4 Ways Technology Is Changing Recruiting — from blog.hrtechweekly.com by Ji-A Min

Excerpt:

AI for recruiting
Industry statistics estimate 75 percent of resumes received for a role are screened out. This adds up to the hundreds of hours a recruiter wastes reading unqualified resumes per year. As one of recruiting’s biggest bottlenecks, resume screening is in dire need of better tools to help recruiters manage their time more effectively. This is why AI for recruiting is the biggest topic in HR tech right now. AI and recruiting are a natural fit because AI requires a lot of data to learn and large companies often have millions of resumes in their ATS.

Recruiting software that uses artificial intelligence can automate the screening process by learning the experience, skills, and qualifications required for the job and then shortlisting, ranking, and grading new candidates who match the requirements (e.g., from A to D). This type of AI recruiting software can also be used to source candidates from external databases such as Indeed and CareerBuilder or find previous candidates in your existing ATS database by applying the same learning ability to match candidates to an open req. By automating the manual processes of resume screening and candidate matching, companies who use AI recruiting software have reduced their screening costs by 75%.

Comment from DSC:
This is exactly why I tell my students to be sure they have an account on LinkedIn — which is owned by Microsoft. A piece of Microsoft will likely traverse down the AI-based pathway. (I also encourage them to have other pieces of their digital/online-based footprint such as an account on Twitter as well as their own WordPress-based blog).  Data mining and the use of AI for hiring will only pick up steam from here on out. If you don’t exist online, you had better have a lot of contacts and foots in the doors elsewhere.

 

 

Today more than ever, finding top talent will depend on a recruiter’s ability to intelligently automate their workflow.

 

 

 

Google is shifting their focus from Search to artificial intelligence, CEO says — from zmescience.com by

Excerpt:

While delivering Google’s first quarterly income report on Thursday, the company’s CEO said that Google is transitioning — the search-engine giant will become an A.I.-first company.

“We continue to set the pace in machine learning and A.I. research,” said Google CEO Sundar Pichai said in a call [embedded at the end of the article] to investors on Thursday to report the company’s Q1 2017 earnings.

“We’re transitioning to an A.I.-first company.”

 

 

 

A revolutionary partnership: How artificial intelligence is pushing man and machine closer together — from pcw.com

Excerpt:

With more than $5 billion in 605 deals of VC investment over last 2 years, artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. We dug deeper into those perceptions through an online survey of consumers and business decision makers, and an expert salon with thought leaders in the field. This original research unpacks key ways AI may impact our world, delving into its implications for society, service, and management.

 

Also see:

AI has the potential to become a great equalizer. More than half of consumers believe AI will provide educational help to disadvantaged schoolchildren. Over 40% also believe AI will expand access to financial, medical, legal, and transportation services to those with lower incomes.

Consumers also see the value in sharing their personal information for the greater good: 62% would share their data to help relieve traffic in their cities and 57% would do so to further medical breakthroughs.

 

 

 

Making sure the machines don’t take over — from raconteur.net by Mark Frary
Preparing economic players for the impact of artificial intelligence is a work in progress which requires careful handling

 

From DSC:
This short article presents a balanced approach, as it relays both the advantages and disadvantages of AI in our world.

Perhaps it will be one of higher education’s new tasks — to determine the best jobs to go into that will survive the next 5-10+ years and help you get up-to-speed in those areas. The liberal arts are very important here, as they lay a solid foundation that one can use to adapt to changing conditions and move into multiple areas. If the C-suite only sees the savings to the bottom line — and to *&^# with humanity (that’s their problem, not mine!) — then our society could be in trouble.

 

Also see:

 

 

 

From DSC:
The recent pieces below made me once again reflect on the massive changes that are quickly approaching — and in some cases are already here — for a variety of nations throughout the world.

They caused me to reflect on:

  • What the potential ramifications for higher education might be regarding these changes that are just starting to take place in the workplace due to artificial intelligence (i.e., the increasing use of algorithms, machine learning, and deep learning, etc.), automation, & robotics?
  • The need for people to reinvent themselves quickly throughout their careers (if we can still call them careers)
  • How should we, as a nation, prepare for these massive changes so that there isn’t civil unrest due to soaring inequality and unemployment?

As found in the April 9th, 2017 edition of our local newspaper here:

When even our local newspaper is picking up on this trend, you know it is real and has some significance to it.

