Month: October 2018

15 Oct 2018
Manahel Thabet

What Innovative CEOs and Leaders Need to Know about AI

Artificial intelligence (AI) is a rising global imperative. Enterprise software companies are rushing to incorporate AI functionality into its product offerings. Venture-capital funding is pouring into AI startups globally. AI is a geopolitical movement with many countries putting it as a top priority. Top-ranked MBA schools are including AI in the curriculum. AI can be found in neuroscience, life sciences, health care,  financial services, esports, art, science, entertainment, and many more industries. Forward-thinking companies are starting to realize returns on their investments as early adopters of AI. It’s not a question of whether or not AI should be incorporated in your company, but rather when it should be implemented. Where is AI in the technology adoption life-cycle? Where is AI being used and how? Here is an executive summary of what a few of the leading global management consulting companies have to say about how artificial intelligence will impact businesses and economies worldwide.

McKinsey Global Institute

In a McKinsey Global Institute (MGI) discussion paper published in September 2018 titled Notes From The AI Frontier – Modeling The Impact Of AI On The World Economy,” the estimated impact of AI is $13 trillion additional economic activity worldwide by 2030. MGI is led by three McKinsey & Company senior partners — Jacques Bughin, Jonathan Woetzel, and James Manyika, the MGI chairperson.

In the report, MGI estimates that 14 percent of the global workforce, up to 375 million employees, may need to change jobs due to AI automation. The occupations most likely to be automated with AI are data collection, data processing, and jobs that require “performing physical activity and operating machinery in predictable environments.”

MGI predicts that the AI adoption rate by companies over time will resemble an S-shape curve — initial adoption will be slower due to the requisite learning involved, then expand rapidly as competition and “improvements in complementary capabilities” increases. Interestingly, MGI predicts a significant first-mover advantage for companies who are early AI adopters. Companies that fully deploy AI throughout the enterprise over the next five to seven years may double their cash flow, whereas the long tail of laggards may experience a 20 percent decrease in cash flows by 2030.


In the Harvard Business Review January-February 2018 edition, Thomas H. Davenport and Rajeev Ronanki advise a highly pragmatic versus “moon shot” approach to AI implementations based on a Deloitte Study of 152 cognitive (AI) projects. Davenport is a senior advisor at Deloitte Analytics, a research fellow at the MIT initiative on the Digital Economy, and a professor at Babson College. Ronanki is a principal at Deloitte Consulting focused on cognitive computing and health care innovation.

The authors view AI as “performing tasks, not entire jobs.” Out of the 152 AI projects, 71 were in the automation of digital and physical tasks, 57 were using algorithms to identify patterns for business intelligence and analytics, and 24 were for engaging employees and customers through machine learning, intelligent agents, and chatbots.

In the Harvard Business Review article, a 2017 Deloitte survey of 250 executives who were familiar with their companies’ AI initiatives, revealed that 51 percent responded that the primary goals was to improve existing products. 47 percent identified integrating AI with existing processes and systems as a major obstacle. When it comes to employment impact, within “the next three years, 69 percent of enterprises anticipate minimal to no job loss and even some job gains.” Early adopters of AI in the enterprise are reporting benefits — 83 percent indicated their companies have already achieved “moderate (53 percent) or substantial (30 percent) economic benefits. 58 percent of respondents are using in-house resources versus outside expertise to implement AI, and 58 percent are using AI software from vendors. Only 20 percent of those surveyed are developing AI applications themselves “from scratch.”

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14 Oct 2018

Daytime Naps Boost Brain Power in Mysterious Ways

Recent sleep research has unearthed some fascinating correlations between the duration of time someone spends sleeping and his or her cognitive functions. One of the most extensive studies ever conducted on the link between sleep duration and cognition recently reported that sleeping more or less than seven to eight hours per night impairs specific cognitive abilities. Surprisingly, the brain researchers from Western University in Canada found that oversleeping can be just as detrimental to cognition as sleeping too little. (For more see, “Does Too Much Sleep Have Negative Repercussions?”)

This massive worldwide survey also identified that getting too much sleep isn’t a problem for most of us; on average people around the globe only sleep about 6.3 hours per night. Unfortunately, this creates a sleep deficit that can cause the body, brain, and mind to function at a subpar level.

