Category Archives: Technology

The best inventions of 2016, according to TIME magazine

Written by Alex Gray Senior Writer, Formative Content
Published Friday 16 December 2016
A man plays a video game with Sony's PlayStation VR headset at Tokyo Game Show 2016 in Makuhari, east of Tokyo, Japan, September 15, 2016.

From an artificial pancreas to a life-saving potato … some of TIME magazine’s top 25 inventions
Image: REUTERS/Kim Kyung-Hoon
Every year TIME magazine publishes a list of what it considers to be the year’s 25 great inventions. This year’s selection covers a broad range of ideas, from a levitating light bulb to shoes that tie themselves.

And some of them are truly life-changing.

1. Uneven football fields

Who says a football field has to be rectangular? Thai property development company AP Thailand has turned unused and unevenly shaped spaces in Bangkok into football fields.

The Unusual Football Field was first developed in the Khlong Toei community, a highly populated area in Bangkok. If found unused but awkwardly sized spaces and converted them into spots where schoolchildren could go to play the nation’s favourite sport.

This video shows how the company converted the spaces to give children a valuable outlet, and why they did it.

Image: AP Thai

2. A roof over their heads

For years, the Swedish furniture group IKEA has been designing functional and affordable furnishings. But it has also been solving problems outside the home.

The company has supported the work of Better Shelter, an organization that, with the help of the IKEA Foundation and the UNHCR, has created a safer and more durable shelter for refugee families around the world.

Better Shelter in Kara Tepe, Lesvos

Image: Better Shelter

The Better Shelter is designed to last for at least three years and is suitable for areas in which local materials or construction workers are in short supply. It is “a ground-breaking example of democratic design form, function, quality, sustainability and an affordable price,” says the IKEA Foundation.

But they can provide more than homes for refugees. They act as registration centres, medical facilities and food distribution points in Africa, Asia, Europe and the Middle East.

“The refugee housing unit is an exciting new development in humanitarian shelter … Its deployment will ensure dramatic improvement to the lives of many people affected by crises,” says Shaun Scales, Chief of Shelter and Settlement, at UNHCR.

3. A life-saving potato

Food – its quality, quantity, availability – is one of the biggest issues of our time.

The World Food Prize is a Nobel-like international award that recognizes achievements of individuals who have advanced human development by improving the quality, quantity or availability of food in the world.

This year’s winners are four people who, between them, have developed a nutrient-rich vegetable and helped to extend the reach of food crops whose nutritional quality has been improved.

Three members of the team, Dr Maria Andrade of Cape Verde, Dr Robert Mwanga of Uganda, and Dr Jan Low of the United States, have created a sweet potato enriched with vitamin A. Deficiency in vitamin A contributes to high rates of blindness, diarrhoea, immune-system disorders and premature death in children and pregnant women in Africa. The potato is “the single most successful example of micronutrient and vitamin biofortification,” say the award organizers.

 World Food Prize Laureates 2016

Image: World Food Prize

The fourth winner of the award, Dr Howarth Bouis, has been recognized for pioneering the concept of “biofortification” – improving the nutritional content of a food – and turning the idea into a global movement.

“Through the combined efforts of our four Laureates, over 10 million persons are now positively impacted by biofortified crops, with a potential of several hundred million more having their nutrition and health enhanced in the coming decades,” say the World Food Prize panel.

4. Artificial pancreas

The number of diabetics worldwide is expected to double in the next 20 years to 700 million, according to the World Health Organisation (WHO).

Diabetes occurs when the pancreas cannot produce enough insulin, or the body cannot effectively use the insulin that it does produce.

Without insulin, sugar can build to harmful levels in the blood. This can cause damage to all our major organs, from our hearts to our kidneys. It can also cause blindness, impotence and infections that can result in amputation.

It’s a chronic disease that impacts the daily lives of its sufferers, who have to regularly check their blood sugar levels.

In September of this year, a new device was approved for use by the Federal Drug Administration in the US.

The MiniMed® 670G system is an insulin pump that automatically adjusts the delivery of insulin based on its sensor, which measures blood-sugar levels every five minutes. The company says it aims to help people living with Type 1 diabetes “spend less time managing their disease and more time enjoying life”.

 The MiniMed 670G system

5. Artificial limbs

Colombia’s 50-year civil war turned it into one of the most heavily landmined countries in the world. Many children have borne the brunt, losing their lives or surviving with lost limbs.

IKO Creative Prosthetic System

Image: IKO

Colombian-born designer Carlos Arturo Torres took the typical prosthesis design and viewed it from the eyes of a child. ‘What if kids could make their own prosthetics?’ he asked himself.

The result is the IKO Creative Prosthetic System, an adaptable prosthesis designed on the back of extensive research into how children feel about wearing a prosthesis. It is both functional – it has a functioning hand attachment – and convertible into a toy. It has a LEGO space ship with a laser and a remote-controlled digger arm that can be attached instead.

“Missing a limb shouldn’t be a disability for a kid when you have the opportunity to explore and augment their potential by creating, playing and learning,” says Torres, “It’s all about kids being kids.”

6. The fundraising Power Band

Wearable fitness devices have been one of the hottest products of recent years. Now there is one that aims to solve two problems at once: reducing obesity among children in wealthier nations while also helping children at risk of starvation in poorer ones.

Created by the Ammunition Group, the UNICEF Kid Power Band encourages children to get active and earn points. The points are converted into funding used by UNICEF to deliver lifesaving packets of therapeutic food to severely malnourished children around the world.

To date, the UNICEF Kid Power Team has earned more than 12 million Kid Power Points, enough to unlock more than 5.1 million packets of therapeutic food.