 

Then, as I was listening to the radio a day or two after seeing the above article, I heard of another related piece on NPR.  NPR is having a journalist travel across the country, trying to identify “robot-safe” jobs.  Here’s the feature on this from MarketPlace.org

 

 

What changes do institutions of traditional higher education
immediately need to begin planning for? Initiating?

What changes should be planned for and begin to be initiated
in the way(s) that we accredit new programs?

 

 

Keywords/ideas that come to my mind:

  • Change — to society, to people, to higher ed, to the workplace
  • Pace of technological change — no longer linear, but exponential
  • Career development
  • Staying relevant — as institutions, as individuals in the workplace
  • Reinventing ourselves over time — and having to do so quickly
  • Adapting, being nimble, willing to innovate — as institutions, as individuals
  • Game-changing environment
  • Lifelong learning — higher ed needs to put more emphasis on microlearning, heutagogy, and delivering constant/up-to-date streams of content and learning experiences. This could happen via the addition/use of smaller learning hubs, some even makeshift learning hubs that are taking place at locations that these institutions don’t even own…like your local Starbucks.
  • If we don’t get this right, there could be major civil unrest as inequality and unemployment soar
  • Traditional institutions of higher education have not been nearly as responsive to change as they have needed to be; this opens the door to alternatives. There’s a limited (and closing) window of time left to become more nimble and responsive before these alternatives majorly disrupt the current world of higher education.

 

 

 



Addendum from the corporate world (emphasis DSC):



 

From The Impact 2017 Conference:

The Role of HR in the Future of Work – A Town Hall

  • Josh Bersin, Principal and Founder, Bersin by Deloitte, Deloitte Consulting LLP
  • Nicola Vogel, Global Senior HR Director, Danfoss
  • Frank Møllerop, Chief Executive Officer, Questback
  • David Mallon, Head of Research, Bersin by Deloitte, Deloitte Consulting LLP

Massive changes spurred by new technologies such as artificial intelligence, mobile platforms, sensors and social collaboration have revolutionized the way we live, work and communicate – and the pace is only accelerating. Robots and cognitive technologies are making steady advances, particularly in jobs and tasks that follow set, standardized rules and logic. This reinforces a critical challenge for business and HR leaders—namely, the need to design, source, and manage the future of work.

In this Town Hall, we will discuss the role HR can play in leading the digital transformation that is shaping the future of work in organizations worldwide. We will explore the changes we see taking place in three areas:

  • Digital workforce: How can organizations drive new management practices, a culture of innovation and sharing, and a set of talent practices that facilitate a new network-based organization?
  • Digital workplace: How can organizations design a working environment that enables productivity; uses modern communication tools (such as Slack, Workplace by Facebook, Microsoft Teams, and many others); and promotes engagement, wellness, and a sense of purpose?
  • Digital HR: How can organizations change the HR function itself to operate in a digital way, use digital tools and apps to deliver solutions, and continuously experiment and innovate?
 

Looking to build the campus of tomorrow? 5 trends you should know — from ecampusnews.com by Laura Ascione
Today’s trends will bring about a new vision for the traditional college campus.

Excerpt:

“Innovations in physical space must be made to accommodate demands for accessibility, flexibility and affordability,” according to The State of Higher Education in 2017, a report from professional services firm Grant Thornton.

Changes in infrastructure are being driven by a handful of trends, including:

  • Digital technology is decoupling access to the classroom and information from any specific geographic location.
  • Learning is becoming more “modular,” credentialing specific competencies, such as certificates and badges,, rather than the model of four years to a degree via fixed-class schedules. This requires a less broad range of academic buildings on campus.
  • Students will engage with their coursework at their own time and pace, as they do in every other aspect of their lives.
  • Price pressure on colleges will create incentives for cost efficiencies, discouraging the fixed-cost commitment embodied in physical structures.
  • Deferred maintenance is a problem so large that it can’t be solved by most colleges within their available resources; the result may be reducing the physical plant footprint or just letting it deteriorate further.

These developments will prompt physical space transformation that will lead to a new kind of campus.

 

 


The State of Higher Education in 2017 — from grantthornton.com

 

Browse the report articles:

 

 

Innovative thinking will be vital to successfully moving into the future.

