What are some bleeding-edge information technology developments that a forward-thinking CIO should keep an eye on?

Here are a few emerging technologies that have caught my attention. These are likely to have an increasing impact on the world of business in the future. Consider which ones you should follow a little more closely.

A recent advance in quantum computing that a Google team achieved indicates that quantum computing technology is making progress out of the lab and closing in on practical business applications. Quantum computing is not likely to change routine business transaction processing or data analytics applications. However, quantum computing is likely to dramatically change computationally intense applications required for:

Since most businesses can benefit from at least a few of these applications, quantum computing is worth evaluating. For a more detailed discussion of specific applications in various topic areas, please read: Applying Paradigm-Shifting Quantum Computers to Real-World Issues.

Machine learning is the science of computers acting without software developers writing detailed code to handle every case in the data that the software will encounter. Machine learning software develops its own algorithms that discover knowledge from specific data and the softwares prior experience. Machine learning is based on statistical concepts and computational principles.

The leading cloud computing infrastructure providers machine learning routines that are quite easy to integrate into machine learning applications. These routines greatly reduce expertise barriers that have slowed machine learning adoption at many businesses.

Selected business applications of machine learning include:

For summary descriptions of specific applications, please read: 10 Companies Using Machine Learning in Cool Ways.

Distributed ledger technology is often called blockchain. It enables new business and trust models. A distributed ledger enables all parties in a business community to see agreed information about all transactions, not just their own. That visibility builds trust within the community.

Bitcoin, a cryptocurrency, is the mostly widely known example application of blockchain.

Distributed ledger technology has great potential to revolutionize the way governments, institutions, and corporations interact with each other and with their clients or customers.Selected business applications of distributed ledger technology include:

For descriptions of industry-specific distributed ledger applications, please read: 17 Blockchain Applications That Are Transforming Society.

The Industrial Internet of Things (IIoT) is a major advance on Supervisor Control and Data Acquisition (SCADA). SCADA, in many forms, has been used for decades to safely operate major industrial facilities including oil refineries, petrochemical plants, electrical power generation stations, and assembly lines of all kinds.

IIOT is a major advance over relatively expensive SCADA. IIoT relies on dramatically cheaper components including sensors, network bandwidth, storage and computing resources. As a result, IIoT is feasible in many smaller facilities and offers a huge increase in data points for larger facilities. Business examples where IIoT delivers considerable value include production plants, trucks, cars, jet engines, elevators, and weather buoys.

The aggressive implementation of IIoT can:

For summary descriptions of specific IIOT applications, please read: The Top 20 Industrial IoT Applications.

RISC-V is an open-source hardware instruction set architecture (ISA) for CPU microprocessors that is growing in importance. Its based on established reduced instruction set computer (RISC) principles. The open-source aspect of the RISC-V ISA is a significant change compared to the proprietary ISA designs of the dominant computer chip manufacturers Intel and Arm.

RISC-V offers a way around paying ISA royalties for CPU microprocessors to either of the monopolists. The royalties may not be significant for chips used in expensive servers or smartphones, but they are significant for the cheap chips required in large numbers to implement the IIOT applications listed above.

For an expanded discussion of RISC-V, please read: A new blueprint for microprocessors challenges the industrys giants.

What bleeding edge information technology developments would you add to this list? Let us know in the comments below.

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Bleeding edge information technology developments - IT World Canada

Space X CEO Elon Musk

Photo by Kevork Djansezian

For a long time, American space exploration was a closed circle: There was just one customer, the U.S. government (NASA) and a handful of giant defense contractors. Then in 2008 Elon Musk's SpaceX put the first privately-financed rocket into orbit, Jeff Bezos' Blue Origin promised private flights, and space was suddenly a lively market with companies vying to put satellites and humans into orbit.

A decade later hundreds of start-ups have flocked to the space sector, bringing sophisticated technologies that include artificial intelligence, quantum computing, phased array radar, space-based solar power, "tiny" satellites and services that could not be imagined just a few years ago.

Space Angels, an early stage investor that also tracks investments in the sector, reported that venture capitalists invested $5 billion into space technologies in the first three quarters of 2019, putting the year on track to be the biggest year yet, with Blue Origin pulling in $1.4 billion from Bezos. Since 2009, said Chad Anderson, CEO of Space Angels, investors have poured nearly $24 billion into 509 companies.

Anderson said that SpaceX triggered the transformation not just by offering competition to NASA but publishing its prices for a launch. Before that revelation, space was really an opaque market, making it difficult for potential competitors to price their products. "It's been a really big decade for commercial space," said Anderson.

