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Month: June 2019

18 Jun 2019

Ethereum Public Blockchain Favoured

A Polish bank has bucked the trend of companies and institutions experimenting with blockchain technology using private, permissioned ledgers. Warsaw-based Alior will reportedly use the Ethereum public blockchain to build a system to let customers check the authenticity of documents they receive from the bank.

By using a completely permissionless, public blockchain, the Alior authenticator feature is something of a world first. Previously, banks have been reluctant to work with any true crypto project and have favoured permissioned systems based on Ethereum or another blockchain platform.

Alior to Build Directly on the Ethereum Blockchain
The news of the Alior authenticator feature broke earlier today via Forbes. The publication described the use of a public blockchain as being “among the very first.”

According to Tomasz Sienicki, the blockchain strategy lead at the bank:

“Our mission is to be disruptive, so we want to provide innovative solutions, and we want other banks to follow us as well. We welcome if somebody copied our solution… We are showing that it’s possible to use public blockchain even if some people think it’s impossible.”

The decision to build such a feature using the Ethereum blockchain has been driven by financial regulations in Poland. The law states that the public must have access to all documents from a bank in a durable medium. In 2017, the nation’s Office of Competition and Consumer Protection ruled that a bank website was not an adequate way to deliver information to a customer since it could easily be changed.
This prompted Alior to explore how they could bring similar online banking convenience to their customers, whilst remaining compliant with regulations. Their experimentation led to the founding of what the bank calls its Blockchain Center of Excellence. Established last October, the department’s first implementation of blockchain technology so far is the bank document authenticator.
Public vs Private Blockchains
There has been a lot of debate over public and private blockchains in recent years. Many crypto naysayers hold the opinion that projects such as Ethereum will be ignored in favour of distributed, permissioned ledgers that the entities using them can control. However, many of those more learned on the subject of cryptocurrencies argue that a permissioned blockchain sacrifices almost everything remotely innovative about the technology.

Blockchain is great for removing the need for different parties to trust each other when doing a transaction of some kind. If you have to request permission from a central authority that created and controls the network to participate, then there is very little ground being broken by such a system.

Hopefully, more companies like Alior will see how much more powerful a tool for disruption a public blockchain like Ethereum is over those glorified, centralised databases that are being created by numerous companies trying to get their heads around this new technology, whilst not rendering their own business obsolete in the process.


17 Jun 2019
Meet The World's Most Valuable AI Startup

Meet The World’s Most Valuable AI Startup

In just four years, SenseTime went from being an academic project to become the world’s most valuable artificial intelligence (AI) company with a current valuation of $4.5 billion. Based in China, the company has a portfolio of 700 clients and partners, including the Massachusetts Institute of Technology (MIT), Qualcomm, Honda, Alibaba, Weibo, and more. They use their proprietary artificial intelligence and machine vision technology to drive its success and “redefine human life as we know it.” With the number of core technologies, products, and services SenseTime offers, it’s hard to believe it’s such a young company. Here are just a few ways SenseTime uses artificial intelligence to “power the future.”

SenseTime’s Core Technologies

SenseTime developed several AI technologies including face, image, object and text recognition; medical image and video analysis; remote sensing; and autonomous driving systems. These AI technologies have been deployed in a variety of industries from healthcare to finance, online entertainment to education, retail to security, smart cities to smartphones, and more. They also created a deep learning platform. SenseTime is now the largest algorithm provider in China, as well as the fifth largest AI platform. Along with other tech titans, SenseTime is working with the Chinese government on Made in China 2025, an initiative to make the country economically autonomous.

One of the reasons SenseTime has been able to grow so successful so quickly is that it has government support and direct access to China’s vast databases regarding its 1.4 billion residents. The more data algorithms are given, the better they become. And, with plans to triple its revenue to $300 million in the coming year by putting its technology as the foundation of image recognition everywhere, there doesn’t seem to be any signs of this growth slowing.

