In humans and animals, curriculum learning -- presenting data in a curated order - is critical to rapid learning and effective pedagogy. Yet in machine learning, curricula are not widely used and empirically often yield only moderate benefits. This stark difference in the importance of curriculum raises a fundamental theoretical question: when and why does curriculum learning help? In this work, we analyse a prototypical neural network model of curriculum learning in the high-dimensional limit, employing statistical physics methods. Curricula could in principle change both the learning speed and asymptotic performance of a model. To study the former, we provide an exact description of the online learning setting, confirming the long-standing experimental observation that curricula can modestly speed up learning. To study the latter, we derive performance in a batch learning setting, in which a network trains to convergence in successive phases of learning on dataset slices of varying difficulty. With standard training losses, curriculum does not provide generalisation benefit, in line with empirical observations. However, we show that by connecting different learning phases through simple Gaussian priors, curriculum can yield a large improvement in test performance. Taken together, our reduced analytical descriptions help reconcile apparently conflicting empirical results and trace regimes where curriculum learning yields the largest gains. More broadly, our results suggest that fully exploiting a curriculum may require explicit changes to the loss function at curriculum boundaries.

The Indian Institute of Technology (IIT), Roorkee will be offering two new Masters of Technology (MTech) programmes in Artificial Intelligence (AI) and Data Science from the academic session 2021-22. The programmes will be launched through the recently established Centre for Artificial Intelligence and Data Science (CAIDS) of the institute. Before applying for the programmes, candidates need to register on COAP 2021 portal at coap.iitd.ac.in. As many as 23 faculty members from 15 different departments of the institute have joined CAIDS as joint faculty of the centre. Candidates must have BE/BTech/Integrated MSc or equivalent degree in any engineering course to be able to apply.

It seems that we have two very different problems here: There is the PAC-learning problem from theoretical computer science discussing whether or not machines can learn certain functions. And there is the Continuum Problem asking whether there are infinite sets of a certain size. What does this have to do with each other? In 2019, a group of researchers, Ben-David et al., published an article entitled "Learnability can be undecidable" in Nature Machine Intelligence: We describe simple scenarios where learnability cannot be proved nor refuted using the standard axioms of mathematics. Our proof is based on the fact the continuum hypothesis cannot be proved nor refuted.

A Bachelor's study usually takes six semesters; a Master's study takes four. But this is only an outline. I've witnessed people doing their BA in three semesters and some taking nine semesters. Sometimes there are so many exciting courses that you voluntarily stay longer to learn it all. Therefore, I've loosely structured the recreated curriculum into four semesters.

Each year, Microsoft Build is like Christmas for developers, especially those focused on the Microsoft stack. It's when they make the most exciting announcements, release the best new software, and start to solidify the roadmap of innovation for the next year. While we don't get to enjoy the conference in person this year, we do get the same great collection of announcements. This year's highlight for engineers and developers interested in machine learning is the Azure Applied AI Services. Microsoft has been Democratizing AI since 2016. Applied AI Services takes this ideal another step forward. Microsoft has already made machine learning accessible to developers through their outstanding Cognitive Services, and this offering will make certain scenarios even more accessible.

Award-winning game designer Jessica Hammer will soon level up when she takes on the role of interim associate director of the Human-Computer Interaction Institute in Carnegie Mellon University's School of Computer Science. "We are lucky to do exceptional research and teaching at the HCII. Even better, we get to combine the two to shape the future of human-computer interaction," said Hammer, the HCII's Thomas and Lydia Moran Assistant Professor of Learning Science. "I want to make sure that all members of our community -- from our first-year undergraduates to the most senior faculty -- can contribute to this mission." Hammer, who has often learned the hard way how to develop a game to meet its vision, said that her training will help her ensure the department's practices and organizations are best suited to meet its goals.

If you've ever shopped online, subscribed to an OTT platform, or stumbled upon a self-driving Tesla's clip on YouTube, you're probably familiar with terms such as Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision and so on. These are the driving concepts behind such technologies. While most feel that the revolution is here, the untapped potential of this domain of computer science and its impact on the global economy is a hot topic. Research by McKinsey depicts with simulations, that by 2030, about 70% of companies could be reliant on AI based services. While Artificial General Intelligence, or systems capable of performing different tasks and accumulating intellect are far from reality (like the ones we come across in sci-fi movies), AI for specific tasks give us a lot to work with.

Natural language processing (NLP), or NLP for short, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is broadly defined as the automatic manipulation of natural language, like speech and text, by software or technology. Natural language processing is a form of AI that is easy to understand and start using. It can also do a lot to help you in making better business decisions. In order to make your website worth your user's time, NLP can do help you a lot.

There are certainly reasons to be cautious in the use of artificial intelligence, but, if used correctly, this technology has the potential to solve the most pressing problems the world is facing today. We all know how technology and artificial intelligence has made remote learning possible since the start of the pandemic. Remote learning through AI technologies can help reduce the drop-out rates, particularly for girls in middle-school in Pakistan and other developing countries. AI is already assisting the management of the current global health crisis through its countless applications for remote medical consultations and contact tracing applications used by governments around the world. AI technologies have the potential to worsen global inequalities, but they also present a valuable opportunity to make this world more sustainable.

Abstract--Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood that class imbalance harms the performance of supervised methods, limited research examines the impact of imbalance on the FSL evaluation task. Our analysis compares 10 state-of-the-art meta-learning and FSL methods on different imbalance distributions and rebalancing techniques. Our results reveal that 1) some FSL methods display a natural disposition against imbalance while most other approaches produce a performance drop by up to 17% compared to the balanced task without the appropriate mitigation; 2) contrary to popular belief, many meta-learning algorithms will not automatically learn to balance from exposure to imbalanced training tasks; 3) classical rebalancing strategies, such as random oversampling, can still be very effective, leading to state-of-the-art performances and should not be overlooked; 4) FSL methods are more robust against meta-dataset imbalance than imbalance at the task-level with a similar imbalance ratio ( ρ < 20), with the effect holding even in long-tail datasets under a larger imbalance ( ρ = 65). We identify well to new examples. However, large datasets can be costly and examine three levels of class imbalance: task-level, to obtain and annotate [1]. This is a particularly limiting dataset-level, and combined (task-level and dataset-level) issue in many real-world situations due to the need to perform imbalance. In contrast to previous work on CIFSL [12], [13], real-time operations, the presence of rare categories, [14], [15], we explicitly attribute and quantify the impact on or the desire for a good user experience [2], [3], [4], [5]. the performance caused by class imbalance for each model. Few-Shot Learning (FSL) alleviates this burden by defining Moreover, we study multiple class imbalance distributions, a distribution over tasks, with each task containing a few giving a realistic assessment of performance and revealing labeled data points (support set) and a set of target data previously unknown strengths and weaknesses of 10 stateof-the-art (query set) belonging to the same set of classes. Additionally, we offer practical advice, way to train FSL methods is through Meta-Learning (ML). Figure 1 the model is repeatedly exposed to batches of tasks sampled shows a graphical representation of the CIFSL problem.