7. Cleaner air

The World Health Organization says the number of deaths attributed to air pollution is 6.5 million a year. That’s more than the number of people killed by HIV/AIDS, tuberculosis and road injuries combined.

Wynd is a portable air cleaner that monitors the air around you and purifies where needed. It removes dust, allergens, smoke, and pollution from your personal space, in a device that is the size of a water bottle.

 A portable air cleaner

Image: Wynd Technologies

It has been created by Wynd Technologies, Inc., a start-up based in California. Founder Ray Wu often travelled to Asia, where poor air quality is a well-known problem. “We created Wynd out of a need to create clean environments for ourselves and our families wherever we go,” says the company.

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[Info] How artificial intelligence could change the face of business

Written by Paul Daugherty Chief Technology Officer, Accenture
Published Monday 12 December 2016

A technician makes adjustments to the "Inmoov" robot from Russia during the "Robot Ball" scientific exhibition in Moscow May 17, 2014. Picture taken May 17, 2014. REUTERS/Sergei Karpukhin (RUSSIA - Tags: SCIENCE TECHNOLOGY SOCIETY) - RTR3PNJ5

Paul Daugherty, Accenture CTO, explores the possible impacts of artificial intelligence on the business world.
Image: REUTERS/Sergei Karpukhin
Artificial Intelligence (AI) may be the single most disruptive technology the world has seen since the Industrial Revolution. Granted, there is a lot of hype out there on AI, along with doomsday headlines and scary movies. But the reality is that it will positively and materially change how we engage with the world around us. It’s going to improve not only how business is done, but the kind of work we do – and unleash new levels of creativity and ingenuity.

In fact, research from Accenture estimates that artificial intelligence could double annual economic growth rates of many developed countries by 2035, transforming work and fostering a new relationship between humans and machines. The report projects that AI technologies in business will boost labor productivity by up to 40 percent. Rather than undermining people, we believe AI will reinforce their role in driving business growth. As AI matures, it will potentially serve as a powerful antidote for the stagnant productivity and shortages in skilled labor of recent decades.

While it is early days, we are already seeing AI’s impact. Combined with cloud, sophisticated analytics and other technologies, it is starting to change how work is done by both people and computers. It’s also changing how organizations interact with consumers, sometimes in startling ways.

AI is flourishing now because of the rise of ubiquitous computing, low-cost cloud services, near unlimited inexpensive storage, new algorithms, and other related technology innovations. Cloud computing along with advances in Graphical Processing Units (GPU’s) has provided necessary computational power. AI algorithms and architectures have progressed rapidly, often enabled by open source software.

Artificial intelligence

Image: Techonomy

Artificial Intelligence is not just one technology, but rather a variety of different sorts of software that can be applied in numerous ways for different applications.

But equally important is a vast increase in the availability of data. AI does not think for itself. Its insights are possible because the software gets fed information, and the more information it gets, the more insight it can produce. Over the last decade, crowdsourced data in particular has proliferated on internet and social media. People in their daily lives upload massive quantities of images, videos, social media comments, and chat dialogues. All that creates labelled data that is available for machines to use in what’s called machine learning.

While many believe that AI will supplant humans, we think it will instead mostly enable people to do more exceptional work. Certainly, AI will cause displacement of jobs, but it may also significantly boost the productivity of labor. Innovative AI technologies will enable people to make more efficient use of their time and do what humans do best – create, imagine and innovate new things.

With technology overall and AI in particular, the key ingredient for success and creating value is taking a “people first” approach. But to make this transition means both companies and governments must acknowledge the challenges and change how they behave. They must be thoroughly prepared—intellectually, technologically, politically, ethically and socially.

Governments and businesses will need to take several steps, many of which are not easy:

-Prepare the next generation. Re-evaluate the type of knowledge and skills required for the future, and address the need for education and training. AI presents the opportunity to prepare an entirely new sort of skilled and trained workers that do not exist today. This training should be targeted to help those who are disproportionately affected by the coming changes in employment and incomes.

-Advocate for and develop a code of ethics for AI. Ethical debates, challenging as they will be, should be supplemented by tangible standards and best practices in the development and use of intelligent machines.

-Encourage AI-powered regulation. Update old laws and use AI itself to create adaptive, self-improving new ones to help close the gap between the pace of technological change and the pace of regulatory response. This will require government to think and act in new ways appropriate to the new landscape, and means more technologically-trained people must play an active role in government.

-Work to integrate human intelligence with machine intelligence.Businesses must begin reimagining business processes, and reconstructing work to take advantage of the respective strengths of people and machines.

The market demand and opportunity for AI is expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from $4.5 billion in 2014 to $9.2 billion in 2019. In fact, Accenture’s Technology Vision 2016—research that gathers input from more than 3,100 global business and IT executives—found that 70% of them are making significantly more investments in AI-related technologies than two years ago, with 55% planning to use machine learning and embedded artificial intelligence. Equity financings for AI companies have risen from $282 million in 2011 to $2.4 billion in 2015, or 746%, according to researchers at CB Insights. AI patents are being granted at a rate five times greater than 10 years ago. AI start-ups in the US alone have increased 20-fold in just 4 years.

A major Italian government agency offers a good example of how AI can dovetail with the work people do and enable them to be more effective. Employees there were spending the majority of their time attending to routine customer queries. The agency worked with Accenture to automate the process with AI. An intelligent Virtual Agent application now handles real-time voice calls and webchat interactions, using a combination of cognitive-semantic analysis and machine-learning algorithms. After just three months, the Virtual Agent application has already successfully served more than 70,000 users. Employees can now take on more demanding and rewarding tasks, which can positively impact their engagement.

AI is also positively impacting how governments operate. The Singapore government’s Safe City program uses the latest in video analytics and image recognition to assist in public safety. It increases security, delivers services more effectively and makes more efficient use of city resources.

