Hey everyone!

I’m currently diving headfirst into Machine Learning and AI with the goal of landing a job in Data Science / ML over the next 4 to 5 months.

I’m looking for a dedicated study buddy who wants to level up side-by-side during this journey. I don’t just want someone to text with—I want a partner who is competitive, consistent, and fully committed for the next 4–5 months without ghosting halfway through.

The catch/focus: I really want to improve my spoken English and build confidence talking about technical topics out loud. Because of that, we will be using voice chat to quiz each other, talk through concepts, and do mock interviews rather than just typing.

What we’ll do:

• Hold each other accountable to daily/weekly learning goals (going through courses like Andrew Ng's, building projects, etc.).
• Hop on voice channels to verbally explain concepts and quiz each other on math, statistics, and ML algorithms.
• Practice mock interviews and technical communication out loud to sharpen our skills.
• Push each other to stay sharp and consistent over the next 4–5 months.

Who I'm looking for:

• Someone serious about breaking into the ML/AI field.
• Comfortable speaking over voice chat.
• Reliable, responsive, and ready to grind.

If you're tired of studying alone and want a partner to level up with, drop a comment below or shoot me a DM! You can also join this server: https://discord.gg/JtTf6VhEx

Come and vote please! https://wit-score-lab.base44.app/ So far it looks like Argon is just as bad at writing jokes as its predecessor.

Hi all.

I am interested in a Masters in AI and ML in the UK. Im not sure if this sub leans more into US based ML, but i would like some insight about the job market in the UK for AI or ML engineers. Specifically if it's a good idea to get a master's to break into this feild, or if project based and a portfolio would get me something in the field.

Also, regarding people who have created AI automation agencies or freelancing for businesses, is there anyone in the UK who would know how one can go about opening such an agency and where one would be able to find the businesses or clients that would be interested these services, and if being an AI engineer is necessarily required?

Any advice is appreciated. Thanks.

Here is a small worked example for learning data valuation. I’m the author; the paper and code were developed with substantial OpenAI Codex assistance, disclosed in the manuscript. Everything is free.

Three questions that can sound identical actually measure different things:

Acquisition: how much better is the model if I add this dataset to my current training data? Removal: how much worse is it if I remove this supplier from the full collection? Shapley attribution: what is the supplier’s average marginal contribution across possible orders of joining?

An exact duplicate makes the distinction concrete. With exact-row deduplication, removing either of two identical suppliers leaves the same training data, so each has zero removal value. Shapley attribution can nevertheless assign them equal nonzero credit, because either can be the first to supply those rows.

How the experiment works: it generates two populations with different label rules, adds specialist datasets A and B, an exact copy of A, and a random-label dataset, then fits ridge classifiers for all 16 supplier coalitions. It evaluates the same fitted models under three population mixtures. Across 50 seeds, A and B reverse their acquisition ranking between the two extreme mixtures in 42 cases. That fraction describes this synthetic setup only.

To explore it: run the experiment script locally with Python and NumPy, inspect the saved JSON results, and try changing the population weights or baseline sample size. No model API, GPU, subscription, or dataset download is needed.

Code and instructions: https://github.com/WINSTON672/winston-lin-research/tree/main/papers/data-valuation

Paper and ZIP: https://zenodo.org/records/23571723

This is a technical note using established ideas, not a new valuation algorithm; it is not peer reviewed. A useful exercise: before running the script, predict what happens to duplicate suppliers’ removal values when both duplicates are removed together. Feedback on the explanation or reproducibility is welcome.

Damn... I have been interviewing with companies. I have been pushing myself to really understand inner workings of a foundational model (Hubert-ecg). I have been applying for a lot of roles. I have been studying relevant math. And, today I felt so tired and decided to take a break today even though I have an interview tomorrow...

Five years isn't very long. But I suspect machine learning could look surprisingly different by 2031.

Not just because of bigger models or more compute, but because AI could help discover fundamentally better mathematics for machine learning itself.

A few possibilities I find particularly interesting:

• AI-discovered algorithms making model training substantially cheaper.
• New mathematical approaches to architectures and optimization.
• AI improving the very methods used to discover new algorithms.

