xAI has recently released "Sexy 18+" and "Romantic 18+" for Grok 3 users. It appeared in my Android app a couple of days ago... I usually appreciate the quality of xAI's platform and I think it's a very interesting alternative to OpenAI and Anthropic. But providing sexual voice assistants to everyone without even asking users to opt in is definitely a NO GO for me! AI fans like to say "exciting times ahead", "the future will be amazing" or other naive things like that. Well, flirting with an AI instead of a real human is definitely not part of an "amazing future" according to my standards... Studies show that the level of depression among youngsters is higher than ever. They also show that birth rates are going down all around the world. Pushing AI chatbots as sex partners will make things even worse, no doubt about that. submitted by /u/juliensalinas [link] [comments]

For context, I'm a mixed Software / Data Engineer with a few years experience working on various ML projects as part of my day job. I'm not professing to be an expert on GenAI, but I've been thinking about this a lot recently. Is it a commonly held opinion amongst practitioners that the name "AI" for the recent batch of LLMs is in a way harmful to the industry? My understanding of transformers and current LLMs is very far from Artifical Intelligence in a true sense. I don't really see how these models are any more like AI than many traditional ML models on a massive scale. To me, this seems like a misappropriation of the term to drive stock value and convince the public that the tools they are using are more advanced than they actually are. And I feel like when I first started working in ML and GenAI was closer to infancy than widespread adoption, the use of the term AI seemed a bit more guarded and less commonly thrown around. Additionally, is any consensus forming about whether GenAI LLMs are actually a stepping stone towards more advanced AI? Or more of a "side quest" diverting resource and investment away from potential advancements? I'm thinking of opinions shared in posts like this from a while back. Interested to hear your thoughts & happy to be corrected if you feel differently. submitted by /u/MatchaWarrior [link] [comments]

I’m a 25-year-old recent Computer Engineering graduate from the University of Zimbabwe, and I’m aspiring to become an AI Engineer. Is there a clear learning roadmap I can follow to achieve this? Are there reputable self-study resources or platforms you’d recommend? How long does it typically take to gain the necessary skills? I’m also wondering, by the time I’m job-ready, would I be considered too old to be hired as a junior? submitted by /u/Uncle_Remus_________ [link] [comments]

Hi everyone, We invite you to submit your work to the Reinforcement Learning and Video Games (RLVG) workshop, which will be held on August 5th, 2025, as part of the Reinforcement Learning Conference (RLC 2025). Call for Papers: We invite submissions about recent advances, challenges, and applications in the intersection of reinforcement learning and videogames. The topics of interest include, but are not limited to, the following topics: RL approaches for large state spaces, large action spaces, or partially observable scenarios; Long-horizon and continual reinforcement learning; Human-AI collaboration and adaptation in multi-agent scenarios; RL for non-player characters (NPCs), opponents, or QA agents; RL for procedural content generation and personalization; Applications of RL to improve gameplay experience. Confirmed Speakers: James MacGlashan, Sony AI Ida Momennejad, Microsoft Research Roberta Raileanu, Meta AI Pablo Samuel Castro, MILA, Google Deepmind Julian Togelius, NYU, modl.ai Michael Bowling, University of Alberta Important Dates: Submission Deadline: May 30th, 2025 (AOE) Acceptance Notification: June 15th, 2025 Submission Details: We accept both long-form (8 pages) and short-form (4 pages) papers, excluding references and appendices. We strongly encourage submissions from authors across academia and industry. In addition to mature results, we also welcome early-stage ideas, position papers, and negative results that can spark meaningful discussion within the community. For more information, please refer to our website. Contacts: Please send your questions to rlvg2025[at]gmail.com, and follow our Bluesky account u/rlvgworkshop.bsky.social for more updates. submitted by /u/RLVideoGamesWorkshop [link] [comments]

Hi everyone. I have to automate a process using a local LLM to generate the tree structure based on the input given. Input and output are as follows: Input: Fruits (100 | 50) Apples (50 | 30) Mangoes (50 | 20) Vegetables (50 | 20) Onions (30 | 20) Cabbage (20 | NA) Output: Groceries (Total: 150 | 70) |_ Fruits (100 | 50) | |_Apples (50 | 30) | |_Mangoes (50 | 20) |_ Vegetables (50 | 20) . . .|_Onions (30 | 20) . . . |_Cabbage (20 | NA) The two values in each category are from the current and previous years. Values have to be preserved. I'm currently training seq2seq models, but I'm failing to get proper results. Top node contains the overall total of parent nodes (Fruits and Vegetables). Parent node contains the total of child nodes. Can anyone help me what is the best way to train a model based on this information? Fyi, my dataset contains: instruction: " ", input: " ", output: " " Edit: Onions and Cabbage have to be aligned right below Vegetables. Ignore the dots used. submitted by /u/False-Fig-8535 [link] [comments]

