finger spinners laser cut

I'm still pinching interesting designs off the web. I signed up to DXFdownloads. Lots of adverts (annoying) but they do have some interesting free downloads. The DXF format can be imported into Lightburn (and Inkscape).

The finger spinner design loaded in Lightburn looked like this:

Quite confusing. But it turned out that this was three different versions of the spinner. Thanks for the help from George and Pat for sorting this out.

The construction took a while since it required gluing many fasteners into place. So far I've constructed two out of three of the finger spinners. Some of the fasteners fell through the laser cutter grid and I couldn't retrieve them all.

Anyway, they spin nicely! A satisfying project!

mechanical iris laser cut

I’m still on a laser cutter learning curve. Not ready to do my own complex designs yet so am searching the web for interesting designs done by others.

I found a free, mechanical iris Creative Commons SVG design on this Maker Design Lab site and gave it a go. I have purchased a copy of Lightburn software.

Simple design

More artistic design

What have I learnt so far: That setting up with Lightburn and doing the cutting is straightforward. I'm working at the Adelaide Maker Space and they have a great setup there. Putting the pieces together after the cutting is fiddly but doable. A friend has been helping me with that (thanks Pat!).

Four guidelines to become anti-fragile

Notes on this video Nassim Taleb - 4 Rules To Become Antifragile (For A Better Life)

1) Do hard things (Embrace adversities)
Post traumatic growth is possible, rather than Post traumatic stress
"The blazing fire makes flame and brightness out of everything thrown into it"
- Marcus Aurelius
"I finally got being a good startup founder down to two words - relentlessly resourceful"
- Paul Graham
When luck gives you lemons, you make lemonade

2) Go through life as a Flaneur. My translation of this is Be Adventurous.
Flaneur: Someone who unlike a tourist, makes a decision opportunistically at every step to revise his schedule (or his destination) so he can imbibe things based on new information obtained.

An adventurer likes disorder, has a different mindset than a tourist. Adventurers welcome uncertainty.

If you know in the morning what your day looks like with any precision, you are a little bit dead - the more precision, the more dead you are.

3) Develop an anti-education. My translation here is Follow your curiousity and passions
Great scientists like Darwin and Einstein didn't like the education system. It kills creativity. It's better to read a lot of books. Focus on things that are really important to you.
An apprenticeship is more important than an academic education

"Only the autodidacts are free"

4) Develop an anti-fragile life philosopy. My translation here is develop a Stoic philosophy (Marcus Aurelius, Seneca)
Keep the upside; don't be hurt by the downside
A Stoic is someone who transforms:

• Fear into Prudence
• Pain into Information
• Mistakes into Initiation
• Desires into Undertaking

initial laser cuts

After my ruptured achilles accident I had to relocate from Alice Springs to Adelaide for surgery. Adelaide has an excellent Maker Space facility so I've joined up and am learning how to use one of their laser cutters.

Last year, as part of an Inventiveness course, I did these two as 3D prints (here) to make painted tiles. So, interesting to compare the 3D print version with a laser cut engraved version.

One of the helpful people at Maker Space gave me a link to a great site about making boxes. Here are some of the products:

Hinged box

Flex box

Shutter box

Then I received a request to make the dungeon and dragons dice box on that site. I then added a couple of dragon images obtained from the Free SVG site. This worked well but there was some burn marks on the surface. Perhaps I can find a work around to eliminate this?

update 6/10/24

Escher

Bear on acrylic

The Conversational Framework

- A brief introduction
- the Diana Laurillard section is partly plagarized!!
- click on Diana's diagrams to see them more clearly
- bit of a preamble first before I get to The Conversational Framework.

I’ve been looking for some time, on and off, for a resolution of a problem I’ve had with learning theory.

Sometimes I am this teacher: just do what I think will work and also, as a bonus, is also interesting. Those things that might work vary from year to year depending on the school environment which varies a lot, school to school.

Sometimes I am this teacher: A teacher who has studied a lot of different learning theories and am still surprised about how little interested in learning theories that other teachers seem to be. Some staff and schools hardly ever discuss learning theory.

For most teachers practice is primary. Find something that works. And then for those who try out different approaches – which seems necessary because students cohorts vary widely – it becomes apparent after a while that there is no magic bullet. There is no unified learning theory.

I’ve had a long time interest in learning theories which may have originated from the traditional teaching method – instruction based around a textbook – being so uninspiring.

Early on (the 1980s) I cottoned onto Seymour Papert’s Constructionism after reading his book “Mindstorms”. This got me interested in computers through the Logo coding language which promised to make maths more engaging. The challenge of Constructionism was for the teacher to create an engaging microworld where the student would learn without being formally taught. Turtle geometry was one such microworld. I’ve spent time exploring other microworlds and found some of them to be very successful, eg. recently, the Turtle Art tile project developed by Josh Burker.

Seymour did setup an ideological polemic of Constructionism (intrinsic learning in a computer generated microworld) versus Instructionism (traditional school based) and in his second book “The Children’s Machine” more or less called for the overthrow of traditional schooling.

This was fine by me and where possible I modelled my teaching along the lines he suggested.

However, when I taught at a disadvantaged school in the northern suburbs Adelaide (Paralowie R12) I found that many of the students had missed out so much from before they went to school that they needed lots of instruction to fill in the many gaps in their knowledge.

This created an epistemological crisis for me (a Skinner moment!) which after some agonising I resolved my deciding that teachers needed both Constructionism and Instructionism and awareness of when to use them. Walk the walk along the spectrum of learning theories.

So for years this went on. I would delve into different learning theories and extract what I saw as useful things from each of them. There are some great learning theorists out there IMO and many of them have valuable things to offer. Probably best to provide details of this some other time.

Recently, my interest in learning theory was reawakened. It’s shocking to say but in schools there is very little discussion of learning theories! But what happened in my school is that there was a problem with quite a few year 7s and 8 engagement. So the school hired a learning consultant to fix things up. In my opinion, the theories he talked about were often not the best ones. But anyway it did induce me to start exploring learning theories again.

