This week, Cloudflare introduced its Workers platform to the world as a new form of edge computing. The news is worth taking a closer look at given all the intense focus on edge computing today. For example, the telcos are all pushing forward with their version of edge computing, contained on servers at the edge of their cellular networks.
And not a week goes by without some startup claiming it has a new edge computing platform or tool. Part of the ubiquity of the phrase “edge computing” comes from the fact that every player in the IoT thinks of the edge in a different way.
Sensor companies think of the edge as tiny, battery-powered devices that gather data, while industrial manufacturers consider it a computer on a machine that gathers data from multiple sensors. Intel and Dell think of the edge as a gateway, or as servers on a factory floor. While the telcos — along with content delivery and internet security provider Cloudflare — view the edge as the limits of their own networks.
For Matthew Prince, CEO of Cloudflare, the edge touted by industrialists and sensor folks will eventually disappear. “Any on-premise devices are going away,” he says. Instead, he sees a future where there is device-side computing, back-end computing in the cloud, and what he calls the “third place” of computing, which happens in between those two.
The benefits of such an architecture are that a company can take advantage of computing power that’s geographically closer to the device, and build devices at the edge that are cheaper because they have no need for big CPUs. As an added bonus, because those devices connect through Cloudflare’s network, they aren’t directly on the public internet and as such, have some security protection. The downside to this architecture is that when the internet fails, so do all the programs you have running in the cloud. Basically one might make the trade-off of putting expensive compute chips in an edge device to putting in dual forms of connectivity.
I’m not sure all on-premise devices will go away, especially not in the next five to 10 years, but I do think the idea of having a third place for computing makes sense. Some of the examples Prince offered by way of customer stories really resonate. For example, a company building an edge device designed to take in constant data, such as a thermometer, could send the data to a Cloudflare Worker program that aggregates it and then sends a sample to the cloud for storage or for processing later on. But if the temperature data spikes, the Worker program can take action and send an alert to the end user.
And ideally, that alert would take less time to reach the end user and would be more resilient than a function hosted on the cloud that’s dependent on a single data center location. Another advantage of this approach is that it makes managing the equipment a bit easier. In the temperature sensing example, for instance, the end user just has to buy the sensors tied to the Cloudflare Worker program and put them in his or her location.
As those sensors age, they can be updated remotely and even replaced without having to futz with a gateway box. One of the more challenging aspects of deploying IoT offerings is that provisioning connected devices can be a nightmare of typing in passwords or snapping pictures of QR codes. In this case, devices can arrive pre- provisioned.
What I’d like to see is a robust discussion of the merits of each approach and a clear understanding of their related trade-offs. There’s obviously an opportunity for this version of edge computing with some connected devices, especially those that need to be cheap and easily deployed.
Like every other major tech company, Google has designs on being the first to achieve quantum supremacy — the point where a quantum computer could run particular algorithms faster than a classical computer. Today it's announced that it believes its… Engadget RSS Feed
Microsoft has announced the first major upgrade to its Quantum Development Kit since its introduction last year. It has added several new features designed to open the platform to a wider array of developers, including support for Linux and macOS, as well as additional open source libraries. Further, the kit will be interoperable with the Python computing language. Microsoft announced the Quantum Development Kit at its Ignite conference last fall. It launched the free preview about three months later, featuring the new Q# programming language. TechNewsWorld
MWC VMware has unveiled an advancement of its IoT strategy focusing on new edge computing solutions for specific use cases including Asset Management and Smart Surveillance at Mobile World Congress 2018.
To be featured at the new edge computing solutions are VMware vSAN hyper-converged infrastructure (HCI) software, VMware vSphere and VMware Pulse IoT Center. For the formulations, VMware will collaborate with several partners including Axis Communications and Wipro Limited.
Targeting the surveillance industry, VMware and Axis Communications are partnering on an IoT solution that features Axis’ advanced surveillance capabilities including IP cameras and 4G/LTE routers which can be deployed for protection of employees and properties. With VMware Pulse IoT Center, customers can manage, monitor, and secure their Axis cameras and routers. In the initial phase, the solution will be available on select Dell EMC servers and include the option of Dell Edge Gateways.
VMware and Wipro are also working together to provide manufacturers a complete edge to cloud IoT solution for enhanced productivity and efficiency of machineries and other assets. The new solution will feature Wipro's IoT offerings and integrate many IoT platforms that are hosted on-premises or in the cloud.
