​Every job consists of tasks. No matter how much we would like to avoid it, habits form over time, and those habits can be structured and eventually automated.

​Taylor’s scientific management (1911) invited systematic, standardized thinking to all aspects of work, essentially making…

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Some follow-up thoughts from the discussion at the second AI Literacy salon that took place at Index Chinatown on May 13th1.

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I recently met myself. Or something that was supposed to be me. Delphi had been trained on my content. ElevenLabs had been trained on my voice. By any reasonable technical standard, the result was impressive. The cadence was right. The vocabulary was plausible. And yet the moment I heard it, I knew something was off. Not broken. Off. The simulatio…

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In the early 2000s, researchers studying epilepsy patients identified a single neuron in one patient’s brain that fired only when she encountered Jennifer Aniston. Her photograph, her name in text, even a stylized cartoon drawing — the same cell, every time. That is how human cognition organizes meaning: symbolically.

Large language models work dif…

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Most conversations about AI start with what it can do. This one started with what it actually is.

At the first AI Literacy Salon, hosted by In Process Coaching and joined by guests Noa Dolberg (Google’s Material Design), Cherae Robinson (Head of Platform, Flybridge), and (Founder, Deliberate Works), we spe…

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In a recent AI Literacy workshop, I asked the prompt: *What do you wish an algorithm could predict?* As the answers moved from the practical towards bigger, harder questions, I arrived at a realization I haven’t been able to shake since.

Algorithms cannot predict anything subjective.

That sounds obvious when you say it like that. But the implication…

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The first AI Literacy Salon is here — an in-person gathering that extends the weekly workshop.

Join us on March 26th at Index in Greenpoint.

AI Literacy is a space to consider where AI meets human process — to decipher how these tools can shape our attention, our communication, and the ways we work with computers and each other.

Most conversations ab…

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Marvin Minsky (1927–2016) and John McCarthy (1927–2011) founded MIT’s CSAIL Lab1 and are responsible for many of the field’s foundations, including LISP2 and early attempts at …

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How many of you like asking tough, creative questions? What if I told you that you already are active in the field of AI?

What if I told you that in the future, all we’ll need to do is ask really tough, wicked questions, and that the answers will always be there?

To understand what I mean by that, we first need to revisit the founding thinkers of AI and IA, namely Minsky, McCarthy, Engelbart, and Kay. Then, we can engage in speculative design thinking about the future and ultimately return with some valuable insights.

But first, let’s look at the basic unit of our relationship with a machine: a machine on one side, a human on the other, and there’s an interaction point—an interface point—that could be your phone, a website, a kiosk.

The AI approach to looking at things is making the machine intelligent and bringing that intelligence to humans.

John McCarthy, who co-founded the MIT AI Lab with Marvin Minsky in 1959, once said that you can view computing AI from the perspective of biology or computer science. You could imitate the nervous system as far as you understand the nervous system, or you could imitate human psychology as far as you understand human psychology.

Marvin Minsky, his counterpart at MIT, was the first person to develop a neural network simulator—a system modeled after the human brain, with neural connections—back in 1951.

He once said, for a machine to be intelligent, we have to give it several different kinds of thinking. When it switches from one of those to another, we will say that it is changing emotions. Emotion in itself is not a very profound thing—it’s just a switch between different models of operation.

So to these thinkers (and some thinkers today as well), my entire being, with all of our ambitions and magic of being alive and human, is a computer problem—quantified as buckets of knowledge and equations and mathematics.

The other school of thought is intelligent augmentation, which posits that tools can be made smarter by integrating intelligence into machines and moving the interface point closer to the human. All it means is that the tools become smarter and more intuitive.

So we need to stretch ourselves less in extending to the machine.

In this school of thought, the two leading thinkers are Doug Engelbart and Alan Kay.

Doug Engelbart is known for numerous contributions to the field, almost all of which were demoed in one iconic demo—aptly named “the mother of all demos”—where he showcased the first mouse, windows, teleconferencing, live document editing—basically, everything we use today daily.

He once said, “The better we get at getting better, the faster we will get better.” Engelbart’s philosophy was that, if you pair groups of thoughtful people with purposeful tools, you get something bigger than just efficiency improvements.

To complement that, Alan Kay’s work at Xerox PARC Lab was also versatile—on educational devices, music synthesizers, and software paradigms. Kay was one of the first to consider the concept of user interface, a field that is one of the most prominent in design today.

Alan Kay once said, “Reality is a reconstruction based on our beliefs of the world.” It is that reconstruction that allows for theatrical performance. It was his understanding that the user interface is the theater that advances us.

