3 technologies that could define the next decade of cybersecurity 06-22

In little over a decade, cybercrime has moved from being a specialist and niche-crime type to one of the most significant strategic risks facing the world today, according to the World Economic Forum Global Risks Report 2019. Nearly every technologically advanced state and emerging economy in the world has made it a priority to mitigate the impact of financially motivated cybercrime. 

The global experience of the past decade has largely been dominated by the emergence of a professional underground economy that provides scale, significant return-on-investment and entry points for criminals to turn a technical specialist crime into a global volume crime. The cybersecurity landscape in the past decade has been shaped by the targeting of financial institutions, notably with malware configured to harvest payment information and target financial platforms. The early cybercrime market that gave rise to the criminal online ecosystem was centred on the trading of harvested stolen credit cards, and some of the most high-profile and sophisticated global attacks focus on the penetration and manipulation of the internal networks of complex global payment systems. 

The Russian-speaking world has not been immune from these trends. Cyberattacks on financial organizations in Russia, Central Asia and Eastern Europe by some of the most sophisticated cybercrime gangs in the world have targeted clients, digital channels and networks. The Russian-speaking underground economy is one of the most active globally, with hundreds of fora and tens of thousands of users. Criminal groups exploit the margins of co-operation to conduct global campaigns, and their threat capacity is always adapting as groups work together in a borderless environment to combat technical defences. 

The past 10 years mark only the start of the global cybersecurity journey. New architectures and cooperation are required as we stand at the brink of a new era of cybercrime, which will be empowered by new and emergent technology. These three technologies might very well define the next 10 years of global cybersecurity: 

1. 5G networks and infrastructure convergence

A new generation of 5G networks will be the single most challenging issue for the cybersecurity landscape. It is not just faster internet; the design of 5G will mean that the world will enter into an era where, by 2025, 75 billion new devices will be connecting to the internet every year, running critical applications and infrastructure at nearly 1,000 times the speed of the current internet. This will provide the architecture for connecting whole new industries, geographies and communities - but at the same time it will hugely alter the threat landscape, as it potentially moves cybercrime from being an invisible, financially driven issue to one where real and serious physical damage will occur at a 5G pace. 

5G will potentially provide any attacker with instant access to vulnerable networks. When this is combined with the enterprise and operational technology, a new generation of cyberattacks will emerge, some of which we are already seeing. The recent ransomware attack against the US city of Baltimore, for example, locked 10,000 employees out of their workstations. In the near future, smart city infrastructures will provide interconnected systems at a new scale, from transport systems for driverless cars, automated water and waste systems, to emergency workers and services, all interdependent and - potentially - as highly vulnerable as they are highly connected. In 2017, the WannaCry attack that took parts of the UK’s National Health Service down took days to spread globally, but in a 5G era the malware would spread this attack at the speed of light. It is clear that 5G will not only enable great prosperity and help to save people’s lives, it will also have the capacity to thrust cybercrime into the real world at a scale and with consequences yet unknown. 

2. Artificial intelligence

To build cyber defences capable of operating at the scale and pace needed to safeguard our digital prosperity, artificial intelligence (AI) is a critical component in how the world can build global immunity from attacks. Given the need for huge efficiencies in detection, provision of situational awareness and real-time remediation of threats, automation and AI-driven solutions are the future of cybersecurity. Critically, however, the experience of cybercrime to-date shows that any technical developments in AI are quickly seized upon and exploited by the criminal community, posing entirely new challenges to cybersecurity in the global threat landscape. 

The use of AI by criminals will potentially bypass – in an instant – entire generations of technical controls that industries have built up over decades. In the financial services sector we will soon start to see criminals deploy malware with the ability to capture and exploit voice synthesis technology, mimicking human behaviour and biometric data to circumvent authentication of controls for people’s bank accounts, for example. But this is only the beginning. Criminal use of AI will almost certainly generate new attack cycles, highly targeted and deployed for the greatest impact, and in ways that were not thought possible in industries never previously targeted: in areas such as biotech, for the theft and manipulation of stored DNA code; mobility, for the hijacking of unmanned vehicles; and healthcare, where ransomware will be timed and deployed for maximum impact. 

3. Biometrics

To combat these emerging threats, biometrics is being widely introduced in different sectors and with various aims around the world, while at the same time raising significant challenges for the global security community. Biometrics and next-generation authentication require high volumes of data about an individual, their activity and behaviour. Voices, faces and the slightest details of movement and behavioural traits will need to be stored globally, and this will drive cybercriminals to target and exploit a new generation of personal data. Exploitation will no longer be limited to the theft of people’s credit card number, but will target theft of their being – their fingerprints, voice identification and retinal scans. 

Most experts agree that three-factor authentication is the best available option, and that two-factor authentication is a must. ‘Know’ (password), ‘have’ (token) and ‘are’ (biometrics) are the three factors for authentication, and each one makes this process stronger and more secure. For those charged with defending our digital future, however, understanding an entire ecosystem of biometric software, technology and storage points makes it still harder to defend the rapidly and ever-expanding attack surface.

What next?

Over the past decade, criminals have been able to seize on a low-risk, high-reward landscape in which attribution is rare and significant pressure is placed on the traditional levers and responses to crime. In the next 10 years, the cybersecurity landscape could change significantly, driven by a new generation of transformative technology. To understand how to secure our shared digital future we must first understand how the security community believes the cyberthreat will change and how the consequent risk landscape will be transformed. This critical and urgent analysis must be based on evidence and research, and must leverage the expertise of those in academia, the technical community and policymakers 

around the world. By doing this, the security ecosystem can help build a new generation of cybersecurity defences and partnerships that will enable global prosperity. 

