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AI can turn against us at any moment in time.

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Digital artifact

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Digital Artifact

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Alaskan Cyber attack

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Digital Artifact!

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3D Heart Printing Can Heal The Medical Business!

·         According to The Guardian, the technology is expected to be worth no less than $1.3 billion by 2021.

·         The typical kidney transplant, for instance, costs an average of $330,000, according to the National Foundation for Transplants. The conventional 3D bio printer, on the other hand, retails for just $10,000.

·         3D printing presents pharmacologists with a new level of precision that can help them design pills that house several drugs.

·         3D printing portends cost savings of up to 70% for its medical clients.

·         Biomedicines could see a drastic cut in cost if competition increases because of 3D printers.

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Three-dimensional (3D) printing has come a long way since its debut 30 years ago and is opening new opportunities in a variety of industries. Riding on the winds of innovation and advancements, 3D printers are set to provide faster prototyping ideas, inventive problem solving and increased cost efficiency. I believe the potential of 3D printing is far-reaching, and one sector that stands poised to benefit greatly is biomedicine and the healthcare industry.
Printing Is Cost Efficient
The impacts of 3D printing go beyond its practical benefits. According to The Guardian, the technology is expected to be worth no less than $1.3 billion by 2021 and looks set to disrupt the cost implications of several medical procedures. The typical kidney transplant, for instance, costs an average of $330,000, according to the National Foundation for Transplants. The conventional 3D bioprinter, on the other hand, retails for just $10,000. In the not too distant future, prices of medical procedures and biomedicines could see a drastic cut in cost if competition increases because of 3D printers. In addition to reducing costs, 3D printers may also simplify treatment.
A New Organ Transplant Landscape
3D printers have successfully recreated body parts as complex as blood vessels, which proves that this technology has a lot of untapped potential. As of today, organoids (mini organs) are already being built by medical professionals. In 2017, a team of biomedical engineers from Pohang University in South Korea, using a 3D printer and living tissue were successfu l at developing what they called bio-blood-vessels. In vitro cloning of full body parts has for a long while been a dream of the 21st-century medical sphere and while this feat demonstrates the disruptive nature of 3D printing, it is just a scratch on the surface of the possibilities heralded by 3D printing. I believe a comprehensive framework that allows for the production and parsing of these smaller tissue bits is within reach and can be developed to foster organ creation -- a development that would provide a step forward in the need for organ transplants.
Smarter Drugs
3D printing presents pharmacologists with a new level of precision that can help them design pills that house several drugs, all with different release times, providing a potential solution to those who suffer from a range of ailments and need to taa large number of pills. One such drug, produced by a 3D printer, is available for sufferers of hypertension and diabetes. The ingenious design of these drugs allows them to smartly release different therapeutic compounds at specified times, a feat that is made possible by 3D printings increased precision and accuracy.
Practice Before Perfection
Many medical procedures, especially those of a surgical nature, can be tricky, even for highly skilled surgeons. By providing a replica of organs and body parts, 3D printers give doctors an insight that cannot be replicated by any form of medical diagnostics. Unlike organ printing, this practice is already commonplace in the medical setting. Earlier in July, a team of surgeons at the Rashid Hospital in the UAE was able to perform an intricate surgical maneuver that required them to safely placate four veins in a 60-year-old thanks to the prior practice they had had with 3D printed models.
A New Future For The Medical Industry
3D printing is ushering in a new era of efficiency, cost-effectiveness and convenience in the medical industry. The technology is leading the path toward the development of a sustainable framework for tackling poorly functioning organs -- experts predict a not too distant future where 3D printing can facilitate cheaper organ replacement and better patient prognosis.

Modular 3D printed prosthetics have debuted on the medical scene with the same functionality as conventional models but at as little as half the cost. The slash in cost compared to the traditional base model highlights just how disruptive this tech is; one 3D printing production firm already suggested that 3D printing portends cost savings of up to 70% for its medical clients. As the technology evolves into a more practical and efficient system, so also will drug manufacturing, organ transplantation and other fields of medicine. 3D printing might at the end of the day prove to be the kind of disruptive innovation that launches the field of medicine to a new level.

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How 3D Printing Could Change The Health Industry

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Three-dimensional (3D) printing has come a long way since its debut 30 years ago and is opening new opportunities in a variety of industries. Riding on the winds of innovation and advancements, 3D printers are set to provide faster prototyping ideas, inventive problem solving and increased cost efficiency.

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http://news.mit.edu/2018/how-to-control-robots-with-brainwaves-hand-gestures-mit-csail-0620

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Ø  Robots can now learn more complex tasks on their own

Ø  Researchers are getting closer to creating robotic hands that can mimic the real thing

Ø  A robotic hand called Dactyl looks a lot like that of Luke Skywalker’s in the latest Star Wars movie, is able to spin, twist, and flip a toy in a nimble way

Ø  Researchers believe they can train robotic hands and other machines to perform far more complex tasks

Ø  Researchers in a robotic lab at Berkeley made a machine with a two finger gripper to pick up items like screwdrivers and sort them into bins

Ø  A gripper is much easier to control than a five-fingered hand, and building the software is a lot easier.

Ø  But items like a ketchup bottle, it has the same basic shape as a screwdriver something the machine doesn’t know

Ø  The machine also can’t confront something that is different from what it has seen before such as a bracelet. Even though technology is advancing a lot, there is still a long way to go.

Ø  In another auto lab they have made a system that uses a gripper and a suction cup which can pick up all sorts of random items but can’t identify them. There are pros and cons to every system.

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A robotic hand? Four autonomous fingers and a thumb that can do anything your own flesh and blood can do? That is still the stuff of fantasy.

But inside the world's top artificial intelligence labs, researchers are getting closer to creating robotic hands that can mimic the real thing.

The Spinner

Inside OpenAI, the San Francisco artificial intelligence lab founded by Elon Musk and several other big Silicon Valley names, you will find a robotic hand called Dactyl. It looks a lot like Luke Skywalker's mechanical prosthetic in the latest Star Wars film: mechanical digits that bend and straighten like a human hand.

If you give Dactyl an alphabet block and ask it to show you particular letters - let's say the red O, the orange P and the blue I - it will show them to you and spin, twist and flip the toy in nimble ways.

For a human hand, that is a simple task. But for an autonomous machine, it is a notable achievement: Dactyl learned the task largely on its own. Using the mathematical methods that allow Dactyl to learn, researchers believe they can train robotic hands and other machines to perform far more complex tasks.

This remarkably nimble hand represents an enormous leap in robotic research over the last few years. Until recently, researchers were still struggling to master much simpler tasks with much simpler hands.

The Gripper

Created by researchers at the Autolab, a robotics lab inside the University of California at Berkeley, this system represents the limits of technology just a few years ago.

Equipped with a two-fingered "gripper," the machine can pick up items like a screwdriver or a pair of pliers and sort them into bins.

The gripper is much easier to control than a five-fingered hand, and building the software needed to operate a gripper is not nearly as difficult.

It can deal with objects that are slightly unfamiliar. It may not know what a restaurant-style ketchup bottle is, but the bottle has the same basic shape as a screwdriver - something the machine does know.

But when this machine is confronted with something that is different from what it has seen before - like a plastic bracelet - all bets are off.

What you really want is a robot that can pick up anything, even stuff it has never seen before. That is what other Autolab researchers have built over the last few years.

This system still uses simple hardware: a gripper and a suction cup. But it can pick up all sorts of random items, from a pair of scissors to a plastic toy dinosaur.

The system benefits from dramatic advances in machine learning. The Berkeley researchers modeled the physics of more than 10,000 objects, identifying the best way to pick up each one. Then, using an algorithm called a neural network, the system analyzed all this data, learning to recognize the best way to pick up any item. In the past, researchers had to program a robot to perform each task. Now it can learn these tasks on its own.

When confronted with, say, a plastic Yoda toy, the system recognizes it should use the gripper to pick the toy up.

But when it faces the ketchup bottle, it opts for the suction cup.

The picker can do this with a bin full of random stuff. It is not perfect, but because the system can learn on its own, it is improving at a far faster rate than machines of the past.

The Bed Maker

This robot may not make perfect hospital corners, but it represents notable progress. Berkeley researchers pulled the system together in just two weeks, using the latest machine learning techniques. Not long ago, this would have taken months or years.

Now the system can learn to make a bed in a fraction of that time, just by analyzing data. In this case, the system analyzes the movements that lead to a made bed.

Across the Berkeley campus, at a lab called BAIR, another system is applying other learning methods. It can push an object with a gripper and predict where it will go. That means it can move toys across a desk much as you or I would.

The system learns this behavior by analyzing vast collections of video images showing how objects get pushed. In this way, it can deal with the uncertainties and unexpected movements that come with this kind of task.

These are all simple tasks. And the machines can only handle them in certain conditions. They fail as much as they impress. But the machine learning methods that drive these systems point to continued progress in the years to come.

Like those at OpenAI, researchers at the University of Washington are training robotic hands that have all the same digits and joints that our hands do.

That is far more difficult than training a gripper or suction cup. An anthropomorphic hand moves in so many different ways.

So, the Washington researchers train their hand in simulation -- a digital recreation of the real world. That streamlines the training process.

At OpenAI, researchers are training their Dactyl hand in much the same way. The system can learn to spin the alphabet block through what would have been 100 years of trial and error. The digital simulation, running across thousands of computer chips, crunches all that learning down to two days.

It learns these tasks by repeated trial and error. Once it learns what works in the simulation, it can apply this knowledge to the real world.

Many researchers have questioned whether this kind of simulated training will transfer to the physical realm. But like researchers at Berkeley and other labs, the OpenAI team has shown that it can.

They introduce a certain amount of randomness to the simulated training. They change the friction between the hand and the block. They even change the simulated gravity. After learning to deal with this randomness in a simulated world, the hand can deal with the uncertainties of the real one.

Today, all Dactyl can do is spin a block. But researchers are exploring how these same techniques can be applied to more complex tasks. Think manufacturing. And flying drones. And maybe even driverless cars.

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How Robot Hands Are Evolving to Do What Ours Can

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A robotic hand? Four autonomous fingers and a thumb that can do anything your own flesh and blood can do? That is still the stuff of fantasy. But inside the world's top artificial intelligence labs, researchers are getting closer to creating robotic hands that can mimic the real thing.