What makes him stand out is his ability to design tools that weren’t possible at the time—a Dynabook, a hybrid of a laptop and an iPad, meant as an early education device with software named Smalltalk—intuitive and easy to use.

He invited Trygve Reenskaug, a Finnish computer scientist, who conceived the idea of building software architecture that matches user mental models to computer operations—mapping the way we think to the way the machine operates.

That was something that philosophers and psychologists long debated, but these technologists had the foresight to incorporate it into everyday tools.

On the left, mental models; on the right, computer models; in the middle, controller and view—essentially, the tool. The modern manifestation of Model-View-Controller (MVC) is a system where a database is on the left, a view (such as an app or website) is on the right, and controllers facilitate communication between the two.

The user only interacts with the view.

For example, all the entries in Wikipedia are stored in a single, massive table (the computer model or database); Wikipedia.com is the view. Editing is done via the controller, which updates the database.

MVC was popular, but really took off in 1993 when the internet opened up indexable systems—the foundation for early internet popularity.

Now, think about all the sites in the world—their knowledge, utilities—they all fit this structure: stationary databases and proprietary interface points waiting to be accessed. A couple of hundred years ago, we entered the Information Age, marked by new communication tools, printing, and other innovations. We then transitioned from hand tools to factories, and subsequently shifted from a material economy to a data economy.

Are we too efficient now? What’s next, with all this quick movement of data?

Consider a scenario: I want to start a business, a system, a tool. What should I ask myself? Is it faster, better-looking? What data am I protecting? Am I being wasteful by copying data?

We are producing a lot more data, much faster than ever before—soon there won’t be time for data to reach a database (i.e., the internet). Phones and wearables constantly produce data—some of which may not have time to get to the cloud or internet. We’ll have to do something with offline data.

Interface points that are bigger than our phones—in the future, will be everywhere. Utilities will come to us instead of us having to find them.

We’ll need to build tools for data that is not online. Utilities will travel instead of data. Currently, we upload data from our devices, but in the future, utilities will locate us.

Lastly, data and interfaces will merge. Right now, there’s a clear boundary between app, website, database—like a form and stack of papers—but in the future, that’ll change.

Data is cheap; tools create themselves.

Kevin Kelly, a founder of Wired, said that cognition should soon be as accessible as AWS or electricity—we could plug cognition right into any system.

Endless knowledge, but not necessarily endless utility—if you had a blank piece of paper with all the answers, what question would you ask?

I see the future as a convergence of humans and machines, communicating through data and intelligence, rendering tools of purpose and function.

The future of universal interfaces will be the question. What would we ask if everything is possible? What would you ask if everything is known?

You can watch the talk below.

Other talks, essays, and recommended books are at https://library.in-process.net/everything-will-happen

The opportunity to select a product that promises…

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Trying to compute AGI (artificial general Intelligence) is not new.

How far back depends on whom you ask, for this write-up, I will focus on Minsky, McCarthy, and MIT’s AI Lab as the starting point (but we could easily go back to Turing and beyond).

When Minsky and McCarthy started the lab in 1959, they were very much set on computing general intelligence.

Machines that think, paired with consciousness, and capable of learning.

“There are 2 ways of looking at computing artificial intelligence, you can look at it from the point of view of biology or point of view of computer science You could imitative the nervous system as far as you understand the nervous system or you can immediate human psychology as far as you understand human psychology”

To annotate: “looking at it from the point of view of biology…” means the kind of “wet” programming the brain does, neurons, axons, and so forth. Simulating such process is likely to be a reference to neural networks, which are (broadly speaking) sets of networks modeled around the way the brain works: namely, taking a stab at clustering logic in proximity and generating hierarchies of information. “Looking at it from the point of psychology” is equally as provoking, as it refers to human intelligence as buckets of knowledge.

To push that slightly further before annotating, here is a quote by Marvin Minsky, part of a monologue in Machine Dreams.

“In order (for a machine) to be intelligent we have to give it several different kinds of thinking, when it switches from one of those to another we will say that it is changing emotions” “Emotion (in itself) is not a very profound thing, it’s just a switch between different modes of operation”

This is the other way of computing a brain. Clusters of knowledge and sets of actions are compartmentalized in different vertical buckets and switched by emotions.

Beyond being offensive to humanity, this argument is also straightforward to debunk using a quick thought experiment.

Imagine an intellectual person sitting in a chair, doing nothing at all, staring into thin air. That person is conscious and intelligent. Her lack of action does nothing to rob her of the consciousness and intelligent title.