The good news is that another study by researchers at the University of Bristol in the UK recently reported that taking a power nap can improve domains of cognitive function associated with processing information below conscious awareness. This study, “Nap‐Mediated Benefit to Implicit Information Processing Across Age Using an Affective Priming Paradigm,” was recently published in the Journal of Sleep Research. The primary goal of this study was to identify if a relatively short period of sleep helps people process unconscious information and how this might improve automatic reaction times.

For this pioneering research on how short bouts of sleep improve memory consolidation of implicit tasks, the researchers hid information by “masking” it and then presenting it to study participants without their conscious awareness. Although the “masked” information was hidden from conscious perception, this research shows that it was being absorbed on a subliminal level somewhere in the brain.

For this study, 16 healthy participants practiced a masked task (unconscious processing) and a control task that involved conscious information processing. One group stayed awake after practicing both tasks while the other group took a 90-minute nap. Then, participants were monitored using an EEG as they performed both tasks again while researchers monitored pre-and-post nap brain activity.

The group that stayed awake throughout the experiment did not show significant improvements on either task. Interestingly, the researchers found that taking a nap improved the processing speed of the masked task — which required learning on an unconscious level — but not the control task, which involved explicit memory and conscious awareness. According to the researchers, this suggests sleep-specific improvements in subconscious processing and that information acquired during wakefulness can be processed in deeper, qualitative ways during short bouts of sleep.

“The findings are remarkable in that they can occur in the absence of initial intentional, conscious awareness, by processing of implicitly presented cues beneath participants’ conscious awareness. Further research in a larger sample size is needed to compare if and how the findings differ between ages, and investigation of underlying neural mechanisms,” co-author Liz Coulthard of the University of Bristol Medical School: Translational Health Sciences said in a statement.

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13 Oct 2018

Disruptive innovation: The journey of a successful incubator

Disruptive innovation is a buzzword that many organisations strive to achieve in this day and age, but what does it actually mean and why do organisations place copious amounts of emphasis on it? The term, coined by Clayton Christensen, describes a process of developing new products or services to replace existing technologies and gain a competitive advantage in the market.

Disruptive innovations are often produced by start-ups rather than existing market-leading companies because digital innovations are oftentimes a huge risk and are mostly not profitable in the initial stages. The amount of risk involved may not sit well with these revenue-generating companies due to the organisation’s profitable nature, and as such, is not an undertaking that is common for them.

However, Media Prima realises that digital technology continues to evolve rapidly and capturing users’ attention on traditional media is not as easy as it used to be. Users are now looking for content on digital platforms, so it has to pivot from its traditional business model to one that is disruptive to continue its legacy as the market leader in the media industry in Malaysia.

This has led to the birth of Media Prima Labs, an incubator within Media Prima Digital that supports the development of content in the digital sphere. With the abundance of intellectual properties (IPs) owned by Media Prima, it only made sense for it to look into other avenues to reach out to its users, who are now more digitally savvy.

Making hay while the sun shines

With the introduction of cheaper mobile devices in the market and a reduction in mobile internet prices in Malaysia, mobile penetration is growing promptly and users are relying mostly on their smartphones these days. According to the Malaysian Communications and Multimedia Commission, mobile cellular penetration in Malaysia has reached an astounding 131.8% while smartphone penetration was at 70% in 2017.

These impressive statistics made it even more confident to invest significantly in the mobile sphere, and Media Prima Labs was tasked to further extend the group’s unique intellectual properties into the expanse of technology innovation and gaming, an area that other media companies had not ventured into at that point of time.

Media Prima Labs started by experimenting with one of the group’s oldest running IPs – Jalan-Jalan Cari Makan (JJCM). Malaysians are generally motivated by food and the question of where to eat, hence, converting the content of JJCM into a walking food directory where users could look for halal mouth-watering food nearby made a lot of sense. There were other food directory apps in the market; however, Media Prima Labs was looking to fill the gap for individuals looking for halal cuisine.

JJCM has established a strong brand name in Malaysian households and has become an integral reference point for foodies. Not surprisingly, the Jalan-Jalan Cari Makan mobile app was a hit with its viewers and recorded 65,000 downloads in merely three months and also ranked first under the “food and drink” category in the Apple app store.

