Artificial Intelligence from Aperio Systems Protects Critical Infrastructure
Startup Aperio Systems emerged from stealth mode in November, offering “a polygraph for process data, detecting when your system is lying to you,” says CEO Yevgeni Nogin. “We are not a typical cybersecurity company,” Nogin explained in a phone interview on November 13. “We have an unusual number of physicists on board, in addition to cybersecurity experts.”
One of the physicists is vice president of product Michael Shalyt who walked me through Aperio’s clever answer to the recent increase in attacks on critical infrastructure systems. In December 2015, a Ukrainian power grid was taken down, leaving more than 230,000 residents in the dark. A few months later, hackers managed to infiltrate a water treatment plant in the U.S. and change the levels of chemicals being used to treat tap water. But “the vast majority of attacks are not disclosed,” says Shalyt. “Hackers have realized recently that they can attack the physical world using digital code.”
The much talked-about Internet of Things (IoT) is the poster boy for both the accelerated merger of the physical and digital worlds and the inadequate security of physical objects. That became apparent last month with the temporary shutdown of the Internet in parts of the U.S. due to an attack orchestrated by taking control of insecure connected devices such as security cameras and baby monitors. Going beyond consumer devices, the same type of attacks now threaten the sensors and other physical objects in critical infrastructure installations such as power plants and other industrial control systems.
Aperio answers the challenge by non-intrusively plugging into an existing control system and unleashing its advanced machine learning algorithms to study and identify the system’s unique “fingerprints.” That serves as the baseline for determining the validity of the process data produced at any given moment and alerting operators when an anomaly—forged data—is detected. The attackers typically produce forged data because they need to mask their presence and gain the time required for them to inflict long-term damage to the equipment. “Our role is to understand the process well enough that whenever an attacker will send a signal that cannot be generated by this specific equipment or plant or mode of operation, we alert the operators that someone is fooling them,” says Shalyt.
Using a sophisticated combination of physics and state-of-the-art machine learning techniques, Aperio than reconstructs the real values of the forged operational data and reverts it to its original state in real time. Establishing the true state of the system is important because that could mean the difference between the necessity for an emergency shutdown of the system or a more controlled one which is less costly and disruptive. “The beauty of physical systems is that everything is connected to everything else and we use this complexity to our advantage,” says Shalyt.
It is also the beauty of having physicists who understand the laws of physics a system follows and can detect the normal rhythm of the system and its unusual and abnormal behavior. The same laws of physics operate across many specific domains—power plants, oil and gas facilities, water and waste control, pharma, manufacturing, and transportation—allowing Aperio to apply its artificial intelligence in multiple markets. In the future, Aperio would like to be able to provide a validation for any information communicated by any type of sensor, in both industrial and consumer environments.
Currently tested by four different power plants, Aperio has secured seed funding from a consortium of private investors, including prominent cybersecurity veterans Doron Bergerbest-Eilon, Liran Tancman, and Shlomi Boutnaru. Bergerbest-Eilon has played a major role in establishing the agency charged with protecting all critical infrastructure in the State of Israel and is the former director of the security and protection division of the Israel Security Agency (ISA). Tancman and Boutnaru, who played key roles in building Israel’s cybersecurity capabilities, founded predictive cybersecurity startup CyActive, which was acquired by PayPal in 2015.
Originally published on Forbes.com
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Autonomous Driving and the Future of Transportation
“2017 will show us that limited deployments are technically, legally, and socially possible, even under today’s laws,” says Bryant Walker Smith, a professor at the University of South Carolina.
The cars themselves, with their distinctive sensor rigs mounted onto their rooftops, will be most visible as they putter down our roads. Behind the scenes, their makers are hurrying to iron out the technical, regulatory, and economic details needed so that one day somewhat soon, most of us will get to take our stupendously fallible meat gloves off the wheel.
BCG:
By 2035, 12 million fully autonomous units could be sold a year globally, and the market for partially and fully autonomous vehicles is expected to leap from about $42 billion in 2025 to nearly $77 billion in 2035. By 2035, autos with autonomous vehicle features are expected to capture 25% of the new car market.
‘The best marketer has been elected president’
After following the presidential election for 18 months and experiencing up close and personal the “best marketing case study ever,” marketing guru David Meerman Scott has concluded: “The best marketer has been elected president.” Here’s what I learned from his presentation at Inbound16, a day after Donald J. Trump became the 45th president of the United States.
Keep it simple: Trump relied on just two strategies, one online—real-time Twitter, the other offline—mega rallies. Both strategies aimed at getting free publicity, and with the aid of provocative sound bytes, Trump ended up generating $5 billion in free media. And the twin strategies were tightly integrated, with online and offline activities and events feeding each other (e.g., proper follow-up online after an event).
