X-Frame-Options: SAMEORIGIN

Category: Digital Economy

02 Nov 2019
AI Stats News: 64% Of Workers Trust A Robot More Than Their Manager

AI Stats News: 64% Of Workers Trust A Robot More Than Their Manager

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted workers’ positive attitudes toward AI and robots, challenges in implementing enterprise AI, the perceived benefits of AI in financial services, and the impact of AI on the business of Big Tech.

AI business adoption, attitudes and expectations

50% of workers are currently using some form of AI at work compared to only 32% last year; workers in China (77%) and India (78%) have adopted AI over 2X more than those in France (32%) and Japan (29%); 65% of workers are optimistic, excited and grateful about having robot co-workers and nearly a quarter report having a loving and gratifying relationship with AI at work; 64% of workers would trust a robot more than their manager and half have turned to a robot instead of their manager for advice; workers in India (89%) and China (88%) are more trusting of robots over their managers, but less so in the U.S. (57%), UK (54%) and France (56%); 82% think robots can do things better than their managers, including providing unbiased information (26%), maintaining work schedules (34%), problem solving (29%) and managing a budget (26%); managers are better than robots in understanding workers’ feelings (45%), coaching them (33%) and creating a work culture (29%) [Oracle survey of 8,370 employees, managers and HR leaders in 10 countries]

The growth of AI applications in deployment was actually less this year than last year, with the total percentage of CIOs saying their company has deployed AI now at 19%, up from 14% last year—far lower than the 23% of companies that thought they would newly roll out AI in 2019 [Gartner]

74% of Financial Services Institutions (FI) executives said AI was extremely or very important to the success of their companies today, while 53% predicted it would be extremely important three years from now; about 75% expected that over the next three years their organizations will gain major or significant benefits from AI in increased efficiency/lower costs; while 61% of FI executives said they knew about an AI project at their companies, only 29% of these executives reported on a project that had been fully implemented; only 29% of AI projects are within full implementation phase, with 46% still pilots, 35% in proof of concept and 24% in initial planning; challenges include securing senior management commitment (45%) and securing adequate budget (44%); technologies used in AI projects include virtual agents (72%) and natural language analysis (56%); 50% found it extremely or very challenging to secure talent and 49% found it extremely or very challenging to attract and retain professionals with appropriate skills [Cognizant survey of FI executives in US and Europe]

82% of CEOs say they have a digital initiative or transformation program, but only 23% think their organizations are very effective at harvesting the results of digital, and even fewer CIOs would say they are very strong at this [Gartner surveys of CEOs and CIOs]

Read more: https://www.forbes.com/sites/gilpress/2019/11/01/ai-stats-news-64-of-workers-trust-a-robot-more-than-their-manager/#777497912b21

27 Oct 2019
Big Tech Is Making A Massive Bet On AI … Here’s How Investors Can, Too

Big Tech Is Making A Massive Bet On AI … Here’s How Investors Can, Too

Artificial intelligence is becoming the future of everything. Yet, only a few large companies have the talent and the technology to perfect it.

That’s the gist of New York Times story published late last week. Rising costs for AI research are locking out university researchers and garage entrepreneurs, two of the traditional — and historically best — founts of innovation.

But it’s not all bad news for investors.

In the past, software engineers used code to build platforms and new business models. A prime example is Netflix.

Managers there transformed the mail-order DVD business into a digital media behemoth. They revolutionized how we view and interact with media. They also shook up traditional Hollywood studios by giving new and independent voices a huge platform.

In the process, the companies with the best algorithms will start to solve the medical, economic and social problems that have vexed researchers and scientists for decades.

Investors need to understand that winners and losers are being determined right now as the cost of AI research becomes prohibitive.

Think of the research process as a set of increasingly complex math problems. Researchers throw enormous amounts of data at custom algorithms that learn through trial and error. As the number of simulations mount, so do costs.

Big problems like self-driving cars or finding the cause of disease at the cellular level require immense amounts computing power.

An August research report from the Allen Institute for Artificial Intelligence determined that the number of calculations required to perform cutting-edge AI research soared 300,000x over the course of the past six years.

Only a handful of companies have the resources to compete at that level.

Long ago, executives at Amazon.comMicrosoftAlphabet and Facebook had the foresight to begin building massive cloud computing scale. Their data centers, many the size of football fields, are strewn all over the globe. Millions of servers, connected with undersea cables and fiber optic lines, have replaced the mainframes of old.

