The increasing deployment of multi-agent systems (MAS) in critical infrastructures such as autonomous transportation, disaster relief, and smart cities demands robust formation control mechanisms resilient to adversarial attacks. Traditional consensus-based controllers, while effective under nominal conditions, are highly vulnerable to data manipulation, sensor spoofing, and communication failures. To address this challenge, we propose Second-Order State Hallucination (SOSH), a novel framework that detects compromised agents through distributed residual monitoring and maintains formation stability by replacing attacked states with predictive second-order approximations. Unlike existing mitigation strategies that require significant restructuring or induce long transients, SOSH offers a lightweight, decentralized correction mechanism based on second-order Taylor expansions, enabling rapid and scalable resilience. We establish rigorous Lyapunov-based stability guarantees, proving that formation errors remain exponentially bounded even under persistent attacks, provided the hallucination parameters satisfy explicit conditions. Comprehensive Monte Carlo experiments on a 5-agent complete graph formation demonstrate that SOSH outperforms established robust control schemes, including W-MSR and Huber-based consensus filters, achieving faster convergence rates, lower steady-state error, and superior transient recovery. Our results confirm that SOSH combines theoretical robustness with practical deployability, offering a promising direction for securing MAS formations against sophisticated adversarial threats.

This study aimed to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. Power spectral densities of brain frequency waves were then analyzed. Testing different k-intervals with XGBoost, Random Forest, and Bagging Classifier revealed that Random Forest performed best, achieving a testing accuracy of 91.07% at an interval size of two. When utilizing all four EEG channels, cognitive flexibility was most recognizable. Task-specific classification accuracy showed the right frontal lobe excelled in mathematical processing and planning, the left frontal lobe in cognitive flexibility and mental flexibility, and the left temporoparietal lobe in connections. Notably, numerous connections between frontal and temporoparietal lobes were observed during STEM activities. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.

As she was looking for a camp last summer, Yabesra Ewnetu, who'd just finished eighth grade, found a reference to MIT's FutureMakers Create-a-thon. Ewnetu had heard that it's hard to detect bias in artificial intelligence because AI algorithms are so complex, but this didn't make sense to her. "I was like, well, we're the ones coding it, shouldn't we be able to see what it's doing and explain why?" She signed up for the six-week virtual FutureMakers program so she could delve into AI herself. FutureMakers is part of the MIT-wide Responsible AI for Social Empowerment and Education (RAISE) initiative launched earlier this year. RAISE is headquartered in the MIT Media Lab and run in collaboration with MIT Schwarzman College of Computing and MIT Open Learning.

As she was looking for a camp last summer, Yabesra Ewnetu, who'd just finished eighth grade, found a reference to MIT's FutureMakers Create-a-thon. Ewnetu had heard that it's hard to detect bias in artificial intelligence (AI) because AI algorithms are so complex, but this didn't make sense to her. "I was like, well, we're the ones coding it, shouldn't we be able to see what it's doing and explain why?" She signed up for the six-week virtual FutureMakers program so she could delve into AI herself. FutureMakers is part of the MIT-wide Responsible AI for Social Empowerment and Education (RAISE) initiative launched earlier this year. RAISE is headquartered in the MIT Media Lab and run in collaboration with MIT Schwarzman College of Computing and MIT Open Learning.

Along with my CTO, Ido Ivri, I Co-Founded Zencity to help local governments make data-driven decisions based on their communities' priorities when creating policies and communicating them to their residents. The Zencity platform gathers and analyzes millions of anonymized, aggregated data points of community feedback from channels like social media, local broadcast media, and government customer service channels (such as 311 and call centers) and turns them into actionable insights about community trends and priorities for local government decision-makers. We analyze these millions of unstructured data points by using advanced AI and NLP algorithms to make the data structured and actionable for these organizations. The algorithms automatically classify data by relevance to the different departments in city hall and then run a sentiment analysis to determine if the data reflects positive, negative, or neutral feedback on a city-related topic. As trends emerge throughout cities or regions, our platform sends alerts to city officials so that they can take immediate action and be proactive.

Tech giants Google, Apple and Intel are in a race with automakers like BMW and Tesla to perfect an autonomous driver system that's reliable and replicable within the next few years. Yet as the world watches on with visions of a Jetsons-like future and city policymakers consider regulations, people who drive for a living in U.S. cities are trying to figure out what it all means for them. Technical advances have always come with negative impacts like job losses for some, but driving careers in particular are worth watching because they're economic multipliers at the city level. Ninety-three percent of the 4.1 million employed drivers in the U.S. don't have bachelor's degrees, yet drivers on the whole average a poverty rate that's lower than workers who aren't in the driving field, at 7.32 percent compared to 8.08 percent. And while nearly two-thirds of drivers identify as white, D.C.-based think tank Center for Global Policy Solutions reports that minorities in driving careers have a "premium" in this field, meaning they're getting paid better than what they'd likely get paid in other non-driving careers without a college degree, according to U.S. Census data. That's something that Jose Garcia, a construction waste driver in New York City, can attest to.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.