Wearable Camera Lets You 'Go Back in Time' to Record Fleeting Moments

                                     
  Ever wish you could go back and record that custom sports car that just raced by, or that awesome jump shot your kid just made? A new wearable camera lets you do just that: go back in time, so to speak, to retroactively capture those fleeting moments you thought you missed.

Called the Perfect Memory camera, developed by New York-based General Streaming Systems, the 12-megapixel device is pocket-size and lightweight. With a tap of its touch screen, it can record video and audio, and is capable of full-high-definition (HD) 1080p video.

Here's how it works: With its AutoEdit mode, the camera is continuously recording, and when you tap its touch screen, it saves footage from the previous 5 minutes, or any other duration you want to set. This allows people to retroactively save a video of an event after the fact. [Photo Future: 7 High-Tech Ways to Share Images]

"You don't know when a surprising, magical moment will happen ... capturing a baby's first words, for instance," said Jules Winnfield, chief operating officer of General Streaming Systems.

Perfect Memory can be worn as a hands-free bodycam. Depending on the camera's accessories, it can also attach to the dash of a car, be paired with sports action mounts, stick to virtually any surface and even hang around a pet's neck.

Perfect Memory can also snap photos, act as a regular video camera and shoot time-lapse photography, according to General Streaming Systems. The device can accept microSD cards with up to 128GB of storage space.

A free iOS or Android app can control the camera. The camera can use Wi-Fi to wirelessly connect with a smartphone via the app, and stream video and photos live, according to the company.

When the camera is recording video continuously at the highest level of resolution, its battery can last up to 4 hours, Winnfield said. When the camera is recording continuously and using Wi-Fi to stream video live, the battery will last up to 2 hours. If the camera is not recording continuously, its battery may last up to several days. The battery takes up to 1.5 hours to recharge, according to the company.

General Streaming Systems started developing the Perfect Memory camera in 2015. An Indiegogo crowdfunding campaign for the device raised more than $171,000 in two months. When the campaign ended on Aug. 20, it had raised almost six times more than its $30,000 goal. Mass production of the cameras (which can be purchased for an early-bird price of $119) will begin this month, and the devices are expected to ship to the campaign's backers beginning in October.

Why Did Yahoo Take So Long to Disclose Security Breach?

In late September, Yahoo announced that at least 500 million user accounts had been compromised. The data stolen included users’ names, email addresses, telephone numbers, dates of birth and encrypted passwords, but not credit card data. Large data breaches have become increasingly common: Just in 2016 we have found out about Yahoo’s breach as well as the LinkedIn hack (compromising 167 million accounts) and the MySpace breach (360 million accounts).

The Yahoo breach affected more users than the other two, but all of them share a crucial element: They were announced to the public years after the fact. The LinkedIn hack happened in 2012, MySpace was breached in 2013 and Yahoo was hacked in 2014. Not until 2016 did users of the three sites found out their information had been stolen.

When personal information is stolen, rapid response is important. Customers need to change their passwords, and take other steps to protect their identity, including securing bank accounts and credit records. If people don’t know a breach has occurred and that they need to take these protective steps, they remain vulnerable.

So why does it take such a long time for companies to disclose that they have been hacked? It’s not as simple as you might think – or hope.

Time is a key factor

It’s not yet clear when Yahoo learned about its attack, though in this case the timing is questionable. A news article published on August 1 quoted a company spokesperson saying Yahoo was “aware” a hacker was sellinglogin details for 200 million Yahoo accounts in an online black market.

But more than a month later, the company filed a document with U.S. financial regulators saying it didn’t know of any claims of “unauthorized access” that might have an effect on its pending sale to Verizon. And Verizon said publicly that it had heard about the breach only two days before Yahoo announced it to the world.

All those events, of course, were years after the breach had actually happened. This is an uncommonly long delay. According to a recent report from network security firm FireEye, in 2015 the median amount of time an organization’s network was compromised before the breach was discovered was 146 days.

That includes all sizes of companies in all types of business. As a major internet company with an extremely large user base, it’s reasonable to expect Yahoo might detect – and disclose – breaches much sooner than other firms.

Detecting, and confirming, the hack

The company has said it believes the attack was conducted by a national government, though it hasn’t said from what country. That may suggest the attack was more sophisticated, and therefore harder to detect – butit’s impossible to know if that’s true, because the company has declined to offer details of how the breach was achieved.

In addition, anyone on the internet can claim anything they want –companies have to investigate their systems to find out whether someone who is advertising they have login information for sale actually took anything, or is just making it up to cause trouble.

Nontechnical reasons that Yahoo took so long to discover the hack could include frequent changes in leadership of its security team and the companywide stress of finding a buyer.

Notifying the public

Once a company has learned it has been hacked, it’s important to tell customers – and the public – so that people can take proper measures to protect their information, privacy and identities.