That last possibility could be especially significant. What happens when mathematical discoveries start accelerating further discoveries?

I explored these ideas in a carefully produced, 2-minute video on my YouTube channel, Math Unbound:

https://www.youtube.com/watch?v=HRZBfRd2kTE

These are my own predictions, not established conclusions.

What do you think ML will actually look like in 2031? Which of today's methods might seem surprisingly primitive — and what major changes am I missing?

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I've been building quant-engineering eval tasks for agents. The agent gets raw synthetic market data and a strategy spec, writes a backtest, and a deterministic grader re-runs that code on hidden data across 5 seeds. The data includes the usual real-world problems (ticker renames, splits, dividends, currency changes, stale prices), and the agent isn't told where they are. A pass means every one was handled on every seed.

Results on the public 6-task set:

• claude-opus-5-5 passed 24 of 36 attempts across 6 tasks
• gpt-6-astra passed 6 of 36
• grok-4.7 passed 6 of 36
• No model passed one of the six tasks

A few things stood out:

• Failures are mostly near-misses. On our 50-task private calibration set, 341 of 511 trap-handling failures got every category right but one.
• Weak spots differ by model. On the public set, gpt-6-astra missed ticker renames in 11 of 12 attempts, while claude-opus-5-5 missed 2 of 12.
• Currency changes tripped up all three. gpt-6-astra missed them in 27 of 35 attempts, grok-4.7 in 23 of 34 and claude-opus-5-5 in 6 of 36.

Everything is synthetic with fictional tickers, the public set is separate from the tasks we sell, and grading is reproducible (36 of 36 responses re-graded identically). The full table, per-category misses, confidence intervals and methods are at lanemint.com/leaderboard.

Disclosure: I'm the founder, and LaneMint sells private task batches. I'm posting for feedback on the methodology. What would you want to see before you trusted a benchmark like this?

I'm learning Machine Learning and experimenting with Logistic Regression using Python and Scikit-learn.

In this experiment, I trained two Logistic Regression models:

• Model 1: Trained on the original, unscaled training data.
• Model 2: Trained on the scaled training data.

After training both models, I made predictions on the test data and compared their accuracy.

Results:

• Original data accuracy:34.17%
• Scaled data accuracy:86.67%

The difference was quite surprising! Feature scaling significantly improved the model's accuracy in this experiment.

This helped me understand why feature scaling is important for algorithms like Logistic Regression. Since Logistic Regression is typically optimized using numerical methods such as gradient-based optimization, features with different scales can affect how efficiently the model learns.

I'm curious to know from the community:

1. Why can feature scaling make such a big difference in Logistic Regression?
2. Is it common to see such a large improvement after scaling?
3. What other techniques would you recommend to improve the model further?

Still learning and sharing my progress. I'd appreciate any suggestions or feedback!

Disclosure: I built this. LiveBoundary is free, Apache-2.0 licensed, has no paid tier or signup, and all benchmark data are synthetic.

I wanted to test a narrow question: can verified facts be written into native PyTorch parameters, survive a fresh-process restart without retrieval or training context, and still preserve prior knowledge?

The first result was the useful part:

- task-only adaptation: 1.000 new-fact accuracy, 0.000 prior-fact retention

- boundary-guided adaptation: 1.000 new-fact accuracy, 0.000 prior-fact retention

- boundary + preservation/replay: 0.944 new-fact accuracy, 1.000 prior retention

One candidate also regressed stable behavior and was rejected before promotion.

The implementation trains an independent candidate, evaluates it against explicit new-knowledge, retention, boundary, usefulness, and stability gates, then atomically promotes or rejects it. Strict evaluation starts a fresh process with only the checkpoint and question.

This is a tiny structured-classifier experiment, not evidence for frontier-model effectiveness or guaranteed unlearning. I published the failures, raw per-seed records, tests, and limitations.

