States across the Southeast are experiencing sharp jumps in gas prices after a major gasoline pipeline ruptured in central Alabama, spilling as many as 336,000 gallons of fuel upstream from a national wildlife refuge. But thanks to a few strokes of luck, the environmental damage is minimal. The pipeline breached near an old coal mine pit, and much of the fuel flowed into a water retention pond. With local streams dry -- much of central Alabama is suffering from moderate to severe drought -- the gasoline did not find its way down into the Cahaba River, home to 64 rare and endangered plant and animal species, including the Cahaba lily. "We really did bypass the bullet," said Myra Crawford, executive director at Cahaba Riverkeeper, which has been monitoring the area by canoe and foot."It

If there was any doubt that AI has officially arrived in the world of enterprise software, Salesforce just put it to rest. The CRM giant on Sunday announced Einstein, a set of artificial intelligence capabilities it says will help users of its platform serve their customers better. AI's potential to augment human capabilities has already been proven in multiple areas, but tapping it for a specific business purpose isn't always straightforward. "AI is out of reach for the vast majority of companies because it's really hard," John Ball, general manager for Salesforce Einstein, said in a press conference last week. With Einstein, Salesforce aims to change all that.

Feel free to test any machine learning, deep learning or whatever algorithm you think it could fit, we only ask for acknowledging the source and please let us know of your performance!

Sprinkled throughout Andrew Ng's machine learning class is a lot of practical advice for applying machine learning. That's what I'm trying to compile and summarize here. The key is dividing data into training, cross-validation and test sets. The test set is used only to evaluate performance, not to train parameters or select a model representation. The rationale for this is that training set error is not a good predictor of how well your hypothesis will generalize to new examples.

In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning. Besides teaching model evaluation procedures and metrics, we obviously teach the algorithms themselves, primarily for supervised learning. Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned. One of the skills that I want students to be able to take away from this course is the ability to intelligently choose between supervised learning algorithms when working a machine learning problem. Although there is some value in the "brute force" approach (try everything and see what works best), there is a lot more value in being able to understand the trade-offs you're making when choosing one algorithm over another.

Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning. The technology has already made inroads with the public through platforms such as Amazon's Echo and Google's Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning's breakthrough moment, but it's coming -- and it will change everything. Humans could be incredibly effective given endless timelines, budget, and energy.

After months of physician-failed diagnosis, a super computer steps in and saves the life of a female patient from Japan, suffering from leukemia. IBM Watson Health has committed to developing a partnership between humanity and technology with the goal of transforming global health. With the ability to read 40 million documents in 15 seconds, IBM's Watson –super computer powered with artificial intelligence- studied the patient's medical records for ten minutes and was able to compare her type of cancer against 20 million oncological records, according to International Business Times. Physicians in Japan decided to try out IBM's Watson on patients after all other treatment options had failed. The Watson revealed that the patient's condition was another form of leukemia and required a different treatment from the one originally prescribed.

A major conference on genomics has moved its West Coast location to San Diego, a leading hub of genomic technology. The Festival of Genomics California runs Tuesday and Wednesday, Sept. 20-21 at the San Diego Convention Center The festival itself is free, People can register at j.mp/fog2016. A pre-event "Base Camp on Monday, Sept. 19 costs 699; information is available at j.mp/2016basecamp. This year's Festival offers sessions on clinical access to genomic medicine, applied technology and data sharing, along with artificial intelligence and machine learning applications. Tuesday is devoted to the effect of genomics on health care, especially in pediatrics.

As industry embraces AI, computers aren't the only ones that have to learn new tricks. Jen-Hsun Huang: 2015 was a big year. Artificial intelligence is moving into the commercial world. AI has been worked on for many years, largely in research. Various aspects of commercial use of AI, otherwise known as machine learning, is used for advertising and web searches and things like that. It wasn't until the last few years that AI could do things that people can't do. Several milestones were achieved in 2015 in particular that made it possible for us to use it in all kinds of areas. Yes, in an area of AI called "deep learning." The system basically learns by itself using a lot of data and computation.

The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market -- efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.