Print Vs Digital (Infographic)


Brian Wallace:

When you reach for your book at the end of the day, is it paperback or electronic? Though eBooks were slated to overtake print books in 2015 it just didn’t happen. There was a 10% drop in eBook sales and a 2% increase in paper book sales during that time period. Because there are pros and cons to both, most people go back and forth depending on their needs.

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The State of Decision-Making by Machines and by Humans

A new PwC survey provides fresh and illuminating data on the burning questions of the day:  Are machines going to take over our jobs? And how much do we rely (or over-rely) today on machines, automation, and algorithms?

Experts are confident that machines are going to replace many workers. A much-quoted report from Oxford University has estimated that “about 47% of total US employment is at risk” for being fully automated. The machine threat to employment is even greater in developing economies—a  recent report from Oxford estimates that 77% of jobs in China and 69% of jobs in India are “at high risk of automation.”

But maybe estimating the type of jobs that the machines are going to replace is the wrong approach. Tom Davenport, who just published a book on strategies for coping with automation, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (co-authored with Julia Kirby), told the Wall Street Journal recently: “Computers don’t tend to replace whole jobs; they replace specific tasks.”

The McKinsey Global Institute (MGI) agrees: “…a focus on occupations is misleading. Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined.”

MGI estimates that 45% of work-related tasks can be automated. This finding does not bode well for knowledge workers who were sure their cognitive skills could not be automated and that they will always outrun the machines. Even CEOs, according to MGI, spend over 20% of their time on activities that can be automated with current technology.

What has been missing in this discussion is data on how much we rely (or don’t) on machines today, rather than estimates based on experts’ assessments of how automation-prone are various occupations and activities. Specifically, has the era of big data and increasingly sophisticated algorithms changed the nature of business decision-making? What is the extent by which business executives rely on machines today when they make strategic decisions?

A new PwC survey of more than 2,100 business decision-makers across more than 10 countries and 15 industries sheds new light on these questions. It frames the discussion as follows: “Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data and inform decisions like never before.”

59% of the decision-makers PwC surveyed say that the analysis they require relies primarily on human judgment rather than machine algorithms. That means that 41% already tend to rely more on algorithms than their own experience, judgement, and intuition. “We are not talking about pricing a seat on an airline,” says (via email) Dan DiFilippo, Global & US Data and Analytics Leader at PwC. “We are talking about big, strategic decisions that almost certainly involve some combination of human and machine, but clearly we see a significant involvement of the machine.”

The most interesting findings are about the type of decisions that tend to be assisted by machine algorithms and the ones that rely more on human judgement. In the chart above, “respondents who answered closest to zero are nearest to the survey’s overall average reliance on analysis from machine algorithms and human judgment. The farther away from the center point, the greater reliance on either mind or machine,” says PwC.

“Shrinking existing business” was deemed by survey respondents as the type of decision that relies most on human judgement and “Investment in IT” as the one relying most on algorithms. “Investment in IT,” says DiFilippo, “can cover many areas including shop floor automation, CRM systems, HR systems, risk management systems, etc., all of which have varying degrees of machine algorithms and can be assessed by machine algorithms.”

The breakdown of results by country offers a striking juxtaposition of China and Japan with the former as the country/region relying more than others on machine algorithms and the latter as the country/region second only to Central and Eastern Europe in its reliance on human judgement. One would think that China and Japan will have similar attitudes toward and use of algorithms in decision making but this is apparently not the case. It’s possible, however, that the results are due to different interpretations of the survey questions. Says DiFlippo: “We don’t have a precise answer or explanation for this—we are still working to gather more on this front.”

Finally, the breakdown of results by industry shows that different economic sectors differ in the degree by which decision makers rely on their own judgement vs. relying on machine algorithms. Conclude DiFilippo: “Involving the machine can help reduce/eliminate bias (at the individual, department or organization level), add more accuracy and/or more computing power to crank through a high volume of scenarios that human can’t do (or can’t do in a timely manner), and importantly—and the data supports this—there is a sense that the machine can help de-risk the strategic decision… we see that those who had a high degree of machine algorithms felt a high degree of managed and known risks.”

