UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations. The collaboration brings together Intel's leading edge computer science and deep learning capabilities with UCSF's clinical and research expertise to create a scalable, high-performance computational environment to support enhanced frontline clinical decision making for a wide variety of patient care scenarios. Until now, progress toward this goal has been difficult because complex, diverse datasets are managed in multiple, incompatible systems. This next-generation platform will allow UCSF to efficiently manage the huge volume and variety of data collected for clinical care as well as newer "big data" from genomic sequencing, monitors, sensors and wearables. These data will be integrated into a highly scalable "information commons" that will enable advanced analytics with machine learning and deep learning algorithms.

The industrial equipment on their ships have thousands of sensors that collect data in real time. As part of their digital transformation, the company's marine service unit, Carnival Maritime, wanted to explore how it might take advantage of this data to find opportunities for operational improvement. One of the areas they looked at was that of water consumption onboard their ship. This is a complex problem, as consumption patterns can vary widely. Passengers of different nationalities shower for different durations at different temperatures and times of the day, for instance, and there are numerous other variables that make such consumption challenging to predict.

Clustering is one of the most powerful and widely used of the machine learning techniques. Throw some data into the algorithm and let it discover hitherto unknown relationships and patterns. K-means is the most popular of all the cluster algorithms. It's easy to understand--and therefore implement--so it's available in almost all analysis suites. When people are first exposed to machine learning k-means clustering is one of the techniques that creates immediate excitement.

I have had it with the stream of articles about what an "AI" can do. It is possible to analyze key words, correlate them with other key words, do a massive amount of statistics, and find out some stuff. People cannot do that and computers can. It sure isn't anything people can do, and it also doesn't correspond to anything I understand about what it means to be intelligent. So, to make clear what AI is really about I propose the ten question test.

Medical progress is leading to an increasing number of patients, overwhelming medical practices and hospitals. AI could well offer a response to this difficult situation. Initiatives proposed by tech giants are set to improve doctors' everyday lives in many ways. Today, doctors need to stay up to date with the latest developments in their field, including new drugs, clinical studies and regulations, while continuing to take account of their patients' specific needs. This amount of information is becoming increasingly difficult for a single human being to process.

Sometimes you just need a little fun in your life! I mean, technology is not all about work! So today, I wanted to share a fun resource with you called Quick, Draw! This tool is made in collaboration with Google and is quite amazing. This Artificial Intelligence experimental tool tries to guess what you are drawing all while you are training it.

The advent of certain technologies -- notably inexpensive high-speed internet, secure cloud storage, mobility solutions, and low-cost devices -- has allowed the fantastic possibilities of the past to become reality. One of the platforms that has been enabled is artificial intelligence. And AI's ability to process information, at high speeds and at scale, unleashes endless new opportunities and infinitely better management of processes, systems, networks, and information. In 2016, we use voice-recognition systems to turn on lamps, driverless cars are being trialed, and robot hotel receptionists work in Japan. In fact, every industry -- from health care to finance to travel to fashion -- is being affected by new AI technologies.

Work on the artificial intelligence team to build robust, scalable, AI algorithms to power the Platform. Work on the artificial intelligence team to build robust, scalable, AI algorithms to power the Platform.

Artificial intelligence is a stream of computer science which deals with making computers act like humans. In other words AI involves intelligent machines. Gartner research suggests that, in a few years, for up to 89% of companies, customer experience will be the factor that decides on success or failure. Also, in five years, 85% of relationships between a customer and a business enterprise will not involve interacting with a human. With the development of AI, machines are able to carryout actions that were once believed to require human intelligence.

As a Scala Engineer your primary role will be to design and implement machine learning algorithms while working closely with the data science team. The Scala Engineer will be contributing to the design and overall architecture of the system, implementing APIs and mentoring and assisting more junior members of the team.