How good are you working with intelligent machines?
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The document discusses the impact of automation on jobs, highlighting that while many tasks can be automated, complex and creative roles are less likely to be fully replaced by machines. It emphasizes the need for individuals to adapt their skills in collaboration with intelligent machines as the nature of work evolves, affecting both low and high-wage occupations. The analysis includes data from Oxford and McKinsey, which indicate that a significant proportion of activities across various jobs can be automated, leading to potential job redefinitions and the emergence of new strategic opportunities in different sectors.
How good are you working with intelligent machines?
- 1. How good are you working with intelligent machines?
- 2. “Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing be;er without you?”
- 4. Overview Social DisrupAon -‐ Some data/research • how work gets done within companies • loss of jobs/acAviAes and changing nature of work • augmented rather than fully replaced Systems Thinking and IntenAonal Networks – ExplanaAon and Examples • enhanced decision making • become informed and engaged in use or understanding of network analysis at scale (individual, group/project, organizaAonal) as automaAon transforms work. Ethics • social research without our knowledge
- 8. Oxford University report 2011 and McKinsey research Key findings Oxford: • 47% of all US jobs were at risk from automaAon Key findings McKinsey: • Less than 5% of of jobs can be fully automated • Below the job or occupaAon level to work acAviAes 45% of work is automatable by current technologies. Included were high wage, high skilled jobs. h;p://bits.blogs.nyAmes.com/2015/11/06/automaAon-‐will-‐ change-‐jobs-‐more-‐than-‐kill-‐them/?_r=0
- 9. … while sophisAcated algorithms and developments in Mobile RoboAcs (MR), building upon with big data, now allow many non-‐rouAne tasks to be auto-‐mated, occupaAons that involve complex percepAon and manipulaAon tasks, creaAve intelligence tasks, and social intelligence tasks are unlikely to be subsAtuted by computer capital over the next decade or two. The probability of an occupaAon being automated can thus be described as a funcAon of these task characterisAcs … h;p://www.oxfordmarAn.ox.ac.uk/downloads/academic/ The_Future_of_Employment.pdf
- 10. More specifically, our research suggests that as many as 45 percent of the acAviAes individuals are paid to perform can be automated by adapAng currently demonstrated technologies.4 In the United States, these acAviAes represent about $2 trillion in annual wages. Although we oeen think of automaAon primarily affecAng low-‐skill, low-‐wage roles, we discovered that even the highest-‐paid occupaAons in the economy, such as financial managers, physicians, and senior execuAves, including CEOs, have a significant amount of acAvity that can be automated.
- 11. The Four Fundamentals: 1. AutomaAon of acAviAes 2. RedefiniAon of jobs and business acAviAes 3. Impact on high-‐wage occupaAons 4. Future of creaAvity – 4% and meaning – 29% (emoAon)
- 12. ConnecAons below the surface are where tacit informaAon is mined, machine learning begins and is applied via algorithms at massive scale.
- 15. Everything we do at Facebook is seen as a graph. (2012) Cameron Marlow Former Head and Founder, Data Science Facebook h;p://www.scienAficamerican.com/arAcle.cfm?id=social-‐scienAsts-‐ might-‐gain-‐access-‐facebooks-‐data-‐use
- 17. Predict 2 week market adopAon lead Ame! TradiAonal Network Science Friend Paradox TED -‐ Christakis
- 22. It may not qualify as a lightning-‐bolt eureka moment, but Jeffrey R. Immelt, chief execuAve of General Electric, recalls the June day in 2009 that got him thinking. He was speaking with G.E. scienAsts about new jet engines they were building, laden with sensors to generate a trove of data from every flight — but to what end? That data could someday be as valuable as the machinery itself, if not more so. But G.E. couldn’t make use of it. “We had to be more capable in soeware,” Mr. Immelt said he decided. Maybe G.E. — a maker of power turbines, jet engines, locomoAves and medical-‐imaging equipment — needed to think of its compeAtors as Amazon and IBM. Predix Soeware When he lee Apple, Mr. Haas was head of cloud engineering, managing the compuAng engine behind Siri, iTunes and iCloud. At GE Digital, Mr. Haas has a similar Atle, head of plasorm cloud engineering, but in a different setng. He describes his job as applying modern soeware technology — machine learning, arAficial intelligence and cloud compuAng — to the industrial arena. “I’ve got my work cut out for me,” he said. GE Backstory OrganizaAonal Business Case Individual who automates work
