On July 22, Microsoft announced a $1 billion investment in OpenAI, a lab focused on "artificial general intelligence," or the goal of creating artificial intelligence with human-like observation and learning capabilities. With this announcement, Microsoft becomes the "exclusive" cloud computing provider for OpenAI and will have access to productizing OpenAI capabilities as they come to market. Key Takeaways: Microsoft makes a long-term investment in "general intelligence" to start on the next generation of AIs that will be coming to market in five-to-ten years and will be able to recoup some costs back as OpenAI's cloud provider and monetizer of OpenAI technologies. From a practical perspective, how does this affect Microsoft Azure Cloud Services and their current AI portfolio? First, OpenAI is still several years away from having any sort of launchable product. Its approach on imitating the human mind as an artificial intelligence approach is still far from mature.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 It's pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models. A model can be a black box for one of two reasons: (a) the function that the model computes is far too complicated for any human to comprehend, or (b) the model may in actual fact be simple, but its details are proprietary and not available for inspection. In explainable ML we make predictions using a complicated black box model (e.g., a DNN), and use a second (posthoc) model created to explain what the first model is doing. A classic example here is LIME, which explores a local area of a complex model to uncover decision boundaries.

A few years ago this month, Portland, Oregon artist Darius Kazemi watched a flood of tweets from would-be novelists. November is National Novel Writing Month, a time when people hunker down to churn out 50,000 words in a span of weeks. To Kazemi, a computational artist whose preferred medium is the Twitter bot, the idea sounded mildly tortuous. "I was thinking I would never do that," he says. "But if a computer could do it for me, I'd give it a shot."

This repository contains the source code and trained model for a large-scale pretrained dialogue response generation model. The human evaluation results indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The repository is based on huggingface pytorch-transformer and OpenAI GPT-2, containing data extraction script, model training code and pretrained small (117M) medium (345M) and large (762M) model checkpoint. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. The largest model can be trained in several hours on a 8 V100 machines (however this is not required), with distributed training and FP16 option.

From there, Mitchell described how machine learning has become a more significant part of the field since the 1980s. Prior to then, so-called intelligent machines learned by having people manually program them with rules. In machine learning, which became more prevalent in the field between the 1990s and 2000s, machines actually learn by being given data rather than through human programming. By 10 years ago, machine learning had become prevalent in AI, and "deep learning," a type of machine learning design inspired by the way the human brain works, had taken over machine learning. Today, she noted "all of the things you use" that fall under AI--speech recognition, Google search, facial recognition--"are powered by deep learning."

Why did you think to combine Qiskit, a quantum-computing framework, with PyTorch, a machine-learning framework? Karel Dumon: Classical machine learning is currently benefiting hugely from the open-source community, and this is something we want to leverage in quantum too. Our project focuses on the potential application of quantum computing for machine learning, but also on the use of machine learning to help progress quantum computing itself. Through our project, we hope to make it easier for machine learning developers to explore the quantum world. Patrick Huembeli: To that effect, it makes Qiskit very accessible for people with a classical machine learning background -- they can treat the quantum nodes just as another layer of their machine learning algorithm.

The open source AI projects particularly pay attention to deep learning, machine learning, neural network and other applications that are extending the use of AI. Those involved in deep researches have always had the goal of building machines capable of thinking like human beings. For the last few years, computer scientists have made unbelievable progress in Artificial Intelligence (AI) to this extent that the interest in AI project ideas keeps increasing among technology enthusiasts. As per Gartner's prediction, Artificial Intelligence technologies going to be virtually prevalent in nearly all new software products and services. The contribution of open source software development to the rise of Artificial Intelligence is immeasurable.

As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters. Although hyper-parameter optimization is crucial in training deep learning models for high model performance, effectively executing such a computation-heavy workload still remains a challenge. We observe that numerous trials issued from existing hyper-parameter optimization algorithms share common hyper-parameter sequence prefixes, which implies that there are redundant computations from training the same hyper-parameter sequence multiple times. We propose a stage-based execution strategy for efficient execution of hyper-parameter optimization algorithms. Our strategy removes redundancy in the training process by splitting the hyper-parameter sequences of trials into homogeneous stages, and generating a tree of stages by merging the common prefixes. Our preliminary experiment results show that applying stage-based execution to hyper-parameter optimization algorithms outperforms the original trial-based method, saving required GPU-hours and end-to-end training time by up to 6.60 times and 4.13 times, respectively.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.