Yesterday, I listed some of the major changes that L&D needs to acknowledge. What we need now is to look at the top steps that need to be taken.  As serious practitioners in a potentially valuable field, we need to adapt to the changing environment as much as we need to assist our charges to do so. So what’s involved?

We need to get a grasp on technology  affordances. We don’t need to that the latest technology exists, whether AI, AR, or VR.  Instead, we have to understand what they mean  in the context of our brains.  What key capabilities are brought?  Can VR go beyond entertainment to help us learn better? How can AI partner with us?  If we can make practical use of AR, what would we do with it?

In conjunction, we need to  understand the realities about us.  We need to take ownership and have a suitable background in how people  really think, work, and learn. Further, we need to recognize that they’re all tied together, not separate things. So, for instance, we learn as we work, we think as we learn, etc.

For example, we need to understand situated and distributed cognition. That is, we need to grasp that we’re not formal logical thinkers, but instead very context dependent, and that our thinking is across our tools. As a consequence, we need to design solutions that recognize our individual situations, and leverage technology as an augment. So we want to design human/computer system solutions to problems, not just human or system solutions.

We also need to understand cultural elements. We work better when we are given meaningful work, freedom to pursue those goals, and get the necessary support to succeed. This is  not micromanagement, but instead, is leadership and coaching. We also need an environment where it’s safe, expected even, to experiment and even to make mistakes.

We also need to understand that we work better (read: produce better results), when we work together in particular ways. Where we understand that we should allow individual thought first, but then pool those ideas. And we need to show our work and the underlying thinking. Moreover, again, it has to be safe to do so!

And, these are all tied together into a systemic approach!  It can’t be piecemeal, because working together and out loud can’t be divorced from the technology used to enable these capabilities. And giving people meaningful work and not letting them work together, or vice-versa, just won’t achieve the necessary critical mass.

Finally, we also need to do this in alignment with the business. And, lets be clear, in ways that can be measured!  We need to be understanding what are the critical performance needs of the organization, and demonstrate that we’re impacting them in the ways above.

This can be done, and it will be the hallmark of successful organization. We’re already seeing a wide variety of converging evidence that these changes lead to success. The question is, are you going to lead your organization forward into the future, or keep your head down and do what you’ve always done?

There are a serious number of changes that are affecting organizations.  We’re seeing changes in the information flow, in technology, and in what we know about ourselves. Importantly, these are things that L&D needs to acknowledge and respond to.  What are these changes?

It’s old news that things are happening faster. We’re being overwhelmed with information, and that rate is accelerating. On the other hand, our tools to manage the information flow are also advancing.

Which is the second topic. We’re getting more powerful technology. We can create systems that do tasks that used to be limited to humans. They can also partner with us, providing information based upon who we are, what we’re doing, and what else is going on.

And there are increasing demands for accountability (and transparency). Your actions should be justified. What are you doing, why, and what effect is it having? If you can’t answer these questions, you’re going to be looking for a job.

Most importantly, we’ve learned quite a bit about ourselves that is contrary to many pre-existing beliefs. Specifically ones that influence organizational approaches.  Our myths about how we think, work, and learn are holding us back from achieving optimal outcomes.

For one, there’s a persistent belief that our thinking is in our heads.  Yet research shows that our thinking is distributed across our tools. We use external representations to capture at least part of our thinking, and access information that we can’t keep in our heads effectively.  Yet we seem to depend on courses to put it in the head instead of tools to put it in the world.

Our thinking is also distributed across others. “You’re no longer what you know, but who you know” is a new mantra. So is “the room is smarter than the smartest person in the room” (with the caveat: if you manage the process right ;). Informal and social learning is the work.  Yet we still act as if we believe that people should solve problems independently.

And we also act as if how we learn is by information dump.  Add a quiz, so we know they can recognize the right answer if they see it, and they’ve learned!  Er, no. Science tells us that this is perhaps the worst thing we could do to facilitate learning.

In short, our practices are out of date. We’re using patch-it (or ignore-it) solutions to systemic issues.  We address simple things as if they’re not all connected. It’s time to get on top of what’s known, and then act accordingly.  Are you ready to join the 21st century?

Listening to a talk last week led me to ponder the different terms for what it is I lobby for.  The goal is to make organizations accomplish their goals, and to continue to be able to do so.  In the course of my inquiry, I explored and uncovered several different ‘organizational’ terms.  I thought I should lay them out here for my (and your) thoughts.

For one, it seemed to be about organizational  effectiveness. That is, the goal is to make organizations not just efficient, but capable of optimal levels of performance.  When you look at the Wikipedia definition, you find that they’re about “achieving the outcomes the organization intends to produce”.  They do this through alignment, increasing tradeoffs, and facilitating capacity building.  The definition also discusses improvements in decision making, learning, group work, and tapping into the strictures of self-organizing and adaptive systems, all of which sound right.

Interesting, most of the discussion seems to focus on not-for-profit organizations. While I agree on their importance, and have done considerable work with such organizations, I guess I’d like to see a broader focus. Also, and this is purely my subjective opinion, the newer thoughts seem grafted on, and the core still seems to be about producing good numbers. Any time you use the phrase ‘human capital’, I am leery.