- 25. biochemical diagnostics online recruiting music financial payments e-commerce networks securitysecurity cloud storagecloud storage data analytics telecom health carehealth care IT semiconductors biologicsbiologics search biofuels education wind solar smart grid travel real estate geolocation imaging medical devices batteries lighting LEDs Locating Your Next Strategic Opportunity To map semantic clus- ters, Quid software first identifies hundreds of key phrases associated with individual companies and organizations, or their “n-grams.” Applying algo- rithms and other analyti- cal tools, the technology parses text in millions of corporate documents, from patent filings, to press releases, to Twitter posts. The software then creates a map with lines connecting companies whose n-grams are alike. The lines act like gravita- tional pull: The more lines there are between com- panies, the more tightly together those companies are drawn. Similar firms become clustered into industry sectors. The result is a multi- dimensional industry map like the one below. It represents 4,000 tech- nology enterprises—from venture-backed start-ups to established public companies—that received media coverage and Where and how do strategists find growth opportunities? Sometimes by literally drawing a map, using a technique called semantic-clustering analysis. Such maps can reveal not only which sectors are thick with competition but where in the market white spaces are open for the taking. For example, while it may seem odd to find opportunity in the nexus between gaming and biopharma, seeing is believing. Data and visualization by Sean Gourley of Quid; graphic design by Open gaming social media genomicsbiopharma ad targeting IDEA WATCH 34 Harvard Business Review March 2011 VisionStatement
- 26. Semantic-clustering software locates and analyzes the documents in a company’s digital footprint. Documents are catego- rized and weighted for importance. The software then identifies the company’s n-grams, or key phrases. The company’s n-grams are then compared with other companies’ n-grams. The process is then repeated for every company in the sample to generate the map. When at least 80% of their n-grams are similar, companies are linked on the map. How N-Gram Mapping Works showed capital growth last year. Such maps expose surprising relationships between and across sectors and, even more tantalizing, the white spaces among them—which can offer firms strategic opportunities to connect companies operat- ing in different markets, to take existing products into new sectors, or to innovate with products and services no one has even dreamed up yet. HBR Reprint F1103Z The Pharma-Gaming Connection One of the most intriguing white spaces on this map is surrounded by some industry sectors that at first glance may seem unlikely to be connected: biopharma, gaming, social media, and ad targeting. As shown in the box below, Selventa, Proximic, Vivo, Insilicos, Foldit, and Nvidia are some of the ventures seizing the strategic opportunities in this space. Sean Gourley is CTO and cofounder of Quid, in San Francisco. Open is a design studio in New York. Nvidia Foldit Vivo Selventa Insilicos Proximic gaming social media genomicsbiopharma ad targeting Profiling and Per- sonalized Medicine Selventa makes targeted drug discoveries by analyzing large amounts of patient data and statistically identify- ing patient cohorts that will respond well to special- ized treatments. To do so it borrows mathematical techniques from ad targeting companies like Proximic. Gaming Meets Drug Discovery Nvidia builds graphics pro- cessing units used in video games, among other things. Recognizing that work done by biomarker discovery and diagnostic development companies like Insilicos requires similarly intense graphics processing, Nvidia has edged into the drug discovery space. Solving Business Problems Socially Foldit is an online social game for science geeks based on the challenge of finding the most efficient way to fold proteins. But the thousands who play it can help solve real protein-folding challenges for biopharma companies, which have begun putting the gaming platform to work. Scientific Social Networking Vivo jumped into the white space between social gaming and pharma by building a Facebook-like online collabo- ration platform that helps scientists connect and share research and data. March 2011 Harvard Business Review 35 HBR.ORG
- 27. We believe that the same AI technology that gives big tech companies a compeAAve edge should be available to developers or businesses of any size or budget. That’s why we built our new Custom Training and Visual Search products – to make it easy, quick, and inexpensive for developers and businesses to innovate with AI, go to market faster, and build be;er user experiences.
- 28. Sales to physicians confirmed at 95% rate using Nugget
- 29. Ethics – Who is minding the transforma<on on the Future of Work?
- 30. Thank You! Victoria G. Axelrod Principal, Axelrod Becker ConsulAng 445 East 86th Street New York, NY 10028 212-‐369-‐2885 vaxelrod@axelrodbecker.com www.axelrodbecker.com Blog: 21st Century OrganizaAon h;p://c21org.typepad.com
- 31. What’s your comfort level working with intelligent machines?