Keep it Inbound: Clinton (and Sanders and Bush) followed the old rules of marketing, relying on outbound marketing—spending heavily on advertising, acting like a large company pushing out messages, “targeting” what it thinks are the right “buyers.” The Trump campaign acted like today’s startups, relying on inbound marketing—“instead of the old outbound marketing methods of buying ads, buying email lists, and praying for leads, inbound marketing focuses on creating quality content that pulls people toward your company and product, where they naturally want to be.” Trump relied on “the proven methodology for the digital age.”
Keep it focused: Trump created (or vastly expanded) a powerful brand, using a memorable message (and a hashtag #MAGA) while many of her supporters could not recall Clinton’s slogan (“Stronger Together”).
Keep it energized: On Twitter, Trump reacted to news in real-time, generating reactions, conversations, free media, and more and more followers. Offline, Trump energized large audiences—“crowd size does matter,” to quote Kellyanne Conway—and showing off the size of the crowds on TV (for free) certainly mattered a lot.
Keep it up: Trump used every opportunity, including adversarial ones, to promote his message. When Gawker published Trump’s cell phone number (urging readers to call and ask him about his important ideas), anyone who called, as David Meerman Scott did, heard the following recorded message: “I’m Donald Trump and I’m running for the presidency of the United States of America. With your help and support, together we can make America truly great again. Visit me at Twitter @RealDonaldTrump and check out my campaign website at www.donaldtrump.com. Hope to see you on the campaign trail.”
David Meerman Scott called Trump “a marketing genius.” That may be true, but I think it’s more than just the mastery of the mechanics of digital marketing or intuitively understanding why inbound marketing may work better than outbound marketing. Trump understands the most fundamental underpinning of marketing, whether of the old or new variety: Know your customers, what they care about, what will make them act. And in 2016, the pundits suffering from Post-Trump-Matic-Stress-Syndrome notwithstanding, it was not “the economy, stupid.” It was not “jobs” or “globalization.”
It was the people revolting against political correctness, against arrogant and ossified political elites, and Trump’s sometimes revolting statements ensured them, even if they did not agree with some or all of these statements, that this time, they can really hope for a change. When asked in exit polls which candidate quality mattered most, only 35% of the respondents who answered “cares about me,” 26% that said “good judgment” and 8% of the respondents that opted for “right experience,” were Trump voters. But 83% of the respondents that answered “change” were Trump voters (as opposed to the 14% who voted for Clinton).
So I would venture to add another marketing lesson: Keep looking, keep asking what really motivates my customers.
Originally published on Forbes.com
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2017 Predictions for Digital Transformation
IDC has released its 2017 predictions for digital transformation and for CIOs, in addition to its IT industry predictions. Digital transformation is a technology-centric transformation, says IDC, that is profoundly changing business and society. Here is what IDC expects to see in the coming years:
By 2021, one third of CEOs and COOs of Fortune 2000 companies will have spent at least 5 years of their career in a technology leadership role.
This will be a new career path for current CIOs who can forge digital-based alliances across the enterprise and lead digital transformation initiatives.
Only 40% of CIOs will lead the digital transformation of the enterprise by 2018.
CIOs leading digital transformation will build organizational linkages with line of business technology teams and across IT organizational silos, and will empower changes in thinking, culture, and practices.
By 2018, companies investing in IoT-based operational sensing and cognitive-based situational awareness will see 30% improvements in the cycle times.
IT will need human resources that have detailed process knowledge, as well as IT capabilities to implement IoT technologies.
By 2019, 40% of IT projects will create new digital services and revenue streams that monetize data.
IT will have to drive data and analytical strategies for companies. (See also Tom Davenport’s IT Organizations: The Shoemaker’s Analytical Children).
By 2019, 5% of revenue will come through interaction with a customer’s digital assistant.
Intelligent agents will create an array of conversational interaction points that need to be defined, maintained and managed. Organizations will need to develop a new set of skills in sales, marketing, and product organizations to serve a connected user.
By 2018, 45% of CIOs will shift primary focus from physical to digital and move away from BPM and optimization to deliver scale, predictability, and speed.
Leveraging digital technologies, new business models, and entrepreneurial cultures will be required for success.
By the end of 2017, revenue growth from information-based products will be double that of the rest of the product/service portfolio for one third of all Fortune 500 companies.
Analytics will become value-added services that will increase the revenue potential of information-based products. Organizations will need to develop new data mining skills that will be required to fully realize this potential.
By 2017, 80% of CIOs will help drive global risk portfolios that enable adaptive responses to security, compliance, business, or catastrophic threats.
This will be a significant expansion in the scope of responsibilities of the CIO and will lead the further integration of IT with the business and the opening of new career paths for CIOs.
The vast impact of the digital transformation of the enterprise will include the emergence of new funding models, acceleration in industry disruption and business innovation, data becoming the new capital and rapid increase in worldwide demand for digital workers. The end result will be the transformation of all enterprises into digital natives.
Originally published on Forbes.com
81% of US companies will have deployed IoT by 2018
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.
The market for smart lighting and connected lighting controls to reach more than $12 billion by 2020
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.
Statistics and Machine Learning
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: Top 2017 Predictions
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.
Streaming Music: Age Demographics
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.
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