Ray O’Farrell, executive vice president & chief technology officer, VMware, said: “Building an edge computing solution today is a time-intensive exercise most enterprises can’t afford. Today, VMware unveils hyper-converged edge computing solutions that are cost-effective and will enable customers to build and scale secure, use case-specific IoT solutions that work for them from the edge all the way to the cloud, relying on proven, tested software they already use and trust.
“Together with ecosystem partners Axis, Wipro Limited and Dell EMC, we’re excited to deliver the first of many tailored solutions to meet the unique IoT needs of our enterprise customers,” O’Farrell added.
After Microsoft signed a deal to test Windows 10 on Xiaomi devices in 2015 and then Xiaomi bought a trove of patents to help run other Microsoft services on its devices in 2016, today the two companies announced another chapter in its collaboration. Xiaomi and Microsoft have signed a Strategic Framework Memorandum of Understanding (MoU) to work more closely in the areas of cloud computing,… Read More Mobile – TechCrunch
With 17-qubit chips and IBM’s 50-qubit computer, quantum computing is coming — that much is undeniable. But if quantum computers are ever going to be used for more complex tasks, they’re going to need thousands — if not millions — of qubits. And we’re not quite there yet.
Whether the machines are primarily tasked with performing calculations or correcting incorrect information caused by external forces (which qubits are very sensitive to) practical quantum computers are going to require a lot of qubits. Therefore, we’ll need to manufacture processors capable of handling all the qubits needed for these machines to run. That’s the challenge a team of scientists from the Delft University of Technology in the Netherlands hopes they’ve found a solution to, by using silicon to make a programmable quantum processor.
In their research, published in the journal Nature, the team describes how they controlled the spin of a single electron using microwave energy. In silicon, the electron would spin up and down simultaneously, effectively keeping it in place. Once this was achieved, the team linked two electrons together and programmed them to perform quantum algorithms. The data from the new processor matched the data from a traditional computer running the same algorithms.
What’s most notable about the team’s research is that they successfully created a 2-qubit silicon-based quantum processor. It’s not all that surprising that it worked: silicon is a material the computer industry is already familiar with, as it’s readily used to manufacture computer chips currently in use.
“As we’ve seen in the computer industry, silicon works quite well in terms of scaling up using the fabrication methods used,” Dr. Tom Watson, one of the authors of the research, explained to the BBC. If Watson and his team can manage to link even more electrons successfully, it could lead to qubit processors that could be mass-produced, which would bring us one step closer to the quantum computers of the future.
Professor Lieven Vandersypen, another author of the research, is already looking ahead to such developments. He told the BBC that next up, the team plans to “develop silicon quantum chips with more qubits, both in the Delft cleanrooms and in industrial cleanrooms with our partner Intel.”
Eight years ago today, Steve Jobs introduced iPad, positioned as a new device category between the highly-mobile iPhone and conventional Macs. Some critics were disappointed that it wasn’t a Mac in tablet form; others were upset it wasn’t a telephone, that it wasn’t smaller, that wasn’t larger–or that it effectively was a larger iPod touch. All critics have since agreed that iPad is a disastrous, disappointing problem Apple should feel bad about despite it being the most popular, most profitable, most influential new form factor in personal electronics since iPhone itself. AppleInsider – Frontpage News
The science and tech world has been abuzz about quantum computers for years, but the devices are not yet affecting our daily lives. Quantum systems could seamlessly encrypt data, help us make sense of the huge amount of data we’ve already collected, and solve complex problems that even the most powerful supercomputers cannot – such as medical diagnostics and weather prediction.
That nebulous quantum future became one step closer this November, when top-tier journal Naturepublishedtwo papers that showed some of the most advanced quantum systems yet.
If you still don’t understand what a quantum computer is, what it does, or what it could do for you, never fear. Futurism recently spoke with Mikhail Lukin, a physics professor at Harvard University and the senior author of one of those papers, about the current state of quantum computing, when we might have quantum technology on our phones or our desks, and what it will take for that to happen.
This interview has been slightly edited for clarity and brevity.
Futurism:First, can you give me a simple explanation for how quantum computing works?
Mikhail Lukin: Let’s start with how classical computers work. In classical computers, you formulate any problem you want to solve in the form of some input, which is basically a stream of 0s and 1s. When you want to do some calculation, you basically create a certain set of rules depending on how this stream should actually move. That’s the process of calculation — addition, multiplication, whatever.