I’m one of those strange people who reads conference papers and PhDs for fun. Luckily for me Diana Laurillard had been invited to present a key note to the Constructionist conference in Ireland 2020. I really liked her approach because she saw the distinctive strengths of Constructionism but also saw it as not the whole deal. It was part of the jigsaw, albeit a big part, with her whole learning jigsaw being made up of Instructionism, Constructionism, Social-cultural learning and Collaborative learning. We could call these the Big Four. There are others too but those four cover most of the ground I want to cover.

Her framework, which integrates these theories, is called The Conversational Framework. I think the way she presents it can be used as a guide for teachers to develop engaging lessons for students which covers most of the ways in which learning occurs. This could be a formal development process. There is an online website for doing that (see References). But I’m using it here as a self check that I’m offering all of these different ways of learning to students. And as a justification that my preferred ways of teaching are supported by learning theory. It's a big step up from "Walk the walk along the spectrum from Constructionism to Instructionism"

I won’t attempt a detailed explanation or historical origins of Diana’s whole framework (best to read her originals for that, see references) but rather introduce some of her marvellous schematics and argue a claim that my preferred way of teaching does cover all of the methods she recommends.

So the 6 Learning Types are Acquisition, Inquiring, Producing, Discussing, Practising and Production. All of them will be explained somewhere in this article.

learning through acquisition: the teacher (human, book, website, etc) communicates (one-way) concepts and ideas, and the learner reads, watches or listens

learning through investigation’ or ‘inquiry’: the teacher asks learners to explore or question the teacher's concepts (two-way). In this case they generate their own ideas of what they want to know.

learning through practice: the learner uses the learning environment set up by the teacher to create exercises for the learners. Ideally it includes a goal, the means for learners to put their concepts into practice to achieve it, feedback on their action, and the opportunity to revise and improve it.

Learning through practice may be guided, with extrinsic feedback, or unguided (after the teacher has setup the learning environment), with instrinsic feedback. Here's another of Diana's diagrams to help explain this:

"This is why Papert could say that constructionist exercises enabled learning without a teacher. The teacher, in the form or a person, or a computer program running a multiple choice exercise, is not needed to comment or inform. The microworld, like the real world in the right context, can provide the ‘informational feedback’ the learner needs"

Learning through Discussion: questions and answers including through peers (social construction of ideas)

However, learning through collaboration is more demanding than simple ‘discussion’ in the top right-hand corner, as Figure 4 shows, because the learners are necessarily collaborating on constructing something together: that is the nature of collaboration. It involves Q&A, shared practice, tinkering / debugging / repeated iterations. The teacher may play no role at all.

Here, each learner is learning through practice by using the learning environment. And at the same time, they are discussing and sharing that practice. In order to do that, they are necessarily also linking the two, which helps them develop both concepts and practice with each other. The teacher need play no role at all, and yet there is a lot of active internal processing required of the learner during this process.

Learning through production: Here the learner must connect up concepts and practice, and then produce an essay, or performance, or design, or presentation to show what they have learned. Throughout this process the learner is actively processing both concepts and practice and the integration of the two. This is akin to what Papert referred to as constructing personally meaningful and shareable artefacts, where the sharing is part of the motivation to construct a successful artefact.

The main issue for a teacher is to be aware of the full range, and the extent to which their teaching embraces all these different types of conversation, between teacher and learner, learner and peers, and on the levels of both concepts and practice.

Constructionism is represented best through four of the 6 learning types. Learning through acquisition, and inquiry are not a particular focus. The role of the teacher is still critical, however, as it is a real design challenge to generate and modulate the learning environment that could achieve learning without a teacher. Very few achieve that as most rely greatly on the teacher to provide additional guidance and feedback. The teacher will also be the recipient of the artefacts produced by a constructionist pedagogy, able to use these for judging the value of it as a learning process.

Putting all these pedagogic approaches together defines the superset of essential requirements for supporting the learning process, a ‘Conversational Framework’, as shown in Figure 5 (Laurillard, 2002). The full framework embraces all the elements prioritized by each of the main pedagogic approaches, and demonstrates the complexity of what it takes to learn: a continual iteration between teachers and learners, and between the levels of theory and practice. It is not symmetrical: the teacher is privileged as defining the conception and designing the practice environment to match. The teacher also learns, from receiving learners’ questions and products, as well as reflecting on their performance. But teachers are learning about teaching, rather than learning about the concept or practicing the skill.

REFERENCE

Diana Laurillard. Profile

Diana Laurillard. Significance of Constructionism as a distinctive pedagogy (2020)
In Constructionism 2020 conference proceedings (Ireland), pp. 29-37

Diana Laurillard. The pedagogical challenges to collaborative technologies (2009)
In Computer supported collaborative learning

Diana Laurillard. Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology (2012). This book was too expensive at $200! link, but then I found it at anna's archive.

Applying the Conversational Framework using an online learning design tool
Diana Laurillard talks through how to use a free online learning design tool which applies the Conversational Framework to build courses using the six key learning types

Learning Designer
At this site you need to sign up and login. It then lets you design your own lessons using The Conversational Framework.

messy AI milestones

For me it is VERY useful to have a list of AI milestones with dates. This defines the ball park which is much, much bigger than ChatGPT. It provides a framework which helps inform future focus. The comments I've added are there as a self guide to future research. So, they often do hint at my favourites.

Keep in mind that there are at least four different types of AI: Symbolic, Neural Networks aka Connectionist, Traditional Robots and Behavioural Robotics, as well as hybrids. For some events in the timeline it is easy to map to the AI type but for others it is not so easy.

1943: Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician, teamed up to develop a mathematical model of an artificial neuron. In their paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" they declared that:

Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms.

1950: Alan Turing publishes “Computer Machinery and Intelligence” (‘The Imitation Game’ later known as the Turing Test)

1952: Arthur Samuel implemented a program that could play checkers against a human opponent

1954: Marvin Minsky submitted his Ph.D. thesis in Princeton in 1954, titled Theory of Neural-Analog Reinforcement Systems and its Application to the Brain-Model Problem; two years later Minsky had abandoned this approach and was a leader in the symbolic approach at Dartmouth.