The Accenture Institute for High Performance and Accenture Technology, in collaboration with Frontier Economics, modeled the impact of artificial intelligence on 12 developed economies that together generate more than 50 percent of the world’s economic output. The research compared the size of each country’s economy in 2035 under a baseline scenario, in which economic growth continues under current conditions, with an AI scenario, in which the impact of AI has been absorbed into the economy.

AI was found to yield the highest economic benefits for the United States, increasing annual growth from 2.6 percent to 4.6 percent by 2035, translating to an additional $8.3 trillion in gross value added (GVA). In the United Kingdom, AI could add an additional $814 billion to the economy in the same period. Japan has the potential to more than triple its annual rate of GVA growth by 2035, and Finland, Sweden, the Netherlands, Germany and Austria could see their growth rates double.

AI can empower people to create, imagine and innovate at entirely new levels to drive growth and productivity. Far from simply eliminating repetitive tasks, AI should put people at the center, augmenting the workforce by applying the capabilities of machines so people can focus on higher-value analysis, decision-making and innovation.

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[INFO] AI, robotics, deep learning, automated delivery… how Amazon, Google, Tesla, and startups use these Edge Strategies to punish slower rivals

 Published on December 7, 2016

Elon Musk (Tesla, Solar City, SpaceX); Jeff Bezos (Amazon, BlueOrigin, Washington Post); Mark Zuckerberg (Facebook); Larry Page (Google, Alphabet). All are CEOs. All are entrepreneurs. All remain disruptive. All are Edge Strategists.

Each operates a core business that has “about-faced” an entire industry … or two. Each continues to invent at the Edge, ignoring old-fashioned industry boundaries. The “former Google, Inc.” operates 11 core subsidiaries: Deep Mind, Access, GoogleX, Jigsaw, Calico, Google, G Ventures, Google Capital, Self-Driving Car Projects, Sidewalk Labs, Nest, Google, Verily. Entities like Youtube live beneath Google itself, two layers down. Google invests in and acquires startups — like Deep Mind and YouTube — to build this ecosystem.

Bezos, Musk, Page, and Zuckerberg are disrupting multiple industries at once: tech (cloud computing, AI), automotive, entertainment (production, streaming), space (re-usable rockets), health…. (Note: watch the Blue Origin and WagonBot videos below.)

Challenges Faced By Older, Traditional Companies

Each companymentioned above are teenagers. They were simple startups 10 to 22 years ago. They behave like Millennials (Gen Y) and Gen Z’s.

For survival’s sake, older companies — born before 1980 and molded in our grandparents’ era — have developed cultures that need to adapt to the Millennial world. This new corporate world is defined by exponential innovation (versus linear) and by disruptive hyper-adoption (think Uber, AirBnB, AWS, Amazon Prime, Pokemon Go).

If you work in an older firm, your team might be constrained by corporate culture. Your team may work as a “incrementalist” … continually tweaking your core revenue streams. Thankfully, your company needs “incrementalism” … like Amazon needs its Weblab. But a singular focus on core incrementalism is short-sighted. Below are suggestions that might help your company maneuver into an Edge Strategist position.

The Edge Strategist’s DNA + Psyche

To survive over the next 5 to 10 years, your organization needs to do be an incrementalist AND an Edge Strategist. As Amazon, Google, and Facebook prove, The Edge can be accessed by inventing internally (intrapreneurs), licensing advanced technologies from startups, or acquiring startups. But — above all — to succeed, your CEO needs to carry the “Innovation Torch”.

The most disruptive Edge Strategists are a handful of well-capitalized CEOs who double as entrepreneurs: Bezos (Amazon), Musk (Tesla), Zuckerberg (Facebook), Page (Google), Ma (Alibaba).

One great example of Edge Thinking, is Blue Origin, the space company that Bezos dreamt up in high school, and founded in 2000. Its rockets land upright (see video):, media companies, NASA, and tech companies (like HP) — all — have to keep a watchful eye on Bezos’ Edge-moves. His companies iterate on the core (Amazon’s Weblab) … while (b) inventing at the Edge.

To grasp how ingrained “Edge Thinking” is in Bezos’ psyche, look at the timing of Blue Origin. It was founded in September 2000 — while Amazon’s stock was dropping 67% (from $106 to $25).

Imagine “having the courage” to ask Amazon’s Board for permission to start a spaceship company (on the side) in 2000. Bezos has backed Blue Origin with $500 million of his own money, demonstrating his capacity for patience and bold moves.

Today’s New Forces: Hyperadoption and Techno-Exponentialism

Executives need to embrace “The Edge” today more than 10 years ago because new forces exist. Forrester calls one of them hyperadoption. Singularity University calls the other exponentialism … the advancement and convergence of many technologies all at once.

Last Mile Supply Chain is likely to completely change as Edge Strategists combine AI with robotics. 20% of package deliveries may not involve people by 2020 (in just 4 years). McKinsey predicts 80% of all packages will be delivered autonomously within 9 years (2025). Starship‘s self-driving WagonBots (below) are possible solutions. These Edge Inventions are being tested today in DC, Europe, and San Francisco! the video … you’ll see that WagonBots (AGV’s — autonomous ground vehicles) can navigate sidewalks and travel for a few miles to deliver your pizza, your medicine, a new pair of shoes. Maybe Amazon will use them to deliver from the 2,000 Amazon grocery stores it’s building. Or maybe Amazon will use drones Or maybe a combo of tools.

McKinsey & Company, September 2016: “Get ready for a world where … Autonomous vehicles including drones will deliver close to 100 percent of X2C and 80 percent of all items. Only ~ 2 percent will be delivered by bike couriers in the relatively small instant delivery segment. Traditional delivery will account for the remaining ~ 20 percent of all items.”

