Ten days ago, we brought you news that Nike had unleashed its latest apps on the public realm – Nike+ Basketball and Nike+ Training .
Whilst the apps were freely available to download from late June, both are designed to work specifically with special footwear equipped with the latest Nike+ tech. Not something you can freely try out without first forking out big bucks for the appropriate sneakers.
With that in mind, The Next Web got a chance to try out the aforementioned footwear in London earlier today. What do the new range of shoes look like?
On show today were the Nike Hyperdunk+, which were first unveiled a couple of weeks back. They’re not cheap though – around $250/£190 is what you can expect to pay for these gems, and that obviously includes the little chips and cables needed to ensure your shoes are synced with your iPhone and/or computer.
The shoes let you know how high, quick and hard you play. It’s easy to see how these can become addictive – even if you lose a game of b-ball, you can retrospectively ‘challenge’ your buddies by analyzing and comparing your vital stats to separate the slackers from the superheroes.
We also got to try out the new Nike+ Training shoes which are geared more towards general workouts than shooting hoops.
We wore the Nike Lunar TR1+ shoes, where we skipped, jumped and sprinted on the spot to try and beat the competition. Thanks to a giant screen that displayed the iPhone data, we could easily see how we were faring against each other.
I had no idea I was such a good skipper, but that’s a story for another day…
You can read more about what the associated apps offer in our previous coverage here .
My first impressions of the shoes were, well, they were great. But would I pay $200+ for a pair of smartshoes? Personally, no I wouldn’t. But there are plenty of people that would, and I really can see these taking off in a big way amongst amateur athletes.
With that in mind, it’s worth looking at exactly who these shoes are aimed at.
During a series of presentations in London today, one point became abundantly clear. Nike is continuing in its quest to take its tech from the lab where superstar athletes have typically worked with the sports giant to improve their performance, and embed this tech in the public domain: in shoes sold in the local mall.
Former NBA star Charles Barkley took to the stage, alongside track-and-field legend Carl Lewis, to talk about the latest developments in sporting apparel.
More specifically, Barkley took an interesting stance on Nike’s innovations in the tech space, noting that to be the best you can be at any given (sporting) discipline, you have to know how you’re performing before you can improve. In other words, you have to compete against yourself.
So, when Barkely learned about the data-transmitting footwear, what did he think?
“I started with Nike in 1984, and like Carl (Lewis) we had these great big concrete shoes. We thought they were the best. And now you see how far they’ve come with their technology, it’s amazing,” he says.
“The thing that people are going to understand…is that it’s going to be great for normal people,” he says. “To become a professional, or to get to the Olympics, you are training at the highest level. One thing that normal people are not able to do is compete with themselves. The toughest part about getting in condition is that you compete against yourself. Normal people don’t get to see their times, how high they jump, how much energy they are putting out. You can’t compete and make a living if you are not pushing yourself to the next level.”
Newly developed neuromorphic hardware will emulate the human brain
While most believe that the brain of the most primitive animals trumps that of any “intelligent robot”, we are actually fast-approaching the day when we’ll build hardware sophisticated enough to replicate a natural brain.
This is not a Transformers-like fantasy. There’s no reason to fear a malicious robot army trying to seize world domination in the (near) future. Reality is much more interesting and could potentially bring a giant leap for modern robotics, medicine and neurology.
The BrainScaleS project is a collaboration of fifteen research institutions, led by the Kirchhoff-Institute for Physics in Heidelberg, Germany, that is creating hardware that emulates parts of a natural brain. The project is based on developing what was named “neuromorphic hardware” by the researchers in Heidelberg, simulative electronic systems that reconstruct the behavior of synapses through electrical components, like transistors and microchips.
The team of scientists have just launched their first prototype: an eight inch large wafer equipped with 51 million of artificial synapses (pictured right). A “quantum leap” for the project, senior researcher Dr. Johannes Schemmel describes the launch:
The prototype only represents a tiny fraction of a working brain, but is efficient for examining the processing of natural nerve signals in time-lapse. Three years from now when the project is finished, the hardware-brain model will re-create neurological processes 10,000 times faster than they take place in a natural biological system. “That means that if we want to study a behaviour [in the nervous system] that would take a few minutes in the real biology, it will only take us split seconds,” says Johannes.
But why do all this? So far, brain research has mostly relied on computer simulation. Researchers would use a high-capacity computer system able to run a program that tries to simulate the behaviour of a nervous system. There have been popular attempts at creating a synthetic brain in software, such as Henry Markram ‘s Blue Brain Project . The initial goal of the Blue Brain Project was the simulation of a rat neocortical column, which was achieved in 2006. Building a functional simulation of a human brain is Markram’s next ambitious target.
