A Guide to Building Your Robotics Toolkit
Pliers Set
Wire Strippers
Multimeter
Soldering Iron and Solder
Long precision screwdrivers
Multi-bit Screwdriver Set
Miter Saw
Hammer
Drill Index and Cordless Drill
Breadboards
Notebook
Creative Inspiration: The Robot Science Coloring Book
Conclusion
A History of Robotics: John Dee's Beetle
- "Mathematics, navigation and empire", https://www.rmg.co.uk/stories/royal-history/curatorial-library-archive/mathematics-navigation-empire-reassessing-john.
- Wood, Anthony à. Athenae Oxonienses: An Exact History of All the Writers and Bishops Who Have Had Their Education in the University of Oxford. London: Thomas Bennet, 1691.
- Ashmole, Elias. Theatrum Chemicum Britannicum. London: J. Grismond for Nathaniel Brooke, 1652.
- Yates, Frances A. The Occult Philosophy in the Elizabethan Age. London: Routledge & Kegan Paul, 1979.
- Harkness, Deborah E. John Dee's Conversations with Angels: Cabala, Alchemy, and the End of Nature. Cambridge: Cambridge University Press, 1999.
The Robot Science Coloring Book
Action figures, a comic book, and altered standards of communication
A few month's back I reposed an older article I had written nearly a decade ago detailing predictions about the future of AI and its effect on jobs. Since then I've had a few related thoughts that fall closer to the human aspect of the work economy.
Currently, ideal interactions with AI assistants and agents occur in a positive but invariably request-response style exchange. For example, I've been having fun with generating realistic images of Salvius toy designs. The exchange is something along the lines of the following:
Me: "I'm looking to generate realistic images of toy designs based on the attached image. Can you create a version with classic bubble packaging and include the name Salvius on it?"
AI: "I would be happy to create that design for you. Here are a few examples..."
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Me: "Those are great, could you also create a few variations that show the robot depicted as a comic book illustration?"
AI: "I'd be happy to. Here are a few examples of Salvius depicted as a comic book illustration..."
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So what's so future-shaping about this style of conversation? My prediction is that they'll charge the way people communicate by altering expectations around standards of communication. Professional conversations (especially ones that occur in text format) will begin to mirror this dialog format as the expectation of a "good response" is pushed further towards the model set by AI. As is, this has already been the norm for many decades in customer-service occupations where the typically asinine "customer is always right" mentality is pushed by the corporation such that underpaid employees are made to circumvent standard practice and common sense to prevent obtuse customers from causing a scene.
On the other hand, as text based communication becomes more robotic, perhaps there will be an upside in which in-person interactions become more human. One could anticipate more candid conversations with fellow humans as the norm becomes to communicate in a way that deliberately contracts anything "bot-like" (often bots are just telling you what they predict you want to hear).
Anyway, that's all I've got for now. With any luck, I'll be able to look back another decade from now and reflect on how well or poorly these predictions held up. In the meantime, I'll be looking to see if I can find any services that can convert an image into a boxed action figure because I'm slightly obsessed with a few of the concepts that I was able to generate.
Rise of the Robots: Technology and the Future of Work
This is a re-post of an article that I wrote for a now defunct blog almost ten years ago. I feel that it has become more relevant given the recent rise of artificial intelligence as well as its rapid and widespread adoption across numerous industries.
Original post (February 20, 2016, via the Kindred Robot blog):
I went to see a the keynote address of Rise of the Robots. Technology and the Future of Work presented by Martin Ford at Mount Holyoke College this weekend. Ford is an entrepreneur and author who has brought to light a selection of very interesting trends in employments and their relation to automation.
It is possible that increased advances in technology will create new job opportunities within fields that are impossible to predict at the current time. Just as a job as a social media marketer could never have been anticipated as a possible career several years ago, it is possible that new research into synthetic biology or nanotechnology will yield new occupations for the next generation of workers.
Flexural Strength of Oak Branches vs Aluminum and Carbon Fiber
Image: A sketch of a robotic arm built from a frame of interwoven oak branches
It's springtime and the frost of winter has yielded to the explosion of buds and blossoms across various species of plants and trees. Recently while cleaning, I came across an oak twig I had saved sometime last year, having whittled the bark off of it, the twig seems exceptionally strong. This left me thinking about the attributes of various materials, and wondering how something like oak compares to items such as aluminum or carbon fiber when it comes down to evaluating their strength vs their costs.
