Tech companies should stop pretending AI won’t destroy jobs — from technologyreview.com / MIT Technology Review by Kai-Fu Lee
No matter what anyone tells you, we’re not ready for the massive societal upheavals on the way.

Excerpt (emphasis DSC):

The rise of China as an AI superpower isn’t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind’s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-­collar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we’re not ready for it.

And finally, there are those who deny that AI has any downside at all—which is the position taken by many of the largest AI companies. It’s unfortunate that AI experts aren’t trying to solve the problem. What’s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can’t do and train people to do them. We need to reinvent education. These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what’s best rather than wallow in what’s worst.

 

From DSC:
If a business has a choice between hiring a human being or having the job done by a piece of software and/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever/whomever will be less expensive will get the job.

However, that way of thinking may cause enormous social unrest if the software and robots leave human beings in the (job search) dust. Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and/or to pursue their vocation/callings — to have/find purpose in their lives.

 

Others think we’ll be saved by a universal basic income. “Take the extra money made by AI and distribute it to the people who lost their jobs,” they say. “This additional income will help people find their new path, and replace other types of social welfare.” But UBI doesn’t address people’s loss of dignity or meet their need to feel useful. It’s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.

 

 

To Fight Fatal Infections, Hospitals May Turn to Algorithms — from scientificamerican.com by John McQuaid
Machine learning could speed up diagnoses and improve accuracy

Excerpt:

The CDI algorithm—based on a form of artificial intelligence called machine learning—is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force—likely to alter the way medical decisions are made and put some people out of work.

 

 

Lawyer-Bots Are Shaking Up Jobs — from technologyreview.com by Erin Winick

Excerpt:

Meticulous research, deep study of case law, and intricate argument-building—lawyers have used similar methods to ply their trade for hundreds of years. But they’d better watch out, because artificial intelligence is moving in on the field.

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer’s job and 35 percent of a law clerk’s job can be automated, which means that while humanity won’t be completely overtaken, major businesses and career adjustments aren’t far off (see “Is Technology About to Decimate White-Collar Work?”). In some cases, they’re already here.

 

“If I was the parent of a law student, I would be concerned a bit,” says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. “There are fewer opportunities for young lawyers to get trained, and that’s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.”

 

So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.

People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.

 

 

Lessons From Artificial Intelligence Pioneers — from gartner.com by Christy Pettey

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.

Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations

“AI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,” says Whit Andrews, vice president and distinguished analyst at Gartner. “However, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.”

So what lessons can we learn from these early AI pioneers?

 

 

Why Artificial Intelligence Researchers Should Be More Paranoid — from wired.com by Tom Simonite

Excerpt:

What to do about that? The report’s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly—including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.

 

 

How to Prepare College Graduates for an AI World — from wsj.com by
Northeastern University President Joseph Aoun says schools need to change their focus, quickly

Excerpt:

WSJ: What about adults who are already in the workforce?

DR. AOUN: Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.

That is the part that I see that higher education has not embraced. That’s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don’t embrace it as part of our core mission.

 

 

Inside Amazon’s Artificial Intelligence Flywheel — from wired.com by Steven Levy
How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.

Excerpt:

Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable—and in certain cases scoops up yet more data to level up the technology even more.

 

 

 

 

AI plus human intelligence is the future of work — from forbes.com by Jeanne Meister

Excerpts:

  • 1 in 5 workers will have AI as their co worker in 2022
  • More job roles will change than will be become totally automated so HR needs to prepare today


As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

How does all of this impact HR in your organization? The following ten HR trends will matter most as AI enters the workplace…

The most visible aspect of how HR is being impacted by artificial intelligence is the change in the way companies source and recruit new hires. Most notably, IBM has created a suite of tools that use machine learning to help candidates personalize their job search experience based on the engagement they have with Watson. In addition, Watson is helping recruiters prioritize jobs more efficiently, find talent faster, and match candidates more effectively. According to Amber Grewal, Vice President, Global Talent Acquisition, “Recruiters are focusing more on identifying the most critical jobs in the business and on utilizing data to assist in talent sourcing.”

 

…as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience.

