Lifeboat Foundation

This means that BPG not only is way smaller than JPEG but also delivers a better quality. And that’s not all! It also supports animations!

And when I say animation, I actually say GIF-like movies with MP4 quality that are actually smaller than the mp4 it was built from.

Let’s see an example (I have not included a GIF example because the same quality size and frame rate means that the GIF will have exactly 33.8MB)

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Can the laws of physics change over time and space?

As far as physicists can tell, the cosmos has been playing by the same rulebook since the time of the Big Bang. But could the laws have been different in the past, and could they change in the future? Might different laws prevail in some distant corner of the cosmos?

“It’s not a completely crazy possibility,” says Sean Carroll, a theoretical physicist at Caltech, who points out that, when we ask if the laws of physics are mutable, we’re actually asking two separate questions: First, do the equations of quantum mechanics and gravity change over time and space? And second, do the numerical constants that populate those equations vary?

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Self-driving cars are already cruising the streets. But before they can become widespread, carmakers must solve an impossible ethical dilemma of algorithmic morality.

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Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin. Life requires what software does (it’s foundationally algorithmic).

1. “DNA is multibillion-year-old software,” says Chaitin (inventor of mathematical metabiology). We’re surrounded by software, but couldn’t see it until we had suitable thinking tools.

2. Alan Turing described modern software in 1936, inspiring John Von Neumann to connect software to biology. Before DNA was understood, Von Neumann saw that self-reproducing automata needed software. We now know DNA stores information; it’s a biochemical version of Turning’s software tape, but more generally: All that lives must process information. Biology’s basic building blocks are processes that make decisions.

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Tesla’s new autopilot system is relying on the cutting edge of machine learning, connectivity and mapping data.

While Tesla’s new hands-free driving is drawing a lot of interest this week, it’s the technology behind-the-scenes of the company’s newly-enabled autopilot service that should be getting more attention.

At an event on Wednesday Tesla’s CEO Elon Musk explained that the company’s new autopilot service is constantly learning and improving thanks to machine learning algorithms, the car’s wireless connection, and detailed mapping and sensor data that Tesla collects.

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Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

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Madrid, Spain (Scicasts) — A novel way to count white blood cells without a blood test, simply by applying a small device on the fingertip, is being developed by a team of young bioengineers.

The technology, that combines an optical sensor with algorithms, has already three prototypes on the go and is specially designed to be used on chemotherapy patients, who could know their immune system levels in real time. It could also serve to detect serious infections.

A group of young bioengineers from various countries, including Spaniard Carlos Castro, is developing a portable device capable of counting white blood cells in real time, without requiring a blood test. The system includes an innovative optics sensor through the skin that can observe white cells as they flow past a miniature lens. This new device could be applied to improve the treatment of patients who are left immunosuppressed after chemotherapy treatments and to prevent sepsis.

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The Blue Brain Project is a vast effort by 82 scientists worldwide to digitally recreate the human brain. While still far from that goal, the team revealed a breakthrough that has already provided insight into sleep, memory and neurological disorders. They created a simulation of a third of a cubic millimeter of a rat’s brain. While that might not sound like much, it involves 30,000 neurons and 37 million synapses. In addition, the simulated level of biological accuracy is far beyond anything so far. It allowed them to reproduce known brain activities — such as how neurons respond to touch — and has already yielded discoveries about the brain that were impossible to get biologically.

To create the simulation, researchers did thousands of experiments on rat brains over a 20 year period, logging each type of synapse and neuron discovered. That led to a set of fundamental rules describing how neurons connect to synapses and form microcircuits. Using the data, they developed an algorithm to pinpoint the synapse locations, simulating the circuitry of a rat’s brain. All of that data was then run through a supercomputer: “It was only with this kind of infrastructure that we could solve the billions of equations needed,” said software lead Felix Schurmann.

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Adobe engineer Ashutosh Jagdish Sharma demonstrated the technology on stage, enlisting the help of host Kim Chambers and Parks and Rec star Nick Offerman to act as stand-in tourists who were getting in the way of the desired shot. When the smartphone was held in place, Monument Mode was able to slowly erase the “tourists” from the image, building up a clear version of the photo slowly as human obstructions moved around. Even though Chambers and Offerman remained inside the frame, the final result showed the background only, the feature able to create a clear image from multiple shots.

Traditionally photographers have been able to remove tourists and other obstructions after their photos are taken with clever Photoshop work, by taking multiple shots, or by taking them from various angles. But Monument Mode works in real-time, cutting down on legwork, and requiring fewer photo-editing skills. The company says it the feature ”uses a new algorithm to distinguish moving objects from fixed ones,” but notes that it’s still only a tech preview, and that it may not come to fruition. That said, the company has a history of swiftly incorporating technology shown off at its MAX conferences. Adobe first detailed its “dehaze” feature during the same segment at last year’s show — it now comes as standard in Lightroom.

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