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Month: July 2019

30 Jul 2019
How Blockchain Will Change Construction

How Blockchain Will Change Construction

Blockchain technology is among the most disruptive forces of the past decade. Blockchain’s power to record, enable, and secure huge numbers and varieties of transactions raises an intriguing question: can the same distributed ledger technology that powers bitcoin also enable better execution of strategic projects in a conservative sector like construction, involving large teams of contractors and subcontractors and an abundance of building codes, safety regulations, and standards?

“Increasingly, we are thinking more carefully about when and where we need to compete and what can we share and collaborate on,” said David Bowcott, global director of growth, innovation, and insight in Aon’s global construction and infrastructure group. Using blockchain to automate the contractual processes and paperwork underpinning these complex projects could save money, free-up valuable resources, and speed up project delivery. (Unless otherwise noted, quotes are from interviews we conducted as part of our research.)

Blockchain-enabled real-estate development projects

In commercial real estate, Amsterdam-based HerenBouw is applying blockchain to a large-scale development project in Amsterdam harbor. According to Propulsion Consulting founder Marc Minnee, HerenBouw’s objective was to set up a blockchain-enabled project management system to make the building development lifecycle more efficient. Minnee’s blockchain application for HerenBouw focused on registering transactions at legally binding moments, where accuracy and an audit trail are essential. “Blockchain provides a platform for clearly cascading work products down the chain and holding everyone accountable for completing key tasks,” Minnee said.

The system’s benefits include timely information, unambiguous communication, and fewer mistakes. “Stakeholders have a clear and evenly distributed incentive to register these facts on-chain: either you won’t get what you ordered or you won’t get paid,” said Minnee. They also develop trust, which reduces friction in their mutual business processes. “Stakeholders spend more time discussing creative design and building method options.”

Blockchain pilots in construction achieve liftoff

Aon, global risk advisor to the construction industry, estimates that 95 percent of building construction data currently gets lost on handover to the first owner. Briq, a California-based blockchain firm, is demonstrating the potential to capture and secure a construction project’s documentation in a blockchain ledger that parties can navigate and give to the owner as a deliverable.

Working on behalf of Minneapolis-based Gardner Builders, Briq developed a “digital twin” of a new office construction with a room-by-room inventory of every asset. “When a product or specification needs to be found in a building, there is finally a place to go to simply search for what is actually in that building,” said Briq CEO, Bassam Hamdy. The blockchain-encoded specifications are granular—paint colors, ceiling fixtures, LED bulbs, door hardware—plus manuals, warranties, and service life in a countdown clock that building owners can monitor.

“Any improvements and refurbishments to the building can be documented, and the whole repository can be transferred to new owners if the asset is put up sale,” said Ellis Talton, Briq’s director of growth marketing. In other words, building owners get a living ledger of everything that has happened with the building.

Overcoming cultural obstacles

Engrained practices in the construction sector will likely prolong widespread blockchain adoption. “The construction industry is technologically advanced in many aspects of what it does,” said Talton of Briq. “But the industry is very relationship based. There are many family-owned firms and private companies. The selection of contractors and subcontractors can be based on relationships that have existed for decades.”

Talton also noted that very little money—less than one percent of revenues (versus 3.5 to 4.5 percent in aerospace and automotive)—is invested in upfront contracting and technology infrastructure for managing complex construction projects. “The vast majority of the projects costs are in the building process, including the people and materials,” he said.

Scott Nelson, CEO of Sweetbridge (where one of us — Don — is an advisor), finds construction a natural for blockchain-based project management: “Projects are well-structured and contract-based. Objectives are clear—be on-time, on-spec, and avoid rework. Classic project management techniques still work, but projects can benefit from a more decentralized and agile approach where transparency is high and parties can be compensated for outcomes as well as work performed.”

Identifying applications of blockchain in project management

Over time, blockchain will have breakthrough applications for project management. We encourage organizations to explore and capitalize on this potential. Here are a few next steps.

Identify use cases for blockchain adoption. Look where success depends on mobilizing resources across enterprise boundaries; where identities, contracts, and payments must be audited and protected; and where the provenance and ownership of assets must be tracked. Here are a few ideas:

  • A reputation ledger that tracked subcontractors’ deliverables could help to identify reliable subcontractors for a project.
  • Smart contracts that identified accountabilities and triggered milestone-based payments could automate agreements.
  • Blockchain-enabled applications that aggregated data into a shared project management dashboard could help to manage workflow.
  • A distributed ledger that chronicled the construction process end to end could record all building inputs and assets, including warranties and maintenance checkpoints.
  • Blockchain-enabled apps that tracked materials, testing, and results against building codes and standards could streamline inspections.