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Augmented Reality Spending Exploding 11X To $36.4B in 2023, Greenlight Says 09-22

Apple just released ARKit and Google just released ARCore in the last few months. But revenue for augmented reality devices and content will hit a massive $36.4 billion in 2023, according to Greenlight Insight's newest report.

That's 11 times higher than the estimated $3.4 billion in revenue in 2019.

Greenlight Insights

Estimated augmented reality revenue from devices and content

Current devices in the space include Microsoft's Hololense, Google's second version of the Google Glass, and the Meta 2. Apple's new iPhone X and high-end Android-powered devices are the thin edge of the wedge driving augmented reality experiences into the consumer consciousness.

We're about to see a lot more devices, however:

According to the report, the total number of augmented reality (AR) head-mounted displays will grow from two million in 2019 to 30 million in 2023. That means, of course, that the tipping point is still a ways off ... several years, in fact.

“We are expecting a faster adoption of AR headsets than what we have seen with virtual reality headsets," Clifton Dawson, CEO of Greenlight Insights, said in a statement. "But optimism should be tempered as the AR ecosystem must address substantial problems on numerous base levels.”
Head-mounted AR revenues should reach $12.9 billion in sales by the end of 2020, the report says. Device revenue is most of that: $7.1 billion, with content revenue taking the rest.

And device revenue should grow fast: the report estimates a compound annual growth rate of 98% from 2019 to 2023.

Early devices are going to hit industrial and professional workplaces first, with prices for head-mounted displays in the $1000-3000 range currently. Those prices will come down, of course, and phone-driven systems with lower quality can be priced as low as $100.

Key markets the report identifies include healthcare, industrial design, manufacturing, education, and training. As device penetration grows, so will demand for content and software.

"In the five years to 2023, consumer and commercial spending on AR content and software is forecasted to grow at an average annual rate of 78% to $15.4 billion," the report says.

By 2023, Greenlight says that 53% of spending will be consumer spending. The key driver will be no surprise to technology industry veterans: gaming.

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Leading to Become Obsolete 07-07

Image credit : Shyam's Imagination Library

Haier CEO Zhang Ruimin is transforming a manufacturing giant into a platform for entrepreneurship — and his employees into self-governing entrepreneurs.We live and work in an age when the need for corporate reinvention is treated almost as a given. Countless CEOs talk about reducing hierarchy and increasing agility, flexibility, and connectedness to the market, and virtually every large company is “transforming for digital.” Yet in most organizations, lip service to change remains more the order of the day than real change itself.

Then again, you might work with Zhang Ruimin. The CEO and chairman of the white goods giant Haier Group Corp., based in Qingdao, China, has done what most chief executives dare not even dream about. He blew up much of the administrative structure of a global manufacturing enterprise, eliminating 10,000 management jobs that once held it together. And he has guided the organization to reemerge as a network of entrepreneurial ventures run by employees, whose compensation is based on the success of their products in the market.

In its transformed state, Haier is no longer a traditional manufacturer corporation so much as a platform that provides financing, support, and coordination for microenterprises all focused on developing products and services for the “smart home,” the internet of things (IoT)-based concept of a fully connected and networked household.

Haier calls its management model Rendanheyi, a term that refers to connecting employees with users. The company sees it as a “win-win” model for reducing the distance between the organization and its end users to zero and moving as close as possible to a state of co-creation with the customer.
This isn’t the first organizational innovation Zhang has led at Haier during his three decades with the company, but it is certainly the most profound. The 68-year-old executive, who has been named to a number of “most admired” and “top thinker” lists, received the Legend in Leadership Award from the Yale School of Management’s Chief Executive Leadership Institute in 2016. In noting the honor, Jeffrey Sonnenfeld, a senior associate dean of leadership programs at Yale, called Zhang “a genuine global business giant who inspires mythic awe in his competitors, his peers, and his fellow Chinese business leaders.”

During a spring 2017 trip to Washington, D.C., Zhang sat down with MIT Sloan Management Review editor in chief Paul Michelman to discuss Haier’s latest reinvention. The interview was conducted through a translator, and a further exchange took place via email. What follows is an edited and condensed version of the conversation.

MIT Sloan Management Review: The strategic transformation that you are undertaking right now is unprecedented in many ways. I’d like to begin by asking: Why now?

Two things make us believe now is the time. One is the internet, and the other is the internet of things. The internet has closed the distance between parts of the organization and between the organization and its customers to zero. This means that traditional management models — like Taylorism and bureaucracy as proposed by Max Weber — are no longer relevant. Then there is the internet of things, which represents the next generation of the internet. Despite a dozen years of development, the idea of IoT has not taken off — or as we like to say, it has not been ignited. We are undertaking this fundamental transformation of our corporate structure using the internet in the hope of becoming a leader in IoT.

Do you believe that this is the only viable path to lead in IoT? Did you consider other possible organizational forms?

We looked at this question from two different angles. First, we have been coming to the U.S. for years. We’ve talked to many corporations in the hope of finding a management model from which we can learn. But we have failed to identify the right one. So we decided to explore on our own. And we have come to believe that the traditional corporate model has to be upended and disrupted to survive in the internet era.

Secondly, what’s called for in the IoT age? It’s a direct interaction with users and a focus on creating the best user experience. However, in the traditional economy, there are no “users,” there are only “customers.” Customers are anonymous; users are real people who are directly involved in the process of creation.