Conventional wisdom in the AI community is that while Europe and the United States might be better at developing AI breakthroughs, China uses and improves AI thanks to the data generated by its enormous population. This had led to some concern about an “AI arms race” and how to manage ethics and privacy considerations among governments and cultures that see these issues very differently. In one example, there are concerns that algorithms using facial recognition technology are being used for racial profiling and to track the actions of a Muslim minority group in China. This is something that human rights groups are watching very closely.

Applications of SenseTime Technologies

Since we’re talking about a Chinese company with facial recognition technology (called DeepID), you won’t be surprised that a core focus for SenseTime is security. You get a sense for what’s behind the screen when you visit the company’s Beijing office lobby and see employees gain access after a camera scans their face and checks it against company records. There’s also a panel at the entrance with a built-in camera that can analyze your face and even assign an “attractiveness rating.”


16 Jun 2019
Developing Talent For The Digital Economy: The Business-Higher Education Forum Shows The Way

Developing Talent For The Digital Economy: The Business-Higher Education Forum Shows The Way

The Business-Higher Education Forum (BHEF) is setting the standard for how to craft partnerships between universities and private employers that address the talent needs of the nation’s workforce, particularly those enterprises requiring digital and technological skills.

Nearly 40 years old, the BHEF is a nonprofit membership organization comprised of Fortune 500 executives, major university presidents and other national educational leaders. In addition to its influential national policy voice, BHEF has focused on regional collaborations between its business and academic members that build durable degree programs, curricula and work-based learning in high-demand fields.

The maturation of BHEF’s work, supported in part by a five-year grant from the National Science Foundation, is exemplified in a recent report, Creating Purposeful Partnerships: Business and Higher Education Working Together To Build Regional Talent Ecosystems for the Digital Economy. The report details the BHEF partnership process and how it works to embed crucial skills in existing courses and majors as well as create new majors, minors and curricula that are tightly aligned with the knowledge, skills and abilities (KSA) that employers need. Six ongoing partnerships are highlighted:

Northrup Grumman and the University of Maryland (cybersecurity);
IBM and the City University of New York (data science and analytics and urban sustainability);
NextEra Energy and Miami Dade College (data science and analytics);
Raytheon and Northeastern University (IT and cybersecurity);
The Water Council and the University of Wisconsin, Milwaukee (water science);
Boing and Washington University (engineering).


15 Jun 2019
What Is AI, and Will It Take Over Your Service-Based Business Job?

What Is AI, and Will It Take Over Your Service-Based Business Job?

Most factories make use of machines in the production process and, as a result, lots of people have been put out of work. But are service-based businesses about to face a similar fate? Will you be replaced by a robot one day?

That particular question has likely brought chills down many people’s spines, but could this really be the reality we’re facing? Imagine all the different kinds of service-based jobs poised to crumble because some app or robot can do the work faster than a human would.

Will AI take over service-based business jobs?
There have been a series of debates on whether AI will take over jobs or not. A study by Boston Consulting Group predicted that by 2025, a quarter of jobs will be replaced by either smart software or robots. Kai Fu Lee, a renowned AI expert and venture capitalist, believes that 40 percent of the jobs in the world will be replaced by robots.

Without a doubt, AI is gradually infiltrating and disrupting different industries, including healthcare and banking. But you mustn’t forget that machines have their limitations. Although technology has advanced to the point that software mimic humans, they can never totally take our place. In fact, AI will help create more jobs than it seeks to eliminate. You may need to acquire new skills, but it doesn’t mean that you’ll be replaced.

Read more:

13 Jun 2019


The Disruptive Technologies New Project

The Hydro-fish project will provide both environmental and economic impact. One of the key concerns in the aquaculture industry is around sustainability as much of it depends on fishmeal and plant ingredients. The Hydro-fish project will use natural resources efficiently by treating fish with enzymes that make higher value feed ingredients. The result will be a fish product supply chain that is more sustainable. This would be a significant gain for the aquaculture industry in Ireland as it develops to balance increasing consumer demand with a need for more sustainable practices.