If you want to do great things in AI research, you’ll probably need to deal with at least one of these four big firms.

It’s a pinch being felt even by large technology companies …

Adobe and SAP joined an open data alliance with Microsoft in September 2018. A day later, salesforce.com hooked up with Amazon Web Services, Amazon’s cloud computer arm.

There has been some effort to break up the concentration of power. But critics are still mostly focused on the wrong things. In their view, data is the new oil, and it begs for regulation.

In the early 1900s, oil was the lifeblood of industry. It was central to the development of new game-changing chemicals. It powered the nascent automobile and steel complex.

The oil barons were the gatekeepers to innovation. In the process, they amassed fantastic wealth, as did many other industrialists. Income inequality soared.

Eventually, this led to calls for regulation, and trust-busters were brought in to break up (and control) the oil giants.

The parallels to today are convenient, and lazy.

Writers at The Economist in 2017, painted a dystopian picture of our future — one where the tech giants remain unregulated. The influential finance magazine concluded antitrust regulators must step in to control the flow of data, just as they did with oil companies in the early 1900s.

However, data is not oil. It’s not dear. It’s abundant.

Thanks to inexpensive sensors and lightweight software, there is a gusher of digital information everywhere. It comes from our wrists, cars and television sets. Soon it will shoot out of traffic lights, buses and trains; mining pits, farm fields and factories.

The limited resource is computing power. Enterprises, governments and researchers will need to pay up if they want to turn their data into something of value.

McKinsey, a global research and consulting firm, argues unlocking data should be a strategic priority at every enterprise. Analysts predict data will change business models in every industry, every business going forward.

The most important takeaway is that all future key AI breakthroughs are likely to come out of the big four. They have the technological and financial resources to attract talent. They have the scale to push the envelope.

It’s not a surprise that Amazon is leading in advanced robotics and language processing, or that Alphabet started developing self-driving cars in 2009.

Microsoft is building the biggest connected car platform in the world: Its engineers in Redmond, Wash., imagine a world of vehicle synchronization and the end of traffic.

Across town, Facebook researchers are working on augmented reality and brain computer interfaces.

These are big ideas with huge potential payoffs.

Amazon, Microsoft, Alphabet and Facebook are as important today as Standard Oil, Royal Dutch Shell and British Petroleum were a century ago.

Their resource is not oil, or data for that matter. It’s computing power. They’re leveraging that position to dominate AI research, the most important technology of the future.

For their investors, this is a good thing.

Growth investors should consider buying the stocks into any significant weakness. The story of AI is only getting started.

Source: https://www.forbes.com/sites/jonmarkman/2019/10/26/big-tech-is-making-a-massive-bet-on-ai–heres-how-investors-can-too/#a3cfea856d73

23 Oct 2019
How Should We Measure the Digital Economy?

How Should We Measure the Digital Economy?

Suppose we make you an offer. You give up access to Google search for one month, and we pay you $10. No? How about $100? $1,000? What would we need to pay you to forgo access to Wikipedia? Your answer can help us understand the value of the digital economy.

In 2018, Americans spent an average of 6.3 hours a day on digital media—not just Google and Wikipedia but social networks, online courses, maps, messaging, videoconferencing, music, smartphone apps, and more. Digital media consumes a large and growing share of our waking lives, but these goods and services go largely uncounted in official measures of economic activity such as GDP and productivity (which is simply GDP per hour worked). We listen to more and better music, navigate with ease, communicate with coworkers and friends in a rich variety of ways, and enjoy myriad other benefits we couldn’t have imagined 40 years ago. But if you were to look only at GDP numbers, you’d think that the digital revolution never happened. The contribution of the information sector as a share of total GDP has barely budged since the 1980s, hovering between 4% and 5% annually and reaching a high of only 5.5% in 2018. To paraphrase the economist Robert Solow, we see the digital age everywhere except in the GDP statistics.

The reason the value of digital offerings is underrepresented is that GDP is based on what people pay for goods and services. With few exceptions, if something has a price of zero, then it contributes zero to GDP. But most of us get more value from free digital goods such as Wikipedia and online maps than we did from their more expensive paper predecessors.

Policy makers use GDP data to make decisions about how to invest in everything from infrastructure and R&D to education and cyberdefense. Regulators use it to set policy that affects technology firms and other organizations. Because the benefits of digitization are dramatically underestimated, those decisions and policies are being made with a poor understanding of reality.

