Can Scientists 'Upload Knowledge' Directly into your Brain to Teach New Skills?

Imagine the world where you do not have to make any efforts to learn new skills or knowledge.

Just like new programs are uploaded to a Robot to teach them new skills, What if new skills are uploaded to your brain to make you learn, say, playing Guitar, a whole language like French or German or anything else you wish?

Do you want a technique, if exists, to make this possible?

Of course, YES! Who would not?

Now, multiple media channels are reporting that a team of researchers from HRL Laboratories in California has developed a new technology that could be used to feed any skill into the human brain without much effort.

But, Is it possible in reality?

Here's what Scientists have Actually Discovered:

In reality, the recent research shows that it may be possible to enhance a human's existing ability to learn new skills, but to upload any particular skill or talent directly into a person via brain waves is outside the scope of the study.

What Did the Scientists Actually Achieve?

Lead by Matthew Phillips, the HRL Labs research team that does R&D for the Boeing Company and General Motors has made use of a neuro-stimulation technique called transcranial Direct Current Stimulation (tDCS) – a noninvasive, painless shock that makes use of a constant, small electric current to excite specific brain regions.

Using tDCS technique, the researchers excited certain areas in the human brain that are responsible for learning and skill retention.

During their experiment, the researchers first monitored the brain waves of 6 commercial as well as military pilots and then transmitted those patterns into 32 newbies who were learning to pilot an aeroplane in a flight simulator.

The finding suggests that tDCS technique might work to enhance a person's ability to learn, as the newbies who received tDCS brain stimulation were found with improved piloting abilities, especially landing skills.

However, this definitely does not mean that the researchers uploaded or transmitted any particular skill or type of data via this technique, rather they just excited the specific brain regions responsible for learning, so that a person could improve his/her learning ability.

So, don’t think that using this new technique you could "upload" an entire skill set, like Kung-Fu or French language. For now, you have to make efforts to learn them but, who knows, the study could just be a first step towards this whole new FUTURE.

Why do we cry? The science of tears

Crying is part of our human emotional package – love it, or hate it. 

Of course, women are definitely better at it than men, with the number of cries per year estimated at 50 and 10, respectively.
It begs the questions how does it all work, and what triggers our waterworks when we are both sad - and happy?

Crying can be scientifically defined as the shedding of your tears in response to an emotional state;
very different from ‘lacrimation’, which is the non-emotional shedding of tears.
With that said, your plumbing apparatus that makes your tears is all the same.
So before I dazzle you with the fact that we have more than one type of tear,
let us explore the science of tear production and how it links to the emotional center of your brain.
To do this, we are going to use the classic example: the break-up.

“What do you mean it’s over?” you whimper, quivering lip in full frenzy. With the ‘beginning of the end’ of the relationship, the production of your tears can begin.
It is all down to your lacrimal system (think of it like your inbuilt Thames Water supply) that sits next to your eyeball.
It is both a secretory system that produces your tears, and an excretory system, that drains them.

When a tear is produced from the lacrimal gland that sits in-between your eyeball and eyelid,
you spontaneously blink, spreading the tear as a film across your eye. Your tear then has two fates; firstly it can drain-off down the lacrimal punctum, like the sink plug in your kitchen, subsequently draining through your nose (hence why your nose runs when you cry).

Of course in this break-up, you are having a really good old sob, and so your lacrimal drainage system simply cannot deal with the volume of tears. The resultant excess fluid now cascades over your eyelids and down your cheeks – for your ex-partner to bear witness to and begin to feel really, really bad.

Of course, your body being the incredible feat of engineering that it is, you don’t just make one type of tear - you make three: basal, reflex and psychic tears.

Your basal tears are what I like to call the ‘worker tears’ and they keep your cornea (the transparent front of your eye) nourished and lubricated so your eyes don’t dry out.

Then there are your reflex tears which that help you to wash out any irritations to your eyes from foreign particles or vapours (onion, being the classic example).

Finally, there are the ones we all know about, and that are florid in your current break-up scenario - the psychic, or ‘crying’ tears. These are the tears produced in response to that strong emotion you may experience from stress, pleasure, anger, sadness and suffering to indeed, physical pain.
Psychic tears even contain a natural painkiller, called leucine enkephalin – perhaps, part of the reason why you might feel better after a good cry!

So here you are - floods of tears cascading onto old photos of you and your ex together, ‘your song’ playing on repeat - but how does your in-built shower-system link to these emotions? Well, there is an area of your brain specifically to deal with your emotions, called the limbic system (specifically the part of it called the hypothalamus), which is hard-wired into your autonomic nervous system
(that’s the part you don’t have any control over). This system, via a neurotransmitter called acetylcholine, has a degree of control over the lacrimal ‘tear’ system; and it is this tiny molecule which then stimulates tear production.
So in short, your emotional reaction to the break-up triggers your nervous system, which in turn, orders your tear-producing system to activate.

So there you are, still heart-breakingly sobbing your cascade of psychic tears. What is the point of them though? Is it as simple as an expression in response to a stimulus, as some suggest, or a more complex primal call out - a form of non-verbal communication to elicit help and support from those around you in your time of need?
There are some psychologists who believe you feel better after a cry because of this social input,
solidifying of relationships with those sharing in the experience, and collaborative helplessness.
How often we see this displayed in the Hollywood movies when the friends rally around the dispatched.

Your crying can even be divided into spatial - and temporal-types; the former being when you cry over wanting to be somewhere e.g. home, versus the latter which is about looking into the past or the future and eliciting an emotion e.g. that one week anniversary with your now, ex. One study even suggested an evolutionary role of crying as a means of displaying vulnerability or submission to an ensuing attacker. Perhaps if you had pre-empted the break-up you could have started the tears
early and quelled any potential break-up!

