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Machines Should Work. People Should Think.

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Machines Should Work. People Should Think.

Machines should work. People should think. This was the mantra of International Business Machines (IBM) in the late 1960s. It was a time when machines were entering the workspace and accelerating human workflows. IBM had contracted the Jim Henson Company to create the short film “Paperwork Explosion” to explain how their newly invented word processor would help control the massive amount of business documents generated by the standard business. Over half a century later, this mantra feels relevant again.

Today we’re experiencing a “Data Explosion”, with each company creating lifetimes of data to wrangle and analyze. We may need a short film to explore the powers of machine learning, one not based on false fears of the singularity and escaping the matrix. The rapid evolution of machine learning (AI) has created confusion, fear, and questions. Have we developed a new super-intelligent entity, and what are the ethics behind this? Will this compete for our jobs? How will this new technology reshape our daily lives?

The IBM MT/ST Word Processor

Like a typist or bookkeeper clerk in the 1960s, there’s an understandable ambiguity of where our role as humans plays in this future. The future of work is at the tipping point of a massive paradigm shift, where data, automation, and machine learning become the organization’s lifeblood. As with any paradigm shift, some roles will be impacted, but this is not super intelligence run amok; this is our civilization building the next age of computing, moving from the information age to the knowledge age. Today, the novel tricks of LLMs like ChatGPT can feel enchanting, but we must keep our perspective on what technology is. These are machines that work to accelerate and bring value to our lives.

Technics Throughout Time

The word technology comes from Greek τεχνη (technē), meaning “systemic tools.” For centuries, we have utilized systemic tools. Now, in the digital age, we are being propelled towards not only sharing information but also unlocking a revolutionary potential for collective knowledge. A.I., also known as Machine Learning (ML), represents a systemic approach to human understanding and creation, shaped by our desires and necessities. These advanced tools allow us to think, act, and communicate in ways never before possible and create new, exciting work possibilities with them.

Natural Born Cyborgs

Our understanding of “human” has been inextricably tied to our environment, relationships, and the tools at our disposal. We are natural-born cyborgs. Throughout our history as a species, we have been adept at connecting our cognitive abilities with external tools. This bond has been essential to our physical development and survival, blurring the lines of human cognition.

This mutual reliance between the self and technology creates an intertwined existence. We craft tools that then shape our minds, patterns of behavior, and collective cultures. This continuous cycle means technology and humans are growing in parallel.

Andy Clark, philospher and author of books such as"Mindware" and "Supersizing the Mind: Embodiment, Action, and Cognitive Extension" calls this cognitive transformation “External Mind Theory,” an excerpt from a New Yorker article illustrates the concept:

Where does the mind end and the world begin? Is the mind locked inside its skull, sealed in with skin, or does it expand outward, merging with things and places and other minds that it thinks with? What if there are objects outside—a pen and paper, a phone—that serve the same function as parts of the brain, enabling it to calculate or remember? You might say that those are obviously not part of the mind because they aren’t in the head, but that would be to beg the question. So are they, or aren’t they?

Consider a woman named Inga, who wants to go to the Museum of Modern Art in New York City. She consults her memory, recalls that the museum is on Fifty-third Street, and off she goes. Now consider Otto, an Alzheimer’s patient. Otto carries a notebook with him everywhere, in which he writes down information that he thinks he’ll need. His memory is quite bad now, so he uses the notebook constantly, looking up facts or jotting down new ones. One day, he, too, decides to go to MoMA, and, knowing that his notebook contains the address, he looks it up.

Before Inga consulted her memory or Otto his notebook, neither one of them had the address “Fifty-third Street” consciously in mind; but both would have said, if asked, that they knew where the museum was—in the way that if you ask someone if she knows the time she will say yes, and then look at her watch. So what’s the difference? You might say that, whereas Inga always has access to her memory, Otto doesn’t always have access to his notebook. He doesn’t bring it into the shower and can’t read it in the dark. But Inga doesn’t always have access to her memory, either—she doesn’t when she’s asleep, or drunk.

10x Evolutionary Tools

The success of humans as a species lies not in our minds but in the ongoing evolution of our technology. Our ability to use external tools for storing memories and creating shared myths through language and art has provided the foundation for our complex tools and social systems today. The momentum began with primitive stone tools, followed by revolutions in agriculture and industry, leading up to our current era of interconnectedness and information exchange. Where does it go next?

Building upon the information age

With the proliferation of digital technologies and the internet, humans increasingly define themselves through their interconnectedness and access to information. This has created the infrastructure and connective tissue towards shared, connected knowledge. With machine learning, we’re entering an exciting new age of computing powered by this collective knowledge. How we manage and treat this will reshape what is possible in our lives, work, and society.

To understand this shift, we must understand how we use data today to store, manage, and create knowledge and how ML enables humans to take advantage of a new realm of tools. The DIKW (Data , Information , Knowledge & Wisdom) pyramid is a simplified way to understand the hierarchy of data as it evolves into actionable intelligence (or wisdom)

DIKW Breakdown:

Humans Should Think: Taking Advantage of The Knowledge Era

Until recently, most computing labor focused on the information layer, relying on human labor to analyze data and document knowledge to take action. LLMs are filling this gap and creating the systematic analysis and action of this knowledge. They can distill patterns, connections, and answers rapidly and regenerate concepts input by their human counterparts. Machine learning allows us to shift the labor of the tool a layer up, removing the burden of complex data analysis on humans and instead creating actionable understanding at scale.

The Knowledge Era

New Tools:

New Impacts:

This is where the typists and bookkeepers were in 1967 when IBM created their MT/ST word processor. They are not being replaced but empowered by new tools to manage this data explosion. These human roles will now have accuracy, automation, and decision-making speed. This is the power of knowledge computing.

These new tools allow humans to focus on what we do best, thinking critically about our surroundings and reality to reshape antiquated systems and constructs and innovate for a better life.

Managing Machine Context & Reality

With change comes new problems; the machine's understanding of reality is based on the input and data it receives. Many companies struggle today to manage their data strategy; with data silos, data swamps, and low resources to fix existing infrastructure, how can we add more complexity to strained systems? There’s also the concern of ethics, biased algorithms, and the potential for inequality. These problems must all be addressed to begin to build better systems and generate value.

Knowledge management will become the critical infrastructure in which to invest our efforts. The new systematic treatment of truth, knowledge, and context requires unique tools, human roles, and new approaches to data management to power machine learning at scale.

The Future of Work: Enterprise Intelligence & Knowledge Architecture

So, where does this knowledge explosion leave the future of work? How will it bring new value and create new roles? First, we must build the bridge from existing data operations towards an intelligent enterprise architecture. There is a growing demand for data meshes, vector databases, knowledge graphs, semantic frameworks, and data ontologies, supplying the context needed to power machine learning.

In the second part of this "Knowledge Era" series, I will delve into the infrastructure of future enterprises and how these tools begin to shape Enterprise Intelligence.