Develop prototypes and pilot projects. Do preliminary analysis: audit the systems in use, consult their users, and think about who would need to be involved in identifying viable options, selecting one to prototype, designing the pilot, and participating in testing.

Make a business case for investing in blockchain. Identify ways blockchain can increase project success, such as improving processes and organizational capacity to locate and share large quantities of data with specific individuals and entities.

Said Bowcott of Aon, “Collectively, we are all better off if we encourage data collaboration and use blockchain and machine learning to help us establish longer-term industry roadmaps for investments, and technologies that can boost productivity and efficiency and lessen risk.” While the fundamentals of project management will remain important, blockchain enables managers to focus their talents on solving problems and achieving better project outcomes.

Source: https://hbr.org/2019/07/how-blockchain-will-change-construction

29 Jul 2019
Five Excuses Leaders Use To Justify Stifling Innovation

Five Excuses Leaders Use To Justify Stifling Innovation

Working in large companies nowadays is interesting. On the one hand, leaders now understand that they need to innovate. On the other hand, they are struggling to balance the demands of running their core business, with the exploration of new opportunities. In publicly traded companies, this tension is at its most acute. The demands of quarterly reporting take up a lot of time and resources. So as much as they may want to innovate for the future, the demands of running their current business can be overwhelming. 

This paradox creates a dissonance within leaders, especially in markets or industries that are facing disruption. What really brings this dissonance to the surface is when some entrepreneurial employees inside the company start asking for time and resources to innovate. Leaders know that they need to create space for this to happen. So the question then becomes, why are they not doing it? To resolve their dissonance around innovation, leaders start making excuses. I spend a lot of my time in conversations with leaders and I have heard every excuse in the world. Below are five of the most common excuses I hear:  

  1. We Are Already Doing It: This is when leaders argue that there is no need for concern, the company is already innovating. Improvements to our current products are a form of innovation. When we take our current products to new markets, that is also innovation. So there is no need to worry. I once heard a leader say that, what she had was a communication problem. She needed to help her employees understand that what they were already doing is innovation. Once they understood this, they would stop asking for time and resources to innovate. 
  2. We Don’t Need To Do It: If I succeed in convincing leaders that they are not already innovating, the next excuse is that their company does not need to do it. This is disruption denial. I have heard leaders say that in their industry they are the leading company, so they do not need to innovate. When I point to examples of other leading companies that have been disrupted, the response is that these companies did not have the barriers to entry that their company and industry have. So even if we are not already doing it, we will be fine. 
  3. We Don’t Have Time For It: After debating at length, I sometimes succeed in convincing leaders that they not already innovating and that they do need to it. This is when leaders move to their next excuse. With all the work we are already doing inside the company, there is no time for innovation. The roadmap is already packed with activity for this year. All our employees are at full tilt delivering against this roadmap. So when will they find the time to innovate. Indeed, the fact that we are so busy is an indication of how successful we are as a company. We have a lot of customer demands to meet. 
  4. We Don’t Know How To Do It: If I work hard enough and succeed in convincing leaders that they need to make time for innovation, their next excuse shifts the blame to their employees. Even if we give them the time and resources to innovate, our employees don’t know how to do it. They don’t have the right entrepreneurial mindset to drive innovation. Whenever we give them a chance to innovate, they don’t come up with any breakthrough ideas. 
  5. We Know It Doesn’t Work: This the final nuclear excuse. Once leaders run out of excuses, they go for the jugular. They challenge the innovation methods I am bringing to the table. They argue that all these lean innovation and design thinking methods don’t work. There is not one company in our industry that has used these methods to launch a multi-million dollar business. We know that these methods don’t work. This is why we are not doing it. 

As you can tell, leaders come up with a number of excuses for stifling innovation. The interesting thing is that I have sometimes heard all five excuses stated by the same group of leaders in one meeting. While making excuses helps to ease their dissonance, it does not help their companies prepare for the future. The leaders I have seen succeed at innovation, acknowledge the paradoxical management challenges they are facing and actively seek solutions. In the 21st century, companies need active and engaged innovation leadership from executives.

