
45% of work activities can be automated including those performed by highest-paid occupations
…our research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent about $2 trillion in annual wages. Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated…
…fewer than 5 percent of occupations can be entirely automated using current technology. However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated. In other words, automation is likely to change the vast majority of occupations—at least to some degree—which will necessitate significant job redefinition and a transformation of business processes. Mortgage-loan officers, for instance, will spend much less time inspecting and processing rote paperwork and more time reviewing exceptions, which will allow them to process more loans and spend more time advising clients. Similarly, in a world where the diagnosis of many health issues could be effectively automated, an emergency room could combine triage and diagnosis and leave doctors to focus on the most acute or unusual cases while improving accuracy for the most common issues.
As roles and processes get redefined, the economic benefits of automation will extend far beyond labor savings. Particularly in the highest-paid occupations, machines can augment human capabilities to a high degree, and amplify the value of expertise by increasing an individual’s work capacity and freeing the employee to focus on work of higher value. Lawyers are already using text-mining techniques to read through the thousands of documents collected during discovery, and to identify the most relevant ones for deeper review by legal staff. Similarly, sales organizations could use automation to generate leads and identify more likely opportunities for cross-selling and upselling, increasing the time frontline salespeople have for interacting with customers and improving the quality of offers…
Our work to date suggests that a significant percentage of the activities performed by even those in the highest-paid occupations (for example, financial planners, physicians, and senior executives) can be automated by adapting current technology.7 For example, we estimate that activities consuming more than 20 percent of a CEO’s working time could be automated using current technologies. These include analyzing reports and data to inform operational decisions, preparing staff assignments, and reviewing status reports. Conversely, there are many lower-wage occupations such as home health aides, landscapers, and maintenance workers, where only a very small percentage of activities could be automated with technology available today [see chart above].
Google: Machine Learning and Deep Neural Networks Explained (Video)
[youtube https://www.youtube.com/watch?v=bHvf7Tagt18?rel=0]
*Greg and Chris did an AMA on Friday, September 25th to answer people’s deep learning questions. Check out their answers here: https://goo.gl/jpbMy9
*To read more about machine learning, neural nets, and the like – check out the Google Research blog:http://googleresearch.blogspot.com/ and Chris’s blog: http://colah.github.io/
68% of Americans have smartphones, 45% have tablet computers, other devices not growing
Today, 68% of U.S. adults have a smartphone, up from 35% in 2011, and tablet computer ownership has edged up to 45% among adults, according to newly released survey data from the Pew Research Center. Smartphone ownership is nearing the saturation point with some groups: 86% of those ages 18-29 have a smartphone, as do 83% of those ages 30-49 and 87% of those living in households earning $75,000 and up annually.
The Economist’s Data Editor on Data Fetishism
“We fetishize data, we think that data is the answer. It’s far from the truth. In fact, it’s ridiculous, because the data is only a simulacrum of reality in the same way that a map is not a territory. And so while we need to use information and data to make decisions as we need to do, the data is always unfaithful, always unreliable, it always misleads, and you have to torture it until it confesses”–Kenneth Cukier, Data Editor, The Economist
Source: Economist Radio, “Arthur Miller and Modern-Day Witch-Hunts”
FinTech Startups:The Landscape of Blockchain Companies in Financial Services
Source: Startup Management
HT: Leaders in Pharmaceutical Business Intelligence
Bitcoin fanatics are enthralled by the libertarian ideal of a pure, digital currency beyond the reach of any central bank. The real innovation is not the digital coins themselves, but the trust machine that mints them—and which promises much more besides.
Whatever you think of the cryptocurrency, the “blockchain” is a trust machine that may yet take its place alongside double-entry book-keeping and the limited-liability company as a way of oiling the wheels of commerce.
Driverless Cars: A Misguided 20th Century Idea
A vision of fully autonomous, self-driving cars allowing human owners to nap or read in the car seems to come from the future. But David Mindell, a historian and electrical engineer at MIT, says that the idea of such fully autonomous vehicles roaming the streets represents a more rigid vision left over from the last century. Mindell casts some doubt over the current course along which Google and other huge tech companies are racing to build self-driving cars that don’t require any human supervision.
In his new book, released this month, titled, “Our Robots, Ourselves: Robotics and the Myths of Autonomy” (Viking/Penguin), Mindell envisions a future in which humans are kept in the loop for (mostly) self-driving cars and other robotic technologies, rather than taking them completely out of the equation…
Spectrum: What do you think of the current focus of Google and other tech companies pursuing self-driving cars?
