Where the AI Rubber Hits the NHTSA Road: Letter to GM Describes State of Autonomy

The U.S. National Highway Traffic Safety Administration (NHTSA) has released a letter in response to a GM query regarding use of warning lights in self-driving vehicles that provides interesting details on the state of the GM autonomous vehicle program and highlights some of the challenges in this critical area of AI. Autonomous vehicles are almost literally “where the rubber hits the road,” for recent developments in AI and Machine Learning, so these details point to the larger issues confronting all autonomous systems, and the regulatory issues that they expose.

Following are key points of the letter:

You state that GM is developing a new adaptive cruise control system with lane following (which GM has referred to as Super Cruise ) that controls steering, braking, and acceleration in certain freeway environments. When Super Cruise is in use, the driver must always remain attentive to the road, supervise Super Cruise’s performance, and be ready to steer and brake at all times. In some situations, Super Cruise will alert the driver to resume steering for example, when the system detects a limit or fault. If the driver is unable or unwilling to take control of the wheel (if, for example, the driver is incapacitated or unresponsive), Super Cruise may determine that the safest thing to do is to bring the vehicle slowly to a stop in or near the roadway, and the vehicle’s brakes will hold the vehicle until overridden by the driver.

You indicate that GM plans to develop Super Cruise so that, in this situation, once Super Cruise has brought the vehicle to a stop, the vehicle’s automated system will activate the vehicle’s hazard lights. You state that you believe that this automatic activation of the hazard lights complies with the requirements of FMVSS No. 108 for several reasons….

GM states that in the event that a human driver fails to respond to Super Cruise’s request that the human retake control of the vehicle, and Super Cruise consequently determines that the safest thing to do is to bring the vehicle slowly to a stop in or near the roadway, Super Cruise-equipped vehicles will activate the vehicle’s hazard lights automatically once the vehicle is stopped….

We note that GM indicates that when the driver is unable or unwilling to take control of the vehicle the system will bring the vehicle to a stop in or near the roadway. A vehicle system that stops a vehicle directly in a roadway might, depending on the circumstances, be considered to contain a safety-related defect–i.e., it may present an unreasonable risk of an accident occurring or of death and injury in an accident. Federal law requires the recall of a vehicle that contains a safety-related defect. We urge GM to fully consider the likely operation of the system it is contemplating and ensure that it will not present such a risk.

This letter addresses concerns that autonomous vehicles are not yet ready for unsupervised operation, as indicated in recent incidents such as the June Tesla Autopilot crash.

While the description of GM’s Super Cruise system is illuminating, the letter also draws attention to the innumerable points that need to be considered as autonomous systems become a part of everyday reality.

In an April, 2015 letter to the California Department of Motor Vehicles, the NHTSA described its 24 month research program into 10 areas of autonomous vehicle operation:

Current Research Questions
1. How can we retain driver’s attention on the driving task for highly automated systems that are only partially self driving and thus require a driver to cycle in and out of an automated driving mode during a driving trip?
2. For highly automated systems that envision allowing the driver to detach from the driving task, but safely resume with a reasonable lead time.
3. What types of driver misuse/abuse can occur?
4. What are the incremental driver training needs for each level of automation?
5. What functionally safe design strategies can be implemented for automated vehicle functions?
6. What level of cybersecurity is appropriate for automated vehicle functions?
7. What is the performance of Artificial Intelligence (AI) in different driving scenarios, particularly those situations where the vehicle would have to make crash avoidance decisions with imperfect information?
8. Are there appropriate minimum system performance requirements for automated vehicle systems?
9. What objective tests or other certification procedures are appropriate?
10. What are the potential incremental safety benefits for automated vehicle functions/concepts?

These research points provide useful guidance for companies developing or employing autonomous systems, as well as pointing to areas in which regulation is likely to occur.

As society fits AI-driven autonomy into both consumer and work environments, a broad spectrum analysis of impact is becoming increasingly urgent. Each case requires different treatment, but moving ahead without understanding the full range of interactions across operation, society, and security is likely to be perilous, indeed.

A detailed report on NHTSA policy on autonomous vehicles can be found here: Federal Automated Vehicles Policy: Accelerating the Next Revolution In Roadway Safety.

