Category: Innovation

20 Jul 2019

Skills Innovations For An Ever Changing World

Countries around the world are faced with considerable skills, productivity and social inclusion challenges that require novel and innovative approaches to skills development. In a global marketplace there has been a number of innovative solutions emerging that provide telling glimpses into the future of education, and a recent report from the Royal Society of Arts, Manufactures and Commerce (RSA) and WorldSkills UK uncovers a handful of the most innovative ones from Switzerland, Shanghai, Russia and Singapore.

“Skills improvements complement other key building blocks of an innovative and inclusive economy,” the authors explain. “The effectiveness of investment in infrastructure, new technologies, research and innovation, regional growth and improved business practices and processes is influenced by how well skills are cultivated (their supply) and applied (their utilisation).”

The Swiss regularly top league tables such as the Global Innovation Index, and the human capital available to the country is a key factor behind that success. They have a robust technical and vocational education system that revolves around apprenticeships, professional education and connectivity between vocational and general education.

They also place a high degree of emphasis on careers guidance, with individuals at risk of exclusion from either learning or work given a caseworker to help them bounce back effectively. This is aligned with a number of high-quality institutions that allow scope for innovation, with the Universities of Applied Sciences helping to connect vocational and academic streams of learning.

By contrast, Shanghai provides some invaluable lessons in terms of their ability to reinvent their economic purpose after a period of deindustrialization.

“Skills provision was reformed to be much more market-oriented and aligned with the city’s economic development strategy,” the authors explain. “The approach addressed both higher level skills and also upskilling the segments of the workforce with low level or outdated skills.”

Central to their approach to reskilling was an intensive amount of local experimentation, with a high degree of local autonomy granted to the region to do so. A process of testing, piloting and scaling was instigated to ensure citizens had the skills needed as the local economy refocused on services and high technology industries.

Lifelong learning
Asian Tiger economy Singapore has seen well-documented economic growth in the past few decades, and education has been a central driver of this growth. Whilst the economy has well known successes in services, the report highlights the technical and vocational education provided as an unsung factor behind the lifelong learning culture found in the country.

“Lifelong learning is now viewed as an important component of the Singapore’s overall education system, as it enables workers to continue their professional development throughout their working lives, and to update their skills in line with the demand in the country’s economy,” the authors explain. “Specific programmes exist to support mid-career workers to convert to a new profession in Singapore’s growth sectors, either through in-work training or training and then job placement.”

Central to this philosophy has been their SkillsFuture program, which offers a one-stop education and career guidance portal to help every Singaporean plan their lifelong learning journey. The program is supported by placements and learning credits for those starting out on their professional life, and a range of courses and development opportunities for those already into their careers.

Retraining is encouraged via skills competitions that contain personal training accounts that have underpinned considerable growth in adult participation in learning.

Another country that has undergone a fundamental restructuring of their economy is Russia, and the report highlights how benchmarking has been used to ensure they learn from the best practices of other countries around the world.

Their first entry into the WorldSkills competitions saw them finish near to the bottom, but since then they have improved considerably, and now regularly finishes in the top positions. Indeed, the competitive nature of these events has been a major factor in their improvement, with public, private and academic sectors working together to move skills development in the right direction.

The authors pull together a number of factors from these case studies that they believe are crucial in equipping countries with the means to support citizens as they adapt to changes in the labor market. These include ensure there is a parity of esteem between vocational and academic education; that policies are led by stakeholders and rooted in local governance; that an experimental approach supports learning across the ecosystem; and they are enacted behind a clear vision to unite all stakeholders.

Local efforts
Perhaps the most important of these is the importance of local governance, as change unfolds in distinct ways in each location, especially in areas where a single employer or industry dominates.

“Place, including how it is shaped by local and regional formal and informal networks, is at the centre of a social ecosystem,” the authors explain. “It constitutes a “complex dynamic of economic, social, political, cultural and institutional factors” that play out in a locality. This ranges from the structure of the local labour market, local traditions and the economic and social geography, all the way through to the capacity and leadership of local government, the actions of employers and the institutional and cultural configurations of education and training providers.”