Since there is a large population of Muslims in Malaysia, Media Prima Labs wanted to continue to tap into this market, and as such, incubated two religious apps – Waktu Solat and Raudhah.  The high engagement rate on both these apps was proof of success. Waktu Solat recorded average monthly active users (MAUs) of 1.5 million and Raudhah achieved an average MAUs of 62,000. The monthly average time spent for Waktu Solat and Raudhah was 165s and 371s respectively.

To further benefit users, it also partnered with Tripfez, a Muslim-friendly travel company to offer special Umrah packages in the Raudhah app.

With the success of these lifestyle mobile apps, Media Prima Labs hoped this would have a snowball effect on mobile games as well, and as such, partnered with the Malaysian Digital Economy Corporation (MDEC) to kick-start this vertical by organising its very first hackathon.

MDEC’s expertise in this area allowed Media Prima Labs to reach out to the best talent in the mobile game development industry and these developers were put together for 36 hours to conceptualise a mobile game for a new animation IP from TV3 – Ejen Ali. On top of the prize money, winners of the hackathon were given an additional sum of investment to further develop the game within a given time frame.

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11 Oct 2018

Can Neuroscience Teach Robot Cars to Be Less Annoying?

Robot cars make for annoying drivers.

Relative to human motorists, the driverless vehicles now undergoing testing on public roads are overly cautious, maddeningly slow, and prone to abrupt halts or bizarre paralysis caused by bikers, joggers, crosswalks or anything else that doesn’t fit within the neat confines of binary robot brains. Self-driving companies are well aware of the problem, but there’s not much they can do at this point. Tweaking the algorithms to produce a smoother ride would compromise safety, undercutting one of the most-often heralded justifications for the technology.

It was just this kind of tuning to minimize excessive braking that led to a fatal crash involving an Uber Technologies Inc. autonomous vehicle in March, according to federal investigators. The company has yet to resume public testing of self-driving cars since shutting down operations in Arizona following the crash.

If driverless cars can’t be safely programmed to mimic risk-taking human drivers, perhaps they can be taught to better understand the way humans act. That’s the goal of Perceptive Automata, a Boston-based startup applying research techniques from neuroscience and psychology to give automated vehicles more human-like intuition on the road: Can software be taught to anticipate human behavior?

“We think about what that other person is doing or has the intent to do,” said Ann Cheng, a senior investment manager at Hyundai Cradle, the South Korean automaker’s venture arm and one of the investors that just helped Perceptive Automata raise $16 million. Toyota Motor Corp. is also backing the two-year-old startup founded by researchers and professors at Harvard University and Massachusetts Institute of Technology.

“We see a lot of AI companies working on more classical problems, like object detection [or] object classification,” Cheng said. “Perceptive is trying to go one layer deeper—what we do intuitively already.”

This predictive aspect of self-driving tech “was either misunderstood or completely underestimated” in the early stages of autonomous development, said Jim Adler, the managing director of Toyota AI Ventures.

With Alphabet Inc.’s Waymo planning to roll out an autonomous taxi service to paying customers in the Phoenix area later this year, and General Motor Co.’s driverless unit racing to deploy a ride-hailing business in 2019, the shortcomings of robot cars interacting with humans are coming under increased scrutiny. Some experts have advocated for education campaigns to train pedestrians to be more mindful of autonomous vehicles. Startups and global automakers are busy testing external display screens to telegraph the intent of a robotic car to bystanders.

But no one believes that will be enough to make autonomous cars move seamlessly among human drivers. For that, the car needs to be able to decipher intent by reading body language and understanding social norms. Perceptive Automata is trying to teach machines to predict human behavior by modeling how humans do it.

Sam Anthony, chief technology officer at Perceptive and a former hacker with a PhD in cognition and brain behavior from Harvard, developed a way to take image recognition tests used in psychology and use them to train so-called neural networks, a kind of machine learning based loosely on how the human brain works. His startup has drafted hundreds of people across diverse age ranges, driving experiences and locales to look at thousands of clips or images from street life—pedestrians chatting on a corner, a cyclist looking at his phone—and decide what they’re doing, or about to do. All those responses then get fed into the neural network, or computer brain, until it has a reference library it can call on to recognize what’s happening in real life situations.

