Come March 31, and an exclusive rumour from an industry insider has given us wind of a France-based startup developing a smartphone concept that fuses the power of two full-fledged smartphones into one. Too good to be true? That’s what we thought at first. As it turns out, the Vota Dual Superfone is not a far-fetched rumour or any random prank that we often come across. The company’s initial round of spreading word will be a slow rollout across social media, and while word remains slim regarding its exact launch date, a teaser poster has been given out by Vota, which reveals much about the device (or two-devices-in-one, to be accurate). Vota says that its initial target will be on Indian shores, backed by the rampant promise of the Indian smartphone market of today.
The Vota Dual Superfone is reportedly powered by two 64-bit quad-core processors, the exact details of which have not been released yet. It presents two, 5.5-inch AMOLED display panels, one on each side, and a staggering 8GB of RAM for each processor adding up to a combined 16GB of RAM! Both the cameras are powered by 21-megapixel sensors. We have not received any information on the make of the sensors, but Vota says that both are present-generation image sensors from a leading camera sensor maker. To top it all, the Vota Dual Superfone will house a Graphene-based battery, which would allow the battery pack to remain ultrathin and flexible.
So, how does all of this work? Vota has remained tight-lipped so far, but stated that each screen on each end will function as independent devices. The power between the two separate hardware modules will be optimised to make one end performance-focussed, while the other will be optimised to maximise battery stamina. This way, you can game and stream video content endlessly on one panel, and once it dies out, the other end will serve as your decent, flawless daily driver. The entire notion may seem a little far-fetched as of now, but Vota claims that this is indeed the reality. The Vota Dual Superfone is not just a concept, but is apparently real. To support its claim, Vota has even shared sample photographs, apparently clicked by this device itself.
It all seems too fantastic for now, but with Vota’s strong claims of this being real, we asked them to give us a further glimpse to it. The company has agreed to comply with our request and will give us more details on the Superfone, some time today. Stay tuned to our website, because this might just change the game in the smartphone industry.
Indian banks are reportedly on alert after India’s national security establishment cited the possibility of the involvement of Pakistan’s intelligence agency, the Inter-Services Intelligence, in the theft of $81 million from Bangladesh’s Central Bank. According to a report by the Economic Times, a high level government official communicated the concerns of India’s security establishment to the Reserve bank of India, urging it to be cautious.
In February 2016, the world saw its latest and one of the largest bank heists till date. The robbers hacked into U.S. account of Bangladesh’s Central bank and were able get away with $81 million. The bank robberies which are done by men dressed in black with shotguns have been replaced by a person typing codes on his computer.
In the Bangladesh heist, the hackers used malware to access the bank’s computers and spoof messages to the U.S. Federal Reserve Bank. According to the testimony at a senate hearing in Philippines, $81 million was transferred from New York Fed to Philippine banks and then, the funds were distributed to casinos and then cashed at Manila.
The ongoing senate hearing regarding the heist is still in a tussle to determine exactly how the money was stolen, and so another hearing has been scheduled next week. Cases like these mostly go unpunished because the perpetrators remain a mystery.
Hundreds of millions of dollars have been stolen in recent years from banks and financial services. Last year, Kaspersky, the software security maker publicised the activities of the Carbanak gang which hacked money from almost a 100 banks. They had hacked into banks and ordered fraudulent money transfers and were able to steal about $2.5 million to $10 million per heist. They had also forced ATMs to cough out cash. In March, in a U.S. court, a Turkish hacker was found guilty, having stolen about $40 million from ATMs in 24 countries within 10 hours!
Owing to the digital revolution and the alliance between digital and traditional criminals, many victims do not report the thefts for fear of damage of reputation.
When the artificial intelligence, AlphaGo, beat Go champion Lee Seedol at his own game, it created history. AlphaGo didn't just beat Lee at Go, it won four games out of five, an unprecedented victory that many hadn't expected. AlphaGo's neural networks took thousands of Go matches, played by human players, and added its own learning on top of that, to come out with moves that even a legend like Lee Seedol didn't expect. But is AlphaGo the breakthrough in machine learning that the world has been waiting for? We talked to David Silver, Research Scientist, Google Deep Mind, to learn the same.
Silver is the main programmer on the AlphaGo algorithm, which makes him perhaps the best person to answer questions about its victory and the future. AlphaGo's victory is an important step in the field of machine learning, opening avenues for more advancements in future. It is still the first step to achieving true artificial intelligence though, and Deep Mind's work continues in that direction. The ancient Chinese game of Go, has long been considered the game to beat for AI, thanks to the fact that it is enormously complex, as Silver explains below.
