Despite a continuing shortage of data science skills, teams do exist in businesses across many industries. Expectations are high and the promises of predictive analytics, prescriptive analytics and artificial intelligence (AI) has captured the imagination of many. Now that the role, the skill-sets and the responsibilities of data science are becoming better defined, how do… Read More » The post Is analytics operations the key to successful data science? appeared first on Data Points.
Over the past year, it’s become pretty clear that machines can now beat us in many straightforward zero-sum games. A new study from an international team of computer scientists set out to develop a new type of game-playing algorithm – one that can play games that rely on traits like cooperation and compromise – and the researchers have found that machines can already deploy those characteristics better than humans.
Chess, Go and Poker are all adversarial games where two or more players are in conflict with each other. Games such as these offer clear milestones to gauge the progress of AI development, allowing humans to be pitted against computers with a tangible winner. But many real-world scenarios that AI will ultimately operate in require more complex, cooperative long term relationships between humans and machines.
“The end goal is that we understand the mathematics behind cooperation with people and what attributes artificial intelligence needs to develop social skills,” says lead author on the new study Jacob Crandall. “AI needs to be able to respond to us and articulate what it’s doing. It has to be able to interact with other people.”
Today, we’re holding the second TensorFlow Developer Summit at the Computer History Museum in Mountain View, CA! The event brings together over 500 TensorFlow users in-person and thousands tuning into the livestream at TensorFlow events around the world. The day is filled with new product announcements along with technical talks from the TensorFlow team and guest speakers.
Machine learning is solving challenging problems that impact everyone around the world. Problems that we thought were impossible or too complex to solve are now possible with this technology. Using TensorFlow, we’ve already seen great advancements in many different fields. For example:
Astrophysicists are using TensorFlow to analyze large amounts of data from the Kepler mission to discover new planets. Medical researchers are using ML techniques with TensorFlow to assess a person’s cardiovascular risk of a heart attack and stroke. Air Traffic Controllers are using TensorFlow to predict flight routes through crowded airspace for safe and efficient landings. Engineers are using TensorFlow to analyze auditory data in the rainforest to detect logging trucks and other illegal activities. Scientists in Africa are using TensorFlow to detect diseases in Cassava plants to improving yield for farmers.
TensorFlow is Google’s open source framework for machine learning. Rainforest Connection is using this framework to protect the rainforests, such as the Amazon. The method is ingenious and representative of a utilitarian society that we should all be lurching towards.
It’s pretty much impossible to expect investments in human guards across the entirety of the Amazon rainforest. So what’s the next best thing? The people living in the rainforest themselves. Collaborating with local tribes in the Amazon, such as the Tembé tribe from central Amazon, Rainforest Connection has come up with a solution that combines the best of the old world with the new.
