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Showing posts with label UpI. Show all posts
Showing posts with label UpI. Show all posts

Saturday, January 6, 2018

Artificial Intelligence, AI in 2018 and beyond


Or how machine learning is evolving into AI
These are my opinions on where deep neural network and machine learning is headed in the larger field of artificial intelligence, and how we can get more and more sophisticated machines that can help us in our daily routines.
Please note that these are not predictions of forecasts, but more a detailed analysis of the trajectory of the fields, the trends and the technical needs we have to achieve useful artificial intelligence.
Not all machine learning is targeting artificial intelligences, and there are low-hanging fruits, which we will examine here also.

Goals

The goal of the field is to achieve human and super-human abilities in machines that can help us in every-day lives. Autonomous vehicles, smart homes, artificial assistants, security cameras are a first target. Home cooking and cleaning robots are a second target, together with surveillance drones and robots. Another one is assistants on mobile devices or always-on assistants. Another is full-time companion assistants that can hear and see what we experience in our life. One ultimate goal is a fully autonomous synthetic entity that can behave at or beyond human level performance in everyday tasks.
See more about these goals here, and here, and here.

Software

Software is defined here as neural networks architectures trained with an optimization algorithm to solve a specific task.
Today neural networks are the de-facto tool for learning to solve tasks that involve learning supervised to categorize from a large dataset.
But this is not artificial intelligence, which requires acting in the real world often learning without supervision and from experiences never seen before, often combining previous knowledge in disparate circumstances to solve the current challenge.
How do we get from the current neural networks to AI?
Neural network architectures  — when the field boomed, a few years back, we often said it had the advantage to learn the parameters of an algorithms automatically from data, and as such was superior to hand-crafted features. But we conveniently forgot to mention one little detail… the neural network architecture that is at the foundation of training to solve a specific task is not learned from data! In fact it is still designed by hand. Hand-crafted from experience, and it is currently one of the major limitations of the field. There is research in this direction: here and here (for example), but much more is needed. Neural network architectures are the fundamental core of learning algorithms. Even if our learning algorithms are capable of mastering a new task, if the neural network is not correct, they will not be able to. The problem on learning neural network architecture from data is that it currently takes too long to experiment with multiple architectures on a large dataset. One has to try training multiple architectures from scratch and see which one works best. Well this is exactly the time-consuming trial-and-error procedure we are using today! We ought to overcome this limitation and put more brain-power on this very important issue.
Unsupervised learning —we cannot always be there for our neural networks, guiding them at every stop of their lives and every experience. We cannot afford to correct them at every instance, and provide feedback on their performance. We have our lives to live! But that is exactly what we do today with supervised neural networks: we offer help at every instance to make them perform correctly. Instead humans learn from just a handful of examples, and can self-correct and learn more complex data in a continuous fashion. We have talked about unsupervised learning extensively here.
Predictive neural networks —  A major limitation of current neural networks is that they do not possess one of the most important features of human brains: their predictive power. One major theory about how the human brain work is by constantly making predictions: predictive coding. If you think about it, we experience it every day. As you lift an object that you thought was light but turned out heavy. It surprises you, because as you approached to pick it up, you have predicted how it was going to affect you and your body, or your environment in overall.
Prediction allows not only to understand the world, but also to know when we do not, and when we should learn. In fact we save information about things we do not know and surprise us, so next time they will not! And cognitive abilities are clearly linked to our attention mechanism in the brain: our innate ability to forego of 99.9% of our sensory inputs, only to focus on the very important data for our survival — where is the threat and where do we run to to avoid it. Or, in the modern world, where is my cell-phone as we walk out the door in a rush.
Building predictive neural networks is at the core of interacting with the real world, and acting in a complex environment. As such this is the core network for any work in reinforcement learning. See more below.
We have talked extensively about the topic of predictive neural networks, and were one of the pioneering groups to study them and create them. For more details on predictive neural networks, see here, and here, and here.
