Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary. We study restrictions on the adversary that enable efficient minimization of the \emph{complete policy regret}, which is the strongest possible version of policy regret. We identify a gap in the current theoretical understanding of what sorts of restrictions permit tractability in this challenging setting. To resolve this gap, we consider a generalization of the stochastic multi armed bandit, which we call the \emph{tallying bandit}. This is an online learning setting with an $m$-memory bounded adversary, where the average loss for playing an action is an unknown function of the number (or tally) of times that the action was played in the last $m$ timesteps. For tallying bandit problems with $K$ actions and time horizon $T$, we provide an algorithm that w.h.p achieves a complete policy regret guarantee of $\tilde{\mathcal{O}}(mK\sqrt{T})$, where the $\tilde{\mathcal{O}}$ notation hides only logarithmic factors. We additionally prove an $\tilde\Omega(\sqrt{m K T})$ lower bound on the expected complete policy regret of any tallying bandit algorithm, demonstrating the near optimality of our method.

The graph represents a network of 2,275 Twitter users whose tweets in the requested range contained "iot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 23 March 2022 at 11:42 UTC. The requested start date was Wednesday, 23 March 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 17-hour, 13-minute period from Monday, 21 March 2022 at 06:46 UTC to Wednesday, 23 March 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.

We all now how boring it gets after a while to attend online classes. So, I made an AI to attend them for me. Let's see how will the AI get the data from the class? So from the above image I hope you get the basic gist of how the data collection will work. So, basically what the above code does is take the screenshoted image and make it negative so that the black turns white and the white turns black and then detect the the text and draw rectangles around it on the original image.

Python is one of the fastest-growing programming languages. It is widely used in various business sectors, such as programming, web development, machine learning, and data science. It is a high-level, object-oriented programming language with built-in data structures and dynamic semantics. Python supports different modules and packages, which allows program modularity and code reuse. The language has become so popular in recent times that aspirants are flocking to learn the language and acquire programming skills.

This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.

Did a student write that essay or a robot? Did a teacher provide that six trait feedback or was it an automated feedback system? Did that student understand that Mandarin dialog or did they translate it on the fly? Is that student talking to a mental health professional or a therapy-bot? If you are worried about plagiarism, things just got a lot more complicated with developments in Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, process, and generate language.

This is a completely free course and a good first step towards understanding the data analysis process. In this course, you will learn the entire data analysis process including posing a question, data wrangling, exploring the data, drawing conclusions, and communicating your findings. This course will also teach Python libraries NumPy, Pandas, and Matplotlib.

Continual learning, or lifelong learning, is a formidable current challenge to machine learning. It requires the learner to solve a sequence of $k$ different learning tasks, one after the other, while retaining its aptitude for earlier tasks; the continual learner should scale better than the obvious solution of developing and maintaining a separate learner for each of the $k$ tasks. We embark on a complexity-theoretic study of continual learning in the PAC framework. We make novel uses of communication complexity to establish that any continual learner, even an improper one, needs memory that grows linearly with $k$, strongly suggesting that the problem is intractable. When logarithmically many passes over the learning tasks are allowed, we provide an algorithm based on multiplicative weights update whose memory requirement scales well; we also establish that improper learning is necessary for such performance. We conjecture that these results may lead to new promising approaches to continual learning.

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