Now the interesting thing about crying is that it doesn’t just make your face wet, or your non-waterproof mascara become a form of combatant face paint.
It in fact has a whole host of other effects; your heart rate increases, you sweat, your breathing slows and you can get a lump in your throat – known as the globus sensation. This all occurs as a result of your sympathetic nervous system (that’s your ‘fight or flight’ system) activating in response to your break-up situation – and any psychic tear-producing one for that matter.

Now before we wrap up I wanted to just tell you a fun fact about the origin of crocodile tears.
You know the ones - those insincere, fake tears that people can sometimes display, such as the Z-list celebrity getting off charges in court - again.
They originate from the ancient Greeks who had an anecdote in which crocodiles would pretend to weep while luring their prey in.
Clever crocodile, I say. So, when your ex who has just done the breaking up with you starts crying too – you can call them out and demand all those shared CDs back (or should that be, iTunes downloads? Oh I’m old…).

When it come to those still fresh in the world, babies use crying more than just a means of emotional expression but as a form of communication to us grown ups. After all they are fairly limited in how they can express themselves! It may surprise you to know (and it definitely did me) that there are three types of baby cry - the basic, angry and pain cry.

So now you know why, apart from your break-up, why that tear-jerker movie, friend that made you laugh so hard you cried, and unsuspectingly discovered nostalgic photo of a past grandparent, brings a tear to your eye.

Tears are a positive representation of who we are. It demonstrates not only our deep emotional connections with our world – past, present, and future – but allows us to visibly celebrate that fact. They are also scientifically proven to make you feel better. So go on and wear your tears with pride.
If you are concerned though, that you are too tearful, too quickly, for no reason or you have worries over your mood, your GP will always be happy to chat with you in confidence. Now, having just that X Factor is back on television, where’s that handkerchief…

" क्षणां मधे आनंद शोधायचा असतो , आनंदामधे क्षण नाही "

" क्षणां मधे आनंद शोधायचा असतो , आनंदामधे क्षण नाही "

 Today, I heard this quote from one of my friend.... I love that what my friend told me....

It's a great thought ... This quote will remind me when I will use dream some nonsense thoughts in my mind....Thank You dear...!!! 

How the brain can handle so much data ?

Humans learn to very quickly identify complex objects and variations of them. We generally recognize an "A" no matter what the font, texture or background, for example, or the face of a coworker even if she puts on a hat or changes her hairstyle. We also can identify an object when just a portion is visible, such as the corner of a bed or the hinge of a door. But how? Are there simple techniques that humans use across diverse tasks? And can such techniques be computationally replicated to improve computer vision, machine learning or robotic performance?

Researchers at Georgia Tech discovered that humans can categorize data using less than 1 percent of the original information, and validated an algorithm to explain human learning -- a method that also can be used for machine learning, data analysis and computer vision.

"How do we make sense of so much data around us, of so many different types, so quickly and robustly?" said Santosh Vempala, Distinguished Professor of Computer Science at the Georgia Institute of Technology and one of four researchers on the project. "At a fundamental level, how do humans begin to do that? It's a computational problem."

Researchers Rosa Arriaga, Maya Cakmak, David Rutter, and Vempala at Georgia Tech's College of Computing studied human performance in "random projection" tests to understand how well humans learn an object. They presented test subjects with original, abstract images and then asked whether they could correctly identify that same image when randomly shown just a small portion of it.

"We hypothesized that random projection could be one way humans learn," Arriaga, a senior research scientist and developmental psychologist, explains. "The short story is, the prediction was right. Just 0.15 percent of the total data is enough for humans."

Next, researchers tested a computational algorithm to allow machines (very simple neural networks) to complete the same tests. Machines performed as well as humans, which provides a new understanding of how humans learn. "We found evidence that, in fact, the human and the neural network behave very similarly," Arriaga said.

The researchers wanted to come up with a mathematical definition of what typical and atypical stimuli look like and, from that, predict which data would hardest for the human and the machine to learn. Humans and machines performed equally, demonstrating that indeed one can predict which data will be hardest to learn over time.

Results were recently published in the journal Neural Computation (MIT press). It is believed to be the first study of "random projection," the core component of the researchers' theory, with human subjects.

To test their theory, researchers created three families of abstract images at 150 x 150 pixels, then very small ``random sketches" of those images. Test subjects were shown the whole image for 10 seconds, then randomly shown 16 sketches of each. Using abstract images ensured that neither humans nor machines had any prior knowledge of what the objects were.

"We were surprised by how close the performance was between extremely simple neural networks and humans," Vempala said. "The design of neural networks was inspired by how we think humans learn, but it's a weak inspiration. To find that it matches human performance is quite a surprise."

"This fascinating paper introduces a localized random projection that compresses images while still making it possible for humans and machines to distinguish broad categories," said Sanjoy Dasgupta, professor of computer science and engineering at the University of California San Diego and an expert on machine learning and random projection. "It is a creative combination of insights from geometry, neural computation, and machine learning."

Although researchers cannot definitively claim that the human brain actually engages in random projection, the results support the notion that random projection is a plausible explanation, the authors conclude. In addition, it suggests a very useful technique for machine learning: large data is a formidable challenge today, and random projection is one way to make data manageable without losing essential content, at least for basic tasks such as categorization and decision making.

The algorithmic theory of learning based on random projection already has been cited more than 300 times and has become a commonly used technique in machine learning to handle large data of diverse types.

"A task ahead of you is never Greater than the strength within you ."

Today I started learning  Eclipse IDE and wrote my first Hello world program.... :)

IF NO ONE THINKS YOU CAN THEN YOU HAVE TO......!!!