Effective management of the digital economy depends on our ability to accurately assess the value of free digital goods and services. That’s why we developed a new technique to measure not only how much consumers pay for digital products but how much they benefit from them. And that uncounted benefit is substantial. For example, our research with Felix Eggers, of the University of Groningen, found that Facebook alone has created more than $225 billion worth of uncounted value for consumers since 2004.

Capturing the unmeasured benefit of free goods is not a new problem: Think of earlier waves of innovation that produced free and nearly free offerings like antibiotics, radio, and television, which clearly delivered significant value to the consumer. Given the exceptionally rapid growth of digital goods and services in our economy, it’s past time to solve this problem.

What GDP Doesn’t Measure

GDP is often used as a proxy for how the economy is doing. It’s a relatively precise number that signals every quarter whether the economy is growing or shrinking. However, GDP captures only the monetary value of all final goods produced in the economy. Because it measures only how much we pay for things, not how much we benefit, consumer’s economic well-being may not be correlated with GDP. In fact, it sometimes falls when GDP goes up, and vice versa.

GDP can be a misleading proxy for economic well-being.

The good news is that economics does provide a way, at least in theory, to measure consumer well-being. That measure is called consumer surplus, which is the difference between the maximum a consumer would be willing to pay for a good or service and its price. If you would have spent as much as $100 for a shirt but paid only $40, then you have a $60 consumer surplus.

To understand why GDP can be a misleading proxy for economic well-being, consider Encyclopedia Britannica and Wikipedia. Britannica used to cost several thousand dollars, meaning its customers considered it to be worth at least that amount. Wikipedia, a free service, has far more articles, at comparable quality, than Britannica ever did. Measured by consumer spending, the industry is shrinking (the print encyclopedia went out of business in 2012 as consumers abandoned it). But measured by benefits, consumers have never been better off. Our research found that the median value that U.S. consumers place on Wikipedia is about $150 a year—but the cost is $0. That translates into roughly $42 billion in consumer surplus that isn’t reflected in the U.S. GDP.

Consumer spending—the basis for GDP—can be counted at the cash register and shows up on companies’ revenue statements. In contrast, consumer surplus cannot be directly observed, which is one reason it hasn’t been used much for measuring the economy. Fortunately, the digital revolution has created not only tough measurement challenges but also powerful new measurement tools. In our research, we use digital survey techniques to run massive online choice experiments examining the preferences of hundreds of thousands of consumers. The results allow us to estimate the consumer surplus for a great variety of goods, including free ones that are missing from GDP statistics. We start by asking participants to make choices. In some cases, we ask them to choose between various goods (for example, “Would you rather lose access to Wikipedia or to Facebook for one month?”). In others, they choose between keeping access to a digital good or giving it up in exchange for monetary compensation (“Would you give up Wikipedia for a month for $10?”). To make sure that people have revealed their true preferences, we follow up with experiments in which participants actually must give up a service before they can receive compensation.

Source: https://hbr.org/2019/11/how-should-we-measure-the-digital-economy

21 Oct 2019
AI Can Help You—And Your Boss—Maximize Your Potential. Will You Trust It?

AI Can Help You—And Your Boss—Maximize Your Potential. Will You Trust It?

Would you trust an Artificial Intelligence (AI) to tell you how to become more effective and successful at your job? How would you feel if you knew your HR department uses AI to determine whether you are leadership material? Or that an AI just suggested to your boss that she should treat you better or else you might soon quit and join a competitor—well before the thought of jumping ship entered your mind?

Meet Yva, introduced by her creator David Yang in this fascinating podcast discussion.

David Yang is an impressive serial entrepreneur: he has launched twelve companies, beginning when he was in fourth grade. David started training as a physicist, to follow in his parents’ footsteps. He won math and physics Olympiads; then his first entrepreneurial detour “distracted” him from his studies for a while and sparked his passion for computer science and AI—it’s really worth hearing the story from David’s own voice, especially his concern of possibly disappointing his parents even as he was launching a hugely successful entrepreneurial and scientific career.

Yva, David’s latest creation, is an AI-powered people analytics platform—a remarkable example of the powerful role that AI is starting to play in the workplace, with the ethical implications that quickly come to the fore.

Yva’s neural network can mine and analyze workers’ activities across a range of work applications: email, Slack, G-Suite, GitHub. With these data, the AI can pick up a treasure trove of nuanced insights about employee behaviors: how quickly an employee responds to certain types of emails; or the tree structure of her communications: how many to subordinates, how many to peers or superiors, how many outside the company; and much more.

