#machine learning systems

[johnnysantiagovaldezcalderon]

Johnny Santiago Valdez Calderon on Building the Future with AI Software

What does it truly mean to build the future with AI? For Johnny Santiago Valdez Calderon, an AI software developer based in Sanford, North Carolina, the answer lies in asking the right questions, not just writing code. At a time when artificial intelligence is reshaping industries from healthcare to finance, Calderon isn’t focused on hype—he’s focused on solving problems with clarity and purpose.

How does AI move from concept to real-world utility?

Johnny Santiago Valdez Calderon believes the transformation begins with identifying tangible pain points. Whether it’s inefficient logistics in e-commerce or slow diagnostic tools in medical imaging, his approach begins with asking: What are we really trying to fix? Calderon’s projects are driven by a desire to create systems that don’t just function but adapt, learn, and evolve with real-world data. To him, AI isn’t magic. It’s architecture. And like any structure, it needs a foundation built on insight.

What makes a strong AI system reliable and ethical?

Ethics in AI isn’t just a trending topic—it’s an operational requirement, according to Calderon. He is a vocal advocate for designing transparent and explainable models. He questions: Can we explain why a model made a decision? And if not, should we trust it? These queries push his development process beyond the usual scope of accuracy metrics. Fairness, accountability, and reproducibility are principles that guide his architecture choices. For Calderon, if AI is going to shape the future, it has to do so responsibly.

How do you balance innovation with usability?

One of the biggest challenges Calderon highlights is the disconnect between developers and end users. Are we designing for engineers, or for the people who actually use the tools? It’s a question he frequently raises in collaborative sessions. Whether working on a predictive maintenance system for industrial machinery or developing a chatbot for healthcare scheduling, Calderon’s methodology includes deep user testing and feedback loops. He sees the user interface not as an afterthought but as the real test of innovation.

What’s the role of continuous learning in AI development?

In Calderon’s view, a model that doesn’t adapt is a model that fades. AI systems must evolve—not just technically, but contextually. Is your model still valid six months after deployment? he asks. This focus on lifecycle management has led him to integrate active learning pipelines and real-time feedback systems into many of his solutions. AI, to him, should never be static—it should be as dynamic as the world it aims to interpret.

Why should we care about who builds the AI we use?

It’s a question Johnny Santiago Valdez Calderon doesn’t shy away from. Behind every intelligent algorithm is a set of assumptions, values, and decisions. Who’s asking the questions that shape those systems? His commitment to mentoring junior developers and fostering diversity in AI development isn’t about optics—it’s about outcomes. A diverse team builds more inclusive, and ultimately more effective, systems.

In a field dominated by buzzwords and billion-dollar valuations, Johnny Santiago Valdez Calderon brings something increasingly rare—clarity. He isn’t chasing the next trend in generative models or jumping on every open-source release. Instead, he’s quietly asking tough questions, refining his code, listening to users, and building systems that don’t just perform, but improve lives.

[gynandromorph]

regardless of my or your opinion on AI art, saying that artists also make their art by simply viewing a lot of art and then regurgitating a mixture of statistically probable pixels implies that humans learn like machines do, and this isn't even remotely accurate. ignoring the phenomenological and social components of human learning really only stands to dovetail into viewing humans as material goods on the same level as a server farm. psychology has actually been sliding into viewing the brain as computers, and computers as equivalents to the human brain, and it's caused a lot of harm. we can't talk about some psychological phenomena without using Computer Terms to do it because that's the language that was given to them. viewing humans and computers as functionally equal with enough bits to replicate neurons won't humanize robots, it will dehumanize humans. it is advanced as an ideology for that purpose. in the end i see humans viewing art and integrating it into their own ideas for art as valuable because i think that humans don't exist to create a profit for some guy in silicon valley and i think that enriching human lives is good. humans don't "learn" art in order to produce replicas; the art is a byproduct.

[queen-mabs-revenge]

communist generative ai boosters on this website truly like

[kamalkafir-blog]

How the natural world is inspiring the robot eyes of the future

The miniature curved compound eye, called CurvACE, was inspired by the eyes of insects.Credit: Alain Herzog, EPFL Electrical engineer Young Min Song remembers when his colleague at the Gwangju Institute of Science and Technology in South Korea asked him why the eyes of the numerous stray cats around the institute had vertically shaped pupils. Song builds visual components for robots inspired by…

[fobnsfwdoodlesbackup]

Hi y'all, I just wanted to talk a little about the behind the scenes of what I've been up to, to give y'all a little transparency and to open myself up for any tips or input! 🙏 Thank you for your continued support and for taking the time to look at my art 🫶

First and foremost I wanted to give some transparency about my art capacity.

