Ethan Mollick discusses the impact of ‘The Button’ on our writing. The Button is in Google Docs but similar GPT-LLM tools are or will soon be available in many other writing tools. They can immediately create a credible piece of writing, such as a letter of recommendation, or a proposal, increasing anyone’s speed of writing and avoiding writer’s block. These tools are exceptionally convenient, so of course most people will use them. But at what cost?

“But then The Button starts to tempt everyone. Work that was boring to do, but meaningful when completed by humans (like performance reviews) becomes easy to outsource – and the apparent quality actually increases. We start to create documents mostly with AI that get sent to AI-powered inboxes where the recipients respond mostly with AI. Even worse, we still create the reports by hand, but realize that no human is actually reading them. This kind of meaningless task, what organizational theorists have called mere ceremony, has always been with us. But AI will make a lot of previously useful tasks meaningless. It it will also remove the facade that previously disguised meaningless tasks. We may not have always known if our work mattered in the bigger picture, but in most organizations, the people in your part of the organizational structure felt that it did. With AI-generated work sent to other AIs to assess, that sense of meaning disappears.” —Ethan Mollick

These tools are likely of most use for mediocre writers.  In Roald Dahl’s 1954 short story, The Great Automatic Grammatizator (PDF), Adolph Knipe invents a machine that can write novels. At first it’s a hard sell, but he soon finds his market.

“Thereafter, Knipe wisely decided to concentrate only upon mediocrity. Anything better than that — and there were so few it didn’t matter much — as apparently not quite so easy to seduce. In the end, after several months of work, he had persuaded something like seventy per cent of the writers on his list to sign the contract. He found that the older ones, those who were running out of ideas and had taken to drink, were the easiest to handle.”

Mollick however, makes some positive conclusions.

“But there is a potentially empowering outcome as well, if we want to pursue it. We can view the destruction of busy-work as freeing. We do not have to set our time on fire as a signal. We don’t have to do work that is mere ceremony. Doing that will require thoughtfulness from organizations and leaders as they redesign work for our new AI-haunted world. But the incentives are huge as well. Stripping away meaningless work removes a huge burden from workers, while reducing inefficiencies and broken processes. This is an amazing opportunity, but only if we are forward-thinking about the future of a world where most work starts by pressing The Button.” —Ethan Mollick

I am not so positive, given how the fruits of improved productivity from personal computers did not result in improved wages, as I noted in augmentation vs automation — “The productivity software that ran on personal computers was a perfect example of augmentation rather than automation: word-processing programs replaced typewriters rather than typists, and spreadsheet programs replaced paper spreadsheets rather than accountants. But the increased personal productivity brought about by the personal computer wasn’t matched by an increased standard of living.”—The New Yorker

On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“Until a drag queen walks into a school and beats eight kids to death with a copy of ‘To Kill A Mockingbird’, I think you’re focusing on the wrong shit.” —Wanda Sykes

This is the best advice ever —”Live so that if your life were turned into a book, Florida would ban it.” —@GigidiGranat

Edmund Burke also paraphrased nicely, “Because half a dozen grasshoppers under a fern make the field ring with their importunate chink, whilst thousands of great cattle, reposed beneath the shadow of the British oak, chew the cud and are silent, pray do not imagine that those who make the noise are the only inhabitants of the field.” Social Media is not the universe of true comment. —@AndrewTrickett

A step in the right direction —@RadicalEgoCom

I asked my friend
the rabbi
if there is a blessing
that is also a curse
and after thinking for a moment
she replied that yes
there is a blessing
that is also a curse:
may you be given
the same amount of empathy
as you give unto others.
—@PlaguePoems

“Women didn’t get the right to vote …by voting.” Sticker spotted in Paris via @RadicalGraffiti

In 2005 I wrote a business plan for a client that was based on an operational model of employing ‘knowledge artisans’.

Next-gen knowledge artisans are amplified versions of their pre-industrial counterparts. Equipped with and augmented by technology, they rely on their human capital and skill to solve complex problems and develop new ideas, products and services. Highly productive, knowledge artisans are capable individually and in small groups of producing goods and services that used to take substantially larger teams and resources. In addition to redefining how work is done, knowledge artisans are creating new organizational structures and business models.

I later followed this up by discussing how knowledge artisans choose their tools.

Knowledge Artisans not only design the work but they can do the work. It is not passed down the assembly line. Many integrate marketing, sales and customer service with their creations. To ensure that they stay current, they become members of various Guilds, known today as communities of practice or knowledge networks. One of the earliest guilds was the open source community which developed many of the communication tools and processes used by Knowledge Artisans today: distributed work (computer supported collaborative work); results-oriented work (your code speaks for you); RSS, blogs, wikis, flattened hierarchies, etc.

I continued to build on the idea of new artisans of the network era.

As more organizations engage with connected workers who have seen the new workplace structures, they will need to change some habits, like letting workers choose their own tools. Knowledge artisans are often more contractual, more independent and shorter-term than previous information age employees. Because of their more nomadic nature, artisanal workers will bring their own learning networks. Companies will need to accept this in order to get work done. Also, training departments must be ready to adapt to knowledge artisans by allowing them to collaborate and connect with their external online networks. When the future of learning is the future of work, then learning support has to adapt to the new reality of an artisanal workforce. But it’s also worth noting that to be a successful knowledge artisan will take a lot more than just being a good employee.

I then summarized the situation in 2014 in —engaging knowledge artisans.

Most organizations are playing with all these new digital technologies and not putting in place structures to support knowledge artisans. But all these levels of hierarchy and control processes, based on a systemic lack of trust, will be overwhelmed by the resulting complexity of a hyper-connected economy. Overarching knowledge work principles have to be first established. An adult-to-adult relationship model like wirearchy is one example; “a dynamic two-way flow of power and authority based on knowledge, trust, credibility and a focus on results, enabled by interconnected people and technology.” Complex environments are the new normal. Relationship building is needed in order to share complex knowledge. Implicit knowledge takes time to share, so time has to be set aside for sense-making, reflecting, and conversing. These are significant workplace changes, but can be mastered with a stable foundation of PKM practiced by interdependent and autonomous knowledge artisans. When everybody is engaged in sense-making, then any organization can better sense where it needs to go.

I related the knowledge artisan to Jane McConnell’s gig mindset advantage in 2019 — from knowledge worker to master artisan.

“The formula for the gig mindset emergence is clear:
High people capabilities + stagnating work cultures + rigid leadership = birth of the gig mindset.” —Jane McConnell

A gig mindset is a change in the status quo. While creating a status quo is more difficult than maintaining an existing one, the time may be right for systemic change in how work gets done.