A one-size-fits-all national approach simply doesn’t cut it in a world in which social, demographic and institutional contexts vary so significantly from place to place. The authors rightly advocate ‘devolution by default,’ with local regions empowered to respond to their particular circumstances in the way they see fit, with each region encouraged to share the outcomes of their experiments so that learning can flourish throughout the nation.

All of this will, inevitably, require investment, but perhaps more importantly it requires a shift in mindset to not only place vocational training on a par with academic education, but to also underpin a culture of lifelong learning that will be so important in the future of work. As the paper ably shows, there are regions of the world where these developments are taking place. Time will tell whether other regions learn from these vanguards and give citizens the support they so dearly need.


18 Jul 2019
Intel Developing an AI Chip That Acts Like a Human Brain

Intel Developing an AI Chip That Acts Like a Human Brain

Intel is aiming to develop semiconductors that mimic the way human brains work, announcing a new product dubbed Pohoiki Beach.

The neuromorphic chip processes data similar to how a human brain does, overcoming the challenges plaguing the first generation of artificial intelligence chips. With this product, Intel is extending AI into the areas that work similar to human cognition including interpretation and autonomous adaptation.

Intel’s Betting Next-Generation Chip Will Take AI to New Level
“This is critical to overcoming the so-called ‘brittleness’ of AI solutions based on neural network training and inference, which depend on literal, deterministic views of events that lack context and commonsense understanding,” Intel wrote in a research report. “Next-generation AI must be able to address novel situations and abstraction to automate ordinary human activities.”

Intel pointed to self-driving vehicles as one example where this new AI chip would be necessary. As it stands, the semiconductors used in autonomous cars can navigate along a GPS route and control the speed of the vehicle. The AI chips enable the vehicle to recognize and respond to their surroundings and avoid crashes with say a pedestrian.

But in order to advance self-driving cars, the systems need to add the experiences that humans gain when driving such as how to deal with an aggressive driver or stop when a ball flies out into the street. “The decision making in such scenarios depends on the perception and understanding of the environment to predict future events in order to decide on the correct course of action. The perception and understanding tasks need to be aware of the uncertainty inherent in such tasks,” researchers at Intel wrote.

New Chip Approach Will Speed Up Processing Times for AI Workloads

According to the Santa Clara, California semiconductor marker, with this new approach to computer processing, its new chips can work as much as 1,000 times faster and 10,000 times more efficiently when compared to the current central processing units or CPUs for artificial intelligence workloads. The Pohoiki Beach chip is made up of 64 smaller chips known as Loihi which when combined can act as if it is 8.3 million neurons, which according to one report is the same as the brain of small rodent. A human brain has nearly 100 billion neurons. 

Intel said the new chip can be particularly useful in the processing for image recognition, autonomous vehicles, and robots that are automated. The chip is free for developers focused on neuromorphic, including its more than sixty partners in the community. The aim is to commercialize the technology down the road. 


17 Jul 2019
Disruptive technology that comes at a price

Disruptive technology that comes at a price

New disruptive breakthroughs in technology can come in many packages — new devices, new software, new medicine. Some show up and force change overnight, and others percolate for years. Some grab headlines but do not change things much, others fundamentally change the world and we hardly notice. One of the most overlooked technologies that upended billion-dollar industries was the introduction of fracking for oil and gas extraction. It reduced the U.S. trade deficit, the global power of other countries, and carbon emissions. And yet, we usually only talk about its adverse side effects, of which there are many.

What is fracking?

Fracking is a nickname for hydraulic fracturing. Most oil and natural gas is extracted from large reservoirs in the ground. To get at it, you drill a hole down to the underground pool and pump it up. But a vast amount of fossil fuel is trapped in what is essentially compressed sand or coal. If you injected water at very high pressure into that sand, it breaks it up to create cracks. They put small particles into the water to hold open the cracks when the pressure is removed. Once enough cracks are made, the oil or gas is free to flow into the well hole, and up to the surface. Other chemicals are added to the mix to increase the efficiency of the process.

Why do people dislike fracking so much?

This article is about the disruption caused by fracking, but I should add a word about the downsides. And they are significant. In short, the process consists of taking a lot of nasty chemicals, a massive amount of water, and injecting it into the ground to break up rocks. You end up with those nasty chemicals in the water table, natural gas leaking into the water table, and it changes the geological structure of the ground. The visible effects of this are water faucets that can catch on fire, pollutants in the drinking water, and earthquakes in places that usually don’t have earthquakes. On top of that, the cheap fuel fracking delivers reduces the economic viability of non-carbon based energy.

