Lifeboat Foundation

Ask the average passerby on the street to describe artificial intelligence and you’re apt to get answers like C-3PO and Apple’s Siri. But for those who follow AI developments on a regular basis and swim just below the surface of the broad field , the idea that the foreseeable AI future might be driven more by Big Data rather than big discoveries is probably not a huge surprise. In a recent interview with Data Scientist and Entrepreneur Eyal Amir, we discussed how companies are using AI to connect the dots between data and innovation.

Image credit: Startup Leadership Program Chicago

According to Amir, the ability to make connections between big data together has quietly become a strong force in a number of industries. In advertising for example, companies can now tease apart data to discern the basics of who you are, what you’re doing, and where you’re going, and tailor ads to you based on that information.

“What we need to understand is that, most of the time, the data is not actually available out there in the way we think that it is. So, for example I don’t know if a user is a man or woman. I don’t know what amounts of money she’s making every year. I don’t know where she’s working,” said Eyal. “There are a bunch of pieces of data out there, but they are all suggestive. (But) we can connect the dots and say, ‘she’s likely working in banking based on her contacts and friends.’ It’s big machines that are crunching this.”

Continue reading “Connecting The Dots to Get the Big Picture with Artificial Intelligence” »

Humor, a distinctly human quality, could arguably be one of the most efficient ways to distinguish humans from machines. But not anymore.

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“[H]alf the people in the world cram into just 1 percent of the Earth’s surface (in yellow), and the other half sprawl across the remaining 99 percent (in black).”

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Deep Learning in Action | A talk by Juergen Schmidhuber, PhD at the Deep Learning in Action talk series in October 2015. He is professor in computer science at the Dalle Molle Institute for Artificial Intelligence Research, part of the University of Applied Sciences and Arts of Southern Switzerland.

Juergen Schmidhuber, PhD | I review 3 decades of our research on both gradient based and more general problem solvers that search the space of algorithms running on general purpose computers with internal memory.

Continue reading “Deep Learning in Action | How to learn an algorithm” »

“You like your Tesla, but does your Tesla like you?” My new story for TechCrunch on robots understanding beauty and even whether they like your appearance or not:

Robots are starting to appear everywhere: driving cars, cooking dinners and even as robotic pets.

But people don’t usually give machine intelligence much credence when it comes to judging beauty. That may change with the launch of the world’s first international beauty contest judged exclusively by a robot jury.

Continue reading “The First International Beauty Contest Judged By Robots” »

Better cameras, along with more powerful algorithms for computer vision and emotion-sensing facial analysis software, could transform the way we interact with our devices.

By Andrew Moore on December 28, 2015.

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A researcher at Singapore’s Nanyang Technological University (NTU) has developed a new technology that provides real-time detection, analysis, and optimization data that could potentially save a company 10 percent on its energy bill and lessen its carbon footprint. The technology is an algorithm that primarily relies on data from ubiquitous devices to better analyze energy use. The software uses data from computers, servers, air conditioners, and industrial machinery to monitor temperature, data traffic and the computer processing workload. Data from these already-present appliances are then combined with the information from externally placed sensors that primarily monitor ambient temperature to analyze energy consumption and then provide a more efficient way to save energy and cost.

The energy-saving computer algorithm was developed by NTU’s Wen Yonggang, an assistant professor at the School of Computer Engineering’s Division of Networks & Distributed Systems. Wen specializes in machine-to-machine communication and computer networking, including looking at social media networks, cloud-computing platforms, and big data systems.

Most data centers consume huge amount of electrical power, leading to high levels of energy waste, according to Wen’s website. Part of his research involves finding ways to reduce energy waste and stabilize power systems by scaling energy levels temporally and spatially.

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In 2010, a Canadian company called D-Wave announced that it had begun production of what it called the world’s first commercial quantum computer, which was based on theoretical work done at MIT. Quantum computers promise to solve some problems significantly faster than classical computers—and in at least one case, exponentially faster. In 2013, a consortium including Google and NASA bought one of D-Wave’s machines.

Over the years, critics have argued that it’s unclear whether the D-Wave machine is actually harnessing quantum phenomena to perform its calculations, and if it is, whether it offers any advantages over classical computers. But this week, a group of Google researchers released a paper claiming that in their experiments, a quantum algorithm running on their D-Wave machine was 100 million times faster than a comparable classical algorithm.

Scott Aaronson, an associate professor of electrical engineering and computer science at MIT, has been following the D-Wave story for years. MIT News asked him to help make sense of the Google researchers’ new paper.

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Machine learning is a bit of a buzz term that describes the way artificial intelligence (AI) can begin to make sense of the world around it by being exposed to massive amount amounts of data.

But a new algorithm developed by researchers in the US has dramatically cut down the amount of learning time required for AI to teach itself new things, with a machine capable of recognising and drawing visual symbols that are largely indistinguishable from those drawn by people.

The research highlights how, for all our imperfections, people are actually pretty good at learning things. Whether we’re learning a written character, how to operate a tool, or how to perform a dance move, humans only need a few examples before we can replicate what we’ve been shown.

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