Con Job: Hackers Target Millennials Looking for Work – from wsj.com by Kelsey Gee
Employment scams pose a growing threat as applications and interviews become more digital

Excerpt:

Hackers attempt to hook tens of thousands of people like Mr. Latif through job scams each year, according to U.S. Federal Trade Commission data, aiming to trick them into handing over personal or sensitive information, or to gain access to their corporate networks.

Employment fraud is nothing new, but as more companies shift to entirely-digital job application processes, Better Business Bureau director of communications Katherine Hutt said scams targeting job seekers pose a growing threat. Job candidates are now routinely invited to fill out applications, complete skill evaluations and interview—all on their smartphones, as employers seek to cast a wider net for applicants and improve the matchmaking process for entry-level hires.

Young people are a frequent target. Of the nearly 3,800 complaints the nonprofit has received from U.S. consumers on its scam report tracker in the past two years, people under 34 years old were the most susceptible to such scams, which frequently offer jobs requiring little to no prior experience, Ms. Hutt said.

 

 

Hackers are finding new ways to prey on young job seekers.

 

 

 

A leading Silicon Valley engineer explains why every tech worker needs a humanities education — from qz.com by Tracy Chou

Excerpts:

I was no longer operating in a world circumscribed by lesson plans, problem sets and programming assignments, and intended course outcomes. I also wasn’t coding to specs, because there were no specs. As my teammates and I were building the product, we were also simultaneously defining what it should be, whom it would serve, what behaviors we wanted to incentivize amongst our users, what kind of community it would become, and what kind of value we hoped to create in the world.

I still loved immersing myself in code and falling into a state of flow—those hours-long intensive coding sessions where I could put everything else aside and focus solely on the engineering tasks at hand. But I also came to realize that such disengagement from reality and societal context could only be temporary.

At Quora, and later at Pinterest, I also worked on the algorithms powering their respective homefeeds: the streams of content presented to users upon initial login, the default views we pushed to users. It seems simple enough to want to show users “good” content when they open up an app. But what makes for good content? Is the goal to help users to discover new ideas and expand their intellectual and creative horizons? To show them exactly the sort of content that they know they already like? Or, most easily measurable, to show them the content they’re most likely to click on and share, and that will make them spend the most time on the service?

 

Ruefully—and with some embarrassment at my younger self’s condescending attitude toward the humanities—I now wish that I had strived for a proper liberal arts education. That I’d learned how to think critically about the world we live in and how to engage with it. That I’d absorbed lessons about how to identify and interrogate privilege, power structures, structural inequality, and injustice. That I’d had opportunities to debate my peers and develop informed opinions on philosophy and morality. And even more than all of that, I wish I’d even realized that these were worthwhile thoughts to fill my mind with—that all of my engineering work would be contextualized by such subjects.

It worries me that so many of the builders of technology today are people like me; people haven’t spent anywhere near enough time thinking about these larger questions of what it is that we are building, and what the implications are for the world.

 

 


Also see:


 

Why We Need the Liberal Arts in Technology’s Age of Distraction — from time.com by Tim Bajarin

Excerpt:

In a recent Harvard Business Review piece titled “Liberal Arts in the Data Age,” author JM Olejarz writes about the importance of reconnecting a lateral, liberal arts mindset with the sort of rote engineering approach that can lead to myopic creativity. Today’s engineers have been so focused on creating new technologies that their short term goals risk obscuring unintended longterm outcomes. While a few companies, say Intel, are forward-thinking enough to include ethics professionals on staff, they remain exceptions. At this point all tech companies serious about ethical grounding need to be hiring folks with backgrounds in areas like anthropology, psychology and philosophy.

 

 

 

 

The Internet’s future is more fragile than ever, says one of its inventors — from fastcompany.com by Sean Captain
Vint Cerf, the co-creator of tech that makes the internet work, worries about hacking, fake news, autonomous software, and perishable digital history.