Firstly, could you explain the significance of AlphaGo's victory over Lee Seedol? Why does a victory in Go matter so much?
Go represents a major milestone in AI research. Go is enormously complex, and such an intuitive game that so-called “brute force” search techniques are not sufficient. Until recently, computers have only played Go as well as amateurs.
Go’s simple rules and profound complexity resemble the real-world in many ways. Therefore we think Go is an important stepping stone towards a more general AI.
How does AlphaGo's victory in Go differ from Deep Blue's victory earlier. How do Chess and Go differ when it comes to being played by an AI?
The number of possible moves in chess is much lower. The search space in Go is vast — more than a googol times larger than chess (a number greater than there are atoms in the universe!) So traditional “brute force” AI methods, which construct a search tree over all possible sequences of moves, don’t have a chance in Go. In addition, it’s much easier to evaluate who is ahead in chess - for example, by adding up piece values.
Human grandmasters handcrafted the chess knowledge used by Deep Blue to evaluate chess positions. For AlphaGo, we did not tell AlphaGo what strategy to use — instead it learnt for itself from hundreds of thousands of human expert games, and from millions more games of self-play.
Finally, Deep Blue used a search algorithm that was based more on brute force - it evaluated thousands of times more positions than AlphaGo. Instead, AlphaGo searches much more smartly and selectively.
I understand AlphaGo actually has two neural networks working inside it. Could you explain these and how they work?
One neural network, the “policy network,” selects the next move to play. The other neural network, the “value network,” predicts the winner of the game. Each neural network takes the board position and passes it through many computational layers with millions of tunable weights, to come up with its answer.
AlphaGo's gameplay in this tournament has been described as aggressive. How does it do that? Why is that significant? Is there no rule-based programming at play here?
AlphaGo always selects the move that maximises its chances of winning the game. This can sometimes results in aggressive moves, but can also sometimes result in seemingly quiet and defensive moves, especially once AlphaGo already has a lead.
How easy or difficult will it be to adapt AlphaGo to other activities?
The methods we’ve used for AlphaGo are general-purpose, so our hope is that one day they could be extended to help us address other challenges from making smartphone assistants more useful to helping scientists with some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis. That being said, it’s very early days. We are decades away from human level AGI.
Is it true that games like Go can only serve for testing such algorithms?
Like many researchers before us, we've been developing and testing our algorithms through games, but we have much bigger goals than games. In the case of AlphaGo, we gave the AI a goal — to win at Go — and then it learns for itself the best way to achieve that goal. That’s a much more general way of learning, and a way that’s similar to the way you and I, as humans, learn.
AI programmers have been known to not recognise their own algorithms after a while. That said, doesn't it make it difficult to tweak algorithms when needed? (Not suggesting lack of control over AI here, simply the ways to tweak algos)
When training neural networks from scratch, the most important factor is designing the goals and objectives that you set the algorithm, and the mechanism for learning those goals, so it can learn for itself to achieve the desired behaviour.
If yes, does that make it difficult to scale or adapt AlphaGo to other applications?
This is the beginning, and there is a lot more work to be done before we can begin to apply these techniques to real world problems.
Do you plan things like AlphaGo to be used for consumer applications, like in smartphone voice assistants?
Not necessarily AlphaGo, but the machine learning techniques will be a tool that helps us do what we already do, but better — whether that’s instant translation, a smarter assistant on our phone that can plan a trip for us just by knowing what we like or by helping doctors to diagnose a disease much earlier.
Google, on Tuesday, announced the Fiber Phone via its official blog. To use Google’s Fiber phone service, you’ll need to attach the ‘Fiber phone box’ to an existing landline handset to make calls from it. The interesting bit is, you’ll also be able to take and dial phone calls using the same landline number but on-the-go, using wireless devices like smartphones or tablets. The Fiber Phone is based on cloud services, which enables users to keep the phone number on the cloud.
Google’s Fiber phone service will be sold in the U.S. for now, alongside its broadband and video services. Initially, it will be offered in select cities where the company has set-up its all-fiber network. The service costs $10 a month, which allows unlimited local and national calls. For international calls, it charges the same rates as its Voice service.
