This is an IoT story combined from what was delivered during Q1’15 in Stockholm, Copenhagen and Bad Homburg (Frankfurt).
We stuck in the past
When I first heard Peter Thiel about our technological deceleration, it collided in my head with technological acceleration by Ray Kurzveil. It seems that both gentlemen are right and we [humanity] follow multi-dimensional spiral pathway. Kurzveil reveals shorter intervals between spiral cycles. Thiel reveals we are moving in negative direction within single current spiral cycle. Let’s drill down within the current cycle.
Cars are getting more and more powerful (in terms of horse power), but we don’t move faster with cars. Instead we move slower, because of so many traffic lights, speed limits, traffic jams, grid locks. It is definitely not cool to stare at red light and wait. It is not cool either to break because your green light ended. In Copenhagen majority of people use bikes. It means they move at the speed of 20 kph or so… Way more slower than our modern cars would have allowed. Ain’t strange?
Aircrafts are faster than cars, but air travel is slow either. We have strange connections. A trip from Copenhagen to Stockholm takes one full day because you got to fly Copenhagen-Frankfurt, wait and then fly Frankfurt-Stockholm. That’s how airlines work and suck money from your pocket for each air mile. Now add long security lines, weather precautions and weather cancellations of flights. Add union strikes. Dream about decommissioned Concord… 12 years ago already.
Smartphone computing power equals to Apollo mission levels, so what? Smartphone is used to browse people and play games mainly. At some moment we will start using it as a hub, to connect tens of devices, to process tons of data before submitting into the Cloud (because Data will soon not fit into the Cloud). But for now we under-use smartphones. I am sick of charging every day. I am sick for all those wires and adapters. That’s ridiculous.
Cancer, Alzheimer and HIV still not defeated. And there is not optimistic mid term forecast yet.
We [thinking people] admit that we have stuck in the past. We admit our old tools are not capable to bring us into the future. We admit that we need to build new tools to break into the future. Internet of Things is such a macro trend – building those new tools what would breakthrough us into the future.
Where exactly we stuck?
We are within 3rd wave of IoT called Identification and at the beginning of 4th wave of IoT called Miniaturization. Those two slightly overlap.
Miniaturization is evidence of Moore’s Law still working. Pretty small devices are capable of running same calculations as not so old desktops. Connecting industrial machinery via man-in-the-middle small device is on the rise. It is known as Machine-to-Machine (M2M). Two common scenarios here: wire protocol – to break into dumb machine’s wires and hook it there for readings and control; optical protocol – read from analog or digital screens and do optical recognition of the information.
More words about optical protocol in M2M. Imagine you are running biotech lab. You have good old centrifuges, doing their layering job perfectly. But they are not connected, so you need to read from display and push the buttons manually. You don’t want to break into the good working machines and decide to read from their screens or panels optically, hence doing optical identification for useful information. The centrifuges become connected. M2M without wires.
Identification is also on the rise in manufacturing. Just put a small device to identify something like vibration, smoke, volume, motion, proximity, temperature etc. Just attach a small device with right sensors to dumb machine and identify/measure what you are interested in. Identification is on the rise in life style. It is Wearables we put onto ourselves for measure various aspects of our activity. Have you ever wondered how many methods exist to measure temperature? Probably more than 10. Your (or my?) favorite wearables usually have thermistors (BodyMedia) and IR sensors (Scanadu).
Optical identification as powerful field of entire Internet of Things Identification requires special section. Continue reading.
Why optical is so important? Guess: at what bandwidth our eyes transmit data into the brain?
It is definitely more than 1 megabit per second and may be (may be not) slightly less than 10 megabit per second. For you geeks, it is not so old Ethernet speed. With 100 video sensors we end up with 1+ Terabyte during business hours (~10 hours). That’s hell a lot of data. It’s better to extract useful information out of those data streams and continue with information rather than with data. Volume reduction could be 1000x and much more if we deal with relevant information only. Real-time identification is vital for self-driving cars.
Even for already accumulated media archives this all is very relevant. How to index video library? How to index image library? It is the work for machines, to crawl and parse each frame and sequences of frames to classify what it there, remember timestamp and clip location, make a thumbnail and give this information to those who are users of the media archives (other apps, services and people). Usually images are identified/parsed by Convolution Neural Networks (CNN) or Autoencoder + Perceptron. For various business purposes, the good way to start doing visual object identification right away is Berkeley Caffe framework.
Ever heard about DeepMind? They are not on Kaggle today. They were there much earlier. One of them, Volodymyr Mnih, won the prize in early 2013. DeepMind invented some breakthrough technology and was bought by Google for $400 million (Facebook was another potential buyer of DeepMind). So what is interesting with them? Well yeah, the acquisition was conditional that Google would not abuse the technology. There is special Ethical Board set up at Google to validate use of DeepMind technology. We could try to figure out what their secret sauce is. All I know is that they went beyond dumb predefined machine learning by applying more neuroscience stuff which unlocked learning from own experience, with nothing predefined a priori.
Volodymyr Mnih has been featured in recent (at the moment of this post) issue of Nature magazine, with affiliation to DeepMind in the references. Read what they did – they build neural network that learns game strategy, ran it on old Atari games and outperformed human players on 43 games!! It is CNN, with time dimension (four chronological frames given to input). Besides time dimension, another big difference to classic CNN is delayed rewards learning mechanism, i.e. it’s true strategy from your previous moves. The algorithm is called Deep Q-learning, and the entire network is called Deep Q-learning Network (DQN). It is a question of time when DQN will be able to handle more complicated graphical screens than old Atari. They have tried Doom already. May be StarCraft is next. And soon it will be business processes and workflows…
Those who subscribed to Nature, log in and read main article, especially Methods part. Others could check out reader-friendly New Yorker post. Pay attention to Nature link there, you might be lucky to access Methods section on Nature site without subscription. Check out DQN code in Lua and DQN + Caffe and DQN/Caffe ping pong demo.
