There's an old joke that physicists like to tell: Everything has already been discovered and reported in a Russian journal in the 1960s, we just don't know about it. The volume of knowledge is vast and growing quickly: The number of scientific articles posted on arXiv (the largest and most popular preprint server) in 2021 is expected to reach 190,000--and that's just a subset of the scientific literature produced this year. It's clear that we do not really know what we know, because nobody can read the entire literature even in their own narrow field (which includes, in addition to journal articles, PhD theses, lab notes, slides, white papers, technical notes, and reports). Indeed, it's entirely possible that in this mountain of papers, answers to many questions lie hidden, important discoveries have been overlooked or forgotten, and connections remain concealed. Artificial intelligence is one potential solution.

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Chris Meah walked into class on the first day of his computer studies degree at University of Birmingham and noticed a complete and utter lack of diversity - just a room full of mainly young men who had been into computing since childhood. There were not many others who strayed too far from this stereotype. "I have four sisters which is more women than there were in my class and my family is also mixed race so there's more diversity at home than there was on my course," he quipped. It was this observation that sent him on a journey to launching a free bootcamp for would-be software coders in Birmingham called School of Code which is now set to embark on a nationwide expansion. And the 32 year old credits being a winner at our first two West Midlands Tech Awards with helping to boost the visibility and credibility of the school to businesses and potential students (2021 entry details below and categories at the bottom).

This position requires a Master's degree or equivalent in Computer Science, Mathematics & Computing, or Engineering. Candidates in the final phase of their Master study may apply. A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, and interpretable learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary. Applicants must document fluency of in English and be able to work in an international environment.

Simplr, a human-first, machine-enabled customer experience platform, announced that they are now accepting applications for the 2022 Simplr Artificial Intelligence and Technology Scholarship. Now in its third year, the scholarship was established to support and encourage students who are pursuing an undergraduate or graduate degree in Computer Science, Mathematics, or Information Technology or are attending or will attend law school with a focus on intellectual property. "As a successful startup involved in machine learning and AI, we like to give back by encouraging students to be creative while also earning degrees in these more difficult and technical fields," said Daniel Rodriguez, Simplr CMO. "Our hope is that winners of this scholarship become even more inspired to take a seat among the crop of leaders who will define the promise of technology for the next generation." The $7500 scholarship will be given to the applicant who writes the most compelling essay on why they have chosen their field of study and how it applies to the development of artificial intelligence and machine learning, Blockchain technology, and the Internet of Things.

Over the past few months I've been learning a lot about data science and the tools that come along with the trade. A very prominent tool that I've become aquatinted with are data science libraries. These libraries have provided me a fantastic resource while I've been in school. Libraries like Numpy, a math based library, and Pandas, a dataframe based library, have given me access to tools and functions with out having to code them all out my self. Instead of hardcoding the standard deviation of my data set I can use Numpy and all it takes is one line of code.

Intelligent data-driven fault diagnosis methods have been widely applied, but most of these methods need a large number of high-quality labeled samples. It costs a lot of labor and time to label data in actual industrial processes, which challenges the application of intelligent fault diagnosis methods. To solve this problem, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method is proposed for the bearing fault diagnosis under the insufficient labeled samples situation. This method includes three stages: pre-training, deep clustering and enhanced supervised learning. In the first stage, a skip-connection based convolutional auto-encoder (SCCAE) is proposed and pre-trained to automatically learn low-dimensional representations. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) model that integrates the pre-trained auto-encoder with a clustering layer is proposed for deep clustering. Additionally, virtual adversarial training (VAT) is introduced as a regularization term to overcome the overfitting in the model's training. In the third stage, high-quality clustering results obtained in the second stage are assigned to unlabeled samples as pseudo labels. The labeled dataset is augmented by those pseudo-labeled samples and used to train a bearing fault discriminative model. The effectiveness of the method is evaluated on the Case Western Reserve University (CWRU) bearing dataset. The results show that the method can not only satisfy the semi-supervised learning under a small number of labeled samples, but also solve the problem of unsupervised learning, and has achieved better results than traditional diagnosis methods. This method provides a new research idea for fault diagnosis with limited labeled samples by effectively using unsupervised data.

End-to-end speech-to-intent classification has shown its advantage in harvesting information from both text and speech. In this paper, we study a technique to develop such an end-to-end system that supports multiple languages. To overcome the scarcity of multi-lingual speech corpus, we exploit knowledge from a pre-trained multi-lingual natural language processing model. Multi-lingual bidirectional encoder representations from transformers (mBERT) models are trained on multiple languages and hence expected to perform well in the multi-lingual scenario. In this work, we employ a teacher-student learning approach to sufficiently extract information from an mBERT model to train a multi-lingual speech model. In particular, we use synthesized speech generated from an English-Mandarin text corpus for analysis and training of a multi-lingual intent classification model. We also demonstrate that the teacher-student learning approach obtains an improved performance (91.02%) over the traditional end-to-end (89.40%) intent classification approach in a practical multi-lingual scenario.

The successful training of neural networks typically involves careful and time consuming hyperparameter tuning. Population Based Training (PBT) has recently been proposed to automate this process. PBT trains a population of neural networks concurrently, frequently mutating their hyperparameters throughout their training. However, the decision mechanisms of PBT are greedy and favour short-term improvements which can, in some cases, lead to poor long-term performance. This paper presents Faster Improvement Rate PBT (FIRE PBT) which addresses this problem. Our method is guided by an assumption: given two neural networks with similar performance and training with similar hyperparameters, the network showing the faster rate of improvement will lead to a better final performance. Using this, we derive a novel fitness metric and use it to make some of the population members focus on long-term performance. Our experiments show that FIRE PBT is able to outperform PBT on the ImageNet benchmark and match the performance of networks that were trained with a hand-tuned learning rate schedule. We apply FIRE PBT to reinforcement learning tasks and show that it leads to faster learning and higher final performance than both PBT and random hyperparameter search.

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 with logarithmic static regret in a dynamic environment. 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 an online mixture framework that uses these static solvers as the base algorithm. We show that with the suitable selection of hyper-expert creations and weighting strategies, we can achieve logarithmic and squared logarithmic regret per switch in quadratic and linearithmic computational complexity, respectively. For the first time in literature, we show that it is also possible to achieve near-logarithmic regret per switch with sub-polynomial complexity per time. Our results are guaranteed to hold in a strong deterministic sense in an individual sequence manner.