Source: https://www.forbes.com/sites/tendayiviki/2019/07/28/five-excuses-leaders-use-to-justify-stifling-innovation/#4ce100237dc4

28 Jul 2019
Why Innovation Labs Fail, and How to Ensure Yours Doesn’t

Why Innovation Labs Fail, and How to Ensure Yours Doesn’t

What do Walmart, Facebook, and Lockheed Martin have in common? They all recently unveiled lavish new innovation labs. These kinds of labs go by different names — accelerators, business incubators, research hubs — and my research suggests their numbers are growing. Over half of financial services firms have started their own creative spaces, and you’d be hard-pressed to find a health care company or retailer without at least one innovation lab, whether it’s a conference room with sticky notes or a 20,000-square-foot incubator space, like the one launched by Starbucks in November of last year.

That’s all great news, generally speaking. Innovation labs are a safe place for organizations to run experiments and iterate on projects, and they’re an important investment for firms that have rigid approaches or that work in highly regulated industries. But do they actually add value and generate growth? According to a report from Capgemini, the vast majority of innovation labs — up to 90%, one expert says — fail to deliver on their promise.

From doing extensive research for my book Disrupt-It-Yourself and advisory work with large corporations in various sectors, I’ve found that there are three reasons many labs come up short. Here’s what companies should watch out for.

Lack of Alignment with the Business

Legendary innovation spaces like Xerox PARC and Bell Labs can evoke images of extreme secrecy and complete isolation from the core business. That sort of separation can be important, especially in companies where bureaucracy tends to neutralize new ideas. But separation alone is seldom a problem.

The problem tends to be that the innovation center doesn’t have a clear strategy that’s aligned with the company’s — or doesn’t have one at all. Many labs install kegs and offer kombucha on tap to get the creative gears turning, and then begin to ideate with only a limited idea of their goals. Some of the innovation teams I’ve met recently seem unsure if they are charged with serving the core business or with disrupting it. This lack of strategy is a common symptom of “innovation theater”: Boards and C-suite leaders unveil labs that are mostly for show, so they can check the box of having a team dedicated to innovation — and especially to disruption. Yet the curtain comes down quickly, either because ideas from these labs are disconnected from real customer needs or because no one is on the hook to carry the ideas through to implementation.

Leaders need to think through the implications of opening a lab, decide how it will complement or disrupt current and future business, and do the difficult work of determining how new ideas will be executed. This starts with a few considerations:

Vision. Creating clear goals for the lab helps both intrapreneurs and company leaders understand the direction and purpose of innovation initiatives. My firm recommends using “from/to” statements. For example, “We want to go from placing big innovation bets to trying many small experiments and rapid prototyping,” or “We want to go from having a limited range of innovation to being able to test out lots of new ideas while simultaneously growing the main business.”

Growth. What happens when an idea has been validated and needs to keep growing outside of the lab? Where will the idea go for additional support? Options may include going back into the core business or to an incubator or an accelerator. Potentially disruptive innovations may go somewhere outside of the core organization, where they can be further developed while being protected from corporate antibodies and business-as-usual fingerprints.

People. How will the lab support the intrapreneurs doing the hard work of bringing forward new ideas and executing them in unchartered territory? How will the lab facilitate close connections with the end users or customers for whom these ideas will solve a problem? People, along with their passions and purpose, are at the heart of the most successful innovation initiatives.

Lack of Metrics to Track Success

Innovation needs to be driven by much more than caffeine. After all, labs lose their luster in the eyes of executives when they fail to contribute to the bottom line over time. The irony? Many never have metrics to begin with. The truth is that innovation labs that don’t have or can’t manage metrics are essentially set up to fail.

Admittedly, innovation requires different thinking around financial support — labs need the space to iterate, evolve, and incubate ideas. Yet even if your lab is a cost center with a mandate to experiment over the long term, some type of return, financial or otherwise, needs to be specified in advance and tracked over time. Specific metrics serve at least two purposes: define what’s at stake for lab innovators as well as company leaders, and remind people that the benefits of innovation range from the very tangible (financial return on investment) to the less tangible but perhaps more valuable (return on intelligence in the form of new knowledge and insight).

But if the yardsticks for mature lines of business don’t apply to the lab, how do you come up with rigorous alternatives? When testing new ideas with potential customers, for example, early-stage metrics could be as simple as: How many users are finding value in the idea? How often do they tell their friends about it? How often do they return to use it? Financial metrics can be introduced as early as the prototype stage: Are users willing to pay for this? And later: How much revenue is being generated from these new offerings? Innovation cannot be sustained, however, without a holistic approach to metrics, one that includes focused organizational capabilities as well as leadership metrics that support the behaviors required to build a culture of innovation.