Today, Gianfranco Chicco described a need to move from artificial to artisanal intelligence.

In craftsmanship, different kinds of knowledge are already at play. Intellectual knowledge residing in our brain and tacit knowledge residing in our body. What AI adds is the possibility for a different, non-human kind of intelligence to have a role … What often gets lost in the current narrative about AI is the why we do what we do. The act of creation, of bringing something into this world that didn’t exist before is not just a transactional act to favour convenience or reduce friction. We create because it’s a way to manifest our humanity, to understand the world and our role in it, and to connect with fellow humans. Removing that creation from our beings, outsourcing it onto another entity — in this case AI — because it can make it easier or even better is to miss the point completely.

Knowledge artisans provide an opportunity to reconnect humans and work. Industrialization compartmentalized tasks and jobs. Artisans work the entire creative process. Yes, we can use tools like GPT and LLMs, but it’s our humanity that creates something unique, and not an iteration of what has been scraped from that which has already been created and shared. As Chicco concludes, “I’m excited to see how (if) craftspeople might embrace artificial intelligence and how it might allow us to develop our natural intelligence further by creating things (objects, ideas, environments) that make life better, not just for humans but for Gaia as a whole.”

Principle of Network Management — It is only through innovative and contextual methods, the self-selection of the most appropriate tools and work conditions and willing cooperation that more productive work can be assured. The duty of being transparent in our work and sharing our knowledge rests with all workers, especially management.

I have written almost 100 posts referencing Marshall McLuhan’s Laws of Media using the media tetrad. I recently came across a tweet where the author, Ethan Mollick asked ChatGPT, “Create a Marshall McLuhan tetrad for larger language models. Be creative and insightful. Think like a theorist.” The image below shows the ChatGPT response.

The ChatGPT response is similar to my tetrads created for these posts:

GPT-3 through a glass darkly

auto-tuning work

capitalism > automation > gpt

But of course the ChatGPT response would reflect my own. ChatGPT is merely a stochastic parrot of what humans have shared on the web. We are getting scraped by these tools, such as Google’s C4 dataset which in my case was confirmed by the Washington Post.

This use of LLMs has limited value in my opinion. I think a more useful application of LLMs would be in helping individuals interpret complicated pieces of information such as — end-user license agreements, contracts, patient records and diagnoses, user manuals.

Understanding machine learning (ML), generative pre-trained transformers (GPT), and large language models (LLM) has become a part-time job for me. Not only is there a lot of of information and discussion, but a wide range of opinions. The topic of ‘AI’ constantly pops up in professional meetings. Researcher danah boyd discusses the difference between the perspectives of automation vs. augmentation as ‘AI’ develops.

“When it comes to AI’s potential future impact on jobs, Camp Automation tends to jump to the conclusion that most jobs will be automated away into oblivion … most in Camp Automation tend to panic and refuse to engage with how their views might intersect with late-stage capitalism, structural inequality, xenophobia, and political polarization … Camp Augmentation is more focused on how things will just change. If we take Camp Augmentation’s stance, the next question is: what changes should we interrogate more deeply?” —Zephoria 2023-04-21

I am mostly in the augmentation camp, though I am concerned that automation + capitalism = a perfect storm. This was the case with the augmented work enabled by the personal computer. Knowledge work improved significantly but wages did not.

“The fact that personal computers didn’t raise the median income is particularly relevant when thinking about the possible benefits of AI. It’s often suggested that researchers should focus on ways that A.I. can increase individual workers’ productivity rather than replace them; this is referred to as the augmentation path, as opposed to the automation path. That’s a worthy goal, but, by itself, it won’t improve people’s economic fortunes. The productivity software that ran on personal computers was a perfect example of augmentation rather than automation: word-processing programs replaced typewriters rather than typists, and spreadsheet programs replaced paper spreadsheets rather than accountants. But the increased personal productivity brought about by the personal computer wasn’t matched by an increased standard of living.” —The New Yorker 2023-05-04

These are my observations and conclusions to date, which of course may change in a month or two, given the speed of technology development in this field.

• It will become much more difficult to be an average — or even above average — writer. Only the best will flourish.  —2022-12-13
• At this time we do not need more ChatGPT experts. What we need are people who can see these technologies in the context of human work. We need more ‘what-if’ questions to be asked. —2023-01-21
• Human oversight of machines and software will be essential as these systems permeate our economy and society. —2023-02-27
• GPT extends our voices, obsolesces many forms of human writing, retrieves the ancient Oracle of Delphi, and when pushed to its limits reverses into Potemkin villages. GPT provides instant synthesis that may be good enough, but often contains errors. If it gets used to answer all our questions then it becomes a tool for “aphoristic nincompoops posing as techno-oracles”. —2023-03-15
• What happens in several years when workers have not had to ‘manually’ develop writing skills? If they have always started with the output of a GPT or LLM tool, will they develop good basic writing skills? —2023-03-19
• “So what happens when we automate our most impactful and superior cognitive capacity—thinking—and we don’t think for ourselves? I think we end up not acting in very smart ways, and then the algorithms are trained by behaviors that have very little to do with intelligence.” —Tomas Chamorro-Premusic—2023-03-26
• The emerging ‘AI’ is creating a situation of, “the total decoding and synthesizing of reality.” Everything we do runs on language — laws, friendships, relationships — and now non-humans can create persuasive narratives. —2023-04-11
• The machines are our tools, not our friends. Let’s make sure we know how they work and what is behind them by learning with and from each other. —2023-04-18
• “Tech companies have grown secretive about what they feed the AI. So The Washington Post set out to analyze one of these data sets to fully reveal the types of proprietary, personal, and often offensive websites that go into an AI’s training data.” — 2023-04-21
• “In a few months, maybe a year, the first wave of AI-driven layoffs slash firings are going to hit the economy.” —Umair Haque … “Thanks to accounting conventions and tax laws dating back centuries, a robot doesn’t need to be better – or more efficient – than a human being at a task to make a business more profitable. It just needs to be 34% as good, or 11% as good, depending on that business’s accounting and amortization policies.”  —John Carolus Sharp —2023-05-10

Five ways people can adapt to automation & smart machines. Based on the book ‘Only Humans Need Apply’ (2016)

Christopher Lydon, host of Radio OpenSource, interviews two humanists on failing intelligence.

