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.

A "robot" lawyer powered by artificial intelligence will be the first of its kind to help a defendant fight a traffic ticket in court next month. Joshua Browder, CEO of DoNotPay, said the company's AI-creation runs on a smartphone, listens to court arguments and formulates responses for the defendant. The AI lawyer tells the defendant what to say in real-time, through headphones. The robot lawyer will take its first case on February 22, Browder announced on Twitter last week. "On February 22nd at 1.30PM, history will be made. For the first time ever, a robot will represent someone in a US courtroom. DoNotPay A.I will whisper in someone's ear exactly what to say. We will release the results and share more after it happens. He did not disclose the name of the client or the court. On February 22nd at 1.30PM, history will be made. For the first time ever, a robot will represent someone in a US courtroom. DoNotPay A.I will whisper in someone's ear exactly what to say. We will release the results and share more after it happens. The artificial intelligence firm has already used AI-generated form letters and chatbots to help people secure refunds for in-flight Wifi that didn't work, as well as to lower bills and dispute parking tickets, among other issues, according to Browder. All told the company has relied on these AI templates to win more than 2 million customer service disputes and court cases on behalf of individuals against institutions and organizations, he added. It has raised $27.7 million from tech-focused venture capital firms, including Andreessen Horowitz and Crew Capital. "In the past year, AI tech has really developed and allowed us to go back and forth in real time with corporations and governments," he told CBS MoneyWatch of recent advances. "We spoke live [with companies and customer service reps] to lower bills with companies; and what we're doing next month is try to use the tech in a courtroom for the first time." If the robot lawyer loses the case, DoNotPay will cover any fines, Browder said. Some courts allow defendants to wear hearing aids, some versions of which are bluetooth-enabled. That's how Browder determined that DoNotPay's technology can legally be used in this case. Some states require that all parties consent to be recorded, which rules out the possibility of a robot lawyer entering many courtrooms. Of the 300 cases DoNotPay considered for a trial of its robot lawyer, only two were feasible. "It's within the letter of the law, but I don't think anyone could ever imagine this would happen," Browder said. "It's not in the spirit of law, but we're trying to push things forward and a lot of people can't afford legal help.

The TriFORCE project, funded by the German Federal Ministry of Education and Research (BMBF), led by Prof. Dr. Carlo D'Eramo, at the University of Würzburg (JMU), is seeking 1 Ph.D. student with a strong interest in Reinforcement Learning and its application to robotics problems. Every student with a master degree and a strong passion for Reinforcement Learning, Robotics, and AI, is strongly encouraged to apply!

In this era, a massive volume of information is available to the users through web which leads to information overload. The Recommender systems are used to facilitate the search through this vast space of items by giving user personalised services and items. The vast majority of traditional recommendation systems consider the recommendation procedure as a static process and make recom- mendations following a fixed strategy. A user interacts with recommendation engine in a sequence of exchanges of recommendations and provides feedback on them. Hence, we should also try to incorporate the feedback ofthe user at each time step while recommending items at the next time step.

In this paper, we examine the influence of communication latency on performance of networked control systems. Even though distributed control architectures offer advantages in terms of communication, maintenance costs, and scalability, it is an open question how communication latency that varies with network topology influences closed-loop performance. For networks in which delays increase with the number of links, we establish the existence of a fundamental performance trade-off that arises from control architecture. In particular, we utilize consensus dynamics with single- and double-integrator agents to show that, if delays increase fast enough, a sparse controller with nearest neighbor interactions can outperform the centralized one with all-to-all communication topology.

Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.

Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction, most of existing approaches works do not allow for uncertainty quantification which is crucial in many applications. In addition, personalisation in the cross-device setting still involves important issues, especially for new clients or those having small number of observations. This paper aims at filling these gaps. To this end, we propose a novel methodology coined FedPop by recasting personalised FL into the population modeling paradigm where clients' models involve fixed common population parameters and random effects, aiming at explaining data heterogeneity. To derive convergence guarantees for our scheme, we introduce a new class of federated stochastic optimisation algorithms which relies on Markov chain Monte Carlo methods. Compared to existing personalised FL methods, the proposed methodology has important benefits: it is robust to client drift, practical for inference on new clients, and above all, enables uncertainty quantification under mild computational and memory overheads. We provide non-asymptotic convergence guarantees for the proposed algorithms and illustrate their performances on various personalised federated learning tasks.

Efficient video-language modeling should consider the computational cost because of a large, sometimes intractable, number of video frames. Parametric approaches such as the attention mechanism may not be ideal since its computational cost quadratically increases as the video length increases. Rather, previous studies have relied on offline feature extraction or frame sampling to represent the video efficiently, focusing on cross-modal modeling in short video clips. In this paper, we propose a semi-parametric video-grounded text generation model, SeViT, a novel perspective on scalable video-language modeling toward long untrimmed videos. Treating a video as an external data store, SeViT includes a non-parametric frame retriever to select a few query-relevant frames from the data store for a given query and a parametric generator to effectively aggregate the frames with the query via late fusion methods. Experimental results demonstrate our method has a significant advantage in longer videos and causal video understanding. Moreover, our model achieves the new state of the art on four video-language datasets, iVQA (+4.8), Next-QA (+6.9), and Activitynet-QA (+4.8) in accuracy, and MSRVTT-Caption (+3.6) in CIDEr.

Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task. The search space for this optimal solution is often very large, so large in fact that this optimal solution is often not computable. Part of the problem is that many candidate solutions found via ML are actually infeasible and have to be discarded. Restricting the search space to only the feasible solution candidates simplifies finding an optimal solution for the tasks. Further, the set of feasible solutions could be re-used in multiple problems characterized by different tasks. In particular, we observe that complex tasks can be decomposed into subtasks and corresponding skills. We propose to learn a reusable and transferable skill by training an actor to generate all feasible actions. The trained actor can then propose feasible actions, among which an optimal one can be chosen according to a specific task. The actor is trained by interpreting the feasibility of each action as a target distribution. The training procedure minimizes a divergence of the actor's output distribution to this target. We derive the general optimization target for arbitrary f-divergences using a combination of kernel density estimates, resampling, and importance sampling. We further utilize an auxiliary critic to reduce the interactions with the environment. A preliminary comparison to related strategies shows that our approach learns to visit all the modes in the feasible action space, demonstrating the framework's potential for learning skills that can be used in various downstream tasks.

In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, in practice human decision-makers often lack understanding of what information an AI model has access to in relation to themselves. There are few available guidelines regarding how to effectively communicate about unobservables: features that may influence the outcome, but which are unavailable to the model. In this work, we conducted an online experiment to understand whether and how explicitly communicating potentially relevant unobservables influences how people integrate model outputs and unobservables when making predictions. Our findings indicate that presenting prompts about unobservables can change how humans integrate model outputs and unobservables, but do not necessarily lead to improved performance. Furthermore, the impacts of these prompts can vary depending on decision-makers' prior domain expertise. We conclude by discussing implications for future research and design of AI-based decision support tools.