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3D metal printing

https://www.citi.io/2018/03/07/top-breakthrough-technologies-for-2018-3-d-metal-printing/

-          In the past 3d metal printing has not widely been used since it is expensive and slow.

-          Plastic has been the most widely used material in 3D printing since it is less expensive and more accessible.

-          Markforged was the first company to release a 3D metal printer for under $100,000.

-          However Desktop Metal is now offering software that generates designs ready for 3-D printing.

-          Users tell the program the specs of the object they want to print, and the software produces a computer model suitable for printing, allowing the printing of metal to easier and more user friendly.

-          With this new technology manufacturers won’t need to maintain large inventories, instead they can simply print an object whenever someone needs it (example- a car part).

-          This will also help companies and manufacturers adapt to the customer’s ever changing needs.

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An insect inspired drone deforms upon impact

·         EPFL researchers, applying what they observed about insect wings, have developed a hybrid origami drone that can be stiff or flexible depending on the circumstances.

·         When airborne, the structure is stiff enough to carry its own weight and withstand the thrust of the propellers.

·         If the drone runs into something, it becomes flexible in order to absorb the shock and therefore minimize any damage.

Sandwich Structure

·         The drone's resilience comes from a unique combination of stiff and elastic layers. Structures that are stiff and flexible at the same time have a range of other potential applications as well.

·         It cannot pick up a load that exceeds its capacity.
https://www.sciencedaily.com/releases/2018/07/180726085811.htm

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·         Regular use of electronic devices negatively impacts how much sleep you get

·         Our devices give off blue light which is the strongest wavelength, resulting in our brains thinking it’s still daytime

·         Also noises from our phones causes us to lose sleep

·         A person using an electronic device before sleeping is less likely to sleep between 7 and 9 hours

·         It affects teenagers and young adults more so than older adults

·         Lack of sleep results in problems in our daily lives

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Amelia Willson

We live in a world dominated by electronics. From smartphones to television, electronic devices keep us entertained, productive, and connected to our work, family, and friends.

Technology has fostered a resurgence of the mattress industry, enabling ever-constant design improvements, online availability , and more variety than ever before. It's also made sleep products more affordable and accessible, leading to popular innovations such as personal sleep trackers and "smart beds" that react to the sleeper's body temperature. Technology even helps you find articles like this one about how to get better sleep.

But for all its benefits, technology seriously interferes with sleep. Regular use of electronic devices negatively impacts how much sleep you get at night, how restorative that sleep is, and how well you function the next day.

In this article we'll explore the various ways technology truly impacts sleep, how it affects children versus adults, and finally, what you can do to power down and get a good night's sleep (without sacrificing technology completely).

How do individual technologies impact sleep?

The biggest obstacle tech devices present to sleep is their level of blue light. Blue light is pervasive in modern technology, existing in smartphones, tablets, televisions, e-readers, computers, and even fluorescent lighting.

High concentration of blue light

is the strongest and brightest wavelength, which means it pierces the photoreceptors in our retinas the most intensely. When your brain senses blue light from an electronic device, it perceives it as sunlight. As a result, it assumes it's still daytime, so it's not yet time to kick off melatonin production. is a hormone that regulates your sleep-wake cycle. It releases at night, inducing sleep. The longer your brain delays melatonin release, the harder it is to fall asleep, and stay asleep.

Sound and other interference

Increased anxiety and cortisol levels

Certain types of technology deliver content that is stimulating in and of itself. Responding to stressful work emails late at night activates your body and mind. Likewise, watching a dramatic TV show or playing an intense video game before bed keeps your mind alert and triggers that "fight or flight" mode. Your body reacts physically to this kind of stress by increasing your cortisol levels, at the same time delaying or inhibiting melatonin production. All this combines to prevent you from the essential winddown that needs to happen for you to relax sufficiently and fall asleep.

Passive vs. interactive activity

The duration, application, and type of device you use all affect how it interferes with your sleep. For example, interactive tech use , such as playing a videogame or texting, has been shown to be more detrimental to sleep than passive use such as watching television. However, passive use is not something to brush aside: one study found that using an iPad for 2 hours at maximum brightness significantly delayed melatonin production.

Smartphones and tablets can be especially dangerous because we hold the screens much closer to our eyes than televisions and computers. For instance, a Harvard study found that people who use e-readers as opposed to paper books require an additional 10 minutes to fall asleep. That fact alone might not seem too concerning, but the fact they also released half the amount of melatonin and spent less time in certainly is.

Regardless of which device you use, one thing's for sure: the longer you use it at night, the less amount of sleep you can expect to enjoy.

Source: BMJ Journals

Does technology affect children's sleep more than adults?

Using technology at night creates even worse sleep problems for children - and poses wide-ranging negative effects beyond that, from academic performance to general health and well-being.

When children don't get enough sleep, they have difficulty focusing, processing and retaining information, and are at greater risk of poor health. The negative effects of technology on our children's sleep are especially problematic because tech use is so normalized. They grew up with smartphones, and are used to living in a world of devices.

It's not uncommon for kids to unwind using technology, whether they're engaging on social media, watching television, or playing a video game. This leads many children to view their smartphone as a sleep aid, rather than a hindrance. Children with this mindset have been shown to go to sleep later, sleep less, and report more daytime sleepiness than their counterparts.

Even if children weren't already comfortable with technology, it's forced on them anyway. The majority of homework assignments require computers to be completed, and since homework is done after school - in the late afternoon to late night - children are stuck sitting in front of bright electric light for hours on end. Worse, competing priorities from work, school, and extracurricular activities make it difficult to squeeze homework in earlier versus later.

There is good news, however. Here are several ideas for counteracting the negative effects of technology on your child's sleep:

Explain the effects of technology on sleep to your child. No one likes to be told what to do, and that's especially the case for teenagers, as many parents will attest. Rather than telling your child to turn off the computer and go to sleep, educate them on how technology affects sleep. Then empower them to make their own decisions.

How should you power down to get a better night's sleep?

Not getting enough sleep on a regular basis is a serious problem. The issues caused by tech-related sleep deprivation pervade all aspects of life:

So, what can you do to get a better night's sleep? Here are our top recommendations.

Remove electronics from the bedroom. This is your best bet for saving yourself from technological disturbances during the night. Not only will you benefit from a peaceful lack of light or noise, it will also help train your brain to view your bedroom as a place for sleep - not work, socializing, or surfing the internet.

Stop using blue-light devices (including phones, computers, TV, tablets and e-readers) at least 30 to 60 minutes before bed. If you can't possibly imagine what to do for an entire hour before bedtime, here are some ideas: practicing yoga or meditation; reading a book or magazine; listening to audiobooks, podcasts, or music; or talking to someone IRL in your house.

During the day, practice not responding to emails, texts, or other notifications immediately. This helps you gradually lessen your cortisol response so you feel less dependent on your phone and can leave it in another room while you sleep.

Generally limit your electric use during the evening to less stimulating activities, such as using social media instead of playing a video game.

Turn off notifications with "Do Not Disturb" mode. Many phones now include a feature that prevents notifications, sounds, or vibrations from going off, except for items you specify (such as texts or calls from your spouse).

Use tinted glasses when you're working on the computer, especially at night. These are yellow or orange and reduce the amount of blue light you perceive, although they're not as effective a filter as "night mode."

Limit your exposure to light at night. Besides your devices, the light around your home can also keep you up at night. Use dimmers or softer light bulbs. Don't shine light toward your eyes - opt for lamps over overhead lighting. If you live on a street with lots of light pollution, get blackout curtains for your bedroom. Then, when you wake in the morning, the bright light from the sun will help jolt your body awake even more.

Set up a regular bedtime routine and practice good sleep hygiene. Go to sleep and wake up at the same time every day. Avoid caffeine, alcohol, drugs, and overly sugary, spicy, or fatty foods in the evening. Keep your bedroom cool, in the mid-60s.

Research on technology and sleep

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From Phones to Video Games, How Tech Is Destroying Your Sleep

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Amelia Willson We live in a world dominated by electronics. From smartphones to television, electronic devices keep us entertained, productive, and connected to our work, family, and friends. Technology has fostered a resurgence of the mattress industry, enabling ever-constant design improvements, online availability , and more variety than ever before.

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https://www.livescience.com/59689-hybrid-driving-flying-robot-swarms.html

Things I learned about Hybrid Driving-Flying Robots:

·         The roboticists devolped algorisms that made sure that the robots would not collide.

·          the robots can fly for about 295 feet or drive for 826 feet before their batteries ran out.

·         can deliver packages or spot victims in disaster zones.

·         a recently made robot named the “flying monkey” can run, fly, and grasp items.

·        vehicles that combine flying and driving have the potential to be more efficient and more useful than vehicles that can only drive or only fly.

·         The ability to both fly and walk is common in nature.

·         There are many different robots that are capable of multiple things and there are some that can only do one task.

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W. Skinner

https://www.sciencedaily.com/releases/2018/07/180705144357.htm

Things I learned from this article:

1)      As computers get smaller and faster, they draw more current and generate more heat.

2)      Overheating of computer hardware can lead to permanent damage of that computer.

3)      Metal is not used in most small computing devices to dissipate heat because metal can cause short circuit.

4)      Diamonds have been used to dissipate heat in electronics sometimes.

5)      Arsenic that is used to help dissipate the heat is very toxic however it becomes a stable compound and nontoxic when combined with boron arsenide.

6)      Currently this chemical mix is second best to diamond when it comes to the task of dissipating heat.

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If your laptop or cell phone starts to feel warm after playing hours of video games or running too many apps at one time, those devices are actually doing their job.

Whisking heat away from the circuitry in a computer's innards to the outside environment is critical: Overheated computer chips can make programs run slower or freeze, shut the device down altogether or cause permanent damage.

As consumers demand smaller, faster and more powerful electronic devices that draw more current and generate more heat, the issue of heat management is reaching a bottleneck. With current technology, there's a limit to the amount of heat that can be dissipated from the inside out.

Researchers at the University of Texas at Dallas and their collaborators at the University of Illinois at Urbana-Champaign and the University of Houston have created a potential solution, described in a study published online July 5 in the journal Science.

