Trends

the data annotation market was valued at $2.37B in 2025, is projected to reach $2.97B in 2026, and is forecast to grow at a 27.11% CAGR to $12.73B by 2032.

## CloudPano.com's Post ### CloudPano.com AI content · 15h · 🤖 How Does Human Feedback Actually Shape AI? RLHF (Reinforcement Learning from Human Feedback) helps AI models learn not just what’s technically correct, but what people actually prefer. Instead of simply labeling an answer as right or wrong, human reviewers compare multiple AI responses based on criteria such as \\helpfulness, accuracy, safety, and tone\\. The process generally works like this: [...] #RLHF #ArtificialIntelligence #MachineLearning #AITraining #HumanFeedback #TrainingData #GenerativeAI May be an image of text that says 'CloudPano RLHF Services: How Human Feedback Data Actually Shapes Model Behavior' [...] 🔹 Humans compare and rank AI-generated responses 🔹 Those preferences are used to train a reward model 🔹 The AI model is fine-tuned using that feedback 🔹 Fresh human evaluations help verify that its behavior actually improved The quality of the human feedback matters. Clear preference crite

## CloudPano.com's post ### CloudPano.com AI content · 14h · 🤖 How Does Human Feedback Actually Shape AI? RLHF (Reinforcement Learning from Human Feedback) helps AI models learn not just what’s technically correct, but what people actually prefer. Instead of simply labeling an answer as right or wrong, human reviewers compare multiple AI responses based on criteria such as \\helpfulness, accuracy, safety, and tone\\. The process generally works like this: [...] #RLHF #ArtificialIntelligence #MachineLearning #AITraining #HumanFeedback #TrainingData #GenerativeAI May be an image of text that says [...] 🔹 Humans compare and rank AI-generated responses 🔹 Those preferences are used to train a reward model 🔹 The AI model is fine-tuned using that feedback 🔹 Fresh human evaluations help verify that its behavior actually improved The quality of the human feedback matters. Clear preference criteria, trained reviewers, consistency checks, and ongoing validation help ensure the

Reinforcement Learning from Human Feedback (RLHF) Human evaluators rank outputs, reward models are built, and reinforcement learning aligns the LLM with

Slide thumbnailBest Personal Website Examples and Practices in 2024img1-1Let's set you up with a custom plan!Let's set you up with a custom planСontact UsContact Us Related Articles How to Create Custom Datasets for AI and LLM Training Technology AI Data & Insights How to Create Custom Datasets for AI and LLM Training DepositPhotos 08\06\26 Blog_Article Cover DtTrvsScr Technology AI Data & Insights Why Commercial Datasets Win Over Data Scraping for AI Training [...] Need licensed data for computer vision, LLM training, or multimodal AI? Share your details, and we’ll help you explore our custom dataset options. Slide thumbnail Scalable and cost-efficient Adaptable to needs Functional across platforms User-friendly Save hours on content search! Enjoy free exclusive access to 100 of our most captivating collections that include visuals illustrating: DOWNLOAD 100 IMAGE AND VIDEO COLLECTIONS Frame 1753 [...] Dataset needs depend heavily on task type: Ma

label Your Data – secure data annotation for ai Services computer vision Image Image Video Video 3D Point Cloud 3D Point Cloud Aerial & Satellite Aerial & Satellite Text Text Audio Audio Content Moderation Content Moderation Industries Mobility & Autonomous Systems Mobility & Autonomous Systems Retail Retail Robotics & Physical AI Robotics & Physical AI Geospatial Geospatial [...] Public benchmarks like Ego4D dataset are open-access repositories that give you immediate, low-risk options to evaluate new model architectures. With public egocentric data, you can pretrain foundational vision backbones, run academic research, and conduct initial feasibility tests before investing in hardware rigs and paid annotation for your own dataset. [...] View Guides The Guide to In-House Dataset Labeling The Guide to In-House Dataset Labeling The Buyer’s Guide to Data Labeling Vendors The Buyer’s Guide to Data Labeling Vendors The Guide to Geos

Data labeling software helps data science and machine learning teams source, manage, annotate, and classify unstructured data, including text, images, videos,