1956: Dartmouth Workshop organised by John McCarthy coined the term Artificial Intelligence. He said would explore the hypothesis that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

The main descriptor for the favoured approach was Symbolist: based on logical reasoning with symbols. Later this approach was often referred to as GOFAI or Good Old Fashioned AI.

Knowledge can be represented by a set of rules, and computer programs can use logic to manipulate that knowledge. Leading symbolists Allen Newell and Herbert Simon argued that if a symbolic system had enough structured facts and premises, the aggregation would eventually produce broad intelligence.

Marvin Minsky, Allen Newell and Herb Simon, together with John McCarthy, set the research agenda for machine intelligence for the next 30 years. All were inspired by earlier work by Alan Turing, Claude Shannon and Norbert Weiner on tree search for playing chess. From this workshop, tree search — for game playing, for proving theorems, for reasoning, for perceptual processes such as vision and speech and for learning — became the dominant mode of thought.

1957: Connectionists: Frank Rosenblatt invents the perceptron, a system which paves the way for modern neural networks

The connectionists, inspired by biology, worked on "artificial neural networks" that would take in information and make sense of it themselves. The pioneering example was the perceptron, an experimental machine built by the Cornell psychologist Frank Rosenblatt with funding from the U.S. Navy. It had 400 light sensors that together acted as a retina, feeding information to about 1,000 "neurons" that did the processing and produced a single output. In 1958, a New York Times article quoted Rosenblatt as saying that "the machine would be the first device to think as the human brain."

Perceptrons were critiqued as very limited in what they could achieve by the symbolic advocates Minsky & Papert in their book Perceptrons. The Symbolists won this funding battle.

1959: John McCarthy noted the value of commonsense knowledge in his pioneering paper "Programs with Common Sense" [McCarthy1959]

1959:Arthur Samuel published a paper titled “Some Studies in Machine Learning Using the Game of Checkers”⁠2, the first time the phrase “Machine Learning” was used–earlier there had been models of learning machines, but this was a more general concept

1960: Frank Rosenblatt published results from his hardware Mark I Perceptron, a simple model of a single neuron, and tried to formalize what it was learning.

1960: Donald Michie himself built a machine that could learn to play the game of tic-tac-toe (Noughts and Crosses in British English) from 304 matchboxes, small rectangular boxes which were the containers for matches, and which had an outer cover and a sliding inner box to hold the matches. He put a label on one end of each of these sliding boxes, and carefully filled them with precise numbers of colored beads. With the help of a human operator, mindlessly following some simple rules, he had a machine that could not only play tic-tac-toe but could learn to get better at it.

He called his machine MENACE, for Matchbox Educable Noughts And Crosses Engine, and published⁠5 a report on it in 1961

1960s: Symbolic AI in the 1960s was able to successfully simulate the process of high-level reasoning, including logical deduction, algebra, geometry, spatial reasoning and means-ends analysis, all of them in precise English sentences, just like the ones humans used when they reasoned. Many observers, including philosophers, psychologists and the AI researchers themselves became convinced that they had captured the essential features of intelligence. This was not just hubris or speculation -- this was entailed by rationalism. If it was not true, then it brings into question a large part of the entire Western philosophical tradition.

Continental philosophy, which included Nietzsche, Husserl, Heidegger and others, rejected rationalism and argued that our high-level reasoning was limited, prone to error, and that most of our abilities come from our intuitions, our culture, and from our instinctive feel for the situation. Philosophers who were familiar with this tradition were the first to criticize GOFAI (Good Old Fashioned AI) and the assertion that it was sufficient for intelligence, such as Hubert Dreyfus and Haugeland.

1963: First PhD about computer vision by Larry Roberts MIT

1963: (1985) The philosopher John Haugeland in his 1985 book "Artificial Intelligence: The Very Idea" asked these two questions:

• Can GOFAI produce human level artificial intelligence in a machine?
• Is GOFAI the primary method that brains use to display intelligence?

AI founder Herbert A. Simon speculated in 1963 that the answers to both these questions was "yes". His evidence was the performance of programs he had co-written, such as Logic Theorist and the General Problem Solver, and his psychological research on human problem solving.

1966: Joseph Weizenbaum creates the Eliza Chatbot, an early example of natural language processing.

1967: MIT professor Marvin Minsky wrote: "Within a generation...the problem of creating 'artificial intelligence' will be substantially solved."

1968: Origin of Traditional Robotics: an approach to Artificial Intelligence by Donald Pieper, "The Kinematics of Manipulators Under Computer Control", at the Stanford Artificial Intelligence Laboratory (SAIL) in 1968.

1969-71: The classical AI "blocksworld" system SHRLDU, designed by Terry Winograd (mentor to Google founders Larry Page and Sergey Brin) revolved around an internal, updatable cognitive model of the world, that represented the software's understanding of the locations and properties of a set of stacked physical objects (Winograd,1971). SHRDLU carried on a simple dialog (via teletype) with a user, about a small world of objects (the BLOCKS world) shown on an early display screen (DEC-340 attached to a PDP-6 computer)

1979: Hans Moravec builds the Stanford Cart, one of the first autonomous vehicles (outdoor capable)

1980s: Back propagation and multi layer networks used in neural nets (only 2 or 3 layers)

1980s: Rule based Expert Systems, a more heuristic form of logical reasoning with symbols encoded the knowledge of a particular discipline, such as law or medicine

1984: Douglas Lenat (1950-2023) began work on a project he named Cyc that aimed to encode common sense in a machine. Lenat and his team added terms (facts and concepts) to Cyc's ontology and explained the relationships between them via rules. By 2017, the team had 1.5 million terms and 24.5 million rules. Yet Cyc is still nowhere near achieving general intelligence. Doug Lenat made the representation of common-sense knowledge in machine-interpretable form his life's work

Alan Kay's speech at Doug Lenat's memorial

1985: Robotics loop closing (Rodney Brooks, Raja Chatila) – if a robot sees a landmark a second time it can tighten up on uncertainties

1985: Origin of behavioural based robotics. Rodney Brooks wrote "A Robust Layered Control System for a Mobile Robot", in 1985, which appeared in a journal in 1986, when it was called the Subsumption Architecture. This later became the behavior-based approach to robotics and eventually through technical innovations by others morphed into behavior trees.