At present there is no federal law regarding when companies must tell the public about information security breaches. In 2015, Democratsproposed giving firms 30 days from discovering a hack to announcing it had happened. That effort failed because many states, which have varying requirements, have stricter standards that the federal law would have overruled.

Recovering a corporate reputation

Tech companies can typically recover quickly from data breaches – if they respond fast and take the necessary steps to notify their users. That’s true even for corporations whose data breaches resulted in the compromise of customers’ credit card information, such as Target in 2013 and Home Depot in 2014.

Lawsuits filed after the breaches have cost companies millions in settlement costs, not to mention legal fees and lost business. The lesson is clear: Early disclosure of a data breach is better. If Yahoo knew about its hack as early as August – or even years ago – and took this long to announce it to the public, the company has manifestly betrayed its users’ trust.

Though Yahoo urged users to change their passwords and security questions after the public disclosure of the security breach, thousands of users took to social media to express anger that it had taken the company two years to uncover the data breach. The lawsuits filed against Yahoo are mounting.

It can be extremely difficult for companies, even tech-focused ones like Yahoo, to protect themselves from skilled and determined hackers. But not reporting the attack as soon as it’s suspected can be almost as damaging as the hack itself.

3 Scientists Win Nobel in Chemistry for Creating World's Smallest Machines

A trio of scientists — Jean-Pierre Sauvage, Sir J. Fraser Stoddart and Bernard L. Feringa  — has won the Nobel Prize in Chemistry for designing and creating the world's smallest machines, turning linked-up molecules into contraptions that could do work, the Royal Academy of Swedish Sciences announced this morning (Oct. 5). These include a tiny lift, artificial muscles and a mini motor. 

The molecular machines, which are 1,000 times thinner than a strand of hair, have "taken chemistry to a new dimension," according to a Nobel Prize statement.

The story begins in 1983, when Sauvage, who is now at the University of Strasbourg, France, linked two ring-shaped molecules into a chain; but rather than connecting the molecules by having them share electrons, Sauvage used a freer mechanical bond. "For a machine to be able to perform a task it must consist of parts that can move relative to each other. The two interlocked rings fulfilled exactly this requirement," according to the statement. [Nobel Prize 2016: Here Are the Winners (and What They Achieved)]

In 1991, Stoddart, now at Northwestern University, in Evanston, Illinois, took a molecular ring and threaded it onto a molecular axle. Then, he closed the opening of the ring to keep it attached to the molecular axle. From this teensy feat, Stoddard crafted a molecular lift, a molecular muscle and a molecular computer chip.

In 1999, Feringa created the world's first molecular motor. Now at the University of Groningen, in the Netherlands, Feringa created a molecular rotor blade and got it to spin in the same direction. Feringa also designed a nanocar using a molecular motor.  

Though tiny, these feats are revolutionary: "In terms of development, the molecular motor is at the same stage as the electric motor was in the 1830s, when scientists displayed various spinning cranks and wheels, unaware that they would lead to electric trains, washing machines, fans and food processors," according to the statement. "Molecular machines will most likely be used in the development of things such as new materials, sensors and energy storage systems."

The three scientists will split the Nobel Prize amount of 8 million Swedish krona (about $937,000).

What Makes the Google Pixel Different from Other Smartphones?

Google debuted its first smartphone this week, dubbed "Pixel," signaling the company's move into an industry long dominated by the likes of Apple and Samsung. And considering Google is already the developer of the Android mobile operating system, what will make the Pixel different from other smartphones already on the market?

Google unveiled the Pixel Oct. 4, saying it will provide the best experience "by bringing hardware and software design together under one roof."

This isn't Google's first foray into the smartphone industry: The company's Android operating system is available on smartphones from a number of companies, including Samsung, HTC and Motorola. Previous smartphone releases from Google were part of the Nexus program, in partnership with other smartphone providers. But Pixel is all Google's — the first smartphone built entirely by the company. [9 Odd Ways Your Tech Devices May Injure You]

"With Pixel, we obsessed over every detail, from the industrial design to the user experience," Brian Rakowski, vice president of product management at Google, wrote in a blog post to introduce the new phone.

With its curved edges, sleek design and two size options, the Pixel is reminiscent of Apple's iPhone. The 5-inch Pixel has a 1080p screen — 1440 x 2160 on the 5.5-inch Pixel XL — 4GB of RAM and either 32GB or 128GB of storage. The new iPhone 7 has similar specs: the screen resolution is 1334 x 750, with 2GB of RAM and storage options of 32GB, 128GB or 256GB.

Google has highlighted that Pixel users also will have unlimited photo and video cloud storage, which may quell some smartphone users' fears of reaching their storage capacity.

Pixel also has what Google is touting as the "best smartphone camera ever." The 12.3-megapixel rear camera received top marks from DxOMark, which measures camera image quality through rigorous testing. With an overall score of 89, the Pixel's camera surpassed every other smartphone camera currently on the market, reported Tom's Guide.

