Artificial intelligence has come a long way since the 1950s when the concept has entered the minds of scientists, mathematicians, and philosophers. Alan Turing is often mentioned as one of the precursors of the artificial intelligence we know and use in our times. Moreover, his 1950 paper, Computing Machinery and Intelligence details how to build such intelligent machines and how to assess their intelligence. Since then, the concept of machine learning has made incredible progress. Deep learning systems are no longer in need of human programming. They learn on their own based on data sets to identify patterns and anomalies.

Researchers from NVIDIA, Aalto University, and MIT have developed an AI that can remove noise from grainy photos and automatically enhance them. This technology can be beneficial in several real-world situations where it is difficult to obtain clear image data like MRI scans, astronomical imaging, and more. Existing noise-reduction AI systems require both noisy and clean input images, but NVIDIA's AI can restore images without being shown what the noise-free image looks like. It just needs to look at examples of corrupted images. The researchers trained the AI on 50,000 images and the deep-learning algorithm was able to produce impressive results.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Safety is one of the emerging concerns in deep learning systems. In the context of deep learning systems, safety is related to building agents that respect safety dynamics in a given environment.

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."

Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.

Using artificial intelligence and deep learning, the detection process will be automated and rapid.

Artificial intelligence (AI) and machine learning are helping analyse enormous amounts of data around the human genome and drug molecules, and these new-age technologies can play an important role in the battle against COVID-19, industry experts said on Saturday. Speaking at KnowDis Machine Learning Day, Avantika Lal – Senior Scientist (Deep Learning and Genomics) at NVIDIA – stated bigger data sets on genome sequences (DNA arrangement) are being obtained, and this data is being studied for multiple parameters. "As the cost of sequencing goes down, more and more people can get their genome sequence and in actuality, governments, research institutes and public health organisations around the world are attempting to sequence many thousands of people so as to be develop an idea of the genomes of the inhabitants of the countries," she said. Lal added that enormous data sets are collected that are extremely complicated and contain many different related sorts of information. These data sets may also help understand the mechanisms by which a specific disorder arises in people, or how does one identify patients who may respond differently or become more sensitive to a particular kind of medication or treatment, she further said.

The Deep Learning Market report 2020 provides a detailed impression as well as describes the industry scope and also the market expanded insights and forecasts up to 2025. The study highlights a detailed assessment of the Deep Learning Market and displays market size, trends by revenue, current growth factors, expert opinions, facts, and industry validated market development data. The study sheds a lights on a market interpretations on a global scale which is further distributed through distribution channels and marginalized market space where most trade occurs. These insights are quite promising for various businesses and industries to cope up with this unprecedented downturn and take effective strategic decisions to thrive and proliferate within the ever competitive business ecosystem. Reports Intellect is your one-stop solution for everything related to market research and market intelligence.

A CycleGAN is designed for image-to-image translation, and it learns from unpaired training data. It gives us a way to learn the mapping between one image domain and another using an unsupervised approach. Jun-Yan Zhu original paper on the CycleGan can be found here who is Assistant Professor in the School of Computer Science of Carnegie Mellon University. These images do not come with the labels, i.e. the generator creates the training data X from the Y datasets. We do not have to extract all the corresponding features from the individual images.

Transfer learning (TL) in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. Importance of transfer of learning, The main purpose of any learning or education is that a person who acquires some knowledge or skill in a formal and controlled situation like a classroom, or a training situation, will be able to transfer such knowledge and skill to real life situations and adapt himself more effectively.