This has lead to more than 20 million robots in people’s homes, numerically more robots by far than any other robots ever built, and behavior trees are now underneath the hood of two thirds of the world’s video games, and many physical robots from UAVs to robots in factories.

1986: Marvin Minsky publishes "The Society of Mind". A mind grows out of an accumulation of mindless parts.

1986: David Rumelhart, Geoffrey Hinton, and Ronald Williams published a paper Learning Representations by Back-Propagating Errors, which re-established the neural networks field using a small number of layers of neuron models, each much like the Perceptron model. There was a great flurry of activity for the next decade until most researchers once again abandoned neural networks.

1986: Perhaps the most pivotal work in neural networks in the last 50 years was the multi-volume Parallel Distributed Processing (PDP) by David Rumelhart, James McClellan, and the PDP Research Group, released in 1986 by MIT Press. Chapter 1 lays out a similar hope to that shown by Rosenblatt:

People are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at. ...We will introduce a computational framework for modeling cognitive processes that seems… closer than other frameworks to the style of computation as it might be done by the brain.

Rumelhart and McClelland dismissed symbol-manipulation as a marginal phenomenon, “not of the essence human computation”.

1986: The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986

1987: Chris Langton instigated the notion of artificial life (Alife) at a workshop11 in Los Alamos, New Mexico, in 1987. The enterprise was to make living systems without the direct aid of biological structures. The work was inspired largely by John Von Neumann, and his early work on self-reproducing machines in cellular automata.

1988: One of Hinton's postdocs, Yann LeCun, went on to AT&T Bell Laboratories in 1988, where he and a postdoc named Yoshua Bengio used neural nets for optical character recognition; U.S. banks soon adopted the technique for processing checks. Hinton, LeCun, and Bengio eventually won the 2019 Turing Award and are sometimes called the godfathers of deep learning.

Late 1980s: The market for expert systems crashed because they required specialized hardware and couldn't compete with the cheaper desktop computers that were becoming common

1989: “Knowledge discovery in databases” started as an off-shoot of machine learning, with the first Knowledge Discovery and Data Mining workshop taking place at an AI conference in 1989 and helping to coin the term “data mining” in the process

1989: “Fast, Cheap, and Out of Control: A Robot Invasion of the Solar System”, by Rodney Brooks and Anita Flynn where we had proposed the idea of small rovers to explore planets, and explicitly Mars, rather than large ones that were under development at that time

1991: Rodney Brooks published “Intelligence without Reason”. This is both a critique of existing AI being determined by the current state of computers and a suggestion for a better way forward based on emulating insects (behavioural robotics)

1991: Simultaneous Localisation and Mapping (SLAM) Hugh Durrant-Whyte and John Leonard: symbolic systems replaced with geometry with statistical models of uncertainty ( used in self-driving cars , navigation and data collection from quadcopter drones, inputs from GPS )

1997: IBMs Deep Blue defeats world chess champion Gary Kasparov

1997: Soft landing of the Pathfinder mission to Mars. A little later in the afternoon, to hearty cheers, the Sojourner robot rover deployed onto the surface of Mars, the first mobile ambassador from Earth

Early 2000s: new symbolic-reasoning systems based on algorithms capable of solving a class of problems called 3SAT and with another advance called simultaneous localization and mapping. SLAM (Simultaneous Localisation and Mapping) is a technique for building maps incrementally as a robot moves around in the world

2001: Rodney Brooks company iRobot, on the morning of September 11, sent robots to ground zero in New York City. Those robots scoured nearby evacuated buildings for any injured survivors that might still be trapped inside.

2001-11: Packbot robots from irobot were deployed in the thousands in Afghanistan and Iraq searching for nuclear materials in radioactive environments, and dealing with road side bombs by the tens of thousands. By 2011 we had almost ten years of operational experience with thousands of robots in harsh war time conditions with human in the loop giving supervisory commands

2002: iRobot (Rodney Brooks company) introduced the Roomba

2005: The DARPA (Defense Advanced Research Projects Agency) Grand Challenge was won by Stanford Driverless car by driving 211 km on an unrehearsed road

2006: Geoffrey Hinton and Ruslan Salakhutdinov, published "Reducing the Dimensionality of Data with Neural Networks", where an idea called clamping allowed the layers to be trained incrementally. This made neural networks undead once again, and in the last handful of years this deep learning approach has exploded into practicality of machine learning

2009: Foundational work on neurosymbolic models is (D’AvilaGarcez,Lamb,& Gabbay,2009) which examined the mappings between symbolic systems and neural networks

2010s: Neural nets learning from massive data sets

2011: A week after the tsunami, on March 18th 2011, when Brooks was still on the board of iRobot, we got word that perhaps our robots could be helpful at Fukushima. We rushed six robots to Japan, donating them, and not worrying about ever getting reimbursed–we knew the robots were on a one way trip. Once they were sent into the reactor buildings they would be too contaminated to ever come back to us. We sent people from iRobot to train TEPCO staff on how to use the robots, and they were soon deployed even before the reactors had all been shut down.

The four smaller robots that iRobot sent, the Packbot 510, weighing 18kg (40 pounds) each with a long arm, were able to open access doors, enter, and send back images. Sometimes they needed to work in pairs so that the one furtherest away from the human operators could send back signals via an intermediate robot acting as a wifi relay. The robots were able to send images of analog dials so that the operators could read pressures in certain systems, they were able to send images of pipes to show which ones were still intact, and they were able to send back radiation levels. Satoshi Tadokoro, who sent in some of his robots later in the year to climb over steep rubble piles and up steep stairs that Packbot could not negotiate, said⁠3 “[I]f they did not have Packbot, the cool shutdown of the plant would have [been] delayed considerably”. The two bigger brothers, both were the 710 model, weighing 157kg (346 pounds) with a lifting capacity of 100kg (220 pounds) where used to operate an industrial vacuum cleaner, move debris, and cut through fences so that other specialized robots could access particular work sites.

