# valuation Published articles for valuation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimal Regret for Profit Maximization in Bilateral Trade DevFeed: [Optimal Regret for Profit Maximization in Bilateral Trade]() Original publisher: [Read original article]() Author: Matteo Castiglioni, Anna Lunghi, Alberto Marchesi Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Algorithms]() Tags: [algorithm](), [cs-gt](), [cs-lg](), [curse-of-dimensionality](), [dimensions](), [distribution](), [fixed](), [learning](), [lower-bound](), [pricing](), [trade](), [valuation]() ### AI overview In repeated bilateral trade, an intermediary must learn a seller's acceptable purchase price and a buyer's acceptable sale price while observing only whether trade occurs, not either valuation. The paper presents an algorithm with regret \(\widetilde{\mathcal{O}}(T^{2/3})\) relative to the best fixed pair of prices, matching the classical one-dimensional dynamic-pricing lower bound up to logarithmic factors. The authors conclude that learning both sides is no harder in regret terms than learning one side, and pose whether this lack of a dimensionality penalty extends to higher dimensions. ### Source excerpt arXiv:2611.09558v1 Announce Type: cross Abstract: Learning to profit from trade requires learning both sides of a market: what a seller is willing to accept and what a buyer is willing to pay. We study repeated bilateral trade, where an intermediary posts a purchase price to a seller and a sale price to a buyer whose valuations are drawn from an unknown joint distribution. Trade occurs only if both parties accept their respective prices, in which case the intermediary earns the difference between the two prices. The intermediary observes only whether trade occurs, without observing either valuation. We show that learning both sides of the market is no harder, in terms of regret, than learning just one. Specifically, we design an algorithm achieving regret $\widetilde{\mathcal{O}}(T^{2/3})$ against the best fixed pair of prices, matching, up to logarithmic factors, the classical lower bound for one-dimensional dynamic pricing. Our result shows that the classical one-dimensional regret rate survives the transition to two dimensions, raising the question of whether this absence of a curse of dimensionality extends to arbitrarily high dimensions. ## OpenAI reportedly raises $11B in new funding at $1.25T valuation DevFeed: [OpenAI reportedly raises $11B in new funding at $1.25T valuation]() Original publisher: [Read original article]() Author: Maria Deutscher Published: 2026-11-06T23:43:40Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [OpenAI]() Tags: [ai](), [amazon](), [amazon-com](), [anthropic](), [anthropic-pbc](), [chatgpt](), [chatgpt-work](), [chip](), [coding](), [coding-tools](), [cyber](), [funding](), [funding-round](), [gpt-6-astra](), [initial-public-offering](), [investment](), [investors](), [ipo](), [microsoft](), [microsoft-corp](), [news](), [openai](), [openai-group](), [openai-group-pbc](), [raises](), [the-latest](), [top-story-5](), [valuation]() ### AI overview OpenAI reportedly received $11 billion in a funding round that could nearly double in size, with the article saying up to $9 billion more may follow. The backers were not named; Microsoft was cited as a possible participant based on its involvement in earlier rounds. The reported $1.25 trillion valuation is presented alongside OpenAI's revenue growth, enterprise expansion, and substantial chip and data center commitments. The article says the funding could help cover infrastructure costs before a planned 2027 public listing. These details are reported claims, not confirmed terms. ### Source excerpt OpenAI Group PBC has reportedly raised $11 billion as part of a funding round that could nearly double in size over the coming weeks. Sources told Bloomberg that investors wired the funds to the company today. The tipsters didn't name the backers. The list of possible participants includes Microsoft Corp., which contributed to at least [...] The post OpenAI reportedly raises $11B in new funding at $1.25T valuation appeared first on SiliconANGLE. ## Autonomous site reliability engineering startup Traversal bags $120M in funding at a $1B valuation DevFeed: [Autonomous site reliability engineering startup Traversal bags $120M in funding at a $1B valuation]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-11-04T03:47:11Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [site-reliability-engineering](), [AIOps](), [Agentic AI](), [Production Engineering](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-sre](), [anish-agarwal](), [artificial-intelligence](), [artificial-intelligence-agents](), [automate](), [autonomous](), [autonomous-site-reliability-engineer](), [autonomous-sre](), [cloud](), [funding](), [it-environments](), [it-infrastructure](), [khosla-ventures](), [kleiner-perkins](), [news](), [production-environments](), [reliability](), [reliability-engineering](), [site](), [site-reliability](), [site-reliability-engineer](), [site-reliability-engineering](), [startup](), [the-latest](), [traversal](), [valuation]() ### AI