China Files a Million Patents: Implications for AI and the IoT

The World Intellectual Property Organization (WIPO) reports that China has become the first country ever to file 1 million patent applications in a single year. While this was in the context of growing patent applications around the world, the size of the difference is remarkable. Chinese patent applications totaled as many applications as the next three offices combined: the U.S. (589,410), Japan (318,721) and the Republic of Korea (213,694). Chinese applications were led by electrical engineering and telecom, followed by computer technology and semiconductors. Growing innovation in these areas shines a spotlight on the potential for new opportunities in the burgeoning Internet of Things (IoT). It also points to an important shift in innovation toward Asia at a critical point where AI technologies, Industry 4.0, and consumer devices are starting to move forward.
The Chinese patent applications were largely for home consumption rather than for international patent protection. China’s applications flow from a highly competitive economy in which domestic protection is important and current government policies have a focused upon developing innovation in all sectors—but particularly in electronics and IT. The huge number of Chinese patent applications reflects this policy, which applies pressure to municipal governments, educational institutions, and companies to produce patent as a demonstration of support.

The recent DDoS attack on the Dyn server in the US used a botnet largely comprised of inexpensive Internet-connected Chinese cameras. The desire to innovate in these types of devices and the growing market is creating the elements of a security problem. As firms continue to release devices at a faster and faster rate at lower cost and without security procedures, security issues will proliferate. Chinese ability to innovate, manufacture, and market in this area is likely to lead to a tsunami of unprotected devices. Rapid innovation will lead to new opportunities as well as new threats as devices offered at extreme low cost are adopted before their impact can be fully understood.

Chinese innovation is also of importance in considering the growth of new product ideas within Asia. It is interesting to note that three of the top five patent producers are in Asia (China, Japan, and Korea). Asian firms have focused upon industrial automation and ability to produce inexpensive devices. This makes it likely to create a hub of innovation in IoT within Asia generally, adding to existing concentrations in robotics and mobility. As frictions continue to develop between China and the West, it is possible that new concentrations of innovation will emerge. The IoT presents a wide range of possibilities for growing markets in a stagnant economy. The connected consumer device territory is still a greenfield area. Low cost in this market is an absolute advantage, since it reduces consumer resistance in experimenting with new technology. By boosting innovation and also controlling low cost manufacture the Chinese are in an ideal position to pursue these types of developments.

For the rest of the world, patent applications continue to grow, particularly in information technology. Computer technology (7.9% of total) leads global applications, followed by electrical machinery (7.3%) and digital communication (4.9%). While the Chinese have focused upon internal patents, most other countries pursue international protection. Offsetting the million internal patents, Chinese international patents remain a relatively tiny number (42,154). While this is growing somewhat, it also demonstrates the country’s ability to isolate itself; or, perhaps, in the words of Deng, “hide your strength, bide your time.”

Four Courses of Artificial Intelligence: The Video

With the speed at which cognitive computing and AI are now developing, it is important to keep up with evolving concepts and trends. Courses have been developed at major institutions providing in-depth training opportunities. Many of these courses are available at low cost, or can be audited for free with materials online. Following is a series of Introductory Lectures for courses on AI that are freely available. The description provided in each case is from the course material. These lectures offer an entry point to the wealth of training that is now becoming available in this sector. Course videos can be used as an introduction, to bolster training programs, or to preview instruction prior to enrollment.

Course: Stanford, Machine Learning

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera

Course: Berkeley, CS188 Introduction to Artificial Intelligence–Spring 2014

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

Instructor: Prof. Pieter Abbeel

Excerpts from Artificial Intelligence for Robotics Course (Georgia Tech College of Computing)

Course Creator and Instructor Sebastian Thrun gives an overview of his Artificial Intelligence for Robotics course, part of Georgia Tech’s Online Master of Science in Computer Science. Thrun is Co-Founder and CEO of Udacity and a Stanford research professor.

Course: Knowledge Based Artificial Intelligence (Udacity)

Georgia Tech, Udacity, and AT&T have teamed up to offer an online Master´s degree in Computer Science—the first of its kind delivered through a MOOC platform. This is a whole new way to advance your knowledge and skills with advanced computer science classes. And there are flexible options for learning with us: you can apply for the full degree through Georgia Tech or take individual classes with the Udacity full course experience. Read on to learn more about your options and choose the path that works best for you.