As with all Disruptive Technologies Innovation Fund projects, the Hydro-fish project is co-funded and collaborative. NUI Galway is the lead partner on this innovative project and Prof Mark Johnson is leading the NUIG team. Jason Whooly, CEO of Bio-Marine Ingredients Ireland heads up the BII team, Catherine Stanton, Teagasc Research Officer is the Teagasc lead and Jamie Downes is leading the Marine Institute team. While Teagasc and the Marine Institute are collaborating on this project, as public bodies they will not receive co-funding from the Fund, which is a testament to this consortium’s commitment to developing an innovative and disruptive solution to address short-comings in the aquaculture industry.

Speaking at Bio-Marine Ingredients Ireland today, Minister Heather Humphreys said:

“The Disruptive Technologies Innovation Fund is a key part of both Project Ireland 2040 and the Government’s new Future Jobs Ireland initiative. It is one of the first funds of its kind in the World and it will ensure that Ireland is at the cutting edge in terms of developing new technologies which will change the way we live and work in the future.”

“The Hydro-fish project represents innovation, collaboration and disruption, all the hallmarks of a successful DTIF project. NUI Galway is a collaborative partner in nine of the twenty-seven successful projects which clearly indicates the quality, novelty and industry-relevance of the research conducted at NUIG, the largest and oldest university in the West of Ireland. I wish to congratulate the Hydro-Fish consortium here today on their success under the first ever DTIF Call.

“The Disruptive Technologies Innovation Fund has attracted huge interest and is supporting really ground-breaking projects right across the country. I look forward to launching Call 2 of the Fund in the coming weeks and the success stories it will bring”.

Disruptive technology is a technology which has the potential to very significantly alter markets and their functioning and significantly alters the way that businesses operate. While it involves a new product or process, it can also involve the emergence of a new business model. Disruption is not about technology alone but the combination of technology and business model innovation. It is certainly not “Business as Usual”. This Fund is about the deployment and commercialisation of technology to deliver new solutions and to create and safeguard the jobs of the future.

Read more:

12 Jun 2019
Why Learn Machine Learning and Artificial Intelligence?

Why Learn Machine Learning and Artificial Intelligence?

Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own.

Some Statistics

Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use. ML-AI and Big Data are offering growth opportunities not just to established large businesses and employees but are becoming backbones of new-age, innovative startups. Startups employ 28per cent of analytics professionals and have created a huge impact in this area. The rate at which freshers are hired in the analytics industry increased by 33per cent in the year 2018; whereas about 40per cent of area-experts are below 5 years of experience.  Delhi and Bengaluru lead the share in market size by revenue, followed by Mumbai, Hyderabad, Pune, Chennai and Kolkata in that order.

ML & AI techniques are very powerful and are already creating a ripple effect in the existing businesses; especially in startup-ecosystems across the globe. To help an uninitiated person relate with common examples when someone’s Youtube, Netflix and Amazon accounts give him/her suggestion on what to watch next it’s based on ML.  When someone types search-keywords on google he/she gets suggestions to what to type and search that is also based on ML. Startups are coming up with innovative products and services aided by ML & AI. Food ordering apps are trying to predict most popular dishes pertaining to different geographies and seasons, improve delivery services, food quality, restaurant and dishes-listing; hospitality industry is trying to optimize discounts, increase room bookings and hence maximize revenues. The medical industry is trying to predict and diagnose risky health conditions with more precision. The application of data analytics is increasing incrementally by the day; so are the jobs that this area offers.

Big data analytics is another area that will see exponential growth in coming times; companies, entrepreneurs and researchers, in general, are scrapping data and using other methods and sources where the data is available, structured or unstructured, to find patterns in the data that was not really explored or anticipated till recent times. Earlier data analysts were constrained by conventional methods of extracting maximum information from minimum data that was available or rather used sampling techniques and also depended on hypothesis; However with big data, analysts can now use new techniques and multiple algorithms/models to find correlation between important variables and find better insights from much larger dataset that is available, without starting with a hypothesis. In big data, more emphasis is on correlation and patterns than causality.  Businesses want to harness the power of ML-AI and Big Data to grow and profit exponentially. It is becoming a race that everyone is trying to win. Anyone who can help them is welcome and is sought after.