What did it change?

In short, fracking freed up a lot of oil and gas in the U.S. Areas that had been pumped dry or that had never been explored could now be tapped. And a larger portion of the hydrocarbons being pulled out is in the form of natural gas. This cheap and abundant resource of fuel here in the U.S. resulted in two disruptive changes — we import less fuel and we burn more natural gas.

The U.S. produced 50% more crude oil in the last decade domestically, and imports dropped from 60% of consumption to 45%. The U.S. is now tied with countries like Saudia Arabia and Russia as leaders in crude oil production. All of that money that was leaving the U.S. to pay for jobs and equipment in other nations is staying here.

Burning natural gas has a disruptive impact because it’s cleaner than coal and produces fewer carbon emissions than other carbon-based ways of generating electricity. So, all around the country utilities are converting coal and oil power plants to natural gas. U.S. carbon emissions have actually gone down, not because of any policy changes or drop in energy usage.

Why did it force such a huge change?

It’s all about economics. Market forces are far stronger than regulation or policy. Natural gas is now cheaper than coal to get out of the ground and transport. When we can produce our own oil and gas, we import less, and the economic and political power of countries we buy from is lessened.

And all of this change happened in spite of the significant negative impacts of fracking. Why? Because it’s a lot of money. Billions of dollars pulled from the ground. And billions for those that make the equipment that does the fracking, pumping, transportation and refining. It’s hard to say no to all that revenue, even if you are looking at flames shooting from a faucet in your back yard.

When we think about innovation, we usually focus on computers, medicine and communication. But innovation can be about low-tech applications like how to get more hydrocarbons out of the ground. And the impact can be just as, or even more, significant in both in a positive and a negative way.

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16 Jul 2019


Launching large man-made structures into orbit poses extraordinary challenges. But cutting-edge 3D-printing technology could make space manufacturing far more practical — by moving the manufacturing process into the near-zero gravity environment of outer space.

NASA just awarded Made In Space a $73.3 million contract to demonstrate 3D-printing spacecraft parts while in orbit using a small spacecraft called Archinaut One. The craft will attempt to print two 32-foot beams that will eventually be used to hold solar arrays to both sides of itself.

Archinaut One

Archinaut One is scheduled to launch on a Rocket Lab Electronrocket from New Zealand “no earlier than 2022” according to NASA.

“In-space robotic manufacturing and assembly are unquestionable game-changers and fundamental capabilities for future space exploration,” said Jim Reuter, associate administrator of NASA’s Space Technology Mission Directorate in a statement.

Today’s news is actually the start of the second phase of NASA’s partnership with Made in Space. Made in Space has already successfully 3D-printed a structural beam in a NASA facility that mimics the conditions of space in 2017.

But actual orbit will undoubtedly pose its own set of challenges.


15 Jul 2019

Artificial intelligence (AI) in Construction Market to Hit Value of USD 3,161 Million By 2024

According to the report, the global AI-in-construction market was valued at USD 312 million in 2017 and is expected to reach USD 3,161 million by 2024, growing at a CAGR of 38.14% between 2018 and 2024.

New York, NY, July 14, 2019 (GLOBE NEWSWIRE) — Zion Market Research has published a new report titled “AI-In-Construction Market by Technology (Natural Language Processing and Machine Learning and Deep Learning), by Component (Solutions and Services), by Deployment (On-Premises and Cloud), and by Application (Project Management, Risk Management, Field Management, Supply Chain Management, and Schedule Management): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2017—2024”.

According to the report, the global AI-in-construction market was valued at USD 312 million in 2017 and is expected to reach USD 3,161 million by 2024, growing at a CAGR of 38.14% between 2018 and 2024.

Artificial Intelligence allows computer systems to make intelligent decisions by applying the required skills. Artificial Intelligence has been beneficial in the development of applications that comprise machine vision for easy analysis and surveying of buildings and structures. Additionally, the development of creating information modeling is software that gives information on a construction project, warranty details regarding material used, and commissioning data. This has resulted in increased AI adoption by most of the construction start-ups globally for various applications.