But the robots we sent to Fukushima were not just remote control machines. They had an Artificial Intelligence (AI) based operating system, known as Aware 2.0, that allowed the robots to build maps, plan optimal paths, right themselves should they tumble down a slope, and to retrace their path when they lost contact with their human operators. This does not sound much like sexy advanced AI, and indeed it is not so advanced compared to what clever videos from corporate research labs appear to show, or painstakingly crafted edge-of-just-possible demonstrations from academic research labs are able to do when things all work as planned. But simple and un-sexy is the nature of the sort of AI we can currently put on robots in real, messy, operational environments.

2011: IBM’s Watson wins Jeopardy

2011-15: Partially in response to the Fukushima disaster the US Defense Advanced Research Projects Agency (DARPA) set up a challenge competition for robots to operate in disaster areas

The competition ran from late 2011 to June 5th and 6th of 2015 when the final competition was held. The robots were semi-autonomous with communications from human operators over a deliberately unreliable and degraded communications link. This short video focuses on the second place team but also shows some of the other teams, and gives a good overview of the state of the art in 2015. For a selection of greatest failures at the competition see this link.

2012: Nvidia noticed the trend and created CUDA, a platform that enabled researchers to use GPUs for general-purpose processing. Among these researchers was a Ph.D. student in Hinton's lab named Alex Krizhevsky, who used CUDA to write the code for a neural network that blew everyone away in ImageNet competition, which challenged AI researchers to build computer-vision systems that could sort more than 1 million images into 1,000 categories of objects

AlexNet's error rate was 15 percent, compared with the 26 percent error rate of the second-best entry. The neural net owed its runaway victory to GPU power and a "deep" structure of multiple layers containing 650,000 neurons in all.

In the next year's ImageNet competition, almost everyone used neural networks.

2013-18: Speech transliteration systems improve and proliferate – we can talk to our devices

2014: Google program had automatically generated this caption: “A group of young people playing a game of Frisbee”. (reported in a NYT article)

2015: LeCun, Bengio, Hinton (LeCun 2015)
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

2015: Diffusion models were introduced in 2015 as a method to learn a model that can sample from a highly complex probability distribution. They used techniques from non-equilibrium thermodynamics, especially diffusion. Diffusion models have been commonly used to generate images from text. Still, recent innovations have expanded their use in deep-learning and generative AI for applications like developing drugs, using natural language processing to create more complex images and predicting human choices based on eye tracking.

2016: Google's AlphaGo AI defeated world champion Lee Sedol, with the final score being 4:1.

2017: In one of Deep Mind’s most influential papers “Mastering the game of Go without human knowledge”,the very goal was to dispense with human knowledge altogether, so as to “learn, tabularasa, superhuman proficiency in challenging domains”(Silveretal.,2017).
(this claim has been disputed by Gary Marcus)

2017-19: New architectures, such as the Transformer(Vaswanietal.,2017) developed, which underlies GPT-2(Radfordetal.,2019)

2018: Behavioural AI:
Blind cheetah robot climbs stairs with obstacles: visit the link then scroll down for the video

2019: Hinton, LeCun, and Bengio won the 2019 Turing Award and are sometimes called the godfathers of deep learning.

2019: The Bitter Lesson by Rich Sutton, one of founders of reinforcement learning.

The biggest lesson that can be read from 70 years of AI research is that general methods thatleverage computation are ultimately the most effective, and by a large margin…researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation.…the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation.

(This analysis is disputed by Gary Marcus in his hybrid essay)

2019: Rubik’s cube solved with a robot hand: video

2020: Open AI introduces GPT3 natural language model which later spouts bigoted remarks

2021: DALL-E images from text captions

2022: Text to images
Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. It is considered to be a part of the ongoing artificial intelligence boom. It is primarily used to generate detailed images conditioned on text descriptions.

Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, and it can run on most consumer hardware equipped with a modest GPU with at least 4 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services.

2022, November: ChatGPT is a chatbot and virtual assistant developed by OpenAI and launched on November 30, 2022. Based on large language models (LLMs), it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive user prompts and replies are considered at each conversation stage as context.

ChatGPT is credited with starting the AI boom, which has led to ongoing rapid investment in and public attention to the field of artificial intelligence (AI). By January 2023, it had become what was then the fastest-growing consumer software application in history, gaining over 100 million users and contributing to the growth of OpenAI's current valuation of $86 billion.

an AI taxonomy

When I dig below the hype and both positive and critical evaluations of ChatGPT what I discover is that AI thought leaders disagree and argue with each other a lot. As well as the disagreements about the reliability of ChatGPT there is also the issue of different types of AI. The ascendancy of Deep Learning in the public consciousness is a relatively recent phenomenon in the 60 plus years history of AI. As part of my research I found the need to clarify a broad bunch of terms that refer to very different approaches. A fundamental part of understanding AI is knowing the different types of AI.

Aside for future article: Moreover, underlying these different approaches originally were different ideas about how the human mind and / or brain works.

What is AI?

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. An AI can do discovery, inference and reasoning just like a human. I’ve underlined robots because they, too, have been very much sidelined in the current ChatGPT hype.

The difference between AI and AGI, which also introduces the terms narrow and weak AI

The phrase “Artificial Intelligence” originated from John McCarthy at the Dartmouth Conference in 1956. In the beginning there was only AI, the goal to build a machine from which we couldn’t tell the difference from a human. This was Alan Turing’s imitation game, aka the Turing test. In 1950 Alan Turing published “Computer Machinery and Intelligence” which presented his Imitation Game challenge which later became known as the Turing Test. The Turing Test has now become outdated but that is a side issue to the present article.

Imitating humans in all or most respects turned out to be very hard to do. Over time, as researchers built machines that could perform a particular sub task that humans could do, such as playing chess or correctly labelling images or self driving cars then those sub goals retained the AI label. So, new labels, AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence) were invented to renew the original goal of imitating or surpassing all of human intelligence.