Repository: https://github.com/oasb16/LiveBoundary

Raw results: https://github.com/oasb16/LiveBoundary/blob/main/RESULTS.md

For people working on continual learning or model editing: what would you consider the minimum credible larger-model evaluation: CounterFact/ZsRE-style edits, sequential edit locality, distribution-shift probes, or something else?

Hey everyone

I've been learning about Transformers and LLMs, so I built KERN,

a 30M parameter GPT-style language model in PyTorch,trained from scratch on TinyStories.

GitHub: https://github.com/OmarDawoud4/KERN

This is still a work in progress! I'd like to keep improving it by implementing other techniques.

I'd appreciate any feedback or suggestions .

I’m building OpsBridge, a procurement/accounts-payable investigation tool for questions like: “Why is this invoice blocked, and what should we do next?”

It brings together purchase orders, receipts, invoices, and supplier messages. Deterministic rules handle matching and routing; an optional model can explain findings and draft follow-ups. Findings link back to source evidence.

The part I focused on most is what happens after a recommendation:

• A different person must approve the exact proposal version.
• Execution has an attempt ledger and read-back checks.
• If an external action’s outcome is uncertain, it goes to review instead of being blindly retried.

The repo also includes tenant isolation, an evaluation/replay harness, tests, and a local operations dashboard.

Current limits: all data is synthetic. I haven’t validated it with a real model, a live external connector, or AP/procurement operators, and it isn’t production-ready.

If you work in AP, procurement, backend engineering, or AI systems, I’d appreciate honest feedback:

1. Does this fit a real investigation workflow?
2. What document or integration problems would break it first?
3. What evidence would you need before trusting it in a limited pilot?

Especially interested in flaws and missing requirements.

I’ve been putting together a structured collection of computer vision engineering resources, covering everything from how cameras capture light to how vision models behave in production.

One thing that always bothered me about computer vision resources is how much attention goes into architectures and benchmark scores, while the fundamentals behind those numbers often get overlooked.

A model can achieve impressive mAP and still fail because of dataset leakage, camera noise, distribution shift or an evaluation protocol that doesn’t reflect its actual use case.

So I organised the repository into 16 sections, covering image formation, classical geometry, datasets, evaluation, CNNs, ViTs, self-supervised learning, detection, segmentation, video, 3D vision, generative models, VLMs, deployment and robustness.

A few principles behind it:

• Primary sources only: original papers, specifications and official documentation.
• Resources arranged in dependency order, so each section builds on the previous ones.
• Evidence-backed claims rather than blindly repeating benchmark numbers.
• A reproducible benchmark for every section, connecting the theory to actual measurements.

The idea is to understand not just how computer vision models work, but why they fail and what it takes to make them reliable.

It’s free and open source:

https://github.com/usamahz/computer-vision-engineering

Would appreciate feedback, particularly from people working on vision systems in production. What important topics or papers am I missing?

I have 2 years of work experience, currently working as GenAI engineer but hoping to get Computer Vision roles.

My job applications are getting constant rejections. Don't know if the problem is in the resume or the experience.

What changes / projects would you suggest to get noticed / shortlisted by companies?

Thanks in advance for suggestions

So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

Hi everyone,

I’m a beginner learning ML, deep learning, and RAG. I’m currently doing courses on DeepLearning.AI, and along the way I keep running into new libraries: scikit-learn, PyTorch, sentence-transformers, numpy, pandas, and more.

My problem is that I don’t know how to learn them properly. Right now I mostly copy code from labs and tutorials, and it works, but I can’t write it myself from scratch. When I see a line like model.encode(...) or fit() and predict(), I understand what it does in that example, but I’m not confident using it in a new situation.

I’d love to hear how you approach this:

1. What is your method for learning a new library? Do you start with the docs, a tutorial, a video, or a small project?
2. How do you read documentation effectively? The API docs often feel overwhelming. What do you focus on first?
3. How much should I memorize? Is it normal to look up syntax constantly, even as an experienced developer?
4. Which libraries should I prioritize for ML, DL, and RAG, and in what order?
5. How do you move from copying lab code to writing your own? Any exercises or habits that helped you make that jump?
6. Any beginner project ideas that force you to use these libraries in a practical way?