"Pixel puts cutting-edge computational photography in an ultra-fast and easy-to-use camera," Rakowski wrote in the blog post. "Our team of photography gurus and image-processing experts have spent the last year designing and tirelessly optimizing our entire camera stack. Pixel's camera lets you take stunning photos in low light, bright light or any light."

Another feature touted by Google is the Pixel's battery and charge time. According to the company, 15 minutes of charge will yield 7 hours of use. Pixel is also the first phone with the company's virtual "smart" service, known as Google Assistant, built in, allowing users to have "a natural conversation with Google" to search or complete tasks.

Pixel phones will start at $649 (equivalent to the price of the new iPhone 7) for the smaller versions and $769 for the "XL" model. The phone comes in three colors: "really blue," "very silver" and "quite black." In addition to being available for preorder from Google directly, Pixel can be bought at all Verizon retail outlets, including Best Buy stores.

At the Google event earlier this week, the search-engine company also announced the launch of a new virtual reality headset and a home assistant to rival Amazon's Alexa.

The Spooky Secret Behind Artificial Intelligence's Incredible Power

Last year, AI accomplished a task many people thought impossible: DeepMind, Google's deep learning AI system, defeated the world's best Go player after trouncing the European Go champion. The feat stunned the world because the number of potential Go moves exceeds the number of atoms in the universe, and past Go-playing robots performed only as well as a mediocre human player.

But even more astonishing than DeepMind's utter rout of its opponents was how it accomplished the task.

"The big mystery behind neural networks is why they work so well," said study co-author Henry Lin, a physicist at Harvard University. "Almost every problem we throw at them, they crack."

For instance, DeepMind was not explicitly taught Go strategy and was not trained to recognize classic sequences of moves. Instead, it simply "watched" millions of games, and then played many, many more against itself and other players.

Like newborn babies, these deep-learning algorithms start out "clueless," yet typically outperform other AI algorithms that are given some of the rules of the game in advance, Tegmark said.

Another long-held mystery is why these deep networks are so much better than so-called shallow ones, which contain as little as one layer, Tegmark said. Deep networks have a hierarchy and look a bit like connections between neurons in the brain, with lower-level data from many neurons feeding into another "higher" group of neurons, repeated over many layers. In a similar way, deep layers of these neural networks make some calculations, and then feed those results to a higher layer of the program, and so on, he said.

Magical keys or magical locks?

To understand why this process works, Tegmark and Lin decided to flip the question on its head.

"Suppose somebody gave you a key. Every lock you try, it seems to open. One might assume that the key has some magic properties. But another possibility is that all the locks are magical. In the case of neural nets, I suspect it's a bit of both," Lin said.

One possibility could be that the "real world" problems have special properties because the real world is very special, Tegmark said.

Take one of the biggest neural-network mysteries: These networks often take what seem to be computationally hairy problems, like the Go game, and somehow find solutions using far fewer calculations than expected.

It turns out that the math employed by neural networks is simplified thanks to a few special properties of the universe. The first is that the equations that govern many laws of physics, from quantum mechanics to gravity to special relativity, are essentially simple math problems, Tegmark said. The equations involve variables raised to a low power (for instance, 4 or less).  [The 11 Most Beautiful Equations]

What's more, objects in the universe are governed by locality, meaning they are limited by the speed of light. Practically speaking, that means neighboring objects in the universe are more likely to influence each other than things that are far from each other, Tegmark said.

Many things in the universe also obey what's called a normal or Gaussian distribution. This is the classic "bell curve" that governs everything from traits such as human height to the speed of gas molecules zooming around in the atmosphere.

Finally, symmetry is woven into the fabric of physics. Think of the veiny pattern on a leaf, or the two arms, eyes and ears of the average human. At the galactic scale, if one travels a light-year to the left or right, or waits a year, the laws of physics are the same, Tegmark said.

Tougher problems to crack

All of these special traits of the universe mean that the problems facing neural networks are actually special math problems that can be radically simplified.

"If you look at the class of data sets that we actually come across in nature, they're way simpler than the sort of worst-case scenario you might imagine," Tegmark said.

There are also problems that would be much tougher for neural networks to crack, including encryption schemes that secure information on the web; such schemes just look like random noise.

"If you feed that into a neural network, it's going to fail just as badly as I am; it's not going to find any patterns," Tegmark said.

While the subatomic laws of nature are simple, the equations describing a bumblebee flight are incredibly complicated, while those governing gas molecules remain simple, Lin added. It's not yet clear whether deep learning will perform just as well describing those complicated bumblebee flights as it will describing gas molecules, he said.

"The point is that some 'emergent' laws of physics, like those governing an ideal gas, remain quite simple, whereas some become quite complicated. So there is a lot of additional work that needs to be done if one is going to answer in detail why deep learning works so well." Lin said. "I think the paper raises a lot more questions than it answers!"