This graphic below is from that McKinsey report (how customer demands are reshaping last mile delivery):

The WagonBots are point solutions. But … combine autonomous deliveries with an Amazon Dash Button (another Edge Strategy) … and combine those with Private Label products like Amazon batteries … and the economics become dangerous. Costs plummet. First movers gain advantages. The dynamics of Edge Strategy start to appear as many Edge Inventions coming together, harmoniously.

Imagine a button summoning a Drone …(this is Edge Strategy):

Imagine your voice, using Echo Dot, to summons the WagonBot:

Legislating the Edge

Amazon invests $9.5 million per year to educate the U.S. government … to influence regulation affecting The Edge … which is one more reason why Amazon is dangerous.

If you look at Gur Kimichi’s LinkedIn Profile (Gur is VP of Amazon Air), you will see that Amazon started working on Commercial Drones in 2012 … giving Amazon a four-year head-start on the U.S. Government it seems.

Contrast Amazon’s Drone launch in 2012 with the White House Drone-announcement in 2016. In August 2016, the White House set up two of groups — the FAA’s new Unmanned Aircraft Safety Team and their new Drone Advisory Committee. As they set up those committees, Amazon was lobbying and conducting real life Drone tests in the U.K. (blessed by the British Government).

When the U.S. Government says, “Go” on Drones, Amazon will be prepared to be first-mover … AGAIN.

Pursuits of Imagination” requires CEO support and blessing

Edge Strategy is disruptive by nature. Thus, for it to succeed inside large organizations that value collaboration and predictable process, “Innovation” (with a capital “I”) and intrapreneurs need CEO level backing. Tommy Knoll says it well:

“The key to innovation management, and (a supportive culture) … is to have the innovation function report directly the CEO and/or the Board. The risks increase for the innovation program being cut/divested with a bad earnings call if the function reports any lower in the org.”

CEO as Chief Edge Strategist (CES)

Bezos is arguably the most commercially-dangerous Edge Strategist. As he tinkers with 4.4 MPH rocket landings at Blue Origin (below), his Amazon troops work on Drones.

As Bezos invests over $500 million of his own money in Blue Origin, Amazon plows part $15 billion into R&D aimed at Voice technologies, AI, and Deep Learning. Then, similar to with Blue Origin, Amazon “works on our government” to mold policies while patiently and continuously conducting R&D with a long-term outlook.

Amazon “Thinks at the Edge” … because its CEO expects it

This Blue Origin behavior is consistent with the way Bezos behaves at Amazon. This is’s “innovations” page

Soon, new grocery formats may be added to the Amazon Innovations page. The new Grab-n-Go store format includes a cocktail of Edge technologies — from Deep Learning to Visual Computing to advanced Sensors.

Using startup technologies, (my company) helped build technologies like this by March 2016– no lines, no checkout — with Twyst. Many retailers have been excited to see it — but tentative on trying it. Too futuristic.

But, as demonstrated in many areas, Amazon likes creating the headwinds, and they tend to just GO: the end — Edge Strategists create the headwinds that “Followers Firms” are forced to face. Edge Strategists, like Bezos and Jobs, enjoy headwinds. These people consistently pursue the unexpected. The undoable. The unimaginable. The world scoffs at them. But, that’s okay (and they expect it).

A Few Edge Strategies To Think About

Beyond self-checkout (which all retailers are now probably evaluating), here are Edge Technologies and Concepts that many retailers and non-retailers need to think about.

  1. Automated Delivery (supply chain)
  2. AI, Deep Learning, and Neural Networks (for many use cases)
  3. New Business Models including Asymmetric ones

Automated Deliveries

Delivery automation is one Edge frontier that sure to be disruptive. Yet, despite being on the Edge, it’s will mainstream soon.

Self-driving DeliveryBots could drive out of Autonomous Vans. Watch this short video from Mercedes and Starship (out of Estonia and London):…%2522%257D%257D%2C%2522title%2522%3A%257B%2522localized%2522%3A%257B%2522en_US%2522%3A%2522Robovan%2520-%2520Future%2520Proof%2520Local%2520Delivery%2522%257D%257D%2C%2522type%2522%3A%2522video%2522%257D&signature=AWlCyP7o8Aw8Ck0-ykNAoKbKVWgwDrones could also fly out of Autonomous Vans. Maybe Amazon will try this … as they are known for experimenting with many concepts at once? Watch this short video:…%2522%257D%257D%2C%2522title%2522%3A%257B%2522localized%2522%3A%257B%2522en_US%2522%3A%2522Drone%2520Delivery%2520Service%3A%2520What%2520Is%2520Drone%2520Delivery%253F%2520Mercedes%2520Van%2520Electric%2520Autonomous%2520Drone%2520Video%2520CARJAM%2522%257D%257D%2C%2522type%2522%3A%2522video%2522%257D&signature=ASQVxP14a9I_2–7nhss_L18WUo3Starship Technologies created the Robotic Delivery Vehicles showcased here. Like Amazon technologists, the founders of Starship are not lightweights, true for many startups. Starship’s CEO / CTO, Ahti Heinla, writes that he’s “happiest when he lits up his laptop and shoots another module of a brilliant code.” Ahti was a co-founder and Chief Technical Architect of Skype and KaZaA.

Like Google Self-Driving Cars, these WagonBots or AGV’s have been racking up miles:

This Edge Strategist, Starship, has started deliveries in Europe. Trials make Starship believe that the cost of on-demand package deliveries in central London will drop from £12 ($15) to have a package delivered to £1 by using their six-wheeled WagonBots. That’s a 70% cost reduction for last-mile delivery.