Browse through 56 Tables & 29 Figures spread over 145 Pages and in-depth TOC on “Global AI-In-Construction Market: By Technology, Size, Share, Types, Trends, Industry Analysis and Forecast 2017—2024”.

Artificial Intelligence has the ability to perform tasks similar to that performed by human intelligence, such as planning, recognition, and decision making. The construction sector is adopting AI to obtain precise data and insights to increase productivity, operational efficiency, and ensure safety at work. AI operates on algorithms related to image recognition to find out search criteria. For instance, it includes hard hats and safety vests to search construction workers, those who are not wearing proper safety gears. The primary applications for AI-In-Construction market include planning, safety, monitoring and maintenance, and autonomous equipment.

AI’s capability in construction services and solutions to reduce production costs is the major factor expected to drive the global AI-In-Construction market. In addition, the need for safety measures on construction sites is also projected to drive this market’s growth. Furthermore, huge investments made by construction companies from the emerging economies globally in the adoption of the advanced AI technology for construction applications is also likely to contribute toward the global growth of the AI-In-Construction market. However, the low technological investments in R&D for developing new technologies might hamper this market. Nonetheless, the increasing demand for integrated AI in construction activities is estimated to create new market opportunities.

By technology, the AI-In-Construction market is divided into natural language processing and machine learning and deep learning. By component, this market includes solutions and services. By deployment type, the market is bifurcated into on-premises and cloud. A cloud deployment type is estimated to grow at a higher CAGR during the projected period, owing to its cost-effectiveness. Project management, field management, risk management, supply chain management, and schedule management comprise the application segment of the AI-In-Construction market.

North America dominated the global AI-In-Construction market in 2017, due to the lack of a skilled workforce that has driven the key construction enterprises to invest in robotics-based solutions. The real estate organizations are developing solutions that can detect the risks and perform the labor tasks repetitively, which can enable the non-experienced staff to complete the complex tasks. In addition, the high AI demand for various applications, such as field management, project management, and risk management, is likely to contribute toward this regional market’s growth.

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13 Jul 2019
Artificial Intelligence and Machine Learning Empowering Business Growth for Entrepreneurs Today

Artificial Intelligence and Machine Learning Empowering Business Growth for Entrepreneurs Today

In 2018 alone, AI-related startups and ventures have secured $9.3 billion in VC funding, according to a report from PwC and CB Insights. The Indian unicorns PayTm, Swiggy, and Oyo have been investing resources to gain AI capabilities and have acquired at least one AI company.

In 2018, VCs have funded Indian AI startups with USD 478.38 million in 111 funding rounds. One of the reasons why AI & machine learning-based technologies on the rise are that the competitive advantages one can develop using them, especially with the customer experience and cost optimization. In e-commerce, for example, understanding consumer behaviour and product demand and making the right offer at the right time can be the difference between winning or losing over the competition. 

Change in Working

AI in the early stages was mostly based on rule-based systems, whose ability to deliver value is limited by how well the rules are defined, which requires human expertise. In machine learning (ML) — a subset of AI — once a model is trained, the ML model can learn further through inferencing — where the ML model is put to work on real-time data. 

The ability of ML models to self-learn with minimal to no human interventions has been the key in gaining interest from entrepreneurs and innovators. Whether it’s shopping on Flipkart or watching movies on Netflix, the customers’ experiences are vastly touched my machine learning and its subset deep learning. Curating vast amount of content and making purchase recommendations has been one of the commercially well-recognized use cases that saw huge interest from tech giants as well as well-established startups. UBS estimates that AI as a standalone industry has the potential to reach a market cap of USD 120-180 billion by 2020. 

Health Care and AI

Healthcare is another field where AI and machine learning systems are expected to make a big impact. For example, a Bangalore based startup delivers precision medicine using AI and machine learning. Another Bangalore based startup analyzes medical data and generates reports using deep learning systems, which can make a huge difference in delivering timely patient-care. 

Other Sectors

Machine learning is the turnkey technology that impacts many industry sectors: robotics, retail, banking, finance, self-driving, fraud-detection, weather forecasting, finding medicine for HIV, examining extraterrestrial objects, and so on. That is, AI & ML provide massive opportunities for entrepreneurs to explore, experiment, and build new businesses. 