Lack of Balance on the Team

I’ve seen more than one lab languish because it was headed by a leader who had deep industry knowledge and good intentions but simply didn’t understand how innovation works. These kinds of leaders approach innovation much the way they approach problem solving in the core business, which often leads to little more than incremental improvements.

I’ve also seen labs struggle when they’re staffed solely by a squad of external entrepreneurs, whose eagerness to set fire to traditional ways of working promptly burns bridges within the company. This is the classic folly of entrepreneur-in-residence programs: Participants know how to launch startups and build businesses but are missing the internal knowledge and networks required to navigate large corporate systems.

It takes a diverse team to succeed. You do need a few people from outside the four walls of the organization, but they should be surrounded by others who bring a mix of skills — including long-standing employees who have a passion for innovation and know how the company works.

Getting this balance right can be messy, but there’s no other way. Effective innovation teams, whether inside or outside of a lab, are purpose-driven and diverse, often in terms of function and background as well as cognitive style. Here’s a quick litmus test: You’ll know you have the wrong team when everything is running along smoothly but the team’s output doesn’t look much different from business as usual. You’ll know you most likely have it right when the team emerges with good ideas, has plenty of the healthy tension that arises when diverse voices challenge one another, and effectively manages the ambiguity inherent to innovation.

Given all the ways that innovation labs can go wrong, should your company hesitate to set one up? Not necessarily. As Kyle Nel, the former executive director of Lowe’s Innovation Labs, and now a faculty member at Singularity University, told me, “Large organizations have just as much a right to play into that future, if not more so, than these kids in a garage somewhere that we’re scared of.” And Nel should know. At Lowe’s he oversaw breakthroughs that included a 3D printer designed for deep space (now used at the International Space Station) and LoweBots (autonomous robots that help customers find the products they’re looking for).

How did Nel and his team at Lowe’s beat the odds? He spent a whole lot of time “building and putting process and rigor to…identify what to work on; helping people on all different levels, from the CEO and board down, understand and have a substantive conversation about the outcomes of what you’re trying to build (and not the steps along the path to build it, because that’s unknown); and create new systems for getting KPIs and metrics of success, so you can see if you’re on the right track.”

The beauty of Nel’s approach is that it gets more people in the organization thinking about problem solving. A main point of these investments in facilities and talent is to fuel the innovation pipeline by encouraging creativity across the organization, while addressing the vision, growth, and people issues described above. Creating a cordoned-off space for innovation can signal that creativity happens only in specially designated spots and only among the people whose job it is to be creative — as in the R&D labs of yore.

Bottom line: The place for creativity is everywhere. Innovation labs are useful because they can provide training, networks, and other resources to help intrapreneurs succeed — regardless of where they work in the company. And, ideally, there should be cells of innovation (often driven by those intrapreneurs) across the organization. This is how real change starts to occur, altering the company’s cultural DNA to make the whole business more like an innovation lab.

Source: https://hbr.org/2019/07/why-innovation-labs-fail-and-how-to-ensure-yours-doesnt

22 Jul 2019
Learn the Ins and Outs of AI With This Artificial Intelligence Engineer Master Class Bundle

Learn the Ins and Outs of AI With This Artificial Intelligence Engineer Master Class Bundle

Right now technology is evolving so fast, it’s starting to get difficult to imagine what the future might look like. The only thing we know for sure is that pretty much everything will be powered by AI. So if you want to be a part of that future you’d better get up to speed on AI right now. And luckily, that’s actually easier than it sounds thanks to The Artificial Intelligence Engineer Master Class Bundle from Certs School.

Broadly speaking, artificial intelligence is the science of simulating human intelligence, or teaching machines and computers to perform human tasks. Of course, when most people think of AI, they think of perfectly rational self-aware robots and computers like you see in popular science fiction. However, while some scientists are trying to create computer programs that can correctly interpret human emotions and social situations, we’re nowhere near being able to teach computers to think for themselves. The kind of AI that’s going to change the world is called machine learning, and machine learning is basically just math.

