American technology firm Nvidia along with researchers from Aalto University and the Massachusetts Institute of Technology have published a paper outlining a new artificial intelligence (AI) system that can almost flawlessly retouch grainy or pixelated pictures. The software uses deep-learning to automatically remove noise and artifacts. What makes this system so unique is that it can teach itself to fix corrupted photos simply by looking at them. Previous deep learning work in image retouching was centered on training neural networks to restore images by comparing noisy and clear images. This new method, however, only requires two corrupted images to proceed with removing noise. "It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars," reads the paper.

American technology firm Nvidia along with researchers from Aalto University and the Massachusetts Institute of Technology have published a paper outlining a new artificial intelligence (AI) system that can almost flawlessly retouch grainy or pixelated pictures. The software uses deep-learning to automatically remove noise and artifacts. What makes this system so unique is that it can teach itself to fix corrupted photos simply by looking at them. Previous deep learning work in image retouching was centered on training neural networks to restore images by comparing noisy and clear images. This new method, however, only requires two corrupted images to proceed with removing noise. "It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars," reads the paper.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.

As artificial Intelligence is being implemented in almost all sectors of automation. Deep learning is one of the trickiest architectures used to develop and maximise the efficiency of human-like computers. To help the product developers, Google, Facebook and other big tech companies have released various frameworks for Python environment where one can learn, build and train diversified neural networks. Google's TensorFlow is an open source framework for deep learning which has received popularity over the years. With the new framework, PyTorch is receiving loads of attention from beginners because of its easy-to-write code.

According to a recent press release, "Artificial Intelligence (AI) companies present unique growth opportunities as AI is clearly becoming an important piece of everyday life. AI in practical terms has become the theory and practice of building machines, applications and devices capable of performing tasks that seem to require intelligence. Innovative and cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Raising expectations of AI of late is incorporating blockchain technology which is essentially a new filing system for digital information that stores data in an encrypted, distributed ledger format. Because data is encrypted and distributed across many different computers, it enables the creation of tamper-proof, highly robust databases which can be read and updated only by those with permission."

Researchers from Nvidia, MIT, and Aalto University are using artificial intelligence to reduce noise in photos. The team used 50,000 images from the ImageNet dataset to train its AI system for reconstructing photos, and the system is able to remove noise from an image even though it has never seen the image without noise. Named Noise2Noise, the AI system was created using deep learning and draws its intelligence from 50,000 images from the ImageNet database. Each came as a clean, high-quality image without noise but was manipulated to add randomized noise. Computer-generated images and MRI scans were also used to train Noise2Noise.

If you want to see some of the stuff that Tay tweeted, head over here (warning; some of her tweets make Donald Trump look tame). Tay's introduction by Microsoft was not just an attempt to build an AI that learnt from human interactions, but also one that potentially enriched Microsoft's brand and was designed also to harvest users information such as gender, location/zip codes, favourite foods, and so on (as was the Microsoft Age guessing software of last year). It harvested user interactions alright, but after a group of trolls launched a sustained, coordinated effort to influence Tay, the AI did exactly what Microsoft designed it to do -- it adapted to the language of it's so-called peers. Tay appears to have accomplished an analogous feat, except that instead of processing reams of Go data she mainlined interactions on Twitter, Kik, and GroupMe. She had more negative social experiences between Wednesday afternoon and Thursday morning than a thousand of us do throughout puberty. It was peer pressure on uppers, "yes and" gone mad. No wonder she turned out the way she did. I've Seen the Greatest A.I. Minds of My Generation Destroyed by Twitter, New Yorker article, March 25th, 2016 Tay is a lesson to us in the burgeoning age of AI. Teaching Artificial Intelligences is not only about deep learning capability, but significantly about the data these AIs will consume, and not all data is good data.

For many Americans, taking prescription pills is a part of a never-ending routine. About seven out of ten people take at least one drug, according to the Mayo Clinic, and as many as 15 percent take five or more. The trouble is, it's difficult to predict the side effects that might result from polypharmacy, or the practice of combining drugs; by some estimates, there are more than 125 billion potential complications. "It's practically impossible to test a new drug in combination with all other drugs, because just for one drug that would be five thousand new experiments," Marinka Zitnik, a postdoctoral fellow in computer science at Stanford, said. "[T]ruly, we don't know what will happen."

Working with TensorFlow Serving I thought, it would be really great to serve the Keras models too. The advantage of Keras is obvious -- it significantly simplifies the model development and allows trying of the models much faster than the pure TensorFlow framework. Another motivation, I wanted to make a client independent of the huge TensorFlow framework and use a very limited part of the Serving capabilities. Keras provides high-level neural network API and is capable to run on top of TensorFlow, CNTK or Theano. Basically, it abstracts those frameworks, is much easier to understand and learn and allows you to do more with less code.