When we think about artificial intelligence (AI), humans are rarely what springs to mind. And understandably so, as AI is all about machine intelligence and automation. AI has become an essential business tool, so we often commend the pioneering work of AI companies to support businesses as they digitally evolve. To shed a light on the importance of people in the creation of intelligent machines, we take a look at the best-in-class executives in the AI field who continue to push the technology – and its boundaries – forward. With a passion for AI, Andrej Karpathy is interested in training deep neural nets on large datasets.

A detailed article on the Georgia Tech and Facebook partnership aimed at building diversity in the AI field appears in the January issue of University-Industry Engagement Advisor. In the initial steps of a program of collaboration with U.S. universities "that serve significant populations of Black and Latino students," Facebook has partnered with Georgia Tech to develop, co-teach, and fund graduate-level online deep learning courses. The program will be expanded in 2021 to include additional institutions. The collaboration at Georgia Tech came about through discussions between Facebook and the university's Machine Learning Center and the School of Interactive Computing, says Zsolt Kira, PhD, associate director of the Machine Learning Center at Georgia Tech and an assistant professor in the School of Interactive Computing. This is not the first collaboration between the two partners, says Paco Guzmán, research scientist manager at Facebook and a lecturer for Facebook's Co-teaching AI Program.

GitHub is a goldmine of free resources. But with so much information, it's hard to know what to prioritize. Bookmark these 10 repositories to guarantee you learn from the best. Start with a strong base in Python and related libraries, then work your way through each relevant application of ML and DL. Jeande's work is based on his own experience and is crafted for users at a range of experience levels.

In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease.

Contrastive learning has been demonstrated to be effective in enhancing pre-trained language models (PLMs) to derive superior universal sentence embeddings. However, existing contrastive methods still have two limitations. Firstly, previous works may acquire poor performance under domain shift settings, thus hindering the application of sentence representations in practice. We attribute this low performance to the over-parameterization of PLMs with millions of parameters. To alleviate it, we propose PromCSE (Prompt-based Contrastive Learning for Sentence Embeddings), which only trains small-scale \emph{Soft Prompt} (i.e., a set of trainable vectors) while keeping PLMs fixed. Secondly, the commonly used NT-Xent loss function of contrastive learning does not fully exploit hard negatives in supervised learning settings. To this end, we propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power, inspired by the connection between the NT-Xent loss and the Energy-based Learning paradigm. Empirical results on seven standard semantic textual similarity (STS) tasks and a domain-shifted STS task both show the effectiveness of our method compared with the current state-of-the-art sentence embedding models. Our code is publicly avaliable at https://github.com/YJiangcm/PromCSE

The Manitoba First Nation School System is encouraging teachers to leverage students' love for video games and educate them about traditional teachings via e-sports clubs and classes. Over the last decade, a growing number of school leaders both on and off-reserve have started using online applications such as Minecraft. By forcing e-learning into the mainstream, COVID-19 has made unconventional educational tools even more popular. Not only are video games an engaging way to teach collaboration and digital literacy, said Karl Hildebrandt, but the education technology facilitator at MFNSS said they pair well with foundational Anishinaabe principles on conducting oneself towards others. Video games are an engaging way to teach collaboration and digital literacy.

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Medical text learning has recently emerged as a promising area to improve healthcare due to the wide adoption of electronic health record (EHR) systems. The complexity of the medical text such as diverse length, mixed text types, and full of medical jargon, poses a great challenge for developing effective deep learning models. BERT has presented state-of-the-art results in many NLP tasks, such as text classification and question answering. However, the standalone BERT model cannot deal with the complexity of the medical text, especially the lengthy clinical notes. Herein, we develop a new model called KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the BERT model for long and multi-type text with the integration of the medical knowledge graph. Our model can outperform all baselines and other state-of-the-art models in diagnosis-related group (DRG) classification, which requires comprehensive medical text for accurate classification. We also demonstrated that our model can effectively handle multi-type text and the integration of medical knowledge graph can significantly improve the performance.

The last decade has seen development of reinforcement learning algorithms with strong empirical performance in domains including robotics (Kober et al., 2013; Lillicrap et al., 2015), dialogue systems (Li et al., 2016), and personalization (Agarwal et al., 2016; Tewari and Murphy, 2017). While there is great interest in applying these techniques to real-world decision making applications, the number of samples (steps of interaction) required to do so is often prohibitive, with state-of-the-art algorithms requiring millions of samples to reach human-level performance in challenging domains. Developing algorithms with improved sample efficiency, which entails efficiently generalizing across high-dimensional states and actions while taking advantage of problem structure as modeled practitioners, remains a major challenge. Investigation into design and analysis of algorithms for sample-efficient reinforcement learning has largely focused on two distinct problem formulations: Online reinforcement learning, where the learner can repeatedly interact with the environment by executing a policy and observing the resulting trajectory. Offline reinforcement learning, where the learner has access to logged transitions ands reward gathered from a fixed behavioral policy (e.g., historical data or expert demonstrations), but cannot directly interact with the underlying environment. While these formulations share a common goal (learning a near-optimal policy), the algorithms used to achieve this goal and conditions under which it can be achieved are seemingly quite different.

Teachers often conduct surveys in order to collect data from a predefined group of students to gain insights into topics of interest. When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report. In the analysis step, traditionally, the teacher has to read each of the responses and decide on how to group them in order to extract insightful information. Even though it is possible to group the responses only using certain keywords, such an approach would be limited since it not only fails to account for embedded contexts but also cannot detect polysemous words or phrases and semantics that are not expressible in single words. In this work, we present a novel end-to-end context-aware framework that extracts, aggregates, and abbreviates embedded semantic patterns in open-response survey data. Our framework relies on a pre-trained natural language model in order to encode the textual data into semantic vectors. The encoded vectors then get clustered either into an optimally tuned number of groups or into a set of groups with pre-specified titles. In the former case, the clusters are then further analyzed to extract a representative set of keywords or summary sentences that serve as the labels of the clusters. In our framework, for the designated clusters, we finally provide context-aware wordclouds that demonstrate the semantically prominent keywords within each group. Honoring user privacy, we have successfully built the on-device implementation of our framework suitable for real-time analysis on mobile devices and have tested it on a synthetic dataset. Our framework reduces the costs at-scale by automating the process of extracting the most insightful information pieces from survey data.