Lucidea’s Lens: Knowledge Management Thought Leaders
Part 108 – George Siemens

George Siemens is an author, researcher, and theorist in the fields of learning, knowledge management, and technology. He focuses on connectivism, MOOCs (Massive Open Online Courses), learning analytics, human and artificial cognition, and systems.

A professor at multiple universities, George’s research focused on how people learn and build knowledge in digital environments. This involved four key areas of exploration:

1. Sensemaking and technology in classrooms and online learning, resulting in proposing Connectivism as a theory of learning in social and distributed networks.
2. The impact of systemic changes, such as open education, and how traditional assumptions of learning in classrooms are inadequate online, reflective of his research in Massive Open Online Courses (MOOCs).
3. The data that learners generate when engaged in digital environments. The research value of data in understanding learning and social and cognitive processes led him to found the Society for Learning Analytics Research, which became the largest academic organization internationally to support researchers working with learning and knowledge process data.
4. How human and artificial cognition is changing society and how this intersection impacts how humans know and are intelligent.

Here are definitions for five of George’s specialties:

• Cognition: the sensory processes, general operations, and complex integrated activities involved in interacting with information. Sensory processes include vision, perception, and attention. General operations involve language, memory, recognition, recall, information seeking and management behaviors. Complex integrated activities include reasoning, judgement, decision making, problem solving, sensemaking, and creativity.
• Connectivism: the integration of principles explored by chaos, network, and complexity and self-organization theories. Connectivism is driven by the understanding that decisions are based on rapidly altering foundations. New information is continually being acquired. The ability to draw distinctions between important and unimportant information is vital. The ability to recognize when new information alters the landscape based on decisions made yesterday is also critical.
• Learning: actionable knowledge; it can reside outside of ourselves (within an organization or a database), and is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing. It is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual.
• Learning Analytics: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs
• MOOCs (Massive Open Online Courses): online courses aimed at unlimited participation and open access via the web. In addition to traditional course materials, such as filmed lectures, readings, and problem sets, many MOOCs provide interactive courses with user forums or social media discussions to support community interactions among students, professors, and teaching assistants, as well as immediate feedback to quick quizzes and assignments.

George created the following content. I have curated it to represent his contributions to the field.

Books by George Siemens

Not everything we call AI is actually ‘artificial intelligence’. Here’s what you need to know

What does ‘AI’ actually mean?

To qualify as AI, a system must exhibit some level of learning and adapting. For this reason, decision-making systems, automation, and statistics are not AI.

AI is broadly defined in two categories: artificial narrow intelligence (ANI) and artificial general intelligence (AGI). To date, AGI does not exist.

The key challenge for creating a general AI is to adequately model the world with all the entirety of knowledge, in a consistent and useful manner. That’s a massive undertaking, to say the least.

Most of what we know as AI today has narrow intelligence – where a particular system addresses a particular problem. Unlike human intelligence, such narrow AI intelligence is effective only in the area in which it has been trained: fraud detection, facial recognition or social recommendations, for example.

AGI, however, would function as humans do. For now, the most notable example of trying to achieve this is the use of neural networks and “deep learning” trained on vast amounts of data.

Neural networks are inspired by the way human brains work. Unlike most machine learning models that run calculations on the training data, neural networks work by feeding each data point one by one through an interconnected network, each time adjusting the parameters.

As more and more data are fed through the network, the parameters stabilize; the final outcome is the “trained” neural network, which can then produce the desired output on new data – for example, recognizing whether an image contains a cat or a dog.

What does AI need to work?

AI needs three things to be successful.

1. First, it needs high-quality, unbiased data, and lots of it. Researchers building neural networks use the large data sets that have come about as society has digitized.
2. AI also needs computational infrastructure for effective training. As computers become more powerful, models that now require intensive efforts and large-scale computing may in the near future be handled locally. Stable Diffusion, for example, can already be run on local computers rather than cloud environments.
3. The third need for AI is improved models and algorithms. Data-driven systems continue to make rapid progress in domain after domain once thought to be the territory of human cognition.

However, as the world around us constantly changes, AI systems need to be constantly retrained using new data. Without this crucial step, AI systems will produce answers that are factually incorrect, or do not take into account new information that’s emerged since they were trained.

Learning in Context

Changed Characteristics and Flow of Knowledge (in Knowing Knowledge)

Changes in Knowledge

Half Life of Knowledge

Knowledge Spaces

Knowledge and Learning Ecology

Knowledge Structures