Read more:

11 Jun 2019


Many countries are looking to transform their economies into digital ones, with the aim to progress from a production-based to a service-based economy, and also to increase economic and societal value for their nations. A digital economy requires more than just ambition. It needs the development and establishment of ecosystems that bring a nation’s economy, businesses and society together into an integrated and coherent system.

Governments, especially those of developing economies, realise that their countries might be caught in middle-income traps, income inequalities and/or social and economic imbalances. This leads to the desire for long-term, national development strategies with ambitious objectives, not only economically but also socially, specifically to raise competitiveness and increase the value of products and services delivered to their societies.

Such aims are usually based on multiple values and driven by innovation—moving from producing commodities and agricultural goods to technology-based, innovative products. Many countries call this a digital or smart economy by emphasising technology, creativity and innovation in industries and areas with significant growth potential. Other objectives for such an economy might include sustainability, environmental protection, health, prosperity, social welfare and poverty reduction, many of which are represented by the United Nations’ 17 Sustainable Development Goals (SDGs).

What is a digital economy?

In a digital economy, everything is connected with anything at any time, which is why it is often also called a smart economy. Three fundamental elements required for such an economy are:

  1. Digital assets,
  2. Digital identities and
  3. Smart contracts.

This article’s main focus is on the significance of digital assets in the context of a digital economy. Digital assets are understood to cover not only the traditional financial-instruments space but also non-materialistic values, such as personal data—including identity, health and medical, geo, behavioural and social data.

To make this less abstract and more tangible, the envisaged ecosystem(s) could be represented in the following diagram, with the Digital Asset Platform (DAP) at its core.

Given this—admittedly simplistic—high-level structure, it becomes clear that the foundation of value creation consists of infrastructure, utilities and public services, including non-material factors. Most importantly, solid financial infrastructures and systems are required to enable the exchange of products and services within and between the various ecosystems.

A digital-assets ecosystem in the context of a digital economy

A digital-assets ecosystem can have a widespread transformative impact on an economy as a whole as it is based on data generated from transactions across the ecosystem and on allowing other ecosystems to collaborate with digital assets and their marketplaces. If implemented across a whole country, digital assets, in conjunction with distributed-ledger technology, can integrate and interconnect all parts of the economy and society from healthcare to education, human resources, taxes, welfare systems, pensions, energy, transportation, cyber-security, secure and trustworthy identity documents, law enforcement, government, taxes, financial services… the breadth of applications is enormous.

Additionally, utilising digital assets and diverse, distributed yet interconnected blockchain variants will enable these sectors of economies, societies and financial markets to grow at a scale corresponding to their priorities and needs. A trusted digital-assets ecosystem is the foundation of an inclusive economy and, therefore, facilitates the means by which products and services are exchanged among participants within and across ecosystems.

Digital assets will allow the introduction of new forms of asset ownership by issuing various forms of digital tokens. The aim is to reduce friction in the current system, increase transparency, reduce costs and directly link the input with the output of a tokenised asset. Also, digital assets allow fractional ownership, thereby contributing to financial inclusion for underprivileged citizens.

Use cases

Some use cases below illustrate the application of digital assets and how they can underpin a financial ecosystem to enable financing and the use of underlying assets. There are two fundamentally different strategic goals to achieve: innovative or disruptive business cases. In both cases, a DAP can serve a national economy and act as a marketplace facilitator:

  • Innovative: Fundraising for new infrastructure—benefiting the society:
  • Raising capital, e.g., by a public-private partnership and directly linking the output generated with the end-users by issuing asset, utility and/or payment tokens.
  • Examples: electricity, clean water, transport, digital services, etc.
  • Disruptive: Change the current state of personal-data ownership and offer a fair economic reward for data use (gig economy):
  • The owners of personal data decide on the use of their data by third-parties and are fairly rewarded for allowing such third-parties to use their data.
  • Tokenised personal data sourced from social-media platforms can be shared on commercial digital platforms.
  • Stakeholders involved could be data owners, utilities, insurance companies, specialised investment firms, commercial data vendors.
  • Innovative: Enable use-case restricted funding (financial inclusion):
  • Social initiatives and programs established to assist low-income beneficiaries.
  • Tokenization allows governments to manage how the recipients of government support for basic necessities can use tokens for only specific purposes, such as housing costs, medical treatment, utility bills, food costs.
  • Examples:
    • Facilitate innovative micro-enterprises that need funding for their purpose-driven business ideas.
    • Offer easy access to funding by issuing equity/debt on the chain (micro-finance as a service).
    • Improve access to public and private services for underprivileged citizens; tokenise goods and services that can be consumed with a payment or utility token on a marketplace (token economy). Government-supported programs through which private-sector entities could be token-issuers.

Ecosystem dynamics and the need for an “ecosystem of ecosystems”

Creating one or many digital ecosystems will not suffice. The reality is that ecosystems (co-)exist and evolve in many different forms of networks and horizontal-to-vertical structures. Given this complexity, we should expect a non-linear transition from the existing environment to a multi-ecosystem landscape in the future.

This situation will create the need for an “ecosystem of ecosystems”, which will align interests and consider the different maturities and structures of each ecosystem, thereby enabling interoperability.

Such a complex super-ecosystem requires significant thought leadership and design effort. This can be visualised and documented in a blueprint that integrates the vision of a digital economy with the fundamental elements to create a functioning digital economy. A digital-assets ecosystem is an integral part of such a super-ecosystem. Its purpose is to bring capital and non-capital markets together with end-users, i.e., the ultimate beneficiaries, and to facilitate primary and secondary markets for digital assets. A pragmatic approach would be to consider a hybrid system—i.e., a decentralised architecture with a governance structure in line with centralised governance and regulations.

Read more:

10 Jun 2019
Is there a weak link in blockchain security?

Is there a weak link in blockchain security?

Recent research revealed that blockchain is set to become ubiquitous by 2025, entering mainstream business and underpinning supply chains worldwide.

This technology is set to provide greater transparency, traceability and immutability, allowing people and organizations to share data without having to be concerned about security. However, blockchain is only as strong as its weakest link. Despite the hails surrounding blockchain’s immutable security, there are still risks surrounding it that organizations must be aware of – and mitigate – prior to implementation.

It is important to understand that there are two types of blockchain – permissionless and permissioned. The most prominent example of permissionless blockchain is Bitcoin – a public blockchain network that anyone can participate in. Cryptocurrencies like bitcoin favor this type of blockchain technology because it enables all users to track, verify and confirm transactions, regardless of whether users choose to be anonymous or not.

The other blockchain model is permissioned (also known as private blockchain) – and is mainly used for business applications. These networks are only accessible to known entities such as partners, suppliers or customers. With permissioned blockchain, a company establishes protocols to achieve consensus, and verify and assemble blocks. This set up can deliver thousands of transactions per second and provide granular management and control over who sees and accesses the transactions.

In both cases, the main benefit is the trust and transparency that blockchain brings – all parties involved in the network have total visibility into the transactions recorded in the blockchain ledger and each block is tied to the block before it.

This transparency makes blockchain extremely difficult to manipulate at scale. While the blockchain platform itself may be secure, there is still some work to be done to ensure organizations are equipped to make their networks secure end to end. For true security, organizations must focus on the last mile connection between a physical event and the digitized record of this event.

If these points of entry to the platform are tampered with, the blockchain is rendered worthless. It is therefore imperative that organizations secure all points of entry, and assess the risks, before they consider deploying blockchain on a broad scale. They will need to consider security at all layers, most importantly:

This starts with ensuring data and transactions entered in the blockchain ecosystem are adequately protected from manipulation. The infrastructure these networks resides on must also have the necessary protections in place. With blockchain, you are only as strong as your weakest link.