Hi everyone, I'm working on a task dependency prediction problem using Graph Neural Networks with PyTorch Geometric. The goal is to predict directed precedence links (A -> B) between tasks within specific sets (called "gammes", typically ~50-60 tasks at inference). Data & Features: I'm currently training on a subset of historical data related to one equipment type family ("ballon"). This subset has ~14k nodes (tasks) and ~15k edges (known dependencies), forming a Directed Acyclic Graph (DAG). Node features (data.x fed into the first GNN layer, dim ~401): Sentence Embeddings (from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, dim 384) for the task name (Nom de l'activite), which is semantically important. Learned categorical embeddings (via torch.nn.Embedding, dim 16) for the specific equipment type variant (3 unique types in this subset). Normalized duration (1 dim). The original Gamme name and Projet source were found to be uninformative and are not used as input features. Data Splitting: Using torch_geometric.transforms.RandomLinkSplit (num_val=0.1, num_test=0.1, is_undirected=False, add_negative_train_samples=True, neg_sampling_ratio=1.0, split_labels=True). Model Architecture: Encoder: 2-layer GraphSAGEEncoder (using SAGEConv) that takes node features + type embeddings and edge_index (training links) to produce node embeddings (currently dim=32). Includes ReLU and Dropout(0.5) between layers. class GraphSAGEEncoder(nn.Module): def init(self, input_feat_dim, hidden_dim, output_dim, num_types, type_embed_dim, num_layers=2): """ Initializes the GraphSAGE encoder. Args: input_feat_dim (int): Dimension of continuous input features (e.g., 384 name embedding + 1 normalized duration = 385). hidden_dim (int): Dimension of GraphSAGE hidden layers and learned embeddings. output_dim (int): Dimension of the final node embedding. num_types (int): Total number of unique 'Equipment Type'. type_embed_dim (int): Desired dimension for the 'Equipment Type' embedding. num_layers (int): Number of SAGEConv layers (e.g., 2 or 3). """ super(GraphSAGEEncoder, self).__init__() # Embedding layer for Equipment Type self.type_embedding = nn.Embedding(num_types, type_embed_dim) # Input dimension for the first SAGEConv layer # It's the sum of continuous features + type embedding actual_input_dim = input_feat_dim + type_embed_dim self.convs = nn.ModuleList() # First layer self.convs.append(SAGEConv(actual_input_dim, hidden_dim)) # Subsequent hidden layers for _ in range(num_layers - 2): self.convs.append(SAGEConv(hidden_dim, hidden_dim)) # Final layer to output dimension self.convs.append(SAGEConv(hidden_dim, output_dim)) self.num_layers = num_layers def forward(self, x, edge_index, type_equip_ids): """ Forward pass of the encoder. Args: x (Tensor): Continuous node features [num_nodes, input_feat_dim]. edge_index (LongTensor): Graph structure [2, num_edges]. type_equip_ids (LongTensor): Integer IDs of the equipment type for each node [num_nodes]. Returns: Tensor: Final node embeddings [num_nodes, output_dim]. """ # 1. Get embeddings for equipment types type_embs = self.type_embedding(type_equip_ids) # 2. Concatenate with continuous features x_combined = torch.cat([x, type_embs], dim=-1) # 3. Pass through SAGEConv layers for i in range(self.num_layers): x_combined = self.convs[i](x_combined, edge_index) # Apply activation (except maybe for the last layer) if i < self.num_layers - 1: x_combined = F.relu(x_combined) x_combined = F.dropout(x_combined, p=0.5, training=self.training) # Dropout for regularization return x_combined Link Predictor: Simple MLP that takes embeddings of source u and target v nodes and predicts link logits. (Initially included pooled global context, but removing it gave slightly better initial AUC, so currently removed). Input dim 2 * 32, hidden dim 32, output dim 1. class LinkPredictor(nn.Module): def __init__(self, embedding_dim, hidden_dim=64): super(LinkPredictor, self).__init__() self.layer_1 = nn.Linear(embedding_dim * 2, hidden_dim) self.layer_2 = nn.Linear(hidden_dim, 1) def forward(self, emb_u, emb_v): # Concatenate only emb_u and emb_v combined_embs = torch.cat([emb_u, emb_v], dim=-1) x = F.relu(self.layer_1(combined_embs)) x = self.layer_2(x) return x # Still returning the logits Training Setup: Optimizer: AdamW(lr=1e-4, weight_decay=1e-5) (also tried other LRs and weight decay values). Loss: torch.nn.BCEWithLogitsLoss. Process: Full-batch. Generate all node embeddings using the encoder, then predict logits for positive and negative edge pairs specified by train_data.pos_edge_label_index and train_data.neg_edge_label_index, combine logits and labels (1s and 0s) for loss calculation. Validation is similar using val_data. The Problem: The model learns the training data (training loss decreases steadily, e.g., from ~0.69 down to ~0.57). However, it fails to generalize: Validation loss starts okay but increases epoch after epoch (overfitting). Crucially, Validation AUC consistently drops well below 0.5 (e.g., starts around 0.5-0.57 in the very first epoch, then quickly drops to ~0.25-0.45) and stays there. This happens across various hyperparameter settings (LR, weight decay, model dimensions). What I've Tried: Reducing model complexity (hidden/output dimensions). Adjusting learning rate (1e-3, 1e-4, 1e-5). Adding/adjusting weight_decay (0, 1e-6, 1e-5). Removing the explicit global context pooling from the link predictor. Verified input features (data.x) don't contain NaNs. Training runs without numerical stability issues (no NaN loss currently). My Question: What could be causing the validation AUC to consistently be significantly below 0.5 in this GNN link prediction setup ? What changes could i possibly do in my architecture if it is too simple ? submitted by /u/Head_Mushroom_3748 [link] [comments]

