As part of Microsoft's research into ways to use machine learning and AI to improve security defenses, the company has released an open source attack toolkit to let researchers create simulated network environments and see how they fare against attacks. Microsoft 365 Defender Research released CyberBattleSim, which creates a network simulation and models how threat actors can move laterally through the network looking for weak points. When building the attack simulation, enterprise defenders and researchers create various nodes on the network and indicate which services are running, which vulnerabilities are present, and what type of security controls are in place. Automated agents, representing threat actors, are deployed in the attack simulation to randomly execute actions as they try to take over the nodes. "The simulated attacker's goal is to take ownership of some portion of the network by exploiting these planted vulnerabilities. While the simulated attacker moves through the network, a defender agent watches the network activity to detect the presence of the attacker and contain the attack," the Microsoft 365 Defender Research Team wrote in a post discussing the project.
Artificial Intelligence might be a term for collecting concepts that allow computer systems to vaguely work like a brain. However, the use of numbers to represent complex social reality is flawed. AI might seem factual and precise when it isn't as the results that AI produces depend on how it is designed and what data it uses. At the moment in our everyday world, AI performs narrow tasks such as facial recognition, natural language processing, or internet searches but the pace of its progress is exponential and regardless of its benefits. The impact it is having is hard to ignore with more and more of the world's commerce becoming automated and trading going online.
PVA is a low-code chatbot building tool with which you can build and deploy chatbots in the shortest time possible. This democratises the technology to non-technical users and reduces the dependency on IT expertise. Using PVA, powerful chatbots can be built using a guided, no-code graphical interface that can be deployed for sales, HR, finance, customer service and virtually on all channels where customers need to be engaged. Bot Framework and Azure Bot Service and Cognitive Services provide the technological foundation for Power Virtual Agents. A power business user can go from zero to a working bot in a matter of minutes!
Cresta, an AI-powered platform that offers real-time support to help customer service agents respond to inquiries on calls or in chats, has raised $50 million in a series B round of funding. The company's latest investment, which was led by Sequoia Capital, with participation from Greylock Partners, Andreessen Horowitz, Allen & Company, and Porsche Ventures, comes after a year of growth that saw its revenues quadruple. It's difficult to read too much into any first-year revenue growth metrics, but it's clear that companies are hankering for technology that helps them optimize their customer-facing operations. Contact centers have proven fertile ground for AI, with a slew of companies emerging to offer their own take on how automation can improve companies' interactions with their customers. Just today, Uniphore announced a fresh $140 million investment to analyze emotion and engagement in both voice and video-based calls, while Talkdesk launched a new "human-in-the-loop" AI trainer for contact centers.
We've all seen the super cool demonstrations of Boston Dynamics' two-legged Atlas robot doing backflips, and been properly amazed! Today, an article about a new robot from Boston Dynamics caught my eye. Stretch is designed for logistics applications. It lifts and moves boxes in warehouses. Unlike traditional industrial robots, it isn't fixed in place, but is mobile instead.
Amanda Prorok is an Assistant Professor (University Lecturer) in the Department of Computer Science and Technology, at Cambridge University, and a Fellow of Pembroke College. She serves as Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Associate Editor for Autonomous Robots (AURO). Prior to joining Cambridge, Prorok was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, USA, where she worked with Prof. Vijay Kumar. She completed her PhD at EPFL, Switzerland, with Prof. Alcherio Martinoli.
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipped with two 9-DOF articulated arms is spawned randomly in a simulated physical home environment. The agent is required to find a small set of objects scattered around the house, pick them up, and transport them to a desired final location. We also position containers around the house that can be used as tools to assist with transporting objects efficiently. To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints. We build this benchmark challenge using the ThreeDWorld simulation: a virtual 3D environment where all objects respond to physics, and where can be controlled using fully physics-driven navigation and interaction API. We evaluate several existing agents on this benchmark. Experimental results suggest that: 1) a pure RL model struggles on this challenge; 2) hierarchical planning-based agents can transport some objects but still far from solving this task. We anticipate that this benchmark will empower researchers to develop more intelligent physics-driven robots for the physical world.
Predicting accurate future trajectories of multiple agents is essential for autonomous systems, but is challenging due to the complex agent interaction and the uncertainty in each agent's future behavior. Forecasting multi-agent trajectories requires modeling two key dimensions: (1) time dimension, where we model the influence of past agent states over future states; (2) social dimension, where we model how the state of each agent affects others. Most prior methods model these two dimensions separately; e.g., first using a temporal model to summarize features over time for each agent independently and then modeling the interaction of the summarized features with a social model. This approach is suboptimal since independent feature encoding over either the time or social dimension can result in a loss of information. Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time. To this end, we propose a new Transformer, AgentFormer, that jointly models the time and social dimensions. The model leverages a sequence representation of multi-agent trajectories by flattening trajectory features across time and agents. Since standard attention operations disregard the agent identity of each element in the sequence, AgentFormer uses a novel agent-aware attention mechanism that preserves agent identities by attending to elements of the same agent differently than elements of other agents. Based on AgentFormer, we propose a stochastic multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep when inferring an agent's future position. The latent intent of all agents is also jointly modeled, allowing the stochasticity in one agent's behavior to affect other agents. Our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
Motivation: Several accurate deep learning models have been proposed to predict drug-target affinity (DTA). However, all of these models are black box hence are difficult to interpret and verify its result, and thus risking acceptance. Explanation is necessary to allow the DTA model more trustworthy. Explanation with counterfactual provides human-understandable examples. Most counterfactual explanation methods only operate on single input data, which are in tabular or continuous forms. In contrast, the DTA model has two discrete inputs. It is challenging for the counterfactual generation framework to optimize both discrete inputs at the same time. Results: We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex. Our proposed framework provides human-interpretable counterfactual instances while optimizing both the input drug and target for counterfactual generation at the same time. The result on the Davis dataset shows the advantages of the proposed MACDA framework compared with previous works.
A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.