Why hasn’t IoT been ignited? Because an interactive platform for users — where companies can take direction from the people who will buy their products — has yet to be created. We need to establish a “community economy” with zero distance between customers and companies. Our end goal is to have a true connection with our users and to create legitimate lifetime value for them via the internet of things.

Will every Haier business run on the platform? Will anything be carved off and managed in a more traditional way?

The platform is the only place for a business to go to. By eradicating our middle management layer — and laying off more than 10,000 middle-level managers — we have destroyed the original hierarchical structure. So we are merely a platform for entrepreneurs. All the businesses have to succeed as innovative entrepreneurial enterprises, or they will be kicked off the platform. The platform is also accessible to entrepreneurial projects from outside Haier. Today, we have more than 3,000 microenterprises operating on it.

What has surprised you the most along this transformative journey?

Three things. The first is our transformation from a traditional hierarchical organization to one with more than 200 different entrepreneurial teams operating thousands of microenterprises on our platform. This was totally unimaginable back in 2005, when the idea for this strategic transformation was proposed. The structure we have now is totally different.

The second thing is the variety of markets the entrepreneurial teams can enter. For example, our gaming laptop has grown to become the No. 1 market player in China in the short span of two to three years since the laptop team became entrepreneurial. The team did not come to me for approval. All the decisions were made by the [team].

But what has surprised me most is that employees have accepted the radical compensation change. Previously, we used IBM’s broadbanding model, where pay was determined based on an employee’s position and contribution. Now, compensation is determined by how much value is created for the user. When employees create value, they get paid. If they don’t create measurable value, they don’t get paid. Ultimately, if they don’t create value, they have to leave.

As we think about the Haier platform as a place where entrepreneurship occurs, many of us will draw on what we’ve learned and witnessed about successful entrepreneurs — that they possess a set of skills and characteristics that differ significantly from people who succeed in more directed environments.

We don’t require employees to possess certain skills. We don’t impose a training system or coach employees on how to be entrepreneurial. I don’t believe there is any training that is so effective as to transform people into entrepreneurs overnight. If someone can meet the requirements — if they can help start up a business — then they will prosper on the platform. If they cannot, they probably have to leave.

At the same time, we have external IoT entrepreneurs joining our platform because they believe it offers resources and support that other platforms do not. We have developed a networked organization that attracts the most capable people. We often say that the whole world is now our human resources department.

Was there anything done to support employees’ transition?

What we do is help people form communities of interest so that they can work together as entrepreneurs.

The process begins with an objective. For instance, someone comes up with an idea for a product targeting a certain niche of the market. And then people from different departments or disciplines — research and development [R&D], sales, manufacturing, marketing — will sit down and analyze its viability across all the relevant dimensions. If they believe it is viable, they will form a community to bring it forward as a new microenterprise.

Then they need to attach their plan to their compensation. We call it a predefined value adjustment mechanism, or VAM, which defines what goal the plan has to realize and how the members of the community will be paid if the goal is achieved. This is a signed agreement between Haier and its microenterprises.

We also have microenterprises that focus on more cutting-edge projects. These teams may not plan to achieve revenue for a couple of years. Here, we set different targets and schedules. For example, at a certain point of this endeavor, they must be able to attract external venture capital. If they can’t achieve the investment by an agreed-upon time, then they have to let it [the project] go, or we might invite another entrepreneurial team to work on the project.

Many leaders have a vision for the way people in their organizations will act. I’m curious to know if you’ve imagined certain core behaviors that indicate whether an individual will be successful?
I think most business leaders tend to view their employees as passive performers who take orders from their superiors. According to traditional management philosophy, there are managers and those to be managed. But in my opinion, everyone is capable of leadership — or in our words, “Everyone can be their own CEO.”

The reason why a company’s employees are not leaders is that they have not had the soil or platform to grow upon. With access to such a platform and with entrepreneurial competence, anyone can prosper.

In our model we have delegated the major powers of corporate executives to the employees — or at least to the microenterprises — including the power of decision-making, the power of selecting and appointing personnel, and the power of financial allocation. Other companies would not do that. They believe that if these powers are delegated, managers will lose control. Our goal is different: We are trying to motivate employees to unleash their potential and realize their own value. We don’t want to control them.

How does this transformation affect frontline employees, particularly in the manufacturing area? What has changed with respect to the factories themselves, such as how they’re run and how individuals in manufacturing jobs are compensated?

That’s a very important question, and one of our biggest challenges. It’s true that manufacturing workers do not typically face the market directly, but we can create a connection to the market by allowing our different production lines to compete with one another.

We have 108 factories all around the world, each possessing many production lines; every production line is a microenterprise. We evaluate the performance of these microenterprises based on cost, delivery and service quality, and market response to the products they make. This evaluation determines how they are qualified to get subsequent orders. Some production lines are able to acquire many orders. Some get fewer — and as a result employees on those lines are not paid as well. Lines gaining more orders can merge with those having fewer.

In this way the production lines are organically connected with the market. Moving forward, we are forging an even tighter connection by allowing users to work directly with the factory to place, customize, monitor, and take delivery straight from the production line. We have eight of these “interconnected factories” operating now.

As a fully realized open platform for entrepreneurship, what will Haier provide or enable that can’t be replicated? Thinking ahead, what will Haier be good for?