“Robert Pogue Harrison is our Dante scholar at Stanford, our professional humanist, and a West Coast friend in smart podcasting. We asked ChatGPT about his voice, and we got the instant answer that his voice “has a certain mellowness and introspection” that go with his ‘keen ear for language and a precise, articulate way of expressing his ideas’. He’s joined by Ana Ilievska, initials A.I. She is Robert’s colleague from Europe in humanistic studies at Stanford. Recently, in the podcast Entitled Opinions, they both defended AI as a wake-up call, maybe in the nick of time, to rescue humanity, human stewardship, human culture from its corrupted condition. They both said they expect their students to use AI and to learn from it.” —2023-05-04

Highlights

“Technology is becoming an ever-sharper mirror of ourselves.”

Generative pre-trained transformers (GPT) & large language models (LLM) are more like humans than previous technologies so we have to train engineers and programmers to better understand human nature as they work with and build machines.

“ChatGPT is designed to flatter the user … self-love is the essential currency of AI … self-love and the common good do not conjugate very well.”

Human fallibility is what distinguishes us from AI. We are bad robots.

“We have no common norms that we all share as a species.”

“Narcissism comes about when the human mind is left idle.”

These technologies are a “genie in a bottle” not a catastrophic metaphor such as the Manhattan Project.

“Stanford [University] will never say no to any new technology.”

“How do you teach ethics? You allow people to discover it. Ethics begins at the individual mind — it begins in discussion with others. It cannot be introduced into the curriculum as a sidekick.”

“The university has become a place of stock phrases … we question very little.”

“We dare to become violent in the midst of the overwhelming.” 

“The whole system is designed to look to technology for our own advancement and enhancement. But we don’t need technology for that. But it’s not going to help the economy if we can do without the technology … It’s a rigged system.”

Alex Colville — Horse and Train

It seems that today everyone is chatting about GPT (generative pre-trained transformers) and what feeds them — large language models (LLM). I am always skeptical when the next techno-hype cycle comes around but this one seems different. The worst case scenario does not look good, especially for knowledge workers.

In a few months, maybe a year, the first wave of AI-driven layoffs slash firings are going to hit the economy. And then? They’ll just keep going. Executives are going to figure out that a whole lot of work — clerical, administrative, accounting, legal, writing, marketing, customer relations, even decision-making and risk analysis and data analysis — can be automated. AI’s going to be like offshoring, but much, much worse. Offshoring wiped out the working class — AI’s going to finish the job of wiping out the middle class. Offshoring eviscerated blue collar jobs — AI’s going to wipe out some pink collar ones, and a whole lot of white collar ones, too. —Umair Haque 2023-04-28

This is especially concerning in view of how our capitalist economy works. People are liabilities, not assets. Automation plus capitalism make for a perfect storm to eviscerate knowledge work. In addition, the entire profit & loss bookkeeping system is designed to reduce paid human work as much as possible.

“Thanks to accounting conventions and tax laws dating back centuries, a robot doesn’t need to be better – or more efficient – than a human being at a task to make a business more profitable. It just needs to be 34% as good, or 11% as good, depending on that business’s accounting and amortization policies.” —John Carolus Sharp — Hatcher (2017)

In the book Only Humans Need Apply (2016), the authors identify five ways that people can adapt to automation and smart machines — they call it ‘stepping’.

• Step-up: directing the machine-augmented world
• Step-in: using machines to augment work
• Step-aside: doing human work that machines are not suited to do
• Step narrowly: specializing narrowly in a field too small for augmentation
• Step forward: developing new augmentation systems

I’m interested in what options many of us may soon be left with.

Directing the machine augmented world

This is a very narrow niche limited to those with money and connections. As the techno giants build ever larger armouries of GPT/LLM it will get very difficult for upstarts to break in.

Using machines to augment work

This field of work is extremely popular at the moment and many people are sharing how they use tools like ChatGPT to make their work easier or more productive. One danger of this type of work I think is that it’s like a hamster wheel. You have to keep moving and improving to stay ahead of the technology and the effects of the total decoding and synthesizing of reality. This will be life in high-speed perpetual beta.

Doing human work that machines are not suited to do

The important factor in this type of work is to really understand what machines and code are able to do and what they may be able to do. For example, voice actors are already getting replaced by technology. Perhaps barbers and hair stylists will survive longer. The key for this work is — choose well.

Specializing narrowly in a field too small for augmentation

This type of work could be something like a restorer of old paintings, but even this work might require smart machine augmentation to stay competitive. If your competitors can do the work faster or cheaper with technology you will have little choice but to adapt.

Developing new augmentation systems

I can see two areas of work here. One is to build new systems for the scratch, much like those who direct these systems. The other is to modify these systems. Platforms like HuggingFace offer ways to do this.

What’s next?

I don’t know what is next but I’m interested in collecting examples of these five ways to step with machines and try to find patterns that can inform decisions about work design and career development.

Image: Heather McGowan

The Next Jobs to Go: Knowledge Jobs (or Why You Should Pay Attention)

“This productivity paradox that first affected the hard hat jobs in the American Rust Belt is now creeping into the so-called ‘knowledge worker’ jobs that afforded comfortable upper middle-class incomes. More worrisome for many workers, this fourth wave of automation – sometimes called the Fourth Industrial Revolution – is hitting harder and faster than previous disruptions, and will affect cognitive and physical labor just as severely. While computerized cognitive labor is replacing humans, it is also liberating humans to do other, perhaps more creative, work. Much of this new work is yet to be imagined, yet it will no doubt enable humans to reach new levels of potential.” —Heather McGowan 2017

On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“The ability to learn from experience in the present — from moments, not models — is what is needed when the past has become a hindrance and the future is unclear.” —Gianpiero Petriglieri via Shaun Coffey

“The kind of intelligence [AI] we’re developing is very different from our intelligence. So it’s this idiot-savant kind of intelligence.” —Geoffrey Hinton

“The great merit of the capitalist system, it has been said, is that it succeeds in using the nastiest motives of nasty people for the ultimate benefit of society.” —E.A.G. Robinson (1941) via QuoteInvestigator

The opposite of “return to office” advocates isn’t “work from home” advocates. It’s a rich tapestry of “open offices are distracting” people and “I’ve never gone this long without being sick” people and “commutes are a waste of time I don’t get paid for” people and “I’m an introvert and playing house with coworkers sucks the life out of me” people and “I have a family and appreciate the flexibility” people and “I primarily communicated with coworkers through Slack anyway” people … —Mac in Philly

There is a machine
that can write articles
another machine
that can create images
and a different machine
that can produce songs
at this rate
the only thing that will be left
for humans to do
is to grieve
but I’m sure that someone
is working on a machine
that does that too.
—Plague Poems

If you’re wondering what the rainbow pentagon thing is that a lot of accounts use, it’s the Fediverse logo :fediverse:

It’s a public domain / CC0 design, its original designer shared a high quality copy at https://commons.wikimedia.org/wiki/File:Fediverse_logo_proposal.svg

It represents all the different server types that make up the Fediverse (https://fedi.tips/what-other-kinds-of-servers-are-on-the-fediverse/)

(Note: There can be no official logo on the Fediverse as there’s no central authority to make anything official. However, this logo is by far the most widely used one.) —Fedi Tips

Seth Godin discusses Cliffs Notes and how these could be a path to better understanding.