overview Traversal raised $120 million at a $1 billion valuation in a round led by Kleiner Perkins, Redpoint Ventures and Khosla Ventures, with Sequoia Capital, Battery Ventures and Amex Ventures participating. Its product is an autonomous SRE agent that analyzes operational logs, metrics and traces to establish normal behavior, diagnose infrastructure problems using causal machine learning, and recommend fixes for a human engineer to accept. The article says general-purpose frontier models score barely 25% on medium-difficulty SRE tasks in the ORCA-bench benchmark, while often hallucinating at rates above 40%; it does not provide a comparative accuracy figure for Traversal. The company reports revenue grew more than ninefold and monthly active users increased twentyfold over the past year, and names PepsiCo, DigitalOcean, Nubank and Harvey AI as customers. It plans to use the funding to expand engineering, go-to-market and research and development hiring. ### Source excerpt Traversal Inc., a startup that's trying to automate site reliability engineering tasks with artificial intelligence agents, seems to be closing in on that goal after raising $120 million in fresh funding, bringing its valuation to $1 billion. The newly minted unicorn's round was led by Kleiner Perkins, Redpoint Ventures and Khosla Ventures, three of Silicon [...] The post Autonomous site reliability engineering startup Traversal bags $120M in funding at a $1B valuation appeared first on SiliconANGLE. ## Generative media inference startup fal raises $1.3B at a $16B valuation DevFeed: [Generative media inference startup fal raises $1.3B at a $16B valuation]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-11-03T22:32:19Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [AI Infrastructure](), [Generative AI](), [Image]() Tags: [ai](), [ai-inference](), [ai-video](), [amazon](), [bessemer-venture-partners](), [burkay-gur](), [cloud](), [cloud-platform](), [coinbase-global](), [computing](), [developers](), [fal](), [features-labels](), [fireworks-ai](), [funding](), [generation](), [generative](), [generative-ai](), [generative-media](), [gorkem-yurtseven](), [hiring](), [hof-capital](), [image](), [inference](), [media](), [models](), [news](), [nvidia](), [oracle](), [raises](), [sequoia-capital](), [sound-ventures](), [startup](), [t-rowe-price-group](), [talia-goldberg](), [the-latest](), [top-story-6](), [valuation](), [video]() ### AI overview Fal raised $1.3 billion at a $16 billion valuation to expand computing capacity and hiring after demand outpaced supply and the company rate-limited customers. Its cloud platform serves image, video, and audio generation models through one API; customers cited include entertainment studios, advertising agencies, and game developers. Fal also post-trains some partner models to run faster, with recent work focused on video and arrangements that may involve licensing fees or revenue sharing. CEO Burkay Gur said training a fal model is not a near-term plan. The excerpt reports annualized revenue above $800 million earlier this year and notes that high compute costs keep gross margins thin across inference providers. ### Source excerpt Generative media inference startup fal today announced it has raised $1.3 billion in new funding at a valuation of $16 billion, according to Bloomberg. The company will spend the money on computing power and on hiring beyond its current 185 employees. Fal provides a cloud platform for running image, video and audio generation models, and developers [...] The post Generative media inference startup fal raises $1.3B at a $16B valuation appeared first on SiliconANGLE. ## AI inference startup Fal doubles its valuation to $16B as demand surges DevFeed: [AI inference startup Fal doubles its valuation to $16B as demand surges]() Original publisher: [Read original article]() Author: Marina Temkin Published: 2026-11-03T19:34:43Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [Inference](), [model-serving](), [AI Infrastructure]() Tags: [ai](), [ai-inference](), [bessemer-venture-partners](), [demand](), [fal](), [funding](), [funding-round](), [in-brief](), [inference](), [infrastructure](), [startup](), [startups](), [valuation]() ### AI overview Fal raised $1.3 billion in a Series F led by Bessemer Venture Partners, valuing the AI model hosting and inference startup at $16 billion--double its April valuation. It announced the round alongside its earlier Series E, reporting $1.65 billion raised across both; Sequoia led the earlier round. The Series F comes as demand for inference, especially for open-source models, grows. Fal had surpassed $800 