Monthly Archives: August 2017

Facts About the Smartphones in Our Pockets

Be honest: Do you think you could go a day without checking your phone? Obviously, if the answer is no, you’re far from alone. If you’ve ever stopped and looked around a doctor’s waiting room or a subway car, it’s most likely the people around you are engrossed in their devices.

But whether you’re focused on scrolling through the news, keeping tabs on an ex or getting your inbox down to zero, it seems that there is more than just mental strain we have to worry about with constantly looking at our phones.At the end of 2017, the California Department of Public Health put out a report about the effect that energy that comes from smartphones can have on our long-term health. The CDPH issued some guidelines that essentially boil down to not keeping your phone on your person too much of the time and putting it away from your bed.

They also recommended decreasing cell use when you have a weak signal, streaming less audio or video, reducing downloads of big files on your phone and removing headsets when you aren’t making a call. Also, those products that say they can block radio frequency energy? Turns out, they can actually increase your exposure to it.

Clearly, the force of mobile phones in our lives is powerful, but how much do we really know about them?

Read on facts about the smartphone in your pocket.
1.Seventy-seven percent of U.S. adults say that they own a smartphone, according to a 2017 report from the Pew Research Center.
2.Fifty-one percent of U.S. adults reported to Pew that they have used smartphones to buy items online.
3.Forty-six percent of U.S. adults told Pew that they “couldn’t live without” their smartphones.
4.According to a recent study, the average U.S. adult spends two hours and 51 minutes a day on his or her smartphone, which adds up to 86 hours every month.
5.A 2015 study reported that the average U.S. smartphone user sends and receives 32 texts and makes and receives six phone calls a day. These actions take 26 minutes and 21 minutes, respectively.
6.ComScore’s Mobile Hierarchy Report from the beginning of 2017 found that apps account for 87 percent of total time spent time on smartphones.
7.Sixty-two percent of smartphone owners used their cell phones to research health conditions. Please don’t go down a Google rabbit hole without some sort of medical professional guidance.
8.The first handheld mobile phone went on sale in 1984. It was made by Motorola and called the DynaTAC 8000X. You could only talk on it for up to a half hour.
9.As far as research and development, Motorola spent roughly $100 million to turn the idea for the device into a reality.
As of 2016, the top five countries with the highest number of smartphone subscriptions are South Korea, Australia, Israel, U.S. and Spain.

Free Data!

“What good are wings without the courage to fly?” These words of wisdom come to mind as I consider the open-source craze among leading artificial-intelligence technology providers.

Top firms, including IBM, Google and Facebook, have opened the source code of their artificial intelligence software tools, making them available for developers to use in their own devices and applications. This is most certainly a good thing, for the companies themselves and for the AI business generally.

However, open source is only part of the equation. Unlike previous generations of software, AI algorithms are worthless without a dataset to work on. And in contrast to their open-source code policies, these companies maintain a closed-data stance, hoarding their vast information repositories as a competitive advantage for developing better AI technology.

Essentially these companies have given us wings — but have denied us the sky. What the top tech firms need is the courage to stop hoarding information and embrace open data, giving the rest of the world access to the information required for AI cognitive engines to attain their full potential.

The data-rich get richer.
In the age of AI, a new 1 percent is arising. This upper, upper crust consists of companies blessed both with machine-learning technology and with large quantities of information.

Some companies have been dubbed “the Superrich” of the AI business, including Google, Facebook, Amazon and Microsoft. It has been reported that, while there are very few of these companies in the world, they have a massive advantage over everyone else in the machine learning space because they have access to vast amounts of clean, structured data.

Such data is needed to train machine-learning algorithms, giving them the basic information they need to function on their own in the real world. For example, an object-recognition algorithm designed to recognize cats in photos will be trained by reviewing massive numbers of images depicting felines. These images need to have some structure, i.e., they must be tagged with keywords that properly indicate they are depicting cats.

The larger the quantity of training data, the better the algorithm will perform, with more information providing more examples that can be used to find patterns. Conversely, inadequate quantities of training data can produce algorithms that deliver substandard results—sometimes to the extreme embarrassment of their creators.

Because of this, the usefulness of an AI algorithm is intrinsically tied to the availability of high-quality data. In this regard, AI algorithms are fundamentally different from other types of software, whose code is valuable on its own without any additional data.

Thus, when a company open-sources an AI cognitive engine such as a translation tool, it’s not the same as open-sourcing a piece of traditional software, like a spreadsheet. Without also providing access to the data, open isn’t really open.

Such data-denial is no accident. Rather, it’s part of a deliberate strategy to maintain a competitive advantage. With AI models well known and well distributed, the data set is the one commodity that can be locked away and kept from rivals.

That’s why top technology players are hoarding data. For example, IBM didn’t buy The Weather Channel’s data operations because it wanted to know if it’s going to rain in Tallahassee tomorrow.

Weather is the number-one factor driving global GDP. By combining The Weather Channel’s vast repository of climate-related information with its Watson AI, IBM can take the lead in forecasting the weather for private businesses, allowing it to do everything from predicting winter energy demand to forecasting crop yields.