AI, Deep Learning, and Neural Networks

New waves of innovation will be powered on advanced AI — deep learning and neural networks. AI empowers the drones, chatbots, new retail recommendation and personalization engines, space ships, self-driving wagonbots, and the new Amazon Go grocery store. The interconnected networks function like neurons in our brains — learning from experiences, recognizing patterns, reading visual images, and even writing text like a human copywriter. This type of technology is accessible to all of large companies via the startup ecosystem.

AI and deep learning will (for sure) be disruptive. In the old days, rules-based software ruled the roost. But now robots with embedded deep learning software can teach themselves. Just look at this Softbank AI Lab demo:

AI will change financial dynamics of existing industries. Business models are likely to change too.

New Disruptive Business Models

Technical inventions lead to disruptive business models (like AirBnB and Uber) and new business opportunities.

Tesla’s distribution model is an example of business model disruption. Elon Musk, the CEO, has replaced franchised dealerships with stores in shopping malls. This has worked, and it’s transformative. Within two weeks of taking orders (mid-April 2016), Tesla’s Model 3 reservations had risen to almost 400,000 units. Selling direct (versus through dealerships) helps Tesla control the presentation, achieve greater speed-to-market, and lower the costs of inventory, facilities, and service. If all 400,000 Model 3’s units are sold for $35,000, Tesla will realize $14 billion from those two weeks of selling. That’s an unprecedented sum.

CNBC  reports, “The 24 malls that list Tesla as a tenant average $940 in sales per square foot, compared to $835 for those without the carmaker in their directory, according to research by Green Street Advisors. Tesla’s correlation to high-performing malls mimics that of another star Silicon Valley tenant: Apple.”

As Taxi Drivers fight Uber, some auto-franchisees (from Ford, GM, etc.) are fighting Tesla. Tesla presses on, though, opening retail stores in hot vacation destinations.

Technical inventions can lead to asymmetries exploited by entrepreneur/CEO’s like Musk and Bezos. Amazon believes in exploiting asymmetries as Vision Mobile shows:

Bezos was describing assymetry here:

IoT makes asymmetries accessible to product companies as shown in Vision Mobile’s value quadrant:

Asymmetric business models form when a company “gives away” one product to gain control of another. Asymmetric business models may emerge in the Tesla / Solar City combination. They become more commonplace as companies cross industry boundaries (Apple into music, Amazon into technology hosting) …

Digital allows for distribution channels to blur. Ecosystems — like Amazon’s — may become a dominant “company-type” in the future.

Edge Inventors are Proactive

Yet, Edge Strategists don’t rest on their laurels. They are thought leaders, and they build their dreams. Amazon did this with AWS and S3. They disrupted HP, Google, and Oracle … Google being two years behind Amazon.

Yet, that leadership position doesn’t give AWS any reason to slow down. In fact, they’ve sped up! These are AWS stats revealed last week at its annual Invent conference —

AWS … revealed one new service after another, one thing became clear: the company with a marketshare lead that is by Gartner’s estimate  10 times bigger than its 14 closest competitors combined, has no plans to slow down or rest on its laurels. (Techcrunch: AWS Shoots for Total Domination)

HP, Google, IBM and Oracle — all of a sudden — are chasing a RETAILER: Amazon. As Techcrunch says:

AWS isn’t just dominating because it was first (although that’s part of it), it’s also continuing to innovate at an astonishing rate, adding around 1000 new features every single year up from 722 just last year.

AWS practices a fast paced “feature release” cadence:

This is similar to the way innovates incrementally though Weblab (1,976 experiments in 2013, up from 500 two years prior).

On top new features, AWS cuts prices: “Since its launch in 2006, Amazon’s cloud business has seen 52 price cuts in total.” wrote Business Insider, October 2016. “Our price reductions are a core part of our philosophy,” said Amazon CFO, Brian Olsavsky.

The Edge sneaks up

Edge companies build products proactively … this is “intentional innovation” as Jeff Roster (analyst) says. They invent patiently … yet they operate with a contradictory sense of urgency. Knowing that their inventions will matter five years from now.

That’s Edge Strategy.

11 Ways To Pursue Edge Strategies without Google or Amazon Pursestrings

Today’s enterprise companies — for survival’s sake — need to practice Edge Innovation Strategies (in addition to incremental improvements), even if they don’t have the big R&D budget. Like Amazonians, all companies should be continually scanning the globe to find seemingly wild ideas that could be disruptive to their futures.

1. As a CEO or VP … provide air-cover

Make your support obvious. Get everyone on board. If your’e a leader, this is a key role for you to play. A company’s ability to embrace the Edge is likely to predict future growth. Lack of leadership here might be a “cause of death” in the long run — think Kodak, Nokia, Blockbuster. Show the inventors, both inside and outside your firm, that you value them … and provide air-cover.

2. License technologies and partner with startups (early)

eBags, which has sold 27 million bags through digital channels, is good at this. They move fast, looking quarterly (alongside Iterate) for early stage technologies to integrate into In late 2015 and 2016, they integrated 8 of 12 technologies helped them discover, curate, speed-up-legal barriers, and test. This is on top of all the many Proof of Concept’s they lined up on their own. It’s one reason eBags is growing 25% year-over-year this holiday season.

3. Build technologies internally.

If you have the intrapreneurial DNA … if time’s on your side … if you have air-cover from the CEO — then build technologies internally. eBags built its own drop ship technology which integrates with 900 warehouses now. It built its own ratings and reviews technology in 1998-9 — still use it today. It built it’s own A/B split test technology nearly 10 years before Optimizely and VWO existed.

4. Acquire digital companies

If you need to move fast … if you want a headstart … or, if you need to build a moat around a strong core business, then acquisitions are a good move.