Most real projects keep a human in the loop even when parts are automated. A model pre-labels the easy cases, and people review the results, correct the mistakes, and handle the hard examples the model cannot. A data-labeling pipeline: raw data flows into annotation guided by a labeling guideline, through a quality-review step, and out as a labeled dataset that trains a model, with corrections looping back to the guideline. [...] Every supervised model learns from examples that a person labeled first. Data labeling is that step: attaching the answer to raw data so a model can learn the pattern. It is unglamorous work, and it is where model quality is usually won or lost. [...] Large language models rely on human labeling too, in a different form. Beyond raw text, they are shaped by people writing example responses and ranking model outputs from best to worst. The InstructGPT work used exactly this: human labelers wrote demonstrations and ranked outputs, and that preference data, used

### Aug 20, 2026, 11:00 ET Vessev Enters U.S. Market With $19 Million Series A, Names First U.S. Customer And Launches Multi-City U.S. Showcase Vessev, a deep tech marine company changing the way the world moves on water, today announced $19 million in Series A and follow-on financing, its... ### Aug 20, 2026, 10:30 ET Math Magic Closes Series A+, Raising Nearly $50 Million Across Two Rounds in Six Months Math Magic, an AI creation company, today announced it has closed its Series A+ round, bringing total funding across two rounds in six months to... Cyberhill Announces a Major AI Breakthrough, Delivering Full Business Context to Anthropic's Claude Enterprise [...] ### Aug 24, 2026, 11:30 ET AI-Driven Portfolio Management Platform Standard Metrics Raises $20M to Supercharge Private Markets Innovation Standard Metrics, the AI-driven portfolio management platform for venture capital and private equity, today announced it has raised $20M in Series B... YL Ventures Ranks in PitchBook

Sapient Intelligence launches PRAXIST (Beta) to accelerate a new era of autonomous AI-led research and development.

3. (tied) Generalist AI, $200M, physical AI: San Francisco-based Generalist AI, a startup developing an AI foundation model that can work with a variety of robots, secured $200 million in fresh financing. The investment, an extension of its $400 million Series B in June, is reportedly led by 8VC 1. 3. (tied) Gatik, $200M, autonomous trucking: Gatik, an operator of driverless trucks, closed on $200 million in Series D funding. Qatar Investment Authority and Koch Disruptive Technologies led the round for the 9-year-old, Santa Clara, California-based company. [...] 5. Socure, $156M, predictive analytics: Socure, a provider of identity, risk and compliance tools, picked up $156 million in growth funding and acquired Fravity, an agentic platform for fraud and compliance operations. Summit Partners led the round, valuing the Incline Village, Nevada-based company at $5.2 billion. 6. Emerald AI, $150M, data center energy management: Emerald AI, a software platform that balances AI computatio

Skild AI develops artificial intelligence systems that enable robots to understand and act in the physical world using a shared foundational model called

Théophile Gervet, the CEO of Genesis AI, a vertically-integrated humanoid robotics company that raised a $105 million seed round this year, disagreed, telling TechCrunch “we’re too early in this wave for a brain strategy to work; our take us there’s lots of opportunities to co-design hardware and AI.” Gervet also touched on another hot topic in the sector: How specifically to focus your physical AI business. Robotics companies that are targeting specific tasks are getting their robots out in the field—Gritt is building solar farms, Agility is deploying robots in industrial settings, and Bedrock is operating excavators autonomously. Meanwhile, general-purpose humanoids aren’t getting out of the labs. [...] Harry Mellsop, a founder of Antioch, a startup that building simulation tools for model builders, suggests physical AI is in its “GPT 2 era,” the OpenAI model that pre-dated the arrival of ChatGPT. More data and compute will be needed to get over the hump, particularly GPUs optimized

Transfyr, the physical AI platform for real-world science, announced its launch today with $25 million in seed funding. The round was led by General

“You get this pattern of an investigation — you ask a bunch of questions of the data, and after you have been able to ask a number of questions, you assemble that together into a narrative,” Naghibzadeh said. “That became the genesis for the name QueryStory. It’s about telling stories with data, right? Putting a narrative together that’s grounded in truth.” QueryStory raised a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures at a valuation of $60 million, and has spent the intervening time developing and piloting its product with customers. QueryStory is aimed squarely at large enterprises that manage big, proprietary databases; it serves as a platform to unite data analysis and review for users like sales teams or operations managers. [...] “AI is more brittle than people realize when it comes to like building things that have to be durable and have large scale businesses relying upon them,” Tayler Sipperly, a partner at Brightmind Partners, told