These insights can provide value to an organization in two main ways:

First, in identifying which employees have high potential to be great performers or strong leaders. The company tells Yva which individuals it currently considers as best performers; Yva’s neural network identifies which behaviors are characteristic of these top performers, and then finds other employees who exhibit some if not all of the same traits. It can tell you who has the potential to become a top salesperson, or an extremely effective leader; and it can tell you which characteristics they already possess and which ones they need to develop.

Second, Yva helps minimize “regrettable attrition” by identifying employees who are a high resignation risk. A decision to resign never comes out of the blue. First the employee will feel increasingly frustrated or burnt out; then she will become more open to consider other opportunities; then she will actively seek another job. Each stage carries subtle changes in our behavior: maybe how early we send out our first email in the morning, or how quickly we respond, or something in the tone of our messages. We can’t detect these changes, but Yva can.

For large companies, reducing regrettable attrition is Yva’s top contribution: losing and having to replace valuable employees represents a substantial cost. This, notes David Yang, makes the Return On Investment from deploying Yva very easy to identify. For smaller companies, especially in their growth stage, attrition is less of a concern and the greater value comes from the way Yva helps them build talent and leadership from within their ranks.

Given the ubiquitous concerns that technology will eliminate jobs, it’s refreshing and reassuring to hear that Yva instead proves its value by boosting employee retention.

Yva can also help the individual worker; it can create your personal dashboard with insights and suggestions on how you can change your behavior to become more effective and successful.

There is a trade-off. By default, Yva will respect your privacy, working on anonymized data. But the more individual data you are willing to share, the more Yva can help. The choice is yours.

David Yang notes some interesting geographic differences in the share of employees who opt in; he also notes that across the board, close to one employee in five remains adamantly opposed to disclosing her individual data.

Privacy concerns are fully understandable when faced with an AI that can drive important HR decisions. But is it smart to trust humans more than AI? David Yang notes that AI can help eliminate the human biases that often influence hiring and promotion decisions. Provided—he stresses—that the AI gets trained in the right way, only on final outcomes, on objective performance criteria, without feeding into it intermediate variables such as race, gender or age, which could create a built-in bias in the AI itself.

David Yang, unsurprisingly, is very bullish on the role that AI can play in people analytics and in our lives. Bullish, but very realistic and thoughtful, and willing to put himself on the line—at the end of the podcast discussion he talks of the role that Morpheus, another AI, plays in his personal life.

David thinks that in the future smaller companies (500 employees or less) will rely completely on AI-powered people analytics platform; he believes that AI will play a major role in leveraging the creativity and efficiency of individuals, while HR (human) professionals will focus on business-specific HR-partner roles. He has a horse in the race—Yva. But there seems to be little doubt that whatever role AI takes in HR and people analytics, it will be one of its most powerful influences in our professional—and personal—lives.

Source: https://www.forbes.com/sites/marcoannunziata/2019/10/20/ai-can-help-youand-your-bossmaximize-your-potential-will-you-trust-it/#1b696bef6b7b

17 Oct 2019
Artificial Intelligence Could Be a $14 Trillion Boon to the Global Economy—If It Can Overcome These Obstacles

Artificial Intelligence Could Be a $14 Trillion Boon to the Global Economy—If It Can Overcome These Obstacles

Global growth is stalling. Trade wars are hammering manufacturers, from Shanghai to Stuttgart to Seattle. But, awful as today’s economic outlook appears, Industry 4.0 is alive and well, its most ardent backers say.

Industry 4.0 is the catch-all term for the implementation by businesses of big data, improved robotics and artificial intelligence systems. And it’s still expected to be a major driver in global growth over the next decade, and beyond. Yes, even in manufacturing.

By 2035, this A.I.-powered push will provide a $14 trillion boost to the global economy, consulting giant Accenture predicts.

That’s the assessment of Marc Carrel-Billiard, global senior managing director at Accenture Labs, who rattled off these numbers during his keynote presentation at World Summit A.I. in Amsterdam on Wednesday. By way of example, he cited research that traced the progress in one growing area of A.I.-powered automation: call centers. Five years ago, A.I. bots could successfully resolve one out every ten customer phone calls. Today, he said, it’s 60%.

Moreover, he predicted, this push to automate will not be the jobs-killer the more bearish economists out there fear.

But before technologists take a victory lap, there’s a caveat.