Facebook Acquires FacioMetrics for AI Face Recognition

Facebook is acquiring Carnegie-Mellon startup FacioMetrics for its face recognition technology. FacioMetrics uses machine learning and other techniques to bring face recognition to mobile platforms, focusing at the moment upon Snapchat-style face masking and other cute selfie content capabilities. But it also bolsters Facebook’s move into AI, and aids in its ability to employ facial expression recognition as a social media response. According to Facebook’s intentions in this area, plans include: “Future applications of deep learning platform on mobile: Gesture-based controls, recognize facial expressions and perform related actions,”

The Facebook statement on the acquisition, as reported by Venturebeat, is that the Faciometrics team “will help bring more fun effects to photos and videos and build even more engaging sharing experiences on Facebook.”

According to the FacioMetrics press release from Founder and CEO Fernando De la Torre :

We began our research at Carnegie Mellon University developing state-of-the-art computer vision and machine learning algorithms for facial image analysis. Over time, we have successfully developed and integrated this cutting-edge technology into battery-friendly and efficient mobile applications, and also created new applications of this technology.

Now, we’re taking a big step forward by joining the team at Facebook, where we’ll be able to advance our work at an incredible scale, reaching people from across the globe.

This addition of an AI face recognition interface for social media pushes gesture control and hybrid human/AI systems forward. Voice recognition and face recognition are essential building blocks, and also fold nicely into its VR interest from the Oculus acquisition.

For companies considering the implications of AI, this also points to the importance of gaming and entertainment in driving new technologies, as well as to the need to examine those areas of existing operations that can benefit from new approaches based on AI and cognitive approaches.

For Facebook, this aids in competition with mobile graphics messaging competitor SnapChat initially, but could also make it much easier and quicker to respond to social media conversations from anywhere. Other companies might also consider the power of linking voice recognition with face-based emotion sensing as a means of enriching communications.

Two for the Road: GE Acquires ServiceMax and Bit Stew on the Path to Industry 4.0

ServiceMax and Bit Stew represent two important pathways in the development of Industry 4.0, or the Industrial Internet of Things. ServiceMax, the larger acquisition, points to the need to manage, monitor, and control industrial devices, including handing the complex requirements of service and repair. This functionality, aided by AI, can act as a kind of lymphatic system to keep systems running and provide new service opportunities through availability of status data. Bit Stew, on the other hand, is a pure AI/machine learning play, adding a level of immediate processing to system data to permit its ingestion. It is a smart ETL solution adapted to the IoT and increasing data requirements of AI and advanced analytics.

Service First

GE is buying cloud-based field service management software provider ServiceMax for $915 million as it moves rapidly forward in pursuit of Industry 4.0. ServiceMax is a part of that strategy. As a component of the industrial Internet, ServiceMax is a fully digital solution operating in the cloud to aid service technicians in repairing large machines. It operates on a Salesforce platform and has been used by GE for the past several years. What is new it is that it is now being incorporated within the company and therefore becomes integral to GE’s growing service business.

ServiceMax automates workforce optimization, provides scheduling and dispatch, insures parts logistics and inventory are correct and takes care of routine details of service contracts. The software has been used by more than 300 customers including Kodak, Coca-Cola, at others in fields such as oil and gas and medical devices. One of its chief attributes is capability to instrument equipment, automate repair and service provisions, and provide an increasing level of intelligence in the control of equipment in the field. Earlier this year, ServiceMax introduced its Connected Field Service solution that connects machines directly to technicians; it also added Service Performance Metrics, a measurement framework for field services to optimize business activity.

By incorporating ServiceMax, GE is aided in servicing heavy-duty machinery and can bolster its services revenue. It also gives GE equipment an advantage in its close alignment with the newly acquired firm. Of greater significance, perhaps, it is the fact that ServiceMax will become part of GE Digital, which was established last year and maintains a separate operating system called Predix, which is used to optimize equipment operation. Predix is a core product that makes it possible for GE to expand its digital operations.

The addition of ServiceMax to GE’s Predix platform provides more data which may be leveraged to create new opportunities in the Industrial Internet of Things. The possibility of providing service information along with other instrumentation from large machines and submitting this to analysis makes it possible to create new services based around a growing fabric of connected devices.

