This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.

The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks. However, exactly calculating this new information quantity requires access to the eigenspectrum of a semi-positive definite (SPD) matrix $A$ which grows linearly with the number of samples $n$, resulting in a $O(n^3)$ time complexity that is prohibitive for large-scale applications. To address this issue, this paper takes advantage of stochastic trace approximations for matrix-based Renyi's entropy with arbitrary $\alpha \in R^+$ orders, lowering the complexity by converting the entropy approximation to a matrix-vector multiplication problem. Specifically, we develop random approximations for integer order $\alpha$ cases and polynomial series approximations (Taylor and Chebyshev) for non-integer $\alpha$ cases, leading to a $O(n^2sm)$ overall time complexity, where $s,m \ll n$ denote the number of vector queries and the polynomial order respectively. We theoretically establish statistical guarantees for all approximation algorithms and give explicit order of s and m with respect to the approximation error $\varepsilon$, showing optimal convergence rate for both parameters up to a logarithmic factor. Large-scale simulations and real-world applications validate the effectiveness of the developed approximations, demonstrating remarkable speedup with negligible loss in accuracy.

The parents of several Oxford High School students, including deceased Tate Myre, have filed a lawsuit against shooting suspect Ethan Crumbley, his parents and school staff. The parents of two victims of the Nov. 30, 2021, shooting at Oxford High School in Michigan are demanding more transparency from the Oxford Community School District after the board voted against moving forward with an independent investigation into the tragedy last fall. The Oxford Board of Education on Tuesday announced that the district has, for the second time, declined an offer from Michigan Attorney General Dana Nessel to conduct a third-party investigation into the school shooting with the goal of determining how shooting suspect Ethan Crumbley, 15, managed to kill four students and injure seven others last fall. "To me, this is an admission of guilt," Buck Myre, father of deceased 16-year-old Tate Myre, said during a Thursday press conference. "They know that things didn't go right that day, and they don't want to stand up and fix it. They're going to hide behind governmental immunity and they're going to hide behind insurance and the lawyers. What's this teach the kids? "We just want accountability," he added later when asked why an independent investigation is important to parents. Oakland County Prosecutor Karen McDonald revealed in December 2021 that school officials met with Crumbley and his parents to discuss violent drawings he created just hours before the deadly rampage. The 15-year-old suspect was able to convince them during the meeting that the concerning drawings were for a "video game." His parents "flatly refused" to take their son home. The shooting has also resulted in several lawsuits, including two that seek $100 million in damages each, against the school district and school employees on behalf of the family of two sisters who attend the school. Ethan Robert Crumbley, 15, charged with first-degree murder in a high school shooting, poses in a jail booking photograph taken at the Oakland County Jail in Pontiac, Michigan. Myre and Meghan Gregory, the mother of 15-year-old Keegan Gregory, who survived the shooting but witnessed and was traumatized by Crumbley's rampage, are suing the shooting suspect's parents, James and Jennifer Crumbley, as well as school staff for negligence. JENNIFER CRUMBLEY, ETHAN CRUMBLEY'S MOTHER, SENT OMINOUS TEXTS ON DAY OF SHOOTING: 'HE CAN'T BE LEFT ALONE' "They're the ones that know what happened that day.

Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages. Taking the analogy between categories and languages seriously, this paper defines a probabilistic generative model of morphisms in free monoidal categories over domain-specific generating objects and morphisms. The paper shows how acyclic directed wiring diagrams can model specifications for morphisms, which the model can use to generate morphisms. Amortized variational inference in the generative model then enables learning of parameters (by maximum likelihood) and inference of latent variables (by Bayesian inversion). A concrete experiment shows that the free category prior achieves competitive reconstruction performance on the Omniglot dataset.

The Indian Institute of Technology (IIT), Roorkee is offering a five-month online course on data science and machine learning (ML). The course is conducted by Imarticus Learning in association with iHUB DivyaSampark to enable candidates to leverage data Science and ML for effective decision-making. Prof Sudeb Dasgupta, project director of iHUB DivyaSampark said in a press release, "We bring iHUB DivyaSampark's expertise in building outstanding programs with IITs and Imarticus' technical expertise to deliver an outstanding learning experience through a holistic approach. Together, we envision creating a skilled workforce for innovation and digital growth." For more information, go through the brochure.

In support of the Alberta Technology and Innovation Strategy (ATIS) and in partnership with AltaML, a leading Canadian artificial intelligence company, the AI lab (named GovLab.ai) AltaML will work alongside government staff and post-secondary students and graduates as they work to develop smart products and models that leverage AI to solve complex, real-world problems. The lab will create opportunities for Alberta's public and private sectors to create intellectual property while accelerating Alberta's recovery and economic diversification. "Alberta is a world leader in AI and machine learning research. With the launch of GovLab.ai, Ultimately this will help Alberta's government offer better services, better results and better value to Albertans."

D2iQ, the leading enterprise Kubernetes provider for smart cloud-native applications, announced version 2.0 of Kaptain AI/ML, the enterprise-ready distribution of open-source Kubeflow that enables organizations to develop, deploy, and run artificial intelligence (AI) and machine learning (ML) workloads in production environments. Powered by Kubeflow 1.5, the Kubernetes machine learning toolkit, Kaptain AI/ML now provides data science teams with features such as expanded control for mounting data volumes and increased visibility into idle notebooks, so they can spend more time developing and less time managing infrastructure. The enhanced user experience enables data scientists to more effectively manage the lifecycle of AI and ML models without the need for infrastructure knowledge and skill sets. By simplifying the deployment and full lifecycle management of AI and ML workloads at scale, Kaptain AI/ML 2.0 accelerates the impact of smart cloud-native applications. This enables organizations to drive better business results by more quickly delivering new smart products and services, becoming more agile when updating models, and driving smarter customer experiences.

We are presently living in an age of "artificial intelligence" -- but not how the companies selling "AI" would have you believe. According to Silicon Valley, machines are rapidly surpassing human performance on a variety of tasks from mundane, but well-defined and useful ones like automatic transcription to much vaguer skills like "reading comprehension" and "visual understanding." According to some, these skills even represent rapid progress toward "Artificial General Intelligence," or systems which are capable of learning new skills on their own. Given these grand and ultimately false claims, we need media coverage that holds tech companies to account. Far too often, what we get instead is breathless "gee whiz" reporting, even in venerable publications like The New York Times.

Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.

DAP team has an extensive experience in implementing and deploying a Deep Learning and Machine Learning solution in Azure and AWS. DAP team has worked on text mining using traditional as well as Deep learning NLP technique.