Limitations of current neural networks  — We have talked about before on the limitation of neural networks as they are today. Cannot predict, reason on content, and have temporal instabilities — we need a new kind of neural networks that you can about read here.
Neural Network Capsules are one approach to solve the limitation of current neural networks. We reviewed them here. We argue here that Capsules have to be extended with a few additional features:
  • operation on video frames: this is easy, as all we need to do is to make capsules routing look at multiple data-points in the recent past. This is equivalent to an associative memory on the most recent important data points. Notice these are not the most recent representations of recent frames, but rather they are the top most recent different representations. Different representations with different content can be obtained for example by saving only representations that differ more than a pre-defined value. This important detail allows to save relevant information on the most recent history only, and not a useless series of correlated data-points.
  • predictive neural network abilities: this is already part of the dynamic routing, which forces layers to predict the next layer representations. This is a very powerful self-learning technique that in our opinion beats all other kinds of unsupervised representation learning we have developed so far as a community. Capsules need now to be able to predict long-term spatiotemporal relationships, and this is not currently implemented.
Continuous learning  — this is important because neural networks need to continue to learn new data-points continuously for their life. Current neural networks are not able to learn new data without being re-trained from scratch at every instance. Neural networks need to be able to self-assess the need of new training and the fact that they do know something. This is also needed to perform in real-life and for reinforcement learning tasks, where we want to teach machines to do new tasks without forgetting older ones.
Transfer learning  — or how do we have these algorithms learn on their own by watching videos, just like we do when we want to learn how to cook something new? That is an ability that requires all the components we listed above, and also is important for reinforcement learning. Now you can really train your machine to do what you want by just giving an example, the same way we humans do every!
Reinforcement learning — this is the holy grail of deep neural network research: teach machines how to learn to act in an environment, the real world! This requires self-learning, continuous learning, predictive power, and a lot more we do not know. There is much work in the field of reinforcement learning, but to the author it is really only scratching the surface of the problem, still millions of miles away from it. We already talked about this here.
Reinforcement learning is often referred as the “cherry on the cake”, meaning that it is just minor training on top of a plastic synthetic brain. But how can we get a “generic” brain that then solve all problems easily? It is a chicken-in-the-egg problem! Today to solve reinforcement learning problems, one by one, we use standard neural networks:
  • a deep neural network that takes large data inputs, like video or audio and compress it into representations
  • a sequence-learning neural network, such as RNN, to learn tasks
Both these components are obvious solutions to the problem, and currently are clearly wrong, but that is what everyone uses because they are some of the available building blocks. As such results are unimpressive: yes we can learn to play video-games from scratch, and master fully-observable games like chess and go, but I do not need to tell you that is nothing compared to solving problems in a complex world. Imagine an AI that can play Horizon Zero Dawn better than humans… I want to see that!
But this is what we want. Machine that can operate like us.
Our proposal for reinforcement learning work is detailed here. It uses a predictive neural network that can operate continuously and an associative memory to store recent experiences.
No more recurrent neural networks —  recurrent neural network (RNN) have their days counted. RNN are particularly bad at parallelizing for training and also slow even on special custom machines, due to their very high memory bandwidth usage — as such they are memory-bandwidth-bound, rather than computation-bound, see here for more details. Attention based neural network are more efficient and faster to train and deploy, and they suffer much less from scalability in training and deployment. Attention in neural network has the potential to really revolutionize a lot of architectures, yet it has not been as recognized as it should. The combination of associative memories and attention is at the heart of the next wave of neural network advancements.
Attention has already showed to be able to learn sequences as well as RNNs and at up to 100x less computation! Who can ignore that?
We recognize that attention based neural network are going to slowly supplant speech recognition based on RNN, and also find their ways in reinforcement learning architecture and AI in general.
Localization of information in categorization neural networks — We have talked about how we can localize and detect key-points in images and video extensively here. This is practically a solved problem, that will be embedded in future neural network architectures.