Used as intended, Cliffs Notes and Quicklit were a gold mine of insight. They opened the door for real understanding, and often got to the heart of the literature better than an overworked high school teacher might be able to. —The Cliffs Notes Paradox

But he also notes that even with the widespread availability of these notes, insight in overall society has not improved. When I was in college, I majored in the Arts while most of the students were in engineering or science at Royal Roads Military College. Our cohort was about 25 students and on entering second year, I was given a barrack box filled with Cliffs Notes (and Coles Notes) for our entire curriculum. As the ‘keeper of the notes’ I was responsible for their safe-guarding and adding to the collection. It was a great responsibility.

The notes helped me pass some exams but little insight was gained. The literature I remember best are the books I read completely, like the short Portrait of the Artist as a Young Man, or the longer A History of Western Philosophy. As Godin concludes, “It doesn’t matter how much we summarize, at some point, effort is required. More summaries won’t automatically lead to more understanding.”

In the book Range, David Epstein states that, “learning is most efficient in the long run when it is inefficient in the short run”. For me, the act of writing forces me to think a bit more than clicking once and having it served up from an automated system. The process of seeking out information sources, making sense of them through some actions, and then sharing with others to confirm or accelerate my knowledge are manual activities, requiring effort on my part.

Managing and sharing information artifacts, such as blog posts — especially through conversations — are fundamental processes for my sensemaking. The strength of personal knowledge mastery is not what tools we use, but how each of us seek > sense > share knowledge. Sensemaking frameworks have to take effort because that is how human learning is wired. There is no Red Pill.

“What is dumbing so many people down?”, asks Henry Mintzberg. His explanations 1 and 2 [quote below with my emphasis added] resonate with me, as I have promoted the idea that we need to connect our work, our communities, and our networks to make sense by engaging with people and ideas. The core of this is curiosity, especially about other people, as well as ourselves.

Explanations 1 and 2: Social Pressures and Social Isolation

Like rats in an overcrowded cage, the pressures of modern life, including the pace of change, can certainly be affecting our propensity to pause and think. Thoughtfulness is hardly encouraged in a society plagued with insecurity and anxiety.

Exacerbating this is the decline of community in contemporary society. When we lived in kinship groups, tribes, and clans, we maintained strong social ties. Then we got organized by establishing institutions that make many of our decisions for us. These came at the expense of community ties, with the consequence that many of us now feel separated, isolated, and alone.

And so we seek replacement affiliations — in a religious congregation, fan club, political party, whatever. Instead of deep bonding, these provide status by association. (“We played with passion!” said a soccer fan after his team lost a major game at the World Cup.) These are not communities so much as networks, with easy-come, easy-go relationships. (To understand the difference between a network and a community, ask your Facebook friends to help paint your house.)

Groupthink may be present in communities, but conformity can be greater in these networks. To join a political party, for example, you must leave your judgment at the door: think liberal, or conservative, or populist, whatever, just not nuanced. The group will think for you. After all, it has its dogma, which reduces complexity to category — some incontrovertible truth laid down by some authority, with the express intention of dumbing people down. (“Take back control” with Brexit. “Make America great again” by voting for the ultimate con man.) From white supremacy to neoliberal economics, dogmas are the blinders that block peripheral vision, and thus open the floodgates to mass manipulation, whether to sell cold beer or vile politicians. Eventually, we can hardly distinguish stark reality from blatant lying.

Welcome to our mindless society, poisoned by its own fake facts. —CEO World 2023-03-03

I wrote that media drive organization because I believe we are currently between a triform (T+I+M) and a quadriform (T+I+M+N) society, which accounts for much of the current political turmoil in our post-modern world. This model can help inform us how to build better organizational forms for a coming age of entanglement. If we do not create better organizational forms that can help us think better collectively we may drift into a society that cannot “distinguish stark reality from blatant lying”. We need to get beyond our post-modern angst and retrieve the cooperative imperative.

So what can we do? I would like to share what Esko Kilpi shared in a series of tweets in 2012.

Unlike mechanical systems, human systems thrive on variety and diversity.
An exact replication of behavior in nature would be disastrous and seen as neurotic in social life.
The Internet changes the patterns of connectivity.
The Internet transforms our understanding what “local” is, makes possible wide participation and new enriching variety in interaction.
All human systems are connected and connected systems cannot be understood in terms of isolated parts
The unit of analysis is now communication and emergence, not entities.
The perspective of network science views knowledge as socially created and socially re-created.
Management literature typically emphasizes individuals and locates explanatory power in their personal properties.
The potential of social media cannot be realized without a very different epistemological grounding, a relational perspective.
Independently existing people and things then become viewed as co-constructed in coordinated networked action.

Diversity > Learning > Trust — become the key elements in building better organizational structures that can help us differentiate between stark reality and blatant lies. These elements are how we should connect because it’s in the connections that we can make sense. The lack of diversity, the unwillingness to learn, and the lack of trust are what is dumbing so many people down.

The Washington Post looked at what information feeds Google’s chatbots, particularly the C4 Data Set which scraped 15 million English language websites. This is the ‘artificial intelligence’ that feeds the chat bot — stuff that people have written and posted online. All of this is taken without authorization — “The copyright symbol — which denotes a work registered as intellectual property — appears more than 200 million times in the C4 data set.”

“Chatbots cannot think like humans: They do not actually understand what they say. They can mimic human speech because the artificial intelligence that powers them has ingested a gargantuan amount of text, mostly scraped from the internet.

This text is the AI’s main source of information about the world as it is being built, and it influences how it responds to users. If it aces the bar exam, for example, it’s probably because its training data included thousands of LSAT practice sites.

