Source: I-Scoop
Machina Research: In summer 2016 Machina Research commissioned a study of 420 business decision makers familiar with their company’s use and planned use of the Internet of Things (IoT) to optimize their business operations or to build intelligent, Internet connected products. 81% of US companies will have deployed IoT by 2018.
IHS Markit:
The world market for smart lighting and connected lighting controls was valued at $6 billion in 2015, and is forecast to more than double in size by 2020, according to a new report from IHS Markit. The smart lighting market is being driven by the IoT, as lighting companies look beyond the transition to LED lighting and look to leverage the unique position lighting holds within the IoT.
The smart lighting market is broadly characterised by three application areas: commercial, residential, and outdoor and street lighting. The IoT is a key driver for both commercial and outdoor and street lighting applications, as lighting companies look to leverage lighting systems to act as the ‘backbone’ of an IoT network. Lighting systems have a unique advantage in this respect given that lighting is both powered and ubiquitous across a building or a city.
The residential smart lighting market is seeing adoption increase alongside the wider smart home offerings such as Apple’s HomeKit, Google’s Nest, Samsung’s SmartThings, or Amazon’s Alexa. The residential smart lighting market is estimated to have been worth $1 billion in 2015. The residential market is forecast to be one of the highest growth areas for smart lighting, growing to over $4 billion in 2020, at a compound annual growth rate of 30.5% from 2015 to 2020.
The image above is taken from a data mining primer course SAS offered in 1998.
Aatash Shah, CEO of Edvancer Eduventures, in KDnuggets:
Machine learning requires no prior assumptions about the underlying relationships between the variables. You just have to throw in all the data you have, and the algorithm processes the data and discovers patterns, using which you can make predictions on the new data set. Machine learning treats an algorithm like a black box, as long it works. It is generally applied to high dimensional data sets, the more data you have, the more accurate your prediction is.
In contrast, statisticians must understand how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population they are studying and the kinds of properties you would expect if you did the experiment many times. You need to know precisely what you are doing and come up with parameters that will provide the predictive power. Statistical modeling techniques are usually applied to low dimensional data sets.
IDC released its 10 IT industry predictions for 2017 in a webcast with Frank Gens, IDC’s senior vice president and chief analyst. The predictions covered many trends driving success today and in the future, from how the entire global economy will be re-shaped by digital transformation, the transition of all enterprises from being “digital immigrants” to being “digital natives,” the scaling up of innovation accelerators, the emergence of “the 4thplatform” (a new set of technologies that will become mainstream in ten years), drastic changes in how enterprises connect to their customers, and the ecosystem becoming as important for business success as IP. Here are IDC’s ten predictions:
By 2020, 50% of the G2000 will see the majority of their business depend on their ability to create digitally-enhanced products, services, and experiences.
We will see a “deep core” transformation of what enterprises are all about and how they behave in the marketplace. By the end of 2017, revenue growth from information-based products will be twice that of the rest of the portfolio for a third of G2000 companies. By 2021, a third of CEOs and COOs of G2000 companies will have spent at least 5 years in a tech leadership role. In 2019, worldwide spending on digital transformation initiatives will reach $2.2 trillion, almost 60% larger than 2016.
By 2019, 3rd Platform technologies and services will drive nearly 75% of IT spending – growing at 2X the rate of the total market.
Last year, IDC predicted that 3rd platform technologies and services—cloud, big data/analytics, social, and mobile—will drive 60% (not 75%) of OIT sepnding by 2019. The increase is due to a “snowball effect,” as these technologies are no longer “emerging” but have become the default choice. The innovation accelerators of the 3rdplatform—AI, IoT, AR/VR, robotics, 3D printing, and next-gen security (buttressed by blockchain)—will become mainstream.
By 2020, 67% of enterprise IT infrastructure and software will be for cloud-based offerings.
What clouds are and what they can do will change, IDC predicts: The cloud will be distributed with 60% of IT done off-premise and 85% by multi-cloud by 2018 and 43% of IoT will be processed at the edge in 2019; the cloud will be trusted and by 2020 it will be where trusted and secured IT lives, enhanced by blockchain-based security; the cloud will be concentrated and by 2020, the top 5 cloud Iaas/PaaS players will control at least 75% of the market share (vs about 50% in 2016).
By 2019, 40% of digital transformation initiatives – and 100% of IoT initiatives – will be supported by AI capabilities.
Top 3 AI use cases in terms of spending, says IDC, are: medical diagnostics and treatment, quality management in manufacturing, and automated service agents in retail. By 2018, 75% of developer teams will include AI functionality in one or more applications or services. Last year this prediction was at 50% and the acceleration is due to the fact that the cloud is “democratizing adoption” of AI functionality. By 2019, over 110 million consumer devices with embedded intelligent assistants will be installed in U.S. households. In 2017-2020 period, 7 of the Top 10 AI use cases will be industry-focused and will account for 85% of top 10 use case investment. We will see a “battle of AI platforms,” with a strong competition for developers in AI space.
In 2017, 30% of consumer-facing G2000 companies will experiment with AR/VR as part of their marketing efforts.