Hardware

Hardware for deep learning is at the core of progress. Let us now forget that the rapid expansion of deep learning in 2008–2012 and in the recent years is mainly due to hardware:
  • cheap image sensors in every phone allowed to collect huge datasets — yes helped by social media, but only to a second extent
  • GPUs allowed to accelerate the training of deep neural networks
And we have talked about hardware extensively before. But we need to give you a recent update! Last 1–2 years saw a boom in the are of machine learning hardware, and in particular on the one targeting deep neural networks. We have significant experience here, and we are FWDNXT, the makers of SnowFlake: deep neural network accelerator.
There are several companies working in this space: NVIDIA (obviously), Intel, Nervana, Movidius, Bitmain, Cambricon, Cerebras, DeePhi, Google, Graphcore, Groq, Huawei, ARM, Wave Computing. All are developing custom high-performance micro-chips that will be able to train and run deep neural networks.
The key is to provide the lowest power and the highest measured performance while computing recent useful neural networks operations, not raw theoretical operations per seconds — as many claim to do.
But few people in the field understand how hardware can really change machine learning, neural networks and AI in general. And few understand what is important in micro-chips and how to develop them.
Here is our list:
  • training or inference? —  many companies are creating micro-chips that can provide training of neural networks. This is to gain a portion of the market of NVIDIA, which is the de-facto training hardware to date. But training is a small part of the story and the applications of deep neural networks. For every training step there are a million deployments in actual applications. For example one of the object detection neural network you can now use on the cloud today: it was trained once, and yes on a lot of images, but once trained it can be use by millions of computers on billions of data. What we are trying to say here: training hardware matter as little as the number of times you trained compared to the number of times you use. And making a chipset for training requires extra hardware and extra tricks. This translates into higher power for the same performance, and thus not the best possible for current deployments. Training hardware is important, and a easy modification of inference hardware, but it is not as important as many think.
  • Applications  — hardware that can provide training faster and at lower power is really important in the field, because it will allow to create and test new models and applications faster. But the real significant step forward will be in hardware for applications, mostly in inference. There are many applications today that are not possible or practical because hardware, and not software, is missing or inefficient. For example our phones can be speech-based assistants, and are currently sub-optimal because they cannot operate always-on. Even our home assistants are tied to the power supplies, and cannot follow us around the house unless we sprinkle multiple microphones or devices around. But maybe the largest application of all is removing the phone screen from our lives, and embedding it into our visual system. Without super-efficient hardware all this and many more applications (small robots) will not be possible.
  • winners and losers  — in hardware, the winner will be the ones that can operate at the lowest possible power per unit performance, and move into the market quickly. Imagine replacing SoC in cell-phones. Happens every year. Now imagine embedding neural network accelerators into memories. This may conquer much of the market faster and with significant penetration. That is what we call a winner.
About neuromorphic neural networks hardware, please see here.