If integration points are compromised, the entire blockchain ecosystem could be at risk, meaning that blockchain credentials and data could be exposed to unauthorized users.

Identity and access management
To prevent unauthorized parties from accessing blockchain data, a combination of encryption and identity management tools are needed. Stolen credentials could potentially allow a cybercriminal to access the blockchain platform, regardless of how secure it is. Organizations must deploy identity and access management controls. Encryption should also be deployed to ensure that data is not stolen, manipulated or leaked in transit.

End users
The insider threat should be a focal concern when it comes to blockchain too. Organizations must consider that employees, partners and suppliers – be it unintentionally or maliciously – can cause security incidents that impact the blockchain.

To mitigate this, organizations should deploy security awareness training for employees and outline clear security parameters and responsibilities with partners. This will stop employees from making careless mistakes and may also ward off malicious insiders. In line with these requirements, blockchain can provide advanced security controls – for example, leveraging the public key infrastructure (PKI) to authenticate and authorize parties, and encrypt their communications.

Data governance
Blockchain-based networks are built on shared business interests creating a system of trust. However, as the network grows, participating entities could leave the network and new ones may join, leading to ambiguities around operational considerations around data sharing and data ownership. These could result in serious regulatory and reputational repercussions for organizations as data owners, unable to secure the customer data.

Organizations are multi-faceted and have multiple revenue streams, often linked to each other. One of the major challenges to blockchain adoption has been a lack of interoperability across different blockchain networks. There have been recent developments, with major players embarking on developing interoperable networks, which could boost blockchain interest to a different level, at the same time introducing additional levels of vulnerability.

Smart contracts
A key component of blockchain networks is the Smart Contracts, which are developed using different languages on the platform being used, like Solidity being used in Ethereum. These languages allow developers to make changes to the underlying blockchain networks, causing vulnerabilities. However, from an enterprise blockchain perspective, a solid governance mechanism using permissioned chain can establish a secure system in place to restrict the privileges to governing body.

To achieve the most value from blockchain, both now and in the future, organizations must take responsibility for their safety and security at all levels – application, Infrastructure, data and partners.

By conducting a blockchain risk assessment and addressing key risks, organizations can make sure they are well positioned to leverage the efficiencies, transparency and cost-effectiveness provided by blockchain without opening themselves up to unexpected risks. The most pragmatic way for organizations interested in blockchain is to test the concept through pilot programs. Pilots should be focused on the areas that offer organizations the most control and companies should take these weak links into consideration.

Ultimately, blockchain has the ability to solve business issues relating to traceability, responsiveness, and trust. By taking a carefully planned approach to implementation, and understanding blockchain’s weak links, organizations can unlock the true value of blockchain, creating new opportunities and reducing inefficiencies.


09 Jun 2019
Digitalization central to product reinvention

Digitalization central to product reinvention

Digitalization is one of the hottest buzzwords of late, as company executives are fixated on how their businesses can become digitalized.

According to Eric Schaeffer, senior managing director of Accenture, this is already happening and executives the world over are leveraging digitalization to push for improvements on efficiency and management.

Speaking at a roundtable during the recent Shanghai Forum, held by Fudan University and the Korea Foundation for Advanced Studies, Schaeffer said many digital-savvy companies are reinventing their products and services to make them more “intelligent.”

Part of the reason, said the leader of Accenture’s Products Industry X.0 practice, is that consumers, firms and markets are demanding more of the type of user experience that transcends a product’s role in value creation.

For centuries, a product’s sale marked the end of its value chain, from the sourcing of raw materials to assembling of finished parts, but this routine is about to experience a tectonic tilt toward a new product world where traditional product-making businesses will need to apply much more fluid business practices if they want to survive, said Schaeffer.

Drawing on his experience in industries including the manufacture of cars and industrial equipment, Schaeffer said these sectors are undergoing a systemic transition to stay competitive in what he called the “post-digitalization era.”

Reinventing the product

“By ‘post-digitalization’ I don’t mean digitalization is past its prime,” said Schaeffer. “It’s just that digitalization alone is no longer a comparative edge, because it is on the agenda of almost everyone.”

