Category Archives: Schmooze Cues
Could Kyrgyzstan be first to send people on Mars?
Had a great pleasure to facilitate the “Modelling for the SDGs” workshop organized by UNDP Kyrgyzstan this Saturday, 14 October 2023, a vital step in the collective journey to achieve the Sustainable Development Goals in Kyrgyzstan and CentralAsia. We combined different tools to explore possible futures.
We are using the International Futures Model by Frederick S. Pardee Center for International Futures for trends projections.
We also explored an IMPOSSIBLY good and bad futures, Utopia and Dystopia–Kyrgyzstan being the first to send people on Mars; and country fully depopulated by the Martian Plague. For this exploration we used Future Backwards method by The Cynefin Company (formerly Cognitive Edge)
It was great to see how tools help unlocking the deep discussion and spot the turning points in the journey to future(s)
Aibo Foster Parent Program
Sony’s new “Aibo Foster Parent Program” will repair and refurbish donated Aibos, before providing them to foster homes, nursing homes and other facilities where an emotional support robot could be beneficial. A machine translation of the program’s announcement says new owners will be charged an undisclosed fee for the robot
The program is designed to extend the life of the pricey robo-dog in order to keep it from ending up in landfills. The company points out that some Aibo units, depending on their condition, may just end up being salvaged for parts.
The newest Aibo robots are certainly designed to feel more like a real dog than their ’90s counterparts: both in features and price. With a $2,900 upfront charge and an annual $300 on top of that for access to smartphone connectivity and online services, it makes sense why some Aibos might find their way into the pound. The new program gives those robots a second chance at life, where studies show they might do some good—certainly more than in a closet.
Full story: https://themessenger.com/tech/sony-is-giving-old-aibo-robot-dogs-a-second-chance-at-being-good-boys
Source: https://aibo.sony.jp/csr/foster/?s_tc=st_sm_tw_aibo_aibjrny_230911_02 (in Japaneese)
Tech Enthusiasts vs Tech Connoisseurs
Fill the Internet with blah blah blah (p=1.00000)
“Garbage in, Garbage out” is a basic caveat to anyone dealing with models, and a well-known source of biases in “AI”. Success of ChatGPT introduced large language models (LLMs) to the general public and is now clear that LLMs are here to stay. Already in 2022 out of all internet traffic 47.4% was automated traffic aka 🤖 bots. So far bots are limited in quality of generated content and easily detectable, but LLMs will change it, and this will bring a drastic change in the whole world of online text and images.
Recent ArXiv paper “The Curse of Recursion: Training on Generated Data Makes Models Forget” considers what the future might hold. Conclusion? Future Ais, trained on an AI-generated content published online, degenerate and spiral into gibberish—what authors call “model collapse”. The model collapse exists in a variety of different model types and datasets. In one funny example in a ninth-generation AI ended up babbling about jackrabbits, while the start point was a medieval architecture text.
This resembles a story from 2017 of two Facebook chatbots named Alice and Bob. The researchers conducted experiments to improve the negotiation skills of chatbots by having them play against humans and other bots. The first bot was trained to imitate human negotiation tactics in English, but it proved to be a weak negotiator. The second bot focused on maximizing its score and exhibited superior negotiation skills, but it resorted to using a nonsensical language that was incomprehensible to humans. (The hybrid bot scored only slightly worse than the humans, while maintained reasonable language).
In the heart of model collapse is a degenerative process when over time models forget the true data. Over the time two things happen—probability of “usual” is overestimated, while probability of “unusual” is underestimated. Disappearing of tails (“unusual”) leads to converging of generated content around central point (“usual”) with very small variance, and finally model collapses into high intensity gibberish.