With machine learning, computers use learning algorithms to sort through massive amounts of data, relying on patterns instead of explicit instructions to make inferences and build increasingly complex mathematical models, which can then be used to perform a wide array of tasks. This type of AI is already being used in a number of existing technologies. It powers computer assistants like Siri and Alexa. It powers the image recognition software you use in Google image search, Facebook, and the photo apps on your phone. It powers the speech recognition software on your phone or smart home device. It powers self-driving cars. It powers medical software that calculates the correct dosages of cancer treatments. It powers facial recognition software used by national security agencies. It powers the fraud detection software used by financial institutions. And that’s just scratching the surface. The possibilities for AI are almost limitless. All we need are software engineers who know how to build AI applications.

The Artificial Intelligence Engineer Master Class Bundle from Certs School will get you up to speed on AI, machine learning, deep learning, and data science, providing you with the tools you need to help pioneer the next wave of AI breakthroughs.

Your subscription gives you 24/7 access to five online courses and 85 total hours of training for one full year. And each course is designed to let you learn at your own pace.

With this bundle you will:

Explore new AI applications in fields like customer service, financial services, and healthcare.
Implement AI techniques such as search algorithms and neural networks.
Master key machine learning concepts and skills such as clustering, decision tree algorithms, building neural networks, and supervised/unsupervised learning.
Learn the essentials of the Python programming language and familiarize yourself with TensorFlow.

Read more: https://futurism.com/learn-the-ins-and-outs-of-ai-with-this-artificial-intelligence-engineer-master-class-bundle

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.

Source: https://www.forbes.com/sites/adigaskell/2019/07/19/skills-innovations-for-an-ever-changing-world/#5f5b4fda326c

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. 

Source: https://interestingengineering.com/intel-developing-an-ai-chip-that-acts-like-a-human-brain

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.

Read more: https://www.bizjournals.com/phoenix/news/2019/07/15/fracking-a-disruptive-technology-that-comes-at-a.html

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.

Source: https://futurism.com/the-byte/nasa-approves-plan-3d-print-giant-spaceship-parts-orbit

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.

Read more: https://www.globenewswire.com/news-release/2019/07/14/1882303/0/en/Artificial-intelligence-AI-in-Construction-Market-to-Hit-Value-of-USD-3-161-Million-By-2024.html

14 Jul 2019
Disruptive Technology, Part 2: Where’s the Risk?

Disruptive Technology, Part 2: Where’s the Risk?

The financial services industry is embracing disruptive technology, but executives are also aware of the potential risks it can bring.

This article is the second in a three-part series exploring executives’ perspectives on disruptive technologies in the financial services industry. The first installment looked at how the industry is using these technologies.

The financial services industry is embracing disruptive technology, but executives are also aware of the potential risks it can bring, according to a study conducted by ALM’s Corporate Counsel on behalf of Winston & Strawn.
The survey found that the level of perceived legal risk depends on the specific technology in question. AI is the area of greatest concern, with 51% of companies seeing it as a significant source of risk. Slightly fewer (50%) cited social banking and P2P lending, which bring the potential for shifting business models, while 42% cited blockchain, where there is still a lot to learn. “With distributed ledger and blockchain, there is a lot of promise and investment, but few successful use cases,” says Michael Loesch, co-chair of Winston’s Disruptive Technology Task Force. On the other end of the spectrum, only 34% see significant risk in facial recognition and biosecurity technologies, and 18% see no risk at all in that area.

Concerns about AI risk may stem from the fact that AI is a high-profile technology but one that still holds a number of unknowns—which may be troubling for companies that see it having an increasingly significant role throughout the business. In particular, bias in AI-enabled loan underwriting was cited by 45% of respondents as a risk—presumably because financial services companies are aware of studies that have shown that bias can creep into AI loan underwriting, highlighting the potential for unintended consequences with a powerful technology. They also know that it can be difficult to explain how AI systems—which are often opaque, “black box” technologies—produce their recommendations, which is likely to gain the attention of regulators.
Meanwhile, it is not always clear how evolving regulatory frameworks will affect the use of AI. For example, with AI-enabled personal assistants for customers, “banks are still asking what the restrictions are going to be on what the personal assistant can do,” says Winston litigation partner Danielle Williams. “Are they just able to read and relay account information? Will they execute transactions? There could be regulatory issues with every aspect of that.” AI used in internal processes, such as robotic process automation, could also increase legal risk if the way it is programmed introduces errors into back-office work.