 

 

Federal Reserve Bank of New York: Press Briefing on Household Debt, with Focus on Student Debt — with thanks to Mr. Bryan Alexander for his post on this

 

 

 

 

 

Excerpts:

Student Debt Overview
  • Student debt was $1.3 trillion at the end of 2016, an increase of about 170% from 2006.
  • Aggregate student debt is increasing because:
    • More students are taking out loans
    • Loans are for larger amounts
    • Repayment rates have slowed down
  • About 5% of the borrowers have more than $100,000 debt in 2016, but they account for about 30% of the total debt.
  • Recent graduates with student loans leave school with about $34,000, up nearly 70% from 10 years ago.

 


 

  • While the total level of household debt has nearly returned to the 2008 peak, debt types and borrower profiles have changed.
    • Debt growth is now driven by non-housing sectors, and debt is held by older, more creditworthy borrowers.
  • Student debt has expanded significantly because of higher levels of borrowing and slower rates of repayment.
  • Student debt defaults peaked with the 2011 cohort and have improved somewhat since. However, payment progress has declined.
  • College attendance is associated with significantly higher homeownership rates regardless of debt status. Yet, student debt appears to dampen homeownership rates among those with the same level of education.
  • College attendance appears to mitigate the impact of economic background on homeownership rates.

 

 

 


 

Also see:

 


 

 

 

Retailers cut tens of thousands of jobs. Again. — from money.cnn.com by Paul R. La Monica
The dramatic reshaping of the American retail industry has, unfortunately, led to massive job losses in the sector.

Excerpt (emphasis DSC):

The federal government said Friday that retailers shed nearly 30,000 jobs in March. That follows a more than 30,000 decline in the number of retail jobs in the previous month.

So-called general merchandise stores are hurting the most.

That part of the sector, which includes struggling companies like Macy’s, Sears, and J.C. Penney, lost 35,000 jobs last month. Nearly 90,000 jobs have been eliminated since last October.

“There is no question that the Amazon effect is overwhelming,” said Scott Clemons, chief investment strategist of private banking for BBH. “There has been a shift in the way we buy things as opposed to a shift in the amount of money spent.”

To that end, Amazon just announced plans to hire 30,000 part-time workers.

 

From DSC:
One of the reasons that I’m posting this item is for those who say disruption isn’t real…it’s only a buzz word…

A second reason that I’m posting this item is because those of us working within higher education should take note of the changes in the world of retail and learn the lesson now before the “Next Amazon.com of Higher Education*” comes on the scene. Though this organization has yet to materialize, the pieces of its foundation are beginning to come together — such as the ingredients, trends, and developments that I’ve been tracking in my “Learning from the Living [Class] Room” vision.

This new organization will be highly disruptive to institutions of traditional higher education.

If you were in an influential position at Macy’s, Sears, and/or at J.C. Penney today, and you could travel back in time…what would you do?

We in higher education have the luxury of learning from what’s been happening in the retail business. Let’s be sure to learn our lesson.

 



 

* Effective today, what I used to call the “Forthcoming Walmart of Education — which has already been occurring to some degree with things such as MOOCs and collaborations/partnerships such as Georgia Institute of Technology, Udacity, and AT&T — I now call the “Next Amazon.com of Higher Education.”

Cost. Convenience. Selection. Offering a service on-demand (i.e., being quick, responsive, and available 24×7). <– These all are powerful forces.

 



 

P.S. Some will say you can’t possibly compare the worlds of retail and higher education — and that may be true as of 2017. However, if:

  • the costs of higher education keep going up and we continue to turn a deaf ear to the struggling families/students/adult learners/etc. out there
  • alternatives to traditional higher education continue to come on the landscape
  • the Federal Government continues to be more open to financially supporting such alternatives
  • technologies such as artificial intelligence, machine learning, deep learning continue to get better and more powerful — to the point that they can effectively deliver a personalized education (one that is likely to be fully online and that utilizes a team of specialists to create and deliver the learning experiences)
  • people lose their jobs to artificial intelligence, robotics, and automation and need to quickly reinvent themselves

…I can assure you that people will find other ways to make ends meet. The Next Amazon.com of Education will be just what they are looking for.

