In 2019 Josh Bersin and Marc Zao Sanders ran a survey with Linkedin to find out more about the'flow of work' surrounding knowledge workers, which include people whose jobs involve handling or using information. It turns out there are some common trends amongst them: There are 780 million knowledge workers globally and they spend 19 percent of their time gathering information and searching for data. That's nearly a full day a week spent searching. On the plus side, learning at work is undergoing a bit of a revolution, with many corporate learning and knowledge platforms and learning and development departments turning to AI-based'knowledge intelligence' (KI) to get knowledge workers what they need, when they need it. It all starts with using AI to optimise search, and in this article, we'll look at how KI can help companies save time and boost performance.

Labeled data is essential in modern systems that rely on Machine Learning (ML) for predictive modelling. Such systems may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios. Online financial fraud detection is an example where labeling is: i) expensive, or ii) it suffers from long delays, if relying on victims filing complaints. The latter may not be viable if a model has to be in place immediately, so an option is to ask analysts to label events while minimizing the number of annotations to control costs. We propose an Active Learning (AL) annotation system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where it is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (with 1/10 to 1/50 of the labels).

We introduce an estimator for manifold distances based on graph Laplacian estimates of the Laplace-Beltrami operator. We show that the estimator is consistent for suitable choices of graph Laplacians in the literature, based on an equidistributed sample of points drawn from a smooth density bounded away from zero on an unknown compact Riemannian submanifold of Euclidean space. The estimator resembles, and in fact its convergence properties are derived from, a special case of the Kontorovic dual reformulation of Wasserstein distance known as Connes' Distance Formula.

Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1.

Legged robots have significant potential to operate in highly unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be either manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behavior-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviors, and that neural connections interfacing with existing modules can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviors allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the control approach was successfully demonstrated on a physical hexapod robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control for complex robotic systems.

We are the world's learning company with more than 24,000 employees operating in 70 countries. We combine world-class educational content and assessment, powered by services and technology, to enable more effective teaching and personalized learning at scale. We believe that wherever learning flourishes so do people. At Pearson, we're committed to a world that's always learning and to our talented team who make it all possible. By embracing a massive digital transformation that includes highly experiential and personalized learning, we are always re-examining and continuously improving the way people learn best, whether it's one child in our own backyard or an education community across the globe.

The graph represents a network of 1,490 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 13 July 2021 at 20:16 UTC. The requested start date was Tuesday, 13 July 2021 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, 18-hour, 24-minute period from Sunday, 11 July 2021 at 05:36 UTC to Tuesday, 13 July 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

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After Apple, Microsoft recently became the only publicly traded American company to hit the $2 trillion market cap. The company has reached the milestone just two years after it crossed the $1 trillion mark. In this article, we list major AI projects and initiatives the company undertook post-2019. In 2019, Microsoft said it would invest $1 billion in OpenAI to build artificial general intelligence. The partnership is directed at developing a hardware and software platform within Azure geared towards AGI.

Before I started this MS program, I was looking for course curricula of different Masters programs and trying to find reviews of other people to understand which program is suitable for me. Now, as I am almost done with my MS, I thought I should write a review to help other learners who are looking for an MS program in Data Science or Analytics. Before I dive into the MS program, here is my background. I have a Bachelor's in Civil Engineering and a master's in Environmental Engineering. So, I am not from a computer science background.