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      Paula KleinCreated by Paula Klein on Aug 29, 2012 in Featured Content

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      Recent advances in the impact of deep learning are finally allowing computers to see, hear and read with increased precision. These long-awaited achievements—like those in robotics and automation--are being celebrated by computer scientists, but they also have implications for the economy and society. Jeremy Howard addressed both the technical and societal issues at a recent MIT IDE seminar.


      Machine learning’s history goes back to 1956 when computers amazed the public because they could play against --and in 1962, beat--a human player at checkers. With continual breakthroughs in machine learning over the decades, Howard said that it’s now widely used and “creepily good” at finding your friends and beating humans at highly sophisticated intellectual pursuits such as Jeopardy.


      Howard should know. He was ranked Number 1 in data science competitions globally in 2010 and 2011, and became President and Chief Scientist at Kaggle, where data scientists compete to answer problems using predictive analytics and machine learning algorithms.


      Deep Learning = Good Medicine?

      As deep learning--the evolution of neural networks and artificial intelligence--greatly improved, it has also incorporated more precise speech and object-recognition capabilities. To pursue these new fields, Howard left Kaggle in August to launch; he now serves as CEO.


      Enlitic uses recent advances in machine learning “to make medical diagnostics faster, more accurate and more accessible to medical staff and patients.” The company's mission is “to provide the tools that allow physicians to utilize the vast stores of medical data collected today, regardless of what form they are in--such as medical images, doctors’ notes and structured lab tests.”


      This computing approach involves training systems, called artificial neural networks, on huge amounts of information derived from audio, images and other inputs, and then presenting the systems with new information and receiving inferences in response. Howard (foreground, in photo) wants to mine medical data in this way and provide easy-to-use applications for health care professionals.

      For instance, “there are new ways to think about tumors” that borrow from Kaggle’s analytical and machine-learning methods, Howard said at the MIT IDE seminar. Assumptions can be made by machines based on pathology analysis that are sometimes more accurate—and objective--than human results. Ultimately, however,

      Howard expects that the best diagnoses will result from a combination of humans and machines; where machines crunch the data and humans add history and human interaction to the mix.

      Policy Discussions Needed

      While the technological strides are apparent, Howard acknowledged the potential labor impact that will result from deep learning in the next decade as computers take on more jobs and displace workers. Drawing similar conclusions to those of MIT’s Erik Brynjolfsson and Andrew McAfee, as described in their latest book, the Second Machine Age, Howard said: “As human perception and judgment are replaced, and machine learning grows exponentially, policy discussions are needed about basic living wages and technological unemployment.”

      2014-10-22 16_09_23-MIT_CODE.pptx - PowerPoint.png

      According to Enlitic, each year since 2010, the Imagenet competition has been the proving ground for state-of-the-art computer vision algorithms. The past three years have seen striking improvements in accuracy.


      In addition to his current role as CEO of Enlitic and his former role with Kaggle, Jeremy Howard is on the faculty at Singularity University. He was recently a Distinguished Research Scientist at the University of San Francisco, and he advises Khosla Ventures as their data strategist. Howard founded two successful start-ups – the e-mail provider FastMail and the insurance pricing algorithm company, Optimal Decisions Group – both of which grew internationally and were sold to large international companies. He started his career in management consulting, including at McKinsey & Co. and AT Kearney, where he built a new global practice in what is now called “Big Data”.

      Follow his blog here.

      Last month, I discussed the recent MIT Second Machine Age Conference, an event inspired by the best-selling bookof the same title published earlier this year by MIT’s Erik Brynjolffson and Andy McAfee.  In his closing keynote, McAfee shared with us what to me was a rather surprising paradox:  Entrepreneurship has never been easier, but entrepreneurship is on the decline.  He showed us data from a July article by the Brookings Institution: in 1992, 23% of all firms were 16 year or older, and employed 60% of the private sector workforce;  by 2011, such maturefirms were 34% of the economy, and employed 72% of private sector workers.  But, during the same period, firm share and employment declined for all other companies aged less than 16 years.


      I was frankly not aware that entrepreneurship has been declining for years.  To the contrary, I’ve viewed entrepreneurship as being closely associated with the transformative innovations all around us, and the core of Schumpeter’s 70 year old theory of creative destruction, according to which successful, mature companies that once revolutionized their industries are too slow to respond to the waves of startups now attacking them with innovative products and services.

      Five years ago, The Economist published a special report on entrepreneurship.  “Entrepreneurialism has become cool,” it said, and called it “An idea whose time has come.”  The Economist concluded that “The rise of the entrepreneur, which has been gathering speed over the past 30 years, is not just about economics.  It also reflects profound changes in attitudes to everything from individual careers to the social contract.  It signals the birth of an entrepreneurial society.”


      Moreover, as plenty of books and articles remind us, it’s never been easier to become an entrepreneur and start your own company.  Digital technologies are inexpensive and ubiquitous, startups have access to all kind of cloud-based business services, and customers can now be easily reached and supported over mobile devices.


      What happened to our entrepreneurial society?  Why is entrepreneurship on the decline when starting a company has never been easier?  This is a truly surprising paradox.


      McAfee’s data comes from The Other Aging of America: The Increasing Dominance of Older Firms by economists Ian Hathaway and Robert Litan.  “Like the population, the business sector of the U.S. economy is aging,” write the authors in their summary.  “Our

      research shows a secular increase in the share of economic activity occurring in older firms --a trend that has occurred in every state and metropolitan area, in every firm size category, and in each broad industrial sector.”  It’s not clear why this is happening, but “whatever the reason, it has become increasingly advantageous to be an incumbent, particularly an entrenched one, and less advantageous to be a new entrant.”


      The authors explored the causes of the rising economic activity of older firms by analyzing business data from the US Census Bureau.  They uncovered that new firm formation has been declining across the board since the late 1970s.  Fewer new firms each year means fewer young- and medium-aged firms over the years, leading to an overall decline in entrepreneurship.


      In addition, business failure rates have steadily increased for all except mature firms, - those 16 years or older, - whose failure rates have been flat for the past 20 years.  Failure rates have been particularly high for early-stage firms, especially since the 2000 dot-com crash.


      Thus, the aging of the firm seems to be the result of the declining formation of new firms combined with the increasing failure rates of younger firms.


      Should we be concerned by this shift of economic activity toward older firms?  Yes, say the authors: “An economy that is saturated with older firms is one that is likely to be less flexible, and potentially less productive and less innovative than an economy with a higher percentage of new and young firms.”


      Continue reading the full blog that appeared October 1, here.


      For another view of entrepreneurship and universities, read the blog here.


      The Second Machine Age: Challenges for CXOs

      Erik Brynjolfsson, co-author of the book The Second Machine Age, discussed with I-CIO the profound impact of automation on every industry and how CXOs must play a key role--now.

      Andrew McAfee on Technology and the American Workforce

      On the PBS NewsHour last April, Andy McAfee spoke about how increased use of technology does play a role in the current disappointing job growth statistics.