This is a question we are constantly reflecting upon, and it guides our direction. Though we have turned Haier into an entrepreneurial platform, we are not an investment company. The goal of an investment company is to put in money and take out profit. After an IPO, the goal is fulfilled — that is not our aim.

Our primary aim is to ignite the internet of things. All the entrepreneurial teams on the platform — even though they cross industries — focus on the smart home in some way. This is also why so many teams outside of Haier are willing to start up smart-home businesses on our platform. If they turn to venture capitalists, they will get money but not coordination. On Haier’s platform, businesses gain access to our sales network, logistics operation, and R&D system. Haier’s platform offers the help to IoT businesses that other platforms or funds cannot.

Today you’re working within a certain construct: the smart home. As you explore the potential of a truly open platform for entrepreneurship, how far will you allow yourselves to stray from this focus?
The smart home is already encompassing and covering many different entrepreneurial ventures. If we cannot succeed in this very broad construct, other goals are undoubtedly out of reach.

What’s most important is our resolute aim to be the enterprise that can truly ignite the whole idea of IoT. And this requires evolving from stand-alone products to products connected to the internet and on to a network of products and services all connected to each other.

So, what has become of the electric refrigerator in this scenario? It has transformed from a single appliance into the hub of a network connected to 400 organic food suppliers that monitor inventory levels and keep the refrigerator stocked. This model — and this level of interconnectedness — is really difficult to achieve in terms of both technology and business. For companies, revenue no longer comes solely from selling refrigerators but also from sales of organic food. These are two different concepts. When you’re taking into account this kind of transformation, a true ignition for IoT becomes very hard to reach.

What facets of the transformation have been enabled by Chinese organizational tradition? And what elements, if any, have been made more challenging by the same tradition?

China doesn’t have any well-established corporate models. When it comes to business, Chinese companies basically replicate Western management. So, it’s not as difficult for us to disrupt the model because it’s not Chinese in the first place.

But I do think Chinese traditional culture can aid this transformation. Western culture mainly focuses on dichotomy and atomism. In a typical Western company, activities are siloed by departments and then further cut up into more detailed tasks.

In China, we tend to look at things from the holistic perspective. Consider the difference between traditional Chinese medicine and Western medicine, which tends to focus on the cellular level of the human body. If something is wrong with your stomach, then something is wrong with your stomach.

So, the West will look more closely: What part of the stomach is wrong? Whereas in Chinese traditional medicine, we will not just look at your stomach. We will consider the connection between your stomach and other organs of your body, and we will look at your body as a whole before providing a cure.

So we are applying this traditional holistic thinking to our management transformation. The internet and IoT require enterprises to see things from the whole and systemic perspectives and to stop dividing everything into tiny parts.

That might suggest that the open platform model could find some challenges in scaling across geographies. Do you think that Western companies will have a hard time following suit?

This is a big challenge for us. We are a global business and must be able to globalize Rendanheyi. We acquired a consumer appliance business from Sanyo Electric Co. of Japan and used this model to transform it. We also acquired Fisher & Paykel Appliances of New Zealand, and they too have gradually accepted Rendanheyi. So it’s working, although at present [it is] applied only in the Asia-Pacific region.

The biggest challenge at the moment is GE Appliances [which Haier bought in 2016]. It’s a very large American company with a standard linear management model, where every action has a basis. In its hierarchy, there are protocols that direct employee behaviors at each step.

Rendanheyi is a nonlinear management model in which employees must be able to answer the question, “What do I do next?” for themselves. There is no one for you to ask — and that’s a challenging transformation.

Since we acquired GE Appliances, we have not sent a single executive over to the U.S. to implement our model. Instead, we have focused on communication and education with the existing executive team to make sure they understand and are willing to accept this philosophy. And they are coming around.

You are a student of Western management and familiar with the idea of corporate culture as an adhesive framework that helps ensure that people are all moving in the same direction. But as we think about an organization that is self-organizing, that is freely incorporating internal and external resources, do we have reason to question whether culture remains a significant factor?

I think an organization’s values are very important. The core value of Haier is self-negation. When most companies achieve success, they tend to fall into states of self-satisfaction and complacence, celebrating and falling in love with their achievements. That is not us. Even when we have a great success, we question where we can improve. Instead of being proud, we realize our own defects and mistakes. We challenge ourselves to reach another height.

This core value was essential in our own transformation from an execution culture to an entrepreneurial culture. Because we have a DNA of self-negation, it is easier for us to disrupt ourselves and to accept the need for change. We keep saying internally to our employees that there’s no such thing as a successful business. There’s only a business that is compatible with the task at hand.

So, if you are doing well right now, don’t be conceited. You’re just doing the right thing at the right time. Things change all the time. The only thing that doesn’t change is time itself. So, if you don’t keep up with changes, you’ll be quickly made obsolete.

I have met with many companies all around the world, but few of them possess this virtue. Usually they are arrogant.

Even as you create a business that aims to transcend traditional management and become self-perpetuating, your personal leadership of Haier demonstrates the value of a strategic visionary. How will the organization survive you? I can’t help but think that you may be Haier’s Steve Jobs.
(Laughing) This question has been raised by many people. I often ask it myself. I’ve been working at Haier for more than 30 years, but even if I can keep working and keep leading the organization, it doesn’t guarantee future success. My task is not to cultivate a replacement but to cultivate many people who are willing to challenge both themselves and the status quo.

That’s the reason why we have installed Rendanheyi. We are developing a multitude of microenterprises and entrepreneurial teams with the goal of dispensing with my authority. Rather than listening to my orders, my instructions — which might turn out to be erroneous — our teams follow the demands of the market and of our users. This will lower the failure of the individual microenterprises and the probability of failure for Haier as a whole.