The largest amount of venture capital still goes into the most fundamental task: putting satellites into orbit. Anderson says 89 companies have received funding for so-called small-lift launch vehicles. These are companies promising to put payloads of up to 2,000 kilos (4,400 lbs) into low Earth orbit. Their focus is a new generation of small satellites such as those used by OneWeb and SpaceX's StarLink, which promise broadband internet access in even the most remote parts of the world by deploying "constellations" of hundreds or even thousands of tiny satellites.

Satellites have become so mainstream you can now buy a standard 4-in. by 4-in. "cubesat" kit online. All this activity could mean 20,000 to 40,000 satellites joining the 1,000 now in orbit over the next few years. "It's quickly becoming congested," Anderson said of the market for small-lift launch. Of the venture-backed rocket companies, SpaceX and Rocket Lab, with launch sites in New Zealand and Virginia, are making regular launches, although Richard Branson's Virgin Galactic is scheduled to begin flying its manned shuttle this year.

The sky is also getting crowded. Aside from the thousands of new satellites scheduled for launch, there is already a lot of clutter in space as many as 250,000 pieces of junk and debris circle the Earth. Up to now the U.S. Air Force has taken the lead role in tracking debris and warning satellite operators about possible collisions. But the military's tracking radar, with some components dating back to the cold war, can only detect pieces 10 cm (4 in.) across or larger. LeoLabs, a start-up based in Menlo Park, California, has developed an advanced radar system that can detect objects in orbit as small as 2 cm (less than an inch) long.

LeoLabs' Kiwi Space Radar was set up in Central Otago, New Zealand, in 2019. It is the first in the world to track space debris smaller than 10 cm.

LeoLabs

A tiny object traveling at several thousand miles an hour can cause severe damage to a satellite. LeoLabs enables customers to track their small satellites more easily and to safely move them to a new position. "That will take a lot of the collision risks off the table," says founder and CEO Dan Ceperley. His company has built phased array radars that steer the radar beam electronically faster than a traditional dish antenna in three locations: Alaska, Texas and New Zealand. To date, LeoLabs has raised $17 million from venture funds, including Marc Bell Capital Partners, Seraphim Capital and Space Angels.

Many of the 1,000 satellites now in orbit are engaged in observing Earth. They monitor the weather, humidity and temperature, among dozens of other phenomena, and capture millions of images. SkyWatch, based in Waterloo, Ontario, recently closed a $10 million round of funding led by San Francisco's Bullpen Capital to develop its service to make satellite data easily available to companies.

SkyWatch would handle licensing and payment for data through subscription fees, and companies could use its software to build their own apps for tasks such as tracking crops or assessing damage from natural disasters. SkyWatch CEO James Slifierz compares his timing to the aftermath of the creation of the global positioning system infrastructure. Once GPS was in place, civilian applications followed.

The growing flow of data from satellites has raised concerns about data security. SpeQtral, based in Singapore, plans to build encryption keys based on the laws of quantum physics to protect space-to-Earth communications. "The security of any communications is essential," says Chune Yang Lum, CEO of SpeQtral, which has raised a $1.9 million seed round led by Space Capital, the venture arm of Space Angels. Quantum encryption has been touted as practically unbreakable.

An illustration of the SPS-ALPHA (Solar Power Satellite by means of Arbitrarily Large Phased Array) transmitting energy to Australia. This approach, in concept phase, includes a series of enormous platforms positioned in space in high Earth orbit to continuously collect and convert solar energy into electricity.

SPS-ALPHA concept and illustration, courtesy John C. Mankins

Start-ups don't have a monopoly on developing new space applications. Tech giant Google has sought ways to commercialize its growing expertise in artificial intelligence and its vast computing power in the cloud. "We work with some of our largest and most transformative customers to do something epic," said Scott Penberthy, director of applied AI at Google Cloud.

He said Google Cloud has done a number of projects with NASA's Frontier Development Lab, including one that takes low-resolution photographs and combines them using AI to create a high-resolution image. Another proposal from Google would enable navigation on the moon's surface (which has no GPS) by having AI comparing an astronaut's surroundings with photos of the moon taken from space.

NASA is itself trying to benefit from the innovations brought by start-ups. In December, NASA's Ames Research Center announced a deal with the Founders Institute, a renowned start-up accelerator, to make some of its technology available to start-up entrepreneurs. In September 2019 the space agency announced the latest round of its Tipping Point Program, a public-private initiative, was distributing $43.2 million to 14 American companies whose technologies could contribute to NASA's plan for its Moon-to-Mars project. Participants include SpaceX, which will work on nozzles to refuel spaceships, and Blue Canyon Technologies, a Denver start-up developing autonomous navigation systems to enable small satellites to maneuver without communicating with "Earth."