An alternative could have been using the terms narrow AI or weak AI. Narrow AI is a good descriptor for one narrow task. Weak AI is a similar term, an AI that implements a limited part of the mind.

What is Machine Learning (ML)?

Some experts refer to their work as Machine Learning. For me this needs clarification because there is overlap in the usage of ML and AI. ML is a subset of AI but in what way?

ML means that the machine is Learning (duh!). It is learning by example. In some cases lots of examples. It improves its performance with training over time. This is a different type of coding to the one I am used to where you write code to do something and that something is predetermined by the code and doesn’t change over time. With machine learning, we can use algorithms that have the ability to learn.

There are lots of these algorithms. See this geeks for geeks page for a comprehensive list. One simple example is linear regression. The machine can take an input of a series of points on a graph and map a straight line of best fit to those points. To do this you would need the data (the points), the linear regression algorithm and some python code to do the work. I provide links to a couple of beginner's hands on AI courses in the reference section below that take you through this process.

Phrases that go with ML: data, big data (especially for Deep Learning which is a subset of ML), self learning, statistical models, self correction, can only use structured and semi-structured data

I've been searching for a relatively simple example of making Machine Learning (making rather than doing ML) suitable for middle school students. Most people are current excitedly focused on the doing but my belief is that to understand it deeply you need to make it.

Rodney Brooks illustration of the early machine learning device by Donald Michie provides an entertaining introduction to the topic. Michie built a machine that could learn to play the game of tic-tac-toe (Noughts and Crosses in British English) from 304 matchboxes, small rectangular boxes which were the containers for matches, and which had an outer cover and a sliding inner box to hold the matches. He put a label on one end of each of these sliding boxes, and carefully filled them with precise numbers of colored beads. With the help of a human operator, mindlessly following some simple rules, he had a machine that could not only play tic-tac-toe but could learn to get better at it. He called his machine MENACE, for Matchbox Educable Noughts And Crosses Engine, and published⁠ a report on it in 1961

Brooks references a 1962 Scientific American article by Martin Gardner which illustrated the concepts with a simpler version to play hexapawn, three white chess pawns against three black chess pawns on a three by three chessboard. As in chess, a pawn may move forward one space into an empty square, or capture an enemy pawn by moving diagonally forward one space. If you get a pawn to the last row, you win. You also win if you capture all the enemy's pawns, or if the enemy cannot move.

Impressively, TheGamer has coded MENACE in Scratch, here and puttering has coded hexapawn in Scratch, here.

What AI isn’t Machine Learning?

You can have forms of AI that don’t learn over time. Symbolic AI, Traditional robotics and Behaviour based robotics could all fit this category. They are programmed, they do some human like stuff but don’t change or improve over time without human reprogramming. They are still important but sidelined at the moment due to the LLM (Large Language Models) hype. A little more on this below.

This diagram, found on the web, is missing the two robotic forms of AI and the Neuro-Symbolic hybrids

What is Deep Learning?

To repeat, Machine learning (ML) is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks.

Deep learning is a subset of machine learning that uses several layers within neural networks to do some of the most complex ML tasks sometimes without any human intervention. But sometimes there is human intervention, in the case of Reinforcement Learning. I won't go into detail about Deep Learning here because it is long as well as smelly. I do provide a link to a detailed version in the Reference.

Four main types of AI

Following Rodney Brooks, there are at least four types of AI. They are, along with approximate start dates:

1. Symbolic (1956) aka Good Old Fashioned AI (GOFAI). Acccording to Herb Simon symbols represent the external world; thought consists of expanding, breaking up, and reforming symbol structures, and intelligence is nothing more than the ability to process symbols. This was the originally dominant of AI (from the 1956 Dartmouth Conference) but the enormous effort over decades by Doug Lenant’s Cyc Project failed so now it has become marginalised due to the successes of Deep Learning.
2. Neural networks (1954, 1960, 1969, 1986, 2006) aka Connectionism, which evolved into Deep Learning…lots of different start dates here since it has died and then returned from the dead a few times). Deep Learning gets all the media attention these days.
3. Traditional robotics (1968)
4. Behaviour-based robotics (1985) aka embodied or situated AI or insect inspired AI! (my term)

One more important thing. Some authors, notably Gary Marcus, say that Neuro-Symbolic hybrids are the way forward to robust, reliable AI.

Here's my crude Venn diagram of the different types of AI:

ML = Machine Learning; DL = Deep Learning; S = Symbolic AI;
H = neuro-symbolic hybrid; TR = Traditional Robotics;
BR = Behavioural Robotics

To understand the current deficiencies of the AI debate / hype it’s necessary to look at the strengths and weaknesses of these different types. Rodney Brooks does evaluate them, in his 2018 blog referenced below, against these criteria: Composition, Grounding, Spatial, Sentience and Ambiguity

AI development has had a tortured zig, zag history. Another fascinating way to view it is from the influences and underlying belief systems / philosophies of the AI founders and developers. If we build our machines in our own image then what is that image?

REFERENCE

Brooks, Rodney. Steps Towards Super Intelligence, 1. How we got here (2018)
Brooks, Rodney. Machine Learning Explained (2017)
Marcus, Gary. The next decade in AI: Four Steps Towards Robust Artificial Intelligence (2020)
Deep Learning (DL) vs Machine Learning (ML): A Comparative Guide
Excellent, wide ranging explanations of ML and DL
The 10 Best Machine Learning Algorithms for Data Science Beginners
Linear Regression to fit points to a straight line is their number one
Your first machine learning project in python step by step
Free introductory hands on course to Deep Learning

short descriptors of different learning theories

Some years ago I organised a wiki called "Learning Evolves". This folded because the hosts, Wikispaces, closed down. At the time I couldn't find an equivalent site (free for educators).

Back then I discovered lots of different learning theories. I was surprised by how many there were. Since then I've often thought of providing succint descriptions of some of the more important learning theories. This is one way (I stress here, not the only way) to make a start on how we learn.

Given my present confinement (recovering from a busted achilles tendon, which gives me more time for theory) I've decided to do it. This is a rough draft. I'm leaving a lot of stuff out. Probably I will return to this page and do updates from time to time.

