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.

 

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

 

 

 

 

From DSC:
Hmmm…how true: “…the digital age rewards change and punishes stasis.” (
source)

Which reminds me of a photo I took just yesterday morning at one of the malls in our area, where a local Sears store is closing.

It made me wonder…if Sears could do it all over again, what would they do differently? If they had a time machine, would they go back in time and work to become the new Amazon.com?

 

 

 

By the way, this picture is for those people who continue to dismiss the need to change and to adapt.  Surveying the relevant landscapes is an increasingly important thing for all of us to do, especially given that we are now on an exponential pace of technological change.

 

 

Companies must be open to radical reinvention to find new, significant, and sustainable sources of revenue. Incremental adjustments or building something new outside of the core business can provide real benefits and, in many cases, are a crucial first step for a digital transformation. But if these initiatives don’t lead to more profound changes to the core business and avoid the real work of rearchitecting how the business makes money, the benefits can be fleeting and too insignificant to avert a steady march to oblivion.

 

 

 



Addendum on 2/10/17

  • Macy’s earnings: Shifts in retail are hurting major players — from marketwatch.com by Tonya Garcia
    Macy’s has assets like real estate and brand identity, but shifts in the sector are putting pressure on earningsExcerpt:
    Even a major player like Macy’s M, +1.51%   isn’t immune to retail’s struggles. The sector is experiencing a dramatic shift to e-commerce and changes in consumer tastes and shopping behavior that have put pressure on department store earnings, and on the industry as a whole. Macy’s has already announced 100 store closures and thousands of job cuts, in addition to a reassessment of its real-estate assets. Now there’s buzz from reports about buyout talks with Hudson’s Bay Co. HBC, parent to Lord & Taylor and Saks Fifth Avenue.

 

 

 

The case for digital reinvention — from mckinsey.com
Digital technology, despite its seeming ubiquity, has only begun to penetrate industries. As it continues its advance, the implications for revenues, profits, and opportunities will be dramatic.

Excerpt:

In the quest for coherent responses to a digitizing world, companies must assess how far digitization has progressed along multiple dimensions in their industries and the impact that this evolution is having—and will have—on economic performance. And they must act on each of these dimensions with bold, tightly integrated strategies. Only then will their investments match the context in which they compete.

 

 

 

 

 

 

 

From J. Walter Thompson Intelligence’s Weekly Roundup 

 

 

From DSC:
For me, using robots for baristas could take away from the charm/overall experience of going into a cafe.  Plus, if Starbucks were to go down this path, many jobs would be lost for our students working their way through college.

 

 

 

 

A world without work — by Derek Thompson; The Atlantic — from July 2015

Excerpts:

Youngstown, U.S.A.
The end of work is still just a futuristic concept for most of the United States, but it is something like a moment in history for Youngstown, Ohio, one its residents can cite with precision: September 19, 1977.

For much of the 20th century, Youngstown’s steel mills delivered such great prosperity that the city was a model of the American dream, boasting a median income and a homeownership rate that were among the nation’s highest. But as manufacturing shifted abroad after World War  II, Youngstown steel suffered, and on that gray September afternoon in 1977, Youngstown Sheet and Tube announced the shuttering of its Campbell Works mill. Within five years, the city lost 50,000 jobs and $1.3 billion in manufacturing wages. The effect was so severe that a term was coined to describe the fallout: regional depression.

Youngstown was transformed not only by an economic disruption but also by a psychological and cultural breakdown. Depression, spousal abuse, and suicide all became much more prevalent; the caseload of the area’s mental-health center tripled within a decade. The city built four prisons in the mid-1990s—a rare growth industry. One of the few downtown construction projects of that period was a museum dedicated to the defunct steel industry.

“Youngstown’s story is America’s story, because it shows that when jobs go away, the cultural cohesion of a place is destroyed”…

“The cultural breakdown matters even more than the economic breakdown.”

But even leaving aside questions of how to distribute that wealth, the widespread disappearance of work would usher in a social transformation unlike any we’ve seen.

What may be looming is something different: an era of technological unemployment, in which computer scientists and software engineers essentially invent us out of work, and the total number of jobs declines steadily and permanently.

After 300 years of people crying wolf, there are now three broad reasons to take seriously the argument that the beast is at the door: the ongoing triumph of capital over labor, the quiet demise of the working man, and the impressive dexterity of information technology.

The paradox of work is that many people hate their jobs, but they are considerably more miserable doing nothing.

Most people want to work, and are miserable when they cannot. The ills of unemployment go well beyond the loss of income; people who lose their job are more likely to suffer from mental and physical ailments. “There is a loss of status, a general malaise and demoralization, which appears somatically or psychologically or both”…

Research has shown that it is harder to recover from a long bout of joblessness than from losing a loved one or suffering a life-altering injury.

Most people do need to achieve things through, yes, work to feel a lasting sense of purpose.

When an entire area, like Youngstown, suffers from high and prolonged unemployment, problems caused by unemployment move beyond the personal sphere; widespread joblessness shatters neighborhoods and leaches away their civic spirit.

What’s more, although a universal income might replace lost wages, it would do little to preserve the social benefits of work.

“I can’t stress this enough: this isn’t just about economics; it’s psychological”…

 

 

The paradox of work is that many people hate their jobs, but they are considerably more miserable doing nothing.

 

 

From DSC:
Though I’m not saying Thompson is necessarily asserting this in his article, I don’t see a world without work as a dream. In fact, as the quote immediately before this paragraph alludes to, I think that most people would not like a life that is devoid of all work. I think work is where we can serve others, find purpose and meaning for our lives, seek to be instruments of making the world a better place, and attempt to design/create something that’s excellent.  We may miss the mark often (I know I do), but we keep trying.

 

 

 

From DSC:
The following questions came to my mind today:

  • What are the future ramifications — for higher education — of an exponential population growth curve, especially in regards to providing access?
  • Are our current ways of providing an education going to hold up?
  • What about if the cost of obtaining a degree maintains its current trajectory?
  • What changes do we need to start planning for and/or begin making now?

 

 

 

 

 

Links to sources:

 

 

With Uber Freight, it’s not just truck drivers whose jobs are at risk — from linkedin.com by John McDermott
The bane of taxi drivers everywhere is now taking on logistics

Excerpts (emphasis DSC):

At the end of December Uber debuted Uber Freight, its foray into the un-sexy yet lucrative world of logistics. Many saw Uber’s entry into freight as a death knell for trucking companies, as Uber is looking to build a fleet of driverless trucks.

And while the threat to trucking is real, Uber Freight poses a more immediate risk to the thousands of mid-level, white-collar support staff jobs in the industry.

Uber is uniquely positioned to streamline the industry, though. Much like the company’s ride-hailing app cuts out the taxi dispatcher and allows people to hail rides directly from drivers, Uber Freight can create a platform where shippers and truckers broker shipping orders directly with one another, effectively rendering obsolete thousands of 3PL (third party logistics) workers. It replaces people with software, and configures a labor-intensive industry into a SaaS business.

Famed venture capitalist Marc Andreessen is fond of the phrase “software is eating the world,” meaning that it’s replacing many of the post-industrial, pre-internet jobs once thought to be essential. Problem is, one man’s efficiency is another’s unemployment.

 

Problem is, one man’s efficiency is another’s unemployment.

 

 

 
© 2016 Learning Ecosystems