A Different Stew

GE is also buying Bit Stew systems for $153 million. Vancouver-based Bit Stew is an AI and IoT company whose base product, MIx Core, uses machine learning to automate the process of acquiring data from machines and readying it for analysis by conventional and advanced analytics. MIx Core automates data modeling, mapping and ingestion in a manner similar to ETL by providing a more sophisticated application of intelligence at the coalface. Its technology greatly reduces the time needed to understand the data and make it accessible to near real time analysis.

The application of machine learning to data input creates another layer of AI. As we had considered earlier in The Composite Nature of Embedded Cognition, the addition of AI capabilities to processes results in a deeper and less predictable system. The capabilities of immediately analyzing data and potentially adding prediction before submitting it to further processing presents some similarities to brain function, which combines different processes at every stage of cognition.

For GE, this provides another tool in extending the reach of its Predix industrial operating system. As Predix grows to increase its capability through addition of more complex data and a greater variety of sources, it becomes possible to integrate a wide range of activities within the industrial and manufacturing sector. As with the ServiceMax, acquisition of Bit Stew brings us closer to an integrated digital manufacturing environment which could be operated remotely, autonomously, or as a service.

All Go for the Industrial IoT

As GE continues to position itself as a digital industrial company we can expect further moves aimed at expanding digital services and automation to create a universal platform of interconnected devices. Other manufacturing companies such as Samsung are moving in the same direction. Mergers and acquisitions in this space tend to be the research tool of choice.

As the digital revolution continues, we can expect equipment to act more and more like software; it will require security updates; continuous change in function; frequent release of updates to physical and digital mechanisms; and a continual reevaluation of processes. These initiatives will frame the digital connected world of the future and create new opportunities for application of artificial intelligence to the optimization not only of digital, but also of physical assets.

GM Gets Personal With IBM Watson: Cars get OnStar AI System

General Motors Corporation is enlisting the aid of IBM’s Watson AI platform to create OnStar Go, a smart interactive assistant for automobiles. While the basic GM OnStar platform has been around for 20 years, every car maker today is working on enhancing its network-connected entertainment and vehicle management system. This is a good opportunity for both companies. By the end of 2016 GM expects to have 12 million OnStar connected vehicles on the road worldwide. OnStar’s primary mission is to supply safety help and communications by connecting to a cellular network, and this role is expanding as the market continues to develop.

OnStar Go will appear early next year in more than 2 million GM vehicles using 4G cellular service. It is a subscription service providing entertainment and safety capabilities. It uses Watson’s cognitive capabilities in a manner similar to a home smart personal assistant such as Amazon Echo or Google Home. OnStar Go bills itself as the first “cognitive mobility platform”.

The capabilities that IBM Watson brings to this platform will include a range of similar services to those provided in varying degrees by smart phones and smart home assistants, plus others that involved its capacity to monitor and control the mechanisms of the vehicle. OnStar Go will be able to help drivers with a growing range of services that demand knowledge of location, personal habits, and vehicular control.

Start with the Car

According to IBM’s press release:

“Combining OnStar’s industry leading vehicle connectivity and data capabilities with IBM Watson APIs will create experiences that allow drivers and passengers to achieve greater levels of efficiency and safety. These experiences could include avoiding traffic when you’re low on fuel, then activating a fuel pump and paying from the dash; ordering a cup of coffee on the go; or getting news and in-vehicle entertainment tailored to your personality and location in real time.”

Cognitive capabilities bring a range of new possibilities for personalizing the driving experience. Initial offerings seem fairly similar to what is already available; but the capability to learn from experience and store vast amounts of data makes it possible to create a more highly customized experience as well as presenting an opportunity to extend marketing and branding possibilities.

“On average, people in the U.S. spend more than 46 minutes per day in their car and are looking for ways to optimize their time,” said Phil Abram, Executive Director, GM Connected Products and Strategy. “By leveraging OnStar’s connectivity and combining it with the power of Watson, we’re looking to provide safer, simpler and better solutions to make our customers’ mobility experience more valuable and productive.”