Nowadays, the management model in many enterprises is “empowerment,” but we are not empowering; we are returning all the power to the employees.

You just mentioned Jobs. There is a book about him titled To Live Is to Change the World. That is the organization we are designing — one meant to keep changing both ourselves and the world.

What is implicit in your answer is that Haier is on this new path permanently. And if it is, then perhaps traditional leadership will not become necessary. Maybe you don’t even need a single CEO or chairman.

Among so many foreigners I have met, you are the only one who truly understands me.

Reproduced from MITSLOAN Management Review

The Democratization of Machine Learning: What It Means for Tech Innovation 04-15

The world of high-tech innovation can change the destiny of industries seemingly overnight. Now we are on the cusp of a new grand leap thanks to the democratization of machine learning, a form of artificial intelligence that enables computers to learn without being explicitly programmed. This process of democratization is already underway.

                                     Image credit : Shyam's Imagination Library

Last month, at the CloudNext conference in San Francisco, Google announced its acquisition of Kaggle, an online community for data scientists and machine-learning competitions. Although the move may seem far removed from Google’s core businesses, it speaks to the skyrocketing industry interest in machine learning (ML). Kaggle not only gives Google access to a talented community of data scientists, but also one of the largest repositories of datasets that will help train the next generation of machine-learning algorithms.

As ML algorithms solve bigger and more complex problems, such as language translation and image understanding, training them can require massive amounts of pre-labeled data. To increase access to such data, Google had previously released a labeled dataset created from more than 7 million YouTube videos as part of their YouTube-8M challenge on Kaggle. The acquisition of Kaggle is an interesting next step.

1. Highly scalable computing platforms
2. Even if specialized processors were available, not every company has the capital and skills needed to manage a large-scale computing platform needed to run advanced machine learning on a routine basis. This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. These services offer developers a scalable infrastructure optimized for ML on rent and at a fraction of the cost of setting up on their own.
3. Open-source, deep-learning software frameworks

A major issue in the wide-scale adoption of machine learning is that there are many different software frameworks out there. Big companies are open sourcing their core ML frameworks and trying to push for some standardization. Just as the cost of developing mobile apps fell dramatically as iOS and Android emerged as the two dominant ecosystems, so too will machine learning become more accessible as tools and platforms standardize around a few frameworks. Some of the notable open source frameworks include Google’s TensorFlow, Amazon’s MXNet and Facebook’s Torch.

4. Developer-friendly tools

The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible to those without doctorate degrees or deep data science training. Microsoft Azure ML Studio offers access to many sophisticated ML models through a simple graphical UI. Amazon and Google have rolled out similar software on their cloud platforms as well.

5. Marketplaces for ML algorithms and datasets

Not only do we have an on-demand infrastructure needed to build and run ML algorithms, we even have marketplaces for the algorithms themselves. Need an algorithm for face recognition in images or to add color to black and white photographs? Marketplaces like Algorithmia let you download the algorithm of choice. Further, websites like Kaggle provide the massive datasets one needs to further train these algorithms.

“The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible.”

All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure.

What are the implications?

Back in the mid- and late-1990s, web development was done by specialists and was accessible only to firms with ample resources. Now, with simple tools like WordPress, Medium and Shopify, any lay person can have a presence on the web. The democratization of machine learning will have a similar impact of lowering entry barriers for individuals and startups.

Further, the emerging ecosystem, consisting of marketplaces for data, algorithms and computing infrastructure, will also make it easier for developers to pick up ML skills. The net result will be lower costs to train and hire talent. We think that the above two factors will be particularly powerful in vertical (industry-specific) use cases such as weather forecasting, healthcare/disease diagnostics, drug discovery and financial risk assessment that have been traditionally cost prohibitive.

Just like cloud computing ushered in the current explosion in startups, the ongoing build-out of machine learning platforms will likely power the next generation of consumer and business tools. The PC platform gave us access to productivity applications like Word and Excel and eventually to web applications like search and social networking. The mobile platform gave us messaging applications and location-based services. The ongoing democratization of ML will likely give us an amazing array of intelligent software and devices powering our world.

Highly scalable computing platforms

Even if specialized processors were available, not every company has the capital and skills needed to manage a large-scale computing platform needed to run advanced machine learning on a routine basis. This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. These services offer developers a scalable infrastructure optimized for ML on rent and at a fraction of the cost of setting up on their own.
Open-source, deep-learning software frameworks

A major issue in the wide-scale adoption of machine learning is that there are many different software frameworks out there. Big companies are open sourcing their core ML frameworks and trying to push for some standardization. Just as the cost of developing mobile apps fell dramatically as iOS and Android emerged as the two dominant ecosystems, so too will machine learning become more accessible as tools and platforms standardize around a few frameworks. Some of the notable open source frameworks include Google’s TensorFlow, Amazon’s MXNet and Facebook’s Torch.
Developer-friendly tools.

The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible to those without doctorate degrees or deep data science training. Microsoft Azure ML Studio offers access to many sophisticated ML models through a simple graphical UI. Amazon and Google have rolled out similar software on their cloud platforms as well.
Marketplaces for ML algorithms and datasets.

Not only do we have an on-demand infrastructure needed to build and run ML algorithms, we even have marketplaces for the algorithms themselves. Need an algorithm for face recognition in images or to add color to black and white photographs? Marketplaces like Algorithmia let you download the algorithm of choice. Further, websites like Kaggle provide the massive datasets one needs to further train these algorithms.