Interestingly, on the same day, the inter-ministerial telecom commission of the Indian government removed a major policy hurdle by issuing clearance for inter-connect agreements between telecom operators and Internet Service Providers (ISPs). We will soon be able to dial landline and mobile numbers from apps like WhatsApp, Skype and Viber. Although a declaration might not be heard any time soon, we might see internet-friendly landline phones like Fiber phone in the Indian market too, some day.
Landline connections have been a dying channel for a while, with many families not having a registered landline in today’s wireless world. With connected services adding wireless capabilities to the perks of a landline connection, will we see a revival of landline services, any time soon?
It’s Saturday and you go to your favourite cafe to chill as usual. You open up Tinder and look for people close to you. But this time you have a specific person in mind, that dancer who was really cute and also were lucky to receive a quick smile back. So you just type in “the dancer” or “the person I met last week (well, you tried!).”
Popular dating app Tinder has acquired Humin, a company that works on figuring out the context of social connections. Notably, Sir Richard Branson and Will.i.am had previously given financial support to it.
Tinder’s long term goals seem to be about managing social interactions and organise collated information. Humin’s CEO, Ankur Jain, who will henceforth be Tinder’s VP of Product and Head of Special Projects, views Tinder as ‘augmented reality’ because it is all about meeting and connecting.
It uses the hardware that you own, in this case your phone, to augment the special information about people around you, and put that into context. Called ‘actionable information’, the social context here means information such as meeting places, mutual friends and trips.
With this acquisition, Tinder will look to consolidate upon becoming an important social media platform, completing its evolution from a dating app.
Microsoft has announced a number of new features for Cortana that would be included in the next major update to Windows 10 called the Anniversary Update. The new update will be available this summer and will see the incorporation of the personal assistant into Outlook. This will allow it to check emails and calendars in order to help the user keep track of their emails. It will be able to take information from emails and automatically create events. Cortana will also become more proactive and will suggest restaurants or arrange transportation. In addition, developers will have complete access to Cortana’s proactive intelligence and this will allow them to make their apps perform tasks based on user context.
Cortana is also getting integrated with Skype and will soon allow users to book trips, shop, plan schedules and more using the service. It is similar to what Facebook to trying to do with its ‘M’ personal assistant. In addition, Android phone users will now be able to receive and reply to text messages from their Windows 10 desktops. Furthermore, Cortana would be enabled on the lockscreen.
MediaTek has only announced the Helio X20 SoC last month, and rumours of Helio X30 have begun. Speculations suggest that the upcoming MediaTek chipset will feature a tri-cluster, deca-core setup similar to the Helio X20, manufactured by TSMC’s 10nm FinFET manufacturing process. The first phones powered by this SoC will probably be seen early next year.
According to rumours, MediaTek's Helio X30 might feature ARM’s latest Cortex-A35 and Artemis cores. Artemis is the new, rumoured core architecture from ARM which will be a direct competitor to Qualcomm’s Kryo cores. The A35 core has been stated to provide up to 40% better performance, and lower power consumption. This new setup should increase the overall performance, while decreasing the power consumption in comparison to the existing X20 SoC. The Helio X30 is also expected to get an upgraded GPU, probably a 4-core PowerVR 7XT series GPU. Rumours also suggest that the new SoC might bring support for upto to 26MP cameras, dual main camera support, VR, as well as a new CAT 13 LTE modem.
In the meantime, the company will be selling its Helio X20 and the upcoming X25 SoC (which also uses a deca-core setup) to manufacturers.The Helio X20 is manufactured using the 20nm manufacturing process. It features two Cortex-A72 cores @ 2.5GHz, four Cortex-A53 cores @ 2.0GHz, and four Cortex-A53 cores @ 1.4GHz. It can support 4GB of DDR3 RAM, and comes with a new Mali GPU. The recently-launched Zopo Speed 8 at MWC 2016 is powered by this SoC.
On the other hand, official specifications for the Helio X25 are yet to be announced, but it is expected that the new SoC might run on a slightly higher clock speed. It is also unclear whether MediaTek is making the SoC using the 20nm manufacturing process, or the newer 16nm FinFET manufacturing process.
The most creative people on the planet? Kids! At ‘Superhero Cyborgs’, kids create their own superhero gadgets using personal wearable devices but with a beautiful difference. For these kids, the superhero gadgets are a potential alternative to their upper limb prosthetic. These are kids between the age 10-15 who have upper-limb differences, either having been born without one or having lost one.