Who eats whom?
All right with importance of optical identification, hope it’s time to switch back to high-level and continue on the Internet of Things as macro trend, at global scale. Many of you got used to the statement that Software is eating the World. That’s correct for two aspects: hardware flexibility is being shifted to software flexibility; fabricators are making hard goods from digital models.
Shifting flexibility from hardware to software is huge cost reduction of maintenance and reconfiguration. The evidence of hardware eaten by software are all those SDX, Software Defined Everything. E.g. SDN aka Software Defined Networks, SDR aka Software Defined Radio, SRS aka Storage, and so of for Data Center etc. Tesla car is a pretty software defined car.
But this World has not been even eaten by hardware yet! Miniaturization of electric digital devices allows the Hardware to eat the World today and tomorrow. Penetration and reach of devices into previously inaccessible territories is stunning. We establish stationary identification devices (surveillance cameras, weather sensors, industrial meters etc.) and launch movable devices (flying drones, swimming drones, balloons, UAVs, self-driving cars, rovers etc.) Check out excellent hardware trends for 2015. Today we put plenty of remora devices onto the cars and ourselves. Further miniaturization will allow to take devices inside ourselves. The evidence is that Hardware is eating the World.
Wait, there are fabricators or nanofactories, producing hard goods from 3D models! 3D printed goods and 3D printed hamburger are evidences of Software directly eating the World. Then, the conclusion could be that Software is eating the World previously eaten by Hardware, while Hardware is eating the rest of the World at higher pace than Software is eating via fabrication.
Who eats whom? Second pass
Things are not so straightforward. We [you and me] have stuck in silicon world. Those ruminations are true for electrical/digital devices/technologies. Things are not limited to digital and electrical. The movement of biohackers can’t be ignored. Those guys are doing garage bio experiments on 5K equipment exactly as Jobs and Woz did electrical/digital experiments in their garage during PC era birth.
Biohackers are also eating the World. I am not talking about standard boring initiation [of biohacker] to make something glowing… There are amazing achievements. One of them is night vision. Electrical/digital approach to night vision is infra red camera, cooler and analog optical picture into your eyes, or radio scanner and analog/digital reconstruction of the scene for your eyes. Bio approach is injection of Chlorin e6 drops into your eyes. With the aid of Ce6 you could see in the darkness in the range of 10 to 50 meters. Though there is some controversy with that Ce6 experiment.
The new conclusion for the “Eaters Club” is this:
- Software is eating the world previously eaten by Hardware
- Hardware is eating the rest of the World, much bigger part than it’d already been eaten, at high pace
- Software is eating the world slowly thru fabrication and nanofactories
- Biohackers are eating the world, ignoring both Hardware & Software eaters
Will convergence of hardware and bio happen as it happened with software and hardware? I bet yes. For remote devices it could be very beneficial to take energy from the ambient environment, which potentially could be implemented via biological mechanisms.
Blending it all together
Time for putting it all together and emphasizing onto practical consequences. Small and smaller devices are needed to wrap entire business (machines, people, areas). Many devices needed, 50 billion by 2020. Networking is needed to connect 50 billion devices. Data flow will grow from 50 billion devices and within the network. Data Gravity phenomenon will become more and more observable, when data attracts apps, services and people to itself. Keep reading for details.
Internet of Things is a sweet spot at the intersection of three technological macro trends: Semiconductors, Telecoms and Big Data. All three parts work together, but have different evolution pace. That’s lead to new rules of the ‘common sense’ emerging within IoT.
Remote devices need networking, good networking. And we got an issue, which will only strengthen. The pace of evolution for semiconductors is 60%, while the pace of evolution of networks is 50%. The pace of evolution of storage technology is even faster than 60% annually. It means that newly acquired data will fit into the network less and less in time [less chances for data to get into the Cloud] . It means that more and more data will be left beyond the network [and beyond the Cloud].
Off-the-Cloud data must be handled in-place, at location of acquisition or so. It means huge growth of Embedded Programming. All those small and smaller devices will have to acquire, store, filter, reduce and sync data. It is Embedded Programming with OS, without OS. It is distributed and decentralized programming. It is programming of dynamic mesh networks. It is connectivity from device to device without central tower. It is new kind of the cloud programming, closest to the ground, called Fog. Hence Fog Programming, Fog Computing. Dynamic mesh networks, plenty of DSP, potentially applicable distributed technologies for business logic foundation such as BitTorrent, Telehash, Blockchain. Interesting times in Embedded Programming are coming. This is just Internet of Things Miniaturization phase. Add smart sensing on those P2P connected small and smaller devices in the Fog, and Internet of Things Identification phase will be addressed properly.
We are building new tools that we will use to build our future. We’re doing it through digitization of the World. Everything physical becomes connected and reflected into its digital representation. Don’t overfocus onto Software, think about Hardware. Don’t overfocus onto Hardware, think about Bio. Expected convergence of software-hardware-bio as most stable and eco-friendly foundation for those 50 billion devices by 2020.
Recall Peter Thiel and biz frustrations nowadays. With digitized connected World we will turn from negative direction within current spiral cycle into positive. And of course we will continue with long term acceleration. The future looks exciting.
Music for reading and thinking: from the near future, Blade Runner, Los Angeles 2019