Perceptive has found it’s important to incorporate regional differences, since jaywalking is commonplace in New York City and virtually non-existent elsewhere. “No one jaywalks in Tokyo, I’ve never seen it,” says Adler of Toyota. “These social mores and norms of how our culture will evolve and how different cultures will evolve with this tech is incredibly fascinating and also incredibly complex.”

Perceptive is working with startups, suppliers and automakers in the U.S., Europe, and Asia, although it won’t specify which. The company is hoping to have its technology integrated into mass production cars with self-driving features as soon as 2021. Even at the level of partial autonomy, with features such as lane-keeping and hands-off highway driving, deciphering human intent is relevant.

Autonomous vehicles “are going to be slow and clunky and miserable unless they can understand how to deal with humans in a complex environment,” said Mike Ramsey, an analyst at Gartner. Still, he cautioned that Perceptive’s undertaking “is exceptionally difficult.”

Even if Perceptive proves capable of doing what it claims, Ramsey said, it may also surface fresh ethical questions about outsourcing life or death decisions to machines. Because the startup is going beyond object identification to mimicking human intuition, it could be liable for programming the wrong decision if an error occurs.

It’s also not the only company working on this problem.  It’s reasonable to assume that major players like Waymo, GM’s Cruise LLC, and Zoox Inc. are trying to solve it internally, said Sasha Ostojic, former head of engineering at Cruise who is now a venture investor at Playground Global in Silicon Valley.

Until anyone makes major headway, however, be prepared to curb your road rage while stuck behind a robot car that drives like a grandma. “The more responsible people in the AV industry optimize for safety rather than comfort,” Ostojic said.

Source: Bloomberg

07 Oct 2018

These Are The Disruptive Technologies That Will Affect Your Industry

Disruption is a hard subject to cover sometimes. The change, the intersections, the arguments about what is and is not disruption. Often it is helpful to step back and really see what is happening, where things are going and who is doing what. Richard Watson, a leading futurist and scenarios planner, is one of the best at this. Watson’s latest work sees a list of technologies with a timeline that is specific to different industries. If you are in the retail, finance, FMCG, food, transport, energy or health industry, knowing what is coming for you has never been easier.

Watson worked with the Tech Foresight team at Imperial College London and used an ex-BBC researcher to source the companies and was surprised by the dominance of the US; “It’s incredible. There is almost a total absence of UK companies…the multiple appearances of Apple, Google, Facebook and Musk is interesting.” The latter point may simply be doing to media reporting biases but still, the number of mentions is high even without Chinese and other startup hubs around the world seeing a look in.

The technology on the table is not all equal. Several of the ‘elements’ are already here (the bottom left corner). Technology like Delivery drones, they are more or less invented and “getting less silly by the hour’ according to Watson. Conversational machine interfaces (Google’s telephone booking interface is a prime example) are improving in leaps and bounds and even Lifelong personal avatar assistants – could this be the next generation of Echo or Alexa? The most interesting? According to Watson, it’s ‘DACs or Distributed Autonomous Corporations’. Not so far fetched if we think about Amazon’s warehouses and pokes at the questions; How far can automation go? How far might we let it?

Watson is most interested in “The Ghost Tech” or the edge or fringe to the table. These technologies (like zero-point energy, force fields, telepathy) are highly improbable but not actually impossible. Watson used science-fiction as the inspiration here and some of the overlaps are indicative of leaps rather than crawls towards success. The biggest issue around disruption, Watson believes, is that the small print gets missed; “This is all tech push and largely logical. It’s all corporate (some military and government) driven and much relates to efficiency, speed, convenience, profitability. What is not considered is a) the way one or more technologies might interact b) psychological factors (illogical and emotional humans), c) government/regulation/societal shifts (e.g. privacy) and d) other factors such as resources/environment and even historical inertia and the state of the economy (e.g. I’d say the future of bitcoin is economy dependent).”

Watson hopes the table is useful above all else but urges viewers and users of the table to look at the why behind the technology. “The other thing that isn’t really discussed is what’s all this tech for? What are we, as a society/world, searching for here? The big question is what is the role of us humans in the midst of all this? I think the big idea falling out of this and tech generally (especially AI) is what are we humans for? Does the human race need a strategy…?”