AI and machine learning applications need the power of massively parallel processing capabilities provided by GPUs. However, owning GPU hardware doesn’t justify 3-year amortization costs, especially for entrepreneurs who are starting out in the AI & ML space. Also, there aren’t many cost-effective GPU solutions available in the Indian market. Getting started with machine learning has become less difficult with the development and production-grade availability of Open Source frameworks.

In Conclusion

AI & ML space is still young and entrepreneurs have a massive opportunity to innovate and disrupt. There have been concerns that AI might replace our jobs on a massive scale. However, according to a study by UBS, in most areas, AI is poised to replace tasks, not jobs. 


11 Jul 2019
Artificial Intelligence And The Challenge Of Global Governance

Artificial Intelligence And The Challenge Of Global Governance

Digitalization is evolving from an economic challenge to a governance and political problem. Some studies suggest that by 2030, Artificial Intelligence (AI) might contribute up to EUR 13.33 trillion to the global economy (more than the current output of China and India combined). The essence of the political conflict that raises the issue of global governance is what type of actor (a state or a digital corporation) will lead this process, creating global asymmetry in terms of trade, information flows, social structures and political power. This means challenging the international system as we know it.

AI is generating new large-scale systems based on (1) services (such as traffic management and smart vehicles, international banking systems, and new healthcare ecosystems); (2) global value chains, the Internet of things (IoT) and robotics (Industry 4.0); and (3) electronics with a new generation of microprocessors and highly specialized chips. The “food” for AI is the Internet – a major source of data, computing power and telecommunications infrastructure.

Not all countries will benefit in the same way, since AI-driven wealth will be dependent on each country’s readiness to be “connected” to the Internet. This is the essence of the political problem between a non-territorial space based on large-scale computer networks and nation-states.

Both democratic and non-democratic governments struggle to assert authority over different dimensions. First, they need to regulate de facto global private monopolies (such as Google, Facebook, Apple and Amazon) that are setting new rules of competition, creating new technology markets and blurring boundaries between industries. Second, many countries perceive Internet governance as too US-centric. A good example is the governance of IP addresses, which created the precedent of having a “universal resource” managed by a private, US-based institution like the ICANN. Third, a vast majority of digital innovations and AI technologies and applications come from a unique public-private ecosystem in the United States: Silicon Valley. Finally, although the Internet is global, investment in infrastructure (such as 5G) requires huge investments driven by local (ex-public) telecom operators whose business models are less sustainable.

While the European Union struggles to regulate Silicon Valley’s global platforms, China has started to block them with a “digital wall,” promoting protectionism to strike and compete in the global-tech game. Examples are tech giants such as Tencent, Baidu, Alibaba and the impressive Digital Silk Road to connect the European Union and China with various types of infrastructure, including satellites, 5G and submarine cables. This project could be the infrastructure that will enable China, by 2030, to become “the world’s primary artificial intelligence innovation center, transforming the country into a leading innovation-style nation and the greatest economic power,” as China’s national AI plan states.


10 Jul 2019
Scientists Create an AI From a Sheet of Glass

Scientists Create an AI From a Sheet of Glass

AI Glass
It turns out that you don’t need a computer to create an artificial intelligence. In fact, you don’t even need electricity.

In an extraordinary bit of left-field research, scientists from the University of Wisconsin–Madison have found a way to create artificially intelligent glass that can recognize images without any need for sensors, circuits, or even a power source — and it could one day save your phone’s battery life.

“We’re always thinking about how we provide vision for machines in the future, and imagining application specific, mission-driven technologies,” researcher Zongfu Yu said in a press release. “This changes almost everything about how we design machine vision.”

Numbers Game
In a proof-of-concept study published on Monday in the journal Photonics Research, the researchers describe how they made a sheet of “smart” glass that could identify handwritten digits.

To accomplish that feat, they started by placing different sizes and shapes of air bubbles at specific spots within the glass. Then they added bits of strategically placed light-absorbing materials, including graphene.

When the team then wrote down a number, the light reflecting off the digit would enter one side of the glass. The bubbles and impurities would scatter the lightwaves in certain ways depending on the number until they reached one of 10 designated spots — each corresponding to a different digit — on the opposite side of the glass.