In Society of Mind Marvin Minsky said the trick is that there is no trick. "There is no single secret, magic trick to learning; we simply have to learn a large society of different ways to learn". So we need to study a wide variety of learning theories to learn about the wide variety of tricks that different people use to learn. It's a lot of work and takes some time. There is no general theory of learning just as there is no general theory of intelligence. So, because learning theories are fuzzy, slippable, embodied and situated things and not sharp, hard edged purely logical things they do require a lot of study to understand them. It doesn't begin or end with study of learning theory. There is philosophy, history, evolution, artificial intelligence, neuroscience and more.

Enactivism: knowledge stored in the form of motor reponses and acquired by the act of "doing". It is a form of cognition inherently tied to actions, as in the handcrafter as way of knowing. It is an intuitive non-symbolic form of learning

Instructionism or Behaviourism: Responses that are rewarded tend to be repeated. Educational outcomes can be identified: fact recall, skills and attitudes. Education can be optimised to achieve measurable changes in these desired outcomes

Cognitivism: the mind is a bit like a computer, it has meaningful structures (schemas, representations, symbols) which receives inputs that are processed and produce outputs.

Constructivism: children build or construct their own intellectual structures

Constructionism: to build personal or social meaning with engaging objects controlled by computer code in a language like Logo which evolved into Scratch

Phenomenology: focuses on an individual’s first-hand experiences rather than the abstract experience of others

Social learning: the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers (the zone of proximal development, Vygotsky)

Update (11/5/24):

What is missing here is the need to combine different learning theories in a way which integrates their various strengths and leaves out their weaknesses. The best effort I have seen which does this is Diana Laurillard's, Conversational Framework. I have written up a summary of that framework which I feel needs some polishing before publishing.

Missing in Australia: 21st C Maker Ed Jobs

Fab Learn Jobs Board

This Job Board is open to "educational makerspaces around the world" but for some strange reason I never see Australian jobs advertised here. Oh, yes, I am being ironic and rhetorical. IMO an important reason, although not the only one, is that we have a one size, (doesn't) fits all, national standards based curriculum called ACARA.

Why aren’t jobs like this being advertised in Australia? Or, if I have missed them then please show me where they are?

Posted March 21, 2024
Westfield State University:
Research, Innovation, Design and Entrepreneurial (RIDE) Center Coordinator
General Statement of Duties: Full time salaried position

The RIDE Center coordinator will support the Executive Director in managing the equipment, space, and programming. The space includes a design studio and MakerSpace with 3D printers, Adobe and other equipment software, laser cutter, woodworking, sewing, computer programing, vacuum formers, and circuitry tools. They will help to coordinate and contribute to a positive user experience, managing classroom, student, and community visits and activities, helping with scheduling events, communication, student support, preparation of reports/assessments, administrative/office tasks, and other RIDE center needs. They will assist with coordination of student interns, work study, and graduate assistants, and community engagements associated with RIDE centers. They will assist the Executive Director with RIDE expositions, workshops, speaker series, and other events, as well as training students, faculty, staff, and community on equipment and software use within the center.

Posted March 21, 2024
St Mary’s School in Orange County

St. Mary’s School is an independent day school that serves over 700 students, Pre-K through Grade 8, in Aliso Viejo, CA. As the only independent school in Orange County that offers the international baccalaureate (IB) program from primary school through middle school, St. Mary’s is committed to a globally-minded and innovative curriculum that, in many ways, stands alone within the educational landscape in Orange County. St. Mary’s students are prepared not only for the next steps in their educational journey, but also to become courageous, caring, global citizens and enlightened leaders of tomorrow.

This summer, construction will begin on a 28,000 square foot facility which will include a design center comprising five specialty labs and a gallery space. The director of the design center will lead the transition into this new space, oversee the design center and its resources, and collaborate with faculty and academic leadership to fully integrate design thinking with St. Mary’s outstanding IB and design-centered curriculum. The director of the design center will report to the director of technology and innovation, and will bring an expertise in design thinking and a relational approach to leadership to the role. St. Mary’s looks forward to welcoming the director of the design center to start July 1, 2024, or later by mutual agreement.

Posted June 1, 2023
Lab Instructor for The da Vinci Lab (DVL) for Creative Arts & Sciences

St. Stephen’s Episcopal School-Houston is looking for a full-time Lab Instructor for The da Vinci Lab (DVL) for Creative Arts & Sciences (DVL) – a makerspace for students in 1st  to 8th grades. The goal of the program is to offer a creative space for students that inspires collaborative learning and cross-pollination of learning techniques and creative skills. The Lab Instructor teaches maintenance and use of equipment, sets and delivers the yearly curriculum for DVL, and records the ways in which learning and making take place within the space.

Posted June 1, 2023
Maker Space Manager, UC Santa Barbara Library

Responsible for the day-to-day operational management of a new Library service for UCSB students to engage in making activities. Develops opportunities for experiential and project based learning with digital and non-digital creative technologies for varying skill levels. Maintains high levels of customer service in the delivery of Makerspace services. Supervises student assistants in providing peer-to-peer support for project design and creation and ensuring safe use of equipment. The inaugural Makerspace Manager will be an integral part of ensuring a smooth launch of the Makerspace and for informing the development of its service portfolio.

Posted February 28, 2022
Location: Cincinnati, Ohio, U.S.A.
Position Overview:

Seven Hills Middle School seeks an inspiring, high-energy, and passionate teacher to serve as Director for our signature Innovation Lab.  Housed in a specially designed makerspace in our new, state-of-the-art Middle School building, the Innovation Lab program engages students in a series of sequenced projects designed to foster design thinking skills. In an empathy-based approach, students consider the needs or challenges faced by others as they work in project teams to conceptualize, design, prototype, test, fail, iterate, and, in many cases, present their fabrications to authentic audiences.