“The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible.”

All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure.
What are the implications?

Back in the mid- and late-1990s, web development was done by specialists and was accessible only to firms with ample resources. Now, with simple tools like WordPress, Medium and Shopify, any lay person can have a presence on the web. The democratization of machine learning will have a similar impact of lowering entry barriers for individuals and startups.

Further, the emerging ecosystem, consisting of marketplaces for data, algorithms and computing infrastructure, will also make it easier for developers to pick up ML skills. The net result will be lower costs to train and hire talent. We think that the above two factors will be particularly powerful in vertical (industry-specific) use cases such as weather forecasting, healthcare/disease diagnostics, drug discovery and financial risk assessment that have been traditionally cost prohibitive.

Just like cloud computing ushered in the current explosion in startups, the ongoing build-out of machine learning platforms will likely power the next generation of consumer and business tools. The PC platform gave us access to productivity applications like Word and Excel and eventually to web applications like search and social networking. The mobile platform gave us messaging applications and location-based services. The ongoing democratization of ML will likely give us an amazing array of intelligent software and devices powering our world.

Market-based access to data and algorithms will lower entry barriers and lead to an explosion in new applications of AI. As recently as 2015, only large companies like Google, Amazon and Apple had access to the massive data and computing resources needed to train and launch sophisticated AI algorithms. Small startups and individuals simply didn’t have access and were effectively blocked out of the market. That changes now. The democratization of ML gives individuals and startups a chance to get their ideas off the ground and prove their concepts before raising the funds needed to scale.
But access to data is only one way in which ML is being democratized. There is an effort underway to standardize and improve access across all layers of the machine learning stack, including specialized chipsets, scalable computing platforms, software frameworks, tools and ML algorithms.

“Just like cloud computing ushered in the current explosion in startups … machine learning platforms will likely power the next generation of consumer and business tools.”

1. Specialized chipsets

Complex machine-learning algorithms require an incredible amount of computing power, both to train models and implement them in real time. Rather than using general-purpose processors that can handle all kinds of tasks, the focus has shifted towards building specialized hardware that is custom built for ML tasks. With Google’s Tensor Processing Unit (TPU) and NVIDIA’s DGX-1, we now have powerful hardware built specifically for machine learning.

Reproduced from Knowledge@Wharton

Unexpected Benefits of Digital Transformation 01-22

Digital tools can be used in many different “right” — and surprising — ways to add value to an organization.

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 Image credit : Shyam's Imagination Library 

                                                                                                                                                           Many different fields of study — such as psychology, human-computer interaction, and ecology —
have employed the concept of “affordances.” The term refers to the different possible actions that someone can take with an object in a particular environment. For example, someone can interact with a beach ball by batting it in the air, letting it float in water, sitting on it, or popping it. The importance of affordances is the shift in focus from the characteristics of the object to what one can do with an object in a particular situation.

The concept of affordances can be particularly useful when applied to digital technologies in organizations. It overcomes many of the key mistakes companies make when trying to update their organizations to compete in an increasingly digital environment.

Having the Technology Is Not Enough

Perhaps the most fundamental implication introduced by the concept of affordances is the shift from the characteristics of the technology itself to what your company can actually do with it. Digital technologies only enable possible actions for people and organizations to engage in; they do not make those actions happen on their own. Simply owning or implementing digital technologies is not enough to derive business value from it.

This insight may sound obvious, but it is stunning how often managers forget this simple fact in practice. They either believe that the mere adoption of the latest technology will improve their business prospects, or they focus all of their efforts on implementation without applying the time or resources to make the types of organizational changes needed to benefit from the possibilities the technologies offer.

For example, one company adopted Twitter in order to be more responsive to customers, but it kept existing processes in place — processes that required multiple approvals before publicly responding on behalf of the company. This negated the benefit of Twitter because it limited the way the technology could be used to respond quickly to customers. This example may be egregious, but it is common for companies to adopt digital technologies without considering how work needs to change to take advantage of the benefits they enable.

There Are Many Different ‘Right’ Ways to Use Digital Technologies

Just as a beach ball can be used in a number of different ways, so can digital technologies enable a number of different possible actions within organizations. One of Twitter’s greatest strengths (and a reason many people and companies find it confusing) is the multiple possible actions it can enable. Some companies — many of the major media outlets, for example — use Twitter as a means of broadening the reach of their content. Others, such as Delta, JetBlue, and KLM, use Twitter as an effective customer service tool, enabling them to support customers in a very fluid service environment. Still others use Twitter as a business intelligence tool. Companies such as Kaiser Permanente used data generated by companies on Twitter to identify areas of improvement in business operations, and T-Mobile used it to identify competitors’ weaknesses to inform their business strategy. The concept of affordances brings the question of how a particular technology will be used within an organization to the forefront.

The Most Valuable Applications Aren’t Always Known in Advance

The affordance literature also introduces the concept of “hidden” affordances, which describes possible actions enabled that are not necessarily known in advance, which is also true of digital technologies in organizations. For example, one company adopted an expertise identification tool to help determine who in the organization needed knowledge. The tool analyzed digital content generated by employees and automatically generated knowledge profiles for them. Although the intention of the technology was to make others in the organization aware of what knowledge employees possessed, the greater impact was in helping employees understand what knowledge they possessed that was most valuable to others. This often differed considerably from their formal roles or how the employees thought they were most valuable. In another organization, the same technology had a very different unanticipated impact, helping improve the performance of women, lower-rank, and newer employees. The technology democratized access to knowledge in the company, access that had previously been controlled through the social networks of senior male employees. Recognition of hidden affordances helps keep managers aware of unexpected or unanticipated benefits of digital technologies that may not have been considered in advance.