Applications

We talked briefly about applications in the Goals section above, but we really need to go into details here. How is AI and neural network going to get into our daily life?
Here is our list:
  • categorizing images and videos  — already here in many cloud services. The next steps are doing the same in smart camera feeds — also here today from many providers. Neural nets hardware will allow to remove the cloud and process more and more data locally: a winner for privacy and saving Internet bandwidth.
  • speech-based assistants  — they are becoming a part of our lives, as they play music and control basic devices in our “smart” homes. But dialogue is such a basic human activity, we often give it for granted. Small devices you can talk to are a revolution that is happening right now. Speech-based assistants are getting better and better at serving us. But they are still tied to the power grid. The real assistant we want is moving with us. How about our cell-phone? Well again hardware wins here, because it will make that possible. Alexa and Cortana and Siri will be always on and always with you. Your phone will be your smart home — very soon. That is again another victory of the smart phone. But we also want it in our car and as we move around town. We need local processing of voice, and less and less cloud. More privacy and less bandwidth costs. Again hardware will give us all that in 1–2 years.
  • the real artificial assistants  — voice is great, but what we really want is something that can also see what we see. Analyze our environment as we move around. See an example here and ultimately here. This is the real AI assistant we can fall in love with. And neural network hardware will again grant your wish, as analyzing video feed is very computationally expensive, and currently at the theoretical limits on current silicon hardware. In other words a lot harder to do than speech-based assistants. But it is not impossible, and many smart startups like AiPoly already have all the software for it, but lack powerful hardware for running it on phones. Notice also that replacing the phone screen with a wearable glasses-like device will really make our assistant part of us!
What we want is Her from the movie Her!
  • the cooking robot — the next biggest appliances will be a cooking and cleaning robot. Here we may soon have the hardware, but we are clearly lacking the software. We need transfer learning, continuous learning and reinforcement learning. All working like a charm. Because you see: every recipe is different, every cooking ingredient looks different. We cannot hard-code all these options. We really need a synthetic entity that can learn and generalize well to do this. We are far from it, but not as far. Just a handful of years away at the current pace of progress. I sure will work on this, as I have done in the last few years~

Thursday, January 4, 2018

The Best Electric Skateboard of 2017


The people have spoken! (But let’s run the numbers anyway).

Is the Enertion Raptor 2 the best electric skateboard of 2017? | Follow me on Instagram
On the 19th of December 2017, Jay Boston hosted his own electric skateboard awards initiative. A cool little idea, particularly considering it was the electric skateboard community itself deciding who would receive the honors.
1,387 people participated in an online survey that decided the winners in each category. Granted, I’m sure a lot of the respondents were Australian, hence the results seemed a little top heavy towards boards that are easily accessible to us here downunder. Hopefully the event garners a little more international participation each year to help even out the results a bit. There were categories where such boards as Metroboard, Carvon and Trampa should have been mentioned, but they were no where to be seen! Nevertheless, it’s a great initiative and will hopefully grow from strength to strength in the coming years. A quick shout-out to Jay for having me on as a guest — cheers mate!
The Enertion Raptor 2 was crowned the overall winner of the best electric skateboard of 2017 — as voted for by the people.
You can check out the video of the live event below:
Go to 1:10.38 to see the Raptor 2 announced as the best electric skateboard of 2017
Nominations were only open to boards that had actually delivered production units to customers in 2017. Enertion, with just under a couple of hundred Raptor 2 units in the field at the time the awards were streamed, got in by the skin of their teeth. However, the fact that the Raptor 2 won tells us that those people who have a Raptor 2, as well as the multitude of people who have tested the board on ride days and events, are clearly very, VERY impressed with Enertion’s end result.
I thought it might be interesting to compare the peoples choice with something a little more academic, finishing off with a bit of commentary regarding the results and any differences between them.
Below I’ve selected what are arguably the 10 most popular production boards of 2017.
(Boards selected are single and dual drive boards in street configuration only. This analysis is focused on the upper end of the market towards boards that might be considered “premium” or “top-tier” by companies owned and operated from such places as the United States, Australia and Europe).

Boosted Board Gen2 Dual+

Top Speed: 22mph (35kph)| Range: 7 miles (11 kms)| Hills: 25% | RRP: $1758.90 USD

Carvon EVO V4 Dual

Top Speed: 40mph (64kph)| Range: 25 miles (40km)| Hills: 15% | RRP: $1999 USD

Enertion Raptor 2

Top Speed: 30.5mph (49kph)| Range: 25 miles (40km)| Hills: 30% | RRP: $1759.18 USD

Evolve Bamboo GT

Top Speed: 26mph (42kph)| Range: 19 miles (30km)| Hills: 25% | RRP: $1459.98 USD

Evolve Carbon GT

Top Speed: 26mph (42kph)| Range: 31 miles (50km)| Hills: 25% | RRP: $2069.98 USD

Evolve GTX

Top Speed: 26mph (42kph)| Range: 31 miles (50km)| Hills: 25% | RRP: $1728.99 USD

Inboard M1

Top Speed: 22mph (35kph)| Range: 7 miles (11km)| Hills: 17% | RRP: $1399 USD

Mellow Board (drive only)

