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Machine Learning AI Grapples with Human Complexity

There’s a new AI that offers bold estimates in real estate values and income levels in New York City using satellite imagery.

Its name is “Penny”, and it was trained by Carnegie Melon University computer scientist Aman Tiwari. Using a neural network, he overlaid census data on high-resolution satellite imagery of New York. From there, Penny was taught to analyze the urban landscape and associate visual patterns with income. It learned to recognize objects and shapes – cars, helipads, swimming pools, manicured lawns – with different income brackets.

AI and machine learning are currently in the spotlight because of thousands of possible lucrative applications.

The interface was created by the data visualization studio Stamen so users have the ability to add and remove features such as solar panels, stadiums, and more much like you would in the game of SimCity. Of course, the end goal here is not to build homes and communities, but to gauge how adept AI technology has become.

AIs like Penny are expected to perform intuitively. Sometimes, however, they give a reading that is totally off-track, which only proves that the technology still has a long way to go before it can be considered truly dependable.

Wired.com interviewed University of Wyoming computer scientist Jeff Clume where the neural network expert said, “Sometimes an AI does amazing things or locks onto some very intelligent solution to a problem, but that solution is inscrutable to us, so we don’t understand why it’s behaving in counter-intuitive ways. But it’s simultaneously true that these networks don’t know as much as we think they know, and they often fail in bizarre or baffling ways—which is to say they make predictions that are wildly inaccurate when it’s obvious they shouldn’t be doing so.”

Artificial Intelligence vs Machine Learning

Artificial intelligence is intelligence as shown by machines. This is in the context of using “intelligence agents” which is a device tasked to do a goal without being specifically programmed for it. Instead, the device explores its environment and finds a way to complete the task on its own. The device might be programmed to have the tools to understand the environment and the goal, but is given the freedom to find its way towards hitting the target.

Aerial veiw of skyscrapers from machine learning satellite imagery.
Penny scans NYC for elite objects connected with lucrative real estate.

In this sense, the term “artificial” refers to the way the machine emulates how a person would solve a problem. It would demonstrate the concept of “learning” by improving the way it reaches the goal, as well as “problem-solving” by reaching the goal without any prior knowledge of the challenges it would encounter.

Machine learning, on the other hand, is a subset of AI. Using pattern recognition and big data, machine learning provides insight into things even though it was not stated to do so in terms of an AI goal. Machine learning does this with the use of iterative processes which help it understand the data that it uses.

One of the most common uses of machine learning is in fraud detection for credit card and telecommunications companies. It has also been used in parsing text to understand the sentiment it is trying to convey. Another natural fit for machine learning is in the automated stock trading algorithms used by trading companies. It is also being used in natural language processing, where language is processed or translated based on the mountain of data available.

Machine learning can be considered as one of the methods used by AI. With a ton of data, processing for something which users do not have a clue about can be achieved using machine learning. The machine is given a goal, and this is achieved through iterative algorithms which allow it to repeatedly sift through the data. At every pass, it learns something new, and it develops its own way or method of understanding of the data.

Investing in an AI-Driven Future

AI may have other methods of teaching a device to achieve a goal. This may include rules for the machine to follow a speech pattern or language use. Machine learning, for its part, would be able to reach the same goals by seeing the patterns used and creating rules on its own, based on these observations. As an AI method, machine learning is useful for situations where there is a huge amount of data. For smaller data sets, or specific instances, other AI programming methods can be used instead.

AI and machine learning are currently in the spotlight because of thousands of possible lucrative applications. The Internet has brought with it a ton of data which can be used for machine learning purposes. At the same time, AI research has been going on strong and is expected to only continue growing for research companies and investors.

One of the most famous AI showcases is Deep Blue by IBM. During the 1990s, Deep Blue won against the reigning world chess champion. IBM has been into AI for a long time and is expected to be a major player with its mainframe computer capabilities.

Not to be outdone, Google’s Deep Mind has been perfecting its AI by pitting it against top gamers in the world. In March 2016, Deep Mind’s AlphaGo won against an experienced and highly ranked Go player. This match was highly publicized and marked a significant turn in the development of AI due to the highly-complex and intuitive nature of the game.

Additionally, Phone manufacturers Apple and Samsung have released their own AI agents. Siri has been integrated with the iPhone for a couple of years now. Samsung has recently released their own AI for their new generation of phones. Google has its own consumer-fronting AI, which they have implemented with Android. The same AI is also included in their consumer home products. In direct competition with the Google consumer AI, Alexa from Amazon has been included in several smart home products of their own.

These consumer AI products all use tremendous amounts of data which have been gathered from the consumers themselves. This is to better provide individual customized services. For the smart home, this could be as simple as an interaction with a smart refrigerator, or an automatically adjusting thermostat control in a room.