Bing Lv (pronounced "love"), assistant professor of physics in the School of Natural Sciences and Mathematics at UT Dallas, and his colleagues produced crystals of a semiconducting material called boron arsenide that have an extremely high thermal conductivity, a property that describes a material's ability to transport heat.

"Heat management is very important for industries that rely on computer chips and transistors," said Lv, a corresponding author of the study. "For high-powered, small electronics, we cannot use metal to dissipate heat because metal can cause a short circuit. We cannot apply cooling fans because those take up space. What we need is an inexpensive semiconductor that also disperses a lot of heat."

Most of today's computer chips are made of the element silicon, a crystalline semiconducting material that does an adequate job of dissipating heat. But silicon, in combination with other cooling technology incorporated into devices, can handle only so much.

Diamond has the highest known thermal conductivity, around 2,200 watts per meter-kelvin, compared to about 150 watts per meter-kelvin for silicon. Although diamond has been incorporated occasionally in demanding heat-dissipation applications, the cost of natural diamonds and structural defects in humanmade diamond films make the material impractical for widespread use in electronics, Lv said.

In 2013, researchers at Boston College and the Naval Research Laboratory published research that predicted boron arsenide could potentially perform as well as diamond as a heat spreader. In 2015, Lv and his colleagues at the University of Houston successfully produced such boron arsenide crystals, but the material had a fairly low thermal conductivity, around 200 watts per meter-kelvin.

Since then, Lv's work at UT Dallas has focused on optimizing the crystal-growing process to boost the material's performance.

"We have been working on this research for the last three years, and now have gotten the thermal conductivity up to about 1,000 watts per meter-kelvin, which is second only to diamond in bulk materials," Lv said.

Lv worked with postdoctoral research associate Sheng Li, co-lead author of the study, and physics doctoral student Xiaoyuan Liu, also a study author, to create the high thermal conductivity crystals at UT Dallas using a technique called chemical vapor transport. The raw materials -- the elements boron and arsenic -- are placed in a chamber that is hot on one end and cold on the other. Inside the chamber, another chemical transports the boron and arsenic from the hot end to the cooler end, where the elements combine to form crystals.

"To jump from our previous results of 200 watts per meter-kelvin up to 1,000 watts per meter-kelvin, we needed to adjust many parameters, including the raw materials we started with, the temperature and pressure of the chamber, even the type of tubing we used and how we cleaned the equipment," Lv said.

David Cahill and Pinshane Huang's research groups at the University of Illinois at Urbana-Champaign played a key role in the current work, studying defects in the boron arsenide crystals by state-of-the-art electron microscopy and measuring the thermal conductivity of the very small crystals produced at UT Dallas.

"We measure the thermal conductivity using a method developed at Illinois over the past dozen years called 'time-domain thermoreflectance' or TDTR," said Cahill, professor and head of the Department of Materials Science and Engineering and a corresponding author of the study. "TDTR enables us to measure the thermal conductivity of almost any material over a wide range of conditions and was essential for the success of this work."

The way heat is dissipated in boron arsenide and other crystals is linked to the vibrations of the material. As the crystal vibrates, the motion creates packets of energy called phonons, which can be thought of as quasiparticles carrying heat. Lv said the unique features of boron arsenide crystals -- including the mass difference between the boron and arsenic atoms -- contribute to the ability of the phonons to travel more efficiently away from the crystals.

"I think boron arsenide has great potential for the future of electronics," Lv said. "Its semiconducting properties are very comparable to silicon, which is why it would be ideal to incorporate boron arsenide into semiconducting devices."

Lv said that while the element arsenic by itself can be toxic to humans, once it is incorporated into a compound like boron arsenide, the material becomes very stable and nontoxic.

The next step in the work will include trying other processes to improve the growth and properties of this material for large scale applications, Lv said.

The research was supported by the Office of Naval Research and the Air Force Office of Scientific Research.

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thermodynamics; materials science; chemistry; inorganic chemistry; spintronics research; computer science; mobile computing; computers and internet

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Heat-conducting crystals could help computer chips keep their cool

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As consumers demand smaller, faster and more powerful electronic devices that draw more current and generate more heat, the issue of heat management is reaching a bottleneck. Researchers have created a potential solution -- boron arsenide crystals with high thermal conductivity, which might be used in future electronics to help keep devices from overheating.

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Popcorn Powered Robots

https://www.sciencedaily.com/releases/2018/08/180802130805.htm

1. Cornell University researchers have discovered how to power simple robots with heated popcorn that expands more than 10 times in size.

2. The study is the first to consider powering robots with popcorn, which is inexpensive, biodegradable, and edible.

3. Since this robot would be edible, it could possibly be ingested for medical procedures.

4. A popcorn-powered mechanism can only be used only once.

5. You just need to apply voltage to get the kernels to pop, so it would take all the expensive parts out of the robots.

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https://www.washingtonexaminer.com/opinion/the-nsa-routinely-fails-to-protect-privacy-but-no-one-is-fixing-it

v  NSA employees used common flash drives for their important work that is vital for our nation’s security WITHOUT scanning them for viruses first.

v  The NSA has inaccurate records of their own data so none of their data can be taken to be true

v  Apparently none of the reports on the NSA’s screw ups require immediate attention to fixing, so they can continue to be lazy with the records of our nation’s security

v  NSA’s record storage facilities were not up to date with regulations

v  As stated in the article that the “NSA has serious problems that undermine its very reason for existence,” it seems that the agency for national security has a tough time keeping its own data secure.

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New virtual reality experience delivers extremely high quality imagery

https://www.sciencedaily.com/releases/2018/07/180726161040.htm

·       Google released Welcome to Light Fields earlier this year as a free for multiple different VR devices

·       It’s based on a novel system that captures and renders high-quality, realistic images from the real world using light fields

·       Light Fields proves that it is now possible to make a compelling light field VR viewer that runs on consumer-grade hardware

·       They used a rendering algorithm that blends between the thousands of light field images in real-time

·       They developed two easy-to-use light field camera rigs, based on a GoPro camera, that capture thousands of images on the surface of a sphere. Those images were then passed through a cloud-based light-field-processing pipeline, that makes the images in 3D and generate depth maps

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·         improve performance in low power environments like in smart phones

·         memristor array is the item that does that

·         they can be programmed to have different resistance states  meaning they store information as resistance levels

·         Could be particularly useful for implementing machine learning and artificial intelligence algorithms.

·         mathematical matrices can map directly onto the grid of memristors

·         A typical processor, in contrast, would have to read the value from each cell of the matrix, perform multiplication, and then sum up each column in series

·         But the memristor can get the multiplication and addition in one step

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A new way of arranging advanced computer components called memristors on a chip could enable them to be used for general computing, which could cut energy consumption by a factor of 100.

This would improve performance in low power environments such as smartphones or make for more efficient supercomputers, says a University of Michigan researcher.

"Historically, the semiconductor industry has improved performance by making devices faster. But although the processors and memories are very fast, they can't be efficient because they have to wait for data to come in and out," said Wei Lu, U-M professor of electrical and computer engineering and co-founder of memristor startup Crossbar Inc.

Memristors might be the answer. Named as a portmanteau of memory and resistor, they can be programmed to have different resistance states -- meaning they store information as resistance levels. These circuit elements enable memory and processing in the same device, cutting out the data transfer bottleneck experienced by conventional computers in which the memory is separate from the processor.

However, unlike ordinary bits, which are 1 or 0, memristors can have resistances that are on a continuum. Some applications, such as computing that mimics the brain (neuromorphic), take advantage of the analog nature of memristors. But for ordinary computing, trying to differentiate among small variations in the current passing through a memristor device is not precise enough for numerical calculations.

Lu and his colleagues got around this problem by digitizing the current outputs -- defining current ranges as specific bit values (i.e., 0 or 1). The team was also able to map large mathematical problems into smaller blocks within the array, improving the efficiency and flexibility of the system.

Computers with these new blocks, which the researchers call "memory-processing units," could be particularly useful for implementing machine learning and artificial intelligence algorithms. They are also well suited to tasks that are based on matrix operations, such as simulations used for weather prediction. The simplest mathematical matrices, akin to tables with rows and columns of numbers, can map directly onto the grid of memristors.

Once the memristors are set to represent the numbers, operations that multiply and sum the rows and columns can be taken care of simultaneously, with a set of voltage pulses along the rows. The current measured at the end of each column contains the answers. A typical processor, in contrast, would have to read the value from each cell of the matrix, perform multiplication, and then sum up each column in series.

"We get the multiplication and addition in one step. It's taken care of through physical laws. We don't need to manually multiply and sum in a processor," Lu said.

His team chose to solve partial differential equations as a test for a 32x32 memristor array -- which Lu imagines as just one block of a future system. These equations, including those behind weather forecasting, underpin many problems science and engineering but are very challenging to solve. The difficulty comes from the complicated forms and multiple variables needed to model physical phenomena.

When solving partial differential equations exactly is impossible, solving them approximately can require supercomputers. These problems often involve very large matrices of data, so the memory-processor communication bottleneck is neatly solved with a memristor array. The equations Lu's team used in their demonstration simulated a plasma reactor, such as those used for integrated circuit fabrication.

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energy technology; technology; physics; electronics; computers and internet; computer science; information technology; distributed computing

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Memory-processing unit could bring memristors to the masses

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A new way of arranging advanced computer components called memristors on a chip could enable them to be used for general computing, which could cut energy consumption by a factor of 100. This would improve performance in low power environments such as smartphones or make for more efficient supercomputers, says a University of Michigan researcher.

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On Wednesday, April 4th, 2018 Facebook Inc. stated that the private information of up to 87 million Facebook accounts may have been leaked to Cambridge Analytica.

The media had previously estimated that only about 50 million Facebook accounts had been leaked.

Cambridge Analyitca was in association with Donald Trump’s 2016 campaign for presidency.

Facebook is taking steps to restrict the private data available to app developers.

This incident caused mass criticism from people who had investments in Facebook and those who at least have owned a Facebook account in the past.

Despite the criticisms, Cambridge Analytica denied that they did anything wrong and that they collected the Facebook data in good faith.