 



 

 

 

From DSC:
It seems to me that we are right on the precipice of major changes — throughout the globe — that are being introduced by the growing use and presence of automation, robotics, and artificial intelligence/machine learning/deep learning, as well as other emerging technologies. But it’s not just the existence of these technologies, but it’s also that the pace of adoption of these technologies continues to increase.

These things made me wonder….what are the ramifications of the graphs below — and this new trajectory/pace of change that we’re on — for how we accredit new programs within higher education?

For me, it speaks to the need for those of us who are working within higher education to be more responsive, and we need to increase our efforts to provide more lifelong learning opportunities. People are going to need to reinvent themselves over and over again. In order for higher education to be of the utmost service to people, the time that it takes to accredit a program must be greatly reduced in cost and in time.


 

 

 

 

 

 

 

 

 

 

 


Somewhat relevant addendums:


 

A quote from “Response: What Teaching in the Year 2047 Might Look Like

To end the metaphor, what I am simply trying to say is that schools cannot afford to evolve at ¼ of the pace the world is around it and not face the possibility of becoming dangerously irrelevant. So, to answer the question – do I think the classrooms of 2040 look like the classrooms of today? Yes, I think they look more like them than they do not. Unfortunately, in my opinion, that is not the way to best serve our kids in our ever-changing world. Let me be clear, great teaching and instruction has not fundamentally changed in the past 2000 years and will not in the next 30. The context of learning and doing our best to meet the needs of the society we are preparing kids for is how and why schools must be revolutionized, not simply evolve at their own pace.

 


An excerpt from “
The global forces inspiring a new narrative of progress” (from mckinsey.com by Ezra Greenberg, Martin Hirt, and Sven Smit; emphasis DSC):

The next three tensions highlight accelerating industry disruption. Digitization, machine learning, and the life sciences are advancing and combining with one another to redefine what companies do and where industry boundaries lie. We’re not just being invaded by a few technologies, in other words, but rather are experiencing a combinatorial technology explosion. Customers are reaping some of the rewards, and our notions of value delivery are changing. In the words of Alibaba’s Jack Ma, B2C is becoming “C2B,” as customers enjoy “free” goods and services, personalization, and variety. And the terms of competition are changing: as interconnected networks of partners, platforms, customers, and suppliers become more important, we are experiencing a business ecosystem revolution.

 

38% of American Jobs Could be Replaced by Robots, According to PwC Report — from bigthink.com by David Ryan Polgar

Excerpt:

Nearly 4 out of 10 American jobs may be replaced through automation by the early 2030s, according to a new report by Price Waterhouse Cooper (PwC). In the report, the United States was viewed as the country most likely to lost jobs through automation–ahead of the UK, Germany, and Japan. This is probably not what the current administration had in mind with an “America First” policy.

 

 

 

 

The Enterprise Gets Smart
Companies are starting to leverage artificial intelligence and machine learning technologies to bolster customer experience, improve security and optimize operations.

Excerpt:

Assembling the right talent is another critical component of an AI initiative. While existing enterprise software platforms that add AI capabilities will make the technology accessible to mainstream business users, there will be a need to ramp up expertise in areas like data science, analytics and even nontraditional IT competencies, says Guarini.

“As we start to see the land grab for talent, there are some real gaps in emerging roles, and those that haven’t been as critical in the past,” Guarini  says, citing the need for people with expertise in disciplines like philosophy and linguistics, for example. “CIOs need to get in front of what they need in terms of capabilities and, in some cases, identify potential partners.”

 

 

 

Asilomar AI Principles

These principles were developed in conjunction with the 2017 Asilomar conference (videos here), through the process described here.

 

Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.

Research Issues

 

1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

  • How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
  • How can we grow our prosperity through automation while maintaining people’s resources and purpose?
  • How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
  • What set of values should AI be aligned with, and what legal and ethical status should it have?

3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Ethics and Values

 

6) Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

7) Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.

8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

10) Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

11) Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.

12) Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.

13) Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.

14) Shared Benefit: AI technologies should benefit and empower as many people as possible.

15) Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

16) Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.

17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

18) AI Arms Race: An arms race in lethal autonomous weapons should be avoided.

Longer-term Issues

 

19) Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.

20) Importance: Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.

21) Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.

22) Recursive Self-Improvement: AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.

23) Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.

 

 

 

Excerpts:
Creating human-level AI: Will it happen, and if so, when and how? What key remaining obstacles can be identified? How can we make future AI systems more robust than today’s, so that they do what we want without crashing, malfunctioning or getting hacked?

