Committee

A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs), a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is, but not how it is used. Two coffee shops can share a category, an address block, and a text vector, while one runs commuter turnover and the other holds customers for ninety minutes. ME-POIs encodes each visit as a contextualized vector, then uses contrastive learning to align those visits with one learnable prototype per POI. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs to strong text encoders improved 34 of 35 model-task pairings in Los Angeles, with relative gains up to 81.9% F1 on visit intent and a 24.7% MAE reduction on busyness. Notably, a variant trained on mobility alone beat Gemini embeddings on price-level classification. Is it deployable? Partially, it is a framework you rebuild, not a checkpoint you download. As of publication, Google Research has released the paper but no public code or weights. The compute bar is low: the model is ~53.7M parameters and was pretrained on a single NVIDIA Tesla V100 16GB. The real gate is data — you need licensed foot-traffic or first-party visit logs plus POI polygons. How the framework works Each visit is a triple: coordinates, arrival time and departure time. Three factorized encoders handle them: Space2Vec for multi-scale location, and two Time2Vec encoders for arrival and departure separately, so start time and dwell duration stay distinguishable. The concatenated vectors get sinusoidal positional encoding and pass through a 4-layer, 8-head Transformer (d_h = 512) to produce contextualized visit embeddings. The core objective is contrastive. Every POI owns a learnable prototype, and an InfoNCE loss pulls each visit embedding toward its own POI’s prototype while pushing away the other POIs in the minibatch. The prototype becomes a functional centroid that averages out individual user schedules. Sparsity is the hard part. Only 9.07% of Los Angeles POIs and 7.04% of Houston POIs cleared the anchor threshold (100 and 50 total visits respectively). For the long tail, ME-POIs computes normalized Gaussian kernels at three bandwidths — 0.3 km, 1.0 km, 3.0 km — and transfers anchor visit histograms to sparse POIs, then adds a KL term forcing the sparse embedding to predict that prior. A second KL term supervises anchors against their own empirical distributions. A fourth loss maximizes cosine similarity with projected text embeddings, whose prompts follow the GeoLLM recipe: coordinates, category, address, and the ten nearest POIs with distance and direction. What the numbers say Evaluation covers two anonymized mobility datasets — Los Angeles (39,557 POIs, 6.9M visits, full-year 2019) and Houston (28,419 POIs, 715,604 visits, 20 days in March 2020) — across five map-enrichment tasks with frozen-embedding probing. Labels come from SafeGraph for opening hours and closures, and Google Maps for visit intent, busyness, and price level. Adding ME-POIs improved 34 of 35 model-task pairings in Los Angeles. Peak relative gains: 16.2% F1 on weekly opening hours (OpenAI-large), 81.9% F1 on visit intent (Gemini), 6.5% F1 on permanent closure (E5), and a 24.7% MAE reduction on busyness (Gemini). In Houston, price-level F1 rose 75.1% for GTR-T5. The single regression was Gemini on permanent closure, down 0.4%. The more interesting result is the mobility-only variant. Trained with no text alignment at all, it reaches 0.600 accuracy on Los Angeles price level against Gemini’s 0.559 — collective behavior outperforming the words used to label the place. It also beats every trajectory-based baseline on every task. Explainer: the mechanism, step by step Key Takeaways ME-POIs learns one context-independent vector per POI, not a trajectory-conditioned one. Contrastive alignment plus multi-scale KL transfer fixes the long tail: 91% of LA POIs are sparse. Gains reach 81.9% F1 on visit intent and 24.7% MAE reduction on busyness. Mobility-only embeddings beat Gemini text embeddings on price-level classification. No public code or weights yet; licensed visit data is the real barrier, not compute. Check out the Paper and Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Google Research Introduces ME-POIs: A Mobility-Informed Framework that Adds “How a Place Is Used” to Text-Based POI Embeddings appeared first on MarkTechPost.

