In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton published their results on the ImageNet LSVRC2010 contest, a computer vision challenge to classify 1.2 million high-resolution images into 1,000 different classes automatically. Their use of deep neural networks yielded a substantial improvement in error rate and marks the beginning of the recent wave of interest in machine learning and artificial intelligence. In the subsequent years, deep learning has been applied to a considerable number of other problems and used productively in applications such as voice recognition for digital assistants, translation software, and self-driving vehicles. But despite all these impressive success stories, deep learning still suffers from severe limitations. For one thing, enormous amounts of labeled data are required to train the networks.

Operations based on an MRI or CT scan are made trickier by the fact that people can never lie completely still. Doctoral candidate Koen Eppenhof has shown that an algorithm based on deep learning can be used to correct for the inevitable movements. To administer radiation or to operate as accurately as possible, the area to be treated is first drawn onto a scan (MRI or CT) by the doctor. This area--the site of a tumor, for example--is then localized on the operating table using a new scan. This is no simple matter: the patient's position is never exactly the same in the two scans--and then there's the inevitable movement and deformation of the organs due to the breathing.

In what might only be perceived as a win for Facebook, OpenAI today announced that it will migrate to the social network's PyTorch machine learning framework in future projects, eschewing Google's long-in-the-tooth TensorFlow platform. OpenAI is the San Francisco-based AI research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, Elon Musk, and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman. In a blog post, the company cited PyTorch's efficiency, scalability, and adoption as the reasons for its decision. "Going forward we'll primarily use PyTorch as our deep learning framework but sometimes use other ones when there's a specific technical reason to do so," said the company in a statement. "We're … excited to be joining a rapidly-growing developer community, including organizations like Facebook and Microsoft, in pushing scale and performance on [graphics cards]." OpenAI says that many of its teams have already migrated their work to PyTorch and that they'll contribute to the PyTorch community in the coming months.

A day in the life of a data scientist is, at the very least, multi-threaded (in terms of task processing, that is). Not only do they deal with several internal stakeholders to get their ideas through, they are also required to ensure their machine learning models are adequately trained in the requisite volume and dimensionality of data. Even more so, we may add, if Deep and reinforcement learning is at play. Computer storage has followed Moore's Law, sure, but computing prowess that is a prerequisite for Artificial Intelligence model development, has not. And at the core of this problem lies a technology relic from decades ago – The Central Processing Unit.

Now, there is a growing realization about the potential of Machine Learning that has the ability to give information from numerous sources into an integrated system. Since, Machine Learning operates on algorithms; healthcare experts are leveraging this technology to develop algorithms for the machines or supercomputers that can help them in analyzing abnormalities inside the human body through advanced imagery techniques. For instance Google has developed a Machine Learning algorithm that helps identify Cancerous tumours. Similarly, Stanford University has developed a deep learning algorithm that helps identify Skin Cancer. Machine Learning has the ability to gather and accumulate sufficient information about a patient which enables doctors to devise unique personalized care for each patient. Machine Learning is proving to be another useful option when it comes to Radiotherapy and Radiology.

DeepMind Technologies, a Google subsidiary and Artificial Intelligence (AI) firm, disclosed that it will adopt Blockchain technology and make use of Distributed Ledger Technology (DLT).This move will help the company secure patient data more efficiently. DeepMind creates algorithms designed for applications, gaming protocols and stimulation. It earned fame for developing a machine-learning program that can be capable of playing video games. Likewise, DeepMind developed the so-called "Neural Turing Machine" that copies short-term memory of human beings. It signed a five-year contract with Royal Free London NHS Trust recently so it can apply the technology to healthcare. The problem is this accord created some hullabaloo as it allegedly affected confidentiality of patient data.

The intersection between artificial intelligence(AI) and human cognition is one of the most fascinating areas of research in the modern technology space. Deep learning is constantly trying to emulate mechanisms of the human brain in order to improve the capabilities of AI agents. Many of those mechanisms are centered around how humans learn and build knowledge. A recent research paper from DeepMind is proposing a method that emulates the progressive nature of human learning in deep learning model. DeepMind calls this technique progressive neural networks.

People share millions of photos and videos on Facebook every day, creating some of the most compelling and creative visuals on our platform. Some of that content is manipulated, often for benign reasons, like making a video sharper or audio more clear. But there are people who engage in media manipulation in order to mislead. Manipulations can be made through simple technology like Photoshop or through sophisticated tools that use artificial intelligence or "deep learning" techniques to create videos that distort reality – usually called "deepfakes." While these videos are still rare on the internet, they present a significant challenge for our industry and society as their use increases.

Radiology is one of the most essential fields in clinical medicine. Experts in this field are specialists in deciphering and diagnosing disease based on various imaging modalities, ranging from ultrasound, magnetic resonance imaging (MRI), computerized tomography (CT), and x-rays. Studies have shown that the use of radiology in clinical practice has exponentially grown over the years: at the Mayo Clinic, between the years 1999 to 2010, use of CT scans increased by 68%, MRI use increased by 85%, and overall use of imaging modalities for diagnostic purposes increased by 75%, all numbers that have likely continued to rise, and indicate the sheer demand and growth of this robust field. A unique proposal that has become prominent over the last few years to help alleviate this increased demand is the introduction of artificial intelligence (AI) technology into this field. Simply put, the premise of AI as an addition to the practice of radiology is straightforward, and has been envisioned in two main ways: 1) a system that can be programmed with pre-defined criteria and algorithms by expert radiologists, which can then be applied to new, straightforward clinical situations, or 2) deep learning methods, where the AI system relies on complex machine learning and uses neural-type networks to learn patterns via large volumes of data and previous encounters; this can then be used to interpret even the most complicated and abstract images.

As AI and machine learning (ML) methods become pervasive across all domains from health to urban planning, there is an increasing need to make the results of such methods more interpretable. Providing such explanations has now become a legal requirement in some countries [10]. Many researchers are investigating this topic under supervised learning, particularly for methods in deep learning (see e.g., [21, 22]). Clustering is a commonly used unsupervised ML technique (see e.g., [2, 3, 9, 27, 13, 31]). It is routinely performed on diverse kinds of datasets, sometimes after constructing network abstractions, and optimizing complex objective functions (e.g., modularity [2]). This can often make clusters hard to interpret especially in a post-hoc analysis. Thus, a natural question is whether it is possible to explain a given set of clusters, using additional attributes which, crucially, were not used in the clustering procedure. One motivation for our work is to understand the threat levels of pathogens for which genomic sequences are available.