Others, like physicist-turned-neuroscientist Sebastian Seung , focus on the latest hot space in neuroscience: connectomics , or the wiring of the human brain. Seung’s team at the MIT tries to invent technologies for identifying and describing the connectome, the totality of connections between the brain’s ca. 100 million neurons. In a TED talk two years ago, he took 20 minutes to attempt to explain the inconceivable complexity of our brain’s neuron system, and the challenges to visualize the human mind in order to understand it, maybe only a little bit, better.
So what is the big difference, and which is more important for modelling the brain, of the two: software that visualizes it, or hardware that replicates it? The problem is that the software simulation is hooked on the processing power – and there are no system available yet that could keep up with a biological nervous system. To simulate a mammalian brain, we would need an entire power plant. And that’s “just completely impractical”, in Johannes’s words.
University of Heidelberg
That’s why Johannes and his team have turned away from software, and to electronics. Having a working system that emulates parts of a functioning brain will help us to analyse, and eventually truly understand the way the brain works in an entirely new manner. Hardware like this will allow us to develop intelligent control systems, which will have a tremendous effect on robotics. Again, no Transformers here – but we’ll see systems that are increasingly powerful, adaptive and more resilient to errors.
Goals like these have motivated research into a connection between neuroscience and computing for a long time. Only in autumn 2011 researchers at the Massachusetts Institute of Technology presented a computer chip that mimics how the brain’s neurons adapt in response to new information, simulating the activity of a single brain synapse. “There are about 100 billion neurons in the human brain, each of which has synapses — or gaps — between it and other neurons. Emulating one is a step “for building truly intelligent brain systems,” researcher Chi-Sang Poon told msnbcom’s John Roach in November.
One of the most interesting aspects in neuroscience is the brain’s ability to learn, says Johannes. So when we begin to think about brain-simulating electronic systems, especially in relation to robotics, what comes to mind is the amazing image of technology that is able to learn. The idea of an adaptive computer system is fascinating, but extremely complicated. In biology, the neurons and synapses in our brain allow us to learn. Neurons, millions evolving from a single cell, grow through cell division and simultaneously exchange signals with their environment and so adjust to their individual functions within the nervous system. That is what scientists have to replicate.
“The problem is, we can analyse single neurones, but not millions and millions individually. That’s why we calculate with statistical models. This involves unbelievably many parameters we can’t measure directly.” The one major drawback of neuromorphe hardware is after all that hardware is not as flexible as biology. In this sense, software has the big advantage of being more easily re-programmed and adjusted. “Come new fundamental findings from biologists, we might just have to change our hardware from scratch.”
One of the principal tasks BrainScaleS need to follow is that it creates a platform for collaboration between neurologists, biologists, physicists and IT scholars to combine as much knowledge as possible and create that system that allows as much flexibility as possible.
“How well it all works out we’ll find out in the next few years. We’ll find out the gaps, learn from them and design the next generation [of neuromorphe hardware].”
Image courtesy of SanFranAnnie on Flickr
Xiaomi to begin production of Mi2 smartphone on September 22 ahead of October release
The launch of Chinese smartphone maker Xiaomi ‘s second-generation handset is still over a month away, but anticipation for the quad-core device remains strong. Company executives have revealed that the device will go to production on September 22nd in preparation for a release in mid to late October.
The company has also decided to release a 32GB version of its phone in addition to the previously announced 16GB model, TechWeb reports ( via Tech in Asia ). Both versions will hit the market at the same time, founder Lei Jun said.
Xiaomi took the wraps off the Mi2 in August at an event for media and fans, who have taken to calling themselves “ Mi Fen “. The Mi2 features a quad-core 1.5GHz processor with 2GB of RAM. It also features a high-density 4.3-inch display, an 8-megapixel rear camera and a 2-megapixel front camera.
To tide fans over during the two-month wait for the new phone, Xiaomi released the Mi1S, an upgrade to the first generation. Reservations for the Mi1S topped 1 million units, with the first batch of 200,000 phones selling out in 30 minutes. Xiaomi issued its second batch of Mi1S units last week.
Given the frenzy of preorders for the Mi1S, the Mi2 should easily become one of the hottest smartphone launches in China this year. Xiaomi sold 3.5 million of its first phone and will need to sell almost as many Mi2 units in order to break even.
The rise of Xiaomi has been an impressive one, even attracting comparisons to Apple. Earlier this year, it raised $216 million in funding at a $4 billion valuation .
Image credits: TNW