5 Robots Booking It to a Classroom Near You

IMAGE: ANDY BAKER/GETTY IMAGES

Robots are the new kids in school.

The technological creations are taking on serious roles in the classroom. With the accelerating rate of robotic technology, school administrators all over the world are plotting how to implement them in education, from elementary through high school.

In South Korea, robots are replacing English teachers entirely, entrusted with leading and teaching entire classrooms. In Alaska, some robots are replacing the need for teachers to physically be present at all.

SEE ALSO: 11 Ways to Make Science Fun for Your Kids

Robotics 101 is now in session. Here are five ways robots are being introduced into schools.

1. Nao Robot as math teacher

IMAGE: WIKIPEDIA

In Harlem school PS 76, a Nao robot created in France, nicknamed Projo helps students improve their math skills. It's small, about the size of a stuffed animal, and sits by a computer to assist students working on math and science problems online.

Sandra Okita, a teacher at the school, told The Wall Street Journal the robot gauges how students interact with non-human teachers. The students have taken to the humanoid robotic peer, who can speak and react, saying it's helpful and gives the right amount of hints to help them get their work done.

2. Aiding children with autism

The Nao Robot also helps improve social interaction and communication for children with autism. The robots were introduced in a classroom in Birmingham, England in 2012, to play with children in elementary school. Though the children were intimidated at first, they've taken to the robotic friend, according to The Telegraph.

3. VGo robot for ill children

Sick students will never have to miss class again if the VGo robot catches on. Created by VGo Communications, the rolling robot has a webcam and can be controlled and operated remotely via computer. About 30 students with special needs nationwide have been using the $6,000 robot to attend classes.

For example, a 12-year-old Texas student with leukemia kept up with classmates by using a VGo robot. With a price tag of about $6,000, the robots aren't easily accessible, but they're a promising sign of what's to come.

4. Robots over teachers

In the South Korean town of Masan, robots are starting to replace teachers entirely. The government started using the robots to teach students English in 2010. The robots operate under supervision, but the plan is to have them lead a room exclusively in a few years, as robot technology develops.

5. Virtual teachers

IMAGE: FLICKR, SEAN MACENTEE

South Korea isn't the only place getting virtual teachers. A school in Kodiak, Alaska has started using telepresence robots to beam teachers into the classroom. The tall, rolling robots have iPads attached to the top, which teachers will use to video chat with students.

The Kodiak Island Borough School District's superintendent, Stewart McDonald, told The Washington Times he was inspired to do this because of the show The Big Bang Theory, which stars a similar robot. Each robot costs about $2,000; the school bought 12 total in early 2014.

ORIGINAL: Mashable
BY YOHANA DESTA

Everybody Relax: An MIT Economist Explains Why Robots Won't Steal Our Jobs

Living together in harmony. Photo by Oli Scarff/Getty Images

If you’ve ever found yourself fretting about the possibility that software and robotics are on the verge of thieving away all our jobs, renowned MIT labor economist David Autor is out with a new paper that might ease your nerves. Presented Friday at the Federal Reserve Bank of Kansas City’s big annual conference in Jackson Hole, Wyoming, the paper argues that humanity still has two big points in its favor: People have "common sense,” and they’re "flexible."

Neil Irwin already has a lovely writeup of the paper at the New York Times, but let’s run down the basics. There’s no question machines are getting smarter, and quickly acquiring the ability to perform work that once seemed uniquely human. Think self-driving cars that might one day threaten cabbies, or computer programs that can handle the basics of legal research.

But artificial intelligence is still just that: artificial. We haven’t untangled all the mysteries of human judgment, and programmers definitely can’t translate the way we think entirely into code. Instead, scientists at the forefront of AI have found workarounds like machine-learning algorithms. As Autor points out, a computer might not have any abstract concept of a chair, but show it enough Ikea catalogs, and it can eventually suss out the physical properties statistically associated with a seat. Fortunately for you and me, this approach still has its limits.

For example, both a toilet and a traffic cone look somewhat like a chair, but a bit of reasoning about their shapes vis-à-vis the human anatomy suggests that a traffic cone is unlikely to make a comfortable seat. Drawing this inference, however, requires reasoning about what an object is “for” not simply what it looks like. Contemporary object recognition programs do not, for the most part, take this reasoning-based approach to identifying objects, likely because the task of developing and generalizing the approach to a large set of objects would be extremely challenging.

That’s what Autor means when he says machines lack for common sense. They don’t think. They just do math.

And that leaves lots of room for human workers in the future.

Technology has already whittled away at middle class jobs, from factory workers replaced by robotic arms to secretaries made redundant by Outlook, over the past few decades. But Autor argues that plenty of today's middle-skill occupations, such as construction trades and medical technicians, will stick around, because “many of the tasks currently bundled into these jobs cannot readily be unbundled … without a substantial drop in quality.”

These aren’t jobs that require performing a single task over and over again, but instead demand that employees handle some technical work while dealing with other human beings and improvising their way through unexpected problems. Machine learning algorithms can’t handle all of that. Human beings, Swiss-army knives that we are, can. We’re flexible.