  • Acquisitions can be small like Amazon did to support Echo with voice technoloigies — each costing roughly $20 million (rumored numbers).
  • Acquisitions can be larger, like Uniliver did with Dollar Shave Club, investing $1 billion to buy the $150 million disruptive direct-to-consumer subscription service. Or like Walmart did with Jet … Walmart’s $3 billion consumer-facing acquisition. Or like Amazon did with Zappos.
  • Companies can aggregate a cocktail of acquisitions like Nordstrom did with TrunkClub, investing $350 million as the company projected $100 million in revenues, and HauteLook, which they acquired for $180 million. Or like Under Armour did with MapMyFitness, MyFitnessPal, and Endomondo (buying all three of them for $750 million).

5. Monitor trends and startups

Keep your entire management team aware of large-scale digital trends. Keep your team aligned, on the same page. Iterate’s Tours of the Possible do this.

Or use companies like FitForCommerce in the U.S., Cnetric in APAC, or in Netherlands.

Anyone can monitor startups by using tracks 158,000 emerging technologies … for both innovations you can apply today (applied innovation) and for future Edge concepts:

When you track startups, sometimes it’s good to seek out “the crazy ones”. Big companies can lend a hand, offering support and partnerships. Also remember that the Edge Inventors like Athi of Starship, or Bezos of Amazon, are often viewed as “nuts” … but they are simply Breakthrough Artists who enjoy the headwinds.

“Most of the breakthrough technologies/companies seem crazy at first: PCs, the internet, Bitcoin, Airbnb, Uber, 140 characters…It has to be something where, when people look at it, at first they say, ‘I don’t get it, I don’t understand it. I think it’s too weird, I think it’s too unusual.’” – Marc Andreessen

6. Leverage shared Labs and Learnings

Q&A and Iterate both help on that front. Q&A operates three Innovation Labs in the Netherlands — Store of the Future. Two clean labs, one dirty lab. Join, and you become part of a club of “like retailers” who can share test results with non-competitors. Iterate does this as well when you are a member of Iterate’s Virtual Lab Network.

7. Update your legal framework to test nimble startups

Andy Wichern, with an Opsware, E&Y consulting, and background, invented a PilotPass® program to advance the goal of reducing the needless friction involved in the digital innovation process for large organizations wanting to test startup technologies. PilotPass® is built to reduce the time and cost of pre-pilot legal and procurement administration … and speed up experimentation to establish proof-of-value. This could help you double your experimentation and double your successes.

Old legal frameworks were often built for enterprise software that touched many parts of the business infrastructure … and took many months (or even years) to configure, test, and promote into production. Today, SaaS has changed the way any Enterprise can test and deploy software. Digital innovators can implement experiments in controlled test environments in a matter of minutes. PilotPass® is built for this new digital world.

8. Innovate in many areas … all at once

Innovating on multiple fronts is good for shareholder returns, (research by Doblin).

Your company may say, “Impossible.” But you can do it if you architect your firm to be Exponential Organization. Embrace modularity. Work with people who are not your employees. Embrace the startup community which is can be your $100 billion (Exponential Organization) Lab. ($100 billion is roughly the level of cash invested in the startup ecosystem every year. ) Let third-party investors and passionate bootstrappers “fund the early stages” of YOUR LAB.

Startups can help you innovate in many arenas at the same time. They can help enterprises innovate exponentially instead of linearly.

Just be a good partner to the startups by treating them as important partners.

9. Digitize your HR

Make sure every part of your organization is updating and digitizing. HR is important because all your employees and prospects interact with it. As a recruiting and retention tool, digital HR sends the message that you are advanced.

10. Give your corporate innovators space

The best ideas are often the ones no one agrees with. It conflicts with traditional Corporate Culture which values teamwork and collaboration. So give your innovators space and respect.

11. Reinforce a company culture that values change

“The ability to reinvent yourself and reinvent your organization will be one of the most important competencies to master in the 21st century,” says author, Ron Immink.

What Won’t Work

Doing things the old way might be comfortable (for a while). But it’s high risk. It’s the most likely path to having a Kodak Moment.

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[Info]The current state of machine intelligence 3.0

November 7, 2016

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year’s landscape has a third more companiesthan our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there.

As has been the case for the last couple of years, our fund still obsesses over “problem first” machine intelligence—we’ve invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. (Our fund focuses on the future of work, so there are some machine intelligence domains where we invest more than others.)

At the same time, the hype around machine intelligence methods continues to grow: the words “deep learning” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like “big data” (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one. We favor those who apply technology thoughtfully.

What’s the biggest change in the last year? We are getting inbound inquiries from a different mix of people. For v1.0, we heard almost exclusively from founders and academics. Then came a healthy mix of investors, both private and public. Now overwhelmingly we have heard from existing companies trying to figure out how to transform their businesses using machine intelligence.

For the first time, a “one stop shop” of the machine intelligence stack is coming into view—even if it’s a year or two off from being neatly formalized. The maturing of that stack might explain why more established companies are more focused on building legitimate machine intelligence capabilities. Anyone who has their wits about them is still going to be making initial build-and-buy decisions, so we figured an early attempt at laying out these technologies is better than no attempt.

machine intelligence landscape
Figure 1. Image courtesy of Shivon Zilis and James Cham, designed by Heidi Skinner. (A larger version can be found on Shivon Zilis’ website.)

Ready player world

Many of the most impressive looking feats we’ve seen have been in the gaming world, from DeepMind beating Atari classics and the world’s best at Go, to the OpenAI gym, which allows anyone to train intelligent agents across an array of gaming environments.

