No End to Moore’s Law

Moore's Law

IEEE Spectrum:

This final ITRS report is titled ITRS 2.0. The name reflects the idea that improvements in computing are no longer driven from the bottom-up, by tinier switches and denser or faster memories. Instead, it takes a more top-down approach, focusing on the applications that now drive chip design, such as data centers, the Internet of Things, and mobile gadgets.

The new IEEE roadmap—the International Roadmap for Devices and Systems—will also take this approach, but it will add computer architecture to the mix, allowing for “a comprehensive, end-to-end view of the computing ecosystem, including devices, components, systems, architecture, and software,” according to a recent press release.

Transistor miniaturization was still a part of the long-term forecast as recently as 2014, when the penultimate ITRS report was released. That report predicted that the physical gate length of transistors—an indicator of how far current must travel in the device—and other key logic chip dimensions would continue to shrink until at least 2028. But since then, 3D concepts have gained momentum. The memory industry has already turned to 3D architectures to ease miniaturization pressure and boost the capacity of NAND Flash. Monolithic 3D integration, which would build layers of devices one on top of another, connecting them with a dense forest of wires, has also been an increasingly popular subject of discussion.

The new report embraces these trends, predicting an end to traditional scaling—the shrinking of chip features—by the early 2020’s. But the idea that we’re now facing an end to Moore’s Law “is completely wrong,” Gargini says. “The press has invented multiple ways of defining Moore’s Law but there is only one way: The number of transistors doubles every two years.”

Moore’s Law, he emphasizes, is simply a prediction about how many transistors can fit in a given area of IC—whether it’s done, as it has been for decades, in a single layer or by stacking multiple layers. If a company really wanted to, Gargini says, it could continue to make transistors smaller well into the 2020s, “but it’s more economic to go 3-D. That’s the message we wanted to send.”

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Internet of Things Market Landscape

IoT-landscape2016.png

Matt Turck:

As in previous versions, the chart is organized into building blocks, horizontals and verticals. Pretty much every segment is seeing a lot of activity, but it is worth noting that those parts are not particularly well integrated just yet, meaning in particular that vertical applications are not necessarily built on top of horizontals. To the contrary, we’re very much very much in the era of the “full stack” IoT startup – because there is no dominant horizontal platform, and not enough mature, cheap and fully reliable components just yet, startups tend to build a lot themselves: hardware, software, data/analytics, etc. Some enterprise IoT companies, such as our portfolio company Helium, also have a professional services organization on top, as enterprise customers are at the stage where they try to make sense of the IoT opportunity and are looking for something that “just works”, as opposed to mixing and matching best of breed components. This is a typical characteristic of startups operating in an early market, and I would expect many of those companies to evolve over time, and possibly ditch the hardware component of their business entirely.

 

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Internet of Things in 2025: $3 Trillion Market, 27 Billion Connections

MachinaResearch_IoTapps

M2M Application Groups covered in Machina Research’s M2M Forecast Database

Machina Research:

  • The total number of IoT connections will grow from 6 billion in 2015 to 27 billion in 2025, a CAGR of 16%.
  • Today 71% of all IoT connections are connected using a short range technology (e.g. WiFi, Zigbee, or in-building PLC), by 2025 that will have grown slightly to 72%. The big short-range applications, which cause it to be the dominant technology category, are Consumer Electronics, Building Security and Building Automation.
  • Cellular connections will grow from 334 million at the end of 2015 to 2.2 billion by 2025, of which the majority will be LTE. 45% of those cellular connections will be in the ‘Connected Car’ sector, including both factory-fit embedded connections and aftermarket devices.
  • 11% of connections in 2025 will use Low Power Wide Area (LPWA) connections such as Sigfox, LoRa and LTE-NB1.
  • China and the US will be neck-and-neck for dominance of the global market by 2025. China which will account for 21% of global IoT connections, ahead of the US on 20, with similar proportions for cellular connections. However, the US wins in terms of IoT revenue (22% vs 19%). Third largest market is Japan with 7% of all connections, 7% of cellular and 6% of global revenue.
  • The total IoT revenue opportunity will be USD3 trillion in 2025 (up from USD750 billion in 2015). Of this figure, USD1.3 trillion will be accounted for by revenue directly derived from end users in the form of devices, connectivity and application revenue. The remainder comes from upstream and downstream IoT-related sources such as application development, systems integration, hosting and data monetisation.
  • By 2025, IoT will generate over 2 zettabytes of data, mostly generated by consumer electronics devices. However it will account for less than 1% of cellular data traffic. Cellular traffic is particularly generated by digital billboards, in-vehicle connectivity and CCTV.
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The Digital Ecosystem

Deloitte_digital_eco.jpg

Deloitte_digital_eco_methodoogy.jpg

Deloitte University Press

What is the digital ecosystem? It’s a subset of TMT companies that specialize in the development of hardware, content, and software applications and provide a platform for the creation, distribution, and consumption of content, applications, and services.