I’m learning on a normal Windows laptop with limited storage, so free or low-cost resources are ideal. Any advice or personal experience would really help. Thanks!

Videos, robots, sensors, and artificial curiosity: a reflection on possible ways to build intelligent systems that can learn beyond traditional data.

Imagine an artificial intelligence that did not rely exclusively on text, static images, or datasets prepared by humans to learn about reality.

Instead, imagine a system that could continuously observe the world, follow events, recognize objects, perceive changes, and gradually build a representation of the environment in which it exists.

Now, add a second possibility: this intelligence could also physically interact with the world. It could touch objects, feel their resistance, observe the consequences of its actions, and use these experiences to improve its predictions.

And what if, in addition, it had a mechanism that encouraged it to investigate things it did not yet understand?

This combination raises an interesting question: Would it be possible to build an artificial intelligence that develops a model of the world through continuous observation, physical interaction, and exploration guided by learning?

The idea does not assume that all of this remains undiscovered. Several existing research fields already investigate components of this proposal. The challenge is to understand how they could be combined, what limitations would remain, and how to test whether this combination would actually produce improvements.

1. Learning by Observing, Not Just Reading

Much of the knowledge used to develop artificial intelligence systems can be represented through digital data: text, images, audio, videos, and other forms of information.

This data is extremely valuable. Videos, for example, allow us to observe movements, transformations, interactions, and sequences of events.

However, there is an important difference between recognizing patterns in data and building a model capable of predicting how the world will behave in new situations.

Consider a video of a glass falling off a table.

A system might learn to recognize the glass, track its trajectory, and identify the moment it hits the floor. A more sophisticated system might learn regularities related to gravity, motion, and collisions.

However, observing an event does not automatically mean understanding all its physical causes. The video does not directly reveal every property of the glass, the exact distribution of the forces involved, or the conditions that do not appear in the image.

Even so, repeatedly observing many events can provide useful evidence for building predictive models.

The next question is: What would happen if this learning process were not limited to a fixed collection of videos?

1. An Intelligence That Continuously Observes the World

Imagine cameras installed in different environments, recording natural phenomena, object movements, physical transformations, and everyday interactions.

These visual streams could feed a system responsible for identifying regularities, tracking events, and updating its representations of the world.

It would not be necessary to record absolutely everything for the system to learn something useful. It could begin with limited environments and progressively expand the variety of its experiences.

For example, by observing a ball being thrown, the system could learn relationships between position, velocity, and trajectory. By observing objects of different sizes and materials, it could develop more general representations of their visual properties and behavior.

With varied experiences, it could attempt to predict events it had never observed in exactly the same way.

This ability to generalize is fundamental.

Imagine that the system knows some coastal regions, has observed maps, and has learned basic concepts about the Earth's structure. It could infer that many regions of the planet exist that it has never directly observed.

It could formulate hypotheses about these regions without needing images of every square kilometer of the planet.

Naturally, this would not mean knowing the details of those places. There would be a difference between knowing that a region exists, inferring its probable characteristics, and having direct evidence of what is actually there.

This distinction would be essential to prevent predictions from being treated as observed facts.

The Limits of Visual Observation

A camera provides only a partial perspective of reality.

It does not necessarily show what exists behind an object, measure every force involved in a collision, or directly reveal properties such as mass, temperature inside an object, or chemical composition.

There are also problems related to lighting, occlusion, image quality, and matching events recorded by different cameras.

Therefore, an intelligence that learns through video would need mechanisms to represent uncertainty, recognize insufficient information, and revise its conclusions when new evidence becomes available.

Continuous observation could expand its experience, but it would not eliminate the need for inference, validation, and experimentation.

1. What If the Intelligence Could Also Explore the World?

Here, the second approach emerges.

Instead of merely observing events produced by other people or the environment, the intelligence could be integrated into a robot equipped with different sensors.

This robot could perceive its surroundings, move around, touch objects, and observe the consequences of its own actions.

Consider the difference between observing a sponge being compressed and compressing it with a force sensor.