Just like the dystopian arguments that machines are about to replace a vast swath of the workforce, Autor’s paper is very much speculative. It’s worth highlighting, though, because it cuts through the silly sense of inevitability that sometimes clouds this subject. Predictions about the future of technology and the economy are made to be dashed. And while Noah Smith makes a good point that we might want to be prepared for mass, technology-driven unemployment even if there’s just a slim chance of it happening, there’s also no reason to take it for granted.

Jordan Weissmann is Slate's senior business and economics correspondent.

ORIGINAL: Slate

By Jordan Weissmann

Portable Brain-Scan Headsets: 4 Incredible Applications

ORIGINAL: NatGeo
Brian Handwerk. for National Geographic
August 1, 2013

New technology is moving brain research outside the lab and into the real world.

The Emotiv Insight brain-scanning headset may help scientists understand the mind on the go. Image courtesy Emotiv Lifesciences

Our brain controls what we think, feel, and do, but scientists have a limited capability to watch it at work outside the lab. National Geographic Emerging Explorer Tan Le hopes to change that while, in the process, fighting neurological disorders, enhancing learning, and even helping the disabled move things in the physical world with the power of the mind.

Emotiv Lifesciences, the company Le co-founded, produces portable, high-resolution EEG (electroencephalogram) brain-scanning headsets that Le hopes will open new windows on the complex functioning of our brain. On August 1, Emotiv unveiled Emotiv Insight, a faster, next-generation wireless brain scanner that collects real-time data on the wearer's thoughts and feelings and delivers it directly to a computer, phone, or other device through Android, iOS, OSX, Linux, and Windows platforms.

Tan Le, Photograph from Australia Unlimited/National Geographic

Le hopes the product, which costs $199, can further democratize brain research and help scientists gather more data. Using the EEG headsets, she says, people around the world can study brains under conditions and stimuli as varied as those we encounter in everyday life—because subjects can wear the headset while doing everyday tasks.

"The idea is to empower us all to understand more about ourselves," Le said. "That's really the mystery of the mind."

She added, "It's all very, very personal. Sure, there are some commonalities in the way the brain functions. But what we know now with epigenetics [the study of how the expression of heritable traits is modified by environmental influences] is that every learning experience, every activity we undertake, actually affects our [neural] networks. Your brain yesterday is different than your brain today."

An EEG records the electrical fluctuations in the brain and tracks changes in activity as neurons fire when you are engaged in a cognitive task. It's a time-tested process, but traditionally its use has been limited to the lab and a subset of people because it's relatively costly and time-consuming.

What's needed instead, Le stressed, is as much data as possible on as many brains as possible.

"Until recently there's not been any sort of concentrated effort to collect EEG recordings on well individuals, and that really is an essential part of any sort of background study into abnormalities," Le said. "It's a classic case of science being very skewed toward studying the problem set."

So if we're interested in epilepsy we study patients with epilepsy. If we're interested in Alzheimer's we study patients with Alzheimer's, Le noted.

But with no idea what the norm is, she added, correlations or features that appear in these brains may be present in non-afflicted people as well.

Emotiv hopes to help create a massive digital repository of brain-scan information, as well as a platform for sharing brain data with interested parties around the world. With that information, researchers could send out experiments online and collect data from a wide range of subjects who wear the headsets while performing all types of directed tasks.


"It used to be that you sat in your lab and worked kind of independently of other people," said Kevin Whittingstall, the Canada Research Chair in Neurovascular Coupling and a professor at the University of Sherbrooke. "What we've realized is that the brain is so complex we need to start grouping together data sets to paint a better picture."

"What I think is very promising with the Emotiv system is that the hardware remains constant," added Whittingstall, who has no connection to the project. "With one hospital using one EEG system and another using another system, the electrode positions might be different, for example, and it's hard to integrate the data." (Read "Beyond the Brain" from National Geographic magazine.)

Here are four applications of the portable brain scanners that Le says are already beginning to take shape:

1. Moving Things With the Mind
The EEG headset has helped paralyzed patients control an electric wheelchair and make music via computer using only the power of their minds, said Le.

"The hope is that it's going to be more of a democratizing force, so that whether you are able-bodied or not you're still going to be able to communicate and interact with your world in a meaningful way," Le said during a recent trip to Washington, D.C., for the National Geographic Explorers Symposium. "And we're starting to see the first signs of that."

When patients think of an action—verbalizing a word, for example—the headset can record the brain patterns for that action into a computer via a wireless connection. Then, when the wearer repeats the action, the computer can perform it—allowing or facilitating communication, for example, among those who had lost some or all of that ability.

"I think it speaks to the power of software and algorithms to decipher and interpret electrical signals from the brain," Le said. "When you can start to interpret what's going on in the brain, you can extract unique features that then can help you use them as a command to trigger different events in a device or an application on some sort of computing platform."

The human-machine interface can be used for things as trivial as playing a video game, or as life changing as operating a prosthetic limb—and today's achievements represent only the tip of the iceberg, said Le.