They’re not a threat to jobs, he says, “because these systems are not very intelligent.” AI—and its many iterations: machine learning, natural language processing, machine vision, image- and voice-recognition—is well adapted at highly specialized tasks. It does a decent job telling you what the weather will be tomorrow, or ordering movie tickets or helping you find the fastest route home during the evening commute. All manner of businesses are using A.I. increasingly on the enterprise level to make sense of the vast flows of structured and unstructured data they collect to root out inefficiencies, and save costs.

But, as Carrel-Billiard notes, A.I. still has a blind spot. It’s trained to interpret certain data sets, not infer meaning or context from a complicated world. A.I. is a specialist, not a generalist, he says. And therefore, much work is needed to make these systems truly intelligent.

Gary Marcus, professor of psychology and neural science at New York University and author of Rebooting AI, is even more frank in his assessment. He calls deep learning— the subset of A.I. that can make sense of huge amounts of data with little to no oversight from human minders—a misnomer. It’s good at narrowly focused tasks, but he questions its much-ballyhooed potential to, for example, revolutionize transportation (self-driving cars) and medicine (analyzing huge volumes of MRI scans for signs of cancerous growths). “Deep learning is no substitute for deep understanding,” he says.

“The number of radiologists who’ve been replaced by deep-learning systems?” he asks. “Zero.”

Carrel-Billiard, for one, believes that in order for A.I. systems to be truly effective they need to be designed to be accountable, transparent and free of bias—not just super-fast task rabbits. Only then will such systems reach their full potential.

On day one of the World Summit A.I., much of the early discussion was about the need to build so-called ethical A.I. systems. Marcus and Carrel-Billiard, among others, challenged the development community to build A.I. systems that are accountable, transparent and free of bias.

Unless it’s responsible, Carrel-Billiard says, “nobody will trust it, and nobody will use it.”

Source: https://fortune.com/2019/10/09/artificial-intelligence-14-trillion-boon-only-if-overcome-one-thing/

12 Oct 2019
Demystifying Artificial Intelligence in the Corporation

Demystifying Artificial Intelligence in the Corporation

Artificial Intelligence (AI) is top of mind for leading corporations these days – 96.4% of top executives reported earlier this year that AI was the number one disruptive technology that they were investing in, up from 68.9% just two years ago. In addition, 80% of these executives identified AI as the most impactful disruptive technology, up from 46.6% two years earlier.

Yet, for many organizations, Artificial Intelligence remains a mystery. For specialists, AI implies a very specific connotation in terms of intelligence demonstrated by machines, in contrast to the more common usage of AI which encompasses all varieties of machine assisted learning, most notably machine learning, deep learning, and natural language. For the sake of this discussion, we will assume the broadest definition of AI. As corporations struggle to understand the applications and benefits that AI can deliver for their organizations, firms have established AI Centers of Excellence, AI Labs, and other sandboxes for piloting AI capabilities tied to business use cases.

So, how can we demystify AI to deliver measurable business value? Rob Thomas, a senior IBM executive who is General Manager of IBM Data and AI, which includes Watson, has a few ideas. He has outlined his framework for AI adoption in a newly published report from O’Reilly Media, “The AI Ladder”. In the report foreword, O’Reilly Media Founder and CEO Tim O’Reilly observes, “Everyone is talking about ‘AI’ these days, but most companies have no real idea of how to put it to use in their own business”. O’Reilly emphasizes the cultural change in mindset that accompanies adoption of any new technology approach, noting that “each new technology revolution breeds new business leaders” who let go of old assumptions about the way things work. O’Reilly notes, “only later do companies realize how much they might need to change their business model to truly make use of new capabilities”. IBM’s Thomas observes, “AI is not about executing a single business project – it’s about changing an entire business culture. It’s about creating a culture of iteration, experimentation”.

The report employs the metaphor of the “AI Ladder” to describe a series of stages that a firm must pass through to become AI-enabled and realize measurable business value. The concept of the AI Ladder is premised on the notion that organizations require a prescriptive approach to understand where they are in their AI maturity. By diagnosing and understanding the stage of maturity of each organization, a firm can then employ the AI Ladder as a framework for outlining the steps and capabilities that will guide the organization toward realization of the benefits that result from machine and human augmentation. The basic tenets of the AI Ladder can be summarized as:

  1. Start with the business problem that you are attempting to address
  2. Understand your data requirements – these are the foundation for AI success
  3. Develop the right skills to leverage AI capabilities
  4. Focus on algorithmic trust and data integrity to ensure credibility
  5. Recognize the need for cultural and business model change.