Tech companies have grown secretive about what they feed the AI. So The Washington Post set out to analyze one of these data sets to fully reveal the types of proprietary, personal, and often offensive websites that go into an AI’s training data.” —WaPo 2023-04-19

This website is in that data set, among about 500K blogs, added without my permission and in contravention of my copyright licence — CC-BY-NC-SA 4.0

I doubt there is much I can do about this, as I cannot afford a lawyer to go up against Google. This situation is another one where giant information technology companies move fast and break things, knowing that it will take legislators, regulators, and the courts some time to catch up to the current state of technology. Meanwhile big corporations make big money.

Consider that GPT-3 consumed 700,000 litres of water in its creation or that each question to a ChatGPT could consume 500 ml of water — that is each and every question asked by each person. For now, I am watching these generative large language multi-modal models though I will not use them. But they are using us.

These are some highlights from several sources focused on large language models (LLM) and generative pre-trained transformers (GPT) — all published in 2023. It might be useful to first read — Nobody knows how many jobs will “be automated” Whatever that even means.

But “AI will increase labor productivity while forcing a small number of people to find new jobs” is not the kind of story that goes viral on social media, while “300 million jobs will be lost” definitely is that kind of story. People love to read about the impending apocalypse, and it’s the media’s responsibility not to indulge that desire … Instead of telling us who will be “automated”, they [A Method to Link Advances in Artificial Intelligence to Occupational Abilities – 2018] tell us who’s more likely to be affected by automation in some way. Obviously we’d like to know whether it’ll be a good way or a bad way. But the truth is that no one knows that yet, and economists do the world a service by refusing to pretend that they do know.

ChatGPT is about to revolutionize the economy

The optimistic view: it [GPT] will prove to be a powerful tool for many workers, improving their capabilities and expertise, while providing a boost to the overall economy. The pessimistic one: companies will simply use it to destroy what once looked like automation-proof jobs, well-paying ones that require creative skills and logical reasoning; a few high-tech companies and tech elites will get even richer, but it will do little for overall economic growth.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted … Our findings indicate that the importance of science and critical thinking skills are strongly negatively associated with exposure, suggesting that occupations requiring these skills are less likely to be impacted by current LLMs. Conversely, programming and writing skills show a strong positive association with exposure, implying that occupations involving these skills are more susceptible to being influenced by LLMs.

Secret Cyborgs: The Present Disruption in Three Papers

AI can increase productivity for workers in fields where automation and economies of scale were previously very rare. These jobs often require more autonomy and encompass multiple types of tasks (teachers need to prep lessons, grade, write letters of recommendation, run classes, respond to parents, run after school programs, do administrative work, etc.). With the power to outsource the most annoying and time consuming parts of their jobs, workers in these industries are highly incentivized to adopt AI quickly, either to do less work or to be able to bill out more work themselves. It is a recipe for rapid adoption at the individual level.

Interestingly, these same incentives suggest that many workers may be hesitant to reveal the extent to which they use AI tools. The advantage of producing AI-written letters and reports that seem like they were made by humans diminishes quickly if people know they are generated by AI. I conducted a bit of an unscientific Twitter poll, and over half of generative AI users reported using the technology without telling anyone, at least some of the time. Their are secret cyborgs among us.

CURATED READS FOR GEN Z—AND THEIR Z-CURIOUS COLLEAGUES

Whether you’re a new grad or have a few after-college years under your belt, you might be worried that generative AI is going to disrupt the job market and throw a huge wrench in your career plans, particularly at the entry level. It’s true that generative AI is going to change the way plenty of us work, but there’s no sense in fighting against it. Embracing generative AI and learning the technical skills required to work with it, no matter what industry you’re going into, is probably a good idea.

But there are also “soft skills”—qualities that make you a valuable teammate and leader—that, no matter how the job market changes, will keep you employable: critical thinking, intellectual curiosity, and flexibility.

The Potentially Large Effects of Artificial Intelligence on Economic Growth

Our baseline analysis incorporates our key findings from above, including that about 7% of workers are fully displaced but that most are able to find new employment in only slightly less productive positions, that partially exposed workers experience a boost in productivity consistent with existing estimates (Exhibit 9), and that effects are realized over a 10-year period that starts around the time when roughly half of businesses have adopted generative AI. Under these assumptions we estimate that widespread adoption of generative AI could raise overall labor productivity growth by around 1.5pp/year (vs. a recent 1.5% average growth pace), roughly the same-sized boost that followed the emergence of prior transformative technologies like the electric motor and personal computer.

Do I contradict myself?
Very well then I contradict myself,
(I am large, I contain multitudes.)
—Walt Whitman (1855)

For over a decade, social media have helped me explore different aspects of my learning and my profession, much more than I could have on my own or in my community. I often feel more affinity for some of my online connections than for my local neighbours. Living with contradictions can help develop critical thinking. Social media have enabled more of us to live like artists, constantly redefining ourselves and our work. Despite what is happening at Twitter, other options for connecting online are emerging, such as the open covenant ‘fediverse’ and Mastodon.

I intend on continuing to embrace contradictions, explore new ideas, and be a work of art in progress. Much of this I will do while connected via some form of social media. As more of us do so, we might be able strengthen our commons, work for a better society, and promote democracy. The past two decades of living a very active online life has helped me contain more multitudes. I would highly recommend it.

I find the manual nature of my sense-making is an essential part of it. The continuous act of writing a blog post — for over 19 years — forces me to think a bit more than clicking once and filing or having it served up from an automated system. The weekly routine of reviewing my social media favourites and creating Friday’s Finds is another manual routine that I find helps to reinforce my learning and add to my knowledge.

The juggernaut of emerging generative large language multi-modal models may result in the total decoding and synthesizing of reality. These tools are removing the requirement to think first and make sense by doing. They can obscure our sensemaking. Relying too much on artificially-generated knowledge may stunt our own sensemaking. Even more importantly, it’s the connections and knowledge-sharing between humans that keeps civilizations alive — how culture drove human evolution.

“What happens when we stop pooling our knowledge with each other & instead pour it straight into The Machine? Where will our libraries be? How can we avoid total dependency on The Machine? What content do we even feed the next version of The Machine to train on? … We already have an irreversible dependency on machines to store our knowledge. But at least we control it. We can extract it, duplicate it, go & store it in a vault in the Arctic (as Github has done). So what happens next? I don’t know, I only have questions. None of which you’ll find on StackOverflow.” —Peter Nixey 2023-03-26

The machines are our tools, not our friends. Let’s make sure we know how they work and what is behind them by learning with and from each other.

A seminal moment in my work came when I saw my first web page on a computer at Montreal’s CRIM in 1994. I finally saw computers as things that connect people around the globe. From here I completed a Master’s degree focusing on how people learn at work with information technology.