Excerpts:

The term “digital literacy” is often referred to as if you can use a spreadsheet or a text editor. But I think digital literacy is closer to looking both ways before you cross the street. It’s a warning to think about what you’re seeing, what you’re hearing, what you’re doing, and thinking critically about what to accept and reject . . . Because in the absence of this kind of critical thinking, it’s easy to see how the phenomena that we’re just now labeling fake news, alternative facts [can come about]. These [problems] are showing up, and they’re reinforced in social media.

What are the criteria that we should apply to devices that are animated by software, and which we rely upon without intervention? And this is the point where autonomous software becomes a concern, because we turn over functionality to a piece of code. And dramatic examples of that are self-driving cars . . . Basically you’re relying on software doing the right things, and if it doesn’t do the right thing, you have very little to say about it.

I feel like we’re moving into a kind of fragile future right now that we should be much more thoughtful about improving, that is to say making more robust.

 

 

Imagine a house that stops working when the internet connection goes away. That’s not acceptable.

 

 

 

 

AI will make forging anything entirely too easy — from wired.com by Greg Allen

Excerpt:

Today, when people see a video of a politician taking a bribe, a soldier perpetrating a war crime, or a celebrity starring in a sex tape, viewers can safely assume that the depicted events have actually occurred, provided, of course, that the video is of a certain quality and not obviously edited.

But that world of truth—where seeing is believing—is about to be upended by artificial intelligence technologies.

We have grown comfortable with a future in which analytics, big data, and machine learning help us to monitor reality and discern the truth. Far less attention has been paid to how these technologies can also help us to lie. Audio and video forgery capabilities are making astounding progress, thanks to a boost from AI. In the future, realistic-looking and -sounding fakes will constantly confront people. Awash in audio, video, images, and documents, many real but some fake, people will struggle to know whom and what to trust.

 

 

Also referenced in the above article:

 

 

 

 

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.

 

 

 

Tech giants grapple with the ethical concerns raised by the AI boom — from technologyreview.com by Tom Simonite
As machines take over more decisions from humans, new questions about fairness, ethics, and morality arise.

Excerpt:

With great power comes great responsibility—and artificial-intelligence technology is getting much more powerful. Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations.

“We’re here at an inflection point for AI,” said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review’s EmTech conference this week. “We have an ethical imperative to harness AI to protect and preserve over time.”

Horvitz spoke alongside researchers from IBM and Google pondering similar issues. One shared concern was that recent advances are leading companies to put software in positions with very direct control over humans—for example in health care.

 

 

59 impressive things artificial intelligence can do today — from businessinsider.com by Ed Newton-Rex

Excerpt:

But what can AI do today? How close are we to that all-powerful machine intelligence? I wanted to know, but couldn’t find a list of AI’s achievements to date. So I decided to write one. What follows is an attempt at that list. It’s not comprehensive, but it contains links to some of the most impressive feats of machine intelligence around. Here’s what AI can do…

 

 

 


Recorded Saturday, February 25th, 2017 and published on Mar 16, 2017


Description:

Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, or could it present a threat to the very basis of human civilization? The future of artificial intelligence is up for debate, and the Origins Project is bringing together a distinguished panel of experts, intellectuals and public figures to discuss who’s in control. Eric Horvitz, Jaan Tallinn, Kathleen Fisher and Subbarao Kambhampati join Origins Project director Lawrence Krauss.

 

 

 

 

Description:
Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, and Jaan Tallinn discuss with Max Tegmark (moderator) what likely outcomes might be if we succeed in building human-level AGI, and also what we would like to happen. The Beneficial AI 2017 Conference: In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients and followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial.

 

 


(Below emphasis via DSC)

IBM and Ricoh have partnered for a cognitive-enabled interactive whiteboard which uses IBM’s Watson intelligence and voice technologies to support voice commands, taking notes and actions and even translating into other languages.

 

The Intelligent Workplace Solution leverages IBM Watson and Ricoh’s interactive whiteboards to allow to access features via using voice. It makes sure that Watson doesn’t just listen, but is an active meeting participant, using real-time analytics to help guide discussions.