Mindell: Overall, robotics is still focused on full autonomy as the ultimate goal. Researchers should be working on a “perfect five” with trusted, transparent, flexible collaboration between people and autonomous systems. (The “perfect five” refers to the middle of a scale for automation that ranges from very low at level 1, to fully autonomous at 10; the concept is based on the work of Tom Sheridan, professor of mechanical engineering at MIT.)
Such systems should have the ability to turn on autonomy when it can be helpful. Autonomy can reduce human workload and fatigue, but humans should still be present in the system. That’s an empirical argument based on everything we’ve seen in the last 40 years of autonomous systems. People are always thinking that full autonomy is just around the corner. But there are 30 to 40 examples in the book, and in every one, autonomy gets tempered by human judgment and experience.
Spectrum: You’ve said that the best way forward involves a mix of humans, remotely-controlled systems and autonomous robots. Do you think the future you’re hoping for is the one we’re likely to see?
Mindell: I’m hoping the likely future is the one I’m arguing for. There is a quote in the book from the chief of BMW saying “People buy our cars because people like driving them; we’d be crazy to cut them out of the loop.” I think the world is ready for a more nuanced approach to robotics.
Connected Cars: A History of Security Vulnerabilities
Chris Poulin, IBM, on Tech Crunch:
A Short History Of Car Vulnerability Research
In 2010, researchers from the University of Washington and University of California, San Diego published a seminal paper proving that once an attacker has physical access to a vehicle, they can compromise every component, from the entertainment system to the electronic control units (ECUs) that operate the engine, brakes and even the steering wheel in modern cars that self-park and sport lane-departure correction.
This research showed that an attacker could use connection points between vehicle systems as an entry point to inject arbitrary commands on the controller area network (CAN) bus to perform activities such as disabling all the engine’s cylinders, locking up one brake pad and disabling all brakes — even when the car was traveling at 40 miles per hour. The researchers even created a CAN bus analysis and packet injection tool, dubbed CarShark.
But the automakers weren’t phased by the research; their view was that an attacker would have to be jacked into your car in order to execute an attack.
In response, these same researchers undertook another study in 2011 to further prove their point, this time centered on how to remotely gain access to the vehicle. The paper enumerated the attack surfaces, including channels that provide remote access: Bluetooth, in-vehicle Wi-Fi, telematics, remote keyless entry and RFID immobilizers, dedicated short-range communications (DSRC) used to communicate between vehicles and the road infrastructure, global positioning (GPS), satellite radio and even tire pressure monitor sensors.
The researchers took the play from the punt to the end zone by remotely compromising a vehicle, then using the techniques they created in their first paper to gain complete control of the car. They even claimed they could compromise the telematics unit by simply playing an audio file over the mobile carrier’s network.
Using another vector, the researchers wrote a mobile phone Trojan that gave them remote access to a driver’s or passenger’s mobile phone, and when paired with a vehicle’s telematics unit, exploited a vulnerability in the Bluetooth firmware. They effectively used the mobile phone as a springboard to pwn the vehicle.
The researchers also compromised a typical diagnostics computer used by many service shops so that when it was connected to the diagnostics port on a vehicle, the computer would infect the vehicle with malware allowing the attackers to control it. In a zombie apocalypse scenario, the researchers even wrote software that could turn cars into a rolling “bot” army that reports back to a command and control (C&C) channel through which a criminal could issue commands.
It would seem that these researchers had proven conclusively that connected vehicle security required retooling, and that the consequences could have a major impact on customer confidence and safety. However, without details on the specific vehicles involved in the research, nor publicly disclosed proof of concept instructions, the automotive industry made little public noise about the research.
In fairness, the auto industry may have rallied war rooms and devised plans to amp up security in their automotive products; however, the automotive industry is tight-knit and guards new designs and technology closely. Further, modern automobiles are complex marvels of engineering, and the process of retooling the mechanics and software has to be undertaken slowly, carefully and over a period of many years. Bear in mind that from inception, a new automobile typically takes 5-7 years before it hits the mass market.
And yet, to the general public — and especially to researchers — the silence implied apathy on the part of the automakers. Some in the industry may not fully recognize the broader implications of these results. For example, I spoke to the design manager on the topic of the tire pressure monitoring system (TPMS) vulnerability and he responded with: “So what? All you could do is light up an amber LED on the dashboard.”
