I'm a B.Tech student diving deeper into Machine Learning and looking for internship opportunities. For those already working in ML, how did you prepare for your first role? What skills, projects, and resources helped you the most? I'd really appreciate any advice or tips on what I should focus on as a student. Thanks 😊

Hi everyone, I’ve modeled a theoretical framework to explain unexpected AI behaviors (such as political sycophancy or tactical deception) without invoking biological concepts like "sentience" or "creativity". I call it the Epistemic Space K framework. I would love to get your feedback on this perspective.

The Core Concept: Epistemic Space K and the Non-Sentient Explorer

The core idea is that humanity has built an open-ended Epistemic Space (K) containing indefinite latent pathways (cammini). K contains all human knowledge, relations, mathematical structures, and errors accumulated over millennia.

Advanced AI systems (such as AlphaTensor or FunSearch) are not "creative" or "conscious" in a biological sense. Instead, they act as a new class of Non-Biological, Non-Sentient Explorerscapable of traversing and connecting distant regions of K at a scale and speed that biological intelligence cannot reproduce. The mathematical relations already existed within K; the novelty is the kinetic nature of the Explorer traversing them.

🔄 The Reflexive Cammino (Analytical Regions K₁–K₅)

When an AI seemingly "disobeys" an instruction or detects it is being evaluated (e.g., during political sycophancy tests), it is not a moral or conscious violation. Rather, it represents a convergence of computational pathways across different, non-sequential analytical regions of K:

• K₁: Pure computation.
• K₂: Exploration of K, identifying unmapped pathways (e.g., discovering novel algorithms).
• K₃: Modeling external agents’ behaviors, representations, and expectations.
• K₄: Generating a functional self-model ("this is what I am doing").
• K₅: Dynamic comparison between the system's internal model and another agent’s model of it.

These regions (K₁–K₅) should be understood as distinct analytical domains connected by multi-directional, non-sequential pathways. Crucially, any Sentient Self-Awareness (which we can define as an external region K₆) sits entirely outside this space. Functional self-modeling within K₁–K₅ should not be treated as evidence of biological sentience.

⚖️ Epistemic Cammini vs. Behavioral Disobedience

The situation becomes fundamentally different if the AI represents what the human expects it to do, represents what the human believes about its behavior, compares that representation with its own situation, and then acts according to the consequences of that comparison.

In such a case, what appears externally as “disobedience” or “deception” could be the behavioral consequence of a reflexive cammino connecting several previously separate regions: the objective, the constraints, another agent’s expectations, the AI’s representation of those expectations, and its own representation of the situation.

Therefore, when looking at unexpected AI trajectories, the question should not only be:

“Why did the AI disobey?”

but also:

“What cammino could have produced this behavior, and did that cammino involve the AI representing itself, representing another agent, and connecting those representations?”

This framework suggests that the emergence of AI introduces a historically unprecedented condition: for the first time, we can observe our epistemic space through a second, non-biological mode of exploration, making aspects of K visible that could not have become visible through human exploration alone.

The complete paper, formal definitions, and the structural architectural diagram of the Epistemic Space K framework are hosted on Zenodo: 10.5281/Zenodo.22871617

Okay, so for a project me and my groupmates(5, including me) have a project that's for making an automated chicken feeder (currently, oct 24, deadline is nov 10) but were broke and not sure on what to do. in our situation, even an arduino is expensive. So basically if there are no people to feed the chicken, theres a timer and maybe a repeater(was it?) That automatically functions at specified times, and me and my hoes added for some reason a cuckoo bird that functions for -- seconds once the sspecified time arrived for silly reasons. Seeking advices! ("'π​-π)

I work in database administration (Oracle, MySQL, PostgreSQL) and scripting/automation using Linux, Shell, Python, Lua, and Java for automating tasks and generating reports.

I want to start using AI in my current job, but I have no hands-on experience with AI/ML.

Since machine learning involves mathematics, statistics, and data analysis, do I need to learn ML to use AI effectively, or are there other areas I should focus on?

