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Algorithms can help fight COVID-19. But at what cost?

#artificialintelligence

This past spring, as billions of people languished at home under lockdown and stared at gloomy graphs, Linda Wang and Alexander Wong, scientists at DarwinAI, a Canadian startup that works in the field of artificial intelligence, took advantage of their enforced break: In collaboration with the University of Waterloo, they helped develop a tool to detect COVID-19 infection by means of X-rays. Using a database of thousands of images of lungs, COVID-Net – as they called the open-access artificial neural network – can detect with 91 percent certainty who is ill with the virus. In the past, we would undoubtedly have been suspicious of, or at least surprised by, a young company (DarwinAI was established in 2018) with no connection to radiology, having devised such an ambitious tool within mere weeks. But these days, we know it can be done. Networks that draw on an analysis of visual data using a technique known as "deep learning" can, with relative flexibility, adapt themselves to decipher any type of image and provide results that often surpass those obtained by expert radiologists.


[D] The machine learning community has a toxicity problem

#artificialintelligence

First of all, the peer-review process is broken. Every fourth NeurIPS submission is put on arXiv. There are DeepMind researchers publicly going after reviewers who are criticizing their ICLR submission. On top of that, papers by well-known institutes that were put on arXiv are accepted at top conferences, despite the reviewers agreeing on rejection. In contrast, vice versa, some papers with a majority of accepts are overruled by the AC.


An understanding of AI's limitations is starting to sink in

#artificialintelligence

IT WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. PwC, a professional-services firm, predicts that artificial intelligence (AI) will add $16trn to the global economy by 2030. The total of all activity--from banks and biotech to shops and construction--in the world's second-largest economy was just $13trn in 2018. PwC's claim is no outlier. Rival prognosticators at McKinsey put the figure at $13trn.


Understanding the limits of convolutional neural networks -- one of AI's greatest achievements

#artificialintelligence

After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. Hinton, who attended the conference with Yann LeCun and Yoshua Bengio, with whom he constitutes the Turin Award–winning "godfathers of deep learning" trio, spoke about the limits of CNNs as well as capsule networks, his masterplan for the next breakthrough in AI.


Understanding the limits of convolutional neural networks -- one of AI's greatest achievements

#artificialintelligence

After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. Hinton, who attended the conference with Yann LeCun and Yoshua Bengio, with whom he constitutes the Turin Award–winning "godfathers of deep learning" trio, spoke about the limits of CNNs as well as capsule networks, his masterplan for the next breakthrough in AI.


Hybrid AI systems are quietly solving the problems of deep learning

#artificialintelligence

Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components. This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three "godfathers of deep learning," have all spoken about the limits of neural networks. The question is, what is the path forward?


Why a major AI Revolution is coming, but it's not what you think -- AAAI 2020

#artificialintelligence

You already know that Deep Learning is good at vision, translation, playing games, and other tasks. But Neural Networks don't "learn" the way humans do, instead it's just really good at fast pattern matching. Today's research mainly focuses on bigger models with larger datasets, bigger models, and complicated loss functions. But the next revolution is likely going to be more fundamental. Let's take a look at two approaches: adding logic with Stacked Capsule Auto Encoders and Self-Supervised Learning at scale.


Learnergy: Energy-based Machine Learners

arXiv.org Machine Learning

Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An interesting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle with the most diverse applications, such as classification, reconstruction, and generation of images and signals. Nevertheless, one can see they are not adequately renowned when compared to other well-known deep learning techniques, e.g., Convolutional Neural Networks. Such behavior promotes the lack of researches and implementations around the literature, coping with the challenge of sufficiently comprehending these energy-based systems. Therefore, in this paper, we propose a Python-inspired framework in the context of energy-based architectures, denoted as Learnergy. Essentially, Learnergy is built upon PyTorch for providing a more friendly environment and a faster prototyping workspace, as well as, possibility the usage of CUDA computations, speeding up their computational time.


AAAI 2020 A Turning Point for Deep Learning?

#artificialintelligence

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.


The case for hybrid artificial intelligence

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components. This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three "godfathers of deep learning," have all spoken about the limits of neural networks.