Paving the Road to the Future of Transportation

The rapid advancement of vehicle technology is dramatically altering transportation models around the world. From early stage consumer infotainment features, to ride sharing and on-demand mobility services, to fully autonomous vehicles in the future, connectivity in the car has been the driving force behind recent automotive technology advancements. As a result, vehicles have morphed into much more than just a way to get from one place to another, but extensions of consumer digital lifestyles and a catalyst for significant change in the way society will experience future mobility.

To visually summarize the past, present and future of vehicle connectivity, Airbiquity developed an infographic, Mapping Vehicle Connectivity: The Driving Force Behind Automotive Innovation, comprised of four key phases of the connected car.

Phase 1: Connecting the car.
The first phase of the connected car was establishing connectivity between the vehicle for call center and concierge services like GM’s OnStar. Many automakers followed suit and introduced similar safety and convenience services. Simultaneously, Bluetooth technology was introduced which enabled drivers to safely make and receive “hands free” phone calls while in their vehicle for the first time. This phase wouldn’t last long however, as the widespread consumer adoption of more sophisticated smartphones drastically altered the driving experience and in-vehicle environment.

Phase 2: Infotainment
The second phase of vehicle connectivity was driven by the introduction of smartphones and pervasive use of mobile apps. Leveraging the connectivity that preceded it, infotainment quickly became a ‘must have’ feature for new car buyers, especially millenials. The first infotainment service was announced by Ford in 2007, and by 2015, all major automakers had integrated some type of infotainment system into their vehicles. Infotainment programs allowed popular apps like Spotify and Pandora to enter the vehicle environment, mirroring the already familiar smartphone-based mobile experience and extending consumers’ digital lifestyle into their cars.

Phase 3: Software and data management
As we enter phase three, the phase we’re currently in, vehicle technology is advancing again with the introduction of over-the-air (OTA) services, which enable the transmission of software updates and data between a vehicle and the cloud. For context, imagine if you had to go to a physical retail store every time you needed to update your smartphone operating system or apps. It would be very inconvenient, correct? Similar to a smartphone, vehicle software will increasingly need to be updated as well – and OTA technology allows this to happen remotely. Prior to OTA, consumers had to visit dealerships to get their vehicle software updated, a costly burden for automakers and a hassle for vehicle owners. With OTA, global automaker cost savings for mitigating software recalls and cybersecurity threats alone are forcasted to increase from $2.7 billion in 2015, to $35 billion by 2022, according to research firm IHS. In addition to revolutionizing vehicle vehicle software update and data management, OTA will also serve as a foundation for the fourth phase of connected car: fully autonomous driving.

Designing a Moral Machine

Back around the turn of the millennium, Susan Anderson was puzzling over a problem in ethics. Is there a way to rank competing moral obligations? The University of Connecticut philosophy professor posed the problem to her computer scientist spouse, Michael Anderson, figuring his algorithmic expertise might help.

At the time, he was reading about the making of the film 2001: A Space Odyssey, in which spaceship computer HAL 9000 tries to murder its human crewmates. “I realized that it was 2001,” he recalls, “and that capabilities like HAL’s were close.” If artificial intelligence was to be pursued responsibly, he reckoned that it would also need to solve moral dilemmas.

In the 16 years since, that conviction has become mainstream. Artificial intelligence now permeates everything from health care to warfare, and could soon make life-and-death decisions for self-driving cars. “Intelligent machines are absorbing the responsibilities we used to have, which is a terrible burden,” explains ethicist Patrick Lin of California Polytechnic State University. “For us to trust them to act on their own, it’s important that these machines are designed with ethical decision-making in mind.”

The Andersons have devoted their careers to that challenge, deploying the first ethically programmed robot in 2010. Admittedly, their robot is considerably less autonomous than HAL 9000. The toddler-size humanoid machine was conceived with just one task in mind: to ensure that homebound elders take their medications. According to Susan, this responsibility is ethically fraught, as the robot must balance conflicting duties, weighing the patient’s health against respect for personal autonomy. To teach it, Michael created machine-learning algorithms so ethicists can plug in examples of ethically appropriate behavior. The robot’s computer can then derive a general principle that guides its activity in real life. Now they’ve taken another step forward.

“The study of ethics goes back to Plato and Aristotle, and there’s a lot of wisdom there,” Susan observes. To tap into that reserve, the Andersons built an interface for ethicists to train AIs through a sequence of prompts, like a philosophy professor having a dialogue with her students.

The Andersons are no longer alone, nor is their philosophical approach. Recently, Georgia Institute of Technology computer scientist Mark Riedl has taken a radically different philosophical tack, teaching AIs to learn human morals by reading stories. From his perspective, the global corpus of literature has far more to say about ethics than just the philosophical canon alone, and advanced AIs can tap into that wisdom. For the past couple of years, he’s been developing such a system, which he calls Quixote — named after the novel by Cervantes.

Riedl sees a deep precedent for his approach. Children learn from stories, which serve as “proxy experiences,” helping to teach them how to behave appropriately. Given that AIs don’t have the luxury of childhood, he believes stories could be used to “quickly bootstrap a robot to a point where we feel comfortable about it understanding our social conventions.”