The gaming world offers a perfect place to start machine intelligence work (e.g., constrained environments, explicit rewards, easy-to-compare results, looks impressive)—especially for reinforcement learning. And it is much easier to have a self-driving car agent go a trillion miles in a simulated environment than on actual roads. Now we’re seeing the techniques used to conquer the gaming world moving to the real world. A newsworthy example of game-tested technology entering the real world was when DeepMind used neural networks to make Google’s data centers more efficient. This begs questions: What else in the world looks like a game? Or what else in the world can we reconfigure to make it look more like a game?

Early attempts are intriguing. Developers are dodging meter maids (brilliant—a modern day Paper Boy), categorizing cucumbers, sorting trash, and recreating the memories of loved ones as conversational bots. Otto’s self-driving trucks delivering beer on their first commercial ride even seems like a bonus level from Grand Theft Auto. We’re excited to see what new creative applications come in the next year.

Why even bot-her?

Ah, the great chatbot explosion of 2016, for better or worse—we liken it to the mobile app explosion we saw with the launch of iOS and Android. The dominant platforms (in the machine intelligence case, Facebook, Slack, Kik) race to get developers to build on their platforms. That means we’ll get some excellent bots but also many terrible ones—the joys of public experimentation.

The danger here, unlike the mobile app explosion (where we lacked expectations for what these widgets could actually do), is that we assume anything with a conversation interface will converse with us at near-human level. Most do not. This is going to lead to disillusionment over the course of the next year but it will clean itself up fairly quickly thereafter.

When our fund looks at this emerging field, we divide each technology into two components: the conversational interface itself and the “agent” behind the scenes that’s learning from data and transacting on a user’s behalf. While you certainly can’t drop the ball on the interface, we spend almost all our time thinking about that behind-the-scenes agent and whether it is actually solving a meaningful problem.

We get a lot of questions about whether there will be “one bot to rule them all.” To be honest, as with many areas at our fund, we disagree on this. We certainly believe there will not be one agent to rule them all, even if there is one interface to rule them all. For the time being, bots will be idiot savants: stellar for very specific applications.

We’ve written a bit about this, and the framework we use to think about how agents will evolve is a CEO and her support staff. Many Fortune 500 CEOs employ a scheduler, handler, a research team, a copy editor, a speechwriter, a personal shopper, a driver, and a professional coach. Each of these people performs a dramatically different function and has access to very different data to do their job. The bot / agent ecosystem will have a similar separation of responsibilities with very clear winners, and they will divide fairly cleanly along these lines. (Note that some CEO’s have a chief of staff who coordinates among all these functions, so perhaps we will see examples of “one interface to rule them all.”)

You can also see, in our landscape, some of the corporate functions machine intelligence will re-invent (most often in interfaces other than conversational bots).

On to 11111000001

Successful use of machine intelligence at a large organization is surprisingly binary, like flipping a stubborn light switch. It’s hard to do, but once machine intelligence is enabled, an organization sees everything through the lens of its potential. Organizations like Google, Facebook, Apple, Microsoft, Amazon, Uber, and Bloomberg (our sole investor) bet heavily on machine intelligence and have its capabilities pervasive throughout all of their products.

Other companies are struggling to figure out what to do, as many boardrooms did on “what to do about the Internet” in 1997. Why is this so difficult for companies to wrap their heads around? Machine intelligence is different from traditional software. Unlike with big data, where you could buy a new capability, machine intelligence depends on deeper organizational and process changes. Companies need to decide whether they will trust machine intelligence analysis for one-off decisions or if they will embed often-inscrutable machine intelligence models in core processes. Teams need to figure out how to test newfound capabilities, and applications need to change so they offer more than a system of record; they also need to coach employees and learn from the data they enter.

Unlike traditional hard-coded software, machine intelligence gives only probabilistic outputs. We want to ask machine intelligence to make subjective decisions based on imperfect information (eerily like what we trust our colleagues to do?). As a result, this new machine intelligence software will make mistakes, just like we do, and we’ll need to be thoughtful about when to trust it and when not to.

The idea of this new machine trust is daunting and makes machine intelligence harder to adopt than traditional software. We’ve had a few people tell us that the biggest predictor of whether a company will successfully adopt machine intelligence is whether they have a C-Suite executive with an advanced math degree. These executives understand it isn’t magic—it is just (hard) math.

Machine intelligence business models are going to be different from licensed and subscription software, but we don’t know how. Unlike traditional software, we still lack frameworks for management to decide where to deploy machine intelligence. Economists like Ajay Agrawal, Joshua Gans, and Avi Goldfarb have taken the first steps toward helping managers understand the economics of machine intelligence and predict where it will be most effective. But there is still a lot of work to be done.

In the next few years, the danger here isn’t what we see in dystopian sci-fi movies. The real danger of machine intelligence is that executives will make bad decisions about what machine intelligence capabilities to build.

Peter Pan’s never-never land

We’ve been wondering about the path to grow into a large machine intelligence company. Unsurprisingly, there have been many machine intelligence acquisitions (Nervana by Intel, Magic Pony by Twitter, Turi by Apple, Metamind by Salesforce, Otto by Uber, Cruise by GM, SalesPredict by Ebay, Viv by Samsung). Many of these happened fairly early in a company’s life and at quite a high price. Why is that?

Established companies struggle to understand machine intelligence technology, so it’s painful to sell to them, and the market for buyers who can use this technology in a self-service way is small. Then, if you do understand how this technology can supercharge your organization, you realize it’s so valuable that you want to hoard it. Businesses are saying to machine intelligence companies, “forget you selling this technology to others, I’m going to buy the whole thing.”

This absence of a market today makes it difficult for a machine intelligence startup, especially horizontal technology providers, to “grow up”—hence the Peter Pans. Companies we see successfully entering a long-term trajectory can package their technology as a new problem-specific application for enterprise or simply transform an industry themselves as a new entrant (love this). We flagged a few of the industry categories where we believe startups might “go the distance” in this year’s landscape.

