The first two games that I developed were Battleship and Pac-Man, in Visual Basic 6.0. Nevertheless to say, both games require some kind of AI, which is what makes the game interesting in the first place. Since it was still the beginning of the Internet era, the idea of an online multi-player version of Battleship was not even conceivable to me, therefore playing against my own computer sounded like the most interesting and feasible option. Anyways, this was my very first "White Paper Syndrome" moment during a programming session in my life, when the following mystical question got stuck into my mind: I had to accept my inability to tackle this problem for quite a few years. Looking back at what happened in the next 4 or 5 years, that must be why I ended up studying Software Engineering, after all.

Creating a network capable of automatically responding to its own issues -- congestion, equipment failure, traffic spikes -- is one of the stated goals of today's virtualization push. Artificial intelligence and machine learning are key elements of this effort today, and they become even more critical going forward. That's because the looming trends of 5G wireless and the Internet of Things will reshape networks and network traffic in even more unpredictable ways than are happening already, notes Mazin Gilbert, AVP of Intelligent Services at AT&T Labs. In this second of two stories on artificial intelligence (AI) and machine learning in telecom (you can read the first one here), we take a closer look at how AT&T Inc. (NYSE: T) and Level 3 Communications Inc. (NYSE: LVLT) are deploying technology today to be ready for future services. First, there is a massive spike in data traffic expected with the advent of 5G and IoT, as much as a 10-fold increase in the next four years, Gilbert notes.

The OpenAI site is centered around an Open Source project and community involving artificial intelligence. The term "Open Source" means that the source code for the project is available for free and can be used by others free of charge. Artificial Intelligence refers to the general aim of intelligent computing, making machines think and learn. The project itself is the creation of a set of tools that are considered to be models of human intelligence. These tools are intended to be integrated into programs or used stand alone for research.

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. You can read more about Prof. Rigollet's work and courses on his website.

A benefit of using ensembles of decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from a trained predictive model. In this post you will discover how you can estimate the importance of features for a predictive modeling problem using the XGBoost library in Python. Feature Importance and Feature Selection With XGBoost in Python Photo by Keith Roper, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. A benefit of using gradient boosting is that after the boosted trees are constructed, it is relatively straightforward to retrieve importance scores for each attribute.

Google DeepMindhas announced it is working on a project to improve treatment on head and neck cancers, its third major collaboration with the NHS. The London-based AI research arm of the online search firm is partnering with University College London Hospital in an attempt to improve the scans available for radiotherapists by using machine learning. The project will use anonymised scans from up to 700 former patients. Radiotherapy works by bombarding cancerous cells with radiation to kill them, while minimising damage to the healthy cells around them. Clinicians target the treatment through a process called "segmentation": literally drawing around different parts of the patient's anatomy on scans, letting the radiotherapy machines know which tissue to target and which tissue to leave.

Drive.ai uses deep learning to teach self-driving cars – and to give them a voice Drive.ai wants to help autonomous cars talk with the people around them Drive.ai AI-based startup Banihal helps find a perfect match with neuroscience-based recommendation ...

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has developed an artificial intelligence (AI) method, aimed at training computers to interpret pathology images. The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep multi-layer convolutional network. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 per cent of the time. One of the researchers, Aditya Khosla, said, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "In our approach, we started with hundreds of training slides for which a pathologist has labeled regions of cancer and regions of normal cells," said Dayong Wang.

The New Artificial Intelligence Market by Aman Naimat, published by O'Reilly: There are only 1,500 companies in North America that are doing anything related to AI today, even using its narrow, task-based definition. That means less than one percent of all medium-to-large companies across all industries are adopting AI.

AI machines are racist & it's all our fault - report