The glass could essentially tell the researcher what number it saw — at the speed of light and without the need for any traditional computing power source.

“We’re accustomed to digital computing, but this has broadened our view,” Yu said. “The wave dynamics of light propagation provide a new way to perform analog artificial neural computing.”

Face Time
Teaching machines to accurate “see” will be key to achieving our goals for artificial intelligence — machine vision plays a role in everything from autonomous cars to delivery robots.

This “smart” glass might not be able to complete calculations complex enough for those uses, but the team does have one possible application for it in mind: smartphone security.

Currently, when you attempt to unlock a phone using face ID, an AI within the device has to run a computation, draining battery power in the process. Affix a trained sheet of this smart glass to the front of the device, and it’ll be able to take over the task without pulling any power from the phone’s battery.

“We could potentially use the glass as a biometric lock, tuned to recognize only one person’s face,” Yu said. “Once built, it would last forever without needing power or internet, meaning it could keep something safe for you even after thousands of years.”


08 Jul 2019
Man Vs. Machine: The 6 Greatest AI Challenges To Showcase The Power Of Artificial Intelligence

Man Vs. Machine: The 6 Greatest AI Challenges To Showcase The Power Of Artificial Intelligence

As artificial intelligence (AI) research and development continues to strengthen, there have been some incredibly intriguing projects where machines battled man in tasks that were once thought the realm of humans. While not all were 100% successful, AI researchers and technology companies learned a lot about how to continue forward momentum as well as what a future might look like when machines and humans work alongside one another. Here are some of the highlights from when artificial intelligence battled humans.

World Champion chess player Garry Kasparov competed against artificial intelligence twice. In the first chess match-up between machine (IBM Deep Blue) and man (Kasparov) in 1996 Kasparov won. The next year, Deep Blue was victorious. When Deep Blue won, many talked that it was a sign that artificial intelligence was catching up to human intelligence and it inspired a documentary film called The Man vs. The Machine. Shortly after losing, Kasparov went on record to state he thought the IBM team had cheated; however, in an interview in 2016, Kasparov said he had analyzed the match and retracted his previous conclusion and cheating accusation.

In 2011, IBM Watson took on Ken Jennings and Brad Rutter, two of the most successful contestants of the game show Jeopardy who had collectively won $5 million during their reigns as Jeopardy champions. Watson won! To prepare for the competition, Watson played 100 games against past winners. The computer was the size of a room, was named after IBM’s founder Thomas J. Watson and required a powerful and noisy cooling system to keep its servers from overheating. Deep Blue and Watson were products that came from IBM’s Grand Challenge initiatives that pit man against machines. Since Jeopardy has a unique format where contestants provide the answers to the “clues” they are given, Watson first had to learn how to untangle the language to determine what was being asked even before it could do the work to figure out how to respond—a significant feat for natural language processing that resulted in IBM developing DeepQA, a software structure to do just that.

Could artificial intelligence play Atari games better than humans? DeepMind Technologies took on this challenge, and in 2013 it applied its deep learning model to seven Atari 2600 games. This endeavor had to overcome the challenge of reinforcement learning to control agents directly from vision and speech inputs. The breakthroughs in computer vision and speech recognition allowed the innovators at DeepMind Technologies to develop a convolutional neural network for reinforcement learning to enable a machine to master several Atari games using only raw pixels as input and in a few games have better results than humans.

Next up in our review of man versus machine is the achievements of AlphaGo, a machine that is able to learn for itself what knowledge is. The supercomputer was able to learn 3,000 years of human knowledge in a mere 40 days prompting some to claim it was “one of the greatest advances ever in artificial intelligence.” The system had already learned how to beat the world champion of Go, an ancient board game that was once thought to be impossible for a machine to decipher. The film about the experience is now available on Netflix. AlphaGo’s success, when not being constrained by human knowledge, presents the possibility of the system being used to solve some of the world’s most challenging problems such as in healthcare or energy or environmental concerns.

In another test of artificial intelligence capabilities, DeepMind sought out a more complex game for artificial intelligence to battle that required the use of different features of intelligence that are necessary to solve scientific and real-world problems. They found the next challenge in StarCraft II, a real-time strategy game created by Blizzard Entertainment that features multi-layered gameplay. AlphaStar was the first artificial intelligence to defeat professional players of the game by using its deep neural network that was trained from raw game data by reinforcement and supervised learning.

