By definition, a computer is a machine that processes and stores data as ones and zeroes. But the U.S. Department of Defense wants to tear up that definition and start from scratch.
Through its Defense Advanced Research Projects Agency (Darpa), the DoD is funding a new program called…
Wikipedia defines Machine Learning as “a branch of artificial intelligence that deals with the construction and study of systems that can learn from data.”
(If you arrived here looking how to add or list an API to Mashape, you check out the Tutorials section here).
Below is a compilation of APIs that have benefited from Machine Learning in one way or another, we truly are living in the future so strap into your rocketship and prepare for blastoff.
This is a really fun paper by Saša Petrović and David Matthews about unsupervised joke generation in big data. A few excerpts, including a few computer-generated jokes that are actually funny:Generating jokes is typically considered to be a very hard natural language problem, as it implies a deep semantic and often cultural understanding of text. We deal with generating a particular type of joke – I like my X like I like my Y, Z – where X and Y are nouns and Z is typically an attribute that describes X and Y. An example of such a joke is I like my men like I like my tea, hot and British – these jokes are very popular online.While this particular type of joke is not interesting from a purely generational point of view (the syntactic structure is ﬁxed), the content selection problem is very challenging. Indeed, most of the X, Y, and Z triples, when used in the context of this joke, will not be considered funny. Thus, the main challenge in this work is to “ﬁll in” the slots in the joke template in a way that the whole phrase is considered funny.
Recently, I wrote an article about Disney’s new RFID location and transaction tracking technology, the MagicBand. Perhaps more magical for Walt than it is for you, the band allows Disney to track…
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine a prizewinning paper by Google Research scientists, describes a breakthrough in machine vision that can distinguish between a huge class of objects 20,000 times faster than before.
Most people that talk about algorithm and machine learning have no idea what that even means. This is my conclusion after numerous discussions with startup founders that effusively explain how they are going to build some Über-engine for social gaming personal deal recommendations or other such…
- Our brains will extend to the cloud, which will allow us to learn new things at any age.
- We will be able to selectively erase pieces of our memory.
- We’ll be in augmented reality at all times.
- By 2029, machines will be able to match the intelligence of humans, and they’ll be able to make us laugh and cry.
- Around the 2030s, tiny “nanobots” able to repair and preserve our organs will keep us healthier and smarter.
- 3D printing will be even more common than it is today, with public 3D printing stations for people to print out clothes, toys, and anything else.
- Within 25 years, computers will be the size of a blood cell and we’ll be able to connect it to the brain without the need for surgery.
- Society will reach a state of “technological singularity” in 2045 where technology enables superhuman machine intelligences to emerge and people and machines become deeply integrated.