This publication draws on a wide range of expertise to illuminate the year ahead. Even so, all our contributors have one thing in common: they are human. But advances in technology mean it is now possible to ask an artificial intelligence (AI) for its views on the coming year. We asked an AI called GPT-2, created by Openai, a research outfit. GPT-2 is an "unsupervised language model" trained using 40 gigabytes of text from the internet.

Deep learning, as a class of artificial intelligence technology, has demonstrated its potential to produce results superior to human experts over a broad range of applications such as computer vision, speech recognition, autonomous driving, recommendation systems, and drug design. Unlike many machine learning methods that usually require the learning features provided to the model manually, deep learning utilizes multiple-layer artificial neural network to progressively extract higher level features from the raw input which allows it to automatically discover the features to be used for classification. Considering the ever growing size of the neural network model and the training data as well as the increasing complexity of the graphs to represent the neural networks, a graph database management system shows great advantages in scaling to large models and representing the neural network.

Computers can beat humans at sophisticated tasks like the game Go, but can they also drive a car, ... [ ] speak languages, play soccer, and perform a myriad of other tasks like humans? Here's what AI can learn from biology. In building the world's first airplane at the dawn of the 20th century, the Wright Brothers took inspiration from the "insightful" movements of birds. They observed and reverse-engineered aspects of the wing in nature, which in turn helped them make important discoveries about aerodynamics and propulsion. Similarly, to build machines that think, why not seek inspiration from the three pounds of matter that operates between our ears? Geoffrey Hinton, a pioneer of artificial intelligence and winner of the Turing Award, seemed to agree: "I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain."

There are a variety of neural network model techniques in deep-learning (ex: deep reinforced learning) that have evolved more recently as tangible value-cases surface. One of the more compelling applications of deep reinforced learning is in the pharmaceutical and bio-pharmaceutical industry where this modeling technique can be used to optimize chemical reactions. This allows for scientists to reduce the trial-and-error related with research and development activities. AI platforms can optimize the reagent quantities and compositions based on a positive feedback method after analyzing the reaction outcome. Such platforms are ultimately providing not just granular enhancements in time savings but are leading to faster product-to-market efforts by enhancing various R&D and logistical processes for the pharma and biopharma industries.

Marketers use AI to generate individualized recommendations and automatic order fulfillment. The list is virtually infinite. A host of services taken for granted today, from credit card fraud detection to email spam filters to predictive traffic alerts to personalized reminders, wouldn't be possible without AI. One area where AI is used extensively is business intelligence. Enterprises leverage deep learning algorithms to spot behavioral patterns likely to lead to sales, use cues from IoT sensors for predictive maintenance and inventory optimization and do more.

The skills people and businesses need to succeed are changing. No matter where you are in your career or what field you work in, you will need to understand the language of data. With Java8s, you learn today and apply it tomorrow.

Deep learning papers often have very good diagrams of their architectures. Does anyone know of tools that can be used to generate these sorts of diagrams. I'm not looking for automatically generated diagrams. What kind of software do people use to make nice looking Visualizations for their network architecture. A really nice example is the pointnet architecture.

Face Recognition, Image Classification, Image Enhancement... Is your smartphone capable of running the latest Deep Neural Networks to perform these AI-based tasks? Does it have a dedicated AI Chip? Run AI Benchmark to comprehensively evaluate it's AI Performance! AI Benchmark measures the speed, accuracy and memory requirements for several key AI and Computer Vision algorithms. Among the tested solutions are Image Classification and Face Recognition methods, Neural Networks used for Image Super-Resolution and Photo Enhancement, AI models playing Atari Games and performing Bokeh Simulation, as well as algorithms used in autonomous driving systems.

Introduction: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances in GI endoscopy. Artificial intelligence holds potential to increase and water exchange significantly increases adenoma detection.Areas covered: The authors searched MEDLINE (1998-2019) using the following medical subject terms and keywords: colonoscopy, adenoma, artificial intelligence, deep learning, computer-assisted detection, and neural networks. Additional related studies were manually searched from the reference lists of publications. Only fully published journal articles in English were reviewed. The latest date of the search was Aug 10, 2019.

Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras! This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!