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.

 

 

 

New Google Earth has exciting features for teachers — from thejournal.com by Richard Chang

Excerpt:

Google has recently released a brand new version of Google Earth for both Chrome and Android. This new version has come with a slew of nifty features teachers can use for educational purposes with students in class. Following is a quick overview of the most fascinating features…

 

 

 

 

 

 

 

Google Home’s assistant can now recognize different voices — from cnbc.com

Excerpt:

SAN FRANCISCO (AP) — Google’s voice-activated assistant can now recognize who’s talking to it on Google’s Home speaker.

An update released Thursday enables Home’s built-in assistant to learn the different voices of up to six people, although they can’t all be talking to the internet-connected speaker at the same time.

Distinguishing voices will allow Home to be more personal in some of its responses, depending on who triggers the assistant with the phrase, “OK Google” or “Hey Google.”

For instance, once Home is trained to recognize a user named Joe, the assistant will automatically be able to tell him what traffic is like on his commute, list events on his daily calendar or even play his favorite songs. Then another user named Jane could get similar information from Home, but customized for her.

 

 

 

From DSC:
In terms of learning, having to be in the same physical place as others continues to not be a requirement nearly as much as it used to be. But I’m not just talking about online learning here. I’m talking about a new type of learning environment that involves both hardware and software to facilitate collaboration (and it was designed that way from day 1). These new types of setups can provide us with new opportunities and affordances that we should begin experimenting with immediately.

Check out the following products — all of which allow a person to contribute to a discussion or conversation from anywhere they can get Internet access:

When you go to those sites, you will see words and phrase such as:

  • Visual collaboration software
  • Virtual workspace
  • Develop
  • Share
  • Inspire
  • Design
  • Global teams
  • A visual collaboration solution that links locations, teams, content, and devices in an immersive, shared workspace
  • Teamwork
  • Create and brainstorm with others
  • Digital workplace platform
  • Eliminate the distance between in-office and remote employees
  • Jumpstart spontaneous brainstorms and working sessions

So using these types of software and hardware setups, I can contribute regardless of where I’m located. Remote learning — from anywhere in the world — being combined with our face-to-face based classrooms.

Also, the push for Active Learning Classrooms (ALCs) continues across higher education. Such hands-on, project-learning based, student-centered approaches fit extremely well with the collaboration setups mentioned above.

Then, there’s the insight from Simon Dudley in this article:

“…video conferencing is increasingly an application within in a larger workflow…”

Lastly, if colleges and universities don’t have the funds to maintain their physical plants, look for higher education to move increasingly online — and these types of solutions could play a significant role in that environment. Plus, for working adults who need to reinvent themselves, this is an extremely efficient means of picking up some new skills and competencies.

So the growth of these types of setups — where the software and hardware work together to support worldwide collaboration — will likely create a powerful, new, emerging piece of our learning ecosystems.

 



 

 

 

 

 

 

 

 

 

 



 

Remote learning — from anywhere in the world — being combined with our face-to-face based classrooms.

 



 

 

The Dark Secret at the Heart of AI — from technologyreview.com by Will Knight
No one really knows how the most advanced algorithms do what they do. That could be a problem.

Excerpt:

The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will. That’s one reason Nvidia’s car is still experimental.

 

“Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.”

 


This raises mind-boggling questions. As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers of our time.

 

 

 

Samsung’s personal assistant Bixby will take on Amazon Alexa, Apple Siri — from theaustralian.com.au by Chris Griffith

Excerpt:

Samsung has published details of its Bixby personal assistant, which will debut on its Galaxy S8 smartphone in New York next week.

Bixby will go head-to-head with Google Assistant, Microsoft Cortana, Amazon Echo and Apple Siri, in a battle to lure you into their artificial intelligence world.

In future, the personal assistant that you like may not only influence which phone you buy, also the home automation system that you adopt.

This is because these personal assistants cross over into home use, which is why Samsung would bother with one of its own.

Given that the S8 will run Android Nougat, which includes Google Assistant, users will have two personal assistants on their phone, unless somehow one is disabled.

 

 

There are a lot of red flags with Samsung’s AI assistant in the new Galaxy S8 — from businessinsider.com by Steve Kovach

Excerpt:

There’s Siri. And Alexa. And Google Assistant. And Cortana. Now add another one of those digital assistants to the mix: Bixby, the new helper that lives inside Samsung’s latest phone, the Galaxy S8. But out of all the assistants that have launched so far, Bixby is the most curious and the most limited.

Samsung’s goal with Bixby was to create an assistant that can mimic all the functions you’re used to performing by tapping on your screen through voice commands. The theory is that phones are too hard to manage, so simply letting users tell their phone what they want to happen will make things a lot easier.