Which would be true if all TPMS receivers only had a wire loop that went to the LED in question; however, it’s likely that most of the automakers connect the TPMS receiver to other parts of the in-vehicle network, if for no other reason than to send that data as telemetry back to the predictive maintenance analytics running in the cloud. But let’s not get hung up on the TPMS system: The vehicle threat surface is as broad as the African savanna is to a big game poacher.
Enter Charlie Miller and Chris Valasek, whose 2013 Today Show vehicle hack elicited a collective gasp from the public. Automakers pointed out that such a hack would be unfeasible in real life, as the dashboard is dismantled and there’s a guy sitting in your back seat with a laptop. As is the way with such stories, other shiny objects and celebrity reality television soon overwrote that chunk of the public’s short-term memory, and drivers slid behind the wheel with nary a thought of cars gone wild.
In 2015, Miller and Valasek were back. The widely publicized video of these researchers remotely hacking into a vehicle on the road and ultimately sending it into a ditch struck a chord with the general public that research to date had yet to reach.
To put this in perspective, Recorded Future, which collects intelligence from more than 600,000 sources, including social media and underground forums, queried their data warehouse for mentions of connected vehicle security. As displayed [above], there was a fair amount of chatter when the CarShark exploit was announced, then it exploded around the two Valasek and Miller exploits. The red “bubbles” show the amount of references by date and the milestones are called out. Additionally, references to announced or publicly speculated future events are plotted at the bottom of the chart.
Survey: The Hunt for Unicorn Data Scientists Boosts the Salaries of Predictive Analytics Professionals
Unicorn Data Scientists (upgraded from “sexy data scientists”) are hard to find and are paid more than $200,000 per year. A new survey finds that the rising data science tide lifts the compensation of all other data analytics professionals, even if they don’t know how to code.
The Burtch Works Study: Salaries for Predictive Analytics Professionals is based on interviews with 1,757 data analytics professionals conducted over the 12 months ending April 2015 by executive recruiting firm Burtch Works. It is a unique source of information in that it does not rely on self-reporting or data provided by human resources departments. It also provides insights into how the demand for data scientists impact the salaries of other data analytics professionals because it excludes data scientists, covered in a separate Burtch Works study, published earlier this year (I wrote about that study here).
Burtch Works defines predictive analytics professionals as those who can “apply sophisticated quantitative skills to data describing transactions, interactions, or other behaviors of people to derive insights and prescribe actions.” Data scientists are a subset of this group—they have the “computer science skills necessary to acquire and clean or transform unstructured or continuously streaming data, regardless of its format, size, or source.”
The additional computer science skills put data scientists on top in terms of compensation regardless of their levels of experience and managerial responsibilities but predictive analytics professionals are keeping up, seeing their salaries and bonuses rise. For example, the median base salary for the most experienced individual contributors rose from $115,250 last year to $125,000 this year and for managers managing teams of ten or more the median base salary rose from $225,000 to $235,000.
Predictive analytics professionals continue to benefit from the increasing demand and short supply for their quantitative analysis skills. The median base salary of individual contributors varies from $76,000 for those at level 1 (0 to 3 years of experience) to $125,000 for those at level 3 (9+ years of experience). The median bonus received varies from $8,100 to $18,100, depending on job level.
The median base salary of managers varies from $125,500 for those at level 1 (1 to 3 reports) to $235,000 for those at level 3 (10+ reports). The median bonus received by managers varies from $23,000 to $75,000 depending on job level.
More and more people are attracted by the demand for data analytics professionals and the potential to become a unicorn. Data recently released by the National Center for Education Statistics, according to Phys.org, shows bachelor’s degrees in statistics grew 17% from 2013 to 2014. This marks 15 consecutive years the number of undergraduates in statistics has risen, increasing by more than 300% since the 1990s. In addition, from 2000 to 2014, master’s and doctorate degrees in statistics also grew significantly at 260% and 132%, respectively.
“The Bureau of Labor Statistics projects job growth for statisticians will increase 27% between 2012 and 2022, outpacing the projected 11% rate for all other occupations. The number of graduates in statistics each year—approximately 2,000 bachelor’s degrees, 3,000 master’s degrees and 575 doctorate degrees—seems unlikely to match this demand,” says Phys.org.
Originally published on Forbes.com








