The Precision Medicine World Conference will be one of the most exciting conferences focused on AI in healthcare in 2018. CEOs of cutting edge companies from around the world will come together to discuss how they are using techniques such as computer vision, deep learning and machine learning to make big advances in medicine from drug discovery to patient diagnosis and treatment. The program will traverse innovative technologies and clinical case studies that enable the translation of precision medicine into direct improvements in health care. Attendees will have an opportunity to learn about the latest developments in Precision Medicine and cutting-edge new strategies that are changing how patients are treated. I am looking forward to MCing the AI Company Showcase at PMWC.

We founded DeepMind Health to develop technologies that could help address some of society's toughest challenges. So we're very excited to announce that our latest research partnership will focus on breast cancer. We'll be working with a group of leading research institutions, led by the Cancer Research UK Centre at Imperial College London, and alongside the AI health research team at Google, to determine if cutting-edge machine learning technology could help improve the detection of breast cancer. Breast cancer is a significant global health problem. Every single year, over 1.6 million people are diagnosed with the disease, and while advances in early detection and treatment have improved survival rates, breast cancer still claims the lives of 500,000 people around the world every year, around 11,000 of whom are here in the UK.

Many of these AI-development companies are quickly turning their sights on real-world challenges. Google DeepMind has already moved the AlphaGo Zero system away from the game and onto a comprehensive study of protein folding in the hopes of revealing a treatment for diseases such as Alzheimer's and Parkinson's.

This next section of interesting APIs from 2017 contains notable choices from our Artificial Intelligence (AI), Recognition, Machine Learning, Predictions, Augmented Reality, and Natural Language Processing categories, beginning with three offerings from Google plus three more from Facebook and Microsoft. Incidentally, our two most popular hashtags on Twitter (besides #API), were #AI and #MachineLearning, which must mean developers are quite interested in these categories of APIs! Google's Cloud Video Intelligence API makes it possible to scan and search videos for nearly anything thanks to machine learning. Developers can use the Google Cloud Video Intelligence API to scan every single frame of videos for specific images. This API is driven by machine learning technology and can seek out key subjects and tag metadata found in a video. Google's ARCore aims to bring augmented reality to Android handsets and other devices.

Due to the recent acquisition of DeepMind by Google for an estimated $500 million, and the movement of some academic experts to high-profile tech giants, there has been a lot of buzz surrounding the potential impact deep learning will have in the field of analytics. At FICO, we're excited about this emerging machine learning technology and want to share how we think it fits into the world of analytics. Many advances in analytics and machine learning have been based on our understanding of how the brain works. Deep learning is no exception -- it takes its inspiration from our understanding of the cortex in the brain. The brain has many regions which form a hierarchy of processing, where sensory data flows from one region to another, being transformed and combined with other information along the way.

This repository contains code for the PassGAN: A Deep Learning Approach for Password Guessing paper. The model from PassGAN is taken from Improved Training of Wasserstein GANs and it is assumed that the authors of PassGAN used the improved_wgan_training tensorflow implementation in their work. For this reason, I have modified that reference implementation in this repository to make it easy to train (train.py) Use the pretrained model to generate 1,000,000 passwords, saving them to gen_passwords.txt. Training a model on a large dataset (100MB) can take several hours on a GTX 1080.

I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this growing sphere exciting, but did not have any contextual experience working with these technologies. I am embarking on a two year journey to explore this field. If you haven't read it already, you can see Part 1 here: From webdev to computer vision and geo. I ended up getting myself moving by exploring any opportunity I had to excite myself with learning.

In May this year, SAP launched the SAP Leonardo Machine Learning portfolio at SAPPHIRE NOW in Orlando, Florida and thus demonstrated that it's on the pulse of innovation. Today, it is about time to sum up the latest developments and give an outlook on the potential of intelligent technologies. Deep learning, neural networks, and natural language processing elevated machine learning to new levels. Thanks to mature machine learning algorithms, higher processing power, and the availability of huge data sets, machines are becoming intelligent and able to process unstructured data like pictures, text, or spoken language – often even on a superhuman level. Additionally, deep learning is now stable enough to potentially establish machine learning as a standard commodity across businesses worldwide.

Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through some layers of the nonlinear transformation of the input data to compute the output. This algorithm has a unique feature, i.e., automatic feature extraction. It means that this algorithm automatically grasps the relevant features required for the solution of the problem. It reduces the burden on the programmer to select the features explicitly. It can be used to solve supervised, unsupervised or semi-supervised type of challenges.

If there are things you can trust to not change, that hackers will always be looking for new exploits in security systems is sure to be on that list. Thankfully, there are also those in the security company working toward keeping a step ahead of the would-be hackers. A new study by researchers from Singapore's Nanyang Technological University found hackers could use data collected by various sensors in the device to guess the smartphone's PIN. Instruments like the "accelerometer, gyroscope and proximity sensors represent a potential security vulnerability," according to an NTU statement Tuesday. To prove their point, the researchers took the data gathered by six sensors on Android smartphones and ran it through machine-learning and deep learning algorithms.