I'm a Full-Stack engineer working mostly on serving and scaling AI models. For the past two years I worked with start ups on AI products (AI exec coach), and we usually decided that we would go the fine tuning route only when prompt engineering and tooling would be insufficient to produce the quality that we want. Yesterday I had an interview for a startup the builds a no-code agent platform, which insisted on fine-tuning the models that they use. As someone who haven't done fine tuning for the last 3 years, I was wondering about what would be the use case for it and more specifically, why would it economically make sense, considering the costs of collecting and curating data for fine tuning, building the pipelines for continuous learning and the training costs, especially when there are competitors who serve a similar solution through prompt engineering and tooling which are faster to iterate and cheaper. Did anyone here arrived at a problem where the fine-tuning route was a better solution than better prompt engineering? what was the problem and what made the decision? submitted by /u/Sunshineallon [link] [comments]

Hey im a college student and I was reading a paper on DRTP and it really interested me this is a AI/ML algorithm and they made it hit 95% accuracy in Python with 2 hidden layers eaching having anywhere from 500-1000 neurons I was able to recreate it in C with one hidden layer and 256 neurons and I hit 90% on the MNIST data set (https://github.com/JaimeCasanovaCodes/c-drtp-mnist) here is the link to the repo leave me any suggestions im new to ML submitted by /u/PlugTheGreatest [link] [comments]

Hello everyone! 👋 I'm excited to introduce my Python library pymoors** and its Rust core **moors. You can find both in the moo-rs GitHub repository. moors is a pure Rust crate that implements all the multi-objective evolutionary algorithms. It is designed to be completely independent of Python. **pymoors** is a Python binding built with PyO3, providing access to the moors algorithms from Python. I'm currently benchmarking pymoors against the popular pymoo library. So far, pymoors has shown promising results—achieving up to 6.5x speedups over pymoo. You can check out a comparison example here: py-moors-examples. This is still an early-stage project and there's a lot of room for improvement. I'm actively looking for collaborators—so if you're interested in evolutionary algorithms, Rust/Python interop, or performance optimization, feel free to reach out or contribute! submitted by /u/Heavy_Host_246 [link] [comments]

Hi guys, I've been a data scientist for 5 years. I've done lots of different types of work and unfortunately that has included a lot of dashboarding (no offense if you enjoy making dashboards). I'm wondering what tools people here are using and if you like them. In my career I've used mode, looker, streamlit and retool off the top of my head. I think mode was my favorite because you could type sql right into it and get the charts you wanted but still was overall unsatisfied with it. I'm wondering what tools the people here are using and if you find it meets all your needs? One of my frustrations with these tools is that even platforms like Looker—designed to be self-serve for general staff—end up being confusing for people without a data science background. Are there any tools (maybe powered my LLMs now) that allow non data science people to write prompts that update production dashboards? A simple example is if you have a revenue dashboard showing net revenue and a PM, director etc wanted you to add an additional gross revenue metric. With the tools I'm aware of I would have to go into the BI tool and update the chart myself to show that metric. Are there any tools that allow you to just type in a prompt and make those kinds of edits? submitted by /u/alexellman [link] [comments]