Top Speed: 25mph (40kph)| Range: 8.5 miles (13km)| Hills: 20% | RRP: $1694.53 USD*

Metroboard 41" Slim Stealth Edition (Single)

Top Speed: 24mph (38kph)| Range: 40 miles (64km)| Hills: 25% | RRP: $1649 USD

Metroboard 41" Stealth Dual

Top Speed: 24mph (38kph)| Range: 25 miles (40km)| Hills: 30% | RRP: $1899 USD
A couple of notes on the above: All prices are RRP in USD (specials, sales, shipping, taxes and other fluctuations are not taken into consideration). All specs are taken directly from the US or international websites of the board manufacturers themselves (correct as of December 2017). Boosted finally announced the release of their extended range battery in late 2017, which “doubles the range”. However, not only is the extended range battery not a standard item, I don’t think anyone outside of a few YouTubers actually got their batteries in 2017. It should be noted that Carvon have a second EVO V4 Dual model called the ‘XL’, which has the same range, a lower top speed of 35mph, but a much higher hill climbing capacity of 25%, which rivals many of the other boards on this list. It comes at a cost of $100 more than the standard EVO V4 Dual at $2099 USD. The ‘XL’ was not included in this comparison as to my knowledge no (or very few) units made it into the hands of the public in 2017. I even debated on whether or not to include the regular EVO (known as the R-Spec), as there’s barely any units in public hands, but they are out there. The listed top speed of the Evolve boards is taken from the known achievable top speed on 97mm wheels, the most popular wheel choice for Evolve riders and the standard wheel size on the GTX. As the Bamboo GT and Carbon GT come with 83mm wheels as standard, the RRP has been adjusted to include a set of ABEC11 97mm Flywheels as priced on the Evolve USA website (109.99 USD) in both circumstances. The Mellow Board lists a range bracket between 7.5 and 10 miles on their website. For the sake of simplicity I chose 8.5 miles as somewhere in the middle. Like Evolve, the top speed spec of the Metroboards is based on the 97mm wheel option in both circumstances. Both Metroboards in this comparison have been tricked out — 97mm wheels for both, 10 watt lights for both and the single drive has the biggest battery available included in the comparison. Metroboard hill climbing specs are estimates as they’re not included on the Metroboard website. The single drive is known to rival Boosted’s and Evolve’s (25%), so by virtue of that knowledge the dual drive must exceed this (30% or more).
*Please see further notes about Mellow Board pricing in the ‘Pricing’ section of this article.

Ranking System Used

In each category (top speed, range, hills and RRP) each board is given a number from lowest to highest based on a best-to-worst order: 1 being the best/cheapest then ascending in score until we get to the worst/most expensive.
The board with the lowest scores are the best in each category and overall (avg).

Top Speed

The Carvon EVO V4 Dual. 2017’s fastest production board! | Source: carvonskates.com
The Carvon EVO V4 Dual is the king of speed in 2017. There’s then quite a drop down to the Enertion Raptor 2 in second place, which is still significantly faster than the next bunch of boards — the Evolve line-up, which all punch out the same top speed. The Mellow Board is hovering around the middle followed closely by the two Metroboards, which each punch out the same top speed. Down the bottom of the list we have the Boosted Board Gen2 Dual+ and the Inboard M1.
From where I’m sitting I’d expect anything with a score of 3 to 5 to all be very similar in real life. It’s really splitting hairs. From that bracket it is a significant step up to the Raptor 2 and then an even bigger step up again to the EVO (maybe too much?)
The Boosted Board and Inboard M1 are significantly over-rated in the speed department.