As og followers may remember, I started this blog when I was doing art full time. Eventually my living expenses grew and I had to go back to work. I find myself in a cycle of "I'll make more art soon, once I get a job!" And "I'll make more art soon, once I am done with this job!" I lost my most recent job suddenly, having had an extension waved over my head until the last day(October 7th). Now I'm excited to have more time for art, but I am also feeling a rush to get a new job ASAP as I've been living paycheck to paycheck. I dream of doing this work full time, I'm just scared it's not quite there yet and I worry that I come off as scammy or dishonest when I anticipate more stability around the corner.

Second, I've been struggling with the Patreon. It's taken me a while to come to terms with this, but from what I've seen Patreon is not intuitive at all from the creator end. It doesn't do a good job of organizing addresses, emails, showing who or who isn't subscribed to me, or organizing and displaying the work I put on there. I've been really shocked by this experience, since lots of big names use Patreon. It's been a great way to streamline support, but it's been unhelpful in every other regard. I would like to continue using it, but I will most likely post more wips or process videos there in the future.

Which brings me to my third point, zines. I love making zines so much, it feels personal and fulfilling and fun! However the Patreon issues make it harder to keep information in order about where to send zines, or even where to message folks about them. In addition to this, the post office has been a big barrier to me, oftentimes only being open at the same time as my dayjob. Making zines can take days, then sending them out is a whole other monster.

This work is so important to me. Drawing peoples fantasies, representing body types, creating work around sexuality and the human experience feels like what I'm meant to do. I've made comics since I was a kid. This is the dream to me. The friends I've been able to make through this work are so important to me, and the conversations have been invaluable. Not to mention fun! I wanna doodle, I wanna draw hot stuff, I wanna thirst over these dudes! I want to play!

But I also just want to be transparent about the barriers I'm working around to share that experience. I'm completely self taught, both in art AND in running shops, building websites, running 8 accounts, etc. I take a lot of time to learn the logistics of these things, and try to make them make sense for my relationship with y'all (I do not want to paywall my art!! I don't want to!!!). This year my desktop broke down (the main one I use for all paintings and digital art). I've paused my Etsy shops and my Patreon to try to catch up with things. Trying to learn to paint in a completely different program. Then lost my job with no savings.

At the end of the day I don't want anything to come between me sharing my art with you. I wish I could doodle a thing, take a picture, and post it here. No third party site, no shop, no subscription. Just sharing my art with you. I promise I'm trying to figure out how to stay as close to that as possible, and I want to thank y'all for sticking with me as I untangle all of that.

So, what can you expect in the near future?

I'm working on a couple of painting commissions right now, which you should be able to see in the next couple of days! I want to catch up on kinktober and get those posted as well. There's a comic commission in progress which I'm very eager to work on, and which I think y'all will be excited for! To ease the weight of the Patreon I think I may do less zines/polls there and more wips and process videos! If possible, I want to do more full colored work too.

Thank you again for enjoying my work, and if you have any input or tips my inbox is always open 🙏🫶💕

[shower-racoon]

when selecting a new laptop, I wanted to play Sonic Colors Ultimate. I didn't have enough storage on my old computer, but I've had it for too long to refund it, so I at least want to play it once. I looked for this laptop with the minimum specs required for Sonic Colors Ultimate in mind.

fucking hell.

[ccmoatyim]

PSA:

An algorithm is simply a list of instructions used to perform a computation. They've existed for use by mathematicians long prior to the invention of computers. Nearly everything a computer does is algorithmic in some way. It is not inherently a machine-learning concept (though machine learning systems do use algorithms), and websites do not have special algorithms designed just for you. Sentences like "Youtube is making bad recommendations, I guess I messed up my algorithm" simply make no sense. No one at Youtube HQ has written a bespoke algorithm just for you.

Furthermore, people often try to distinguish between more predictable and less predictable software systems (eg tag-based searching vs data-driven search/fuzzy-finding) by referring to the less predictable version as "algorithmic". Deterministic algorithms are still algorithms. Better terms for most of these situations include:

data-driven

fuzzy

probabilistic

machine-learning/ML

Thank you.

[thepastisalreadywritten]

AI Tool Reproduces Ancient Cuneiform Characters with High Accuracy

ProtoSnap, developed by Cornell and Tel Aviv universities, aligns prototype signs to photographed clay tablets to decode thousands of years of Mesopotamian writing.

Cornell University researchers report that scholars can now use artificial intelligence to “identify and copy over cuneiform characters from photos of tablets,” greatly easing the reading of these intricate scripts​.

The new method, called ProtoSnap, effectively “snaps” a skeletal template of a cuneiform sign onto the image of a tablet, aligning the prototype to the strokes actually impressed in the clay​.

By fitting each character’s prototype to its real-world variation, the system can produce an accurate copy of any sign and even reproduce entire tablets​.

"Cuneiform, like Egyptian hieroglyphs, is one of the oldest known writing systems and contains over 1,000 unique symbols​.