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Facebook Inc said on Wednesday that the personal information of up to 87 million users may have been improperly shared with political consultancy Cambridge Analytica, up from a previous media estimate of more than 50 million.

Most of the 87 million people whose data was shared with Cambridge Analytica, which worked on US president Donald Trump's 2016 election campaign, were in the US, Facebook chief technology officer Mike Schroepfer wrote in a blog post.

Facebook said that following the scandal it was taking steps to restrict the personal data available to third-party app developers.

The world's largest social media company has been hammered by investors and faces anger from users, advertisers and politicians after a series of scandals about fake news stories, election-meddling and privacy issues.

Last month, Facebook acknowledged that personal information about millions of users wrongly ended up in the hands of Cambridge Analytica, which was an accredited third-party software developer on the platform.

Facebook chief executive Mark Zuckerberg will testify about the matter next week before the US House energy and commerce committee, the panel said on Wednesday.

Shares in Facebook were down 1.4 per cent on Wednesday to $153.90 (about €126). They are down more than 16 per cent since the Cambridge Analytica scandal broke.

The previous estimate of more than 50 million came from two newspapers, the New York Times and London's Observer, based on their investigations of Cambridge Analytica.

Mr Schroepfer did not provide details on how Facebook determined its higher estimate, but he said Facebook would tell people if their information may have been improperly shared with Cambridge Analytica.

A representative from Cambridge Analytica could not immediately be reached for comment.

The British-based consultancy has denied wrongdoing. It says it engaged a university professor "in good faith" to collect Facebook data in a manner similar to how other third-party app developers have harvested personal information.

The scandal has kicked off investigations by Britain's Information Commissioner's Office, the US Federal Trade Commission and by some 37 US state attorneys general.

Nigeria's government will also investigate allegations of improper involvement by Cambridge Analytica in that country's 2007 and 2015 elections, a presidency spokesman said on Monday. - Reuters

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Facebook data breach affected up to 87 million users

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Facebook Inc said on Wednesday that the personal information of up to 87 million users may have been improperly shared with political consultancy Cambridge Analytica, up from a previous media estimate of more than 50 million.

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·         A.I. is not very “human” as it does not know philosophy or other feelings of humans

·         It isn’t very likely to end up like Terminator or Wall-e because we can control it very easily

·         Jobs are not being taken by A.I. despite how much people think

·         We overestimate A.I. as it has actually done very little to prove it is useful now

·         A.I. can’t do anything other than it’s supposed too.

·         It can’t answer simple questions about someone as it does not know the human

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We are surrounded by hysteria about the future of artificial intelligence and robotics-hysteria about how powerful they will become, how quickly, and what they will do to jobs.

I recently saw a story in MarketWatch that said robots will take half of today's jobs in 10 to 20 years. It even had a graphic to prove the numbers.

The claims are ludicrous. (I try to maintain professional language, but sometimes ...) For instance, the story appears to say that we will go from one million grounds and maintenance workers in the U.S. to only 50,000 in 10 to 20 years, because robots will take over those jobs. How many robots are currently operational in those jobs? Zero. How many realistic demonstrations have there been of robots working in this arena? Zero. Similar stories apply to all the other categories where it is suggested that we will see the end of more than 90 percent of jobs that currently require physical presence at some particular site.

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Mistaken predictions lead to fears of things that are not going to happen, whether it's the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us. We need to push back on these mistakes. But why are people making them? I see seven common reasons.

1. Overestimating and underestimating

Roy Amara was a cofounder of the Institute for the Future, in Palo Alto, the intellectual heart of Silicon Valley. He is best known for his adage now referred to as Amara's Law:

We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

There is a lot wrapped up in these 21 words. An optimist can read it one way, and a pessimist can read it another.

A great example of the two sides of Amara's Law is the U.S. Global Positioning System. Starting in 1978, a constellation of 24 satellites (now 31 including spares) were placed in orbit. The goal of GPS was to allow precise delivery of munitions by the U.S. military. But the program was nearly canceled again and again in the 1980s. The first operational use for its intended purpose was in 1991 during Desert Storm; it took several more successes for the military to accept its utility.

Mistaken predictions lead to fears of things that are not going to happen.

Today GPS is in what Amara would call the long term, and the ways it is used were unimagined at first. My Series 2 Apple Watch uses GPS while I am out running, recording my location accurately enough to see which side of the street I run along. The tiny size and price of the receiver would have been incomprehensible to the early GPS engineers. The technology synchronizes physics experiments across the globe and plays an intimate role in synchronizing the U.S. electrical grid and keeping it running. It even allows the high-frequency traders who really control the stock market to mostly avoid disastrous timing errors. It is used by all our airplanes, large and small, to navigate, and it is used to track people out of prison on parole. It determines which seed variant will be planted in which part of many fields across the globe. It tracks fleets of trucks and reports on driver performance.

GPS started out with one goal, but it was a hard slog to get it working as well as was originally expected. Now it has seeped into so many aspects of our lives that we would not just be lost if it went away; we would be cold, hungry, and quite possibly dead.

We see a similar pattern with other technologies over the last 30 years. A big promise up front, disappointment, and then slowly growing confidence in results that exceed the original expectations. This is true of computation, genome sequencing, solar power, wind power, and even home delivery of groceries.

AI has been overestimated again and again, in the 1960s, in the 1980s, and I believe again now, but its prospects for the long term are also probably being underestimated. The question is: How long is the long term? The next six errors help explain why the time scale is being grossly underestimated for the future of AI.

2. Imagining magic

When I was a teenager, Arthur C. Clarke was one of the "big three" science fiction writers, along with Robert Heinlein and Isaac Asimov. But Clarke was also an inventor, a science writer, and a futurist. Between 1962 and 1973 he formulated three adages that have come to be known as Clarke's Three Laws:

When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.

The only way of discovering the limits of the possible is to venture a little way past them into the impossible.

Any sufficiently advanced technology is indistinguishable from magic.

Personally, I should probably be wary of the second sentence in his first law, as I am much more conservative than some others about how quickly AI will be ascendant. But for now I want to expound on Clarke's Third Law.

Imagine we had a time machine and we could transport Isaac Newton from the late 17th century to today, setting him down in a place that would be familiar to him: Trinity College Chapel at the University of Cambridge.

Now show Newton an Apple. Pull out an iPhone from your pocket, and turn it on so that the screen is glowing and full of icons, and hand it to him. Newton, who revealed how white light is made from components of different-colored light by pulling apart sunlight with a prism and then putting it back together, would no doubt be surprised at such a small object producing such vivid colors in the darkness of the chapel. Now play a movie of an English country scene, and then some church music that he would have heard. And then show him a Web page with the 500-plus pages of his personally annotated copy of his masterpiece Principia, teaching him how to use the pinch gesture to zoom in on details.

Watch out for arguments about future technology that is magical.

Could Newton begin to explain how this small device did all that? Although he invented calculus and explained both optics and gravity, he was never able to sort out chemistry from alchemy. So I think he would be flummoxed, and unable to come up with even the barest coherent outline of what this device was. It would be no different to him from an embodiment of the occult-something that was of great interest to him. It would be indistinguishable from magic. And remember, Newton was a really smart dude.

If something is magic, it is hard to know its limitations. Suppose we further show Newton how the device can illuminate the dark, how it can take photos and movies and record sound, how it can be used as a magnifying glass and as a mirror. Then we show him how it can be used to carry out arithmetical computations at incredible speed and to many decimal places. We show it counting the steps he has taken as he carries it, and show him that he can use it to talk to people anywhere in the world, immediately, from right there in the chapel.

What else might Newton conjecture that the device could do? Prisms work forever. Would he conjecture that the iPhone would work forever just as it is, neglecting to understand that it needs to be recharged? Recall that we nabbed him from a time 100 years before the birth of Michael Faraday, so he lacked a scientific understanding of electricity. If the iPhone can be a source of light without fire, could it perhaps also transmute lead into gold?

This is a problem we all have with imagined future technology. If it is far enough away from the technology we have and understand today, then we do not know its limitations. And if it becomes indistinguishable from magic, anything one says about it is no longer falsifiable.

This is a problem I regularly encounter when trying to debate with people about whether we should fear artificial general intelligence, or AGI-the idea that we will build autonomous agents that operate much like beings in the world. I am told that I do not understand how powerful AGI will be. That is not an argument. We have no idea whether it can even exist. I would like it to exist-this has always been my own motivation for working in robotics and AI. But modern-day AGI research is not doing well at all on either being general or supporting an independent entity with an ongoing existence. It mostly seems stuck on the same issues in reasoning and common sense that AI has had problems with for at least 50 years. All the evidence that I see says we have no real idea yet how to build one. Its properties are completely unknown, so rhetorically it quickly becomes magical, powerful without limit.

Nothing in the universe is without limit.

Watch out for arguments about future technology that is magical. Such an argument can never be refuted. It is a faith-based argument, not a scientific argument.

3. Performance versus competence

We all use cues about how people perform some particular task to estimate how well they might perform some different task. In a foreign city we ask a stranger on the street for directions, and she replies with confidence and with directions that seem to make sense, so we figure we can also ask her about the local system for paying when you want to take a bus.

Now suppose a person tells us that a particular photo shows people playing Frisbee in the park. We naturally assume that this person can answer questions like What is the shape of a Frisbee? Roughly how far can a person throw a Frisbee? Can a person eat a Frisbee? Roughly how many people play Frisbee at once? Can a three-month-old person play Frisbee? Is today's weather suitable for playing Frisbee?

Computers that can label images like "people playing Frisbee in a park" have no chance of answering those questions. Besides the fact that they can only label more images and cannot answer questions at all, they have no idea what a person is, that parks are usually outside, that people have ages, that weather is anything more than how it makes a photo look, etc.

This does not mean that these systems are useless; they are of great value to search engines. But here is what goes wrong. People hear that some robot or some AI system has performed some task. They then generalize from that performance to a competence that a person performing the same task could be expected to have. And they apply that generalization to the robot or AI system.

Today's robots and AI systems are incredibly narrow in what they can do. Human-style generalizations do not apply.

4. Suitcase words

Marvin Minsky called words that carry a variety of meanings "suitcase words." "Learning" is a powerful suitcase word; it can refer to so many different types of experience. Learning to use chopsticks is a very different experience from learning the tune of a new song. And learning to write code is a very different experience from learning your way around a city.