Range

Metroboard 41" Slim Stealth Edition (single) with a 17.6AH battery for the longest range! | Source: metro-board.com
There are five distinct categories here: We have the Metroboard single that’s in a class of its own! Then we have the Evolve GTX and Carbon GT, which essentially share the same battery. Next we have the upper-middle class of range: The Carvon EVO, Enertion Raptor 2 and Metroboard Dual. The Evolve Bamboo GT stands alone as a mid-range board and our list ends with the low-range, swappable battery category of boards. An optimist might consider the final category to be even better than the ones above it, as swappable batteries can in reality mean “endless range”. The problem being, of course, that more batteries equals more $$$…

Hill Climbing

The Metroboard 41" Stealth Dual. A widely reported incline killer! | Source: metro-board.com
I’d say we’re looking at four distinct categories of hill climbing here. The first category is reserved for certified incline killers! The Enertion Raptor 2 and Metroboard Dual! Then we have a range of aggressive hill climbers ranging from the Evolve line-up, Boosted Board and Metroboard single. The Mellow stands alone as a moderate hill climber, and our list ends with a couple of boards that shy away from inclines, the Carvon EVO and Inboard M1.
It should be noted that with the optional 38T drive gear and hard duro/small wheels, the Evolve GT/GTX line-up are also capable of climbing hills on par with (even better than?) the Metroboard Stealth Dual and Enertion Raptor 2. Video here. However, the 38T drive gear is not standard.
The Enertion Raptor 2. Conquers 30% inclines with ease! | Source: thatesk8life.com
Raptor 2 vs. 30% incline. Raptor 2 wins!

Price

Note: The Mellow Board pricing was taken straight from mellowboards.com and converted from EUR to USD. After publication I was made aware of mellowboardusa.com, where adjusted pricing can be found direct from the US distributor. The difference being that shipping a drive unit from Europe would have a considerable shipping fee attached to it. It’s clear this cost (and other sundry costs) has been incorporated into the US distributor price of $1,995. Please make your own adjustments and determinations regarding this as you read the rest of the article.
The Inboard M1 is the best priced electric skateboard in the upper end of the market. | Source: bestbuy.com
In the Sub-$1500 category we have the Inboard M1 and Evolve Bamboo GT. In the $1500-$1800 category we have the Metroboard single, Mellow Board, Evolve GTX, Boosted Board and Enertion Raptor 2. In the $1800 and above category we have the Metroboard Dual, Carvon EVO and Evolve Carbon GT (man, carbon fiber is expensive!)

And The Winner Is…

The equal winners of this little test couldn’t be more different! According to just raw specs vs. price, the best electric skateboard of 2017 is a tie between the Evolve Bamboo GT and the Metroboard 41" Slim Stealth Edition (single)!
On paper the Evolve Bamboo GT represents well-rounded specs at a reasonable price. In addition, Evolve also have that tempting 2-in-1 conversion capability, allowing you to fit pneumatic all-terrain tyres to your board making it an entirely different beast!
Evolve Bamboo GT. Best bang for your buck! But the battery sag is a killer! | Source: evolveskateboards.com.au
If you can forgo the need for pneumatic all-terrain tyres, I believe the Metroboard single to be a far better option. Top speed between the two is splitting hairs, they both climb the same grade hills, but the Metroboard has insane range! Spend approx $200 more to get the Metroboard single over the Bamboo GT and you instantly upgrade from a 19 mile range board to a 40 mile range board! Again, that’s insane!
Metroboard 41" Slim Stealth Edition (single). Equal score, but an entirely different beast with out of this world range! | Source: metro-board.com
The next issue to tackle is one of aesthetics vs. quality. The Evolve looks better, there’s no denying it. It has nice flex, dual kingpin trucks (if that’s your thing) and is just an all-round slimmer and sexier design. The Metroboard is not as slim and stealth as its namesake. It rides high and stiff compared to an Evolve. When it comes to the argument of quality, however, the opposite is true. Evolve’s quality and reliability has been called into question time and time again, whereas Metroboard’s are known as bullet proof tanks! Then there’s the question of batteries. Paper specs tell us the Bamboo GT has a 19 mile range, but due to the low quality cells Evolve use in their battery packs, Evolve boards generally suffer from the worst battery sag in the industry. I think it would be fair to say that the Bamboo GT actually gets about 14 miles of enjoyable/manageable range, which now really tilts the scales in favor of the Metroboard single.