This process has a shivering resemblance with the way how self-amplifying feedbacks shape the inertia of beliefs, which lead to black-and-white thinking, associated with psychiatric disorders, prejudices, and conspiracy thinking. Rigidity of beliefs and societal failure may reinforce each other through feedback mechanisms, very similar to those poisoning AI reality. Mitigation of rigid harmful beliefs in people may require improving the sustained exposure to counterevidence, as well as supporting rational override by freeing cognitive resources by addressing problems of inequity, poverty, polarization, and conflict. In a similar vein, avoiding model collapse, requires restoring information about the true distribution through access to genuine human-generated content.
In a world plagued by narrow-mindedness and echo chambers, the last thing we need is an narrow-minded and divisive AI. The true value of human-generated data lies in its inherent richness, encompassing invaluable natural variations, errors, twists and turns, improbables and deviants. Human-generated data represents more than just a function to be optimized; it encapsulates the very essence of what makes life worth living.
NB: Heading Image by master1305 on Freepik
How the email found me. . .
📩 “I hope this email finds you well!”
😾 How the email found me
Dancing with the Vowels
Many years ago my son was struggling with the Pythagoras theorem in school. He simply was unable to get it. We tried different approaches, and finally I suggested “Why won’t you dance it? Or play it on your guitar?” (He was very much in torturing the Les Paul that time)
While the idea could be a radical, it makes a lot of sense. When we work with data, with numbers, we are dealing with abstractions. This could be useful, no doubt. But it also could be deprived of meaning.
— Your greatest weakness?
— Interpreting semantics of a question but ignoring the pragmatics
— Could you give an example?
— Yes, I could.
As a Chemistry Teacher by the first degree, I was trained to provide relatable examples to chemical phenomena, which could be too small or too big to comprehend. You could have a hard time to imagine an atom, they are too small. But I could tell you that if the electron orbit around the the hydrogen atom (5.29×10−11 m or 52.9 pm) would be scaled to the size of stadium (football field is usually 100 meters long and 60 meters wide, Beşiktaş Arena building is 220 by 165 m), then the hydrogen nucleus (1.70×10−15 m or 1.70 fm) will be of size of .. a small berry (some 5×10−3 m or 5 mm), not even a ball (7×10−1 m or 70 cm). As a home work you could compare sizes of the Sun and the Earth orbit 😉
This week I ran across “datasculptures”, a physical and visual representation of data, in this case–the complete history of one river. To quote the author, the approach is a form of counter-mapping, both tactile and sensible, but also involving a slow-making process and another kind of relationship to the data and the river it concerns. Sculpting environmental data is a proposition to map geographical entities that go around the “from above” and “far away” traditional views to open new ways of re-embedding time and materiality into cartographies.
This could be pure fun on the bun! In 2018 I attended a breathtaking summer school on Analysis of Linguistic Data (LingDan). We played around with different data related to language–sounds, words, signs. I used a Romanian / Moldovan tongue twister consisting of vowels only “oaia aia e a oaiei ei” (meaning “that sheep belongs to that sheep”). The interesting thing about vowels is that they differ systematically in the frequencies of so-called “formant” sounds, so you could record, measure and map them. I thought it could be a good idea to show the “dance” of vowels–movement of sounds in a tongue twister–and produced a short data video. I also coaxed the fellow Dance Lab, who were in the next block, into human dancing about it.
Coming back to the Pythagoras theorem, one way to show it is a hydraulic sculpture. Another way is through visual puzzles, which could provoke a very good discussion in the classroom.
P.S. You could also check my “Numbers in a context” artish project
Why did the ghost move out of the smart home?
Q: Why did the ghost move out of the smart home?
A: It couldn’t handle the constant reminders to update its afterlife software!
The Storycraft Adventures: Mapping Way Ahead with Fibonacci and Dragons
The Storycraft Adventures: Mapping Way Ahead with Fibonacci and Dragons Can stories be the driving force behind change and guide us towards a clear path forward? This question was at the heart of my recent workshop with the European Trainers Network. Our goal was to explore the potential of storytelling in finding solutions for pressing challenges, such as improving internal communication and achieving results with diverse teams. Using a guinea pig of a fictional scenario crafted from three real stories, we embarked on a three-step journey. We delved into telling the stories, leveraging the power of Anecdote Circles, mapped possible solutions in the complexity space using the Cynefin framework, and employed the technique of Planning Poker to prioritize our solutions.