A Digital Organization-Affordance Cycle

The final benefit of an affordance view of digital technologies in organizations is the recognition of a mutually dependent relationship between the organization and its digital technologies. Digital technologies can change the organizational environment of which they are a part, creating the

possibility of a new set of affordances. Organizations can also implement or emphasize new features in the digital technologies, as the most valuable affordances they enable become more apparent. An affordance perspective suggests that digital transformation, rather than a linear progression, is a recursive process in which technologies and the organizational environment mutually influence one another over time. Digital technology creates new opportunities to work differently, and working differently creates new opportunities to infuse technology into the work process.

Shifting toward an affordance view requires managers to shift from thinking about digital tools themselves to a focus on what the tools help companies do differently. As managers think about whether these changes in how work happens will add value to an organization, they will be able to more easily consider what legacy technologies fulfill similar tasks, and whether these different systems will complement or compete with one another. 



Reproduced from MIT Sloan Management Review















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6 reasons why we’re underhyping the Internet of Things 06-25

6 reasons why we’re underhyping the Internet of Things

Just when you thought the Internet of Things couldn’t possibly live up to its hype, along comes a blockbuster, 142-page report from McKinsey Global Institute (“The Internet of Things: Mapping the Value Beyond the Hype”) that says, if anything, we’re underestimating the potential economic impact of the Internet of Things. By 2025, says McKinsey, the potential economic impact of having “sensors and actuators connected by networks to computing systems” (McKinsey’s definition of the Internet of Things) could be more than $11 trillion annually.

According to McKinsey, there are six reasons we may be underhyping the Internet of Things.

1. We’re only using 1 percent of all data

What McKinsey found in its analysis of more than 150 Internet of Things use cases was that we’re simply not taking advantage of all the data that sensors and RFID tags are cranking out 24/7. In some cases, says McKinsey, we may be using only 1 percent of all the data out there. And even then, we’re only using the data for simple things such as anomaly detection and control systems – we’re not taking advantage of the other 99 percent of the data for tasks such as optimization and prediction. A typical offshore oil rig, for example, may have 30,000 sensors hooked up to it, but oil companies are only using a small fraction of this data for future decision-making.

2. We’re not getting the big picture by focusing only on industries

Rather than focusing on verticals and industries (the typical way that potential economic value is computed), McKinsey takes a deeper look at the sweeping changes taking place in nine different physical “settings” where the Internet of Things will actually be deployed – home, retail, office, factories, work sites (mining, oil and gas, construction), vehicles, human (health and wellness), outside (logistics and navigation), and cities. Of that $11 trillion in economic value, four of the nine settings top out at over $1 trillion in projected economic value – factories ($3.7 trillion), cities ($1.7 trillion), health and fitness ($1.6 trillion) and retail ($1.2 trillion).

Thus, instead of focusing on, say, the automotive industry, McKinsey spreads the benefits of the Internet of Things for automobiles over two different physical settings — “vehicles” and “cities.” In the case of vehicles, sensors are a natural fit for maintenance (e.g. sensors that tell you when something’s not working on your car). In the case of cities, these sensors can help with bigger issues such as traffic congestion.

3. We’re forgetting about the B2B opportunity

If you think the Internet of Things is just about smart homes and wearable fitness devices, think again – McKinsey says the B2B market opportunity could be more than two times the size of the B2C opportunity. One big example cited by McKinsey is the ability of work sites to take better advantage of the Internet of Things.

Think of an oil work site, for example. You have machinery (e.g. oil rigs), mobile equipment (trucks), consumables (barrels of oil), employees, processing plants and transportation networks for taking this oil out of the work site. If all those elements are talking to each via the Internet, you can optimize the work site. Oil rigs can let employees know if something’s broken, trucks can arrive on time to pick up the barrels of oil, and then all that oil can be processed and shipped off to wherever it’s needed on time and on schedule.

4. We’re ignoring that “interoperability” could be the new “synergy”

According to McKinsey, approximately 40 percent of the total economic value of the Internet of Things is driven by the ability of all the physical devices to talk to each other via computers — what McKinsey refers to as “interoperability.” You can think of “interoperability” as a new form of synergy – a way to increase the whole without increasing the sum of the parts.

If machines can’t talk to each other, says McKinsey, the Internet of Things might only be a $3.9 trillion opportunity. One example of interoperability is the ability of your brand-new fitness wearable to talk with your hospital or healthcare provider. What good is your fitness device if it can’t communicate with the people who can actually use all that data? With interoperability in health, the Internet of Things may be able to cut the cost of treating chronic disease by 50 percent.

5. We’re underestimating the impact on developing economies

In terms of pure economic impact, there will be approximately a 60:40 split between economic gains for developed economies and developing economies. As McKinsey points out, some of the greatest gains will be in developing nations, especially in areas such as retail. In some cases, developing nations will be able to leapfrog the achievements in developed nations because they don’t have to worry about retrofitting equipment or infrastructure with sensors and actuators.

6. We’re forgetting about the new business models that will be created

It’s not just that the Internet of Things will lead to efficiencies and cost savings – but also that it will lead to entirely new ways of doing business. As McKinsey points out, we will likely see the rise of new business models that correspond with the way we are monitoring and evaluating data in real-time. The line will blur between technology companies and non-technology companies.