In other words, intelligence is not conditioned by action. It need not be modeled around goals, nor operational switches.

This is a critical point to stay on. This idea that we can compute human intelligence (we can’t), or that the brain is a computer (it’s not), is the underpinning belief that has been fueling generations of researchers, keen and persistent in their pursuit to compute a brain. Allan Watts refers to life as an ongoing oscillation—a continuous frequency of the brain.

You can imagine your intelligence (or soul, or consciousness) as a vibrating frequency. Like the trajectory of an analog sound wave as it travels through space. It is continuous, and unlike a digital factor, it is not broken into a set of high-fidelity instances.

A digital sound wave — say an MP3 file — is economical in storage because it can slice the edges of the frequency range, and then create a high-res sound–alike composition.

Let’s say that I could somehow peer into your brain and start creating a map of your intelligence. It may take me up to a few decades, but eventually, I will succeed in computationally solving your intelligence.

The problem is that I only solved the computation of your brain, and only at one point in time.

In other words, I only solved one instance of intelligence.

Anchored to a point in time and a subject. The brain, as is consciousness and intelligence, is an ongoing analog frequency. Not instance-based digital permutations.

For a much broader palette of opinions on the prospect of AGI (artificial general intelligence), I highly recommend What To Think of Machines That Think, by John Brockman.

If you’re still reading, it is safe for me to assume that you’re on board with my pro–human view. That is the view that the brain is not a computer problem, and hence we should abort the idea of AGI. Narrow AI, on the other hand, is alive and strong.

Once we accept the premise that AGI is of no use, we can start identifying the opportunities in narrow AI. Think of your calculator as a narrow expert in calculation, think of a medical journal scanner as the best tool we have to consume terabytes of medical journals, and machine-vision algorithms as the best face recognizer.

We can do the same tasks the machine is doing: calculate on a piece of paper, skim journals, and classify images, but only to a certain extent. Let’s draw this.

Intelligence on a narrow plain

On the left, there is ‘0’, no intelligence is being used, and nothing is being done. We can learn to perform a task, and as a next step, we might relay this knowledge to a machine so that it could hyper-mathematize it.

We can foresee improvements in computing power, access to data, and other technologies pushing the machine’s ability into infinity and the unknown. ON A NARROW DOMAIN. A singular plain. Life, as a system, contains many of these trajectories: flying, writing, internet browsing, coding, dancing, and driving. We learn new tasks, and some of them can graduate to automation.

New domains are added, while others become obsolete. For example, a self–driving car technician, or a horseshoe maker, respectively. We can imagine the system of life — i.e., humanities — as made of an infinite amount of these single line trajectories.

What would AGI need to achieve

Under this lens, we can position AGI in the bottom right, as holding infinite ability in infinite domains. But we established the fallacies of that view, so that leaves us with a much more current and useful alternative.

In the absence of AGI, is it up to us to navigate this bend? Crossing and linking disparate skills and disciplines. We hold an intellectual monopoly in that regard.

Machines are competent in their unique domains, but are blind to anything else; they only compute and extend the steps we relayed to the domain.

Another way of thinking about it is when we come up with a new skill or a technology, we might slowly improve it, maturing the domain for other humans to participate in it. As part of that onboarding, sets of instructions need to be written. At which point are the domains ready for a machine to excel in them?

That machine is domain-specific and holds no intelligence. Its algorithms could do a lot of things that we could never do. For example, untangle messy data or make assumptions about the future.

But the bend is uniquely creative and can’t be mechanically produced.

This is the core of it. If your product or service is narrow by design, then you’re open for a machine to excel, or replace you.

This is not limited to unskilled jobs, as the machine holds no interest in your prestige. It holds no interest at all. The system can perform tasks efficiently. And if you’re job is on a narrow domain, and can be broken into steps, then you’re not using your human advantage properly.

Our unique, innate ability as creative humans is a crucial aspect of our identity. It will reveal currents in automation, AI, and innovation opportunities.

Nitzan Hermon is a designer and researcher of AI, human-machine augmentation, and language. Through his writing, academic and industry work, he is writing a new, sober narrative in the collaboration between humans and machines.

Welcome to AI literacy, a new section of Being in Space (updated breakdown of sections).

It builds on other writing and spaces (On Creativity, What is Work, Everything Will Happen) that were looking to understand (1) what all of this (tech in this case) means, (2) for you.

You can expect links and essays, original writing, and other material that will h…

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