Love Lost's AI photo-matching technology helped locate Louie, an indoor cat who escaped through a window, connecting his owner Sharon with a helpful neighbor.

Each time a driver in Seattle meets a red light, they wait about 20 seconds on average before it turns green again, according to vehicle and smartphone data collected by analytics company Inrix. The delays cause annoyance and expel in Seattle alone an estimated 1,000 metric tons or more of carbon dioxide into the atmosphere each day. With a little help from new Google AI software, the toll on both the environment and drivers is beginning to drop significantly. Seattle is among a dozen cities across four continents, including Jakarta, Rio de Janeiro, and Hamburg, optimizing some traffic signals based on insights from driving data from Google Maps, aiming to reduce emissions from idling vehicles. The project analyzes data from Maps users using AI algorithms and has initially led to timing tweaks at 70 intersections.

Israeli startup OncoHost announced today an upsized and oversubscribed $35 million Series C funding round, led by ALIVE Israel HealthTech VC, with the participation of Leumi Partners, Menora Mivtachim, OurCrowd and other existing investors. Clinical trial results have shown OncoHost's AI-powered precision oncology platform to have remarkably high accuracy in assessing non-small cell lung cancer (NSCLC) patient response at three months, six months and one year. Through one blood test pre-treatment, the company's multi-patented platform also provides clinicians with potential combination strategies to overcome treatment resistance. Last year, OncoHost CEO Dr. Ofer Sharon told me that "For immunotherapy, the most important treatment modality we have today, the response rate on average across all cancer types is about 20%. With all the promise of immunotherapy, if you have ten patients waiting in your waiting room with advanced cancer, only two will be alive in two years."

Bidirectional search algorithms interleave a search forward from the start state (start) and a search backward (i.e. using reverse operators) from the goal state (goal). We say that the two searches "meet in the middle" if neither search expands a node whose g-value (in the given direction) exceeds C*/2, where C* is the cost of an optimal solution. The only bidirectional heuristic search algorithm that is guaranteed to meet in the middle under all circumstances is the recently introduced MM algorithm (Holte et al. 2016). The feature of MM that provides this guarantee is its unique priority functions for nodes on its open lists. In this short note we present MMe, which enhances MM's priority function and is expected to expand fewer nodes than MM under most circumstances. We sketch a proof of MMe's correctness, describe conditions under which MMe will expand fewer nodes than MM and vice versa, and experimentally compare MMe and MM on the 10-Pancake problem.

One of the signatures of American culture is our respect for the people that fight for our freedom. This has been the case throughout many generations of American history. This is why the statistics on veteran homelessness are rather disturbing. Recent statistics show that veteran homelessness increased by about 4% in 2020, aggravating an already bad situation. The US government and the VA have created very accommodating measures to enable Veterans to become homeowners.

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.

Every word can be represented into N-Dimension Space after applying Machine Learning Algorithms on documents. The most famous algorithms are the Word2Vec built by Google and the GloVe built by Stanford University. We will work with the GloVe pre-trained model. The idea is to represent into a50-D space every Movie Plot Summary and based on this vector to find similar movies. Finally, we will do dimensionality reduction by applying the T-SNE algorithm and to represent the plot summaries into 2-D space.

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for well-formed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities. This paper was published at AAAI 2019.

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time. This paper was published at AAAI 2019.

We formalize the problem of multi-agent path finding with deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within a given deadline, without colliding with each other. We first show that the MAPF-DL problem is NP-hard to solve optimally. We then present an optimal MAPF-DL algorithm based on a reduction of the MAPF-DL problem to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network.