The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future. While business leaders are very interested in leveraging machine learning technology, there's a talent shortage standing in the way. It turns out that there are very few developers that have the skills needed to spearhead serious new AI projects. This means that developers who can acquire these skills will be highly in demand. With all this in mind, let's take a look at several facts about AI every developer should know before changing their focus to machine learning, artificial intelligence, and--while we're at it--deep learning and neural networks.

Artificial intelligence is now becoming an integral part of our lives and has defined its role in various fields. Now Artificial Intelligence is no longer just confined to movies related to science fiction. We find it is a part of our routine lives and in our classrooms. Since there are many AI tools in the market such as Google Assistant, Amazon Alexa, and Apple's Siri, we are just starting to see the presence of AI in the field of education as well. Here are some key benefits of Artificial Intelligence in education which helps in the overall learning experience. The personalization in education is one of the benefits that Artificial Intelligence is expected to contain in its armory.

The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "We don't recognize how much talent is there," said Mr. Cuban, who lives in Dallas. "One of my goals is to really go out and find the superstars. There are so many there that are under-appreciated and don't have access to resources." Mr. Cuban's funding will be spent on resources to educate hundreds of students in AI over the next few years, with the goal of teaching 1,000 students a year from 2023.

Recent state-of-the-art approaches to summarization utilize large pre-trained Transformer models. Distilling these models to smaller student models has become critically important for practical use; however there are many different distillation methods proposed by the NLP literature. Recent work on distilling BERT for classification and regression tasks shows strong performance using direct knowledge distillation. Alternatively, machine translation practitioners distill using pseudo-labeling, where a small model is trained on the translations of a larger model. A third, simpler approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by copying parameters to a smaller student model and then fine-tuning. We compare these three approaches for distillation of Pegasus and BART, the current and former state of the art, pre-trained summarization models, and find that SFT outperforms knowledge distillation and pseudo-labeling on the CNN/DailyMail dataset, but under-performs pseudo-labeling on the more abstractive XSUM dataset. PyTorch Code and checkpoints of different sizes are available through Hugging Face transformers here http://tiny.cc/4iy0tz.

We study two-sided decentralized matching markets in which participants have uncertain preferences. We present a statistical model to learn the preferences. The model incorporates uncertain state and the participants' competition on one side of the market. We derive an optimal strategy that maximizes the agent's expected payoff and calibrate the uncertain state by taking the opportunity costs into account. We discuss the sense in which the matching derived from the proposed strategy has a stability property. We also prove a fairness property that asserts that there exists no justified envy according to the proposed strategy. We provide numerical results to demonstrate the improved payoff, stability and fairness, compared to alternative methods.

Visual categorization and learning of visual categories exhibit early onset, however the underlying mechanisms of early categorization are not well understood. The main limiting factor for examining these mechanisms is the limited duration of infant cooperation (10-15 minutes), which leaves little room for multiple test trials. With its tight link to visual attention, eye tracking is a promising method for getting access to the mechanisms of category learning. But how should researchers decide which aspects of the rich eye tracking data to focus on? To date, eye tracking variables are generally handpicked, which may lead to biases in the eye tracking data. Here, we propose an automated method for selecting eye tracking variables based on analyses of their usefulness to discriminate learners from non-learners of visual categories. We presented infants and adults with a category learning task and tracked their eye movements. We then extracted an over-complete set of eye tracking variables encompassing durations, probabilities, latencies, and the order of fixations and saccadic eye movements. We compared three statistical techniques for identifying those variables among this large set that are useful for discriminating learners form non-learners: ANOVA ranking, Bayes ranking, and L1 regularized logistic regression. We found remarkable agreement between these methods in identifying a small set of discriminant variables. Moreover, the same eye tracking variables allow us to classify category learners from non-learners among adults and 6- to 8-month-old infants with accuracies above 71%.

In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such as processing power and execution time limits, or requirements such as verifiable safety guarantees, it may not be feasible to actually use such high-performing DNNs in practice. Many techniques have been developed in recent years to compress or distill complex DNNs into smaller, faster or more understandable models and controllers. This work seeks to provide a quantitative framework with metrics to systematically evaluate the outcome of such conversion processes, and identify reduced models that not only preserve a desired performance level, but also, for example, succinctly explain the latent knowledge represented by a DNN. We illustrate the effectiveness of the proposed approach on the evaluation of decision tree variants in the context of benchmark reinforcement learning tasks.

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field.

Our focus on AI and Data at University of Birmingham is two-fold, covering education to bridge sector skills gaps with Degree Apprenticeships and MSc programmes, alongside well established research communities promoting new ways of working and new insights into data and AI. We know the tech world changes rapidly. We are collaborating with industry sectors such as IT and computer science, engineering and professional services by developing innovative courses whilst also promoting the latest insights from research directly to business. Researchers at University of Birmingham and experts from industry are working on various projects for the UKRI AI for Services network initiatives. We are a partner in The Alan Turing Institute, the UK's national institute for data science and artificial intelligence.

Predicting student's outcomes is at the heart of the most educational institutions. Parents are no different when it comes to knowing about the future their children will pursue. Artificial Intelligence (AI) and its applications have helped education sector figure out a lot of study related outcomes that will help students follow the right dream. Artificial intelligence models are considered to be outstanding when doing predictive analysis. The technology anticipates the future by analyzing past data.