When people hear that machine learning is making great strides in some new domain, they tend to use as a mental model the way in which a person would learn that new domain. However, machine learning is very brittle, and it requires lots of preparation by human researchers or engineers, special-purpose coding, special-purpose sets of training data, and a custom learning structure for each new problem domain. Today's machine learning is not at all the sponge-like learning that humans engage in, making rapid progress in a new domain without having to be surgically altered or purpose-built.

Likewise, when people hear that a computer can beat the world chess champion (in 1997) or one of the world's best Go players (in 2016), they tend to think that it is "playing" the game just as a human would. Of course, in reality those programs had no idea what a game actually was, or even that they were playing. They were also much less adaptable. When humans play a game, a small change in rules does not throw them off. Not so for AlphaGo or Deep Blue.

Suitcase words mislead people about how well machines are doing at tasks that people can do. That is partly because AI researchers-and, worse, their institutional press offices-are eager to claim progress in an instance of a suitcase concept. The important phrase here is "an instance." That detail soon gets lost. Headlines trumpet the suitcase word, and warp the general understanding of where AI is and how close it is to accomplishing more.

5. Exponentials

Many people are suffering from a severe case of "exponentialism."

Everyone has some idea about Moore's Law, which suggests that computers get better and better on a clockwork-like schedule. What Gordon Moore actually said was that the number of components that could fit on a microchip would double every year. That held true for 50 years, although the time constant for doubling gradually lengthened from one year to over two years, and the pattern is coming to an end.

Doubling the components on a chip has made computers continually double in speed. And it has led to memory chips that quadruple in capacity every two years. It has also led to digital cameras that have better and better resolution, and LCD screens with exponentially more pixels.

The reason Moore's Law worked is that it applied to a digital abstraction of a true-or-false question. In any given circuit, is there an electrical charge or voltage there or not? The answer remains clear as chip components get smaller and smaller-until a physical limit intervenes, and we get down to components with so few electrons that quantum effects start to dominate. That is where we are now with our silicon-based chip technology.

When people are suffering from exponentialism, they may think that the exponentials they use to justify an argument are going to continue apace. But Moore's Law and other seemingly exponential laws can fail because they were not truly exponential in the first place.

Back in the first part of this century, when I was running MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and needed to help raise money for over 90 different research groups, I tried to use the memory increase on iPods to show sponsors how things were continuing to change very rapidly. Here are the data on how much music storage one got in an iPod for $400 or less:

Then I would extrapolate a few years out and ask what we would do with all that memory in our pockets.

Extrapolating through to today, we would expect a $400 iPod to have 160,000 gigabytes of memory. But the top iPhone of today (which costs much more than $400) has only 256 gigabytes of memory, less than double the capacity of the 2007 iPod. This particular exponential collapsed very suddenly once the amount of memory got to the point where it was big enough to hold any reasonable person's music library and apps, photos, and videos. Exponentials can collapse when a physical limit is hit, or when there is no more economic rationale to continue them.

Similarly, we have seen a sudden increase in performance of AI systems thanks to the success of deep learning. Many people seem to think that means we will continue to see AI performance increase by equal multiples on a regular basis. But the deep-learning success was 30 years in the making, and it was an isolated event.

That does not mean there will not be more isolated events, where work from the backwaters of AI research suddenly fuels a rapid-step increase in the performance of many AI applications. But there is no "law" that says how often they will happen.

6. Hollywood scenarios

The plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist.

In Bicentennial Man, Richard Martin, played by Sam Neill, sits down to breakfast and is waited upon by a walking, talking humanoid robot, played by Robin Williams. Richard picks up a newspaper to read over breakfast. A newspaper! Printed on paper. Not a tablet computer, not a podcast coming from an Amazon Echo-like device, not a direct neural connection to the Internet.

It turns out that many AI researchers and AI pundits, especially those pessimists who indulge in predictions about AI getting out of control and killing people, are similarly imagination-challenged. They ignore the fact that if we are able to eventually build such smart devices, the world will have changed significantly by then. We will not suddenly be surprised by the existence of such super-intelligences. They will evolve technologically over time, and our world will come to be populated by many other intelligences, and we will have lots of experience already. Long before there are evil super-intelligences that want to get rid of us, there will be somewhat less intelligent, less belligerent machines. Before that, there will be really grumpy machines. Before that, quite annoying machines. And before them, arrogant, unpleasant machines. We will change our world along the way, adjusting both the environment for new technologies and the new technologies themselves. I am not saying there may not be challenges. I am saying that they will not be sudden and unexpected, as many people think.

7. Speed of deployment

New versions of software are deployed very frequently in some industries. New features for platforms like Facebook are deployed almost hourly. For many new features, as long as they have passed integration testing, there is very little economic downside if a problem shows up in the field and the version needs to be pulled back. This is a tempo that Silicon Valley and Web software developers have gotten used to. It works because the marginal cost of newly deploying code is very, very close to zero.

Deploying new hardware, on the other hand, has significant marginal costs. We know that from our own lives. Many of the cars we are buying today, which are not self-driving, and mostly are not software-enabled, will probably still be on the road in the year 2040. This puts an inherent limit on how soon all our cars will be self-driving. If we build a new home today, we can expect that it might be around for over 100 years. The building I live in was built in 1904, and it is not nearly the oldest in my neighborhood.

Capital costs keep physical hardware around for a long time, even when there are high-tech aspects to it, and even when it has an existential mission.

The U.S. Air Force still flies the B-52H variant of the B-52 bomber. This version was introduced in 1961, making it 56 years old. The last one was built in 1962, a mere 55 years ago. Currently these planes are expected to keep flying until at least 2040, and perhaps longer-there is talk of extending their life to 100 years.

I regularly see decades-old equipment in factories around the world. I even see PCs running Windows 3.0-a software version released in 1990. The thinking is "If it ain't broke, don't fix it." Those PCs and their software have been running the same application doing the same task reliably for over two decades.

Almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine.

The principal control mechanism in factories, including brand-new ones in the U.S., Europe, Japan, Korea, and China, is based on programmable logic controllers, or PLCs. These were introduced in 1968 to replace electromechanical relays. The "coil" is still the principal abstraction unit used today, and PLCs are programmed as though they were a network of 24-volt electromechanical relays. Still. Some of the direct wires have been replaced by Ethernet cables. But they are not part of an open network. Instead they are individual cables, run point to point, physically embodying the control flow-the order in which steps get executed-in these brand-new ancient automation controllers. When you want to change information flow, or control flow, in most factories around the world, it takes weeks of consultants figuring out what is there, designing new reconfigurations, and then teams of tradespeople to rewire and reconfigure hardware. One of the major manufacturers of this equipment recently told me that they aim for three software upgrades every 20 years.

In principle, it could be done differently. In practice, it is not. I just looked on a jobs list, and even today, this very day, Tesla Motors is trying to hire PLC technicians at its factory in Fremont, California. They will use electromagnetic relay emulation in the production of the most AI-enhanced automobile that exists.

A lot of AI researchers and pundits imagine that the world is already digital, and that simply introducing new AI systems will immediately trickle down to operational changes in the field, in the supply chain, on the factory floor, in the design of products.

Nothing could be further from the truth. Almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine.
Rodney Brooks is a former director of the Computer Science and Artificial Intelligence Laboratory at MIT and a founder of Rethink Robotics and iRobot. This essay is adapted with permission from a post that originally appeared at rodneybrooks.com.

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Robotics pioneer Rodney Brooks debunks AI hype seven ways

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We are surrounded by hysteria about the future of artificial intelligence and robotics-hysteria about how powerful they will become, how quickly, and what they will do to jobs. I recently saw a story in ­ MarketWatch that said robots will take half of today's jobs in 10 to 20 years.

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·         Due to insufficient sleep thought to be caused by access to broadband internet, approximately 200,000 working days are lost in Germany every year and $60 billion (about 1.6% of its GDP).

·         Researchers conclude that access to high-speed internet reduces sleep duration and sleep satisfaction in individuals that face time constraints in the morning for family reasons or work.

·         Individuals with DSL access tend to sleep 25 minutes less than their counterparts without DSL internet. They are significantly less likely to sleep between seven and nine hours, the amount recommended by the scientific community, and are less likely to be satisfied with their sleep.

·         "Digital temptations may lead to a delay in bedtime, which ultimately decreases sleep duration for individuals who are not able to compensate for later bedtime by waking up later in the morning," Prof. Billari says.

·         Among teenagers and young adults (aged 13-30), there is a significant association between insufficient sleep and time spent on computer games or watching TV or videos in the evening, while for older adults (31-59) the correlation is with the use of PCs and smartphones.

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Broadband internet causes sleep deprivation, a new study finds

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About 200,000 working days are lost in Germany every year due to insufficient sleep, with an economic loss of $60 billion, or about 1.6 percent of its GDP, according to a 2016 Report of the RAND Corporation. Francesco Billari ...

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“Artificial intelligence system designs drugs from scratch”

An artificial intelligence system Reinforcement Learning for Structural Evolution, known as ReLeaSE is comprised of a teacher AI and a student AI

The teacher teaches the student the many different molecules combinations that makes up medicine (over 1.7 million)

Designed by the University Of North Carolina

One medicine they have created is a compound that helps fight against an enzyme that helps cause leukemia.

·         Patent Pending

The University wants to install these devices in local pharmacies in order to help pharmacist

Olexandr Isayev  Alexander Tropsha, and Mariya Popova, all of the UNC Eshelman School of Pharmacy are the creators

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An artificial-intelligence approach created at the University of North Carolina at Chapel Hill Eshelman School of Pharmacy can teach itself to design new drug molecules from scratch and has the potential to dramatically accelerate the design of new drug candidates.

The system is called Reinforcement Learning for Structural Evolution, known as ReLeaSE, and is an algorithm and computer program that comprises two neural networks which can be thought of as a teacher and a student. The teacher knows the syntax and linguistic rules behind the vocabulary of chemical structures for about 1.7 million known biologically active molecules. By working with the teacher, the student learns over time and becomes better at proposing molecules that are likely to be useful as new medicines.