Source: Forbes


06 Oct 2018

How AI, Machine Learning Are Solving Global Problems

Although developments in the field of artificial intelligence began around the 1950s, its capacities have significantly increased in the recent years. Owing to factors such as the development of faster computers, availability of open-source software and the access to vast amounts of computational data, AI has now branched into machine learning (ML), probabilistic predictions, chaos theory and evolutionary computation. Investing in artificial intelligence courses, therefore, prepares people to undertake not just one, but many AI applications.

AI and ML already play a pivotal role in our day-to-day life, even without our realization. Netflix and Amazon use machine-learning algorithms to recommend shows and products based on our usage history. Similar examples include weather forecasts, voice and face recognition, and image classification (such as tagging of people on Facebook). Recent developments in the field include innovations such as driverless cars, natural language processing, and speech-to-text translation.

The ability of AI and machine learning to discern patterns in complex datasets has allowed scientists to make better predictions and recommend better solutions, which makes AI and ML perfect tools for providing solutions for some pressing global problems:

Climate change

With the number of natural dizasters almost tripling since 1980 and as many as 20 percent of species now facing extinction, the effects of climate change can no longer be ignored — and it could trigger more problems that would eventually make survival difficult. However, AI and machine learning can now be employed to cumber these effects.

Microsoft believes that AI and ML could become game changers for environmental issues. In one of its research grants dedicated to the cause, the tech giant is funding a Columbia University study on the effects of the Hurricane Maria. The research team uses artificial intelligence to analyze high-resolution photographs and match them against data about several plant species, in an attempt to determine how tropical storms may affect the distribution of tree species in a given ecosystem. By protecting such species, the team could take a big step towards environmental conservation.

Renewable energy generation

AI and machine learning can also be used to manage the generation of renewable energy. Wind energy companies are increasingly using AI to optimize turbine propellers to generate more electricity per rotation. By making use of operational and real-time weather data, AI allows each propeller to determine the wind speed and direction and adjust accordingly, thus enabling maximum energy generation.

AI and ML can also be used to maintain energy conversation in power grids. Stanford University’s National Accelerator Laboratory uses both to predict possible vulnerabilities in the power grid and to strengthen them in advance, thus enabling operators to restore power more quickly in case of failures. The entire system first studies the different parts of the grid, and analyses data from satellite images, power sources and battery storage to predict the likelihood of failures.


Despite developments in transport infrastructure, time lags due to traffic congestion is a problem still faced by millions of people all over the globe every day. AI and ML, however, are solving this problem to some extent. By enabling vehicles to communicate with infrastructure and with each other, AI-based models help drivers avoid traffic jams and safety hazards. For instance, an AI system in the city of Pittsburgh makes use of cameras and proximity sensors to monitor the traffic flow, and then adjusts traffic lights when needed. This system not only ensures smooth traffic but also reduces greenhouse gas emissions, as cars spend a shorter amount of time waiting at intersections.


According to a report by CB Insights, approximately 86 percent of healthcare providers spanning across technology vendors, hospitals and life science companies are using artificial intelligence; by 2020, these institutions are expected to invest an average of $54 million on AI projects. Apart from digital consultation and virtual nurses, AI has a more prominent role to play, and could even lead to the cure of serious problems.

Coupled with the capacity of a supercomputer, AI has the potential to develop new drugs from a database that lists out molecular structures. For instance, Atomwize’s AI system was able to predict and develop the drugs that were used to treat the Ebola virus. Its virtual search system used an algorithm to recognize existing medicines that could be repurposed to fight the epidemic.

Future prospects

Despite the advancements in AI and machine learning, the field still holds opportunities for innovation. To begin with, after voice and face recognition, AI has the potential of recognizing emotions in a human face. Microsoft has already started its research in the field through Project Oxford, which uses ML mechanisms to interpret the feelings displayed by an individual. Further contributing to the cause of making AI more human-like is Elon Musk’s recent startup, Neuralink, which aims to merge the human brain with a computer. Through a neural lace framework, Neuralink will enable the human brain to interact with machines and computers directly, without the need for a physical device.

As artificial intelligence and machine learning continue to evolve — and consequently, more intelligent — they are likely to solve more global problems, thus bringing us closer to realizing our goals for truly sustainable development.