The next significant moment arrived with social media. I started blogging and sharing online. When Twitter came along it changed my relationship with hundreds of people. Social media platforms became  the great connectors. But now in 2023 we know that much of web is comprised of surveillance and tracking tools that are designed to influence our behaviour, especially our purchasing behaviour.

In whither Twitter, I wrote that more important than any single platform is our collective ability to seek diversity, think critically, and learn socially. For now I am staying on Twitter and watching the show, muting and blocking with abandon. But we know that platforms like Twitter can undermine democracy and spread disinformation and propaganda. Perhaps that is why Musk bought the company.

I had another seminal moment came when I watched The AI Dilemma recorded on 9 March 2023. It shook my understanding about the current state of machine learning, which I thought I sort of understood conceptually. Tristan Harris and Aza Raskin, from The Center for Humane Technology, present on the new force that has been unleashed by several global companies with no regulatory oversight — the Generative Large Language Multi-modal Model (AKA Gollem-class AIs).

Social Media

When social media came on the scene many of us saw their positive potential — “Give everyone a voice”, “Join like-minded communities.”, “Enable small businesses to reach customers.”. But social media shortly caused many more problems —“Addiction, Disinformation, Polarization.”

What I find most interesting is how the presenters also look at the underlying driver behind social media — Maximize engagement resulting in a race for attention. They show that trying to address the resulting problems without first changing the underlying motive only will result in a game of whac-a-mole. Today, no consumer social media platform is a trusted space though covenants like the fediverse may provide a better way of connecting humans.

AI

There are many who cheer AI in the form of generative pre-trained transformers (GPT) — “AI will us more efficient. AI will make us write faster. AI will make us code faster.” Other people are warning about — “AI bias. AI taking jobs. AI lack of transparency.”

Again, the presenters look at the underlying logic behind Gollem — “to increase its capabilities and entangle itself with human society.”

Shaking my cognitive tree

Here some of the points made in this presentation that definitely shock me.

• An AI can reconstruct what you visually see from an fMRI image of your brain.
• All of these AI are one field now, converting everything to languages that can cross fields, so increases in power are exponential.
• AI can identify human participants and their locations in a room based only on Wi-Fi signals = cameras that can locate people behind walls and in the dark
• Three seconds of a human voice can be replicated by AI and then used to communicate = authentication based verification is dead.
• TikTok could turn every user [not just their avatar] to look and sound like a different person, or even the same person
• The resulting AI is, “The total decoding and synthesizing of reality.”
• Everything we do runs on language — laws, friendships, relationships — and now non-humans can create persuasive narratives.
• Gollems already create new capabilities for themselves without human input.
• An AI can generate its own training data to learn faster —“AI makes better AI.”

I have to reflect on this presentation, and watch it a few more times. I have not made any conclusions on what we should do. I do think that developing and nurturing trusted human relationships will be critical for society and that is likely where I will keep my professional focus. Anyway, a new arms race has begun and our elected officials are once-again clueless in creating the optimal regulations for this fast-moving field.

“I put forward formless and unresolved notions, not to establish the truth but to seek it.” —Michel de Montaigne

The perspective of perpetual beta is to make sense of our experiences by formulating models to help our sensemaking while at the same time being ready to discard these beta models as new information is discovered. It may not be the most comfortable way to understand our world but it may be the most adaptive. This perspective informs the personal knowledge mastery (PKM) framework, which has evolved since 2004.

PKM in perpetual beta describes the most recent changes to the PKM online workshop — starting 17 April & 2 October 2023. Many of the activities in this workshop are aimed at trying new methods or tools. There is even a section on fake news.

Jonathan Malesic says that our challenge today is not the proliferation of misinformation but rather an attitude of ‘knowingness’ — already knowing and not looking for disconfirming data.

“But, in fact, the information age reveals how little we each already know. Knowingness, then, might be a way to manage the flood of information. There is so much we might know, that we perhaps ought to know, that it’s often easiest just to act as if we do. And if everyone else is acting, too, then we’re never caught in our ignorance. Of course, then, we’re stuck living with these falsehoods and their consequences – from plague to political upheaval, the very matter of tragedy.” —Psyche 2023-03-27

PKM is one way to avoid knowingness. Seeking diversity of opinions and perspectives is core to PKM. The concept of half-baked ideas is that even if not fully formed, we can get our ideas in the open so they can be discussed. This is where trusted community spaces become ever more important. Our perpetual beta coffee club is one such space.

The opposite of knowingness is curiosity.

In 2013 I posed this question — ask what value you can add — when it comes to sharing information and knowledge. Ten years later and what has increased is the noise, especially misinformation, disinformation, and propaganda. The release of tools like generative pre-trained transformers (GPT) will only increase the amount of noise online. It is becoming even more important to add value before we share information, especially confirming that the information is valid and reliable. As more machines create ‘answers’ to our questions, we should focus on adding human value.

Jacqueline Bates asks, “how can educators find their way through the sea of new technologies like Artificial Intelligence?”. She recommends using the Feynman Technique, an excellent way of making sense of something and then adding value by explaining it.

Step 1: Choose a concept or topic to explain.
Step 2: Pretend you are teaching the concept or topic to a child.
Step 3: Write down a simple explanation of the concept or topic.
Step 4: Review and revise the explanation until you understand it completely.
—Wayfinding New Technology for Educators

Adding value to information is one way of helping make our networks smarter and able to make better-informed decisions. The fourteen methods I described in 2013 are still valid today. Adding value is one of the topics in the PKM workshop, building on intentionality, media literacy, and understanding fake news. If the use of tools like GPT become widespread then instead of answers to all our questions, we may only get answers without real questions behind them — auto-tuning work.

The next time we share something online, let’s ask — “What value am I adding?”