 

 

From DSC:
Although I like the possibility of using machine learning to help employees navigate their careers, I have some very real concerns when we talk about using AI for talent acquisition. At this point in time, I would much rather have an experienced human being — one with a solid background in HR — reviewing my resume to see if they believe that there’s a fit for the job and/or determine whether my skills transfer over from a different position/arena or not. I don’t think we’re there yet in terms of developing effective/comprehensive enough algorithms. It may happen, but I’m very skeptical in the meantime. I don’t want to be filtered out just because I didn’t use the right keywords enough times or I used a slightly different keyword than what the algorithm was looking for.

Also, there is definitely age discrimination occurring out in today’s workplace, especially in tech-related positions. Folks who are in tech over the age of 30-35 — don’t lose your job! (Go check out the topic of age discrimination on LinkedIn and similar sites, and you’ll find many postings on this topic — sometimes with 10’s of thousands of older employees adding comments/likes to a posting). Although I doubt that any company would allow applicants or the public to see their internally-used algorithms, how difficult would it be to filter out applicants who graduated college prior to ___ (i.e., some year that gets updated on an annual basis)? Answer? Not difficult at all. In fact, that’s at the level of a Programming 101 course.

 

 

 

Artificial intelligence is going to supercharge surveillance – from theverge.com by James Vincent
What happens when digital eyes get the brains to match?

From DSC:
Persons of interest” comes to mind after reading this article. Persons of interest is a clever, well done show, but still…the idea of combining surveillance w/ a super intelligent is a bit unnerving.

 

 

 

Artificial intelligence | 2018 AI predictions — from thomsonreuters.com

Excerpts:

  • AI brings a new set of rules to knowledge work
  • Newsrooms embrace AI
  • Lawyers assess the risks of not using AI
  • Deep learning goes mainstream
  • Smart cars demand even smarter humans
  • Accountants audit forward
  • Wealth managers look to AI to compete and grow

 

 

 

Chatbots and Virtual Assistants in L&D: 4 Use Cases to Pilot in 2018 —  from bottomlineperformance.com by Steven Boller

Excerpt:

  1. Use a virtual assistant like Amazon Alexa or Google Assistant to answer spoken questions from on-the-go learners.
  2. Answer common learner questions in a chat window or via SMS.
  3. Customize a learning path based on learners’ demographic information.
  4. Use a chatbot to assess learner knowledge.

 

 

 

Suncorp looks to augmented reality for insurance claims — from itnews.com.au by Ry Crozier with thanks to Woontack Woo for this resource

Excerpts:

Suncorp has revealed it is exploring image recognition and augmented reality-based enhancements for its insurance claims process, adding to the AI systems it deployed last year.

The insurer began testing IBM Watson software last June to automatically determine who is at fault in a vehicle accident.

“We are working on increasing our use of emerging technologies to assist with the insurance claim process, such as using image recognition to assess type and extent of damage, augmented reality that would enable an off-site claims assessor to discuss and assess damage, speech recognition, and obtaining telematic data from increasingly automated vehicles,” the company said.

 

 

 

6 important AI technologies to look out for in 2018 — from itproportal.com by  Olga Egorsheva
Will businesses and individuals finally make AI a part of their daily lives?

 

 

 

 

 

Will Letter Grades Survive? — from edutopia.org by Laura McKenna
A century-old pillar of the school system is under fire as schools look to modernize student assessment.

Excerpt:

Under pressure from an unprecedented constellation of forces—from state lawmakers to prestigious private schools and college admissions offices—the ubiquitous one-page high school transcript lined with A–F letter grades may soon be a relic of the past.

In the last decade, at least 15 state legislatures and boards of education have adopted policies incentivizing their public schools to prioritize measures other than grades when assessing students’ skills and competencies. And more recently, over 150 of the top private high schools in the U.S., including Phillips Exeter and Dalton—storied institutions which have long relied on the status conveyed by student ranking—have pledged to shift to new transcripts that provide more comprehensive, qualitative feedback on students while ruling out any mention of credit hours, GPAs, or A–F grades.

 

 

“The grading system right now is demoralizing and is designed to produce winners and losers,” said Looney. “The purpose of education is not to sort kids—it’s to grow kids. Teachers need to coach and mentor, but with grades, teachers turn into judges. I think we can show the unique abilities of kids without stratifying them.”