Features of the new cognitive-enabled whiteboard solution include:

  • Global voice control of meetings: Once a meeting begins, any employee, whether in-person or located remotely in another country, can easily control what’s on the screen, including advancing slides, all through simple voice commands using Watson’s Natural Language API.
  • Translation of the meeting into another language: The Intelligent Workplace Solution can translate speakers’ words into several other languages and display them on screen or in transcript.
  • Easy-to-join meetings: With the swipe of a badge the Intelligent Workplace Solution can log attendance and track key agenda items to ensure all key topics are discussed.
  • Ability to capture side discussions: During a meeting, team members can also hold side conversations that are displayed on the same whiteboard.

 


From DSC:

Holy smokes!

If you combine the technologies that Ricoh and IBM are using with their new cognitive-enabled interactive whiteboard with what Bluescape is doing — by providing 160 acres of digital workspace that’s used to foster collaboration (and to do so whether you are working remoting or working with others in the same physical space) — and you have one incredibly powerful platform! 

#NLP  |  #AI  |  #CognitiveComputing  | #SmartClassrooms
#LearningSpaces  |#Collaboration |  #Meetings 

 

 


 

 

 


 

AI Market to Grow 47.5% Over Next Four Years — from campustechnology.com by Richard Chang

Excerpt:

The artificial intelligence (AI) market in the United States education sector is expected to grow at a compound annual growth rate of 47.5 percent during the period 2017-2021, according to a new report by market research firm Research and Markets.

 

 

Amazon deepens university ties in artificial intelligence race — from by Jeffrey Dastin

Excerpt:

Amazon.com Inc has launched a new program to help students build capabilities into its voice-controlled assistant Alexa, the company told Reuters, the latest move by a technology firm to nurture ideas and talent in artificial intelligence research.

Amazon, Alphabet Inc’s Google and others are locked in a race to develop and monetize artificial intelligence. Unlike some rivals, Amazon has made it easy for third-party developers to create skills for Alexa so it can get better faster – a tactic it now is extending to the classroom.

 

 

The WebMD skill for Amazon’s Alexa can answer all your medical questions — from digitaltrends.com by Kyle Wiggers
WebMD is bringing its wealth of medical knowledge to a new form factor: Amazon’s Alexa voice assistant.

Excerpt:

Alexa, Amazon’s brilliant voice-activated smart assistant, is a capable little companion. It can order a pizza, summon a car, dictate a text message, and flick on your downstairs living room’s smart bulb. But what it couldn’t do until today was tell you whether that throbbing lump on your forearm was something that required medical attention. Fortunately, that changed on Tuesday with the introduction of a WebMD skill that puts the service’s medical knowledge at your fingertips.

 

 


Addendum:

  • How artificial intelligence is taking Asia by storm — from techwireasia.com by Samantha Cheh
    Excerpt:
    Lately it seems as if everyone is jumping onto the artificial intelligence bandwagon. Everyone, from ride-sharing service Uber to Amazon’s logistics branch, is banking on AI being the next frontier in technological innovation, and are investing heavily in the industry.

    That’s likely truest in Asia, where the manufacturing engine which drove China’s growth is now turning its focus to plumbing the AI mine for gold.

    Despite Asia’s relatively low overall investment in AI, the industry is set to grow. Fifty percent of respondents in KPMG’s AI report said their companies had plans to invest in AI or robotic technology.

    Investment in AI is set to drive venture capital investment in China in 2017. Tak Lo, of Hong Kong’s Zeroth, notes there are more mentions of AI in Chinese research papers than there are in the US.

    China, Korea and Japan collectively account for nearly half the planet’s shipments of articulated robots in the world.

     

 

Artificial Intelligence – Research Areas

 

 

 

 

 

 

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)

 

 

 

Code-Dependent: Pros and Cons of the Algorithm Age — from pewinternet.org by Lee Rainie and Janna Anderson
Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment

Excerpt:

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns…

 

The use of algorithms is spreading as massive amounts of data are being created, captured and analyzed by businesses and governments. Some are calling this the Age of Algorithms and predicting that the future of algorithms is tied to machine learning and deep learning that will get better and better at an ever-faster pace.

 

 

 

 

 

 

 

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.

 

 

 
© 2017 | Daniel Christian