As a subscription product, OnStar Go will provide GM with a continuous revenue stream in the entertainment and services industry. The platform also provides plenty of opportunities for learning how people prefer to set up their environment and where they would like to go. This data is useful in creating better personal assistants. IBM does not as yet have a personal assistant tool competing in the Google/Apple/Amazon space; it is clear that such platforms are the way of the future. Voice-operated request systems with cognitive interactions that decode naïve requests and formulate a complex response based upon database and Internet search capability will shape home and office environments for years to come.

Caveat Emptor

There are items of particular note in providing cognitive links to automobile functions. The OnStar system has potential to stop the car or manipulate its controls. It provides parking capabilities and navigation capabilities which will grow in importance. This kind of control presents security risks which have already been demonstrated last year with a pre-Watson version of OnStar compromised to remote control a Chevrolet Impala, as reported by the television program 60 Minutes last year. While security problems get patched, the possibility of sophisticated access to vehicle systems will always remain. Watson will require large amounts of data, some of it personal; this data can likely be hacked to provide information about whereabouts and intentions of the user; at the same time it should be able to provide more sophisticated intrusion response. Another issue is mobility. A continuous attachment to the cellular network as a requirement for operation could create possibility for security compromise or interruption of complex actions when the network fails. With the connection to vehicle systems, this could create dangerous vulnerabilities that might impact the driver. Additionally, the data collected will likely be provided to third parties for marketing, making it possible for outsiders to obtain special knowledge for marketing–most of which will be that benign, but some of which may be pernicious in a moving vehicle.

On to the Future

The ability to provide smart assistance within an automobile is probably the most difficult of the tasks to be expected of a personal assistant. These systems have been of marginal value in the home with relatively little to control. Automobiles are different. They are a transportation system in motion in a sophisticated environment, with innumerable demands–some of which might include personal comfort, entertainment, navigation, purchase of outside services, fueling, environmental issues, lighting, safety, and response to the unexpected.

This is a whole new breeding ground for smart assistance. While all major automobile manufacturers are working upon similar types of help, particularly as the era of autonomous vehicles approaches, the Watson approach brings in some new possibilities. The complex, cognitive learning system with integrated database capabilities and search makes it possible to craft a more interactive user interface as well as operate more deeply within the user environment. This will create a challenge for other players in the smart automobile as well as in the personal assistant sectors.

The personal assistant industry is developing quickly as visions of the smart home come together. Companies understand that they need to be visionary here. Those who establish a viable platform and are able to demonstrate success in this category will be able to apply it across the board to the consumer and commercial environments. This Holy Grail of marketing and interactive assistance will be fundamental to the complex operation of the Internet of Things; it will bring on the age of personal assistants, robots, and the advent of ubiquitous intelligence in processes.

Ring the Welkin: Everything is Related, and a New Era has Begun

We cannot ignore the outside world as technology continues to press forward. Technology affects economies, jobs, and public opinion. It will continue to inform aspirations, fears, and political dialogues around the world.

The impact of recent technology has been so profound that society has not really caught up with it. This is the “Cartoon Cliff” effect. People continue forward just as they always have, until that moment where they look down and discover the earth is so very far below. Perhaps with the US Election, that moment has arrived.

Society reacts. Hope for the best, plan for the worst, and remember that the outcome is far from certain. The past is not necessarily a guide to the future. But it is important to remain optimistic. Adjustments are always necessary, no matter how painful they might be. There will be new opportunities and new possibilities. Even as some doors begin to close, others will open. Changes in the geopolitical lineup will hasten trends that have been building for years.

Guest Blog: Lagging In Digital Transformation? 30% Of Your Senior Executives Are Going To Leave Within A Year

Original blog link:

Lagging In Digital Transformation? 30% Of Your Senior Executives Are Going To Leave Within A Year

by Gil Press

Companies lagging in their digital transformation or not even trying to become digital, face the risk of losing substantial portions of their sales, IT leadership, and senior management. About 30% of senior vice presidents, vice presidents, and director-level executives who don’t have adequate access to resources and opportunities to develop and thrive in a digital environment are planning to leave their company in less than one year.

This is one of the key finding of a new research report, Aligning the Organization for its Digital Future. It is based on a worldwide survey of 3,700 business executives, managers, and analysts, conducted for the fifth year in a row by MIT Sloan Management Review, in collaboration with Deloitte.