 

 


There are other unanswered questions and challenges to be worked out, too. Will college admissions counselors have enough time, especially at large public colleges, to look meaningfully at dense digital portfolios of student work? Will the new transcripts create too much work and new training for K-12 teachers, as they struggle to measure hard-to-define categories of learning? Perhaps most importantly, will parents buy in?

 

 

 

Also relevant/see:

What Failing Students Want Us to Remember — from edutopia.org by Rebecca Alber
By seeing students as more than their grades, we can enable them to reach their potential.

Excerpt:

1. I am not my grade. I don’t get good grades or earn a lot of points on assignments even though I know some stuff. I often won’t even try because I know I’m going to get a bad grade. I wish there were other ways besides grades or points to show who I really am.

2. I can still contribute meaningfully. I like to help, but I pretend sometimes like I don’t and that I don’t care about being part of the school or my class. I protect myself because in school, the kids with good grades get picked to help more often.

3. I am not a disappointment. School is hard, and I know I let my teachers down, and when working in a group, I let down my classmates too. Because of this, I struggle to feel good about myself every day. What am I doing right? I wish in school that we could look at all the stuff we do right and not just mostly the things we do wrong.

4. Meet me where I am. There’s stuff I can do—just not this, right now, like this. I wish I had more time. I wish the directions and assignments made more sense to me. So much of school is so rushed and confusing.

5. Don’t give up. Find a way for me. I’m not sure why I don’t get it. I want someone to keep trying to find out. It’s not that I don’t want to do it, even though it sometimes looks like that. It helps when adults ask me questions. I can’t do it right now, but maybe someday I’ll be able to.

 

 

 

 

 

FREEDOM 2.0 – Blockchain’s Biggest Use Case with Richie Etwaru @richieetwaru — with thanks to Mike Mathews for his posting this on LinkedIn

Description:

While the Internet has profoundly impacted global society, new questions must be asked. When the human species reflects on the Internet in 2081 a hundred years after its invention will the Internet be viewed as good for our species, and has the impact of the set of adjacent inventions of the Internet furthered the triumph of the human species? Did we connect the last billion with mobility, did we distribute wealth meaningfully, and was basic healthcare democratized? Or, did social media coupled with mobile cameras create a spike in vanity that affected important social constructs such as love, self-esteem and family? Did AI create a new class system of robo sapiens that constrict freedom? And did we change the core of commerce of trust between citizens, communities and governments? Maybe; the Internet is only 49% of the story of our species, and the remaining 51% of our story is still unfolding. Richie will discuss the other 51% which he believes is blockchain, and how we can change the answers to some of these new types of questions of mankind.

 

 

 

 

 

 

 

The Advantages of Blockchain Technology — from hortonworks.com by Ryan Wheeler

Excerpt:

Blockchain ensures data objectivity—a single source of truth. Blockchain also represents a security layer that ensures that data is encrypted in such a way that only the people you want to can read your data. It makes it next to impossible for people to corrupt or manipulate the data—or even gain wrongful access to it—because the system raises an instant red flag when a problem occurs, and it uses a new, advanced encryption method to secure the data.

Blockchain is both reactionary—alerting users to changes—and proactive, by preventing the security threat. And even if the data is somehow breached, it still can’t be used. The effects have already been seen in the healthcare industry, where technologies using blockchain have provided the proper balance of security and governance for people’s health data.

 

 

 

From DSC:
Interesting to see this new platform developing, one that combines 2 big trends — blockchain and freelancing:

 

 

 

Also interesting to see:

 

“Peculium: The first savings system in cryptocurrency utilizing AIEVE and Blockchain Technology with artificial intelligence. PECULIUM revolutionizes savings management by deploying immutable Smart-Contracts over Ethereum blockchain.”

 

 

 

 

 

Addendum/also see:

 

The blockchain provides a rich, secure, and transparent platform on which to create a global network for higher learning. This Internet of value can help to reinvent higher education in a way the Internet of information alone could not.

 

 

 

The legal and ethical minefield of AI: ‘Tech has the power to do harm as well as good’ — from theguardian.com by Joanna Goodman

Excerpt:

Artificial intelligence and machine learning tools are already embedded in our lives, but how should businesses that use such technology manage the associated risks?