Superhero Cyborgs is a workshop where professional designers and engineers work along with kids in Autodesk’s state-of-the-art shops to prototype their own wearable devices. The kids are taught 3D modelling, digital fabrication and 3D printing. Version 2.0 of Superhero Cyborgs took place recently in January 2016, at Autodesk's Pier 9 shops in San Francisco, CA.
Ten-year-old Jordan Reeves designed and 3D printed a cannon that fits on her upper arm. She called it ‘Project Unicorn’, and when the trigger rope is pulled, it shoots out glitter ammunition!
Thirteen-year-old Kieran Blue Coffee designed his hand and called it ‘e-Nable’. It has LED lights, and an aluminium attachment that can carry heavy loads. By letting children create their own prosthetics, Autodesk enabled customisation of the parts to the core needs, and personalise it to their dreams.
Kate Ganim is the co-founder of KidMob, the non-profit group that organised this project in partnership with California-based 3D software firm, Autodesk. This project is designed to encouraged and re-think the missing limb as an opportunity, and not a disability.
Last year, a Non-profit group called Limbitless Solutions 3D printed a fully functioning bionic prosthetic for seven-year-old Alex. The arm looked exactly like Iron man’s gauntlet, and is a part of the Limbitless’ initiative called The Collective Project. While Superhero Cyborgs is about gifting superhero arms, it allows the kids to design their own superhero powers, thus adding a ray of sunshine to their lives.
When technology does marvellous things like these, it makes the world a happier place to live in, without any catch!
Watch Robert Downey Jr., Iron Man himself, present the specially-designed arm to a happy Alex.
Instagram will soon let users upload videos that are up to a minute long. Previously, the maximum length of a video one could upload was 15 seconds. On its official blog, Instagram stated, “We want to bring you fun, flexible and creative ways to create and watch video on Instagram. As part of our continued commitment, you’ll soon have the flexibility to tell your story in up to 60 seconds of video. This is one step of many you’ll see this year.” The company also announced that it is bringing back the feature that allowed iOS users to create videos by stitching together multiple clips from their camera roll.
Instagram says in its post that the time people spend watching videos has increased by more than 40% in the last six months. As such, it hopes that longer videos will help people create “more diverse stories.” The update for longer videos is rolling out from today, and will be available for everyone in the coming months. The multi-clip video update for iOS users will be available this week, as a part of Instagram v7.19 for iOS.
Ecommerce websites like Amazon, Flipkart and Snapdeal may be in a huge fix thanks to two new conditions attached to the recent approval of 100% ecommerce FDI in India. According to the new rules, no group or seller on any online marketplace can contribute more than 25% of generated sales. Secondly, discounts offered by online marketplaces have been banned completely.
In the first scenario, a restriction of 25% of sales from one particular seller has been made to ensure that no ecommerce website flouts fair trade practices by promoting a particular seller of their choice. For example, WS Retail Services Pvt. Ltd makes for the largest seller on Flipkart, clearly contributing more than 25% to the online marketplace’s sales. This will now have to change and Flipkart will have to increase the number of sellers on its platform in order to fulfill the new conditions. Same rules apply to other ecommerce players who depend on large sellers for a chunk of their sales.
As far as online discounts are concerned, a note released by the Department of Industry Policy and Promotion (DIPP), on Tuesday said “Ecommerce entities providing marketplace will not directly or indirectly influence the sale price of goods or services and shall maintain a level playing field.”
Online sales such as Flipkart’s Big Billion Days Sales or Amazon’s Diwali Sales have been creating a difficult environment for brick and mortar sellers because of the attractive deals offered on these platforms. An example of how online sales are influenced by ecommerce platforms is Amazon’s ‘promotional funding’ route. With promotional funding, Amazon funds the discounts offered by its sellers. It recommends a discounted price to the sellers and the sellers in turn have an option of keeping the suggested discounts. After taking on the suggested discount price by Amazon, sellers then present the company with a debit note of the discounted price and Amazon refunds/finances the discounted sum to the seller by cheque. This way, online sellers don’t actually have to discount their prices, as they are later paid back in full by the ecommerce company.
Such a practice will now have to cease if the new marketplace rules are followed. The news will definitely create an even more challenging environment for the three big ecommerce companies – Amazon, Flipkart and Snapdeal, which already find themselves reeling under huge financial losses.