A hallmark of this coming era of post-digitalization, in his opinion, is a dwindling role of the manufacture of products in the overall industrial value chain, as this process cedes ground to emerging factors such as the platform, service providers and software designers.

Together, they have moved up the value chain in terms of their contribution to making smart, connected products, Schaeffer noted. The veteran French pioneer of industrial digitalization has laid out his argument in a book he co-authored with David Sovie, entitled “Reinventing the Product: How to Transform your Business and Create Value in the Digital Age.”

The key thesis of his book is that day-to-day products, ranging from a printer to industrial machines, from an airplane engine to medical equipment, will become self-adaptive as they are increasingly integrated with software and underlying IT infrastructure, exchanging millions of gigabytes of data in a split second. The constant interaction will turn an originally lifeless, stand-alone product into a “living” product capable of reacting automatically to surrounding environments, making decisions and “communicating” with other players in a collaborate network — or as is now better known — “ecosystem.”

“In the future, products will be reinvented and reconfigured,” said Schaeffer. “This means hardware or the product itself will only be the basics.”

Creation of value will, to a large extent, depend more on software.

He cited the example of Tesla, saying that Tesla is an “evergreen tree” as the in-car software updates itself in a ceaseless cycle, receiving upgrades and delivering the most up-to-date functions and “experience” to users.

“This is a fundamental change,” Schaeffer claimed.

Waves of innovation led by Tesla and other poster boys of digitalization are also redefining the value composition of industrial goods.

In addition to features like connectedness based on the practicality of everyday life, enhanced user experience is also deemed central to the rebirth of mundane industrial products.

The Michelin tire company is an example. Built-in sensors in tyres from Michelin are the underpinnings of the company’s new strategy of “tire as a service.”

Michelin monitors its tires’ performance in whatever weather and road conditions through data transmitted via the Internet of Things.

The followup analysis enables it to forecast potential glitches and send in service staff before a major breakdown. This is a way of ensuring its tyres are “invariably at their best.”

“This is how they turn tyres into a service,” said Schaeffer. “The company also cooperates with other suppliers to help cut fuel costs.”

Through digitalization, Michelin has turned a product into not just a service, but also a part of an ecosystem, a platform for products from other stakeholders. “What used to be at the heart of an ecosystem may now find itself on the periphery,” said Schaeffer.


08 Jun 2019
When AI Becomes an Everyday Technology

When AI Becomes an Everyday Technology

The evolution of AI has been a rich tale of exploration since its origins in the 1950’s, with the last decade providing an especially dramatic chapter of breakthrough innovations. But I believe the real story is what comes next — when the disruption stabilizes and machine learning transitions from a staple of Silicon Valley headlines to an everyday technology. It’ll be a far longer chapter — perhaps decades — in which developers all over the world use a mature set of tools to transform their industries.

In 2019, we find ourselves at the start of this new chapter. AI has undergone a remarkable refinement in recent years, as barriers to entry have fallen and a wide range of products, services, resources, and best practices have emerged. As our focus shifts — finally — from AI itself to the impact that AI can have on your business, the question is no longer how this technology works, but what it can do for you.

In other words, we’re entering the age of deployed AI. Deployed AI is about more than engineering — it’s about a shared vision. Engineering expertise will always play a role in AI. But in the age of deployed AI, our most important asset will be the vision that guides that expertise. What problems can AI solve, and what kind of data might the solution require? By what metrics will success be measured? And how can the result be integrated most effectively with the people and processes already in place in any given business? These are broad, organizational questions, and their answers won’t come from any single stakeholder. Every voice can contribute to deployed AI — technical and non-technical alike — and it’s vital that businesses establish workflows that empower everyone to play a role.