So should we search for the right mix of minds and machines in the context of a specific decision or should we succumb to a universal McAfee’s Law and agree that “as the amount of data goes up, the importance of human judgment should go down”?  What’s your experience with trusting machine algorithms rather than your own judgement?

Originally published on

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Machine Learning Applications by Industry


Louis Columbus, Machine Learning Is Redefining the Enterprise in 2016

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.

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Future Disruption, Disruptors, Disrupted


Deloitte University Press:

More than one-third of the 141 companies in the Americas, Europe, and Asia Pacific that grew to a valuation of greater than $1 billion between 2010 and 2015 were located in the Bay Area, a striking testament to the area’s ability to accelerate commercial success. Perhaps for this reason, 61 percent of companies with innovation centers have a presence in Silicon Valley.

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E-Commerce Startups Market Landscape


CB Insights:

These are the 120+ companies shaping global e-commerce, including startups selling apparel, used cars, baby care, eyeglasses, and more.

We broke down e-commerce into the following categories:

  • Internet Malls – This heavily-funded category includes multi-product online shopping platforms selling a range of household goods, apparel, electronics, toys, and more. Four companies — China Internet Plus Holding (China), Flipkart (India), Snapdeal (India) and Coupang (South Korea) – have each raised more than $1B.
  • Apparel & Accessories – The category includes both online-first clothing brands, such as Bonobos and NastyGal, and shopping platforms focused on fashion, such as Wish (the segment’s most well-funded, with $571M raised) and Keep.
  • Luxury – This includes luxury shopping platforms such as Moda Operandi, and sites for buying and selling pre-owned luxury, such as The RealReal.
  • Fashion Rental – Rent the Runway and The Black Tux focus on luxury clothing rentals for special occasions.
  • Classifieds & Resale – These startups host online classifieds platforms and channels for peer-to-peer selling of used items. Mumbai-basedQuikr, the most well-funded company on the table, has raised $350M.
  • Personal & Grooming – These startups offer cosmetics and personal care products, some on a subscription model. The most well-funded is vertically-integrated razor company Harry’s, with $287M.
  • Maternity & Baby – and BeiBei are two China-based e-commerce platforms for baby supplies, with over $220M in funding each. India-based FirstCry has raised $69M.
  • Athletic Apparel – Florida-based Fanatics, which manages e-commerce and merchandising for sports teams, is one of the most well-funded companies in our table with $695M.
  • Furniture & Decor – These startups provide online shopping platforms for furniture and home decor, including Houzz, with $214M in funding.Casper, with $70M, is an online-first mattress manufacturer.
  • Eyeglasses – This includes online-first eyeglass designers like Warby Parker (the category’s most well-funded with $216M) along with e-commerce platforms for glasses and lenses like MyOptiqueGroup($102M raised).
  • Auctions & Art – These startups offer online bidding and purchasing platforms for art, like HIHEY ($100M), estate sales, like Everything But The House ($43M), and antiques/collectibles, like Catawiki ($95M).
  • Auto – This category, which has had a flurry of $100M+ mega-rounds recently, includes online platforms for buying and selling new and used cars, with a mix of peer-to-peer and business-to-consumer models. The most well-funded is Beijing-based Uxin Pai with $460M.
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Patents and Trademarks by the Top 100 Internet of Things Startups

IP Held by Top IOT Startups .

Unicorn IP

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The Face of Artificial Intelligence


Facebook’s new servers for artificial intelligence research, inside the company’s data center in Prineville, Oregon (Source: Technology Review)

Source: Technology Review

Source: IEEE Spectrum



Ex Machina (2015)

Source: MadAboutMovies




Lee Se-dol, one of the world’s top Go players, won just one of the matches against the AlphaGo program, missing out on the $1 million prize up for grabs. (March 2016)




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