2. Diagnosing Disease
Le stresses that because we lack a large, easily accessible database of "normal" brain data, we're likely missing opportunities to identify and track the causes of brain ailments from their earliest stages—when intervention might be less dramatic and more successful.

"A lot of these conditions are developmental in nature, meaning that you don't get Alzheimer's [or autism] overnight," she said.

Historically, Whittingstall added, most of our information regarding brain function was obtained by studying how damage to one particular area was linked to a cognitive deficit, such as language or memory impairment.

"With EEG, we can now start to non-invasively map out brain function so it enables you to record data from many subjects and improves the statistical power to detect the tiniest differences between experimental conditions," he added.

Le hopes the headset will not only help identify neurological conditions and study their progressions in the ever-changing brain, but also enable intervention.

"The brain is plastic; it's very capable of change, so if it's going down a route that we don't want it to go down, then let's do something about it and fine-tune how we intervene based on feedback from the brain itself," she said. (Video: Visit the Brain Bank at Harvard.)

3. Making Learning Easier
With EEG technology becoming more affordable, scientists and citizens alike can get a more complete picture of how each individual brain operates in real-world situations, ranging from social interactions to studying to intense physical activity, said Le.

"People are more and more interested in quantifying their physical health, and I think we're going to start seeing people more interested in quantifying their cognitive, behavioral, and mental health," Le said.

This window on how the individual brain works could also inspire personally tailored applications to help it learn better. "The value to the individual is that you can start to put together some sort of productivity profile for yourself," said Le. "When am I optimized to do some sort of creative brainstorm work?" she added, as an example.

Whittingstall agreed that such learning boosts could be part of the near-term future. "If you can monitor the brain as someone learns a language, for example, you might correlate their ability to learn through changes in their brain waves," he said.

"However, being able to actually put your brain into that state that's optimized for learning might be further off," he cautioned. (Related: "How Your Brain Cleans Itself—Mystery Solved?")

4. Organizing Data by Thoughts and Emotions
Le suggests that brain-scan information could one day be used to help people organize the avalanche of data, videos, images, audio, and other media that is steadily mounting.

"The first time you view an image [of something you experienced], you're going to have a very strong visceral response because you are reliving that moment," she explained, pointing to the example of watching a video of your child taking her first steps.

Because such images can have a strong emotional cue, there may be a way to label them with some kind of personalized emotional tag based on a brain scan, Le suggests. The concept could allow each of us to build a library of cherished video or images that are organized not just by date or location, but also by the emotional descriptions of what each of them produce in our own brains.

All these applications, exciting as they seem, represent a technology that's still in its infancy—and no one knows where it may lead. "There's no way to tell what the killer app is," Le said.

"The biggest job is to try to get as many people excited about the technology as possible so they can play with it, work with it, and expand it in their own areas of interest and their own passions," she said. "Hopefully we can find some great innovations as a result."

What you can do with a little Insight…

Optimize your brain fitness and performance.

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Tan Le: A headset that reads your brainwaves

http://www.ted.com Tan Le's astonishing new computer interface reads its user's brainwaves, making it possible to control virtual objects, and even physical electronics, with mere thoughts (and a little concentration). She demos the headset, and talks about its far-reaching applications.

Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

ORIGINAL: IntechOpen

Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

By Ernesto Iacucci, Samuel Xavier De Souza and Yves Moreau

1. Introduction   

Protein-protein interactions (PPI) are one of the most important biological events which occur in the cell. As PPIs regulate almost all biological processes in the cell, aberrations in PPI may cause severe health problems. One specific area of PPI is receptor-ligand interactions. These interactions are transient yet account for a large part of cell-to-cell communication. As PPI is an important area of research, many groups have proposed methods to make computational predictions of PPI.  The basis of the majority of these methods rely largely on the phylogenetic profile analysis of candidate interactors. These methods determine the similarity of the phylogenetic history of a protein A and its putative protein partner B, examining the most accurate measure of similarity between the phylogenetic histories of A and B in order to predict interaction. As interacting proteins should co-adapt as they are under the same evolutionary pressures, it is self-evident that interacting receptors and ligands should be identifiable by application of the same methodology.   While several methods, described below, make use of phylogenetic information to predict protein-protein interaction (PPI), more contemporary work has been conducted in the area of data fusion and kernel learning. We describe one method [Iacucci et al. 2011] in detail which does both. In this work, the existing line of phylogenetic research is extended by using phylogenetic data to construct a kernel to train a least square support vector machines (LS-SVM) in order to classify candidate receptors and ligands as interacting or noninteracting.    

In this chapter, we discuss the plethora of various methods for determining protein-protein interactions. In addition, we evaluate the application of LS-SVMs to the sub-problem of receptor-ligand interaction prediction.

Fig. 1. The Receptor Ligand Schematic. Schematic of receptor-ligand and protein-protein interaction model. Top image is a representation of in-vivo interaction of proteins, receptors, and ligands while bottom image is the graph representation from which a PPI adjacency matrix may be derived. (Figure published in Iacucci et al. 2010)   

Fig. 2. Phylogenetic Analysis of Proteins

Fig. 3. Work flow of the combined kernel classifier