, specifically Llama, GPT-3.5 and Mistral 7B. After designing a proper prompt, we tested the models on three real-world mobility datasets. The results show that LLMs can obtain accuracies up to 32.4%, a significant relative improvement of over 600% when compared to sophisticated DL models specifically designed for human mobility. Moreover, we show that other LLMs are unable to perform the task properly. To prevent positively biased results, we also propose a framework inspired by other studies to test data contamination. Finally, we explored the possibility of using LLMs as text-based explainers for next-location prediction showing that can effectively provide an explanation for their decision. Notably, 7B models provide more generic, but still reliable, explanations compared to larger counterparts. Code: github.com/ssai-trento/LLM-zero-shot-NL

In February 2022, a meeting was set up between New York City mayor Eric Adams' team and an artificial intelligence gun-detection company called Evolv. An email thread from Evolv representatives included an accompanying brochure, which listed opportunities to partner together: in the Port Authority Bus Terminal, NYC schools, hospitals, and gathering places such as Times Square. One area conspicuously missing from the list, though, was the subway. After an in-person meeting a few days later, Evolv cofounder Anil Chitkara made another attempt to sell the company's technology--through name-dropping. "As I mentioned, Linda Reid, VP Security for Walt Disney World (Florida) has known us since 2014 and deployed many of our systems at the Parks and Disney Springs," Chitkara wrote in a February 7 email to the Mayor's Office, obtained by WIRED. "They've had success screening for weapons with Evolv Express … There may be some interesting parallels to how you are thinking about everyone's role in security."

An AI chatbot released by the New York City government to help business owners access pertinent information has been spouting falsehoods, at times even misinforming users about actions that are against the law, according to a report from The Markup. The report, which was co-published with the local nonprofit newsrooms Documented and The City, includes numerous examples of inaccuracies in the chatbot's responses to questions relating to housing policies, workers' rights and other topics. Mayor Adams' administration introduced the chatbot in October as an addition to the MyCity portal, which launched in March 2023 as "a one-stop shop for city services and benefits." The chatbot, powered by Microsoft's Azure AI, is aimed at current and aspiring business owners, and was billed as a source of "actionable and trusted information" that comes directly from the city government's sites. But it is a pilot program, and a disclaimer on the website notes that it "may occasionally produce incorrect, harmful or biased content."

CellBlock Fire Containment Systems announced its modular battery charging rack has obtained regulatory approval by the FDNY for use in charging lithium-ion batteries. The Charge Safe Battery Rack is the first and only approved system allowing for the bypass of NYC fire code 309.3, requiring 2 feet of separation between charging batteries not only adjacent but omnidirectional. "Our team is excited to not only offer safety in storage but passive fire suppression and measurable cost savings as perhaps the most impressive added value." "Until now, even a fortified 10 10 battery fire area would be spatially inadequate for 50kWh of stored energy. Our system is not only the safest charging system, it requires 1/12th the space" said Dylan Vandemark, CellBlock FCS CTO and Founder.

With programs like ChatGPT and Dall-E going mainstream, New York City is looking for a director of Artificial Intelligence and Machine Learning to shape how emerging tools are used in everything from policing to urban planning. The newly defined role under the city's Office of Technology and Innovation, with a salary range of $75,000 to $140,000, will be tasked with finding new AI use cases, educating the public and city departments about upsides and risks, and informing and guiding the city's overarching AI and machine learning policies.

This is not Beamery's first audit of its AI tools. It conducted internal audits to test for compliance with General Data Protection Regulation, the 2016 European Union law that protects consumer identity and privacy. For AI anti-bias audits that fall under the New York City law, Beamery sought to test how its talent acquisition tools handle a potential job candidate's gender and ethnicity during the recruitment process. The first audit took place in the summer followed by a month-long audit in October.

YipitData is the leading market research firm for the disruptive economy and recently raised $475M from The Carlyle Group at a valuation of over $1B. We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments, and more. Our data team uses proprietary technology to identify, license, clean, and analyze the data many of the world's largest investment funds and corporations depend on. For three years and counting, we have been recognized as one of Inc's Best Workplaces. We are a fast-growing technology company backed by Norwest Venture Partners and The Carlyle Group.

SingularityNET development and blockchain teams have partnered with MLabs, experts in Plutus development for Cardano, to create the AGIX-ADA staking contracts. The contracts are currently under evaluation in testnet and will soon be audited externally. The final stage of development will be creating the staking portal on the SingularityDAO v2 Dapp. The SingularityDAO v2 Dapp is gearing up for launch in Q3, and the staking portal will be launched shortly after that, approximately mid-Q3. The Loyalty Rewards wallet was created to demonstrate the gratitude of SingularityNET to the Phase One token holders for supporting the Phase Two initiative; as well as an opportunity to incentivize and reward community growth through new token holders on the Cardano blockchain. The technical backend portal has been designed and is under evaluation on testnet, and the team is also actively working to integrate more wallets. The portal is expected to be ready for community launch in Q3, and further details on the program will be coming shortly. We look forward to community feedback and discussion as we work with the community to design an optimal system.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

The move reaffirms Touro's focus on in-person learning.