In the past five years, NASA has awarded five groups of Tipping Point Awards, worth more than $120 million combined. Broadly speaking, a company or project selected for a tipping point award receives NASA resources up to a fixed amount, with the private side paying for at least 25% of the program's total costs. This allows NASA to shepherd the development of important space technologies while trying to save the agency money.

Despite the surge of cash, not all space projects find funding easily. John Mankins, a former NASA physicist, has long been an advocate of space-based solar power. Satellites would capture solar energy, convert it to microwaves and beam it down to Earth, where it would be converted into electricity. Mankins believes such a system taking advantage of recent technological advances can deliver electricity at a competitive price to areas of the world where power is expensive.

Mankins' company, Solar Space Technologies, has formed a joint venture with an Australian company to seek funding to supply power to remote parts of Australia with minimum impact on the environment. While the cost for space-based solar power may have been prohibitive in the past, Dr. Michael Shara, an astrophysicist at New York's Museum of Natural History, said "it really gets interesting" as costs come down.

Anderson, the Space Angels CEO, said venture capitalists hesitate to invest in space solar power because these are large infrastructure projects. "They require a significant amount of capex, and their paybacks are much longer than the typical 10-year lifetime of a venture capital fund." But as concern about climate change increases and the cost of putting "stuff" in orbit drops, clean energy from space may become an attractive entrepreneurial proposition.

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Start-ups join Google, SpaceX and OneWeb to bring new technologies to space - CNBC

Column Just before Christmas, Google claimed quantum supremacy. The company had configured a quantum computer to produce results that would take conventional computers some 10,000 years to replicate - a landmark event.

Bollocks, said IBM - which also has big investments both in quantum computing and not letting Google get away with stuff. Using Summit, the world's largest conventional supercomputer at the Oak Ridge National Laboratories in Tennessee, IBM claimed it could do the same calculation in a smidge over two days.

As befits all things quantum, the truth is a bit of both. IBM's claim is fair enough - but it's right at the edge of Summit's capability and frankly a massive waste of its time. Google could, if it wished, tweak the quantum calculation to move it out of that range. And it might: the calculation was chosen precisely not because it was easy, but because it was hard. Harder is better.

Google's quantum CPU has 54 qubits, quantum bits that can stay in a state of being simultaneously one and zero. The active device itself is remarkably tiny, a silicon chip around a centimetre square, or four times the size of the Z80 die in your childhood ZX Spectrum. On top of the silicon, a nest of aluminium tickled by microwaves hosts the actual qubits. The aluminium becomes superconducting below around 100K, but the very coldest part of the circuit is just 15 millikelvins. At this temperature the qubits have low enough noise to survive long enough to be useful

By configuring the qubits in a circuit, setting up data and analysing the patterns that emerge when the superpositions are observed and thus collapse to either one or zero, Google can determine the probable correct outcome for the problem the circuit represents. 54 qubits, if represented in conventional computer terms, would need 254 bits of RAM to represent each step of the calculation, or two petabytes' worth. Manipulating this much data many times over gives the 10 millennia figure Google claims.

IBM, on the other hand, says that it has just enough disk space on Summit to store the complete calculation. However you do it, though, it's not very useful; the only application is in random number generation. That's a fun, important and curiously nuanced field, but you don't really need a refrigerator stuffed full of qubits to get there. You certainly don't need the 27,648 NVidia Tesla GPUs in Summit chewing through 16 megawatts of power.

What Google is actually doing is known in the trade as "pulling a Steve", from the marketing antics of the late Steve Jobs. In particular, his tour at NeXT Inc, the company he started in the late 1980s to annoy Apple and produce idiosyncratic workstations. Hugely expensive to make and even more so to buy, the NeXT systems were never in danger of achieving dominance - but you wouldn't know that from Jobs' pronouncements. He declared market supremacy at every opportunity, although in carefully crafted phrases that critics joked defined the market as "black cubic workstations running NeXTOS."

Much the same is true of Google's claim. The calculation is carefully crafted to do precisely the things that Google's quantum computer can do - the important thing isn't the result, but the journey. Perhaps the best analogy is with the Wright Brothers' first flight: of no practical use, but tremendous significance.

What happened to NeXT? It got out of hardware and concentrated on software, then Jobs sold it - and himself - to Apple, and folded in some of that software into MacOS development. Oh, and some cat called Berners-Lee built something called the World Wide Web on a Next Cube.

Nothing like this will happen with Google's technology. There's no new web waiting to be borne on the wings of supercooled qubits. Even some of the more plausible things, like quantum decryption of internet traffic, is a very long way from reality - and, once it happens, it's going to be relatively trivial to tweak conventional encryption to defeat it. But the raw demonstration, that a frozen lunchbox consuming virtually no power in its core can outperform a computer chewing through enough wattage to keep a small town going, is a powerful inducement for more work.

