PyData London 2018 This tutorial aims to introduce key theory and methods in Variational Inference and apply these in practice, ending up connecting VI and recent generative model advances such as VAEs and GANs. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations.

Artificial Intelligence is a broader umbrella under which Machine Learning (ML) and Deep Learning (DL) comes. Diagram shows, ML is subset of AI and DL is subset of ML. AI is composed of 2 words Artificial and intelligence. Anything which is not natural and created by humans is artificial. Intelligence means ability to understand, reason, plan etc.

Fusing high performance computing and AI 2. Find your next binge-worthy show with AI 3. The connection between self-driving vehicles and radiology 4. Robots are learning new tasks by mimicking humans 5. How AI could spot a silent cancer in time to save lives 5. FUSING HIGH PERFORMANCE COMPUTING AND AI During GTC Taiwan 2018, NVIDIA CEO Jensen Huang announced HGX-2: a "building block" cloud-server platform that will let server manufacturers create more powerful systems around NVIDIA GPUs for high performance computing and AI. TechCrunch's Ron Miller sums it up best, saying that: "It's the stuff that geek dreams are made of. READ ARTICLE 6. FIND YOUR NEXT BINGE-WORTHY SHOW WITH AI While AI may play a leading role in the entertainment industry's depictions of the future on screen, it's already starring in entertainment behind the scenes, thanks to Netflix. Our latest AI Podcast features the company's research and engineering director, Justin Basilico. LISTEN HERE 7. CONNECTING SELF-DRIVING VEHICLES AND RADIOLOGY According to new commentary published in the Journal of American College of Radiology, AI implementation may not be as far as people believe, as seen in self- driving vehicles. "It is important to realize that many of these features are not far-future applications in radiology but will be incorporated into routine clinical practice over the next few years." "In radiology, having AI perform the mundane tasks that humans may struggle with or find interminable…frees us to interact with the images in ways that can push the boundaries of diagnostic science.

I recently read the "Learning to generate reviews and discovering sentiment" paper by OPENAI and found it to be super cool. But I could not understand how they are using the language model as feature extractor. Suppose we have 150 characters in a review, how do we extract features from these 150 characters when our input is 64 characters at a time.

Im finding it hard to find the information on this, but im sure its fine. The gtx 1050 ti is listed as having a compute capability of 6.1. Or if someone can point me to a list of supported GPUs for matconvnet that would be stellar!

Kids love to play in physical sandboxes. Developers love to "play" in virtual sandboxes. BlueData, which offers a new-gen big-data-as-a-service (BDaaS) software platform, has made available a new environment for AI and machine-learning developers to try out new ideas and have fun testing them. This is a new turnkey package that enables accelerated deployment of artificial intelligence, machine learning and deep learning applications in the enterprise. Turns out you can't build these applications too quickly.

At the DefCon2018 conference held in China on May 12, hackers and data scientists raised vivid discussions on cyberattacks with the use and abuse of machine learning and possible solutions. It goes without saying that artificial intelligence is now actively used in most security technologies as well as in a wide range of attacks. Attack vectors have become more advanced and sophisticated. If you are curious, there is a remarkable series of posts related to AI and cybersecurity on Forbes, revealing how AI-driven system can be hacked, detailing seven ways cybercriminals can use ML, and uncovering the truth about ML in defense. Today cyberattackers are less interested in traditional platforms but target self-driving cars, human-voice-imitation and image-recognition systems.

A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against machine in the quest for better, faster diagnostics, researchers said Tuesday. A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The machine -- a deep learning convolutional neural network or CNN -- was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at "expert" level with more than five years of experience, 19 percent had between two and five years' experience, and 29 percent were beginners with less than two years under their belt. "Most dermatologists were outperformed by the CNN," the research team wrote in a paper published in the journal Annals of Oncology.

Artificial intelligence refers to the ability of computers systems to perform tasks that normally require human intellect and judgment. Also called machine learning, these systems take in data that is structured or unstructured producing algorithms, finding patterns, and continually learning and refining its capacity over time to make decisions, guide behavior, offer solutions, or act. AI is now moving rapidly into "deep learning" which looks not just at the face value of data but draws meaning and representations from initial and cumulative data accelerating its learning and capacity as it grows. IBM's Watson is probably the most commonly know system exercising deep learning. Although the term was first coined in the mid 1950s, we are beginning to see their use in everyday life with self-driving cars, airplane autopilot systems, automatically controlling traffic lights based on flow and density, mobile check deposits deciphering handwriting, detecting human emotion to target advertising, and analyzing electronic trails to learn and exploit human behavior patterns.

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features; and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low-and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium(LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches. Keywords: Lung nodule classification, lung cancer diagnosis, Computed tomography, deep learning, convolutional neural networks, model interpretability 1. Introduction and Background Lung cancer is the leading cause of cancer mortality worldwide [1, 2]. Computed tomography (CT) imaging is widely used to detect pulmonary nodules and forms the basis for diagnosing lung cancer. Based on the findings of the NLST, the United States Preventative Services Task Force (USPSTF) went on to recommend low-dose CT lung cancer screening for current and former smokers aged 55-80 with a smoking history of at least 30 pack-years, or former smokers having quit within the past 15 years [4]. However, the potential consequences of implementing lung cancer screening is an increase in false positive screens that result in unnecessary medical, economic, and psychological costs.