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.

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.

This week, Dessa launched Atlas as an open source platform for developing deep learning projects. The deep learning startup Dessa was acquired by Square in February 2020. Let's see what features this beta version of their deep learning platform has to offer. Atlas has a Python SDK, CLI, GUI and scheduler on board. These aspects should help reduce the effort of managing infrastructure and increase the speed of developing deep learning models.

A research team has proposed non-contrast thoracic chest CT scans as an effective tool for detecting, quantifying, and tracking COVID-19. As of March 16, the COVID-19 pandemic had a confirmed infection total of more than 170,000 people around the globe. The speed of transmission of COVID-19 has surprised the world and had a massive impact on people's daily lives and the global economy. To accelerate COVID-19 detection and support efforts to combat the epidemic, researchers from RADLogics, Tel-Aviv University, New York Mount Sinai Hospital and University of Maryland School of Medicine developed an AI-based approach designed to help identify infected patients and quantify disease burden by analyzing thoracic CT (Computer Tomography, aka CAT) exams. Data sources for new epidemic diseases such as COVID-19 remain limited, as does expertise.

Elmer is now in use on Sperry's entire North American non-stop track monitoring fleet, including ATS rail-road vehicles. Sperry Rail has been active in non-destructive testing for rail faults for more than 90 years. In a drive to deliver greater efficiency in track monitoring, the supplier has developed Artificial Intelligence (AI) and machine learning applications that it is now installing in regular service. As demand for track access grows, infrastructure managers from around the world face a daily battle to keep their railways operational during limited maintenance intervals. Predictive and preventative rather than interval-based maintenance is the Holy Grail for infrastructure managers.

Dr. Alex Antic is a trusted and experienced Data Science leader, with a proven record of delivering innovative, successful and sustainable projects in government, industry and startups (including Sports Analytics), that leverage data and Machine Learning/Deep Learning capabilities to deliver actionable insights. Dr. Antic is currently serving as an Academic at ANU, whilst also being a Principal Data Scientist for Federal Agency. Before his current role at ANU, Alex was a quantitative analyst at hedge fund and investment bank. He has also worked with the actuarial teams in the marketing analytics. Because of this unique career path with the commercial experience, he understands the business needs well as well as able to be pragmatics with the commercial perspective whilst working at different environment.

An artifact-free computational approach to extract the phase of light from noisy intensity signals improves imaging of transparent objects, such as biological cells, under low light conditions. Deep neural networks are trained to operate on these two frequency bands, before a final algorithm recombines them into a full-band phase image. This method avoids the tendency of automatic phase extraction programs to over-represent low frequencies. The retrieval of phase of electromagnetic fields is one of the most important problems in optics as it allows the shape of transparent objects, including cells, to be quantified using visible light. Phase is a quantity that relates to the wave nature of light; it is not directly detectable by our eyes or common cameras, and yet carries important information about objects the light went through.

Recently one morning, I asked my computer a relatively simple question: can artificial intelligence (AI) write? We're not too certain on what artificial intelligence will be able to write, but there are some scenarios in which computers could be responsible for a huge number of word documents … The biggest potential scenarios would involve machines analyzing what has already been written and determining what pieces need to be edited to make the content seem fresh. The above sentences were composed by a machine in a matter of seconds. The tool used is a freely accessible interface based on the GPT-2 text generator released by OpenAI – a company founded by technology industry leaders, including Elon Musk and Sam Altman. Only a limited version of the tool was made available, as it was dubbed "too dangerous" by the company to release fully into the world.

Stepping into the field of deep learning, I've realized that sometimes an informative blog post could serve as an extremely helpful friend to guide you through learning and researching. So I decided to gather all the informative and well-explained blog posts I've ever read and hoping to get some more from you guys:)

The COVID-19 pandemic has caused drama in the local supply chain here, but I am trying to decide on a machine to train and deploy a model built around Mask-RCNN (Resnet 101), and perhaps experiments with VGG-16. The hardware vendor has recommended a system built around a 10900X/X299 with a single RTX5000 (16GB of VRAM). Are the unique extensions in the Intel architecture worth the clock/core/fab gen hit? Would I immediately run into issues with the 11GB VRAM on the 2080Ti cards? The RTX5000 has 16GB of VRAM but is double the cost and has less Stream processors.