million in annualized revenue earlier this year, according to Bloomberg's report. Other inference providers named in the text--Baseten, Fireworks AI and Modal--have also grown rapidly and raised large rounds, while providers' gross margins remain thin amid high compute costs. ### Source excerpt The five-year-old startup raised a $1.3 billion round led by Bessemer. ## PhoneValue-Bench: A Multimodal Benchmark and Datasets for Consumer Smartphone Recommerce Valuation DevFeed: [PhoneValue-Bench: A Multimodal Benchmark and Datasets for Consumer Smartphone Recommerce Valuation]() Original publisher: [Read original article]() Author: Zihan Deng, Jun Gao Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Ground truth / benchmark quality](), [Dataset development](), [Image](), [Benchmark]() Tags: [annotations](), [battery](), [benchmark](), [channel](), [cs-ai](), [cs-cv](), [datasets](), [defect](), [detection](), [evaluation](), [fusion](), [multimodal](), [orders](), [photos](), [production](), [quote](), [ranking](), [reading](), [reference](), [remote](), [smartphone](), [valuation]() ### AI overview PhoneValue-Bench evaluates remote smartphone trade-in valuation by combining cosmetic evidence from unordered multi-view photos with identity, battery, lock, and channel-risk information from diagnostic screenshots and staff forms. It pairs a dual-ground-truth evaluation suite with tier-gated scorecards and PhoneValue-Corpus, a weakly labeled training set covering over 20,000 orders and hundreds of thousands of captioned images. The authors also provide ValueAgent, a staged perception-fusion-valuation pipeline, plus a public scorer and submission schema. In the reported experiments, ValueAgent scores 84.1/100 on Bench-Full, with 97.0 appearance fill and 54.1 staff-field fill. ### Source excerpt arXiv:2611.00791v1 Announce Type: cross Abstract: As consumer smartphone recommerce scales, buyers must quote binding repurchase prices from seller-uploaded multi-view photos and diagnostic screenshots before devices reach the warehouse. Accurate remote valuation requires fusing noisy cosmetic evidence with identity, battery, lock, and channel risk from screenshots and staff forms, since an error in any modality changes both grade and payout. However, existing multimodal resources mainly target general VLM competence, screen reading, or factory defect detection, and largely overlook staff-aligned trade-in fields, dual appearance/staff ground truth, and executable valuation scorecards. This gap stems from two challenges: (1) the lack of large-scale real trade-in data with audited dual annotations and a compact ranking benchmark kept separate from training labels, and (2) the need to jointly reason over unordered multi-view cosmetics and diagnostic screens under operational deduction rules. To address these challenges, we propose PhoneValue-Bench, a dual-GT evaluation suite with tier-gated scorecards, together with PhoneValue-Corpus, a matched weakly labeled training set of over twenty thousand orders and hundreds of thousands of captioned images. We further present ValueAgent, a staged perception-fusion-valuation reference pipeline aligned with production inspection, plus an executable public scorer and submission schema. Experiments show that ValueAgent reaches a measured 84.1/100 on Bench-Full, with 97.0 appearance fill and 54.1 staff-field fill, establishing a practical dual-GT benchmark for multimodal recommerce valuation. ## OpenAI reportedly lowers funding target for new round DevFeed: [OpenAI reportedly lowers funding target for new round]() Original publisher: [Read original article]() Author: Anthony Ha Published: 2026-10-31T16:12:55Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [OpenAI]() Tags: [ai](), [ai-infrastructure-spending](), [anthropic](), [challenges](), [company](), [funding](), [funding-round](), [fundraising](), [in-brief](), [investors](), [ipo](), [openai](), [spending](), [stocks](), [target](), [tech-stocks](), [techcrunch](), [valuation](), [wall-street-journal]() ### AI overview Reports say OpenAI lowered its funding target from previously reported amounts of $30 billion at a $1.4 trillion valuation, later $1.25 trillion, to $20 billion, with the initial close expected to be smaller. The Financial Times reported that UAE investors would lead the round and a later retail offering to wealthy individuals could bring proceeds to the target. The Wall Street Journal reported $11 billion secured so far and a "tepid" response from some large investors. The article suggests investor caution about AI infrastructure spending and a tech stock sell-off may partly explain the reported fundraising difficulty; OpenAI had not commented to TechCrunch. ### Source excerpt OpenAI's latest funding round doesn't seem to be living up to expectations. ## Does 3D Pose Improve Action Valuation in Football? An Empirical Study of Pose Representations and