Project Debater, a project from IBM, tackles another area of expertise for artificial intelligence—debating humans on complex topics. This skill involves dissecting your opponent’s arguments and finding ways to appeal to their emotions (or the audience’s emotions)—something that would seem like a uniquely human ability to do. Even though Miss Project Debater lost when it faced off against one of the world’s leading debate champions, it was still an impressive display of artificial intelligence capabilities. To succeed at a debate, AI needs to rely on facts and logic, be able to make sense of an opponent’s line of reasoning and to navigate human language fully which has been one of the most challenging feats of all for AI to master. While not 100% successful, Project Debater gave a good glimpse of what’s possible in the future where machines can augment human intelligence in powerful ways.


07 Jul 2019

Can You Make Innovation Happen?

Companies used to stay competitive by being reliable. They provided the tried and true. Customers valued companies that reduced the risk in their lives. But the technology boom turned that completely around. Now almost every industry must contend with the need to innovate. Customers want products and services that are high quality but also the latest and newest vs. the tried and true.

This demand for innovation has sent companies and their leaders into a tailspin trying to figure out how to make innovation happen. I’m often brought in to help them try to solve this dilemma. But the hardest thing for them to hear is that you can’t make innovation happen. There, I’ve said it.

So now what? Well, actually, there’s a lot that can be done. But the focus isn’t about making innovation happen. It should be on making innovation probable. And there’s quite a bit a company and its leadership can do for that.

Some key opportunities, that range anywhere from the simple to the complex, include the following:

Take the risk out of risk taking. One of the biggest challenges companies tackle is their fear of failure and mistakes. A great way to do that is to put it right out in the open. From the CEO to the frontline employee, creating a dialogue that tackles that fear head on helps demystify what it takes to make risk taking work for their culture and goals.

This includes sharing lessons learned, clarifying priorities, encouraging a growth mindset and focusing on the value from lessons learned.

Make risk taking more predictable. When leadership discusses how to mitigate risk it helps set a clearer path on how to navigate all the gray area of risk taking. This includes sharing a method for how to propose new ideas, build a business case and conduct low risk trial runs. When people get to take the risk out of sharing their ideas, they are more likely to focus on the risk of genuinely out of the box thinking vs. avoiding rejection or having their reputation ruined.

Get people sharing ideas. Imagination tends to have a fantastic domino effect when shared with others. One out there idea begets another out there idea, until you end up with a genius idea. This is often attempted through the act of group brainstorming. It’s a great concept, in theory. But where it often falls apart is in the execution. Too often the brainstorming sessions become a one or two-person show. Original ideas can get stamped out by group think and seeking approval.

One solution is better facilitated brainstorming sessions. Another option is leveraging collaboration software. Software tools make collaboration independent of time and place, and they also help focus and guide the collaboration to be more productive towards what the company is trying to achieve. Viima Solutions is an example of that kind of software. They focus on providing tools that help facilitate sharing of ideas wherever and whenever.

Measure what’s working and let go of what isn’t. Part of what makes innovation so elusive is people sit around assuming they’ll know what’s innovative or not. But what separates an interesting idea from a truly innovative one is the level of impact it has on the company’s bottom line. If customers don’t care about your idea, then does it matter?

If you know what to measure for, you will be better prepared to gauge whether the issue is the quality of the idea or the readiness of the customer. The latter calls for a phased approach, looking for early adopters and building momentum. The former calls for a post mortem and return to the drawing board. Key things to consider measuring include the effectiveness of collaboration efforts, impact on brand differentiation and consumer behavior.

Have a holistic approach. Though Viima Solutions makes their bread and butter on companies that use their software, they’re the first to admit that the biggest mistake is to think that innovation is easy, or something that can be achieved with a couple of quick superficial projects like introducing a new software tool or organizing a couple of idea challenges. These kinds of tools and methods are essential for driving sustainable results within the organization but won’t lead to innovation in and of themselves.

What’s ultimately needed is a holistic and determined effort that combines all the key aspects of innovation management: strategy, culture, structures and capabilities. The right tools certainly help across all of these factors, and in putting it all together, but you’re still going to need to put in the work to get all of those different aspects right.