“Above the Trend Line” – Your Industry Rumor Central for 7/29/2021

Above the Trend Line

News items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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[Paper Summary] DeepMind Researchers introduce Epistemic Neural Networks (ENNs) for Uncertainty Modeling in Deep Learning

Deep learning algorithms are widely used in numerous AI applications because of their flexibility and computational scalability, making them suitable for complex applications. However, most deep learning methods today neglect epistemic uncertainty related to knowledge which is crucial for safe and fair AI. A new DeepMind study has provided a way for quantifying epistemic uncertainty, along with new perspectives on existing methods, all to improve our statistical knowledge of deep learning.

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Stanford Researchers put Deep Learning on a Data Diet

With the cost for deep learning model training on the rise, individual researchers and small organisations are settling for pre-trained models. Today, the likes of Google or Microsoft have budgets (read:millions of dollars) for training state of the art language models.  Meanwhile, efforts  are underway to make the whole paradigm of training less daunting for everyone. Researchers are actively exploring ways to maximise training efficiency to make models run faster and use less memory.

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[Paper Summary] Scientists have created a new Tool ‘Storywrangler’ that can explore billions of Social Media messages in order to Predict Future Conflicts and Turmoil

Scientists have recently invented an instrument to divulge deeper into the billions of posts made on Twitter since 2008. The new tool is capable of providing an unprecedented, minute-by-minute view of popularity. The research was carried out by a team at the University of Vermont. The team calls the instrument the Storywrangler. 

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[Paper] Google AI introduces a pull-push Denoising Algorithm and Polyblur: A Deblurring Method that eliminates noise and blur in images

Despite the advances in imaging technology, image noise and restricted sharpness remain most critical factors for increasing the visual quality of an image. Noise can be linked to the particle nature of light, or electronic components may be introduced during the read-out process. A photographic editing process will then process the captured noisy signal via the camera image processor (ISP) and be enhanced, amplified, and distorted. Image blur may be caused by a wide range of phenomena, from inadvertent camera shake during capture, incorrect camera focusing, or sensor resolution due to the final lens aperture.

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[Paper] OpenAI reveals details about its Deep Learning Model ‘Codex’: The backbone of Github’s ‘Copilot’ Tool

In their latest paper, researchers at OpenAI reveal details about a deep learning model called Codex. This technology is the backbone of Copilot, an AI pair programmer tool jointly developed by GitHub and OpenAI that’s currently available in beta to select users. The paper explores the process of repurposing their flagship language model GPT-3 to create Codex, as well as how far you can trust deep learning in programming. The OpenAI scientists managed this feat by using a series of new approaches that proved successful with previous models.

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A landscape of Automatic Speech Recognition on Deep Learning — 2021

Automatic speech recognition

Few people know and attentive for this field of Artificial Intelligence and meanwhile is a novel and promissor research area within Deep learning, nowadays is usually to see and not realize technologies which are using ASR and your strives are bigger than ever, Automatic Speech Recognition is a technology that be able recognize a voice, sound or a signal across acoustic waves, sums up the machine recognize your voice and transform it into a text transcription, we’ve seen several high level products and services that itself use ASR behind the hood, I can cite Siri a virtual assistent at Apple, Alexa belongs an Amazon another virtual assistent, Google Home, and so on.

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Deep reinforcement learning helps us master complexity

Deep reinforcement learning—where machines learn by testing the consequences of their actions—is one of the most promising and impactful areas of artificial intelligence. It combines deep neural networks with reinforcement learning, which together can be trained to achieve goals over many steps. It’s a crucial part of self-driving vehicles and industrial robots, which have to navigate complex environments safely and on time.

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[Paper Summary] Facebook AI Introduces few-shot NAS (Neural Architecture Search)

Neural Architecture Search (NAS) has recently become an interesting area of deep learning research, offering promising results. One such approach, Vanilla NAS, uses search techniques to explore the search space and evaluate new architectures by training them from scratch. However, this may require thousands of GPU hours, leading to a very high computing cost for many research applications.

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The user-experience crisis in bioinformatics & artificial intelligence softwares

Cell structure

Biology was once a domain where we could use only sight to make distinct differences between vegetal, animal & bacteria cells. During these times, it was possible for a single person to study a range variety of organisms and to still discover new mechanisms. However, nowadays you cannot simply be a biologist anymore.

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NVIDIA Launches TensorRT 8 that improves AI Inference Performance making Conversational AI smarter and more interactive from Cloud to Edge

Tensor 8

Today, NVIDIA released the eighth generation of the company’s AI software: TensorRT™ 8, which cuts inference time for language queries in half. This latest version of the software allows firms to deliver conversational AI applications with quality and responsiveness that was never possible before.  

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The Paladin, the Cleric, and the… Reinforcement Learning?

Data science and artificial intelligence are everywhere. So are video games. It’s no surprise that it was only a matter of time until people started getting creative with combining the two in unique ways. And no, I’m not talking about improving in-game AI (because clearly, Skyrim doesn’t care), and I’m not talking about analyzing game sales either. Today, I want to look at some fun ways that people have used reinforcement learning in the world of gaming, ranging from creating new AI to beating the game using bots.

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Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals.

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[Paper Summary] DeepMind introduces it’s Supermodel AI ‘Perceiver’: a Neural Network Model that could process all types of input

DeepMind recently released a state-of-the-art deep learning model called Perceiver via a recent paper. It adapts the Transformer to let it consume all the types of input ranging from audio to images and perform different tasks, such as image recognition, for which particular kinds of neural networks are generally developed. It works very similarly to how the human brain perceives multi-modal input.

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CPU vs GPU in Machine Learning Algorithms: Which is Better?


Machine learning algorithms are developed and deployed using both CPU and GPU. Both have their own distinct properties, and none can be favored above the other. However, it’s critical to understand which one should be utilized based on your needs, such as speed, cost, and power usage.

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[Paper Summary] Skoltech Researchers present a Machine Learning Framework involving Convolutional Neural Networks

Skoltech researchers and their partners in the U.S. have created a neural network that can help tweak semiconductor crystals to achieve superior properties for electronics. This is an exciting new direction of development with limitless possibilities for next-generation chips and solar cells. This study is published as a paper in the journal npj Computational Materials.

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[Paper Summary] Researchers at Facebook AI, UC Berkeley, and Carnegie Mellon University Announced Rapid Motor Adaptation (RMA), An Artificial Intelligence (AI) Technique

To achieve success in the real world, walking robots must adapt to whatever surfaces they encounter, objects they carry, and conditions they are in, even if they’ve not been exposed to those conditions before. Moreover, to avoid falling and suffering damage, these adjustments must happen in fractions of a second.

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AI Accelerators – Hardware for Artificial Intelligence

AI accelerators

Though CPUs are no longer viable sources of computational power, they were the pioneers. Today, those CPUs are rightfully replaced by GPUs and AI accelerators, specifically designed for large computing. The main features considered while purchasing an AI accelerator are cost, energy consumption, and processing speed.

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