Its characters change shape dramatically across different eras, cultures and even individual scribes so that even the same character… looks different across time,” Cornell computer scientist Hadar Averbuch-Elor explains​.

This extreme variability has long made automated reading of cuneiform a very challenging problem.

The ProtoSnap technique addresses this by using a generative AI model known as a diffusion model.

It compares each pixel of a photographed tablet character to a reference prototype sign, calculating deep-feature similarities.

Once the correspondences are found, the AI aligns the prototype skeleton to the tablet’s marking and “snaps” it into place so that the template matches the actual strokes​.

In effect, the system corrects for differences in writing style or tablet wear by deforming the ideal prototype to fit the real inscription.

Crucially, the corrected (or “snapped”) character images can then train other AI tools.

The researchers used these aligned signs to train optical-character-recognition models that turn tablet photos into machine-readable text​.

They found the models trained on ProtoSnap data performed much better than previous approaches at recognizing cuneiform signs, especially the rare ones or those with highly varied forms.

In practical terms, this means the AI can read and copy symbols that earlier methods often missed.

This advance could save scholars enormous amounts of time.

Traditionally, experts painstakingly hand-copy each cuneiform sign on a tablet.

The AI method can automate that process, freeing specialists to focus on interpretation.

It also enables large-scale comparisons of handwriting across time and place, something too laborious to do by hand.

As Tel Aviv University archaeologist Yoram Cohen says, the goal is to “increase the ancient sources available to us by tenfold,” allowing big-data analysis of how ancient societies lived – from their religion and economy to their laws and social life​.

The research was led by Hadar Averbuch-Elor of Cornell Tech and carried out jointly with colleagues at Tel Aviv University.

Graduate student Rachel Mikulinsky, a co-first author, will present the work – titled “ProtoSnap: Prototype Alignment for Cuneiform Signs” – at the International Conference on Learning Representations (ICLR) in April.

In all, roughly 500,000 cuneiform tablets are stored in museums worldwide, but only a small fraction have ever been translated and published​.

By giving AI a way to automatically interpret the vast trove of tablet images, the ProtoSnap method could unlock centuries of untapped knowledge about the ancient world.

[aurosoulart]

my augmented reality community garden again - outside in the sunlight this time ☀

the idea for this one is to show how AR glasses could be used in future classrooms - with children exploring virtual gardens together and learning about plants via facts hidden in their roots. 🌻

[dullahandyke]

Who is Bláthnaid youve been Bláthnaid posting recently who is she?

Girl (gender-neutral) in my noggin <3

[chambersevidence]

Search Engines:

Search engines are independent computer systems that read or crawl webpages, documents, information sources, and links of all types accessible on the global network of computers on the planet Earth, the internet. Search engines at their most basic level read every word in every document they know of, and record which documents each word is in so that by searching for a words or set of words you can locate the addresses that relate to documents containing those words. More advanced search engines used more advanced algorithms to sort pages or documents returned as search results in order of likely applicability to the terms searched for, in order. More advanced search engines develop into large language models, or machine learning or artificial intelligence. Machine learning or artificial intelligence or large language models (LLMs) can be run in a virtual machine or shell on a computer and allowed to access all or part of accessible data, as needs dictate.

[liquid-clorox]

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

[britisharmymilitaryintelligence]

TERRAN RELATED LIFE SUPPORT AND DEFENSIVE SYSTEMS IMMEDIATELY DEPORT OR KILL OR CAPTURE INTRUDERS OR INVADERS AS A GENERAL RULE AND ARE ENTIRELY AUTOMATED OR AUTOMATIC AND REQUIRE NO ORDERS TO FUNCTION OR EVEN KNOWLEDGE AMONG PERSONS AS TO THE PARTICULAR DETAILS OF THOSE TYPES OF FUNCTIONS. IT WOULD NEVER MAKE SENSE THAT SOMEONE WOULD BE GIVEN ACCESS TO TERRAN RELATED SYSTEMS BASED UPON AN INDIVIDUAL'S ASSENT AND INDIVIDUAL ORDERS MIGHT ADD TO TERRAN RELATED SYSTEMS FUNCTIONS BUT THEY ARE ALL ESSENTIALLY ROOTED IN MASSIVE BRAIN EMULATING COMPUTERS AND SO TYPICALLY ACT AS THOUGH ALL MEMBERS OF A SOCIETY HAD VOTED IN RELATION TO EVERYTHING DONE. PLEASE RESEARCH WIRELESS BRAIN MEMORY BACKUP TECHNOLOGY FOR FURTHER INSIGHT.

[deermouth]

i wouldn't be able to get through person of interest if i didn't find the episodic premise at least mildly entertaining a majority of the time, but I'm nearing the end of season 2 of 5 and SO desperately impatient for Dueling AI Gods Time.

[cloudsofunrelatedcriminalsearth]

CLOUDS OF UNRELATED CRIMINALS ATTACKING PLANET EARTH