In preparation for these projects, students learn a series of fabrication and design skills. Sixth-grade students develop basic skills as they work with hand, power, and digital tools on projects that include designing for others. Seventh-grade students dive more deeply into the engineering design process. They explore and develop spatial reasoning, empathy, and creative thinking skills as they take on a series of challenges. An eighth-grade Computer Science elective course teaches students to use loops, variables, functions and conditionals to build efficient and adaptable computer programs. Students also design, build, and program robotic devices. In addition to teaching courses, the Innovation Lab Director supports student-driven projects each day during lunch. All student projects increase in scope, complexity, and sophistication as they acquire new skills, but the basic formula is to help students learn to understand and empathize with challenges faced by others and to use their creativity and imagination to design effective solutions.

The gears of my childhood, again!

Lessons from the Gear Thinkers

I’ve been rereading Seymour Papert's Mindstorms. I thought I had understood it. But I needed the update. Recently, I’ve been part of a curriculum reform which overall has created waves. This was partly because of leadership errors (a mix of good and bad interventions) and partly because middle class parents complain when Schools depart from traditional structures.

Whilst I was writing my interpretation (here) of “The Gears of My Childhood” (Preface to Mindstorms) I discovered a bunch of other interpretations in Meaningful Making book 3 (free download!). Some of them I thought enhanced my interpretation of the "Gears" article. I’ll quote some extracts. Hopefully, this might encourage some to read the originals. Even though my main goal is to clarify my own thinking about what to learn from Seymour’s gears reflection.

Gears of Learning by Ridhi Aggarwal, p. 10

Children should be given the opportunity to explore their questions like babies explore the world around them ...

Children would learn by doing only when they make things that are answers to their own questions. Based on this idea, we started a Question Hour in which children could just share their daily curiosities about anything and everything. They raised questions and discussed possibilities, and then they explored the ideas by making things.

Papert reloaded by Federica Selleri, p. 14

As Papert said, we need to create and take care of the conditions in which the learning process takes place, because the creation of cognitive models is closely linked to the experience associated with them.

Therefore, it is important to pay particular attention to the context in which the experience takes place, and to design it in such a way that it can be about generating ideas and not about running into obstacles. This means thinking about the tools you want students to use, and trying them out for yourself to evaluate their possibilities, but listening to the students’ hypothesis about how things work and supporting their investigations.

What makes a project meaningful? by Lina Cannone, p. 16

I believe that a synergy between teacher and learner must be nurtured. We must abandon pre-planned activities and projects that ignore the participation of the learner. We must give way to the co-planning of activities

Finding my Gear at Twenty-Three by Nadine Abu Tuhaimer, p. 21

After graduation, I realized that my love for tinkering with objects outshined my love for programming,

At 24, I decided to take the “Fab Academy – How to Make Almost Anything” course. This is a six month long intensive program that teaches the principles of digital fabrication

Since then, I’ve been teaching in the Fab Academy program and trying to incorporate what I learned with the different educational programs I run at the Fab Lab where I work, the first Fab Lab in Jordan.

Making means heads and heart, not just hands by Lior Schenk, p. 22

Car child did not become car professional — he became a mathematician. He also became a cyberneticist and renowned learning theorist, responsible for both the 1:1 computing initiatives and the constructionist movements rippling across education to this day.

Gears were, he describes, “both abstract and sensory,” acting as “a transitional object” connecting the formal knowledge of mathematics and the body knowledge of the child.

This notion of knowing — what it means to know something, to learn, to develop knowledge formed the central thesis of Papert’s career. Knowledge is not merely absorbed through cognitive assimilation, but actively constructed through affective components as well. Papert would assert, in other words, that we learn best when we are actively engaged in constructing things in the world. Real, tangible things. Things you can hold, manipulate, and feel in order to make sense of them.

Papert’s successes, as he would ascribe, were not due to interacting with gears as objects — rather due to falling in love with the gears as more than objects, as a conduit across intellectual and emotional worlds.

As Dr. Humerto Maturana said, “Love, allowing the other to be a legitimate other, is the only emotion that expands intelligence.”

Time to Tinker by Lars Beck Johannsen, p. 28

I believe that we need to help our students discover their own gears, and help them channel it into their projects whenever possible. I also believe that it is a teacher’s task to help students develop new gears. Another task is being aware of the way you learn. If something is easy to you, it is natural to believe that it is also easy for everyone else, but that is not the case. We need to help our kids to discover their strengths!

There are a few things that could make this happen. One is knowing your students! Not just on a factual basis but also on a more personal basis. How would you otherwise discover, what makes them tick, what they love, who they are?

I strongly urge all the schools I work with to make time for more project based, constructionist, student-centered learning. The after-school programs, which most kids attend because the parents are working, also need to be a more inspiring place to spend your time. A place to tinker, do what you love, make stuff together with other kids, and have fun!

Between the garage and the electronics workshop, by Mouhamadou Ngom, p. 33

To conclude, I would say that the most important part of learning by doing is careful observation. My secret as a specialist in electro-mechanics is to take careful notes. For example, before disassembling a mechanism, I mark the intersections between the different gears. This is why I ask learners to observe well, to listen well, and to document their work.

Find your unique gear by Xiaoling Zhang, p.35

Dr. Papert’s experience makes me think that it might be a natural human instinct to love fiddling with objects as a prompt to explore the world around us. By building and playing with things, we are also building the connections between ourselves and the physical world. When it happens frequently and reliably, then it becomes a way of thinking. It makes it easier when we see consistency in the world to believe that there are laws behind seemingly superficial phenomena and to discover even more possibilities.

… every child or every person has their own unique “gear.” But can everyone find their gear? Or can we help them to find something that THEY love and can be applied as a bridge to understand more abstract ideas and the world. It seems that unique gear can’t be cloned or taught, but must be discovered

SUMMING UP, the lesson from the Gear Thinkers:

• Children should be given the opportunity to explore their questions
• We must give way to the co-planning of activities
• Listen to your students; pay attention to detail
• Be a trail blazer! Setup the first FabLab in your location
• Knowledge is actively constructed using hands, head and heart
• Love is essential for optimal knowledge growth (of the objects we work with as well as human-human)
• Know your students, personally
• Everyone has to find their own gear. They might need help with this
• Observe everything carefully