Information extraction teams face a recurring choice. Small encoder models are cheap but rigid, and large language models are flexible but expensive per document. Fastino released GLiNER2.5 to narrow that gap. The release replaces span enumeration with boundary prediction: the model scores where an entity starts and ends instead of scoring every candidate span against a width grid. That single change removes the maximum entity width, allows a 4,096-word context, and keeps computation linear in sequence length for a fixed schema. It also unlocks joint entity-relation decoding, cross-task label constraints, and per-span attributes. Across 16 zero-shot benchmarks, the multilingual checkpoint reaches 56.17 overall macro F1 against 56.09 for GLiNER2, with a 24.75-point gain on XNLI. Three checkpoints ship on Hugging Face under Apache 2.0 at 74M, 194M, and 287M parameters. Is it deployable? Yes, Fastino released three GLiNER2.5 checkpoints on Hugging Face under Apache 2.0, with local inference on CPU, CUDA, or MPS via pip install “gliner2[local]” (Python 3.10+). No inference provider currently hosts the checkpoints, so self-hosting is the deployment path. Company level: any tier. The 74M and 194M checkpoints run on standard CPU boxes, so a two-person team can ship extraction without GPU budget. Larger orgs get a fine-tunable, privately hosted alternative to per-token LLM extraction. Industries: legal and contract operations, healthcare and clinical documentation, financial services, insurance claims, customer support, and AI safety tooling. Applications: PII detection and redaction, contract clause extraction, knowledge graphs for agent memory, agent and model routing, guardrail classification, clinical entity extraction with negation and dosage attributes. What changed Earlier GLiNER models located entities by enumerating candidate spans: every start position paired with every allowed width, each scored against the schema. That design tied compute to a width axis and imposed a hard ceiling on entity length. GLiNER2.5 removes enumeration. The shared encoder still processes text and schema queries in one pass. Instead of scoring spans, the model predicts start and end scores over token boundaries plus inside scores over tokens. A sparse proposal stage selects the most promising starts and ends per query and pairs them, with no restriction on distance. A reranking head then scores each candidate using boundary evidence and span content. Relation candidates are drawn from the same pool rather than a separate path. Fastino team reports that computation stays linear in sequence length for a fixed schema and candidate budget. Five capabilities that follow Long-context extraction: Removing explicit span representations cut memory enough to train on sequences up to 4,096 words. The checkpoints ship with max_len=4096. The library also adds native chunking helpers (extract_entities_long, extract_long, Classifier.classify_long, JointIE.extract_long) that remap spans to character offsets in the original document. Unlimited span length: GLiNER2 enumerated spans up to a fixed width, typically around twelve words; longer entities were never scored. In GLiNER2.5 a span can open at the first token and close at the last. A forty-word indemnification clause costs the same to locate as a two-word name. Joint entity and relation extraction: Users declare entity types, typed relations, and structural rules (unique_head=True, no_self_loops()), and a beam search assembles a globally consistent graph. Invalid combinations are never admitted, so output conforms by construction. Check result.feasible before using the graph. Constrained classification: C.implies and C.excludes rules bind labels across tasks during decoding. Fastino’s own GLiGuard guardrail model illustrates the problem being solved: without constraints, a prompt can be labeled safe while simultaneously flagged for prompt injection. If no valid assignment exists, the classifier raises an error. Span attributes: Attribute groups such as sentiment attach to specific entity types via applies_to, and are decoded span-by-span in the same forward pass. Entities return qualified rather than flat. The model family Model Params Encoder Language gliner2.5-small-v1 74M DeBERTa-v3-xsmall English gliner2.5-base-v1 194M DeBERTa-v3-base English gliner2.5-multi-v1 287M mDeBERTa-v3-base Multilingual All three share the same public API. Load with AutoExtractor, not the legacy GLiNER2 span loader. Benchmarks Fastino team evaluates zero-shot on 16 public datasets, reporting macro F1 against GLiNER2 at matched sizes. Overall average: GLiNER2.5 Multi reaches 56.17 versus 56.09 for GLiNER2 Multi. GLiNER2.5 Base reaches 54.87 versus 53.34. The headline gain is XNLI, where Multi jumps to 62.30 from 37.55, a 24.75-point increase. Few-NERD improves for Base to 55.14 from 47.22. Romanian RONEC, an untrained language, improves for both. Key Takeaways Boundary prediction replaces span enumeration; entity width no longer costs compute. Three Apache 2.0 checkpoints: 74M, 194M, 287M, all CPU-runnable. Joint decoding returns schema-valid graphs, removing post-hoc validation layers. Overall F1 rises to 56.17 (Multi) and 54.87 (Base); extraction average dips for Multi. Chunking keeps a span only when both boundaries land in one chunk. Check out the Fastino technical blog, the GitHub repo, and the boundary architecture docs. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction appeared first on MarkTechPost.