A central message of The AI Ladder is that AI success depends upon data, and effective data management provides the fundamental building block for AI enablement. It was only with the advent of Big Data in recent years, that proliferating sources and volumes of data could be combined with massive computing power. This has helped AI emerge from decades of nascent experimentation, as a foundation of data collection, organization, and analysis provides the foundation upon which AI capabilities and algorithms are deployed. The AI Ladder describes the challenges that corporation face in organizing their data to enable AI when confronted by issues including lack of data, too much data, and lack of quality data. It’s long been understood that organizations typically invest 80% of their efforts in preparing their data so that it can be used productively. Thomas echoes the critical linkage between successful data management and effective AI enablement, noting “the vast majority of AI failures are due to failures in data preparation and organization”.

New generations of tools and technologies are helping reduce the amount of time that corporations now spend on data preparation, with the result that firms can focus efforts on analysis and business results. Companies are moving to modern data architecture approaches that are enabling simpler data collection and access. The advent of Cloud Computing has accelerated the adoption of AI through the migration of data assets to the Cloud. By renting rather than purchasing data storage capabilities, corporations are transforming the economics of data management, and realizing increases in speed and efficiency. They are no longer responsible for managing data access and security, no longer required to make large capital investments in computer hardware purchases, only pay for and access their data as they need it, and as a result, accelerate adoption of Big Data and AI.

So, what does this all mean for business? A 2017 study by PwC, reported that “Global GDP will be 14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion. This makes it the biggest commercial opportunity in today’s fast changing economy”. A 2017 Gartner study argues that Artificial Intelligence will create more jobs than it eliminates, and that by 2021, “AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity”. IBM’s Thomas suggests that although today there is only a 4%-8% adoption rate of AI within the corporate world, within the next 18-24 months we are likely to see this rate explode t0 80%-90%.   This would suggest that AI is rapidly reaching the moment of critical mass that will lead to business adoption and business value. IBM’s Thomas concludes, “AI is at execution state”. Have you launched your AI Center of Excellence? It appears that the time to act is now.

Source: https://www.forbes.com/sites/ciocentral/2019/10/09/demystifying-artificial-intelligence-ai-in-the-corporation-forbes/#4608fb6e6016

08 Oct 2019


Disruptive innovation of today is going to transform society tomorrow. From the rise of smart voice assistants to venturing into the age of 5G technology, this generation has seen it all. Coming out of the conventional ways and moving forward towards innovative future with determination, has made its footprint in the current market. With the emergence of digital transformation, more and more businesses are adopting disruptive technologies to drive innovation.

In the race of being relevant with the present, digital platforms, in both theoretical and practical manner, have satisfied the definition of disruptive innovation by creating a new market, causing disruption in the existing market, adding potential value to the network and uprooting prevailing well-established markets.

For businesses who adapt to disruptive innovation in their workplace, have to go through a lot of plots and procedures to finally hit the market. The journey includes innovation (doing same things better than before) to bring in new ideas and introducing new things and ultimately reaching to a disruptive phase where they become confident enough to make existing operations obsolete and consume whole new and fresh ecosystem to the business.

Emanation of technologies including Robotics, RPA, Autonomous vehicles, Internet of things, Artificial intelligence, Space Colonization, Machine Learning, Deep Learning, Big Data, 3D Printing, High-speed travels, Blockchain and Cryptocurrency technology, Virtual Reality, has augmented the open-ended process of potential transformation.

The most recent and apt example we can quote here is of the new launch by tech giant Apple. Other than Netflix, Apple’s online streaming media is creating a buzz nowadays. Additionally, the company’s venture into the commerce industry with the introduction of Apple Card (Apple Credit Card), which surely comes forward as post-iPhone strategy, narrates all about the disruptive intervention of Apple into a non-tech industry to drive digital innovation.

As we can see, our regular periodic is enveloped in the layers of varied disruptive innovations which we didn’t realize entering in our lives. Well, we can merely predict how our future filled with technologies would look like whose optimistic propelling possibilities seem to be endless. Although many have asserted that this disruption can act as a destructive catalyst in certain ways for future society but it would be really unfair to land up with any negative conclusion without assessing the binary ends of it. Moreover, it depends on the users and society as a whole to sculpt the ultimate outcomes of these highly innovative and disruptive technologies.

Source: https://www.analyticsinsight.net/disruptive-innovation-will-transform-business-work-and-society/

07 Oct 2019
The Upside of AI Accessibility Now and in the Future

The Upside of AI Accessibility Now and in the Future

AI is often misunderstood by those who lack education or training in the technology. This interview with Max Versace, CEO and co-founder, Neurala offers some new insights into the technology.