My Thoughts on the Results

If you had to call a winner out of the two tied boards, it would have to be the Metroboard 41" Slim Stealth Edition (single). For speed, range and hill climbing vs. dollar + quality and reliability, it just can’t be beat!
Of course, however, there will be people who don’t need 40 miles worth of range and would much prefer to have the option for pneumatic all-terrain tyres, save $200 and get the Bamboo GT. There will also be people who just plain don’t like the look/feel of something like the Metroboard.
One of the most interesting results for me was the gap between the Evolve GTX and Carbon GT. These are essentially the exact same board — they have the same top speed, range and hill climbing capability. The difference is purely cost. That carbon fiber deck must cost a pretty penny! The GTX comes in at $1728.99, whereas the Carbon GT comes in at $2069.98 (which also includes a set of ABEC11 97mm Flywheels, otherwise the board wont reach the quoted top speed — matching the GTX). That’s an insane cost difference for exactly the same performance between the two boards. I personally view the GTX as the preferable choice here. It’s not only cheaper, but it’s more flexy and more modular, as the deck and enclosure are separate pieces, allowing for more modifications down the road (on the Carbon GT the deck and the enclosure are one complete unit). On the other hand, the Carbon GT is longer (40 inches compared to the GTX’s 38), lighter (17lbs compared to the GTX’s 19.4 lbs) and obviously has a far more rigid and stiff feel to it. Some people prefer the latter points.
The Evolve GTX. Every bit the Carbon GT without the price tag! | Source: twelveboardstore.com.au
The Evolve Carbon GT. Probably still the best looking board on the market by a country mile! | Source: techcrunch.com
I guess we also can’t ignore the fact that these paper-based results sees the Boosted Board languishing in last place. The board scores extremely poorly in the speed and range departments. The KO then comes from the high price tag that’s applied to what is now considered a fairly mediocre spec sheet. But (and it’s a big but) SPECS AREN’T EVERYTHING…
Boosted remains the smoothest and most comfortable electric skateboard I’ve ever ridden! A tremendous amount of care and attention to detail is put into their product. Their remote and mobile app are still best in class and their QC and customer service also, arguably, remains unmatched. Yes, there are far better performing electric skateboards you can get for your money, but very few do the “off board” stuff as well as Boosted, very few have such a well-rounded, well-finished, polished and respected product that “just works” as Boosted do. That’s what you pay for.
Boosted Board. If this was a user experience analysis (not based on specs), the Boosted would win! | Source: boostedboards.com
What these results say in the end is that user experience counts for far more than specs ever will. The problem is that user experience is a very hard thing to measure, particularly form an independent, third party perspective.
Or is it?…

The Peoples Choice

This brings us back full circle to Jay Boston’s Electric Skateboard Awards and the overall winner as voted by 1,387 people — the Enertion Raptor 2!
The Raptor 2 comes forth in a straight-up specs showdown, but it’s arguable that the Evolve GT Bamboo is only above it due to its price point. In addition, I’d be surprised if there were any more than five Metroboards in the whole of Australia! Add to that Evolve’s known reliability and durability woes and it’s easy to see why the Enertion Raptor 2 came out on top!
The Enertion Raptor 2 is faster than the Evolve suite of boards, is comparable in range to the GTX and Carbon GT (once you account for the Evolve sag factor) and is an equal or better hill climber in stock configuration. It sits around the same price point as an Evolve GTX, which is also obviously significantly cheaper than a Carbon GT.
If you’re after a performance board packing the latest in motor, battery and VESC/FOCBOX technology that has great specs across the board at a highly competitive price, in my mind, the people got it right!
The Enertion Raptor 2. The peoples (and my) choice! | Follow me on Instagram

The Best Electric Skateboard of 2017?