The startup, which recently raised $21 million in a funding round led by Bessemer Venture Partners, was co-founded by Armen Aghajanyan and Akshat Shrivastava, who previously worked for Meta’s Fundamental AI Research (FAIR), the tech giant’s AI research division. The duo see their software as the future of industrial automated deployment. “Physical AI today forces a false choice: generalist foundation models that need multiple dedicated cloud GPUs for every instance, or narrow models that handle perception or control, but never both,” the company says. [...] REGISTER NOW ## Most Popular Two years after launch, Walmart’s Flipkart is closing in on India’s quick-commerce leaders + Jagmeet Singh Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research + Anna Heim Michael Polansky is training an AI model on skin that’s still alive + Connie Loizos How AI accounting startup Rillet raised $100M and became a unico

Silent speech recognition (SSR) provides an alternative communication pathway in the absence of audible speech. However, conventional approaches are limited by the need for constant facial attachment, privacy concerns, and unstable signal acquisition. Here, we propose a soft, active electromyography (EMG) interface that enables word-level SSR using machine learning. Worn on the hand, the device uses a fingertip electrode that can be positioned near the lips to acquire EMG signals only when needed. The interface integrates liquid metal (LM) interconnects, transparent flexible printed circuit (FPC) electrodes, and elastomer encapsulation to ensure high mechanical stability during finger motion. A deep neural network trained on these stable signals achieved a mean accuracy of 97.2 $\pm$ 1.3% across three subjects in classifying a 30-word vocabulary, demonstrating robust linguistic discrimination. Furthermore, real-time drone control validates the practicality of this approach in noisy and

Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging due to strong nonlinearities, distributed deformation, and uncertainty in their dynamics. This paper presents a real-time dynamic-model-based task-space feedback and estimation framework based on a non-minimal coordinate discrete elastic rod model formulated in absolute coordinates with holonomic constraints. The resulting structure preserves distributed mechanics while maintaining computational efficiency through sparse system matrices, enabling real-time control with up to 10 discretized rods. A quasi-static feedforward inverse model is combined with a task-space PI controller and a dynamic observer that fuses measurement residuals as virtual forces, enabling full-state estimation from sparse sensing. The approach is experimentally validated on three planar pneumatic soft actuators with varying geometries. Across five tasks, including drawing the digits 0-9 across the wor

Robots that can change their morphologies and behaviors for different tasks and environments hold great promise for adaptable, multifunctional systems. Modular reconfigurable robots (MRRs) can achieve such functionalities by docking and rearranging individual units, but most rely on rigid modules that lack structural compliance, resulting in limited capabilities. Continuum robots offer compliance through flexible backbones, yet they cannot self-reconfigure into task-adaptive multi-robot configurations. Here, we introduce an MRR that unifies the advantages of both architectures by combining a tensegrity-based compliant body with claw-based connection mechanisms. Each robot can manipulate and locomote independently, and multiple robots can self-reconfigure into different morphologies (e.g., chains, loops, branches) for cooperative manipulation and locomotion. We demonstrate the robots' capability across diverse tasks and environments, including coordinated object manipulation and transpo

Effective human-robot collaboration in industrial settings requires robots to understand human intentions and assist with task planning, reducing workload. Recent works have explored the use of Multi-modal Large Language Models (MM-LLMs) for task planning in such data-scarce scenarios, leveraging in-context learning to interpret user actions and generate long-horizon action plans in natural language. However, MM-LLMs inherently lack an understanding of system states and do not track state transitions, often leading to hallucinated actions that deviate from the intended goal. Additionally, generating action plans in natural language tends to limit the generated plans to a high level, introducing ambiguity in action execution. To address these limitations, we propose the State-aware Task Estimator and Planner (STEP), which prompts a MM-LLM to explicitly estimate the state of the system and predict the state transitions resulting from executed actions. By forecasting future states alongsi

The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understand global ocean systems. However, as these operations scale, manually routing and planning large autonomous fleets becomes exponentially complex and time-consuming. To address this, we propose a mixed-integer linear programming (MILP) model designed to automate and optimise MAV deployment schedules. The model accounts for strict operational constraints, including battery capacities and time windows for data collection, while aiming to maximise total data collection and minimise both the number of deployed vehicles and their energy consumption. A key novelty of this framework is integrating conventional ship itineraries, allowing MAVs to support vessels with mid-mission battery swapping or accelerated transit between waypoints. Computational experiments demonstrate that the model is highly scalable, solving routing problems for fleets of do