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Machine Learning, Artist’s Rendition

[vimeo 176389707 w=640 h=360]

Motherboard:

Jakob Werner, a 22-year-old animator and visual design student in Germany, decided to interpret the idea of machine learning a bit more literally “in order to create a sarcastic view at our society and into the future.”

The result is a wooden automaton that appears to “read” by holding a book, moving its eyes as if scanning the words, and then flipping the page. “The secret behind machine learning,” he wrote in the video’s description. “This is how machines collect data.”

Werner’s artist statement shows he’s thought deeply about this topic, even though his project is tongue-in-cheek.

“Machine learning distinguishes itself from memorisation because it has the ability to recognize patterns which makes the algorithm seem more human-like,” he wrote. “This is essential for machines in order to be considered as artificial intelligence. Though there are a lot of programs and machines that aren’t intelligent but just seem to be. They don’t solve problems, they are programmed to work around them.”

 

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Artificial Intelligence in the Enterprise (Infographic)

AI_enterprise

HT: Inside Big Data

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Pokemon Go Market to Reach $56.8 Billion by 2020

pokemon

MarketsAndMarkets:

According to a new market research report “Augmented Reality Market by Component (Sensor, Display, & Software), Display Type (Head Mounted, Head-Up, Handheld, & Spatial), Application (Aerospace & Defense, Consumer, Commercial), and Geography – Global Forecast to 2020”, the total augmented reality market is expected to reach $56.8 Billion by 2020, at a CAGR of 79.6% between 2015 and 2020.

The augmented reality technology would drive the specialized applications in the diverse fields. The augmented reality was initially developed for the military purpose to train the military personnel.

It would help the military personnel to be aware of the new instruments and weapons. Augmented reality technology is also used in the medical sector to train the doctors and the nurses to help the patients treat better.

The gaming experience has been much more fruitful with the augmented reality being used in generation of the games.

The augmented reality market is expected to exhibit a high growth in the next five years. The use of augmented reality technology in sectors such as aerospace & defense, consumer, and commercial would be high in the coming years.

The applications such as aerospace & defense and automotive would also help boost the augmented reality market.

The presence of major companies such as Google, Inc. (U.S.), Qualcomm, Inc. (U.S.), Microsoft Corporation (U.S.) in the North American augmented reality market is likely to boost the market growth in the region.

The European augmented reality market would follow North America in terms of market growth. The associations such as the EuroVR, the European Association for Virtual Reality and Augmented Reality and the growth of automotive and aerospace & defense sector in the region would help the growth of European augmented reality market.

The Asia-Pacific AR market is likely to be driven by fast-growing commercial sector in the region.

The major market players covered in the report are:

  1. Google, Inc. (U.S.),
  2. Qualcomm, Inc. (U.S.),
  3. Microsoft Corporation (U.S.),
  4. Samsung Electronics Co., Ltd. (South Korea).
The start-ups such as Magic Leap, Inc. (U.S.) are also the ones to look out for in the AR market.
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Print Vs Digital (Infographic)

BooksVSdigital

Brian Wallace:

When you reach for your book at the end of the day, is it paperback or electronic? Though eBooks were slated to overtake print books in 2015 it just didn’t happen. There was a 10% drop in eBook sales and a 2% increase in paper book sales during that time period. Because there are pros and cons to both, most people go back and forth depending on their needs.

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Machine Learning Applications by Industry

machine-learning-apps

Louis Columbus, Machine Learning Is Redefining the Enterprise in 2016

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.

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The Face of Artificial Intelligence

Facebook_AIservers

Facebook’s new servers for artificial intelligence research, inside the company’s data center in Prineville, Oregon (Source: Technology Review)

Source: Technology Review
[youtube https://www.youtube.com/watch?v=v09-n4buwYE?rel=0]

Source: IEEE Spectrum

 

AI_exmachina

Ex Machina (2015)

Source: MadAboutMovies

AI_ChessPlaying

Source: http://www.lkessler.com/brutefor.shtml

AI_goPlayer

Lee Se-dol, one of the world’s top Go players, won just one of the matches against the AlphaGo program, missing out on the $1 million prize up for grabs. (March 2016)

 

 

 

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