There is remarkable across the board agreement about digital disruption which 87% of those surveyed believe will impact their industry. This is considerably up from last year’s survey, where only 26% said that digital technologies present a threat of any kind. Regardless of the much-increased anticipation of digital disruption, only 44% think their organizations are adequately preparing for it. Similarly, a recent Gartner survey of IT professionals found that 59% said that their IT organization is unprepared for the digital business of the next two years.

“Digital” has a strong external orientation, according to the reported objectives of the digital strategy of the organizations surveyed. 64% “strongly agree” with improving customer experience and engagement as a key objective. Only 41% cite “fundamentally transform business processes and/or business model.”

While the orientation of companies’ digital strategy is primarily external, the perceived obstacles to digital success are primarily internal. The biggest barrier impending the organization from taking advantage of digital trends is too many competing priorities, followed by lack of organizational agility. “Disruption,” to these respondents, begins at home, not with the startups promising to disrupt their industry.

Understanding technology is a required but not the most important skill for success in a digital workplace. Says the report: “In an open-ended question, respondents said that the ability to steer a company through business model change is the most important skill, cited by 22%.” They also think that there are not enough people with the right skills. Only 11% say that their company’s current talent base can compete effectively in the digital economy.

The report goes beyond the raw data to assess “companies’ sophistication in their use of digital technologies.” Explaining the methodology for this assessment, it says:

“For the past two years, we have conducted surveys in which we asked respondents to “imagine an ideal organization transformed by digital technologies and capabilities that improve processes, engage talent across the organization, and drive new value-generating business models.” We then asked them to rate their company against that ideal on a scale of 1 to 10. Respondents fall into three groups: companies at the early stages of digital development (rating of 1-3 on a 10-point scale, 32% of respondents), digitally developing companies (rating of 4-6, 42% of respondents), and businesses that are digitally maturing (rating of 7-10, 26% of respondents).”

The assessment of whether a company is digitally mature or not is a subjective assessment by the respondents, not by outside observers applying objective criteria. It may well be that the respondents who rated their companies low on the digital maturity scale simply are not happy with their current employer—not enough opportunities to develop, generally incompetent leaders, too much hierarchy and not enough collaboration.

Notwithstanding the issue of how digitally mature companies were identified, the report’s conclusion—and prescription—is that to succeed in a digital world you must adopt a digital culture. It says:

“A key finding in this year’s study is that digitally maturing organizations have organizational cultures that share common features…The main characteristics of digital cultures include: an expanded appetite for risk, rapid experimentation, heavy investment in talent, and recruiting and developing leaders who excel at “soft” skills.”

Sounds to me very much like the prescriptions for business success emanating from business schools for at least half a century, way before “digital” has become a set of new technologies, processes, and attitudes companies must invest in and take advantage of to stay competitive.

The importance of becoming digital today is a good enough reason to read the report carefully and take note of how business executives in 131 countries and 27 industries answered the questions posed to them. The Sloan Management Review and Deloitte should be commended for conducting a large annual survey probing the state-of-the-art of digital transformation.

But for a more convincing assessment of what constitutes “digital maturity” we will have to wait until Sloan and Deloitte (or someone else) conduct research that compares objectively companies that have invested heavily in “digital” with companies that have invested only lightly in this new new thing. A difficult research challenge, no doubt, as very few companies willingly admit to falling behind the times.

The findings will be even more meaningful if the research will compare objectively successful companies (e.g., profitable) not investing in digital with not-so-successful companies (e.g., losing money, market share) that have totally embraced digital. Aren’t there out there today companies that are hierarchical, risk-averse, and do not invest in talent and digital but still that make a ton of money ? Can we be absolutely confident that these will not be the characteristics of (at least some) successful companies in the future?

Gil Press

Gil Press is Managing Partner at gPress, a marketing, publishing, research and education consultancy. Previously, he held senior marketing and research management positions at NORC, DEC and EMC. Hewas Senior Director, Thought Leadership Marketing at EMC, where he launched the Big Data conversation with the “How Much Information?” study (2000 with UC Berkeley) and the Digital Universe study (2007 with IDC). He blogs at http://whatsthebigdata.com/ and http://infostory.com/ Twitter: @GilPress