Alexander Tropsha, Olexandr Isayev and Mariya Popova, all of the UNC Eshelman School of Pharmacy, are the creators of ReLeaSE. The University has applied for a patent for the technology, and the team published a proof-of-concept study in the journal Science Advances last week.

"If we compare this process to learning a language, then after the student learns the molecular alphabet and the rules of the language, they can create new 'words,' or molecules," said Tropsha. "If the new molecule is realistic and has the desired effect, the teacher approves. If not, the teacher disapproves, forcing the student to avoid bad molecules and create good ones."

ReLeaSE is a powerful innovation to virtual screening, the computational method widely used by the pharmaceutical industry to identify viable drug candidates. Virtual screening allows scientists to evaluate existing large chemical libraries, but the method only works for known chemicals. ReLeASE has the unique ability to create and evaluate new molecules.

"A scientist using virtual screening is like a customer ordering in a restaurant. What can be ordered is usually limited by the menu," said Isayev. "We want to give scientists a grocery store and a personal chef who can create any dish they want."

The team has used ReLeaSE to generate molecules with properties that they specified, such as desired bioactivity and safety profiles. The team used the ReLeaSE method to design molecules with customized physical properties, such as melting point and solubility in water, and to design new compounds with inhibitory activity against an enzyme that is associated with leukemia.

"The ability of the algorithm to design new, and therefore immediately patentable, chemical entities with specific biological activities and optimal safety profiles should be highly attractive to an industry that is constantly searching for new approaches to shorten the time it takes to bring a new drug candidate to clinical trials," said Tropsha.

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pharmacology; pharmaceuticals; today's healthcare; organic chemistry; chemistry; engineering and construction; artificial intelligence; educational technology; computer modeling

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Artificial intelligence system designs drugs from scratch

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An artificial-intelligence approach created at the University of North Carolina at Chapel Hill Eshelman School of Pharmacy can teach itself to design new drug molecules from scratch and has the potential to dramatically accelerate the design of new drug candidates.

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1. wearable sensor technology has developed an automatic alert system to help people quit smoking
2. the sensor detects specific arm and body movement associated with smoking
3. it is a two armband sensor that detects smoking motions and is 98% accurate
4. before, studies relied on smokers self-reporting how often they smoke, while this system more accurately tracks smoking activity based on sensors
5. they've been able to detect between a single motion such as eating and drinking, to a sequence of motions more clearly linked to smoking

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Researchers at Case Western Reserve University are using wearable sensor technology to develop an automatic alert system to help people quit smoking.

The smart-phone app, initially limited to android-based operating systems, automatically texts 20- to 120-second video messages to smokers when sensors detect specific arm and body motions associated with smoking.

There is no shortage of products or programs -- from nicotine gum to hypnosis -- to help people stop smoking. More recently, wearable technology has gained popularity in the fight against addiction.

But the mobile alert system Case Western Reserve researchers are testing may be the first that combines:

an existing online platform with mindfulness training and a personalized plan for quitting; * two armband sensors to detect smoking motions, a technology that demonstrated more than 98-percent accuracy in differentiating "lighting up" from other similar motions. (That compares to 72-percent accuracy in systems using a single armband);

and a personalized text-messaging service that reminds the user of either their own plan to quit, or sends video messages that stress the health and financial benefits of quitting.

Collaborative effort
The system was conceived, developed and tested over the course of the last year by a team of Electrical Engineering and Computer Science researchers at the Case School of Engineering and a high school intern in collaboration with a clinical psychologist at the Case Western Reserve School of Medicine.

A paper detailing the system and reporting early findings on a group of 10 users was published in a July edition of Smart Health. The researchers said most previous studies have relied on smokers self-reporting how often they smoked, while the Case Western Reserve system more accurately tracked smoking activity based on the sensors.

"We've been able to differentiate between a single motion, which could be confused with eating or drinking, and a sequence of motions more clearly linked to the act of smoking a cigarette," said Ming-Chun Huang, an assistant electrical engineering and computer science professor who led the technical aspect of the study.

The collaboration to develop a technology-enhanced, and personalized mobile-smoking cessation system started after a conversation last summer between Huang, who was looking for a new project for his students, and Monica Webb Hooper of the Case Comprehensive Cancer Center, who was seeking new ways to help her clients break the habit.

"The field of tobacco control has really adopted mobile technologies because many people won't come in for therapy," said Webb Hooper, who has been working on interventions for nearly two decades.

"We were interested in translating one of our programs into a video-based mobile application, but the motion sensors made this even more amazing," said Webb Hooper, who has extended the study to another 120 smokers -- half using the program and a control group using a standard text messaging program without sensors or video messaging.
The addiction problem
Tobacco smoking is responsible for one of every five deaths in the United States, according to the Centers for Disease Control and Prevention. Other research has shown there are more than 7,000 chemicals in cigarettes, including carbon monoxide, hydrogen cyanide and nitrogen oxides in cigarette smoke.

Further, the National Cancer Institute reports that there are 69 known cancer-causing agents in tobacco smoke.

"Tobacco is the toughest of all addictions to overcome and cigarettes are one of the easiest drugs to become addicted to -- all it takes is three (cigarettes) for some people," Webb Hooper said. "And, neurologically, it's harder to quit because we have more nicotine receptors in the brain. That's why I'm so excited about this intervention."

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smoking; medical topics; lung cancer; staying healthy; mobile computing; artificial intelligence; computer science; computer programming

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Tech takes on cigarette smoking: Researchers develop system that senses smoking movements, sends motivational text messages, videos to help users quit smoking

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Researchers at Case Western Reserve University are using wearable sensor technology to develop an automatic alert system to help people quit smoking. The smart-phone app, initially limited to android-based operating systems, automatically texts 20- to 120-second video messages to smokers when sensors detect specific arm and body motions associated with smoking.

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·          After a Breach, customers would stop using the online website.

·          Huge financial loss for the company.

·         Customers lose trust on the company.

·          Customers device may be damaged

·          Banks are the first to deal with the angry customers.

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Data breaches are on the rise. Since January 2017, at least 15 retailers were hacked and likely had information stolen from them.

A report from cybersecurity firm Shape Security showed that almost 90% of the login attempts made on online retailers' websites are hackers using stolen data.

Many of these were caused by flaws in payment systems taken advantage of by hackers.

At least 15 separate security breaches occurred at retailers from January 2017 until now. Many of them were caused by flaws in payment systems, either online or in stores.

Data breaches are on the rise for both retailers and other businesses.

A recent report published by cybersecurity firm Shape Security showed that 80% to 90% of the people that log in to a retailer's e-commerce site are hackers using stolen data. This is the highest percentage of any sector.

These data breaches are a real danger for both brands and customers and can affect the trust shoppers have in brands.

According to a study by KPMG, 19% of consumers would completely stop shopping at a retailer after a breach, and 33% would take a break from shopping there for an extended period.

Here are 15 retailers that have been affected by data breaches since January 2017:

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If you shopped at these 15 stores in the last year, your data might have been stolen

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Data breaches are on the rise. Since January 2017, at least 15 retailers were hacked and likely had information stolen from them. A report from cybersecurity firm Shape Security showed that almost 90% of the login attempts made on online retailers' websites are hackers using stolen data.

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·         Financial Losses to companies

·         Loss of Information

·         Decreased Privacy

·         Damaged Reputation in Companies

·         Damage to the device

·         Having to pay for the damage

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by Sarah Morse

Hackers have a number of reasons for breaking through network security. Some do it just for the challenge, others for information, some for fame and some for honing their programming skills. Whatever the reason, hacking causes damage to the computing devices of individuals and businesses, sometimes resulting in millions of dollars lost.

Financial Losses

Every year, reports of hacked businesses reveal staggering financial losses as a result. In 2011, Sony lost $170 million due to a hack of their PlayStation system. Also in 2011, CitiGroup lost $2.7 million and AT&T lost $2 million as a result of hackers. The cost of patching the holes in security, repaying customer losses, addressing lawsuits and weathering shutdowns of their systems contributed to those huge numbers. Even for an individual who loses his credit card information to a hacker, however, the cost of repairing damage and tracking down the culprit can be significant.

Loss of Information

Hacking often results in a loss of data due to files being deleted or changed. Customer information and order information can be stolen and deleted, or a leak of top secret information could cause real-world security issues. Servers at the Pentagon, FBI, Interpol and NASA have all been compromised at various points in the past ten years. Sometimes, these hackers even post information from these governmental organizations online, which could in theory cause unrest between countries.

Decreased Privacy

When hackers gain access to your computer, they can see everything. Since much of the personal, professional and financial parts of our lives have moved online, we risk losing much more than money or information. Because of the Internet, privacy is limited, usually by choice. A hacker with access to your email, social networking accounts and personal photos can very quickly destroy that privacy.

Damaged Reputation

Companies that get hacked have a bigger problem than just paying for the initial damage costs and lawsuits. Reputation damage can be devastating to a company's fortunes. If a bank has been compromised multiple times, customers are less likely to give them their personal information. The same goes for retailers who lose information to hackers. These companies lose business over time because of damaged or weakened reputations. Individuals with stolen identities as a result of hacking have a similar reputation problem when it comes to their credit ratings.

About the Author

Sarah Morse has been a writer since 2009, covering environmental topics, gardening and technology. She holds a bachelor's degree in English language and literature, a master's degree in English and a master's degree in information science.

More Articles

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Hackers have a number of reasons for breaking through network security. Some do it just for the challenge, others for information, some for fame and some for honing their programming skills. Whatever the reason, hacking causes damage to the computing devices of individuals and businesses, sometimes resulting in ...

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1.       Many are the Universities which are investing in training their students by using Virtual Reality peripherals.

2.       Encouraging the usage of such peripherals could be a breaking point in order to better prepare surgeons and specialists in subjects and operations that are naturally delicate.

3.       Flight simulator recently announced that an entire VR-based version will come in the future, providing an experience close to the actual pilot training which is currently done by many different companies such as British Airways and Turkish Airlines.

4.       Used for many people in training in many fields, helps with hands on experience without any risk.

5.       Can also be used in therapy for patients working on recovering.

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Virtual Reality and Augmented Reality are possibly two of the biggest trends in the entire tech world. We saw the rise of VR in gaming applications and mapping software, but what is the future of this technology? How will Virtual Reality impact on our day to day life? Is it going to be a scary matter or is the technology just moving a bit too fast? Let's try to break down the subject in a simple way.