As artificial intelligence (AI) penetrates deeper into business operations and services, even supporting judicial decision-making, are we approaching a time when the greatest legal mind could be a machine? According to Prof Dame Wendy Hall, co-author of the report Growing the Artificial Intelligence Industry in the UK, we are just at the beginning of the AI journey and now is the time to set boundaries.

“All tech has the power to do harm as well as good,” Hall says. “So we have to look at regulating companies and deciding what they can and cannot do with the data now.”

AI and robotics professor Noel Sharkey highlights the “legal and moral implications of entrusting human decisions to algorithms that we cannot fully understand”. He explains that the narrow AI systems that businesses currently use (to draw inferences from large volumes of data) apply algorithms that learn from experience and feed back to real-time and historical data. But these systems are far from perfect.

Potential results include flawed outcomes or reasoning, but difficulties also arise from the lack of transparency. This supports Hall’s call for supervision and regulation. Businesses that use AI in their operations need to manage the ethical and legal risks, and the legal profession will have a major role to play in assessing and apportioning risk, responsibility and accountability.

 

 

Also see:

 

 

 

 

Blockchain and Distributed Web: Why You Should Care — from ideou.com

Excerpt:

To help get our heads around emerging tech, we invited our IDEO friends, IDEO CoLab, in for a Creative Confidence Series session about emerging tech (and why you should care).

In this first session, we sat down with CoLab’s Joe Gerber and Gavin McDermott to talk about the distributed web and blockchain and why it’s important to experiment with these emerging technologies. Many people conflate blockchain and bitcoin, but as Joe and Gavin discussed, bitcoin is just the tip of the spear and one small piece of a larger movement of blockchain and the distributed web. In this post, we’ll break down why, as Joe and Gavin say, the web is being rewritten from the inside out.

First, some definitions:

  • Blockchain: A blockchain is a peer-to-peer network that logs shared information about transactions. It’s provable because the transaction is validated by a broad network of computers. Joe quoted Vitalik Buterin likening blockchain to “a database we all agree on.” The magic of blockchain is that it solves the problem of digital abundance and computers’ innate ability for infinite copying by creating scarcity, ensuring that only one copy of something exists.
  • Bitcoin: Bitcoin is a cryptocurrency that uses blockchain technology, but there are a number of different applications for blockchain including contracts (smart contracts) and other types of information.
  • Distributed web: A movement that’s a complete reimagining of today’s internet infrastructure, it includes new and different protocols. We rely on protocols every day for things like email (simple mail transfer protocol, SMTP, allows Gmail, Yahoo Mail or Hotmail to all communicate) or for web browsing (hypertext transfer protocol, HTTP). In the distributed web, new peer-to-peer networks that do not rely on centralization are being built.

 

These technologies are still in their early days of construction and the blueprints are changing every day. What Joe and Gavin would recommend is that you start to experiment and prototype with these technologies to test assumptions for how they could affect your business. Don’t get ready, get started.

 

 

 

 

 

Top 10 Technology Trends for 2018: IEEE Computer Society Predicts the Future of Tech — from computer.org

Excerpts:

The top 10 technology trends predicted to reach adoption in 2018 are:

  1. Deep learning (DL)
  2. Digital currencies.
  3. Blockchain.
  4. Industrial IoT.
  5. Robotics.
  6. Assisted transportation.
  7. Assisted reality and virtual reality (AR/VR).
  8. Ethics, laws, and policies for privacy, security, and liability.
  9. Accelerators and 3D.
  10. Cybersecurity and AI.

Existing Technologies: We did not include the following technologies in our top 10 list as we assume that they have already experienced broad adoption:

A. Data science
B. “Cloudification”
C. Smart cities
D. Sustainability
E. IoT/edge computing

 

 

 


Also relevant/see:


 

 

 

From DSC:
Regarding the article below…why did it take Udacity needing to team up with Infosys to offer this type of program and curriculum? Where are the programs in institutions of traditional higher education on this?  Are similar programs being developed? If so, how quickly will they come to market? I sure hope that such program development is in progress..and perhaps it is. But the article below goes to show us that alternatives to traditional higher education seem to be more responsive to the new, exponential pace of change that we now find ourselves in.