“The ways in which AI is being deployed in the financial space, however, is of lower risk than in other industries where we are trusting AI to make potentially life-altering or threatening decisions,” remarked Kathi Vidal, Winston’s Silicon Valley managing partner and a former AI developer. “In the financial space, AI not only has the potential to replicate human decision making but also to improve it. In fraud detection, credit analysis, and other applications, we can train neural networks and other AI systems to better, and more blindly, analyze big data to render more accurate but also more equitable predictions,” concluded Vidal.
In time, more familiarity with disruptive technologies may actually increase awareness of potential risks. “Some institutions just aren’t using disruptive technologies to their fullest capacities yet,” says Basil Godellas, head of Winston’s Financial Services Regulatory Practice. “For example, respondents had fairly low concern about facial recognition, biometrics, and biosecurity solutions. But most institutions are just dipping their toes in the water with these technologies—and I think their concerns about risk may grow as they have more exposure to them.” Evolving legal frameworks may also increase those concerns. The 2008 Illinois Biometric Information Protection Act, for example, regulates how companies collect biometric information, such as fingerprints and retinal scans. More recently, a number of other states have passed or proposed similar biometric data privacy laws.

Beyond any specific technology, financial services companies clearly see risk in the cybersecurity and data privacy realm. (As mentioned above, respondents cited this as a top barrier to implementing disruptive technology.) The industry has long experience with the challenges of keeping sensitive data safe, as well as with the legal and regulatory costs of failing to do so. But disruptive technologies raise the security bar significantly because they rely on large amounts of quality data—often sensitive data about customers—to operate, provide services, and create insights. And that data has to be managed, protected, and shared safely with a growing number of applications.

At the same time, regulations around data privacy and cybersecurity are evolving. Take, for example, the EU’s General Data Protection Regulation, which went into effect last year and significantly strengthened privacy regulations—and penalties for noncompliance. Or consider the California Consumer Privacy Act of 2018, slated to go into effect in 2020, which has some of the most stringent privacy mandates in the United States. Such regulations, coupled with the advent of disruptive technologies, only make compliance more complex.

Regulators: Looking at Disruptive Technology
Executives worry about fintech and disruptive technologies bringing unwanted attention from a variety of legal and regulatory sources—indeed, 41% of respondents point to investigations and civil enforcement actions by federal agencies as the largest technology-related legal/regulatory threat. Many worry about actions by industry groups (32%), state regulators (28%), DOJ and state attorneys general (26%), and private civil plaintiffs (24%).

Meanwhile, nearly half say they are concerned with technology-related antitrust issues and the possibility of antitrust enforcement—driven in part by a sense that the rules are lagging behind advancing technology. “In financial services, there is an overarching concern that the antitrust laws as they are drafted today might not be sophisticated enough or flexible enough to deal with new disruptive technologies,” says Susannah Torpey, a partner at Winston & Strawn.

There are other antitrust implications to consider as well. In a technology-driven world, collaboration and working in partner ecosystems are both easier and more important. When setting up blockchain consortia or working with others in the industry to set technology standards, “you have to be very careful that this increase in coordination is well managed, so that you don’t find yourself exchanging information with your competitors that leads to anti-competitive effects in the market place,” commented Torpey.

Disruptive technologies essentially increase the importance of data in business, and AI specifically makes it easier to gather and use data from a wide variety of internal and external sources. Thus, these new technologies may increase the risk that regulators will consider data a source of market power when assessing mergers and acquisitions. And because disruptive technologies can drive innovations that quickly alter competitive dynamics, companies that succeed with new approaches may find themselves under increased scrutiny. “A startup financial services company that does something new might quickly gain dominant share of a niche market, which could put them at a much higher risk of an antitrust violation,” says Torpey.

In general, fintech and disruptive technology are moving quickly, and regulators sometimes struggle to keep up—which itself presents challenges to financial services companies trying to balance innovation and compliance. But regulators are paying close attention to these technologies. The U.S. Commodity Futures Trading Commission, for example, has established a LabTechCFTC program designed to keep the commission in close touch with technology innovators—and other agencies have set up similar programs. Perhaps with an eye toward those efforts, nearly seven out of 10 financial services companies believe that regulators are keeping up with the use of disruptive technologies in the industry. But in reality, that can be a challenge. “Regulators are doing positive things like LabTech to keep up with technology, but they are often still playing catch-up,” says Loesch. “They have to use the tools and laws that they have, and sometimes those are not fit for purposes in the disruptive technology space. There are tensions because the normal regulatory framework doesn’t always fit precisely with how the new technology operates. And that can lead to costly investigations and even enforcement actions.”

Source: https://www.law.com/corpcounsel/2019/07/12/disruptive-technology-wheres-the-risk/