Sea travel on the Caribbean became a routine for Spain, this is why it had detailed records of ship travels. Storms accounted for many of the shipwrecks in the Caribbean.
Florida Keys' tree-ring records extend all the way back to the 1707. These tree-rings show when there is a hurricane in a particular year, because the ring growth slowed down whenever one occurs. The team gathered wood samples from shipwrecks and began dating them.
The team used two books in the study to combine shipwreck data with tree-rings data, namely "Shipwrecks In The Americas: A Complete Guide To Every Major Shipwreck In The Western Hemisphere" by Robert F. Marx and "Shipwrecks Of Florida: A Comprehensive Listing" by Steven D. Singer.
MIT has developed a predictive tool it says can give ships and their crews a two- to three-minute advanced warning, allowing them to shut down essential operations on a ship or offshore platform.
Combining ocean-wave data available from measurements taken by ocean buoys with a nonlinear analysis of the underlying water wave equations, Sapsis' team quantified the range of wave possibilities for a given body of water. They then developed a simpler and faster way to predict which wave groups will evolve into rogue waves.
The resulting tool is based on an algorithm that sifts through data from surrounding waves. Depending on a wave group’s length and height, the algorithm computes a probability that the group will turn into a rogue wave within the next few minutes.
Currently, as few as 37% of puppies make it through the raising program to become successful service dogs for the blind. Given that it costs Guiding Eyes more than $40,000 to raise each dog, even a 5% increase in performance can yield the non-profit considerable savings.
The first step was to move all the data — which includes 30 years of structured genetic breeding data and thousands of unstructured questionnaire documents — to IBM Cloud.
Now, Professor Chris Tseng of San Jose State University and a group of his machine-learning students are using IBM Watson services on Bluemix to look for insight in all that data.
By combining the hard and soft data, the study will connect complex patterns, and yield useful insights that will help inform every stage of guide dog development.
Medium – thanks to Vijay Vijayasankar of IBM for sharing
Over the past two years, Under Armour has spent close to $1 billion buying and investing in three leading makers of activity- and diet-tracking mobile apps. By doing so, the company has amassed the world's largest digital health-and-fitness community, with 150 million users. Plank envisions all of those users, and their metrics, as a big data engine to drive everything from product development to merchandising to marketing.
Today, Under Armour has 13,500 employees around the world and nearly $4 billion in revenue. But Plank is still every bit the entrepreneur, chasing audacious dreams--chief among them overtaking Nike as the world's largest sportswear maker. Under Armour leapfrogged the longtime number two, Adidas, in the U.S. sportswear market in 2014, but worldwide it's still third. And Nike remains far larger, with more than $30 billion in revenue in 2015 Which is part of why Plank wants to move so aggressively. Nike has about a fifth as many users on its Nike+ platform as Under Armour does on its apps, and in 2014 the shoe giant shut down its FuelBand fitness-tracker business.
“Many of KPMG's audit, tax, advisory and other professional services rely heavily on judgment-driven processes. Adding cognitive technology's massive data analysis and innovative learning capabilities to these activities has the potential to advance traditional views on how talent, time, capital and other resources are deployed by professional services organizations.
KPMG's growing cognitive ecosystem will contribute significantly to the continued evolution of the firm's service offerings. Underscoring this importance is KPMG's deep commitment to working with leading technologies like IBM Watson. This includes promising work with Watson to develop select cognitive services designed to help KPMG meet its extensive audit-specific security, confidentiality and compliance requirements.”
“Sentrian's approach collects data streams from biosensors and uses machine learning algorithms to detect subtle patterns based on general information within the system on ;chronic conditions. These can include heart disease, diabetes and chronic obstructive pulmonary disease (COPD). Data such as heart rate, blood pressure and oxygen saturation from wireless biosensorson the patient are pushed to a cloud-based engine that analyses this data and notifies doctors when needed.
Martin Kohn, chief medical scientist at Sentrian, who practised emergency medicine for 30 years, explains the value in this approach. "It's based on the premise that for many patients with diseases such as congestive heart failure and COPD, the processes that lead to severe illness start days before the patient actually becomes acutely ill," he says. “
The new rules reflect Facebook’s shifting attitude toward third parties using its data, considered one of the world’s richest sources of information on human relationships. In 2007, with great fanfare, Facebook founder and Chief Executive Mark Zuckerberg invited outsiders to access to Facebook’s “social graph,” the friend lists, interests and “likes” that knit Facebook users together.
Facebook said it reversed course after users raised concerns about their data being shared with outsiders without their knowledge.
The new rules don’t “make it harder for developers to build social experiences,” said a Facebook spokeswoman. The rules “simply require them to do so in a more privacy-protective way.”
Other social networks, including like LinkedIn Corp. and Twitter Inc., also have restricted access to their data in recent years. But Facebook’s changes have generated more controversy.
Over the past few years, big companies, including Unilever and Coca-Cola, have used emotions analytics to better understand customers' likes and dislikes and to tailor marketing and advertising campaigns. About a dozen companies are making and supporting such software, according to researcher Crone Consulting.
The market leaders include Emotient, a startup in San Diego, and Affectiva in Waltham, Mass. Unilever relies on Affectiva's emotions analysis to assess customer reactions to its ads. Emotient's software will be used in Stoneware's classroom product. And Emotient tested its software with the NBA's Golden State Warriors to study how spectators respond to activities such as a dance cam.
One of the first Big Data projects I wrote about was about the study at the Singapore-MIT Alliance for Research and Technology (SMART). It triangulated two months of weather data with 830 million GPS records of 80 million trips of over 16,000 Singapore taxicabs. Armed with the data which showed most taxis stopped moving when it rains, they went and talked to some drivers as to why.
So, on my trip to Singapore last week, I fully expected taxis to widely use navigation systems. Actually like London cabbies, Singapore drivers know their streets pretty well and don’t use those on a regular basis. But they have plenty of other tech - front and back cameras with memory slots to record accidents, toll transponders, displays for dispatch addresses, credit card processors, fare meters and printers. And fittingly, plenty of mobile apps like GrabTaxi which promise to find you a cab when it rains
The taxis themselves are somewhat humble – most were Hyundai Sonatas, newer ones Hyundai i40s. But they were clean, and the drivers polite and safe. And the fares reasonable.
Like the rest of the city-state, the taxi network works just fine.
Last season, some 68 billion bytes of data were collected — more than in the previous two decades combined — and this year that number will double. Such a dramatic increase in data could usher in a revolution for the sport. Coaches will be able to use the technology to track players' effectiveness, monitor workloads, and refine a team's in-game strategies. Broadcasters will use it to unveil fancy new graphics and ever more arcane stats to better explain the game. And fantasy owners will no doubt obsessively dissect the data, looking to glean information on player tendencies before their head coaches can.
Shah even predicts that these numbers could be used by research institutions to study safety measures, by agents to craft performance bonuses, and by clubs to institute player evaluations — or root out slackers.