We have to pick up the pace! To do this, we need to identify any obstacles to our institutions adapting to this new pace of change — and then address them immediately. I see our current methods of accreditation as one of the areas that we need to address. We’ve got to get solid programs to market much faster!

And for those folks in higher ed who say change isn’t happening rapidly — that it’s all a bunch of hype — you likely still have a job. But you need to go talk with some people who don’t, or who’ve had their jobs recently impacted big time. Here are some suggestions of folks to talk with:

  • Taxi drivers who were impacted by Lyft and by Uber these last 5-10 years; they may still have their jobs, if they’re lucky. But they’ve been impacted big time…and are likely driving for Lyft and/or Uber as well as their former employers; they’re likely to have less bargaining power than they used to as the supply of drivers has skyrocketed. (By the way, the very existence of such organizations couldn’t have happened without the smartphone and mobile-related technologies/telecommunications.)
  • Current managers and former employees at hotels/motels about the impacts on their industry by AirBnB over a similar time frame
  • Hiring managers at law firms who’ve cut back on hiring entry-level lawyers…work that’s increasingly being done by software (example)
  • Employees who worked at brick and mortar retailers who have been crushed by Amazon.com’s online-based presence (in not that long of time, by the way). For example, below is what our local Sears store looks like these days…go find an employee who used to work at Sears or a Sears automotive-related store:

 

This is what our local Sears store looks like today

This picture is for those who say there is no disruption.
You call
this hype?!

 

The above example list — that’s admittedly woefully incomplete — doesn’t include the folks displaced by technology over the last several decades, such as:

  • Former bank tellers who lost their jobs to ATMs
  • Checkout clerks at the grocery stores who lost their jobs to self-service stations
  • Check-in agents at the airports who lost their jobs to self-service stations
  • Etc., etc., etc.

Institutions of traditional higher education
need to pick up the pace — big time!

 


Infosys and Udacity team up to train 500 engineers in autonomous technologies — from by Leah Brown
Infosys’ COO Ravi Kumar explains how these individuals can apply what they learn to other industries.

Excerpt (emphasis DSC):

Infosys, a global technology consulting firm, recently partnered with online learning platform Udacity to create a connected service that provides training for autonomous vehicles, and other services for B2B providers of autonomous vehicles.

TechRepublic’s Dan Patterson met with Infosys’ COO Ravi Kumar to discuss how autonomous technology can help create new industries.

Autonomous technology is going to be an emerging technology of the future, Kumar said. So Infosys and Udacity came together and developed a plan to train 500 engineers on autonomous technologies, and teach them how to apply it to other industries.

 

Per Wikipedia:
Udacity is a for-profit educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky offering massive open online courses (MOOCs). According to Thrun, the origin of the name Udacity comes from the company’s desire to be “audacious for you, the student.” While it originally focused on offering university-style courses, it now focuses more on vocational courses for professionals.

 


 

But times are changing. Artificial intelligence (AI) and robotics are facilitating the automation of a growing number of “doing” tasks. Today’s AI-enabled, information-rich tools are increasingly able to handle jobs that in the past have been exclusively done by people—think tax returns, language translations, accounting, even some kinds of surgery. These shifts will produce massive disruptions to employment and hold enormous implications for you as a business leader. (mckinsey.com)

 


 

 

AI: Embracing the promises and realities — from the Allegis Group

Excerpts:

What will that future be? When it comes to jobs, the tea leaves are indecipherable as analysts grapple with emerging technologies, new fields of work, and skills that have yet to be conceived. The only certainty is
that jobs will change. Consider the conflicting predictions put forth by the analyst community:

  • According to the Organization of Economic Cooperation and Development, only 5-10% of labor would be displaced by intelligent automation, and new job creation will offset losses.  (Inserted comment from DSC: Hmmm. ONLY 5-10%!? What?! That’s huge! And don’t count on the majority of those people becoming experts in robotics, algorithms, big data, AI, etc.)
  • The World Economic Forum27 said in 2016 that 60% of children entering school today will work in jobs that do not yet exist.
  • 47% of all American job functions could be automated within 20 years, according to the Oxford Martin School on Economics in a 2013 report.
  • In 2016, a KPMG study estimated that 100 million global knowledge workers could be affected by robotic process automation by 2025.

