Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies. However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives. This course is designed to give you an introduction to the amazing world of Artificial Intelligence.

The graph represents a network of 1,369 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 September 2022 at 11:21 UTC. The requested start date was Friday, 09 September 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 8-minute period from Tuesday, 06 September 2022 at 13:51 UTC to Thursday, 08 September 2022 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,430 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 01 February 2022 at 22:57 UTC. The requested start date was Tuesday, 01 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 6-hour, 55-minute period from Friday, 28 January 2022 at 18:04 UTC to Tuesday, 01 February 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

This paper presents a variational representation of the Bayes' law using optimal transportation theory. The variational representation is in terms of the optimal transportation between the joint distribution of the (state, observation) and their independent coupling. By imposing certain structure on the transport map, the solution to the variational problem is used to construct a Brenier-type map that transports the prior distribution to the posterior distribution for any value of the observation signal. The new formulation is used to derive the optimal transport form of the Ensemble Kalman filter (EnKF) for the discrete-time filtering problem and propose a novel extension of EnKF to the non-Gaussian setting utilizing input convex neural networks. Finally, the proposed methodology is used to derive the optimal transport form of the feedback particle filler (FPF) in the continuous-time limit, which constitutes its first variational construction without explicitly using the nonlinear filtering equation or Bayes' law.

Register free for NVIDIA GTC to learn from experts on how AI and the evolution of the 3D internet are profoundly impacting industries--and society as a whole. We have prepared several AWS sessions to give you guidance on how to use AWS services powered by NVIDIA technology to meet your goals. Amazon Elastic Compute Cloud (Amazon EC2) instances powered by NVIDIA GPUs deliver the scalable performance needed for fast machine learning (ML) training, cost-effective ML inference, flexible remote virtual workstations, and powerful HPC computations. AWS is a Global Diamond Sponsor of the conference. Scaling Deep Learning Training on Amazon EC2 using PyTorch (Presented by Amazon Web Services) [A41454] As deep learning models grow in size and complexity, they need to be trained using distributed architectures.

LegalRuleML is a comprehensive XML-based representation framework for modeling and exchanging normative rules. The TPTP input and output formats, on the other hand, are general-purpose standards for the interaction with automated reasoning systems. In this paper we provide a bridge between the two communities by (i) defining a logic-pluralistic normative reasoning language based on the TPTP format, (ii) providing a translation scheme between relevant fragments of LegalRuleML and this language, and (iii) proposing a flexible architecture for automated normative reasoning based on this translation. We exemplarily instantiate and demonstrate the approach with three different normative logics.

Continual learning aims to rapidly and continually learn the current task from a sequence of tasks. Compared to other kinds of methods, the methods based on experience replay have shown great advantages to overcome catastrophic forgetting. One common limitation of this method is the data imbalance between the previous and current tasks, which would further aggravate forgetting. Moreover, how to effectively address the stability-plasticity dilemma in this setting is also an urgent problem to be solved. In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply multiscale knowledge distillation to grasp the evolution of long-range and short-range spatial relationships at different feature levels to alleviate the problem of data imbalance. Besides, our method mixes the samples from the episodic memory and current task in the online continual training procedure, thus alleviating the side influence due to the change of probability distribution. Moreover, we optimize our model via the meta-learning update resorting to the number of tasks seen previously, which is helpful to keep a better balance between stability and plasticity. Finally, our experimental evaluation on four benchmark datasets shows the effectiveness of the proposed MMKDDA framework against other popular baselines, and ablation studies are also conducted to further analyze the role of each component in our framework.

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.

Author: Dariusz Trawinski Introduction Model servers play a vital role in bringing AI models from development to production. Models are served via

Streaming videos is one of the methods for creators to share their creative works with their audience. In these videos, the streamer share how they achieve their final objective by using various tools in one or several programs for creative projects. To this end, the steps required to achieve the final goal can be discussed. As such, these videos could provide substantial educational content that can be used to learn how to employ the tools used by the streamer. However, one of the drawbacks is that the streamer might not provide enough details for every step. Therefore, for the learners, it might be difficult to catch up with all the steps. In order to alleviate this issue, one solution is to link the streaming videos with the relevant tutorial available for the tools used in the streaming video. More specifically, a system can analyze the content of the live streaming video and recommend the most relevant tutorials. Since the existing document recommendation models cannot handle this situation, in this work, we present a novel dataset and model for the task of tutorial recommendation for live-streamed videos. We conduct extensive analyses on the proposed dataset and models, revealing the challenging nature of this task.