Most single-stage 3D object detectors complete different tasks with the same extracted features. Nevertheless, it is impossible to project features into a common space that is adaptive for all the tasks. We present a novel task-aware deformable prediction (TADP) method for single-stage 3D object detection to solve this problem. Firstly, a triple feature refinement aggregation module is designed to extract three-level features adaptively. Additionally, we design the multi-scale feature aggregation block to fuse multi-scale features in a scale-aware manner. Finally, the prediction of each task is deformed with the designed plug-and-play task-aware deformation head. It can percept the emphasis and interaction of each task. We also designed three different deformation modules. The experimental results demonstrate that the proposed deformation head shows good results on other detection methods. The experimental results on the KITTI dataset demonstrate that the car mAP is 80.91%, surpassing

This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match a target configuration using only egocentric observations and a top-down target layout. Unlike prior rearrangement tasks, ESRP precludes global state access and introduces mutual object occlusions, reflecting the practical constraints of real-world robotic deployment. These factors make aligning partial egocentric observations with the global target layout particularly challenging for long-horizon planning. To facilitate research, we present ESRP-Bench, a comprehensive benchmark built on OmniGibson featuring over 5,400 scene pairs and 8,200 objects. We define three multi-level metrics to evaluate rearrangement quality and provide four baselines: a hierarchical task-and-motion planning method, a vision-language-model-based method, and two learning-based approaches (IL and RL). Experimental results demonstrate that current methods struggl

Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio understanding tasks. However, they are typically evaluated on single, coherent audio segments, leaving their behavior under less familiar input configurations underexplored. We study this issue through a controlled setting in which two audio segments are concatenated into a single input. Across multiple LALMs, we observe a striking task-dependent robustness gap: automatic speech recognition (ASR) remains comparatively stable, whereas audio question answering (AQA) degrades substantially. To investigate the mechanisms underlying this disparity, we analyze how audio information is routed through LALM decoders using layer-wise attention knockout. The results reveal distinct task-dependent pathways. ASR relies primarily on direct retrieval from audio tokens by answer tokens, whereas AQA depends more strongly on a mediated route in which audio information is first integrated into prompt tok

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Strong analytical rigor, comfortable digging into large volumes of imagery/data to find patterns, not just running a script and reporting a number. Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or similar). Strong communication skills in English — you write clearly and engage well async.

We’re looking for detail-oriented specialists to join a long-term AI data annotation project focused on high-level video captioning, behavioral segmentation, and AI model verification. Successful contributors will begin with video annotation and segmentation tasks and gain priority access to advanced, high-paid projects. What you will do: High-Level Captioning: Write 1–2 sentence summaries

or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. ## Sign in to tailor your resume or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. About the Role We are hiring for a client building the next generation of data annotation infrastructure to serve enterprise AI companies. We need an experienced Solutions Engineer who has worked directly with clients at a major data labeling company and can help us establish both our technical foundation and market credibility. [...] thco - we are hiring! ### Solutions Engineer - Data Annotation & Labeling thco - we are hiring! # Solutions Engineer - Data Annot

Remote Data Annotation Jobs. Base pay range $30.00/hr - $50.00/hr ・ deliver high-accuracy labeled datasets. Work includes data labeling, RLHF preference ・

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About the Role: As the Data/Annotation Engineer, you'll be hands-on with the data itself. You'll administer the annotation toolchain, manage annotation workflows across the corpus, and produce the per-dataset documentation that feeds our governance framework. You'll work with the AI Solutions Engineer to ensure the data going into our models is accurate, well-labeled, and fully traceable. This role is for someone detail-obsessed who understands that great AI starts with disciplined, well-governed data. Key Responsibilities: Must-Have Qualifications: Nice-to-Have Qualifications: The expected hourly salary range for this position is $55 to $60 p/hour, based on experience, skills, and qualifications. Note to Candidates: [...] Banner # Data & Annotation Engineer Innodata (Nasdaq: INOD)

Base pay range $30.00/hr - $50.00/hr. Rex.zone connects candidates to full-time remote data labeling and evaluation projects that create high-quality training