AI is certainly on the rise, despite some of the concerns some have expressed about it leading to doomsday scenarios or a devastating loss of jobs, as described in AI Attitudes: What the Experts Consider of Concern. There is no cause for concern insists Max Versace, CEO and co-founder of Neurala, who explains the benefits that AI offers now and in the future for improving workflows. 

Is fear of AI holding businesses back from reaping its benefits now?

Fortunately not. In the 1970s, when computers were introduced in enterprises, there was an equally brief and illogical fear that they would take over from humans. The reality is that they augmented us, and the same will be true for AI, despite a few malicious attention-seeking critics spreading fears otherwise.

When it comes to AI and enterprises, many have already begun to implement AI as a part of their business and digital transformation strategy. In fact, about 80% of organizations are already using AI in some form. However, an overwhelming 91% of companies foresee significant barriers to AI adoption, such as a lack of IT infrastructure and a shortage of AI experts to guide the transition.

So, the problem is not fear of AI, rather a lack of education, training and tools preventing businesses from fully realizing the benefits of AI. That’s why there is a need to democratize AI across industries, so that anyone – not just engineers and data scientists – can build AI.

Think of it this way – we need a WordPress for AI: something that allows anyone to build AI, in that same way WordPress made website development accessible for all, without needing extensive expertise.

Only then will organizations be able to truly capitalize on the benefits of implementing AI into their workflows.

Is the fear grounded in actual threats or just the fear of the unknown due to the lack of transparency that makes AI appear to be a black box? Do you see that changing?

If you look at who is warning us about impending AI apocalypses, they are invariably AI outsiders that do not understand much about AI. As someone who has worked in the field both in academia and industry for a couple of decades, I see AI as a fantastic tool that can help make machines less dumb and more useful for us – augment productivity and help us re-allocate our time to more creative and critical tasks.


Also, as I mentioned earlier, there is a lack of accessibility and training around AI which has likely resulted in a lack of understanding. I think that if we can find a way to truly democratize AI, it will go a long way in terms of changing people’s opinions. 

Take the manufacturing industry for example – there is a lot of fear that AI, robotics and automation will take people’s jobs. But this is not the case.

In fact, AI has the ability to augment the workforce, providing new opportunities.

For industrial product managers, one of the biggest challenges is quality control. Product managers struggle to inspect each individual product and component (no human can inspect hundreds of products coming out of an industrial machine every second!), while also meeting deadlines for massive orders.


Moreover, humans are notoriously bad at visual inspection tasks, so why not delegate these tasks to AI? This will free up time for workers who can now focus on other tasks rather than spending time doing visual quality control.

Read more: https://interestingengineering.com/the-upside-of-ai-accessibility-now-and-in-the-future

06 Oct 2019
Southeast Asia's Internet economy to hit $100 billion

Southeast Asia’s Internet economy to hit $100 billion

Bangkok: Southeast Asia’s Internet economy is estimated to hit $100 billion in 2019, a 39-per cent increase compared to 2018, according to industry forecasts released on Thursday.

The annual “e-Conomy SEA” report presented by the Singapore-based investment firm Temasek also projected that the region’s digital economy could reach up to $300 billion by 2025, Efe news reported.

“The region has tremendous potential for further growth thanks to fundamental consumer behaviour changes, growing Internet connectivity and more,” the report said.

“Investors remain bullish about Southeast Asia despite the global economic headwinds, with over $37 billion of capital poured into the region’s Internet economy over the past four years,” it added.

With over 360 million people across the region having online access, users have become used to performing day-to-day tasks such as shopping, booking a cab or making e-payments through the Internet.

Though more than half of Southeast Asia’s Internet economy remains concentrated in just seven sprawling metropolitan areas (Bangkok, Hanoi, Ho Chi Minh City, Jakarta, Kuala Lumpur, Manila and Singapore), activity in other areas is witnessing rapid growth, said the report.

According to the report, 11 e-commerce and ride-hailing unicorns – such as regional giants Grab, Lazada or GoJek – attracted two out of every three dollars raised since 2016.

In addition, $5 billion have been invested in more than 70 so-called “aspiring unicorns” – tech startups valued at between $100 million and $1 billion – that are “on the lookout for late-stage funding to scale further”.

Source: https://gulfnews.com/technology/southeast-asias-internet-economy-to-hit-100-billion-1.1570113619266

05 Oct 2019
How can digitalization be an 'enabler for development'?

How can digitalization be an ‘enabler for development’?