  • Talks:
    • Demis Hassabis (DeepMind)
    • Ray Kurzweil (Google) (video)
    • Yann LeCun (Facebook/NYU) (pdf) (video)
  • Panel with Anca Dragan (Berkeley), Demis Hassabis (DeepMind), Guru Banavar (IBM), Oren Etzioni (Allen Institute), Tom Gruber (Apple), Jürgen Schmidhuber (Swiss AI Lab), Yann LeCun (Facebook/NYU), Yoshua Bengio (Montreal) (video)
  • Superintelligence: Science or fiction? If human level general AI is developed, then what are likely outcomes? What can we do now to maximize the probability of a positive outcome? (video)
    • Talks:
      • Shane Legg (DeepMind)
      • Nick Bostrom (Oxford) (pdf) (video)
      • Jaan Tallinn (CSER/FLI) (pdf) (video)
    • Panel with Bart Selman (Cornell), David Chalmers (NYU), Elon Musk (Tesla, SpaceX), Jaan Tallinn (CSER/FLI), Nick Bostrom (FHI), Ray Kurzweil (Google), Stuart Russell (Berkeley), Sam Harris, Demis Hassabis (DeepMind): If we succeed in building human-level AGI, then what are likely outcomes? What would we like to happen?
    • Panel with Dario Amodei (OpenAI), Nate Soares (MIRI), Shane Legg (DeepMind), Richard Mallah (FLI), Stefano Ermon (Stanford), Viktoriya Krakovna (DeepMind/FLI): Technical research agenda: What can we do now to maximize the chances of a good outcome? (video)
  • Law, policy & ethics: How can we update legal systems, international treaties and algorithms to be more fair, ethical and efficient and to keep pace with AI?
    • Talks:
      • Matt Scherer (pdf) (video)
      • Heather Roff-Perkins (Oxford)
    • Panel with Martin Rees (CSER/Cambridge), Heather Roff-Perkins, Jason Matheny (IARPA), Steve Goose (HRW), Irakli Beridze (UNICRI), Rao Kambhampati (AAAI, ASU), Anthony Romero (ACLU): Policy & Governance (video)
    • Panel with Kate Crawford (Microsoft/MIT), Matt Scherer, Ryan Calo (U. Washington), Kent Walker (Google), Sam Altman (OpenAI): AI & Law (video)
    • Panel with Kay Firth-Butterfield (IEEE, Austin-AI), Wendell Wallach (Yale), Francesca Rossi (IBM/Padova), Huw Price (Cambridge, CFI), Margaret Boden (Sussex): AI & Ethics (video)

 

 

 

A smorgasboard of ideas to put on your organization’s radar! [Christian]

From DSC:
At the Next Generation Learning Spaces Conference, held recently in San Diego, CA, I moderated a panel discussion re: AR, VR, and MR.  I started off our panel discussion with some introductory ideas and remarks — meant to make sure that numerous ideas were on the radars at attendees’ organizations. Then Vinay and Carrie did a super job of addressing several topics and questions (Mary was unable to make it that day, as she got stuck in the UK due to transportation-related issues).

That said, I didn’t get a chance to finish the second part of the presentation which I’ve listed below in both 4:3 and 16:9 formats.  So I made a recording of these ideas, and I’m relaying it to you in the hopes that it can help you and your organization.

 


Presentations/recordings:


 

Audio/video recording (187 MB MP4 file)

 

 


Again, I hope you find this information helpful.

Thanks,
Daniel

 

 

 

Growth of AI Means We Need To Retrain Workers… Now — from forbes.com by Ryan Wibberley

Excerpt:

On the more positive side, AI could take over mundane, repetitive tasks and enable the workers who perform them to take on more interesting and rewarding work. But that will also mean many workers will need to be retrained. If you’re in a business where AI-based automation could be a potentially significant disruptor, then the time to invest in worker training and skill development is now. One could argue that AI will impact just about every industry. For example, in the financial services industry, we have already seen the creation of the robo advisor. While I don’t believe that the robo advisor will fully replace the human financial advisor because of the emotional aspects of investing, I do believe that it will play a part in the relationship with an advisor and his/her client.

 
© 2016 Learning Ecosystems