But we’ve known for more than 100 years that our microscopic world is fundamentally quantum mechanical. And in quantum mechanics, you can have systems. Your computer, for instance, or your chair can be placed in two different states at once — that’s the idea of quantum superpositions. In other words, your computer can be simultaneously both in Boston and in New York. So this quantum superposition, even though it sounds very weird, is allowed by the laws of quantum mechanics. On a large scale, like the example that I gave, it is clearly very strange. But in the microscopic world, like with a single atom, creating this kind of superposition state is actually quite common. So by doing these scientific experiments, scientists proved that a single atom is in two different states at once.
The idea of quantum computers is to basically make use of these rules of quantum mechanics to process information. It’s pretty easy to understand how this can be so powerful. In classical computers, you give me a certain input, I put it in my computer, I give you an output. But if our hardware was quantum mechanical, rather than just sequentially providing some input and reading out the answers, I could prepare the computer register in the quantum superpositions of many different kind of inputs.
This means that if I then take this superposition state and process it using the laws of quantum mechanics, I can process many, many inputs at once. It could be potentially an exponential speedup, compared to the classical programs.
F:What does a quantum computer look like?
ML: If you were to walk into a room with our quantum machine in it you would see a vacuum cell or tube and a bunch of lasers which shine into it. Inside we have a very low density of a certain atom. We use lasers to slow down the atomic motion very close to absolute zero, which is called laser cooling.
F:So how do you program the thing?
ML:. To program a quantum computer, we shine a hundred tightly-focused laser beams into this vacuum chamber. Each of these laser beams acts as a optical tweezer, grabbing one atom or not. We have these atom traps, each of which is either loaded or empty. We then take a picture of these atoms in these traps, and we figure out which traps are full and which are empty. Then we rearrange the trap containing single atoms in any pattern that we wish. This desired arrangement of single atoms, each individually held in and easily controlled, are positioned basically at will.
Positioning these atoms is one way that we can program it. To actually control the qubit, we gently, carefully, push the atoms from their lowest energy state into a higher energy state. We do this with carefully chosen laser beams that shoot to one specific transition. Their frequency is very tightly controlled. In this excited state the atom actually becomes very big and, because of this atom size, the atoms start interacting or – in other words – talking to each other. By choosing the state to which we excite the atoms and choosing their arrangements and positions, we can then program the interaction in a highly controllable way.
F:What kinds of applications would a quantum computer be most useful for?
ML: To be honest, we really don’t know the answer. It’s generally believed that quantum computers will not necessarily help for all computational tasks. But there are problems that are mathematically hard for even the best classical computers. They usually involve some complex problems, such as problems involving complex optimizations in which you try to satisfy a number of contradictory constraints.
Suppose you want to give some kind of collective present to a group of people, each of which has its own niche. Some of the niches might be contradictory. So what happens is, if you solve this problem classically, you have to check each pair or triplet of people to make sure that at least their niche is satisfied. The complexity of this problem grows in size very, very rapidly because the number of classical combinations you need to check is exponential. There is some belief that for some of these problems, quantum computers can offer some advantage.
Another very well-known example is factoring. If you have a small number, like 15, it’s clear that the factors are 3 and 5, but this is the kind of problem that very quickly becomes complicated as the number grows. If you have a large number that is a product of two large factors, classically there is pretty much no better way to find what these factors are than just trying numbers from one, two, three, and so on. But it turns out that a quantum algorithm exists, called Shor’s algorithm, that can find the factors exponentially faster than the best known classical algorithms. If you can do something exponentially faster than using the alternative approach, then it’s a big gain.
F:It sounds like your mission, and that of others in your field, is to help us advance and understand this technology, but the applications are sort of secondary and will come when you have the tools. Does that seem about right?
ML: I will answer your question with an analogy. When classical computers were first developed, they were mostly used to do scientific calculations, numerical experiments to understand how complex physical systems behave. Right now, quantum machines are at this stage of development. They already allow us to study complex quantum physical phenomena. They are useful for scientific purposes, and scientists are already doing it now.
In fact, one significance of our papers [published in Nature] is that we have already built machines, which are large enough, and complex enough, and quantum enough to do scientific experiments that are very difficult to impossible to do on even the best possible classical computers — essentially supercomputers. In our work, we already used our machine to make a scientific discovery, which had not been made up until now in part because it’s very difficult for classical computers to model these systems. In some ways, we are now crossing the threshold where quantum machines are becoming useful, at least for scientific purposes.
When classical computers were being developed, people had some ideas of which algorithms to run on them. But actually it turned out that when the first computers were built, people were able to start experimenting with them and discovered many more practically efficient, useful algorithms. In other words, that’s really when they discovered what these computers can actually be good for.