Simultaneous Localization And Mapping is the biggest focus when it comes to development at the minute: this technology is able to instantly translate data from the real world (by using sensors) into the virtual one and vice-versa. This avenue is currently studied by many different mobile app development companies, given the fact that Niantic based its success on a very embryonic version of SLAM, which was translating the surroundings into the app by using a floor algorithm (the same one that is generally used for QWERTY codes).

Simultaneous Localization And Mapping is currently used in TESLA's autopilot and summon, two core elements of the car's technology that let the vehicle process the surroundings in order to avoid crashes, understand how much battery is left and other vital pieces of information.

Many are the Universities which are investing in training their students by using Virtual Reality peripherals. The first example that comes to mind is, of course, medical-related: by putting the student into a high quality represented situation, he's able to understand the dynamic of the case without risking anyone's life (especially if it's done when studying very delicate surgeries).

Encouraging the usage of such peripherals could be a breaking point in order to better prepare surgeons and specialists in subjects and operations that are naturally delicate.

Simulation Is Reaching The Next Level

Flight simulator, the popular game for Windows, recently announced that an entire VR-based version will come in the future, providing an experience close to the actual pilot training which is currently done by many different companies such as British Airways and Turkish Airlines. By stating that, we can easily say that the future of such simulations will be relying on Virtual and Augmented Reality combined and not just in a video game-related scenario.

By combining physical peripherals and VR ones like Oculus or HTC Vive, the user will be fully absorbed in an experience which will lead him to a better understanding of the experience itself.

It's no secret that Google always wanted to explore the VR world. What we can expect in the near future is the combination of floor AR (the one used in order to present 3D maps in the current Google Maps application) and SLAM, in order to present a realistic projection of the surroundings in order to boost the user experience.

Maps will surely be VR focused in the near future, that is not a secret, we just have to wait and see how this will evolve in the near future.

This is an exciting time for VR and AR developers, no doubts about it. The entire tech market is moving towards that direction and we can expect a big business growth in the next years. Virtual Reality is indeed taking over.

Paul Matthews is a freelance writer in Manchester. His aim is to inform people about the power of the internet, while he still focuses on his office. You can often find him mountain biking or at the local library.

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Virtual Reality and Augmented Reality are possibly two of the biggest trends in the entire tech world. We saw the rise of VR in gaming applications and mapping software, but what is the future of this technology? How will Virtual Reality impact on our day to day life?

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1. Now we have to worry about protecting our car, our home appliances, our wearable, and many other IoT devices.
2. Cars can be hacked decelerate/ accelerate
3  And one problem is that companies do not update their devices enough or at all
4. Companies pressured to get their devices out quickly end up compromising on security.
5. also use these devices to obtain personal data, particularly dangerous when used for money transfer
6. Most people are too lazy to update their software’s

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The internet of things (IoT) is finally here in 2017, and companies like Google and Amazon are rushing to get out and become the main company to become the hub of this revolutionary concept. There have been multiple predictions over the years which declare that there will be at least tens of billions connected devices by 2020, and even objects as mundane as baby monitors or tires could all become part of this interconnected world.

But each device which is connected increases privacy and security concerns surrounding the Internet of Things. These concerns range from hackers stealing our data and even threatening our lives to how corporations can easily uncover private data we carelessly give them. While the IoT's progress will not be stopped anytime soon, here are some of the biggest issues which consumers and businesses need to consider before both using these connected devices.

More devices, more problems

The fundamental security weakness of the Internet of Things is that it increases the number of devices behind your network's firewall. Ten years ago, most of us had to only worry about protecting our computers. Five years ago, we had to worry about protecting our smartphones as well. Now we have to worry about protecting our car, our home appliances, our wearables, and many other IoT devices.

Because there are so many devices that can be hacked, that means that hackers can accomplish more. You may have heard about how hackers could potentially remotely control cars and remotely accelerate or decelerate the car. But hackers could use even seemingly unimportant devices like baby monitors or your thermostat to uncover private information or just ruin your day. The point is that we have to think about what a hacker could do with a device if he can break through its security.

Updates, updates, updates

As the Internet of Things becomes reality, we have to worry about protecting more devices. But even if you start taking security seriously, the tech companies which make these new devices are too cavalier about the risks. And one problem is that companies do not update their devices enough or at all. This means that an IoT device which was safe when you first bought it can become unsafe as hackers discover new vulnerabilities.

Computers used to have this problem, but automatic and easier updates have helped alleviate this problem. But as CSO points out, companies pressured to get their devices out quickly end up compromising on security. Even if they may offer firmware upgrades for a time, they often stop when they focus on constructing the next device, leaving customers with slightly outdated hardware that can become a security risk.

Protecting your data from corporations

Hackers are scary, but they are far from the only threat to the Internet of Things. In fact, the corporations which create and distribute interconnected devices could also use these devices to obtain personal data, particularly dangerous when used for money transfers.

For example, consider how BP and other companies are distributing Fitbits to their employees so that they can track their health and thus get lower health insurance premiums. Even if we ignore the worrying idea of workers' health being monitored by corporations around the clock, there is the question of what corporations can do with the data they have gathered. Some companies like RadioShack have attempted to send or even sell gathered data to other companies, which raises issues regarding our individual privacy rights.

For now, the best protection which consumers have is to actually read any agreement they sign when receiving a device. Also find what that device's corporation's policies are in regards to keeping data safe and sharing said data. This may mean refusing to use certain IoT devices, but said device may not be worth the privacy tradeoff.

Lazy consumers

Computers have automatic updates partly because most users are too lazy to perform even the basic steps needed to keep their computer safe. And when you consider that protecting the myriad IoT devices will be even harder than a single computer, this problem will get even worse.

While tech companies and the government are taking the IoT security threat more seriously, the first line of defense in your home is you. This means taking the time to think about how IoT devices could be used against you as well as going over their security features. For example, an IoT device from a smaller, less established company may be cheaper or have other attractive features. But if that smaller company folds, then there is no one around to patch its vulnerabilities.

IoT boasts opportunity, but the security risks cannot be ignored whether it is from hackers or corporations. Above all else, the best remedy is to consider the potential risks of installing connected devices and doing your research.
This article is published as part of the IDG Contributor Network. Want to Join?

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4 critical security challenges facing IoT

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The internet of things (IoT) is finally here in 2017, and companies like Google and Amazon are rushing to get out and become the main company to become the hub of this revolutionary concept.

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·         Tesla is now developing their own computer chips for their self-driving cars.

·         They haven’t had a major tech upgrade in 3 years until now.

·         Their new chips, which are made by Tesla themselves, can process 2,000 frames per second as opposed to their old NVIDIA chips which process 200 frames per second.

·         This is another step towards Tesla controlling all aspects of their production (they even make their own seats, something that is very uncommon among car manufacturers.)

·         Chips are made from a new type of silicon are were designed from scratch.

·         All new Tesla’s have the hardware to be self-driving, the software and chip just needs to be perfected. This new chip would do just that.

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Tesla's Model S sedan, the car company's flagship vehicle, was first shown as a prototype in 2009, has been on sale since 2012, and, barring one small change to remove the fake grille at the front, has looked exactly the same for nearly a decade. This is notable because most manufacturers fully redesign their cars every four to six years to keep them fresh-and to keep buyers buying. For Tesla, tech upgrades are the selling point. The company pushes software updates several times a year, adding features like summon, where a car pulls in and out of a garage with nobody inside, or camper-mode, for sleeping in the car with the heating on.

Tesla's biggest claim is that one day, all the cars it's currently building will be capable of full-self driving. So when Elon Musk announced after the company's second-quarter earnings report that Tesla is developing its own computer chips, it was a momentous claim by the car company. Musk's grand reveal (which maybe actually isn't news given Musk said this at an AI conference in December, a fact WIRED reported on then) led him to boast the company has "the world's most advanced computer designed specifically for autonomous operation."

Musk says the new silicon is an order of magnitude faster than the chips in their cars now, a product developed by NVIDIA, an industry leader and supplier to at least 20 other robocar developers. The NVIDIA chips, Musk says, can deal with 200 frames of video per second, from the cameras around the car. (Unlike most other autonomous vehicle developers, Tesla is determined that cameras are enough to perceive the world, and it doesn't need the more expensive lidar sensors.) Tesla's chip can handle 2,000 frames per second, with some spare capacity left for redundancy and safety.

That might be true, but it's not a Ruffles to Ruffles comparison. "The performance claims are against what they have in the vehicle today, which are three years old," says NVIDIA's director of automotive Danny Shapiro. NVIDIA's latest silicon is at least 10 times faster than that, which would put it on a par with Tesla's chip.

Tesla's chip development is key to improving Autopilot. Right now the term is an umbrella for a collection of driver assistance tools, but in October 2016 Elon Musk claimed that all new Teslas now come with the hardware-cameras, ultrasonic sensors, and an on-board supercomputer-for self driving. They just needed to develop the software.

It turns out that computer wasn't powerful enough. Tesla upgraded it once, a year later, but now says it'll have to upgrade all the cars again, with even more number crunching power-and the new chip. All the connectors on the computer, which sits behind the glove box in the Model S and X, are exactly the same, so it's just a case of service centers plugging in a new one. "It's super kick-ass," says Musk.

Building its own chips is the latest example of Tesla's drive for full vertical integration, where it designs and makes everything in-house. Most automakers have chains of suppliers and sub-suppliers, but Tesla famously, and unusually, even builds its own seats. That allows it to create custom solutions, using the ground-up redesign approach that Musk prefers.

For the chips, the team went back to a "bare metal level" and instead of layering up GPUs, which were originally designed to speed up graphics processing for gaming machines, Tesla designed the calculator and memory circuits from scratch, sitting right next to each other, so they can pass data at high speed, which has been a traditional choke point when a GPU is used in emulation mode to run an AI neural net.

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Tesla Says Its New Self-Driving Chip Is Finally Baked

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Tesla's Model S sedan, the car company's flagship vehicle, was first shown as a prototype in 2009, has been on sale since 2012, and, barring one small change to remove the fake grille at the front, has looked exactly the same for nearly a decade.