Organizational engineering is a phrase that popped to mind (similar to learning engineering). Here, Wikipedia defines it as an offshoot of org development, with a focus on information processing. And, coming from cognitive psychology, that sounds good, with a caveat.  The reality is, we’re flawed as ideal thinkers. And in the definition it also talks about ‘styles’, which are a problem all on their own. Overall, this appears to be more a proprietary suite of approaches under a label. While it uses nice sounding terms, the reality (again, my inferences here) is that it may be designed for an audience that doesn’t exist.

The final candidate is organizational development. Here the definition touts “implementing effective change”. The field is defined as interdisciplinary and drawing on psych, sociology, and more.  In addition to systems thinking and and decision-making, there’s an emphasis on organizational learning and on coaching, so it appears more human-focused. The core values also talk about human beings being valued for themselves, not as resources, and looking at the complex picture.  Overall this approach resonates with me more, not just philosophically, but pragmatically.

As I look at what’s emerging from the scientific study of people and organizations, as summed up in a variety of books I’ve touted here, there are some very clear  lessons. For, one, people respond when you treat the as meaningful parts of a worthwhile endeavor. When you value people’s input and trust them to apply their talents to the goals, things get done. Caring enough to develop them in ways that are supportive, not punitive, and not just your goals but theirs’ too, retains their interest and commitment. And when you provide them with an environment to succeed and improve, you get the best organizational outcomes.

There’s more about how to get started.  Small steps, such as working in a small group (*cough* L&D? *cough* ;), and developing the practices and the infrastructure, then spreading, has been shown to be better than a top-down initiative. Experimenting and reviewing the outcomes, and continually tweaking likewise.  Ensuring that it’s coaching, not ‘managing’ (managers are the primary reason people leave companies).  Etc.

All this shouldn’t be a surprise, but it’s not trivial to do but takes persistence.  And, it flies in the face of much of management and HR practices.  I don’t really care what we label it, I just want to find a way to talk about things that makes it easy for people to know what I’m talking about.  There are goals to achieve, so my main question is how do we get there?  Anyone want to get started?

Last night I attended a session on “Our Relationship with AI” sponsored by the Computer History Museum and the Partnership on AI. In a panel format, noted journalist John Markoff moderated Apple‘s Tom Gruber, AAAI President Subbarao Kambhampati, and IBM Distinguished Research Scientist Francesca Rossi. The overarching theme was:  how are technologists, engineers, and organizations designing AI tools that enable people and devices to understand and work with each other?

It was an interesting session, with the conversation ranging from what AI is, to what it could and should be used for, and how to develop it in appropriate ways. Addresses were concerns about AI’s capability, roles, and potential misuses.  Here I’m presenting just a couple of thoughts triggered, as I’ve previously riffed on IA (Intelligence Augmentation) and Ethics.

One of the questions that arose was whether AI is engineering or science. The answer, of course, is both. There’s ongoing research on how to get AI to do meaningful things, which is the science part. Here we might see AI that can learn to play video games.  Applying what’s currently known to solve problems is the engineering part, like making chatbots that can answer customer service questions.

On a related note was what  can AI do.  Put very simply, the proposal was that AI could do what you can make a judgment on in a second. So, whether what you see is a face, or whether a claim is likely to be fraudulent.  If you can provide a good (large) training set that says ‘here’s the input, and this is what the output should be’, you can train a system to do it.  Or, in a well-defined domain, you can say ‘here are the logical rules for how to proceed’, and build that system.

The ability to do these tasks, was another point, is what leads to fear. “Wow, they can be better than me at this task, how soon will they be better than me on many tasks?”  The important point made is that these systems can’t generalize beyond their data or rules.  They can’t say: ‘oh I played this video driving game so now I can drive a car’.

Which means that the goal of artificial  general intelligence, that is, a system that can learn and reason about the real world, is still an unknown distance away.  It would either have to have a full set of  knowledge about the world,  or you’d have to have both the capacity and the experience that a human learns from (starting as a baby).  Neither approach has demonstrated any approach of being close.

A side issue was that of the datasets.  It turns out that datasets can have or learn implicit biases. A case study was mentioned how Asian faces triggered ‘blinking’ warnings, owing to the typical eye shape. And this was from an Asian company!  Similarly, word recognition ended up biasing woman towards associations with kitchens and homes, compared to men.  This raises a big issue when it comes to making decisions: could loan-offerings, fraud-detection, or other applications of machine learning inherit bias from datasets?  And if so, how do we address it?

Similarly, one issue was that of trust. When do we trust an AI algorithm?  One suggestions was that it would come through experience (repeatedly seeing benevolent decisions or support).  Which wouldn’t be that unusual. We might also employ techniques that work with humans: authority of the providers, credentials, testimonials, etc. One of my concerns then was could that be misleading: we trust one algorithm, and then transfer that trust (inappropriately) to another?  That wouldn’t be  unknown in human behavior either.  Do we need a whole new set of behaviors around NPCs? (Non Player Characters, a reference to game agents that are programmed, not people.)

One analogy that was raised was to the industrial age. We started replacing people with machines. Did that mean a whole bunch of people were suddenly out of work?  Or did that mean new jobs emerged to be filled?  Or, since we’re now doing human-type tasks, will there be less tasks overall? And if so, what do we do about it?  It clearly should be a conscious decision.

It’s clear that there are business benefits to AI. The real question, and this isn’t unique to AI but happens with all technologies, is how we decide to incorporate the opportunities into our systems. So, what do you think are the issues?