Learning Objectives DevFeed: [Does 3D Pose Improve Action Valuation in Football? An Empirical Study of Pose Representations and Learning Objectives]() Original publisher: [Read original article]() Author: Rikuhei Umemoto, Kenjiro Ide, Soham Majumder, Ulf Brefeld, Keisuke Fujii Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Reinforcement learning](), [Machine learning](), [Representation learning](), [Model Development](), [Predictive AI]() Tags: [3d](), [cs-cv](), [cs-lg](), [football](), [learning](), [valuation]() ### AI overview In this study's SARSA-based football valuation setup, adding 3D pose rarely improved observed-action values relative to attack returns: it helped in 3 of 50 on- and off-ball comparisons, all with torso and head orientation, and worsened performance in 12, mostly with ST-GCN embeddings. Training choices mattered more; removing action-prediction loss improved position-only on-ball skill from 0.003 to 0.040 and its expected-goals correlation from 0.23 to 0.60, with return-prediction gains persisting at matched training length. Pose did help predict imminent behavior: raw joints raised 0.2-second-ahead on-ball action macro AUC from 0.914 to 0.934, and passer pose during execution raised pass-success AUC from 0.844 to 0.856. These findings are limited to the reported UEFA Champions League 2022/23 data and training protocol. ### Source excerpt arXiv:2610.38308v1 Announce Type: cross Abstract: Football action valuation estimates players' contributions to future scoring. Existing multi-agent deep reinforcement learning (MADRL) models use 2D positions and velocities, omitting posture information relevant to imminent actions. We investigate whether adding 3D pose to a SARSA-based model improves observed-action values relative to attack returns: goals, conceded goals or final expected possession value. Using UEFA Champions League 2022/23 event, tracking and 3D pose data, we compare six representations (no pose, raw joints, torso and head orientation, and spatial-temporal graph convolutional network (ST-GCN) embeddings) across five learning settings and three seeds. The settings vary temporal-difference targets and action-prediction loss, with Monte Carlo return regression as a reference. Across 50 on-ball and off-ball comparisons, pose robustly improved return prediction in only three, all using torso and head orientation, while worsening it in twelve, mostly with ST-GCN. Training choices had greater effects: removing the action-prediction loss raised on-ball skill of position-only values from 0.003 to 0.040 and their correlation with expected goals from 0.23 to 0.60; the improvement in return prediction persisted at matched training length. Separately, pose improved prediction of the on-ball action 0.2 s ahead (macro AUC 0.914 to 0.934 with raw joints), while the passer's pose during execution improved pass-success prediction (AUC 0.844 to 0.856). Within this training protocol, pose informed imminent actions but rarely improved observed-action values. ## Wabi, the AI agent that builds mini apps, now valued at $1.25B after latest round DevFeed: [Wabi, the AI agent that builds mini apps, now valued at $1.25B after latest round]() Original publisher: [Read original article]() Author: Sarah Perez Published: 2026-10-29T14:35:26Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [AI Agent]() Tags: [a16z](), [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-assistant](), [ai-companion](), [alexa](), [amazon](), [amazon-alexa](), [app](), [apps](), [ivp](), [mini](), [mini-apps](), [personal](), [personal-agent](), [valuation](), [wabi]() ### AI overview Wabi raised $55 million at a $1.25 billion valuation as it pivots from letting users build apps with prompts to an AI messenger and "personal agent." The company says Wabi 2.0 can build interfaces and mini apps for tasks such as meal planning, tracking local restaurants, and creating fitness plans. The excerpt presents the funding as investor support for this agent-focused direction. IVP led the round; participants included a16z, Forerunner, Amazon Alexa Fund, and several angel investors. ### Source excerpt On Thursday, AI app, Wabi, now describing itself as a "personal agent," said it has raised $55 million at a $1.25 billion valuation. ## Serval raises $150M at $3B valuation to bring its automation agents beyond IT DevFeed: [Serval raises $150M at $3B valuation to bring its automation agents beyond IT]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-29T13:00:58Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [workflow automation](), [Automation](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [alex-mcleod](), [anas-biad](), [apps](), [automation](), [catalyst](), [conviction-capital](), [elad-gil](), [enterprise-service-management](), [evantic-capital](), [first-round-capital](), [fox](), [funding](), [general-catalyst](), [headline](), [it](), [it-service-management](), [jake-stauch](), [live-nation-entertainment](), [news](), [proserval](), [raises](), [redpoint-ventures](), [sequoia-capital](), [series-c](), [serval](), [servicenow](), [startups](), [the-latest](), [top-story-5](), [valuation](), [venture-capital](), [wpp]() ### AI