 

 

Samsung Galaxy S8: Hands on with the world’s most ambitious phone — from telegraph.co.uk by James Titcomb

Excerpt:

The S8 will also feature Bixby, Samsung’s new intelligent assistant. The company says Bixby is a bigger deal than Siri or Google Assistant – as well as simply asking for the weather, it will be deeply integrated with the phone’s everyday functions such as taking photos and sending them to people. Samsung has put a dedicated Bixby button on the S8 on the left hand side, but I wasn’t able to try it out because it won’t launch in the UK until later this year.

 

 

Samsung Galaxy S8 launch: Samsung reveals its long-awaited iPhone killer — from telegraph.co.uk by James Titcomb

 

 

 


Also see:


 

Recent years have brought some rapid development in the area of artificially intelligent personal assistants. Future iterations of the technology could fully revamp the way we interact with our devices.

 

 

 

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)

 

 

 
 

From DSC:
Given the exponential pace of technological change that many societies throughout the globe are now on, we need some tools to help us pulse-check what’s going on in the relevant landscapes that we are trying to scan.


 

 

 

 

 

 

 

 

 

 


Below, I would like to suggest 2 methods/tools to do this.  I have used both methods for years, and I have found them to be immensely helpful in pulse-checking the landscapes. Perhaps these tools will be helpful to you — or to your students or employees — as well.  I vote for these 2 tools to be a part of all of our learning ecosystems. (And besides, they also encourage micro-learning while helping us spot emerging trends.)


 

Google Alerts

 

 

Feedly.com

 

 

 

 

KPMG & Microsoft Announce New “Blockchain Nodes” — from finance.yahoo.com

Excerpt:

NEW YORK, Feb. 15, 2017 /PRNewswire/ — KPMG International and Microsoft Corp. have announced the launch of joint Blockchain Nodes, which are designed to create and demonstrate use cases that apply blockchain technology to business propositions and processes.  The first joint Blockchain Nodes are in Frankfurt and Singapore, with future plans for a location in New York.

The KPMG and Microsoft Blockchain Nodes –innovation workspaces– will expand on a global alliance, which combines Microsoft’s technical expertise with KPMG’s deep industry and blockchain application knowledge, together with strong connections to the start-up and developer communities.

“The Blockchain Nodes will play a critical role in identifying new applications and use cases that blockchain can address,” said Eamonn Maguire, global and US leader for KPMG’s Digital Ledger Services. “They will enable us to work directly with clients to discover and test ideas based on market insights, creating and implementing prototype solutions that use this innovative technology.”

 

 

IBM Brings Machine Learning to the Private Cloud — from finance.yahoo.com
First to automate creation and training of learning analytic models at the source of high value corporate data, starting with IBM z System Mainframe

Excerpt:

ARMONK, N.Y., Feb. 15, 2017 /PRNewswire/ — IBM (NYSE: IBM) today announced IBM Machine Learning, the first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores.  Even using the most advanced techniques, data scientists – in shortest supply among today’s IT skills1 – might spend days or weeks developing, testing and retooling even a single analytic model one step at a time.

IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world’s enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments.

IBM Machine Learning allows data scientists to automate the creation, training and deployment of operational analytic models that will support…

 

 

Amazon Echo and Google Home may soon be able to make voice calls — from financye.yahoo.com and Business Insider by Jeff Dunn

Excerpt:

The Amazon Echo and Google Home could be used to make and receive phone calls later this year, according to a new report from The Wall Street Journal’s Ryan Knutson and Laura Stevens. Citing “people familiar with the matter,” the report says that both Amazon and Google are looking to activate the feature, but that their attempts have been slowed by privacy and regulatory concerns. Amazon has reportedly been working on Echo-specific voice calls since 2015, but has been held up by “employee turnover” as well.

 

 

Amazon unveils Chime, looks to reinvent the conference call with new Skype and GoToMeeting competitor — from geekwire.com by John Cook

Excerpt:

Amazon is looking to transform just about every industry.

Now, the Seattle tech juggernaut wants to reinvent how you conduct meetings and conference calls.

Amazon Web Services today unveiled Chime, a new service that it says takes the “frustration out of meetings” by delivering video, voice, chat, and screen sharing. Instead of forcing participants to call one another on a dedicated line, Amazon Chime automatically calls all participants at the start of a meeting, so “joining a meeting is as easy as clicking a button in the app, no PIN required,” the company said in a press release. Chime also shows a visual roster of participants, and allows participants to pinpoint who exactly on the call is creating annoying background noise.

 

 

 

 

 

 
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