For example, take the makers of industrial equipment. Instead of selling expensive capital goods, they will sell products-as-services. Instead of charging one lump sum upfront, they will charge by usage. In addition, there will be new companies that emerge that bill themselves as end-to-end Internet of Things system providers.

**

Obviously, it’s exciting news that the world is about to get an $11 trillion economic shot in the arm from hooking up every possible object to the Internet with sensors and actuators. At the very least, some companies are going to get awfully rich by selling sensors and RFID tags to everyone trying to cash in on the Internet of Things gold rush.

At the same time, though, isn’t there something very bleak about a future in which sensors are hooked up to every object, every setting is predictable and optimized, and pure data guides every decision rather than the human heart? Imagine a giant planned economy, overseen by a bunch of managers schooled in Frederick Winslow Taylor’s principles of scientific management, figuring out new ways to crunch the data of our daily lives. When it comes to the Internet of Things, be careful what you wish for.

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The World’s Most Tech-Ready Countries 2015 04-16

The World’s Most Tech-Ready Countries 2015

Those able to harness the power of information and communication technology are reaping ever more benefits. But in poor countries, digital poverty is holding back growth and development, leaving them further behind.

Singapore is this year’s leader of the global ICT revolution. Its government has a clear digital strategy and is an exemplar of online services and e-participation tools, which filters down to its industries and population. The country has the highest penetration of mobile broadband subscriptions per capita in the world and more than half of the population is employed in knowledge-intensive jobs. 

The country topped this year’s Global Information Technology Report (GITR), published by INSEAD in partnership with the World Economic Forum and Johnson Cornell University, due to its leadership in business, innovation environment and government usage of ICT.

The report benchmarks 143 economies in terms of their capacity to prepare for, use and leverage ICT.

What gives these high income countries their “networked readiness advantage” is their education systems and concerted policy efforts to facilitate innovation and commerce, which allows them to take advantage of digital innovation in a way that emerging economies cannot.

Hold the champagne 

From a global point of view, however, we see reasons for concern. Not only does this year’s report show that the world’s emerging economies are failing to exploit the potential of ICTs to drive social and economic transformation, but the gap between the digital haves and have nots is increasing. Those in the top 10 percent of the ranking have seen twice the level of improvement since 2012 as those in the bottom 10 percent.  

This is also reflected by progress among the world’s largest emerging markets. Lately, their journey towards network readiness has been disappointing. While the Russian Federation is highest-placed among the BRICS nations, climbing nine places to 41 and China remains at 62, all other members of the group have declined. India dropped six places to 89, Brazil dropped 15 to 84 and South Africa is 75th, down five spots.

And the example of the BRICS is not unique. Many other emerging countries that have improved their networked readiness over the last decade or so are now facing stagnation or regression. Indonesia is one such example which has fallen 15 places this year down to 79th position from 64 last year. This is partly due to persistent divides within these countries between their rural and urban areas and across income groups, which leaves a large portion of the population out of the digital revolution.

Emerging exemplars

Despite the top 30 places being dominated by high-income countries, there are a number of other countries in which ICT is being used to stimulate growth and reduce inequality.  Here, governments are using a number of instruments in their toolkits such as balancing  liberalisation and regulation to stimulate healthy competition across their economies.

Among those that have made considerable improvements in terms of their ranking are Lithuania (31st)), Malaysia (32nd) and Latvia (33rd).  Other examples of countries hitting above their weight include Caucasian countries – Kazakhstan (40th), Armenia (58th) and Georgia (60th) – as well as Mauritius (45th) which is far ahead of the other sub-Saharan African countries.   Even so, in a number of sub-Saharan countries, both large and small, progress is being made where their ICT markets have been liberalised. For example, Kenya, Nigeria and Tanzania are all beginning to see the benefits of market reforms as well as smaller economies like Cape Verde, Lesotho or Madagascar.

Low hanging fruits

There is no doubt that technological innovation is influencing lives in all types of economies – social media are changing the ways in which we interact with each other at an individual level and at an industry level. Big data is being used to create new products and generate new markets.   In developing countries, however, information and communication technologies (ICTs) are even more fundamental to reducing inequalities, to taking people out of poverty and to creating jobs. 

The internet remains nonexistent, scarce, unaffordable or too slow in vast swathes of the developing world. While internet penetration has been growing, its growth has slowed lately. A fresh internet revolution is needed to connect the ‘next two billion people who still do not have online access’. This will require concerted efforts to extend mobile broadband to large parts of the developing world.

To achieve an internet revolution and bridge the digital divide, developing countries must consider long-term investments in infrastructure and education. Governments can accelerate the process through sound regulation and more intense competition.

If fostered properly, ICT can transform economies through productivity gains, reducing information costs, allowing new models of collaboration and changing the way people work. ICT fosters entrepreneurship and wealth creation. But widespread ICT use by businesses, government and the population at large is a pre-condition for all these benefits and opportunities to materialise and to be accessible to the largest numbers. Inclusive growth is indeed within reach, and ICTs can play a critical role in making it a global reality.

The Global Information Technology Report uses a combination of data from publicly available sources and the results of the Executive Opinion Survey of more than 13,000 executives conducted by the World Economic Forum and partners.  It gauges usage, socio-economic impact, political and regulatory environments and the climate for business and innovation as well as ICT infrastructure, affordability and ICT skills.

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