More and more companies will connect with consumers through “immersive interfaces” including augmented reality and virtual reality. In 2018, the monthly active user base of consumers using mobile augmented reality apps (e.g., Pokemon Go) will exceed 400 million. By 2020, over 20% of commercial media on Facebook will be 360-degree VR, as social goes “immersive.” Dark horse scenario: 20% of all social media is 360-degree by 2020. In 2019, companies will deploy earworn wearables, with AI-enabled voice interface, as digital assistants for customer-facing roles (in retail, for example).
By 2018, the number of Industry Collaborative Clouds will triple to more than 450.
By 2020, almost 60% of enterprises will actively participate in compliance Clouds. By 2020, 75% of F500 companies will be suppliers of digital services through Industry Collaborative Clouds. 90%+ of Industry Collaborative Clouds will partner with a Cloud mega-platform provider.
By year end 2017, over 70% of the Global 500 will have dedicated digital transformation/innovation teams.
60% of F100 companies had already formed a dedicated team or a business unit focused on digital transformation. By 2018, enterprises pursuing digital transformation strategies will expand their developer teams by 2-3X. By 2019, more than 50% of the value of software will be monetized through “things” and consumer and business services. By 2020, DX teams will source 80%+ of their solution components from open source communities.
By 2020, over 70% of Cloud services providers’ revenues will be mediated by channel partners/brokers.
There will be a complete “reboot of the channel community,” as cloud providers will need help reaching potential customers and supporting them cloud services. By 2018, major IT distributors will have transitioned at least a third of their business from hardware sales to cloud services sales/brokering. By 2018, most cloud providers outside of the top 10 will offer brokered access to their leading competitors’ cloud services. By 2021, the “cloud broker” landscape serving SMBs will become highly industry-specific, offering cloud-based business services.
By 2020, all enterprises’ performance will be measured by a demanding new set of benchmarks in leadership, customer engagement, digitization of new and traditional offerings, operational efficiency and organizational agility. At least 1/3 of leaders in every industry will fail to clear these digital transformation hurdles.
New benchmarks will include 35% improvement in Net Promoter Score, 100% growth in revenues from information-based products, 20% of processes are self-healing, and 50% reduction in management layers. These performance levels will be “the new normal” for all enterprises.
By 2020, 1/3 of Health/Life Sciences and CP companies will begin to develop the first products and services tightly integrating 3rd Platform technologies with the human body. “Augmented Humanity” offerings will be mainstream in the mid- 2020s.
The 4th Platform will be the integration of digital technologies with human biosystems, and the use of digital technologies to engineer biological systems at the cellular and subcellular level. “The 4thplatform is us,” says IDC. These set of technologies will provide humans with a wide variety of enhancements and we will see early adopters from 2021 to 2026. Ethical and legal issues will emerge, and there a lot of controversy and debate will surround the emergence of the 4th Platform.
What will see as a result of all of these changes, says IDC, is the transformation of the traditional enterprise value chain to a new “enterprise social graph” or “the enterprise innovation graph,” linking the enterprise to its various communities: developers, channel, industry platforms, data providers , and customers and fans, as we already see today with Amazon, Apple, and Salesforce.
Pew: Listening to music and shopping on the go are especially popular among smartphone owners ages 18 to 29: 87% have listened to an online radio or music service on their phone, compared with 41% of those 50 and over, and 73% have shopped online through their mobile device, versus 44% of older users.
Wall Street Journal: Last year less than 2% of consumers above the age of 45 opted to pay for an on-demand music service such as Apple Inc.’s Apple Music or Spotify AB’s Premium, while 11% of 18- to 24-year-olds did so and 8% of 25- to 34-year-olds paid, according to a survey by research firm MusicWatch Inc.
Source: McKinsey, 2011
[In 2011], we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards.
Source: McKinsey, 2016
Investments and acquisitions by Verizon, Comcast, Discovery, Time Warner, and Disney
Investments and acquisitions by Hearst, tronc, The Washington Post Company, News Corp., The New York Times Company, and Gannett
Investments and acquisitions by Facebook, Google, Twitter, and Snapchat
Source: Columbia Journalism Review
The deep learning market is expected to be worth $1,722.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The deep learning market has a huge potential across various industries such as advertisement, finance, and automotive. The major factors driving the deep learning market globally are the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning.
The market for the data mining application is expected to grow at the highest rate between 2016 and 2022
The deep learning market for data mining application is expected to grow at the highest CAGR between 2016 and 2022. The increasing usage of deep learning in data analytics, cyber security, fraud detection, and database systems is fueling the growth of data mining applications in the deep learning market. Medical industries generate huge amounts of data sets related to medication, patient details, and diagnosis. This data is converted into valuable patterns and is used to forecast future trends. Thus, data mining is expected to witness the highest growth rate in the medical industry.
Deep learning hardware market expected to grow at the highest rate between 2016 and 2022
The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others.
North America leads the deep learning market in terms of market size
North America is currently leading the deep learning market and is projected to be in the leading position for the next few years owing to the wide adoption of deep learning technology. The growth of the deep learning market in North America is attributed to the high government funding, presence of leading players, and strong technical base.
Augmented Intelligence (#AI) https://t.co/ZAUL14fT0f
— Gil Press (@GilPress) November 29, 2016
Augmented Intelligence (#AI) https://t.co/t6THNZvtBY
— Gil Press (@GilPress) November 29, 2016
