Lifeboat Foundation

They can increase their income 16-fold using just their phones.

Between the never-ending stream of news linking bad actors to social networks and studies documenting society’s growing smartphone addiction, it seems almost wrong today to think that technology can — ahem — help make the world a better place.

That’s why I am thankful for the annual Inclusive Innovative Challenge, hosted by MIT’s Initiative on the Digital Economy. Launched in 2016, the IIC seeks out and awards entrepreneurs that are leveraging technology advances to reinvent the future of work. That’s right. There remains, even in this news cycle, firms committed to tapping technology’s ability to connect—and not divide—people and build—and not threaten—jobs and other economic activities.

Or, as the challenge organizers put it:

Continue reading “Some Good Tech News: Entrepreneurs Leverage Tech for Economic Inclusion” »

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“Artificial Intelligence is not just a large part of a technological revolution, it’s a major part of a human evolution of going beyond the limits of an environmentally programmed human biological operating system.”

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Today’s optical systems—from smartphone cameras to cutting-edge microscopes—use technology that hasn’t changed much since the mid-1700s. Compound lenses, invented around 1730, correct the chromatic aberrations that cause lenses to focus different wavelengths of light in different spots. While effective, these multi-material lenses are bulky, expensive, and require precision polishing or molding and very careful optical alignment. Now, a group of researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is asking: Isn’t it time for an upgrade?

With nothing but a smartphone and some clever computation, researchers can exploit ambient signals to track individuals in their own homes.

Apps can turn your smartphone into a scientific instrument. These programs let you spot birds, identify stars, learn about the elements, and more.

Xiaomi has a couple of new products out in Walmart and gave press the rundown today at an event in New York City. The first is a 150-inch laser projector going for $1,999.99 at Walmart doubles up as a television with Android TV. It may seem overpriced for what it is, but it also marks one of Xiaomi’s infrequent expansions into American offerings.

There are a couple of things to break down here, namely that projector is oddly expensive for a 1920 × 1080p screen. Xiaomi says it has no concrete plans to bring a 4K version to the US, and that this is the same model it’s sold in China. You could have found cheaper 1080p projectors back in 2016.

Still, the laser projector has a number of positives including an ultra-short throw, so it can be placed 20 inches from the wall and still display a bright, colorful image, in contrast with other projectors that might need to be placed at the back of a room and show a more faded image. There’s also a detector on top of the projector so if you get too close to the laser, the projector will shut off its light automatically to prevent you from damaging your eyes. The device also comes with a corresponding remote control with which you can summon Google Assistant.

Continue reading “Xiaomi made a 150-inch $2,000 laser projector exclusively available at Walmart” »

Biometric features like fingerprint sensors and iris scanners have made it easier to securely unlock phones, but they may never be as secure as a good old-fashioned password. Researchers have repeatedly worked out methods to impersonate registered users of biometric devices, but now a team from New York University and the University of Michigan has gone further. The team managed to create so-called “DeepMasterPrints” that can fool a sensor without a sample of the real user’s fingerprints.

Past attempts to bypass biometric systems usually involve getting access to a registered individual’s data — that could be a copy of their fingerprint or a 3D scan of their face. DeepMasterPrints involves generating an entirely new fingerprint from a mountain of data that’s close enough to fool the sensor. Like so many research projects these days, the team used neural networks to do the heavy lifting.

The process started with feeding fingerprints from 6,000 people into a neural network in order to train it on what a human fingerprint looks like. A neural network is composed of a series of nodes that process data. It feeds forward into additional “layers” of nodes if the output meets a certain threshold. Thus, you can train the network to get the desired output. In this case, the researchers used a “generative adversarial network” to tune the system’s ability to generate believable fingerprints. The network used its understanding of prints to make one from scratch, and then a second network would determine if they were real or fake. If the fingerprints didn’t pass muster, the network could be re-tuned to try again.

Continue reading “Researchers Create ‘Master Fingerprints’ to Unlock Phones” »