Image classification is not a hard topic anymore. Tensorflow has all the inbuilt functionalities that take care of the complex mathematics for us. Without knowing the details of the neural network, we can use a neural network now. In today's project, I used a Convolutional Neural Network (CNN) which is an advanced version of the neural network. If you worked with the FashionMNIST dataset that contains shirts, shoe handbags, etc., CNN will figure out important portions of the images.

The human brain is an incredibly efficient source of intelligence. Earlier this month, OpenAI announced it had built the biggest AI model in history. This astonishingly large model, known as GPT-3, is an impressive technical achievement. Yet it highlights a troubling and harmful trend in the field of artificial intelligence--one that has not gotten enough mainstream attention. Modern AI models consume a massive amount of energy, and these energy requirements are growing at a breathtaking rate.

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Ever trained an image recognition model? What accuracy did you get? 90, 95, or maybe a near-perfect 99 percent? No matter what your answer is, we want to ask for a follow-up. If you get a great accuracy on training as well as test images, does it mean your model is ready to be deployed? Well, even though it once did, now it may not be ready.

Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office. Fun roles you will have in this job: Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration Describe how the work products to which they contributed had solved customers' problems in such domains as energy, health, and national security or in the commercial sector Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers. Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data Independently design and undertake new research as well as partner in a team environment across organizations Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.

The Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) is a collaboration between McGill University and Forschungszentrum Jülich to develop next-generation high-resolution human brain models using cutting-edge Machine- and Deep Learning methods and high-performance computing. HIBALL is based on the high-resolution BigBrain model first published by the Jülich and McGill teams in 2013. Over the next five years, the lab will be funded with a total of up to 6 million Euro by the German Helmholtz Association, Forschungszentrum Jülich, and Healthy Brains, Healthy Lives at McGill University. In 2003, when Jülich neuroscientist Katrin Amunts and her Canadian colleague Alan Evans began scanning 7,404 histological sections of a human brain, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer in three dimensions. At that time, there were no technical possibilities to cope with the huge amount of data.

In 2017, I built this deep learning machine as a solution to my growing AWS bill. In 2020, I struggled to get this machine to run Kubeflow locally. Here are the instructions that worked for me. Some of these instructions are based on this Ubuntu.com MicroK8s is a small version of Kubernetes that will work well locally.

AN ARTIST has used artificial intelligence to create human-like portraits from statues and paintings of famous faces. If you've ever wondered what the Statue of Liberty or Michelangelo's David statue would look like as real people then take a look below. Dutch artist Bas Uterwijk used AI to create the photo-style portraits. He focused on well-known figures including Vincent Van Gogh and Napoleon Bonaparte. The deep learning technology enabled him to take a photo of a statue or a painting and turn it into a more human-like face.

Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.

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