overview Serval raised $150 million at a $3 billion valuation to expand its AI workflow automation platform beyond IT into areas including HR, finance and legal. Employees describe workflows in plain language, and agents create automations to handle incoming requests. Its Catalyst tool scans support tickets for repetitive work and drafts automations for administrator review before launch. The funding also supports ProServal, which plans to place more than 100 automation engineers at large customers to map workflows and build automations. Serval says most customers are operational within 30 days; it did not disclose revenue. The company names ServiceNow as its main competitor and says one automaker replaced that platform in 45 days, including IT service management, configuration management and asset tracking. ### Source excerpt Enterprise service management startup Serval Inc. announced today it has raised $150 million in new funding at a $3 billion valuation to bring its automation agents to more departments beyond information technology. Serval offers a service management platform where staff describe a workflow in plain language and artificial intelligence agents write the automation, then use [...] The post Serval raises $150M at $3B valuation to bring its automation agents beyond IT appeared first on SiliconANGLE. ## Upwind Security Raises $400 Million at $3.8 Billion Valuation DevFeed: [Upwind Security Raises $400 Million at $3.8 Billion Valuation]() Original publisher: [Read original article]() Author: Mike Lennon Published: 2026-10-28T13:56:50Z Content type: news Language: en Sources: [SecurityWeek]() Topics: [Security](), [Cloud & Application Security](), [threat detection](), [network security]() Tags: [acquisition](), [apis](), [apis-and-data](), [attacks](), [bessemer](), [bessemer-venture-partners](), [c](), [cloud-security](), [cloud-security-platform](), [cybersecurity](), [cybersecurity-funding](), [datadog](), [development](), [funding](), [funding-round](), [interest](), [raises](), [security](), [upwind](), [upwind-security](), [valuation]() ### AI overview Upwind Security announced a $400 million Series C at a $3.8 billion valuation, nine months after a $250 million round at a $1.5 billion valuation; total funding now exceeds $800 million. Its runtime-native cloud security platform is described as using runtime intelligence to surface critical exposures, help teams remediate risks, and detect attacks in real time, with visibility into networks, APIs, and data flows. The round was led by Bessemer Venture Partners, with Canapi Ventures and existing investors participating. Reports of acquisition interest are disputed: The Information said Datadog offered nearly $4 billion, while CTech cited information indicating Datadog had made no such offer. ### Source excerpt The massive funding round comes just days after reports emerged that Upwind had attracted acquisition interest from several companies, including Datadog The post Upwind Security Raises $400 Million at $3.8 Billion Valuation appeared first on SecurityWeek. ## Cloud security startup Upwind raises $400M at $3.8B valuation as revenue quadruples DevFeed: [Cloud security startup Upwind raises $400M at $3.8B valuation as revenue quadruples]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-28T12:00:23Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [cloud security](), [ai security](), [alert triage](), [observability](), [Cloud & Application Security](), [Cloud](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-security](), [alert](), [alerts](), [amazon-web-services](), [amiram-shachar](), [artificial-intelligence](), [aws](), [aws-security-hub](), [azure](), [bessemer](), [bessemer-venture-partners](), [canapi-ventures](), [carvana](), [cloud](), [cloud-security](), [craft-ventures](), [cyberstarts](), [datadog](), [devops](), [funding](), [google](), [google-cloud](), [greylock-partners](), [microsoft](), [microsoft-azure](), [netapp](), [news](), [oracle](), [oracle-cloud-infrastructure](), [picture-capital](), [raises](), [revenue](), [roku](), [runtime-security](), [salesforce](), [salesforce-ventures](), [security](), [security-startup](), [series-c](), [siemens](), [startup](), [the-latest](), [top-story-4](), [upwind](), [valuation]() ### AI overview Upwind raised $400 million in a Series C led by Bessemer Venture Partners, valuing the cloud and AI security startup at $3.8 billion; it says revenue has quadrupled so far in 2026. Its platform combines runtime data from cloud workloads, APIs and AI agents into a shared view, with findings traceable to underlying code. Upwind says