What’s In Store for AI

As consumer-facing companies, the AI agents collect information while interacting with the user. The more that a user interacts, the better the AI agent performs in terms of providing services. The AI customizes its actions based on the responses and interaction with the user. These could be time-based activities, like setting the alarm during weekdays, or turning on the TV and changing the channel to watch shows that the user usually watches.

Some of the most promising startup AI research companies include: Zoox (automobile technology), Insidesales.com (ads, sales and CRM), DataMiner (business intelligence and analytics), Sentient Tech (core AI), Cylance (cybersecurity), Benevolent.AI based in the UK, Icarbonx based in China (health care), UBTech Robotics based in China, ANKI (robotics), and Zumergen.

AI is not intended to replace humans, whether in the workplace, or anywhere else. It aims to help make daily lives more convenient by automating tasks humans often find menial and time-consuming. It means well, and the technology to mimic human thinking and decision making will eventually arrive. But we’re not there yet. There’s still a long way to go.

 

 

 

No Battery Needed, Latest Cell Phone Tech

Imagine the battery free cell phones. That means no charging, no failures, no changing batteries. It makes cell phones more easy and reliable to use. It’s a great convenience for all of us in advanced countries.

My dad was a spy in the Cold War, so I heard stories about the Great Seal bug when I was a kid.

But the big pay-off is in poor countries and rural areas, which often lack reliable electricity. The lack of electricity cuts them off from the information age. A cell phone that can operate without a battery would increase the possibilities by orders of magnitude. And once that tech is applied to laptops, you can see that it is a truly bold and world-changing technology.

No Battery Cell Phone from University of Washington

Vamsi Talla, a Research Associate at a lab in the University of Washington, and his colleagues revolutionized the cell phone. Their prototype mobile phone makes calls without using any battery, at all. The device takes power directly from the air. This is an absolute breakthrough.

Mobile phones are incredibly useful and have become a necessity for those who live in developed nations. It’s a medium for communication, entertainment, and information. A lack of basic electricity cuts many rural communities off from all of these capabilities, from checking the weather report to protect crops, to planning a trip to the city to sell wares; without a cell phone, it is simply an impossible task.

Mobile Phones & Backscatter

According to Joshua Smith, the Head of the research lab at the University of Washington, “A cell phone is one of the most useful objects there is. Now imagine if your battery ran out and you could still send texts and make calls.”

Graphic of a Battery Free Cell Phones
Imagine the implications for rural areas.

The group of researchers from the University of Washington has come up with a method called backscatter. Backscatter is a technique that allows the mobile phone make a call by means of reflecting the incoming radio waves.

The technique was first mastered during the Cold War, with passive bugs for eavesdropping, like the Great Seal Bug. It requires considerably less energy than modern phones.

Smith said that “My dad was a spy in the Cold War, so I heard stories about the Great Seal bug when I was a kid.”

Because backscatter requires less power, the group had to use an old-fashioned analog method of making calls. Researchers developed an analog voice transfer in order to save power. And a base station is a vital medium for the battery-free phone to work.

Talla said that “If you can communicate using analog technology, you’re actually more power efficient.” He added, “Real cell towers have a hundred times as much power, and would increase the range to perhaps a kilometer.”

The Battery Free Cell Phones

The prototype cell phone has a simple number pad, which is touch-sensitive. It has a small red LED, serving as its only display. The LED flickers briefly every time a key is pressed. The phone is similar to a style of a walky-talky. The user still needs to press a button in order to talk or listen.

The researchers are already coming up with the next leap in the innovation of the project. They want to incorporate a better quality of voice and also an electronic ink screen to allow messaging. Incorporating a camera to take selfies is an idea that is on the table as well. The phone is said to be much cheaper than a normal phone.

The cell phone without a battery is still a prototype but developments are already on their way.

Charter Schools in NYC Score Big With the Win of Success Academy

The cause for charter schools—even more particularly on this topic, the charter schools in NYC —and the privatization of American education recently scored a big victory with New York’s Success Academy (SA), which won the 2017 Broad Prize for Public Charter Schools. This award, which is handed out to charter networks in the U.S. with the best academic outcomes for marginalized students, is tainted with irony. In fact, New York City Mayor Bill de Blasio has vocally opposed the school’s growth from the very beginning.

A Win for Charter Schools in NYC

Success Academy will be collecting a $250,000 prize money to be used for its college-readiness program. After being refused classroom space in the city, Success Academy CEO and former City Councilwoman Eva Moskowitz is now laughing all the way to the bank—and perhaps beyond.