That's Google's big achievement. So many new and promising technologies have failed not because they could never live up to expectations but because they cant survive infancy. Existing, established technology has all the advantages: it generates money, it has distribution channels, it has an army of experts behind it, and it can adjust to close down challengers before they get going. To take just one company - Intel has tried for decades to break out of the x86 CPU prison. New wireless standards, new memory technologies, new chip architectures, new display systems, new storage and security ideas - year after year, the company casts about for something new that'll make money. It never gets there.

Google's "quantum supremacy" isn't there either, but it has done enough to protect its infant prince in its superconducting crib. That's worth a bit of hype.

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Google and IBM square off in Schrodingers catfight over quantum supremacy - The Register

Way back in the 1960s, Gordon Moore, the co-founder of Intel, observed that the number of transistors that could be fitted on a silicon chip was doubling every two years. Since the transistor count is related to processing power, that meant that computing power was effectively doubling every two years. Thus was born Moores law, which for most people working in the computer industry or at any rate those younger than 40 has provided the kind of bedrock certainty that Newtons laws of motion did for mechanical engineers.

There is, however, one difference. Moores law is just a statement of an empirical correlation observed over a particular period in history and we are reaching the limits of its application. In 2010, Moore himself predicted that the laws of physics would call a halt to the exponential increases. In terms of size of transistor, he said, you can see that were approaching the size of atoms, which is a fundamental barrier, but itll be two or three generations before we get that far but thats as far out as weve ever been able to see. We have another 10 to 20 years before we reach a fundamental limit.

Weve now reached 2020 and so the certainty that we will always have sufficiently powerful computing hardware for our expanding needs is beginning to look complacent. Since this has been obvious for decades to those in the business, theres been lots of research into ingenious ways of packing more computing power into machines, for example using multi-core architectures in which a CPU has two or more separate processing units called cores in the hope of postponing the awful day when the silicon chip finally runs out of road. (The new Apple Mac Pro, for example, is powered by a 28-core Intel Xeon processor.) And of course there is also a good deal of frenzied research into quantum computing, which could, in principle, be an epochal development.

But computing involves a combination of hardware and software and one of the predictable consequences of Moores law is that it made programmers lazier. Writing software is a craft and some people are better at it than others. They write code that is more elegant and, more importantly, leaner, so that it executes faster. In the early days, when the hardware was relatively primitive, craftsmanship really mattered. When Bill Gates was a lad, for example, he wrote a Basic interpreter for one of the earliest microcomputers, the TRS-80. Because the machine had only a tiny read-only memory, Gates had to fit it into just 16 kilobytes. He wrote it in assembly language to increase efficiency and save space; theres a legend that for years afterwards he could recite the entire program by heart.

There are thousands of stories like this from the early days of computing. But as Moores law took hold, the need to write lean, parsimonious code gradually disappeared and incentives changed. Programming became industrialised as software engineering. The construction of sprawling software ecosystems such as operating systems and commercial applications required large teams of developers; these then spawned associated bureaucracies of project managers and executives. Large software projects morphed into the kind of death march memorably chronicled in Fred Brookss celebrated book, The Mythical Man-Month, which was published in 1975 and has never been out of print, for the very good reason that its still relevant. And in the process, software became bloated and often inefficient.

But this didnt matter because the hardware was always delivering the computing power that concealed the bloatware problem. Conscientious programmers were often infuriated by this. The only consequence of the powerful hardware I see, wrote one, is that programmers write more and more bloated software on it. They become lazier, because the hardware is fast they do not try to learn algorithms nor to optimise their code this is crazy!

It is. In a lecture in 1997, Nathan Myhrvold, who was once Bill Gatess chief technology officer, set out his Four Laws of Software. 1: software is like a gas it expands to fill its container. 2: software grows until it is limited by Moores law. 3: software growth makes Moores law possible people buy new hardware because the software requires it. And, finally, 4: software is only limited by human ambition and expectation.

As Moores law reaches the end of its dominion, Myhrvolds laws suggest that we basically have only two options. Either we moderate our ambitions or we go back to writing leaner, more efficient code. In other words, back to the future.

What just happened?Writer and researcher Dan Wang has a remarkable review of the year in technology on his blog, including an informed, detached perspective on the prospects for Chinese domination of new tech.

Algorithm says noTheres a provocative essay by Cory Doctorow on the LA Review of Books blog on the innate conservatism of machine-learning.

Fall of the big beastsHow to lose a monopoly: Microsoft, IBM and antitrust is a terrific long-view essay about company survival and change by Benedict Evans on his blog.

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Were approaching the limits of computer power we need new programmers now - The Guardian