Despite the conflicting views, most analysts agree on one thing: big change is coming. Venture Capitalist David Vandergrift has some words of advice: “Anyone not planning to retire in the next 20 years should be paying pretty close attention to what’s going on in the realm of AI. The supplanting (of jobs) will not happen overnight: the trend over the next couple of decades is going to be towards more and more automation.”30

While analysts may not agree on the timing of AI’s development in the economy, many companies are already seeing its impact on key areas of talent and business strategy. AI is replacing jobs, changing traditional roles, applying pressure on knowledge workers, creating new fields of work, and raising the demand for certain skills.

 

 

 

 

 

The emphasis on learning is a key change from previous decades and rounds of automation. Advanced AI is, or will soon be, capable of displacing a very wide range of labor, far beyond the repetitive, low-skill functions traditionally thought to be at risk from automation. In many cases, the pressure on knowledge workers has already begun.

 

 

 

 

Regardless of industry, however, AI is a real challenge to today’s way of thinking about work, value, and talent scarcity. AI will expand and eventually force many human knowledge workers to reinvent their roles to address issues that machines cannot process. At the same time, AI will create a new demand for skills to guide its growth and development. These emerging areas of expertise will likely be technical or knowledge-intensive fields. In the near term, the competition for workers in these areas may change how companies focus their talent strategies.

 

 

 

 

How artificial intelligence could transform government — from Deloitte University Press
Cognitive technologies have the potential to revolutionize the public sector—and save billions of dollars

Excerpt:

The rise of more sophisticated cognitive technologies is, of course, critical to that third era, aiding advances in several categories:

  • Rules-based systems capture and use experts’ knowledge to provide answers to tricky but routine problems. As this decades-old form of AI grows more sophisticated, users may forget they aren’t conversing with a real person.
  • Speech recognition transcribes human speech automatically and accurately. The technology is improving as machines collect more examples of conversation. This has obvious value for dictation, phone assistance, and much more.
  • Machine translation, as the name indicates, translates text or speech from one language to another. Significant advances have been made in this field in only the past year.8 Machine translation has obvious implications for international relations, defense, and intelligence as well as, in our multilingual society, numerous domestic applications.
  • Computer vision is the ability to identify objects, scenes, and activities in naturally occurring images. It’s how Facebook sorts millions of users’ photos, but it can also scan medical images for indications of disease and identify criminals from surveillance footage. Soon it will allow law enforcement to quickly scan license plate numbers of vehicles stopped at red lights, identifying suspects’ cars in real time.
  • Machine learning takes place without explicit programming. By trial and error, computers learn how to learn, mining information to discover patterns in data that can help predict future events. The larger the datasets, the easier it is to accurately gauge normal or abnormal behavior. When your email program flags a message as spam, or your credit card company warns you of a potentially fraudulent use of your card, machine learning may be involved. Deep learning is a branch of machine learning involving artificial neural networks inspired by the brain’s structure and function.9
  • Robotics is the creation and use of machines to perform automated physical functions. The integration of cognitive technologies such as computer vision with sensors and other sophisticated hardware has given rise to a new generation of robots that can work alongside people and perform many tasks in unpredictable environments. Examples include drones, robots used for disaster response, and robot assistants in home health care.
  • Natural language processing refers to the complex and difficult task of organizing and understanding language in a human way. This goes far beyond interpreting search queries, or translating between Mandarin and English text. Combined with machine learning, a system can scan websites for discussions of specific topics even if the user didn’t input precise search terms. Computers can identify all the people and places mentioned in a document or extract terms and conditions from contracts. As with all AI-enabled technology, these become smarter as they consume more accurate data—and as developers integrate complementary technologies such as machine translation and natural language processing.

We’ve developed a framework that can help government agencies assess their own opportunities for deploying these technologies. It involves examining business processes, services, and programs to find where cognitive technologies may be viable, valuable, or even vital. Figure 8 summarizes this “Three Vs” framework. Government agencies can use it to screen the best opportunities for automation or cognitive technologies.

 

 

 

 

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