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here. Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones that could magically answer almost any homework question or spin up an essay in seconds. Of course, teachers can often tell when a student is using AI—models make mistakes that most humans don’t, and some teachers say that AI-generated text has simple giveaways like too many em dashes.  Nevertheless, the generative AI boom increased the burden on teachers, who were already working long hours to plan lessons, make homework assignments, and grade exams, and now needed to adapt to a new technology. For many, it still feels like there’s no clear path forward. Organizations ranging from OpenAI to UNESCO encourage AI use in the classroom, but many teachers feel confused about how exactly to handle it.  Case study Cheshire Academy is a private boarding and day school in Connecticut with about 400 students in grades 9 through 12. Administrators there don’t force instructors to use AI at all, though the school’s librarian and technology coordinator George Aiello claims the “vast majority” of instructors use it in some way. The educators there are trying a patchwork of programs, including general-purpose chatbots like ChatGPT and Perplexity as well as more specialized tools like MagicSchool, an AI-powered platform meant specifically for educators. That patchwork approach is partly because the school, on the advice of consultants, opted to train its staff on general techniques for how to use AI instead of prescribing certain tech. The staff training covered topics like how to craft useful prompts but also stressed the technology’s limits, highlighting its potential for generating incorrect and biased responses.  Now, teachers there often use generative AI to prepare class materials. This means asking the AI of their choice for help with planning lessons or creating grading rubrics. Some even want to use it to help them give feedback to students, though concerns over quality, personalization, and privacy have prevented any of them from doing that just yet. Others, like Miriam Przybyla-Baum, who teaches French, don’t use AI themselves but do address it with their teaching. Przybyla-Baum says she doesn’t really need AI’s help since she’s built up plenty of classroom materials across nearly 30 years of teaching. But she started seeing students try to use AI-powered tools like Google Translate to take shortcuts on their assignments years before ChatGPT’s launch.  She’s developed a system to make students reflect on how AI can and can’t teach language skills. In one assignment, students let a large language model (LLM) edit their homework. Then, they go through the edits and decide which ones were correct and which ones removed their voice. In another, she has students anonymously grade each other’s AI-assisted assignments, making annotations as to which parts they think are AI-assisted. Cheshire Academy is continuing to experiment with how AI can be harnessed productively in the classroom. The school is piloting a program in which students create media and lead discussions regarding healthy AI use. This program, which they call a “Student AI Council,” aims to push students to reflect on how AI should and shouldn’t be used to benefit the community around them. The academy as a whole has since adopted similar techniques to those Przybyla-Baum introduced to make students reflect on their own use of AI. Assignments are now labelled like traffic lights, with green meaning AI is fully allowed and red banning any AI use. Yellow, then, lets the teacher permit some tools while banning the rest, like allowing students to use spell-check but not message a chatbot. The tool As generative AI was becoming mainstream, Cheshire Academy previewed MagicSchool to its staff.  For many, MagicSchool’s main strength seems to lie in the sheer amount of offerings it provides in one package. It can generate questions and assignments of all kinds, from quizzes to worksheets, across many subjects and grade levels. It has a specialized grading rubric generator, which outputs a ready-to-go table that teachers can use to score assignments. It can make presentations and lesson plans and administrative reports, too.  All of this is done through a single platform where educators enter specific prompts tailored for each task. For example, to make an assignment, a teacher can specify the students’ grade level, number of questions, the types of questions (such as multiple choice or short answer), and more, and include documents to align the questions with.  Not every teacher feels comfortable using LLMs to generate student-facing text, whether because they don’t think an AI can produce effective teaching materials or helpful feedback, or because they’re concerned about accuracy. MagicSchool, which has free and paid versions, does offer tools on its platform for other tasks like lesson planning. If teachers want unlimited access and complete records in the system, though, they need to pay just under $100 per year for an individual plan. Alternatively, many of the general-purpose generative AI tools (think the chatbots on offer from Anthropic, Google, OpenAI, and more) seem suited to administrative tasks, as well, attested by the fact that many of the teachers at Cheshire Academy use those instead. Some of these companies are even rolling out features tailored for schools, to mixed results. How to apply this Meet students where they’re at. Students will be tempted to try AI, and there’s no way to entirely police this for take-home assignments. The internet and social media can spread a lot of misinformation as to what AI can and can’t do, and it’s critical to counter these narratives and teach healthy strategies and relationships. Model best practices. AI is very good at automating tasks, but it struggles with precision and voice. Keep this in mind, especially when generating any text that anyone else may see. Impressionable students who see those in authority using AI in a lazy way

Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59% success (±10% std. dev.) straight from the pretrained model. Ten gradient steps on five minutes of data per task raised that to 83% (±9%). Generalist calls the mechanism physical prompting, and says it was never trained for: no architectural changes, no meta-learning loop, no auxiliary objectives. It emerged from over eight months of continuous pretraining on physical interaction data. The tasks are simple and short-horizon, and the company says so plainly. But this is the first model its team knows of where one-shot learning of physical skills has emerged at scale. Is it deployable? Not yet — this is a research release. There are no public weights, no API, no pricing page and no self-serve product. Generalist AI runs GEN-1.5 on its own fleet and data engine. Anyone who wants it today goes through a direct partnership. What is GEN-1.5? GEN-1.5 is a large multimodal model that takes video, sensor, language and proprioceptive inputs, holds 30 seconds of memory, and emits 100 Hz action trajectories. It has been pretraining continuously for over eight months on physical interaction data captured in homes, warehouses and factories. The main mechanism is physical prompting. A sensorimotor example — sensor streams plus the action trajectory — is inserted into the 30-second context window through a drag-and-drop interface. The remainder of the window holds rolling observations. The model then performs the task immediately, with zero gradient steps and no fine-tuning. Crucially, none of this was designed in. Generalist states there were no architectural changes to promote in-context learning, no meta-learning loop, and no auxiliary objectives encouraging improvisation. The capability emerged from pretraining scale, the same way one-shot prompting emerged in GPT-3. The numbers Across 10 diverse tasks, one-shot in-context prompting averaged 59% success (±10% std. dev.) from the pretrained model, with no training at all. Ten gradient steps on five minutes of data per task — roughly 50 demonstrations — raised that to 83% (±9%). In the extreme case, one gradient step on one minute of data reached 66.5% on a held-out task, with no adaptation-specific hyperparameter sweep. The compute story is the interesting part. Adapting robot policies has typically taken tens of thousands of gradient steps. Ten steps here move the model weights on held-out tasks by less than 0.15%, which suggests fine-tuning is reconfiguring knowledge the model already has rather than building new representations. Generalist frames it as test-time training in an extremely low-data regime. Three transfer results worth knowing Compositional generalization: Two independently recorded prompts placed in context get chained into one continuous behaviour. The model produces the bridging motions — repositioning, regrasping, error recovery — that appear in neither demonstration. Zero-shot sim-to-real: A demonstration recorded entirely in simulation works as a prompt for the real robot, despite pretraining containing no simulation data — neither rendered video nor simulated dynamics. For some tasks, demonstrations no longer need to be collected physically. Human-to-robot imitation: In some cases a person demonstrates with their own hands, in view of the robot’s cameras, and the model reproduces it with the robot’s hands. Generalization also shows up after light fine-tuning. Trained on five minutes of brushing a block into a bowl, the model used a banana as a makeshift brush, and used a dustpan to lift and dump the block instead — a different contact sequence entirely. It also removed a sheet of paper covering the bowl, and worked ambidextrously when demonstrations used one hand. Interactive explainer Key Takeaways GEN-1.5 learns new manipulation tasks from a single 3–12 second demonstration dropped into its 30-second context window. One-shot in-context prompting hit 59% across 10 tasks; 10 gradient steps on 5 minutes of data hit 83%. One-shot, sim-to-real and human-to-robot transfer emerged from pretraining — none of it was explicitly trained for. Ten gradient steps change weights by under 0.15%, collapsing per-task adaptation compute by orders of magnitude. No weights, no API, no product: treat this as a research signal about scaling, not a deployable system. Check out the GEN-1.5 research post and @GeneralistAI announcement thread. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo appeared first on MarkTechPost.