That’s why I’m saying that we really don’t know now the tasks for which quantum computers will be particularly useful. The only way to find these tasks is to build large, functional, quantum machines to try these things out. That’s an important goal, and I should say that we are entering this phase now. We’re very, very close to a stage when we can start experimenting with quantum algorithms on large scale machines
F:Tell me a little bit about your Nature paper. What actually is the advance here? And how close are we to being able to start discovering the algorithms that could work on quantum computers?
ML: So first let’s talk about how one could quantify quantum machines. It can be done along three different axes. On one axis is the scale — how many qubits [a “quantum bit,” the unit that makes up the basis of quantum computer the way “bits” do in classical computing] it is. More is better. Another axis is the degree of quantum-ness, that is, how coherent these systems are. So eventually, the way to quantify it is that if you have a certain number of qubits, and you perform some calculations with that, what’s the probability that this calculation is error-free?
If you have a single qubit, you have a small chance to make an error. Once you have a lot of them, this probability is exponentially higher. So the systems described in our paper, and also in the complementary paper, have large enough qubits and are coherent enough so that we can basically do the entire series of computations with fairly low error probability. In other words, in a finite number of tries, we can have a result that has no errors.
But this is still not the complete story. The third axis is how well you can program this machine. Basically if you can make each qubit talk with any other qubit in an arbitrary fashion, you can also encode any quantum problem into this machine. Such machines are sometimes called universal quantum computers. Our machine is not fully universal, but we demonstrate a very high degree of programmability. We can actually change the connectivity very quickly. This in the end, is what allows us to probe and to make new discoveries about these complex quantum phenomena.
F:Could a quantum computer be scaled down to the size of a phone, or something vaguely portable at some point?
ML: That is not out of the question. There are ways to package it so that it can actually become portable and potentially can be miniaturized enough maybe not to the point of a mobile phone, but perhaps a desktop computer. But that cannot be done right now.
F:Do you think, like classical computers, quantum computers will make the shift from just scientific discoveries to the average user in about 30 years?
ML: The answer is yes, but why 30 years? It could happen much sooner.
F:What has to happen between now and then? What kind of advances need to be made to get us there?
ML: I think we need to have big enough computers to start really figuring out what they can be used for. We don’t know yet what quantum computers are capable of doing, so we don’t know their full potential. I think the next challenge is to do that.
The next stage will be for engineering and creating machines that could be used maybe to target some specialized applications. People, including [my team], are already working on developing some smallscale quantum devices, which are designed to, for example, aide in medical diagnostics. In some of these applications, quantum systems just measure tiny electric or magnetic fields, which could allow you to do diagnostics more efficiently. I think these things are already coming, and some of these ideas are already being commercialized.
Then maybe, some more general applications could be commercialized. In practice quantum computers and classical computers will likely work hand-in-hand. In fact, most likely what would happen is that the majority of the work is done by classical computers, but some elements, the most difficult problems, can be solved by quantum machines.
There is also another field called quantum communication where you can basically transfer quantum states between distant stations. If you use quantum states to send information, you can build communication lines that are completely secure. Moreover, through these so-called quantum networks, sometimes called quantum internet, we should be able to access quantum servers remotely. That way, I can certainly imagine many directions in which quantum computers can enter everyday life, even though you don’t carry it in your own pocket.
F:What’s something that you wish more people knew about quantum computers?
ML: Quantum computing and quantum technology have been in the news for some time. We scientists know that it’s an exciting area. It’s really the frontier of the scientific research across many subfields. Over the last five to 10 years, most people assumed that the developments have been very futuristic. They assumed that it will take a long time before we create any useful quantum machines.
I think that this is just not the case. I think we are already entering the new era with tremendous potential for scientific discoveries, which might have wideranging applications for material science, chemistry — really anything that involves complex physical systems. But I also feel that very soon we will start discovering what quantum computers can be useful for in a much broader scope, ranging from optimization to artificial intelligence and machine learning. I think these things are around the corner.
We don’t yet know what and how quantum computers will do it, but we will find out very soon.
Technology is shrinking. As our gadgets evolve, they become smaller and smaller, so that they’re able to permeate every part of our lives and even our bodies. Headphones have lost their wires and been reduced to the size of buttons, and yet they can produce the sound of a complete orchestra. Now that the power of computers is tightly packed into tiny gadgets and wearables, the only logical next step is for them to disappear. Where will they go? The answer is: everywhere. Twenty years ago, futurist and physicist Michio Kaku wrote the following in his book Visions: “A consensus…