With the help of this list, any person who is interested in artificial intelligence or machine learning can feel free to learn all about it. In this course, the instructor is going to talk about the meaning behind the common AI terminology. It includes explanations about neural networks, machine learning, data science, and deep learning. Then the instructor will talk about what AI can and can't do realistically. Similarly, you will also get to understand how to spot opportunities to apply AI to different problems in your own organization.

With the current pandemic accelerating the revolution of AI in healthcare, where is the industry heading in the next 5-10 years? What are the key challenges and most exciting opportunities? To answer those questions, DeepLearning.AI and Stanford Institute for Human-Centered Artificial Intelligence (HAI) are proud to present our virtual event, Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng, at 10am PT on April 29. What's special about this event is that you get to decide what our speakers talk about. If you'd like to submit and upvote questions for our speakers, please sign up for the Q&A General access ticket.

Many learning problems involve multiple agents optimizing different interactive functions. In these problems, the standard policy gradient algorithms fail due to the non-stationarity of the setting and the different interests of each agent. In fact, algorithms must take into account the complex dynamics of these systems to guarantee rapid convergence towards a (local) Nash equilibrium. In this paper, we propose NOHD (Newton Optimization on Helmholtz Decomposition), a Newton-like algorithm for multi-agent learning problems based on the decomposition of the dynamics of the system in its irrotational (Potential) and solenoidal (Hamiltonian) component. This method ensures quadratic convergence in purely irrotational systems and pure solenoidal systems. Furthermore, we show that NOHD is attracted to stable fixed points in general multi-agent systems and repelled by strict saddle ones. Finally, we empirically compare the NOHD's performance with that of state-of-the-art algorithms on some bimatrix games and in a continuous Gridworld environment.

Extending language models with structural modifications and vision-and-language (V&L) pretraining are successful ways of making V&L models that can ground vision and language. Potential applications of these advanced models include multi-modal machine reading comprehension models and multi-modal dialogue models, which require language ability upon grounding. Although language capability is crucial for such applications, the impact of extending their visual capabilities on their language capabilities is not fully understood. This paper investigates how visual extension affects the language capability of V&L models using the GLUE benchmark. We found that visual extension causes some decreases in language capability and that V&L pretraining has a greater impact than structural modifications on the decreases. Our results suggest the need for further study on pretraining that can maintain or, if possible, improve a model's language capability.

We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary combinatorial problems. We test the proposed algorithm on an ensemble learning problem. Ensembling several models is a common Machine Learning technique to achieve better performance. We consider ensembles of several models trained on disjoint subsets of a dataset. Finding the best dataset partitioning is naturally a combinatorial non-binary optimization problem. Fitness function evaluations can be extremely expensive if complex models, such as Deep Neural Networks, are used as learners in an ensemble. Therefore, the number of fitness function evaluations is typically limited, necessitating expensive optimization techniques. In our experiments we use five classification datasets from the OpenML-CC18 benchmark and Support-vector Machines as learners in an ensemble. The proposed algorithm demonstrates better performance than alternative approaches, including Bayesian optimization algorithms. It manages to find better solutions using just several thousand fitness function evaluations for an ensemble learning problem with up to 500 variables.

Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images. However, very limited work on NMN has been studied in the video-grounded language tasks. These tasks extend the complexity of traditional visual tasks with the additional visual temporal variance. Motivated by recent NMN approaches on image-grounded tasks, we introduce Video-grounded Neural Module Network (VGNMN) to model the information retrieval process in video-grounded language tasks as a pipeline of neural modules. VGNMN first decomposes all language components to explicitly resolve any entity references and detect corresponding action-based inputs from the question. The detected entities and actions are used as parameters to instantiate neural module networks and extract visual cues from the video. Our experiments show that VGNMN can achieve promising performance on two video-grounded language tasks: video QA and video-grounded dialogues.

In Yeshiva University's engineering-focused M.S. in Artificial Intelligence (AI), offered by the Katz School of Science and Health, students will learn the key skills most valued in today's marketplace, including machine learning and deep neural networks, along with cutting-edge technologies such as reinforcement learning, voice recognition and generation, and image recognition and generation. In the program's project-based courses, students will build systems, models and algorithms using the best available artificial intelligence design patterns and engineering principles, all done in the heart of Manhattan, a global epicenter for artificial intelligence work and research. Prof. Andrew Catlin is the program director for the AI program, with a background as a data scientist and production systems developer who has worked with such major clients as Fidelity Investments; Smart Money; Donaldson, Lufkin and Jenrette; Manufacturers Hanover Trust; and the National Football League. He is also a founder of multiple tech startups, including Hudson Technology and Metrics Reporting. He teaches graduate courses in recommender systems, natural language processing and neural networks, among others.

This article is part of our series on "AI education" (In partnership with Paperspace) In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning libraries, optimized implementation, scalability, and versatile features. But Python is not the only option for programming machine learning applications. There's a growing community of developers who are using JavaScript to run machine learning models. While JavaScript is not a replacement for the rich Python machine learning landscape (yet), there are several good reasons to have JavaScript machine learning skills.

Substituting a cup of cocoa throughout the day for other snacks could help obese people lose weight – even if they're on a high-fat diet, a new study claims. In lab experiments, US researchers gave obese mice with liver disease a dietary supplement of cocoa powder, for a period of eight weeks. Even though the mice were on a high-fat diet, the experts found the supplement reduced DNA damage and the amount of fat in their livers. While there is more to learn about the health benefits of cocoa, the researchers believe it may in some way impede the digestion of dietary fat and carbohydrate, thereby avoiding weight gain. Supplementation of cocoa powder in the diet of high-fat-fed mice with liver disease markedly reduced the severity of their condition, according to a new study.

Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However, the evaluation of neural IR has so far been limited to standard supervised learning settings, where they have outperformed traditional term matching baselines. We conduct in-domain and out-of-domain evaluations of neural IR, and seek to improve its robustness across different scenarios, including zero-shot settings. We show that synthetic training examples generated using a sequence-to-sequence generator can be effective towards this goal: in our experiments, pre-training with synthetic examples improves retrieval performance in both in-domain and out-of-domain evaluation on five different test sets.