This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method.

The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases. The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer's disease. In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 "significant" genetic differences among Alzheimer's and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer's symptoms develop and a patient's brain is irreversibly damaged.

Machine learning and Artificial Intelligence developments are happening at breakneck speed! At such pace, you need to understand the developments at multiple levels – you obviously need to understand the underlying tools and techniques, but you also need to develop an intuitive understanding of what is happening. By the end of this article, you will develop an intuitive understanding of RNNs, especially LSTM & GRU. Have a look at this article on NLP. I took a handful of tweets and used the word count of positive versus negative words to classify the sentiment of the tweet.

DCNNs are neural networks that can be used to classify (or "name") images to then cluster them (such as when performing a photo search). These machine learning algorithms belong to the subset of deep learning. Using cameras to take wide-field photographs of large areas of patients' bodies, the program uses DCNNs to quickly and effectively identify and screen for early-stage melanoma, according to Luis R. Soenksen, a postdoc and a medical device expert currently acting as MIT's first Venture Builder in Artificial Intelligence and Healthcare. Soenksen conducted the research with MIT researchers, including MIT Institute for Medical Engineering and Science (IMES) faculty members Martha J. Gray, W. Kieckhefer Professor of Health Sciences and Technology, professor of electrical engineering and computer science; and James J. Collins, Termeer Professor of Medical Engineering and Science and Biological Engineering. Soenksen, who is the first author of the recent paper, "Using Deep Learning for Dermatologist-level Detection of Suspicious Pigmented Skin Lesions from Wide-field Images," published in Science Translational Medicine, explains that "Early detection of SPLs can save lives; however, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking."

The past few years have seen several flashy demonstrations of how artificial intelligence (AI) algorithms may transform biomedical research, particularly with respect to drug discovery. This past November, for example, Google's AI subsidiary, DeepMind, announced that its AlphaFold program could deliver computational predictions of protein structure that approach the quality of those provided by gold-standard experimental techniques such as X-ray crystallography.1 Such high-profile announcements have elicited justifiable excitement about the future of algorithmically guided drug development, but AI's champions in the industry remain wary about overselling the technology's current capabilities. "I still feel that there's a lot of hype around it," says Paul Nioi, PhD, senior director of research at Alnylam Pharmaceuticals. "Companies are springing up that claim to solve all the issues of drug discovery, target discovery, and development using AI. I think that's yet to be proven."

When will we have artificial general intelligence, the kind of AI that can mimic the human mind in all aspect? Experts are divided on the topic, and answers range anywhere between a few decades and never. But what everyone agrees on is that current AI systems are a far shot from human intelligence. Humans can explore the world, discover unsolved problems, and think about their solutions. Meanwhile, the AI toolbox continues to grow with algorithms that can perform specific tasks but can't generalize their capabilities beyond their narrow domains.

Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates. Results of the team's study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels. "Air quality is one of the major issues within an urban area that affects people's lives," said Manzhu Yu, assistant professor of geography at Penn State. "Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead." Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said.

The course aims at teaching foundations of machine learning and AI, machine learning, deep learning, Python ML&AI frameworks, and solving real- …

OpenAI's language model GPT-3 has established a reputation for its flexibility, having been used for everything from AI-generated self-help blogs to identifying paintings with simple captions. In the latest development, the language model was used to generate game code, according to programmer Ivan Mathy (who goes by @OKatBest on Twitter). "All you need to do is describe what you want," Mathy said, "and GPT-3 will try to write it down as a Unity C# script." Just a week ago, the same language model was trained on cringe-worthy data sets of people trying to flirt. The resulting pick-up lines were surprisingly less awful than what we humans come up with when we attempt to woo others.

We're releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore's Law had a 2-year doubling period).[1] Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it's worth preparing for the implications of systems far outside today's capabilities. The total amount of compute, in petaflop/s-days,[2] used to train selected results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used. Three factors drive the advance of AI: algorithmic innovation, data (which can be either supervised data or interactive environments), and the amount of compute available for training.