Alphabet’s latest filings, posted yesterday, gave us an insight into the pay package held by Google CEO, India-born Sundar Pichai. At a staggering gross annual salary of $100.5 million (Rs. 6,67,01,85,000), Pichai now joins a list of top executives in the United States of America with pay packages way beyond what many would even dream of earning. Pichai’s cash earnings stood at $652,500 (Rs. 4,33,06,425), in addition to 273,328 Class C shares amounting to $99.8 million. The shares will compound to quarterly increments through 2019, and will translate to direct revenue for Pichai from next year. Pichai also received $22,935 as “other” compensations.
Pichai, once deputy lieutenant to Google cofounder Larry Page as Senior Vice-President of Products, was deployed to be in charge of all operations under the Google banner, after a reorganisation saw the formation of the Alphabet umbrella, of which Google forms a massive part. Pichai’s present pay package sees him overtake fellow India-born and Microsoft CEO Satya Nadella, who reportedly has a pay package of $84.3 million (Rs. 5,59,49,91,000). Among other India-born CEOs at the helm of international giants, Chairman and CEO of PepsiCo Indra Nooyi earns a gross annual salary of $19.1 million (Rs. 1,26,76,67,000). In comparison to them, Chairman-MD of Reliance Industries, Mukesh Ambani, has a gross annual salary of about $2.2 million (Rs. 15,00,00,000), while the last reported salary of Nokia CEO Rajeev Suri stood at$1.4 million (Rs. 9,29,18,000).
Pichai, right now, seems right up the pecking order in terms of Indian CEOs making it big internationally. At this amount of earning, Sundar Pichai definitely gets access to a number of privilege possessions, in surprisingly large numbers. For instance, if he were to save the entirety of his annual salary and then wish to spend it at one go, he can buy 242 Rolls Royce Phantom Extended Wheelbaseeditions. The latest Nexus smartphone, Nexus 6P, is priced at $650 for the 128GB version. If Pichai took fancy to his company’s latest creation and decided to spend an entire year’s salary behind it, he’d be in possession of about1,54,615 Nexus 6P 128GB smartphones, beating flash sale figures of a number of cellphones of today. He could also choose to be more sensible, and purchase the Gulfstream G650 (incidentally the world’s best private jet) for $65 million, the newly-unveiled Bugatti Chiron for $2.6 million, and maybe choose between purchasing islands or a trip to space, for the remaining money. He can also, if wish be, attempt to purchase Cristiano Ronaldo in the next transfer window, with a bit of bargain.
With all such riches, we heartily congratulate Mr. Pichai for his massive earnings, and for being a calm-headed CEO at Google. Here’s to success, and doing the right thing!
On August 31, 1999, when Nvidia unveiled the GeForce 256, the company called it the "world's first GPU". According to Nvidia's website, "A GPU represents a significant breakthrough in realism. It literally transforms the way you interact with your PC. It accomplishes this by completely offloading all graphics acceleration from the CPU." In essence, a GPU is a specialised chip tasked with taking load off the CPU in order to deliver high level graphics. It was developed out of a need for such performance in computers, because a single chip couldn't perform every task that it was required to.
It was a turning point in computing, leading to a lot of features that we take for granted today. But as then, today we stand at the same hurdle once again: The need for specialised chips is again apparent, thanks in no small part to the obsolescence of Moore's Law. Until now, developments in the chip industry were driven by a prophecy made by Intel's co-founder Gordon Moore that has led us from the Intel 4004 (with around 2,300 transistors embedded) to the Intel Skylake, with approximately 1.75 billion transistors embedded on it.
"Moore's Law: Processing power doubles roughly every two years, as smaller and more transistors are packed on a silicon wafer. This boosts performance and reduces costs"
Death of Moore's law and the rise of VPUs
"Broadly speaking, yes," said Jack Dashwood, Marcom Director, Movidius, when asked whether the obsolescence of Moore's law plays a part in the rise of VPUs. "We are increasingly less reliant on the implicit benefits provided by moving down to a new process node. Purpose built processors and perhaps even more importantly, elegant marriage of software on top of the underlying silicon is going to be a huge source of improvements going forward, both from a technological and economic perspective."
Movidius is a small chip startup that you've most probably not heard of, yet. It's a European company that currently produces specialised chips known as Vision Processing Units (VPUs). The chips are meant for application in areas like Augmented Reality, Virtual Reality and others. Movidius' Myriad 2 chip is running on the recently-announced DJI Phantom 4 drone, and its Myriad 1 chip was used in Google's first Project Tango device. Dashwood explains that while Movidius is a decade-old company, it turned its attention to VPUs in 2009, with Google's first Project Tango phone being the first instance of its chips being implemented. The Myriad 1 and Myriad 2 are high-performance, low-power chips, meant specifically for Computer Vision.