Source: Sustainablebrands

01 Oct 2018

91% Of Enterprises Expect AI To Deliver New Business Growth by 2023

  • AI is already enabling early-adopter manufacturers to better orchestrate analytics, Business Intelligence (BI), mobility and real-time monitoring to enable faster revenue growth and grow faster than their peers.
  • The top 18% of AI adopters today devote over 70% of their efforts to devising new strategies to drive revenue and new sales growth.
  • 91% of all enterprises interviewed expect AI to deliver new business growth by 2023.

These and many other fascinating insights are from the recently published report Artificial Intelligence in Business Gets Real; Pioneering Companies Aim for AI at Scale by MIT Sloan Management Review.  The study is based on a survey of 3,076 business executives, managers, and analysts from organizations around the world. The survey, conducted in Spring 2018, captured insights from individuals working in organizations of various sizes, spread across 29 industries and located in 126 countries, and supplemented by 36 executive interviews. For additional details on the methodology, please see page 3 of the study.

Key takeaways of the study include the following:

  • Four groups of adopters emerged from the analysis based on their AI adoption and understanding, ranging from Pioneers, Investigators Experimenters and Passives with widely varying goals. The study found four distinct groups of organizations based on their level of AI adoption and maturity. 18% of those surveyed are Pioneers, or organizations that both understand and have adopted AI. 33% are Investigators or organizations that understand AI but are not deploying it beyond the pilot stage. Their investigation into what AI may offer emphasizes looking before leaping. 16% are Experimenters, who are organizations that are piloting or adopting AI without deep understanding. And 34% are Passives or organizations with no adoption or much understanding of AI. In the next five years, all expect AI to be a primary catalyst that redefines their business models.


  • 91% of all enterprises interviewed expect AI to deliver new business growth by 2023.  In the many previous studies of AI adoption maturity in enterprises, cost reduction is often the catalyst that nudges late adopters or passive organizations to finally take action.  What’s noteworthy regarding this study is all four categories of adopters are expecting AI to contribute to their new business growth in five years, by 2023.  AI-enabled product development and new products that integrate contextual intelligence and Internet of Things (IoT) sensors provide manufacturers with a rich real-time data stream that can be turned into subscription and services revenue, freeing them of being in a transaction-only business.  That’s just one of a myriad of areas were AI designed into products will redefine manufacturing and services revenue streams.


  •  Early adopters are best at driving revenue from AI by enabling greater scale, speed and responsiveness of centralized data lakes. Chinese AI early adopters benefit from their government’s pro-AI support programs and direction and lead the world in optimizing centralized data lakes to support AI use cases.  MIT and BCG found that 78% of Chinese early adopters or pioneers maintain corporate data in a centralized data lake compared with only 37% and 43% of European and U.S. early adopters respectively. 83% of Chinese AI-leading companies surveyed manage corporate data centrally, while only 39% of European Pioneers and 40% of U.S. Pioneers do so. Ironically, Chinese early adopters are focusing on using AI to cut costs and putting less emphasis on using the technology to generate new revenue streams.



  • Early adopters are finding success using AI to drive new revenue, making the business case for continued re-investment clear.  72% of early adopters or pioneers will prioritize use cases that prioritize revenue first compared to just 48% of passives. In the previous three years, 81% of early adopters prioritized revenue increases over cost reductions. The study cites OPTEL, a Canadian technology company who is on a  mission to build a sustainable world through smarter supply chains. Using end-to-end traceability systems based on advanced AI, OPTEL connects the various segments of the supply chain and provides much-needed visibility over the path of products in a wide range of industries. There are also early adopters or pioneers in manufacturing using AI to optimize production scheduling, logistics, distribution, service, and have products provide real-time status updates on when maintenance is needed, wherever they are in the world at any given moment.


I am currently serving as Principal, IQMS. Previous positions include product management at Ingram Cloud, product marketing at iBASEt, Plex Systems, senior analyst at AMR Research (now Gartner), marketing and business development at Cincom Systems, Ingram Micro, a SaaS start…

Louis Columbus is an enterprise software strategist with expertise in analytics, cloud computing, CPQ, Customer Relationship Management (CRM), e-commerce and Enterprise Resource Planning (ERP).

Source: Forbes