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:
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.

 

 

 

 

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.

 

 

 

Per X Media Lab:

The authoritative CB Insights lists imminent Future Tech Trends: customized babies; personalized foods; robotic companions; 3D printed housing; solar roads; ephemeral retail; enhanced workers; lab-engineered luxury; botroots movements; microbe-made chemicals; neuro-prosthetics; instant expertise; AI ghosts. You can download the whole outstanding report here (125 pgs).

 

From DSC:
Though I’m generally pro-technology, there are several items in here which support the need for all members of society to be informed and have some input into if and how these technologies should be used. Prime example: Customized babies.  The report discusses the genetic modification of babies: “In the future, we will choose the traits for our babies.” Veeeeery slippery ground here.

 

Below are some example screenshots:

 

 

 

 

 

 

 

 

 

Also see:

CBInsights — Innovation Summit

  • The New User Interface: The Challenge and Opportunities that Chatbots, Voice Interfaces and Smart Devices Present
  • Fusing the physical, digital and biological: AI’s transformation of healthcare
  • How predictive algorithms and AI will rule financial services
  • Autonomous Everything: How Connected Vehicles Will Change Mobility and Which Companies Will Own this Future
  • The Next Industrial Age: The New Revenue Sources that the Industrial Internet of Things Unlocks
  • The AI-100: 100 Artificial Intelligence Startups That You Better Know
  • Autonomous Everything: How Connected Vehicles Will Change Mobility and Which Companies Will Own this Future

 

 

 

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.

 

 

 
 

How will leadership change in the cognitive era? — from forbes.com by Chris Cancialosi

Excerpt:

Technological innovation is continuing to accelerate on a hockey stick growth curve. Companies like IBM, Microsoft, Facebook, and Amazon are bringing cognitive computing capability to the masses. And it’s only a matter of time until nearly every aspect of our work and personal lives are impacted.

These advances are still relatively new. Time will tell when and how they change things, but it will happen, and it will happen quickly. In a recent article, Steve Denning reminds us that a repeating pattern of massive transformation has occurred regularly over the last 250 years.

With massive change at our doorstep, now is the time to begin a collective discussion to help leaders navigate this new age.

 

Leadership behaviors that yielded success in the past may no longer be effective as the way we work changes over time.

 

 

SEEK is using artificial intelligence to find your next job — from afr.com by Max Mason

Excerpt:

Employment services business SEEK has begun using machine learning and artificial intelligence to send its users more relevant job advertisements and alerts.

Across its network, which includes Australia, Brazil, Malaysia, Mexico and China among others, SEEK’s machine learning algorithm has made more than 2.5 billion recommendations to jobseekers.

 

From DSC:
With Microsoft investing heavily in AI and with its purchase of LinkedIn (who had already purchased Lynda.com the year before), I’m wondering what Microsoft will be offering along these lines. With AI, #blockchain and other new forms of credentialing, finding work could be very different in the future.

 

 

From DSC:
Hmmm…this is interesting! I ran into a company based out of Canada called Sightline Innovation — and they offer Machine Learning as a Service!

 

Here’s an excerpt from their site:

MLaaS: AI for everyone
Sightline’s Machine Learning as a Service (MLaaS) is the AI solution for Enterprise. With MLaaS, you provide the data and the desired outcome, and Sightline provides the Machine Learning capacity. By analyzing data sets, MLaaS generates strategic insights that allow companies to optimize their business processes and maximize efficiency. Discover new approaches to time management, teamwork and collaboration, client service and business forecasting.

Mine troves of inert customer data to reveal sales pipeline bottlenecks, build more in-depth personas and discover opportunities for upsales.
MLaaS empowers Enterprise to capitalize on opportunities that were previously undiscovered. MLaaS.net is the only system that brings together a full spectrum of AI algorithms including:

  • Convolutional Neural Networks
  • Deep Nets
  • Restricted Boltzman Machines
  • Probabilistic Graphical Models; and
  • Bayesian Networks

I wonder if Machine Learning as a Service (MLaaS) is the way that many businesses in the future will tap into the power of AI-based solutions – especially smaller and mid-size companies who can’t afford to build an internal team focused on AI…?

 

 

Equipping people to stay ahead of technological change — from economist.com by
It is easy to say that people need to keep learning throughout their careers. The practicalities are daunting.

Excerpt (emphasis DSC):

WHEN education fails to keep pace with technology, the result is inequality. Without the skills to stay useful as innovations arrive, workers suffer—and if enough of them fall behind, society starts to fall apart. That fundamental insight seized reformers in the Industrial Revolution, heralding state-funded universal schooling. Later, automation in factories and offices called forth a surge in college graduates. The combination of education and innovation, spread over decades, led to a remarkable flowering of prosperity.

Today robotics and artificial intelligence call for another education revolution. This time, however, working lives are so lengthy and so fast-changing that simply cramming more schooling in at the start is not enough. People must also be able to acquire new skills throughout their careers.

Unfortunately, as our special report in this issue sets out, the lifelong learning that exists today mainly benefits high achievers—and is therefore more likely to exacerbate inequality than diminish it. If 21st-century economies are not to create a massive underclass, policymakers urgently need to work out how to help all their citizens learn while they earn. So far, their ambition has fallen pitifully short.

At the same time on-the-job training is shrinking. In America and Britain it has fallen by roughly half in the past two decades. Self-employment is spreading, leaving more people to take responsibility for their own skills. Taking time out later in life to pursue a formal qualification is an option, but it costs money and most colleges are geared towards youngsters.

 

The classic model of education—a burst at the start and top-ups through company training—is breaking down. One reason is the need for new, and constantly updated, skills.

 

 

 

Lifelong learning is becoming an economic imperative — from economist.com
Technological change demands stronger and more continuous connections between education and employment, says Andrew Palmer. The faint outlines of such a system are now emerging

Excerpt:

A college degree at the start of a working career does not answer the need for the continuous acquisition of new skills, especially as career spans are lengthening. Vocational training is good at giving people job-specific skills, but those, too, will need to be updated over and over again during a career lasting decades. “Germany is often lauded for its apprenticeships, but the economy has failed to adapt to the knowledge economy,” says Andreas Schleicher, head of the education directorate of the OECD, a club of mostly rich countries. “Vocational training has a role, but training someone early to do one thing all their lives is not the answer to lifelong learning.”

To remain competitive, and to give low- and high-skilled workers alike the best chance of success, economies need to offer training and career-focused education throughout people’s working lives. This special report will chart some of the efforts being made to connect education and employment in new ways, both by smoothing entry into the labour force and by enabling people to learn new skills throughout their careers. Many of these initiatives are still embryonic, but they offer a glimpse into the future and a guide to the problems raised by lifelong reskilling.

 

 

Individuals, too, increasingly seem to accept the need for continuous rebooting.

 

 

 
© 2016 Learning Ecosystems