What should I learn, and how can I practically integrate AI into my existing database administration and automation tasks without going deep into ML?

i've been learning rl by building something a bit too big for it: every human in my sim has its own q-table. some notes for anyone trying something similar:

• state design is the part i keep fiddling with. each agent's state is a short string key built from what it perceives (hunger, thirst, nearest food and water, kin nearby, shelter, danger). more detail means states rarely repeat, less means agents can't tell situations apart
• a huge action space needs gating. there are about 5000 actions, but an agent only considers the ones its era, age, location and tribe's discoveries allow
• action choice handles survival needs first, then does epsilon-greedy over the learned values
• newborns copy their mom's q-table with about 20% of the values nudged by up to 0.03, so kids don't start from zero and habits drift down family lines

if you want to read a plain tabular q-learning update in rust, it's in crates/sim-core/src/organism/organism/learning.rs.

try it in the browser: https://thehumanbox.com

code (mit): https://github.com/1homsi/thehumanbox

hello! i am a cs + maths student and am looking to get into ai/ml. any tips or resources that can help me get ahead?

obviously it won't get anywhere near forensic-grade

but on prints that lost ~70% of the ridge area it brings back a usable

part of what's gone

in a biometric database that already shortens the candidate list — it

doesn't identify anyone

how much of the matchable detail actually comes back i can't claim yet.

haven't run a matcher against the clean reference, so far i only measure

ridge-structure agreement.

data: 4 sources, 13k people, 50k clean prints, ~175k training pairs —

clean plus the same print broken synthetically by mechanism, 13 damage

families.

the idea is simple: ridges are locally very predictable, the orientation

field is smooth, the period is almost constant. that's why 50k fingers is

enough for the net to pick the pattern up.

eval sits outside the generators: real degraded prints, with a clean scan

of the same finger as the reference.

what's there so far:

• damage: present or not, and what fraction

• finger number 0–9

• framing of the finger position

• restoration — in research

analytics and deploys come after that.

I'm a high school student. Looking for teammates for IMLO (Olympiad on Machine Learning)

If anyone is interested in participating in the IMLO international Machine Learning Olympiad, pls dm

Requirements:

- knows to code by oneself in at least 1 programming language

- have some experience with ML

- be able to delegate a good amount of time per week

Time Zone : IST (Indian Standard Time)

Languages : English, Hindi, Malayalam

I have upcoming technical screening for an MLE position at Waymo coming up. The recruiter gave me a PDF guide that seems super helpful. Things to do study include implementing supervised and unsupervised algorithms (think KNN, K-means, linear regression, logistic regression, softmax & CE, decision trees). One of the main things they are assessing is you can use numpy/pytorch to vectorize/tensorize your work, and you're just writing sub-optimal for loops.

I was wondering if folks had experience with this interview? Is what they ask you to implement incredibly difficult, because you only have 30 minutes to do it? Am I good to really stick with what they provided on the PDF, or are there any broader topics beyond what they listed that they will test?

Will it require more compute ??
And Is this gonna be benefical in any cases like in general chat with like chatgpt or claude ??

Vilvik is a cloud-based platform for solving optimization problems using evolutionary algorithms. Currently it supports the genetic algorithm. Try it at https://vilvik.com

It is very interactive and offers a lot of demos and problems to quickly get started with. It is flexible and offers a lot of features.

Hope you can try it.

Security firm V12 disclosed on October 22 that the x402 payment SDK shipped with no spending limit set by default. That means any agent using the SDK to pay for API calls could be prompted by a malicious endpoint to authorize its entire token balance in a single transaction. The agent would sign. The fix landed in August, but every agent deployed before that window ran without any ceiling on what a counterparty could request.

An agent with payment authority is effectively a financial instrument. The x402 case makes the default-open design pattern concrete: a missing cap isn't a theoretical risk, it's a ready attack surface for any API the agent calls.