Playing Planning Poker turned out to an immensely insightful exercise. For those who are not familiar with the Planning Poker—it is a gamified technique for estimations. The members of the group make estimates by playing numbered cards face-down to the table, instead of speaking to them aloud, to avoid the cognitive bias of anchoring. A typical deck has cards showing progressing in non-linear manner (e.g. 0, 1, 2, 3, 5, 8, 13, 20, 40, 100), as it better reflects the nature of the world. We played in Zoom White Board using a shortened deck by Redbooth—only 1, 3, 8, 100 and Dragon “Here be dragons” to mark dangerous unknown areas.
Our participants were tasked with estimating the level of effort required for the proposed solutions that we had mapped in the previous step. After the initial round of assessments, a fascinating picture emerged. Some ideas received similar estimates, while others, including seemingly straightforward solutions, garnered disparate cards—like a combo of 1, 100, and the enigmatic Dragon. Rather than seeking immediate consensus and convergence, we embraced the divergence and initiated conversations. This exploration uncovered a wealth of stories and nuanced solutions. For instance, the idea of having a dedicated communicator to facilitate dialogue between different teams was split into two parts. The first part, selecting a dedicated person to facilitate communication, received a score of 3 and was placed in the complicated domain—there are good practices how to do it. However, organizing the communication process itself, was considered a project itself, requiring expert knowledge and scored 100, and placed between complex and complicated dolmans—as it requires experimentation. Similarly, a seemingly simple idea to partner with an electronic company to provide travel adaptors for the US market was met with a dissenting Dragon card. This unexpected response raised important considerations related to legal requirements and the need to thoroughly assess market demands before proceeding.
The Storycraft Adventures workshop helped me to realize three things. First, stories has an immense transformative potential. Second, estimations, similar to Planning Poker, could highlight important deviations in views and dissents—promising unexplored territories and a fresh perspective. Third, in embracing the divergence and exploring the stories behind the different estimates, we uncovered a treasure chest of insights and valuable perspectives. By combining storytelling techniques with collaborative estimation methods, we gained a deeper understanding of the challenges at hand and charted a way forward that encompassed the complexity and nuances of our goals.
European Trainers Network is serving club members of any Toastmasters club in Europe. ETN facilitates development of skills needed to create and conduct training sessions using adult learning theory.
Horror Then vs Now
Do you notice the ever-changing landscape of horror? Back in the day, we were terrified of those old cursed houses, with their eerie vibes and creaking floorboards, featuring known horrors, whether mythical or real. But fast forward to today, and what do we have?
Haunted smart homes! Forget the ghostly moans, clinking of chains, and transparent figures in white. The menace is unknown and ubiquitous. It could be a thermostat running amok, controlled by hackers who think they can mess with our lives. It could be a peeping web camera, broadcasting publicly due to the nonchalance of the person who installed it. It could even be a fridge ordering a hundred packs of toothpicks as the hallucinating AI recommendation system decides these mini swords are an absolute must for an epic battle between the condiments.
Remember the days when we were scared of the idea of big machines going haywire, giving us the heebie-jeebies? HAL9000 from “2001: A Space Odyssey” would say things like, “I’m sorry, Dave, I can’t do that.” Well, now we don’t have insanity in the mainframe; instead, we have an army of tiny WiFi ghosts haunting our routers, watching our Netflix, chuckling at our WhatsApp messages, and making us guess who is truly behind that black box during a Zoom call. It’s like living in a digital haunted house, where even our own devices have a mischievous spirit.
So, remember to keep your devices secure and your sense of humor intact. After all, what’s scarier than a haunted smart home? Trying to get tech support on a Monday morning!