On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“You don’t get to be a VP or CEO of a major corporation by being meek. You get there by being a polite sociopath.” —Dave Rahardja

“Have you ever thought about why there aren’t AI tax prep companies coming out of the woodwork? Perfect application: drudgery! Legible rules! Expensive human experts!
It’s because they (rightly) expect that enough of their customers would get in trouble from bad advice that they’d be sued out of existence, if not indicted themselves.
That’s how it should be with all AI apps. Be accountable for what you claim your model does, or don’t ship it.” —@MattMay

“I’m not worried about the AI apocalypse. I’m worried about the ‘VCs subsidize AI tech and sell it at a loss just long enough to make everyone rely on them (AKA ‘disrupting the knowledge worker industry’) before bumping the price up and quality down just like Uber and Amazon and the rest’ apocalypse.” —Alex Chaffee

“I’m coming to think that releasing these [AI] tools was a reckless act with the potential to generate negative externalities we have barely started to imagine.
The threat isn’t rogue superintelligence. It’s bullshit at unprecedented scale, reflected back upon itself and iteratively amplified.” —Carl T. Bergstrom

“Of all the demoralising AI boosterism, the excitement about automating work emails to send is among the saddest. Why? Because it will increase the volume and deplete the remaining value of humans communicating with humans at work in one snappy move without any real understanding of message overload. I would like a new filter for outlook which treats everything by default as junk and selects no more than five a day for actual reading. Then I’m going to treat those five with reverent attention and the rest I will trebuchet into the ocean in large batches on Fridays.” —Kate Bowles

“The ethics of AI aren’t complicated or exciting, it’s just regular old IP theft. They take other people’s writing, jumble it up a bit, and sell it to you. Unethical and irredeemable. That was easy, next question please. All the hype is necessary because OpenAI is banking on convincing lawmakers that they deserve an exception to copyright law.” —Chris Martin

“We regret that you are no longer able to gaze long into the abyss. Gazing long into the abyss is now a subscriber only service. See terms and conditions to upgrade your access. 30 day free trial.” —Paul Bassett Davies

Image: Gapingvoid.com

I have been keeping an eye on the hype & hope around artificial intelligence (AI), especially:

• ML — machine learning
• GPT — generative pre-trained transformers
• GAI — generative artificial intelligence
• LLM — large language models

“I’ve long been a fan and found value in AI / ML and its capabilities. Learning and finding patterns and causal patterns that in time can lead to outcomes that are problematic (a large fleet of vehicles with hundreds of sensors feeding and AI / ML to detect early engine, transmission, or other failure to address before more expensive damage or at a human cost). Generative AI from large language models is missing core pieces still and had knock-on effects that are really problematic with its lack of understanding facts (or multitudes of facts and truths), but more problematic is it blunts human learning and cognition.”
—Thomas Vander Wal 2023-03-18

How Technology Influences Social Networks

Stewardship of global collective behavior —2021-06-21

“Human collective dynamics are critical to the well-being of people and ecosystems in the present and will set the stage for how we face global challenges with impacts that will last centuries. There is no reason to suppose natural selection will have endowed us with dynamics that are intrinsically conducive to human well-being or sustainability. The same is true of communication technology, which has largely been developed to solve the needs of individuals or single organizations. Such technology, combined with human population growth, has created a global social network that is larger, denser, and able to transmit higher-fidelity information at greater speed. With the rise of the digital age, this social network is increasingly coupled to algorithms that create unprecedented feedback effects.”

Image Source: https://www.bbntimes.com/science/real-vs-fake-ai-how-to-spot-the-difference

Dark Shadows

Google and Microsoft’s chatbots are already citing one another in a misinformation shitshow —2023-03-22

These companies can put as many disclaimers as they like on their chatbots — telling us they’re “experiments,” “collaborations,” and definitely not search engines — but it’s a flimsy defense. We know how people use these systems, and we’ve already seen how they spread misinformation, whether inventing new stories that were never written or telling people about books that don’t exist. And now, they’re citing one another’s mistakes, too.

“From my perspective, the danger isn’t that a new alien entity will speak through our technology and take over and destroy us. To me the danger is that we’ll use our technology to become mutually unintelligible or to become insane if you like, in a way that we aren’t acting with enough understanding and self-interest to survive, and we die through insanity, essentially … You can use AI to make fake news faster, cheaper, and on greater scales. That combination is where we might see our extinction.” —Jaron Lanier 2023-03-23

The Market Shifts Quickly

The genie escapes: Stanford copies the ChatGPT AI for less than $600 —2023-03-19

What does this all mean? Well, it means that unlimited numbers of uncontrolled language models can now be set up – by people with machine learning knowledge who don’t care about terms and conditions or software piracy – for peanuts.

It also muddies the water for commercial AI companies working to develop their own language models; if so much of the time and expense involved is incurred in the post-training phase, and this work can be more or less stolen in the time it takes to answer 50 or 100,000 questions, does it make sense for companies to keep spending this cash?

And for the rest of us, well, it’s hard to say, but the awesome capabilities of this software could certainly be of use to an authoritarian regime, or a phishing operation, or a spammer, or any number of other dodgy individuals.

The genie is out of the bottle, and it seems it’s already incredibly easy to replicate and re-train. Hold on to your hats.

@LinusEkanstam on Twitter —2023-03-23

“I think Apple will be launching their own secure and private LLM that runs on device (edge compute). And when necessary it offloads more heavy workloads to a cloud based LLM that’s optimized for heavier tasks. So we will initially have some hybrid. Single-use apps will be a huge thing. If you need to solve a unique problem, and nobody has ever done software for that because not enough market. With an LLM even a problem with only one user, will be doable, enter your ask, and code gets written, problem gets solved. Runtime ends, app dies. Done. Single use apps are born.”

There is a Regulatory System (sort of)

AI-generated images from text can’t be copyrighted, US government rules —2023-03-16

“Based on the [US Copyright] Office’s understanding of the generative AI technologies currently available, users do not exercise ultimate creative control over how such systems interpret prompts and generate material. In the Office’s view, it is well-established that copyright can protect only material that is the product of human creativity.”

One Risk

OpenAI CEO warns of risks of AI —2023-03-17

“The thing that I try to caution people the most is what we call the ‘hallucinations problem’,” [Sam] Altman said. “The model will confidently state things as if they were facts that are entirely made up.

“The right way to think of the models that we create is a reasoning engine, not a fact database,” he added. While the technology could act as a database of facts, he said, “that’s not really what’s special about them – what we want them to do is something closer to the ability to reason, not to memorize.”

A Possible Solution to that One Risk

ChatGPT Gets Its “Wolfram Superpowers”! —2023-03-23

And now ChatGPT + Wolfram can be thought of as the first truly large-scale statistical + symbolic “AI” system. In Wolfram|Alpha (which became an original core part of things like the Siri intelligent assistant) there was for the first time broad natural language understanding—with “understanding” directly tied to actual computational representation and computation. And now, 13 years later, we’ve seen in ChatGPT that pure “statistical” neural net technology, when trained from almost the entire web, etc. can do remarkably well at “statistically” generating “human-like” “meaningful language”. And in ChatGPT + Wolfram we’re now able to leverage the whole stack: from the pure “statistical neural net” of ChatGPT, through the “computationally anchored” natural language understanding of Wolfram|Alpha, to the whole computational language and computational knowledge of Wolfram Language.