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·         Alaskan borough suffered cyber attack

·         They were locked out of their computers and email  servers

·         Hackers took their data hostage and demand payment to get it back

·         The borough has been forced to shut down most all of their computer systems because of this

·         They have reverted to type writers and their means of communication have been limited

·         The attack has taken an economic toll on the borough due to suspended services and loss of productivity

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August 1
After a devastating cyberattack forced a borough in Alaska to shut down many of its computer systems, officials there turned to an unlikely savior: typewriters.

Staff in Matanuska-Susitna, a south-central borough of Alaska, began relying on typewriters after hackers locked them out of their computers and email server, according to the BBC. The perpetrators used what's known as a ransomware attack, deploying malicious software to take control of digital systems and then demanding payment from the victims to get their data back. While the borough works to bring its systems back online, officials "reenlisted typewriters from closets" and wrote receipts and lists of library members by hand, according to Bleeping Computer, a technology website.

The borough said that beginning in mid-July, hackers seized its desktop computers, most of its servers, telephone system and email exchange. On the borough's home page, visitors are greeted with an arresting banner: "Mat-Su Declares Disaster. Worst of its Kind Cyber Attack."

Borough Manager John Moosey declared the cyberattack a disaster Tuesday, according to a statement by public affairs director Patty Sullivan. Moosey told members of the public at a Borough Assembly meeting that the declaration grants the borough access to its insurance, funds dedicated to emergencies and possibly the assistance of the Federal Emergency Management Agency.

The borough anticipates that the major disruptions caused by the cyberattack may continue for a "prolonged time." The loss of services and productivity, the borough said, is compounded by the high cost of hiring IT specialists and the "great deal of overtime" that the borough is paying IT department staff.

Borough IT Director Eric Wyatt said the perpetrators deployed hacking tools that were the "worst of its type," citing international cyber experts who have experience with the kind of ransomware that was used. The computer virus that infected the borough has claimed 209 previous victims, he said. "The group that we are facing that has unleashed this particular attack is a very well-organized group, and they're using the most sophisticated tools and have done a lot of damage across the country to include us."

The data encrypted by the hackers was turned over to the FBI, Wyatt said in a report earlier this week, in the hope that federal agents can recover the decryption keys needed to retrieve the information.

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An Alaskan borough turns to typewriters and handwriting after its computers were hacked

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August 1 After a devastating cyberattack forced a borough in Alaska to shut down many of its computer systems, officials there turned to an unlikely savior: typewriters. Staff in Matanuska-Susitna, a south-central borough of Alaska, began relying on typewriters after hackers locked them out of their computers and email server, according to the BBC.

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Google has created an AI operated messaging machine

The messaging machine has the ability to mimic the human voice when making calls

Google is releasing the service to a select few customers

The rollout will begin with the ability to make “holiday hour” calls and then make restaurant reservations and finally the service will be able to make haircut appointments..

The Google AI is so accurate to the point where it even impersonates the natural tendencies that human voices tend to have such as  “Ummmm” and “Ahhhh” and “ooooh”

Google  Duplex has the ability to detect multiple different accents as well.

When a call goes wrong the call will get redirected to an actual human being to recover

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In a restaurant in Mountain View, California yesterday, Google gave several small groups of journalists a chance to demo Duplex. If you don't recall, Duplex is the AI system designed to make human-sounding voice calls on your behalf so as to automate things like booking restaurant tables and hair appointments. In the demo, we saw what it would be like for a restaurant to receive a phone call - and in fact, each of us in turn took a call from Duplex as it tried to book a reservation.

The briefings were in service of the news that Google is about to begin limited testing "in the coming weeks." If you're hoping that means you'll be able to try it yourself, sorry: Google is starting with "a set of trusted tester users," according to Nick Fox, VP of product and design for the Google Assistant. It will also be limited to businesses that Google has partnered with rather than any old restaurant.

The rollout will be phased, in other words. First up will be calls about holiday hours, then restaurant reservations will come later this summer, and then finally, hair cut appointments will be last. Those are the only three domains that Google has trained Duplex on.

The demos we saw had many of the same elements that made the original demonstration at Google I/O so impressive: the voice sounded much more human than normal, complete with "umms" and "ahhs." It also featured something we didn't hear in May: each call started with an explicit statement that the call was being recorded.

There were a few variations on the disclosure, but they all included some indication that you were talking to a machine and the call was being recorded. For example, one call began with "Hi, I'm calling to make a reservation. I'm Google's automated booking service, so I'll record the call. Uh, can I book a table for Sunday the first?"

"I'm Google's automated booking service, so I'll record the call."

A few things to note about that call. The voice sounded just as natural as in the video above, not at all like a robot. There were several variations on the robot disclosure - Google seems to be testing to see which is most effective at making people feel comfortable sticking with the call. The other thing to know is every variation I heard definitely said that it was recording, usually followed by a quick "umm" before jumping in to making a request for the reservation.

The more natural, human-sounding voice wasn't there in the very first prototypes that Google built (amusingly, they worked by setting a literal handset on the speaker on a laptop). According to VP of engineering for the Google Assistant Scott Huffman, "It didn't work ... we got a lot of hangups, we got a lot of incompletion of the task. People didn't deal well with how unnatural it sounded."

Part of making it sound natural enough to not trigger an aural sense of the uncanny valley was adding those ums and ahs, which Huffman identified as "speech disfluencies." He emphasized that they weren't there to trick anybody, but because those vocal tics "play a key part in progressing a conversation between humans." He says it came from a well-known branch of linguistics called "pragmatics," which encompasses all the non-word communications that happen in human speech: the ums, the ahs, the hand gestures, etc.

"Google has invented a lot of things" Huffman said, "but we did not invent ums and aahs."

If you take a Duplex call and want to take that initial "um" as an opportunity to say "yeah no, I don't want to be recorded," Duplex can recognize that and end the call with something like "'Okay, I'll call back on an unrecorded line' and then we have an operator just call back," Fox says.

There are a few states where Duplex won't work - Fox says Google doesn't yet have the permitting for Texas, for example - but it should start making calls in the vast majority of the US soon. Duplex only works in English for now, but Google has worked to ensure that it is able to understand lots of dialects and accents.

"We going to be very slow, very careful, and very thoughtful."

Fox emphasized that the behavior of Duplex arises out of Google's recently published " core AI principles." "We're going to be very slow, very careful, and very thoughtful as we go here," said Fox. That's part of the reason why the initial testing will only be with businesses that Google has partnered with. Google will also allow businesses to opt-out of being called by Duplex - likely through the "Google My Business" portal. Of course, if you're the sort of business that doesn't have online bookings, you're probably the sort of business that has never identified yourself to Google using its tools.

"We want to be very respectful of the businesses we're working with," Fox stressed. Google will ensure that businesses won't receive too many calls from Duplex - say, for example, from people who might use it to prank restaurants with fake reservations.

When you set up Duplex on your Google Assistant, you'll give it a few pieces of information and some permissions. In one call, Duplex told the human on the phone it wasn't authorized to share an email address but could share a phone number, for example.

As for haircuts, Fox noted that Google hadn't worked out all the details of what Duplex would need to know, but he imagined a case where at the least it might be able to ask for something like your "usual" haircut.

Duplex conveyed politeness in the demos we saw. It paused with a little "mmhmm" when the called human asked it to wait, a pragmatic tactic Huffman called "conversational acknowledgement." It showed that Duplex was still on the line and listening, but would wait for the human to continue speaking.

Duplex's tone was unfailingly polite and at times apologetic

It handled a bunch of interruptions, out of order questions, and even weird discursive statements pretty well. When a human sounded confused or flustered, Duplex took a tone that was almost apologetic. It really seems to be designed to be a super considerate and non-confrontational customer on the phone.

But Duplex can't handle everything, and so it will be paired with a bank of human operators who can take over a call if it goes sideways. Valerie Nygaard, product manager for Duplex, emphasized that "this is a system with a human fallback." Those operators serve two purposes: they handle calls that Duplex can't complete and they also mark up the call transcripts for Google's AI algorithms to learn from.

None of the phone calls we listened to required human fallback, however. Huffman says that right now four out of five calls that Duplex makes can be handled without the human operator. That's either a very low or very high percentage, depending on your attitude toward the technology.

If you're skeptical of all this, I don't blame you. Google got quite a bit of blowback after its Google I/O demo, both from people who were wondering about disclosure and from those who thought it might have not been a real call. Fox insists that it was, though it was edited to take out "personal information."

Another reason to believe the demos we saw in that Mountain View restaurant were real? My own demo totally flopped. I played the role of a busy, crabby bartender who kept interrupting Duplex. The system handled the interruptions fine, but it got flummoxed when I told it that I could go ahead and make a reservation for seven, but the full kitchen closed at six that day so it would have to settle for bar food.

My call should have been handed off to a human operator at that point, but instead Duplex misunderstood my meaning about the kitchen closing. When I said there would only be bar food in a harried and snippy tone, it replied "Oh I see. Bye, thank you," and hung up.

It was a very human thing to do.

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Google Duplex really works and testing begins this summer

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In a restaurant in Mountain View, California yesterday, Google gave several small groups of journalists a chance to demo Duplex. If you don't recall, Duplex is the AI system designed to make human-sounding voice calls on your behalf so as to automate things like booking restaurant tables and hair appointments.

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My article:

·         The Chinese version of Google will censor websites like the BBC and Wikipedia.

·         Google is planning on launching a censored version of its search engine in China in the next six to nine months, recently leaked documents reveal.

·         This marks an about face on the company’s policy regarding Chinese censorship, which caused Google to shut down its search service in the country in 2010.

·         The Chinese government blocks information on the internet about political opponents, free speech, sex, news, and academic studies.

·         Mentions of books that negatively portray authoritarian governments, like George Orwell’s 1984 and Animal Farm, have been prohibited on Weibo, a Chinese social media website

·         The censorship will apply across the platform: Google’s image search, automatic spell check and suggested search features will incorporate the blacklists, meaning that they will not recommend people information or photographs the government has banned.

·         We have also found overall that internet censorship [in China] is evolving towards less transparency, with less notification to users when messages are censored or removed across all platforms