built-in specialized AI agents can investigate and confirm alerts and carry out fixes, while human teams focus on judgment and response. AWS customers can access it through AWS Security Hub and pay through their AWS account; the platform also runs on Azure, Google Cloud and Oracle Cloud Infrastructure. Upwind plans to use the funding for product development and broader go-to-market efforts. These capabilities and customer figures are reported by the company. ### Source excerpt Cloud and artificial intelligence security startup Upwind Security Inc. today revealed that it has raised $400 million in new funding at a valuation of $3.8 billion. Reports in early September had the round at $300 million. By the time it closed, Upwind had taken in an additional $100 million, and the company used the announcement [...] The post Cloud security startup Upwind raises $400M at $3.8B valuation as revenue quadruples appeared first on SiliconANGLE. ## Upwind Raises $400M Series C at $3.8B Valuation as Revenue Quadruples in 2026 DevFeed: [Upwind Raises $400M Series C at $3.8B Valuation as Revenue Quadruples in 2026]() Original publisher: [Read original article]() Author: Kevin John Published: 2026-10-28T12:00:02Z Content type: news Language: en Sources: [HackerNoon]() Topics: [ai security](), [observability](), [Artificial Intelligence](), [Local AI]() Tags: [2026](), [agents](), [ai](), [ai-agent-cloud-security](), [ai-security](), [amazon-web-services](), [apis](), [attack-surface](), [autonomous-threat](), [aws-security-hub-extended](), [azure](), [bessemer-venture-partners](), [canapi-ventures](), [carvana](), [cloud](), [cloud-attack-path-validation](), [cnapp-runtime-intelligence](), [confluent](), [cybersecurity-funding](), [runtime](), [runtime-cloud-security](), [security](), [series-c](), [upwind](), [upwind-security](), [valuation]() ### AI overview Upwind Security closed a $400 million Series C at a $3.8 billion valuation after reporting that revenue quadrupled in 2026. The round, led by Bessemer Venture Partners, will fund product development and go-to-market expansion. Upwind says its platform combines live runtime intelligence with context from cloud workloads, APIs, and AI agents to help teams prioritize exposures, remediate risks, and detect attacks. It supports AWS, Azure, Google Cloud Platform, and Oracle Cloud Infrastructure; its AWS Security Hub Extended integration offers access through the AWS console, simplified onboarding, and unified billing. The company is also developing AI agents for threat investigation, attack-surface validation, and infrastructure remediation. The article presents customer testimonials but does not provide independent performance measurements. ### Source excerpt Upwind Security reports a $400M Series C at a $3.8B valuation as revenue quadruples, supporting expansion of runtime cloud and AI security. ## Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football DevFeed: [Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football]() Original publisher: [Read original article]() Author: Kenjiro Ide, Taiga Someya, Kohei Kawaguchi, Keisuke Fujii Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Reinforcement learning](), [Machine learning](), [Sports]() Tags: [actions](), [analysis](), [context](), [cs-lg](), [data](), [defensive](), [football](), [game](), [horizon](), [independent](), [interpretability](), [learning](), [linear](), [markov](), [multi-agent](), [multi-agent-system](), [position](), [positioning](), [reinforcement-learning](), [reward](), [valuation]() ### AI overview The proposed MPE-inspired framework values actions for all 22 football players by modeling each possession as a finite-horizon dynamic game. It uses Expandable Decision-Making States (EDMS) and decomposes Q-values into a successor-feature basis and linear reward weights; the basis is initialized with linear temporal-difference learning and refined nonlinearly from observed transitions. On tracking and event data from 95 J1 League matches, the study compares it with an independent reinforcement-learning baseline using Position and Velocity States (PVS) and EDMS. With EDMS fixed, the baseline most often favored forward movement (99.21% of evaluated off-ball states), while the proposed method's most frequent direction accounted for 17.63%, indicating more varied valuations. Qualitative analysis shows context-dependent off-ball values but identifies limitations in representing defensive positioning. The abstract describes an approach that supports analysis under alternative tactical reward weights. ### Source excerpt arXiv:2610.05961v2 Announce Type: replace Abstract: Valuing player actions in football, a complex multi-agent system, requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positioning. Existing reinforcement-learning-based valuation methods commonly incorporate other players through the observed state while retaining