Open AI in the Field

A&O announces exclusive launch partnership with Harvey —2023-02-15

“Allen & Overy (A&O), the leading international law firm, has broken new ground by integrating Harvey, the innovative artificial intelligence platform built on a version of Open AI’s latest models enhanced for legal work, into its global practice. Harvey will empower more than 3,500 of A&O’s lawyers across 43 offices operating in multiple languages with the ability to generate and access legal content with unmatched efficiency, quality and intelligence … Harvey is a platform that uses natural language processing, machine learning and data analytics to automate and enhance various aspects of legal work, such as contract analysis, due diligence, litigation and regulatory compliance. Whilst the output needs careful review by an A&O lawyer, Harvey can help generate insights, recommendations and predictions based on large volumes of data, enabling lawyers to deliver faster, smarter and more cost-effective solutions to their clients.”

Jobs & GPT

Image source: https://mastodon.social/@ChrisPirillo/110035122171768207

Image source: https://xoxo.zone/@_/110035209219943623

Notice that ‘Human Resources’ appears on both lists.

The Human Component

In the ‘age of AI,’ what does it mean to be smart? —2023-03-16

“So what happens when we automate our most impactful and superior cognitive capacity—thinking—and we don’t think for ourselves? I think we end up not acting in very smart ways, and then the algorithms are trained by behaviors that have very little to do with intelligence. Most of the stuff we spend doing on a habitual basis is quite predictable and monotonous and has very little to do with our imagination, creativity, or learnability—which is how we refer to curiosity.” —Tomas Chamorro-Premuzic, author — I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique

Caveat Emptor

Previous Posts on AI

capitalism > automation > gpt

auto-tuning work

stepping in

GPT-3 through a glass darkly

In a local op-ed I recently concluded that curriculum change in education is like fixing a plane in mid-flight especially when the first principles of public education are not clear while the curriculum is the same for everyone. Basically, standardized curriculum is the confinement of the human experience. It is a blunt tool that winds up bullying someone. But nobody can take the government to task on first principles when they do not exist. Much of the blame lies with the professionals managing the system. They have handed over this cobbled-together system to each successive government to do their political whims. And so it will continue.

The question about the role of education will become even more important as artificial intelligence tools become pervasive. Dave Cormier notes that, —“The vast majority of the human experience of learning about something is done at the novice level. That experience is about to be autotuned” — by tools such as ChatGPT. These generative pre-trained transformers are designed to automate human work. Educators should be careful that they don’t automate human learning. The only way to avoid this is to understand these powerful new tools.

Christian Talbot recently presented to ACS Athens school on the role of AI in education. He strongly advocated for ‘Humans + AI’, as opposed to ‘Humans vs. AI’. In the workplace, I refer to this as ‘stepping in‘, or using machines to augment human work, requiring deep thinking. Christian concludes that there are some key questions about education that should inform all students and educators alike.

• Who am I?
• Who are we?
• What matters to us?
• What are we going to do about it?

These questions form a compass for education, as opposed to a curriculum map. Even more succinct, was Nel Nodding’s advice that, “the main aim of education should be to produce competent, caring, loving, and lovable people.” AI, in the form of a tool like GPT, is a mirror of our society says Christian, because our words have informed this AI. Any biases of GPT and large language models are our biases. If we, and our children, don’t learn how to use these tools and understand how they work, then once-again we will be manipulated by those who already own the post-truth machines.

“You don’t need to change everyone’s mind, argues Brittany Kaiser, former director of business development at Cambridge Analytica. You just need to change the minds of the ‘persuadables.’ And the way you identify them is through understanding not just what they buy or say about themselves, but how they think. Through harvesting personal data, Cambridge Analytica could, and did, identify and persuade them.”

Simon Terry has a short post on Microsoft’s new Copilot and how we should be careful in fully adopting some of these generative AI tools.

LLMs [large language models] are improvements on past tools but are hardly perfect. In a world where the volume of information means many people scan everything, we need to remain alert for the risks of the models false inferences or patterns gone awry.

In the history of aviation, it became apparent that pilot personal relationships are critical to avoiding dangerous incidents. Authoritarian cultures meant senior pilot mistakes went devastatingly unchallenged. —Microsoft Co-pilot

I mentioned pilot training on a post recently — experience cannot be automated. I concluded that automation, in all fields, forces learning and development out of the comfort zone of course development and into the most complex aspects of human learning and performance. On that post is also a quote by Captain Sully Sullenberger, the famed pilot who safely landed a passenger jet on the Hudson River. A movie was made about this, which included the subsequent safety investigation. Tom Hanks plays Sully and in this sequence of videos we see the difference between human cognition of experienced pilots versus the best software/hardware simulation of the day. There is no comparison.

Part 1 — Part 2 — Part 3 — Part 4 — Part 5

Let’s get back to Microsoft Copilot, which was demonstrated last week. We should first note that this a demonstration and the software is not in widespread use at this time. Basically, Copilot connects all Microsoft applications, with the Microsoft graph (all user data within an enterprise), and a large language model (the black box). Using natural language processing, this system interacts with people using  an application to automate processes, find information, and summarize information, such as automatic meeting minutes and actions to be taken. The demonstration is definitely worth watching

Based only on the demonstration, my initial conclusion is that this can be a powerful performance support tool. But as the demo shows, there will be mistakes so that any Copilot output has to be checked by a human. Whether this system crosses any ethical boundaries may not be known.

Image source: Microsoft

MS Copilot is a tool that supports collaboration — working together for a common objective. It does not connect outside the enterprise (that I can see) so it does not support cooperation — sharing freely with no expectation of direct recompense. In revisiting cooperation I noted that working cooperatively requires a different mindset than merely collaborating on a defined project. Being cooperative means being open to others outside your group. Copilot will likely only improve actions in the lower left of the image below — collaborating in work teams.

More on Jony Ive and the Stephen Hawking Fellowship award — curiosity & resolve

Finally, Copilot and tools like it may result in auto-tuning work, especially basic skills. In the Copilot demonstration, human oversight of the output is repeatably shown. The worker using O365 reviews the output, perhaps changes the request, and adds or removes components. This requires a certain level of skill and experience, which most workers today will have. But what happens in several years when workers have not had to ‘manually’ develop these skills. I have become a professional writer through 20 years of blogging and writing for clients. I can quickly review text and see if it makes sense. But if I have always started with the output of a LLM tool, will I develop good basic writing skills?

“We become what we behold. We shape our tools and then our tools shape us.” —Father John Culkin (1967) A Schoolman’s Guide to Marshall McLuhan