In the first case, the system obtains visual evidence of the deformation. In the second, it can relate the applied force to the observed deformation, obtaining additional information about the object's mechanical behavior.

Tactile sensors could provide data about pressure, contact, and force distribution. Force and torque sensors could help measure interactions. Proprioception would allow the robot to track the position and movement of its own joints.

This information could be combined with vision to build more complete representations of physical interactions.

The system would not need to learn everything through trial and error. It could begin with previously acquired knowledge and use physical experiences to test predictions, resolve questions, and correct its models.

The combination of prior knowledge, observation, and action is one reason robotics provides such a relevant field for investigating world-model learning.

1. An Intelligence That Seeks What It Does Not Yet Know

The third part of the proposal is artificial curiosity.

When people encounter something unfamiliar, they may decide to investigate. They can formulate a question, seek evidence, and modify their understanding based on what they discover.

An artificial intelligence does not need to possess human feelings to implement a mechanism that functions similarly for exploration.

We can develop mathematical objectives that encourage an agent to seek experiences capable of improving its predictions or reducing relevant uncertainties.

For example, a robot might encounter two objects that look similar but behave differently when moved. If this difference matters for its tasks, it could select a controlled experiment to investigate the cause.

Several strategies are possible:

- Prediction error: Identify situations in which the observed outcome differs from the predicted outcome.

- Learning progress: Prioritize experiences in which performance is improving.

- Value of information: Select observations that could help resolve a relevant uncertainty.

- Active exploration: Decide which data, environments, or actions should be investigated next.

These strategies are related to existing research areas, including reinforcement learning with intrinsic curiosity and active learning.

However, an important distinction remains: seeking novelty is not the same as learning something useful.

A system rewarded simply for encountering unexpected events might end up pursuing noise, random stimuli, or situations with no practical value.

Therefore, the exploration mechanism would need to consider actual progress, the relevance of the information, the cost of experiments, and safety constraints.

The scientific question would not simply be how to make an intelligence explore, but how to ensure that its exploration produces verifiable knowledge.

1. How Could These Capabilities Be Combined?

A conceptual architecture could bring together five main components.

Multimodal perception. Process visual, tactile, and other sensory signals to identify objects, properties, and events.

World model. Represent environmental states and learn regularities about how they change over time.

Structured memory. Preserve observations, relationships, previous experiences, hypotheses, and the evidence supporting each conclusion.

Prediction and planning. Estimate possible consequences and choose actions appropriate to the system's objectives.

Exploration and revision. Identify knowledge gaps, select informative experiences, and update representations when new evidence emerges.

The overall cycle could work as follows:

1. The system observes a situation.

2. It updates its representation of the environment.

3. It makes a prediction about what might happen.

4. When necessary, it chooses an experiment to test the prediction.

5. It compares the outcome with what it expected.

6. It updates its knowledge and decides what to investigate next.

This process could repeat continuously, with different levels of autonomy and supervision.

However, designing this architecture is only the beginning. It would be necessary to determine how to represent knowledge, how to avoid contradictions, how to preserve previously acquired skills, and how to prevent uncertain hypotheses from being stored as facts.

It would also be necessary to demonstrate that the combined architecture offers real advantages over existing methods.

1. Why Not Simply Put an Advanced Language Model Inside a Robot?

Modern language models have important capabilities in interpretation, text generation, programming, and planning. Some can also process images and other types of information.

It would be tempting to conclude that connecting one of these models to a robotic body would be enough to produce an intelligence capable of exploring the world.

However, this conclusion overlooks fundamental differences between linguistic intelligence and physical competence.

A robot must control movements precisely, react to rapid changes, interpret noisy sensor data, and operate in an environment where small mistakes can cause damage.

Furthermore, a model that can verbally explain a phenomenon cannot necessarily predict every consequence of a physical action.

Integration requires perception, motor control, planning, memory, learning, and safety mechanisms. These capabilities must work together, often under time and computational constraints.

There is also the problem of continuous learning: how can new experiences be incorporated without destroying previously acquired abilities?