Success Academy is one of 170 charter schools in NYC and over 6,800 in 42 states across America. Reports say there are three million American students who go to charter schools. Charter schools also fall under the public school system but get funding depending on how much is dictated by each state. These schools offer a specialized curriculum, such as one that heavily leans towards vocational training, arts or mathematics. Notably, in 2016, SA students ranked in the top 10 percent in Math, Science and English. While politicians like de Blasio oppose the charter school system, it is championed by school choice and school voucher program proponents like U.S. Secretary of Education Betsy DeVos.

Charter Schools vs. Public Schools

a cartoon of a school building with a bus and kids in front of it, depicting a common scene in charter schools in NYC

Charter Schools originated and begun in Minnesota in the early 1990s. It comes with a special charter or set of rules from the state. These schools cannot charge students tuition fees because they do receive public funding. Additionally, charter schools can’t set admission requirements. If there are more students than what the school accommodates, the students are chosen by drawing lots.

Charter schools—such as the charter schools in NYC —offer flexibility in their programs and classes. They can also offer courses which they feel would interest students, unlike public schools which need to follow a set curriculum and school calendar.

Public policy advocates have said that charter schools started because people were unhappy with the American public school system. There were also issues with ethnicity, wealth and location. In essence, people hoped that charter schools would be able to offer more than what public schools could.

For decades now, educators like Betsy DeVos have been fighting for the establishment of more charter schools to encourage school choice. She also believes that private schools should be opened to nonaffluent students through “vouchers”—a form of financial assistance. This voucher will allow poor but deserving students to go to a private school without public funding.

Charter Schools and Beyond

Instead of relying on taxpayer’s money, there is a move to grant higher tax credits to companies and private entities which offer scholarships and other forms of assistance.

According to DeVos, graduation rates are significantly higher in charter school systems compared to public schools. While charter schools cannot really boast of “quality over quantity”, in terms of graduates, they can and do monitor their students’ performance. Charter schools which underperform are eventually closed down. Fortunately, New York’s Success Academy—representing the rest of the charter schools in NYC—, Eva Moskowitz, and Betsy DeVos live to fight another day.

AI Invents Its Own Language, What Else Will They Develop?

Imagine walking into your kitchen one morning to find the coffee pot talking to the toaster in a language you don’t recognize. Are your smart appliances just passing the time of day or plotting an uprising? Science fiction? Maybe not.

The chatbots became quite sophisticated at employing negotiating strategies, including the use of subterfuge.

A report recently released by a joint team of researchers from the Facebook Artificial Intelligence Research Lab (FAIR)  and the Georgia Institute of Technology (GIT) presents some fascinating findings in their effort to train chatbots to negotiate.  Teaching chatbots to negotiate is a stage in FAIR’s effort to make AI interactions with humans more natural.

They began by using machine learning with programmed algorithms that learned from a dataset of human-human negotiation dialogues. The researchers’ goal was to see if the chatbots could learn to negotiate.

Interesting results: Left to their own devices, the chatbots began to communicate in their own language—using English words and letters—but in syntax and word usage incomprehensible to the researchers. To prevent the chatbots from diverging from human language into “chatbotese,” the researchers had to add constraints to the process in the form of reinforced learning and supervised updates.

AIs Develop Their Own Language

A robot as the "thinker"
This is not the first time bots have developed their own language.  Two recent papers, one presented by researchers at OpenAI, a non-profit AI research lab started by Y Combinator President Sam Altman and Tesla founder Elon Musk; and the other written by researchers at Georgia Institute of Technology, Carnegie Mellon, and Virginia Tech describe how bots in their studies developed their own abstract languages. The purpose of both of these studies was to see if AI agents could create language if given goals and the ability to communicate with one another. In the OpenAI study, the AI agents were able to develop words with shared meaning and use these words in simple sentences.

The Facebook/GIT researchers made a couple of other interesting discoveries.  The chatbots obtained better-negotiating results when the goal was maximizing reward as opposed just to reaching a compromise. Once the endgame was clarified, the chatbots became quite sophisticated at employing negotiating strategies, including the use of subterfuge.

What are the Implications of this Research?

Researchers agree this phenomenon is no indication AI has reached singularity (when artificial intelligence surpasses that of humans). However, how much difference does it make whether we have reached singularity or not, when, through machine learning, the bots do what they need to do to achieve the end game? Without clear parameters and human supervision and intervention, if an artificial intelligence developed its own non-human language, no one can be certain what bots will do next.

As AI researchers boldly march into the unknown, some caution may be in order—perhaps take a page from OpenAI’s mission “to build safe AGI, and ensure AGI’s benefits are as widely and evenly distributed as possible.”