In the end that’s completely up to you to decide. It’s completely subjective. What’s best for one might not be what’s best for another.
If the best electric skateboard for 2017 to you is simply the fastest electric skateboard, then the best electric skateboard of 2017 is the Carvon EVO V4 Dual.
If the best electric skateboard for 2017 to you is simply the electric skateboard with the most range, then the best electric skateboard of 2017 is the Metroboard 41" Slim Stealth Edition (single).
If the best electric skateboard for 2017 to you is simply the electric skateboard with the best hill climbing capabilities, then the best electric skateboard of 2017 is the Enertion Raptor 2 or Metroboard 41" Stealth Dual.
If the best electric skateboard for 2017 to you is simply the most reliable/durable electric skateboard, then the best electric skateboard of 2017 is the Boosted Board Gen2 Dual+ or maybe one of the Metroboards.
If the best electric skateboard for 2017 to you is simply the most versatile electric skateboard, then the best electric skateboard of 2017 is an Evolve GT/GTX.
I honestly do think the people got it right in selecting the Enertion Raptor 2 as the best all round electric skateboard of 2017, but I also think an honorable mention needs to go to the Metroboard 41" Slim Stealth Edition (single) from a pure specs for dollar + quality point-of-view.
Honorable mention: The Metroboard 41" Slim Stealth Edition (single) | Source: techgearlab.com
It truly is an exciting time to be into electric skateboards!
2018 is going to be a big year!

Monday, January 1, 2018

Hike Wallet crossed 10M transactions in Nov 2017, plans to launch more services


  • Over 25M active Wallets on Hike
  • Redesigns its App for easy discoverability of transactional services
  • Cab booking, bus, train, movie tickets, bill payments and more will soon be possible on Hike




21 December: Hike Messenger, India’s first homegrown messaging app today announced that it has crossed 10 million transactions per month on its Wallet, growing 100% month-over-month. Hike is the first messaging app to integrate payments in India and Hike Wallet has seen exponential growth over the last two months. Of the 10M transactions, 70% were on recharge and the remaining 30% on P2P.
The app also has been redesigned to provide easy access to transactional services on the new homescreen. Users will no longer need to scroll through the chat thread or look for the services they want to go to. One can view the entire portfolio of services and just tap to access these and pay seamlessly through the Hike Wallet.
Seeing this phenomenal response, with just a simple set of services like Recharge & P2P, Hike is planning to add more services like cab bookings, bus, train, movie tickets and pay bills in Q1 2018.
According to Kavin Bharti Mittal, Founder and CEO, Hike Messenger, “The growth on the Wallet has been tremendous and honestly we’re just getting started. On the back of this growth, we’ve launched an updated design to make it easier to discover and transact with services on Hike. It’s also become quite clear to us that our users want more services. So we’re heads down working hard to bring things like booking taxis, movietickets and more to the platform. Expect these to start rolling out as early as next quarter.’
About Hike Messenger
Hike is the first messaging and social technology company made with love in India. It simplifies how people connect with others and changes the way they interact with content and services on mobile. It is the only successful Indian messaging platform with scale.
Hike was launched on 12/12/12 and acquired a user base of over 100 million in January 2016. In August 2016, Hike raised its fourth round of funding of USD 175 million led by Tencent and Foxconn at a valuation of USD 1.4 billion, making it the fastest company in the India to attain a valuation of USD 1 billion, having reached the milestone in just 3.7 years. Investors in Hike include Tencent, Foxconn, Tiger Global, Softbank and Bharti. Apart from these, some of the top tech veterans from the Silicon Valley have also invested in the company and are advisors.
Today, Hike has over 350 employees spread across 2 offices in Delhi and Bangalo
via : Hike

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