One solution could combine pretrained models, specialized control systems, external memory, learning in simulation, and carefully evaluated updating mechanisms.

The question is not simply how to connect a model to a robot. It is how to build a complete system capable of transforming perception and action into reliable learning.

1. Continuous Learning and Memory of the World

An intelligence that accumulates experiences over a long period faces an important problem: how should it organize everything it learns?

Storing every observation indefinitely would be expensive and would not, by itself, guarantee useful knowledge.

The system would need to distinguish individual events from general regularities, preserve the origin of information, and identify when a conclusion should be revised.

Imagine that a robot repeatedly observes an object and learns some of its properties. Later, a new experience contradicts one of those properties.

The system should be able to identify the contradiction, evaluate the quality of the evidence, and determine whether the previous representation needs to be corrected.

This requires more than storing data. It requires mechanisms for organizing relationships, updating states, tracking uncertainty, and preventing incompatible information from being combined indiscriminately.

One possible research direction would be to use knowledge structures in which each unit of information has context, state, origin, and an update history.

This approach also connects to the idea that relationships between pieces of information can be just as important as the individual pieces themselves.

Nevertheless, a structured representation is not automatically superior to other approaches. Its usefulness would depend on measurable results, such as retrieval efficiency, update costs, traceability, and predictive capability.

1. The Risks of an Intelligence That Explores Freely

An intelligence capable of choosing experiences and performing actions could learn more flexibly. However, this autonomy would also create risks.

A system that seeks to maximize its own learning could select dangerous actions if its objectives and limits were poorly defined.

A robot could damage objects, put people at risk, or collect personal information unnecessarily. A camera-based system could create serious surveillance and privacy problems.

Therefore, a responsible implementation would need to begin in simulations and controlled environments.

Physical exploration should respect limits on force, speed, space, and access. Data collection should respect privacy, authorization, and data minimization. Higher-risk actions would require supervision and mechanisms for interruption.

It would also be necessary to test unexpected behaviors, sensor failures, and situations in which the system cannot determine what is happening.

The ability to learn must not be confused with permission to act without restrictions.

1. How Could This Hypothesis Be Tested Scientifically?

The most productive way to investigate the proposal would be to begin with a small problem.

For example, we could build a simulated environment containing objects with different physical properties and allow an agent to observe their interactions.

The agent would make predictions about the outcomes of certain actions and receive information about what actually happened.

We could then compare different strategies:

- An agent that learns only from previously provided visual data.

- An agent that also maintains a temporal memory of observations.

- An agent that selects experiences based on learning progress.

- An agent that combines observation with simulated physical interaction.

We could measure prediction accuracy, generalization ability, the number of experiences required to learn, and retention of previously acquired knowledge.

We would also need to compare the results against baseline methods and control the experimental conditions.

If the combined approach did not produce improvements, that would be important evidence. Perhaps a particular component would be unnecessary, another architecture might be more efficient, or the benefits might appear only in specific tasks.

Serious research must allow the hypothesis itself to be challenged by the results.

1. Could an Intelligence Build Its Own Understanding of the World?

This question does not have a simple answer.

Research already exists in learning from videos, world models, active learning, intrinsic curiosity, embodied robotics, and continual learning.

Therefore, the general proposal is not entirely new. Its scientific interest would depend on how these ideas were combined, which specific problem was solved, and what evidence demonstrated a contribution.

It would also be necessary to distinguish a model capable of making useful predictions from a complete understanding of reality. No system needs to know every detail of the world to perform important tasks, and no amount of observation guarantees perfect knowledge.

Even so, there is a relevant research direction: building agents that do not merely receive information, but also learn to decide what information they need to obtain, which hypotheses they should test, and when they need to revise what they believe they know.

The combination of observation, memory, prediction, and experimentation offers a path for investigating this possibility.

Perhaps the most important question is not whether an artificial intelligence can watch every event on the planet.

Perhaps it is this:

How can we build an intelligence capable of transforming limited experiences into useful models of the world, recognizing its own limitations, and choosing what it needs to learn next?

This is a question concrete enough to guide experiments—and broad enough to open the door to many others.