Inspirational machine intelligence

Once we do figure it out, machine intelligence can solve much more interesting problems than traditional software. We’re thrilled to see so many smart people applying machine intelligence for good.

Established players like Conservation Metrics and Vulcan Conservation have been using deep learning to protect endangered animal species; the ever-inspiring team at Thorn is constantly coming up with creative algorithmic techniques to protect our children from online exploitation. The philanthropic arms of the tech titans joined in, enabling nonprofits with free storage, compute, and even developer time. Google partnered with nonprofits to found Global Fishing Watch to detect illegal fishing activity using satellite data in near real time, satellite intelligence startup Orbital Insight (in which we are investors) partnered with Global Forest Watch to detect illegal logging and other causes of global forest degradation. Startups are getting into the action, too. The Creative Destruction Lab machine intelligence accelerator (with whom we work closely) has companies working on problems like earlier disease detectionand injury prevention. One area where we have seen some activity but would love to see more is machine intelligence to assist the elderly.

In talking to many people using machine intelligence for good, they all cite the critical role of open source technologies. In the last year, we’ve seen the launch of OpenAI, which offers everyone access to world class research and environments, and better and better releases of TensorFlow and Keras. Non-profits are always trying to do more with less, and machine intelligence has allowed them to extend the scope of their missions without extending budget. Algorithms allow non-profits to inexpensively scale what would not be affordable to do with people.

We also saw growth in universities and corporate think tanks, where new centers like USC’s Center for AI in Society, Berkeley’s Center for Human Compatible AI, and the multiple-corporation Partnership on AI study the ways in which machine intelligence can help humanity. The White House even got into the act: after a series of workshopsaround the U.S., they published a 48-page report outlining their recommendations for applying machine intelligence to safely and fairly address broad social problems.

On a lighter note, we’ve also heard whispers of more artisanal versions of machine intelligence. Folks are doing things like using computer vision algorithms to help them choose the best cocoa beans for high-grade chocolate, write poetry, cook steaks, and generate musicals.

Curious minds want to know. If you’re working on a unique or important application of machine intelligence we’d love to hear from you.

Looking forward

We see all this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks, there will be more data, there will be more people interested in learning these methods, and there will always be problems worth solving. We still need ways of explaining the difference between machine intelligence and traditional software, and we’re working on that. The value of code is different from data, but what about the value of the model that code improves based on that data?

Once we understand machine intelligence deeply, we might look back on the era of traditional software and think it was just a prologue to what’s happening now. We look forward to seeing what the next year brings.

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[Info] The Artificial Intelligence Behind Google Translate Recently Did Something Extraordinary Those translations aren’t making us laugh as much as they used to.

By Justin Bariso, Founder, Insight
CREDIT: Getty Images

As someone who lives between two countries, I’ve relied heavily on machine translation for a number of years. It took me years before I could communicate casually with my in-laws (they’re fluent in German and Polish, but not English), without my wife serving as an interpreter. (I don’t need to tell you how dangerous that can be.)

I realize that nothing beats a skilled human translator. But who has time (or money) to hire a person to translate simple, everyday tasks?

That’s why I’ve always been amazed at computer-aided translation, specifically Google Translate.

True, in its earlier days, Google Translate could spit out some pretty laughable, if not completely unintelligible, nonsense in an attempt to convert a message from one language to another. But generally, most of its attempts were helpful.

In recent times, I noticed the quality of these translations steadily improving. Nowadays, I can literally copy and paste a pretty advanced technical document (or God forbid, a letter written in German legalese), and the translation is remarkably good–at least as good as a human translator’s first draft.

Google is consistently at the head of the pack when it comes to A.I. and algorithm-based learning, and Translate’s no exception. The program generates translations using patterns found in huge amounts of text, discovered through millions of documents that have already been translated by humans. As time goes on, the program recognizes more and more patterns, receives input from real people, and continues to refine its translations.

What’s New

Then, recently, something really exciting happened.

As reported by technology blog Tech2:

“In September, Google switched from Phrase-Based Machine Translation (PBMT) to Google Neural Machine Translation (GNMT) for handling translations between Chinese and English. The Chinese and English language pair has historically been difficult for machines to translate, and Google managed to get its system close to human levels of translation by using bilingual people to train the system … Google planned to add GNMT for all 103 languages in Google Translate. That would mean feeding in data for 103^2 language pairs, and the artificial intelligence would have to handle 10,609 models.

Google tackled this problem by allowing a single system to translate between multiple languages … When the translation knowledge was shared, curious Google engineers checked if the A.I. could translate between language pairs it was not explicitly trained on before. This was the first time machine based translation has successfully translated sentences using knowledge gained from training to translate other languages.”

In other words, Google Translate’s A.I. actually created its own language, to enable it to better translate other languages.


So, when was the last time you used Google Translate? If it’s been a while, I suggest you give it another try.

Because those results aren’t as funny as they used to be, but they’re a lot more useful.

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[Video] The Ideas Exchange – 1

Série de conversas entre líderes empresariais com vídeos de curta duração… Vale a pena! Mto interessantes! Primeiro capítulo com Jorgen Vig Knudstorp da Lego (Denmark) e Bethlehem Alemu da SoleRebels (Ethiopia)

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November 10, 2013 · 11:33 am

[Video] Criatividade?! Isso sim foi criativo…

No dia 13 de Abril, Justin pediu a sua namorada Emily em casamento. Ele combinou um encontro com a namorada no restaurante onde tiveram o primeiro encontro e disse-lhe que estava um bocado atrasado. O que a namorada não sabia é que estava a ser filmada por cameras ocultas e que estaria prestes a assistir a um lindo pedido de casamento bastante elaborado e criativo…


November 9, 2013 · 11:24 am