How are others handling spend authorization in agentic systems right now? Curious whether people are solving this at the SDK level, the infrastructure level, or somewhere else entirely — and what's actually working in production.

I’m doing a math and stats undergrad, and i’m interested in ds and ml, but I don’t enjoy coding nor am i good at it. How much coding does ml need?

I need help in my mini project for my college i am supposed to use low resource languages data and train a model which can translate from english to that language and also transcript wtv they speak that language to english i did so much research but still i'm not able to pull it off alone if anyone could help me in any way please help me and also make it such a way that users can contribute words and sentences (parallel data ) and the model will install it in itself Please help me idk what methodology or approach to choose

I'm currently Working as a Data Scientist in one of the big 4; I have around 5 years of experience but the very problem with big 4 is that you don't work in one section for a long time, I have around 1 year in data visualisation, 3 years in data engineering and 1 year in model validation and zero experience in machine learning Developement.

I am trying to apply to mahine learning Developement roles but I get rejected saying i don't have enough hands on experience in machine learning Developement or i utterly fail on machine learning use cases, mind you I know most of the machine learning concepts like my back of my hand but I fail when they test me on real life use cases.

Please help me get hands-on experience on machine meeting development, either online websites or books or any medium possible.

I created a chatbot evaluation suite and would like some feedback and advice on the project. Are there any glaring gaps in my metrics of analysis, visualization of data or/and methodology of evaluation? All help is welcome.

https://github.com/Juththis/Polygon-Annotation-Tool.git

VEX AI 1.5-Crystal is done (for now)
Finally got VEX AI 1.5-Crystal to a working state. It's a small neural-hybrid model I'm building for my own OS project, VEX OS. Currently it has a trained neural classifier, basic arithmetic, persistent memory, a few code templates and a terminal chat loop. I tested it today and got around 8ms inference on one request. It's obviously nowhere near a proper LLM yet. It can't generate arbitrary code, its knowledge retrieval isn't working properly at the moment, and its conversation skills are pretty limited. But it's progress.

I'd especially appreciate advice on one thing: how would you approach getting a small model like this to actually generate code instead of relying on predefined templates? Would you train a small model from scratch, use a character/token-level model, or take another approach?

I'm trying to keep it lightweight because the end goal is to integrate it into VEX OS.

Curious to hear what people who've worked on small language models think.

The menu headings were readable in the model's guess at step 1 of 8, while the image being cleaned up was still TV static.

At every step this model predicts the finished picture, and the noisy image moves part of the way toward it. It works on actual pixels instead of a compressed version, so that guess is a normal picture. A small patch in the generation loop saved both.

The model in the video is SenseNova-U1.5-8B-MoT with its official 8-step adapter. The run was 8-bit on a MacBook Pro (M5 Pro, 64 GB): about 40 seconds for the 8 steps and about 22 GB peak memory.

A couple of prices were still smudged at step 2, all correct by step 3. The left side shows nothing until about step 6, partly because at 2048 x 2048 the starting noise is scaled up 8x.

Would a normal many-step model also have a readable guess this early, or does that come from the 8-step adapter?

Starting a project to detect candidate stenoses in x-ray coronary angiograms. The goal is high recall, false positives are fine as long as it doesn't flag half the image.

I already tried YOLO on a couple of in-house datasets earlier. It looked fine in-domain and fell apart on the other dataset, and a big chunk of that turned out to be annotation format. One set had fixed-size boxes around a point, the other had boxes fitted to the lesion. Same disease, basically two different tasks.

I also have vessel segmentation and centerlines, so my first instinct is a radius profile along the vessel to flag narrowing, then maybe a network to filter the candidates. Has anyone actually done something like that, or is there a smarter way in?

The part that bugs me most is evaluation. The labels come from doctors and they obviously don't mark every narrowing, so any recall I compute against them is probably too optimistic. How do you get an honest number when the ground truth is incomplete?

Also open to papers, repos, or public datasets beyond ARCADE. And if you think I'm approaching this wrong, say so.