NEW YORK — The 2019 United Nations General Assembly saw the launch of a number of data-focused initiatives to track progress on — and accelerate progress toward — achieving the Sustainable Development Goals.

For example, Facebook held a closed-door roundtable on ways to use its data to drive progress on the SDGs, beginning with gender data. The U.N. and several private sector players launched Data For Now, an effort to help governments use satellite imagery and mobile phone data to better understand a range of development challenges. And the Rockefeller Foundation launched a new initiative to ensure frontline health workers have access to data science tools such as predictive analytics, artificial intelligence, and machine learning.

Despite the many ways technology can support the 2030 agenda, for some countries, the digital tools that gain attention during Global Goals Week still seem out of reach, due to barriers including infrastructure and affordability. On Tuesday, new research from the Alliance for Affordable Internet revealed that despite a drop in data costs across Africa, the cost of mobile broadband is still prohibitively high, with 1 GB of data costing 7.1% of average monthly income.

But several experts tell Devex there are already ways the global development community can work to ensure that emerging technologies — which have the potential to transform the way development is done — are deployed rapidly, globally, and responsibly.

The push for universal health coverage, for example, has led to a lot of excitement around the role that technology can play, but many potential solutions run into challenges when it comes to scale, interoperability, and sustainability.

“People’s lives are integrated and whole, and not segmented into diseases,” said Stefan Germann, CEO of Fondation Botnar.

He talked about the need for a move from pilots toward affordable and sustainable business models that fit within “a systems lens of: How do we tackle universal health coverage in a digital age?”

With just 10 years left to achieve the SDGs, a number of conversations organized during Global Goals Week focused on this question of how to move from pilots to scale, not only in health but across sectors. For example, the World Economic Forum previewed a new platform called Accelerating Sustainable Development in the Fourth Industrial Revolution, known as 4IR for SDGs, which will launch at its annual meeting in Davos. From 2020 to 2022, it aims to catalyze action by the public and private sector, develop partnerships around the challenges that prevent the scale-up of technology for the SDGs, and work with governments to test and scale models that might be replicated elsewhere.

The Overseas Development Institute is partnering with WEF on the new initiative, focusing on national partnerships to help governments ensure that every sector of society benefits from this technology transformation.

Sara Pantuliano, acting executive director of ODI, called for the global development community to be a more active partner working with governments to develop data governance principles that support innovation and protect citizens.

“Regulation will be critical in determining whether digitalization will be a barrier or enabler for development,” she told Devex.

In the Fourth Industrial Revolution, the value for developing countries will not come from production, said Stefan Dercon, academic director of the Pathways for Prosperity Commission, an initiative housed at Oxford University’s Blavatnik School of Government that is researching how to turn the risks of technological change into opportunities for the poorest.

Moving forward, value will come from connectivity, he said, and the question is how to help countries become digitally ready.

One of the challenges facing low- and middle-income countries is how to invest in digital infrastructure at the rate that is needed to bring their citizens online. Ensuring digital access globally will require $1 trillion in investment by 2040, WEF representatives explained during a workshop on the topic.

To ensure that the digital revolution does not leave the poorest behind, these technological advances have to make it to hard-to-reach places, said Neal Keny-Guyer, CEO of Mercy Corps.

“There’s just not the return on investment in many places to go the last mile and reach the most vulnerable,” he told Devex. “We are seeing some examples, but when you total all the resources up, it’s not at the scale needed for real progress.”

Blended finance can help to lower the risk for companies that might not otherwise enter these markets, making the terms acceptable — something that is below market but somewhere above zero.

Keny-Guyer mentioned, for example, the role that the U.K. Department for International Development played in the rollout of M-Pesa — the mobile payment system widely credited for transforming banking in Africa — and said that NGOs like Mercy Corps can also help to lower the risk with their knowledge and relationships in these markets.

Despite all the excitement around the potential of technology for the SDGs, some warned that focusing on emerging technology might distract from the very real needs and possibilities that exist today.

“As we think about technology, we should not think so much into the future,” mPharma co-founder and CEO Gregory Rockson, who is working to fix drug supply chains in Africa, said at a high-level dialogue organized by the United Nations Development Programme. “We have to conceptualize what the future will look like … But many people cannot afford to wait, and we have to think about how we can fix their problems today.”

Source: https://www.devex.com/news/how-can-digitalization-be-an-enabler-for-development-95702