The failure of Moore's Law has led companies to look at new methods of adding more computing power, and chips such as the Myriad 2 make for one of the most promising avenues. VPUs, like the Myriad 2, don't sound like much, but like GPUs they come out of a specific need for computer vision. Computer Vision is a branch of computing that deals with processing and understanding real world elements and images. It is the technology behind myriad augmented reality apps that you see today (like Blippar, for instance). It's also important in intelligent drones and robots that can navigate around and interpret real world objects by themselves.
"GPUs are actually quite a good analogy. In the early 1990s, people realised that 3D gaming and visualisation was going to be hugely important for both commercial as well as consumer purposes, but existing architectures were not well suited to the types of computation required for rich graphics. In a similar vein, we are now keenly aware of the value of computer vision, but much of the existing hardware and software approaches aren't optimised for such tasks," said Dashwood.
"VPUs are to Computer Vision, what GPUs are to gaming and graphics"
Obstacles to overcome
Using a specialised chip for a particular purpose is easier said than done. Gamers usually use complex liquid cooling techniques in order to meet the heat requirements that GPUs come with. But, while GPUs were originally meant for PCs where there was enough space to implement such cooling methods, VPUs do not enjoy that luxury. They are meant for drones, smartphones and other smaller devices, becoming an integral part of the mobile environment that the world is rapidly progressing towards.
According to Dashwood, that problem has already been solved. "The Myriad 2 has been developed from the ground up to run in a low power envelope, and at temperatures low enough that they can be embedded on wearable devices." The Myriad 2 can process millions of pixels, while consuming less than one watt of power. This is significantly lower than the power consumed by smartphone processors today and necessary for a chip that is supposed to run alongside those processors. In essence, while the multi-core processor on your phone will be responsible for fast boot-up of an augmented reality app, the VPU will be responsible for what that app does,” he said.
Heat isn't the only hurdle, though. The problem with implementing specialised chips is that it’s harder to program for them. Dashwood explained that the Myriad 2 is aimed at device manufacturers who are competent in this realm. Programmability of the chip should not be confused with end-applications running on Android OS.
The computing industry is no stranger to specialised logic. Intel's newest chips have special programming meant for videos and other tasks; MediaTek's Helio chips come with CorePilot algorithm to improve performance; Qualcomm, the biggest name in smartphone SoCs, recently introduced a bunch of enhancements made to its chips using specialised algorithms. In its data centres, Microsoft uses a specialised FPGA (Field-Programmable Gate Array) chip for Bing. The company told The Economist that it has doubled the number of queries a server can process in a given time. Given the DJI Phantom 4's proficiency in obstacle avoidance and Google's recent showcase of Project Tango devices, it looks like the industry has overcome this hurdle as well.
Lastly, Dashwood says that VPUs take almost no discernible space, which makes them easier to implement in smaller devices such as smartphones and smartwatches. "The additional sensors often involved are much larger considerations when it comes to space," Dashwood said.
Application in Virtual Reality
While the implementation of VPUs in augmented reality is apparent, the industry today has been primarily focused on virtual reality. For starters, VPUs can help make a VR headset less bulky, Dashwood believes. More importantly, it can add hundreds of ways for Virtual Reality applications to interact with the real world. Room-scaling in HTC's Vive headset is one example of how virtual reality can work in conjunction with the real world. Think of this as a union between Virtual and Augmented Reality. What if the virtual space was built around your real space?
VPUs can help in "all sorts of areas", says Dashwood. He lists positional tracking, gesture, environment mapping, eye tracking and object classification as a few examples. These are some of the essential components of Virtual Reality today. If your environment can be effectively mapped, then the virtual reality space that a headset like the Oculus Rift takes you to can be built around it. This means that if you're in your living room, your Minecraft game will be built based on things in the room. Thinking back to the legendary Age of Empires games, imagine players in a single house, building their empires in separate rooms of the house, while the doorways act as borders between their empires.
Microsoft's Hololens is another area where VPUs can come in handy. The augmented reality headset seems to be one of the best things to have come out of Microsoft's stables recently, and it essentially depends on recognising the real world and then overlaying the virtual on top of it.