Relative Cost
Could variables such as availability, cost, and strength leave wood outperforming metal? The difficulty in answering this question is greatly increased by relative costs. For example, location often affects the price of materials - wood not native to an area costs more to ship it to that location. Likewise, scale plays a tremendous factor. It might be efficient for one person to harvest oak branches to build a structure, but less practical to do that at an industrial scale where farming methods and machinery become necessary.
In my case, I want to determine a rough cost evaluation that's just relative to me. I'm just getting twigs and branches from naturally planted trees using regular/sustainable trimming and pruning methods). Additionally, mining ore and refining it might require tools, energy, and materials not currently available to me (although I'm up for attempting this for a future blog post).
Flexural Strength
Given my lack of testing equipment, and materials, I've compiled the following table from a variety of sources. Not that many of these are approximations or can vary significantly depending on how the material is treated, tempered, etc. Additionally conditions such as temperature can impact the strength of materials and how/when they fail.
Table References:
- https://www.atlasfibre.com/understanding-flexural-strength-guide-to-flexural-strength-in-materials/
- https://www.ijert.org/research/processing-and-flexural-strength-of-carbon-fiber-and-glass-fiber-reinforced-epoxy-matrix-hybrid-composite-IJERTV3IS040781.pdf
- https://www.smicomposites.com/carbon-fiber-cost-factors-that-influence-the-most/
- https://alvibel.pl/en/what-determines-the-strength-weight-of-plywood/
- https://workshopcompanion.com/know-how/design/nature-of-wood/wood-strength.html
- https://blog.lostartpress.com/2021/03/21/buying-wood-by-the-pound/
- https://pmc.ncbi.nlm.nih.gov/articles/PMC4233722/
Conclusion
Notes:
- The grain of wood means that, similar to carbon fiber, its strength is not equal in every direction, so design considerations must be made to align the material correctly against the direction of force applied to it
- Not considered here: cost per volume ratios
- Not considered here: suitability of materials (eg. wood is flammable)
The robots are getting stressed out
The idea of machines expressing human traits is fascinating, and I think the challenges around this make some of the most compelling characters, at the very least in science fiction. Perhaps there is something inherently relatable about the challenges humanity faces that we recognize in robots and computer models when they begin to encounter and overcome similar obstacles. As these technologies continue to progress, I suspect we'll push the boundaries of what we know about ourselves even more than what we learn about designing computer models.
I've seen a few articles that cite a recent study on how large language models (specifically GTP-4 in the case of this study) begin to behave differently when presented with emotionally-charged, or stress inducing information. Specifically they become less likely to behave objectively and trend towards expressing biases latent in the data they've been trained with.
One suggested explanation is that this is in-part a "trait" of the LLMs. They've been trained using vast amounts of human generated data, and so they mimic the pattern that humans follow in similar situations. Somewhat humorously, the study showed evidence that using mindfulness meditation techniques on these LLMs helped to "relax" them and counteracted the trend towards biased behavior.
While the study focused on more of a black-box method of working with the GTP-4 model using prompts, I think there's something inherently interesting about the nature of the type problems that were presented to these LLMs.
Recently, I've been working on adding support for vector databases to the chatterbot Python library, and so naturally I've been giving a lot of thought to the idea of vector representations of information. But what are vectors used for in LLMs? In a general sense, "words" as the LLM understands them are represented as vectors, or rather a number that represents that word's relationship to other known words. As a small example, we can consider the relationship between "speaking" and "writing" alongside actions that could be synonymous with either:
(Speaking)
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/ \
/ \
(Poetry) ●-----● (Rhyming)
\ /
\ /
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(Writing)
Speaking and writing have very close contextual relationships but on the other hand something like "running" or "battling" would be a significant distance away from these terms.
Vector relationships like these may be relevant to the responses of LLMs becoming less optimal when presented with stressful information when we start to think about the attributes of those types of situations. Our own brains excel when faced with information and scenarios that we're familiar with, but begin to experience stress when faced with fundamentally challenging data. Much like the vector spaces, stressful scenarios involve points of information that are further apart, they are challenging because it takes greater effort to try to reconcile it issue at hand.
The distance between vectors is still a bit speculative on my part, but might be worth some experimentation to better quantify. More likely than not, the landscape of information shared between humans and machines is highly nuanced, and shaped by an extensive range of both strong and subtle forces.
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