One of my favorite recent examples of this shift in possibilities comes from Carnegie Mellon University (CMU), where I formerly served as dean of the computer science department. While I was there, a student was considering her options for an upcoming artificial intelligence project, and thought of her sister, who happens to be deaf. She wanted to make it easier for her friends to learn the basics of American Sign Language, so she developed an AI-powered tool that tracked their movements and provided automatic feedback as they learned new signs. And here’s the best part: she wasn’t a computer science postdoc or even a grad student — she was a history major, taking an introductory class for fun.

It’s hard to imagine a better example of how accessible and powerful deployed AI can be — or a better indication that this technology is ready to solve problems for every business, in every industry, today.

How does deployed AI actually work? The primary characteristic is a measurable, practical impact. Simply put, a deployed AI project brings dramatic automation to a major part of your business, solving real problems for customers or employees — sometimes both — in new ways. Over the course of my career, I’ve seen countless AI projects that begin by looking for something clever to do with the data or algorithms that happen to be lying around, hoping to justify their existence in the process. In contrast, a deployed AI solution works backwards from the existing needs of the people who will use it.

So how should your own company get started identifying projects that could benefit from deployed AI? Ask yourself these questions:

How can I attract or develop the expertise needed to build the solution?

It’s vital that the members of an AI deployment team share a respect for a range of different skills. For example, imagine you’re building an AI-powered voice assistant. The project will include researchers, dialogue designers and acoustic speech modelers — among many other groups — all of whom must trust each other to solve distinct challenges intelligently. If any group feels left out, the results will range from inconsistent to downright inhumane.

How can I avoid ending up with a stranded proof-of-concept?

It’s easy to get lost in the rush of innovating, especially in a space moving as fast as AI, but it’s vital to focus on change management at the same time. This means utilizing all the traditional practices that would benefit a non-AI project: a clear north star, consistent metrics, high-quality, reliable data sets, and agility. Expect weekly reviews — at a minimum — with a continued emphasis on the end user’s experience.

Who is ultimately responsible for the decisions the AI is making?

At its core, AI is about automating judgments that have previously been the exclusive domains of humans. This is a significant challenge unto itself, of course, but it brings with it significant risk as well. Increasing effort, for instance, is required to make the decisions of AI systems more transparent and understandable in human terms. Additionally, best practices are emerging on how to use data sets and testing to ensure each sub-population of users is treated with fairness and consistency. There are also adversarial examples — deliberately misleading input intended to cause an AI system to misbehave — as well as deepfakes — realistically modified video — among many other emerging challenges. As leaders in AI, it’s our responsibility to face all of these complexities, and provide the expertise our customers and their users need to steer this technology in the right direction.

Deployed AI in action

It’s exciting to think about where deployed AI might take us as more businesses incorporate AI in their products and services. Consider some of these examples of Google Cloud AI customers that are getting creative with AI:

  • Global energy company AES is using drones and AutoML Vision to more safely and efficiently inspect thousands of wind turbines.
  • Real Estate firm Keller Williams is empowering individual realtors to work more efficiently and effectively on their own by allowing home buyers to automatically search listing photos for specific features like “granite countertops.”
  • The New York Times is preserving a priceless archive of millions of photos covering more than 100 years of its history. The media publication is using AI to scan and analyze images and words on thousands of archived photos.
  • Financial Services firm HSBC is using AI to detect fraud at the speed and scale of global commerce by screening vast amounts of customer data against publicly available data in the search for suspicious activity.

Within each of these stories, three fundamental characteristics of deployed AI can be seen in action. First, they identify a long-unsolved problem or unrealized opportunity. Next, they’re solved in a way that simply wouldn’t be possible without AI. Finally, they demonstrate that AI has a role to play in just about every industry, whether tech-focused or not.

Sooner or later, every technology transitions from an elite niche to a mainstream tool. AI is now undergoing a similar transformation. After years of hype around mysterious neural networks and the PhD researchers who design them, we’re entering an age in which just about anyone can leverage the power of intelligent algorithms to solve the problems that matter to them. Ironically, although breakthroughs get the headlines, it’s accessibility that really changes the world. That’s why, after such an eventful decade, a lack of hype around machine learning may be the most exciting development yet.