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Some kids spend their summers swimming and building campfires. This year thousands will head to university and high school campuses to program robots, write software, and design video games. Artificial intelligence--the increasingly ubiquitous technology routing delivery trucks, sifting through job applicants' resumes, and making small business lending decisions--is now a summer camp experience for ages five to 18. At least 447 AI summer camps opened across 48 states in the US this year, according to an August report from Georgetown University's Center for Security and Emerging Technology. Because US schools typically don't cover the subject, these camps may offer one of the only chances for middle and high-school students to study AI, the report notes.

We investigate the problem of online learning, which has gained significant attention in recent years due to its applicability in a wide range of fields from machine learning to game theory. Specifically, we study the online optimization of mixable loss functions in a dynamic environment. We introduce online mixture schemes that asymptotically achieves the performance of the best dynamic estimation sequence of the switching oracle with optimal regret redundancies. The best dynamic estimation sequence that we compete against is selected in hindsight with full observation of the loss functions and is allowed to select different optimal estimations in different time intervals (segments). We propose two mixtures in our work. Firstly, we propose a tractable polynomial time complexity algorithm that can achieve the optimal redundancy of the intractable brute force approach. Secondly, we propose an efficient logarithmic time complexity algorithm that can achieve the optimal redundancy up to a constant multiplicity gap. Our results are guaranteed to hold in a strong deterministic sense in an individual sequence manner.

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).

Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI algorithms that can reason about physical interactions between objects. However, datasets thus far have primarily focused on elementary physical events such as rolling or falling. There is currently a scarcity of datasets that focus on the physical interactions that humans perform daily with objects in the real world. To address this scarcity, we introduce SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments. The SPACE simulator allows us to generate the SPACE dataset, a synthetic video dataset in a 3D environment, to systematically evaluate physics-based models on a range of physical causal reasoning tasks. Inspired by daily object interactions, the SPACE dataset comprises videos depicting three types of physical events: containment, stability and contact. These events make up the vast majority of the basic physical interactions between objects. We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning. Repository: https://github.com/jiafei1224/SPACE

Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled domain under the distribution shift. Thanks to the strong representation ability of deep neural networks, recent remarkable achievements in UDA resort to learning domain-invariant features. Intuitively, the hope is that a good feature representation, together with the hypothesis learned from the source domain, can generalize well to the target domain. However, the learning processes of domain-invariant features and source hypothesis inevitably involve domain-specific information that would degrade the generalizability of UDA models on the target domain. In this paper, motivated by the lottery ticket hypothesis that only partial parameters are essential for generalization, we find that only partial parameters are essential for learning domain-invariant information and generalizing well in UDA. Such parameters are termed transferable parameters. In contrast, the other parameters tend to fit domain-specific details and often fail to generalize, which we term as untransferable parameters. Driven by this insight, we propose Transferable Parameter Learning (TransPar) to reduce the side effect brought by domain-specific information in the learning process and thus enhance the memorization of domain-invariant information. Specifically, according to the distribution discrepancy degree, we divide all parameters into transferable and untransferable ones in each training iteration. We then perform separate updates rules for the two types of parameters. Extensive experiments on image classification and regression tasks (keypoint detection) show that TransPar outperforms prior arts by non-trivial margins. Moreover, experiments demonstrate that TransPar can be integrated into the most popular deep UDA networks and be easily extended to handle any data distribution shift scenarios.

A recent Forbes article reported that over the last four years, the use of artificial intelligence (AI) tools in many business sectors has grown by 270%. AI has a history dating back to Alan Turing's work in the 1940s, and we can define AI as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Four key areas of AI are machine learning, robotics, computer vision, and natural language processing. Other advances in computing technology mean we can now store and efficiently analyse colossal amounts of data (big data); consequently, data science was formed as an interdisciplinary field combining mathematics, statistics, and computer science. Data science is often presented as intertwined with machine learning, as data scientists commonly use machine learning techniques in their analysis.

In machine learning and statistics, computing the accuracy, or loss, of a model is crucial for understanding the quality of the model and what improvements can be made to increase accuracy. Typically, researchers choose a loss function de- pending on their task, and this loss function runs over their test set of data, after training. However, in many cases, researchers want an estimation of their loss either before they test it or in cases when testing data is not yet available. This estimation is known as expected loss, or risk, and is usually utilized in order to assess how precarious an action or event will be. The foundations of Bayesian statistics are rooted in Bayes' Theorem, a theorem developed by Thomas Bayes who was an English mathematician and theologian during the 1700s.

Such is just one of the attributes of Jurassic, a computer program introduced Wednesday by Tel Aviv-based artificial intelligence startup AI21 Labs. GPT-3, of course, is the language program from the San Francisco-based startup OpenAI that rocked the world in 2020 by generating sentences and whole articles that seemed quite human-like. GPT-3 also shocked the world by being kept inside a fairly restrictive beta testing arrangement by OpenAI. AI21 is promising to go OpenAI not one better, but two better, with what it claims are superior benchmark results on a test known as « few shot learning, » and a more open program for beta testers. On the latter score, AI21 is making development use of the program available as an « open beta, » it said, where anyone can sign up to use the program and there is « no wait list.

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