Hi everyone, Has anyone heard any updates about MICCAI 2025 results? It seems like they haven’t been announced yet—has anyone received their reviews? Thanks! submitted by /u/Long_Equal_5923 [link] [comments]

Hello All. As far as I understand, we can add the technical appendices with the main paper before the full paper submission deadline or as a separate PDF with the supplementary materials. Does it have any negative effect if I do the latter one to add more experiments in the appendix with one week extra time? Thanks submitted by /u/Accomplished_Newt923 [link] [comments]

submitted by /u/PraiseChrist420 [link] [comments]

I’ve become an avid open source contributor over the past few years in a few popular ML, Econ, and Jax ecosystem packages. In my opinion being able to take someone else’s code and fix bugs or add features is a much better signal than leetcode and hacker rank. I’m really hoping I don’t have to study leetcode/hackerrank for my next job search (DS/MLE roles) and I’d rather just keep doing open source work that’s more relevant. For the other open source contributors out there - are you ever able to get out of coding challenges by citing your own pull requests? submitted by /u/James_c7 [link] [comments]

Hi, Recently, I was curious why two random vectors are almost always orthogonal in high dimensions. I prepared an interactive post for this explanation https://maitbayev.github.io/posts/random-two-vectors/ Feel free to ask questions here submitted by /u/madiyar [link] [comments]

submitted by /u/ElectrikMetriks [link] [comments]

Hey everyone! I've been learning about optimization algorithms in machine learning, and I kept struggling to intuitively grasp how different ones behave — like why Adam converges faster or how momentum helps in tricky landscapes. So I built a 3D visualizer that shows how these optimizers move across a custom loss surface. You can: Enter your own loss function Choose an optimizer (SGD, Momentum, RMSProp, Adam, etc.) Tune learning rate, momentum, etc. Click to drop a starting point and watch the optimizer move in 3D It's fully interactive and can be really helpful to understand the dynamics. Here’s a short demo (Website): https://i.redd.it/c69gnqn9md0f1.gif I’d love feedback or thoughts from others learning optimization. If anyone's interested, I can post the GitHub repo. submitted by /u/SnooCupcakes5746 [link] [comments]

Hi Everyone! Bit of a technical modeling question here. Apologies if this is very basic preprocessing stuff but I'm a younger data scientist working in industry and I'm still learning. Say you have a pretty standard binary classification model predicting 1 = we should market to this customer and 0 = we should not market to this customer (the exact labeling scheme is a bit proprietary). I have a few features that are in the style "days since last touchpoint". For example "days since we last emailed this person" or "days since we last sold to this person". However, a solid percentage of the rows are NULL, meaning we have never emailed or sold to this person. Any thoughts on how should I handle NULLs for this type of column? I've been imputing with MAX(days since we last sold to this person) + 1 but I'm starting to think that could be confusing my model. I think the reality of the situation is that someone with 1 purchase a long time ago is a lot more likely to purchase today than someone who has never purchased anything at all. The person with 0 purchases may not even be interested in our product, while we have evidence that the person with 1 purchase a long time ago is at least a fit for our product. Imputing with MAX(days since we last sold to this person) + 1 poses these two cases as very similar to the model. For reference I'm testing with several tree-based models (light GBM and random forest) and comparing metrics to pick between the architecture options. So far I've been getting the best results with light GBM. One thing I'm thinking about is whether I should just leave the people who have never sold as NULLs and have my model pick the direction to split for missing values. (I believe this would work with LightGBM but not RandomForest). Another option is to break down the "days since last sale" feature into categories, maybe quantiles with a special category for NULLS, and then dummy encode. Has anyone else used these types of "days since last touchpoint" features in propensity modeling/marketing modeling? submitted by /u/Ok-Needleworker-6122 [link] [comments]

I’m launching a privacy-first mobile assistant that runs a Llama 3.2 1B Instruct model, Whisper Tiny ASR, and Kokoro TTS, all fully on-device. What makes it different: Entire pipeline (ASR → LLM → TTS) runs locally Works with no internet connection No user data ever touches the cloud Built on ONNX runtime and a custom on-device Python→AST→C++ execution layer SDK We believe on-device AI assistants are the future — especially as people look for alternatives to cloud-bound models and surveillance-heavy platforms. submitted by /u/Economy-Mud-6626 [link] [comments]

I'm looking to get my feet wet in egocentric vision, and was hoping to get some recommendations on papers/resources you'd consider important to get started with research in this area. submitted by /u/fullgoopy_alchemist [link] [comments]