"VPUs are not just possible for VR, they're almost essential"
Application in smartphones
VR is still about a year or so away from truly coming to the mainstream, and consumers today are still more focused on smartphones. A very interesting possibility for VPUs in smartphones is in improving cameras on them. "Computational photography is an obvious application," says Dashwood, "there are great deal of ways of working around the physical limitations of optics running on various operations, to construct a visually pleasing photograph." According to him, computational photography has the potential to bring DSLR (or better) quality images to our smartphones.
In essence, the fact is that Computer Vision allows your smartphone to understand the scene in front of you. A photograph can be passed through additional processing, adding inputs from the VPU to generate more realistic representations. This could help in two things -- first, enhancing camera quality without making your phone thicker. One of the main reasons why smartphones cannot attain DSLR-like quality lies in their space constraints. You cannot fit large enough lenses or sensors into them. VPUs, potentially, can solve this. Secondly, it could also improve low-light photography, a major area of focus for smartphone OEMs. While a lot of advancements have been made by companies like Apple and Samsung, low light remains the bane for smartphone cameras, and VPUs may help here as well.
We have reached out to some OEMs to get their take on the use of VPUs for such purposes. The story will be updated when their response is available.
Brains and Brawns
Perhaps the most interesting and potentially scary implementation of VPUs lies in machine learning. A neural network of machine learning algorithm replicates the human brain, which means that a VPU can act as the eyes for that brain. On January 27, 2016, Movidius announced that it is working with Google to accelerate the adoption of deep learning within mobile devices. The partnership gives Movidius access to Google's neural network technology roadmap, while the Search giant will source Movidius' processors and entire software development network.
"What Google has been able to achieve with neural networks is providing us with the building blocks for machine intelligence, laying the groundwork for the next decade of how technology will enhance the way people interact with the world," said Blaise Aguera y Arcas, head of Google's machine intelligence group. Arcas said, working with Movidius allowed Google to expand its technology out of data centres and into the real world. Google is using MA2450, the most powerful iteration of Movidius' Myriad 2 chip for this purpose. According to Remi El-Ouazzane, CEO, Movidius, the challenge in embedding the technological advances that Google has made in machine intelligence is in extreme power efficiency. This needs deep synthesis between the underlying hardware architecture, and that is where neural computer comes in.
In an interview with Digit, David Silver, Research Scientist on Google's Deep Mind, said that it is early days for Artificial Intelligence and we are "decades away from human level AGI". Silver heads the team that developed Deep Mind's AlphaGo algorithm, which recently beat Go champion Lee Sedol in a best-of-five tournament. Dashwood says that machine learning and VPUs go hand-in-hand.
"VPUs will, in future, make for an integral part of artificially intelligent robots, working as the eyes for the neural networks to work with"
The booming market
To add the proverbial cherry on the cake, Computer Vision and VPU markets are at a nascent stage, but is booming. Google and DJI are two of the best-known names, but there are others exploring these avenues. Dashwood says that currently, Movidius is the only viable solution that presents low power, low thermal characteristics and high performance.
According to him, the company's chief competitors come from the GPU and CPU market, as in some cases, they may make for viable solutions for Computer Vision requirements. "In some instances, a CPU or GPU might make for a viable solution for high performance, OR, low power, OR, low thermal characteristics...but all three at the same time? We think we are the only viable solution right now," said he.
The Movidius MA2450 mentioned above is the only commercial solution for computer vision in the market today. While VPUs won't offset chipmakers like Qualcomm, MediaTek and Intel, and they won't compete against Nvidia and AMD in the GPU segment either. Instead, they're creating a whole new segment for themselves.
The telecom commission in India has agreed to allow Virtual Network Operators (VNOs) in the country. This will allow companies that do not have infrastructure in place to provide telecom services by buying bandwidth from established telecom operators, and resell it under their own brand. This will also allow established telecom operators to monetise any unused bandwidth.
According to a report by Business Insider, the telecom commission approved a new category for a unified license for VNOs, at entry fees of Rs. 7.5 crore for companies aiming to provide all services. For others, it would range from Rs. 15 lacs for national-level internet services to Rs. 1.25 crore for a long-distance telecom license. Once these permits are provided, it would remain valid for 10 years. It was also reported that mobile VNOs will not be in direct competition with telecom operators, as they would only target niche market segments such as retail, business, roaming, and so forth. Customers would also benefit from VNOs, as the services provided by these companies may be less expensive.