independent-agent value formulations. To our knowledge, we introduce the first Markov perfect equilibrium (MPE)-inspired framework for valuing the actions of all 22 players using real football tracking and event data, formulating each possession as a finite-horizon multi-agent dynamic game. To improve interpretability, the framework uses Expandable Decision-Making States (EDMS) and decomposes each Q-value into a successor-feature basis and a linear reward-weight vector. The value basis is estimated through a two-stage procedure combining linear temporal-difference initialization with nonlinear iterative refinement from observed state-action transitions. Using tracking and event data from 95 J1 League matches, we compare the proposed formulation with an independent reinforcement learning baseline under both Position and Velocity States (PVS) and EDMS representations. With EDMS held fixed, the independent baseline assigns the highest value to forward movement in 99.21% of evaluated off-ball states, whereas the most frequent direction under the proposed formulation accounts for 17.63%. Qualitative analyses illustrate context-dependent off-ball valuations and highlight limitations of the current state representation for defensive positioning. Overall, the framework provides an MPE-inspired approach to interpretable action valuation in football, enabling analysis under alternative tactical reward weights. ## Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science DevFeed: [Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science]() Original publisher: [Read original article]() Author: Hiroyuki Chuma, Kanji Otsuka, Yoichi Sato, Kodo Yamauchi Published: 2026-10-23T04:00:00Z Content type: opinion Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Computer science](), [knowledge-engineering]() Tags: [ai](), [audit](), [confidence](), [craft](), [cs-ai](), [cs-ne](), [dag](), [generate](), [knowledge](), [llms](), [memory](), [mentor](), [science](), [student](), [tacit](), [trace](), [transmission](), [valuation]() ### AI overview The paper models tacit knowing as a memory's operating regime, using VaCoAl, an XOR-and-shift hyperdimensional memory over GF(2), implemented as CASRAM and PyVaCoAl. Its DAG offers two readings: Rescue repairs every collision for an exactly auditable trace but does not rank paths; Don't Care tolerates collisions and produces path confidence (CRâ‚‚), which favors direct routes. The authors use this audit-valuation tradeoff to distinguish query-revealed tacitness from regime-bound tacitness, which can be stated only after it is lost. For AI-generated science, they propose that LLMs generate results, VaCoAl/PyVaCoAl preserve traces and confidence, and people evaluate their value--a role they argue cannot be delegated. They characterize untraceable, checkable AI results as "rootless explicit knowledge" and frame their broader claims, including conviviality as a basis for evolvability, as conjectures or testable predictions. ### Source excerpt arXiv:2610.16102v2 Announce Type: replace-cross Abstract: We argue that tacit knowing is better modelled as an operating regime of a memory than as a kind of content. Our substrate is VaCoAl, an algebro-deterministic XOR-and-shift hyperdimensional memory over GF(2), realized as CASRAM and PyVaCoAl. In it, a DAG reads two ways. Repairing every collision (Rescue) makes the trace exactly auditable but ranks no path higher; tolerating them (Don't Care) yields a path confidence, CR$_2$, silently favouring direct routes. This audit-valuation complementarity formalizes Polanyi's claim that tacit and explicit are not sharply divided. It separates contingent tacitness, which a new query brings out (AlphaGo's game tree), from regime-bound tacitness, statable only once it is gone (AlphaGo's hunches). (1) Polanyi's from-to structure reads as non-commutative binding. (2) Explicating the valuation destroys it, as with Polanyi's pianist, yet a closed-form law states it. Polanyi, Nonaka, Tsoukas and Collins each capture part of this, narrowly; even Nonaka and von Krogh's continuum cannot tell the classes apart. (3) Collins's three tacit knowledges become strata, one outside the substrate. (4) Our mentor-student DAG is a tacit-transmission graph. (5) Tao's remark that AI-solved problems spawn few follow-ups reveals rootless explicit knowledge: checkable results no one can trace or yet understands. To re-root them, LLMs generate, VaCoAl/PyVaCoAl keeps an auditable trace with its confidence, and people value, a role virtue epistemology shows cannot be offloaded. Art, craft and skill show the same decoupling of form from the maker's becoming. (6) On Mercier and Sperber's account an LLM is reason's arguing side set loose, a sophist; truth-seeking lies deeper, in Polanyi's conviviality, which we conjecture is the root of evolvability and, as the Buddhist idea that plants and lands attain buddhahood suggests, reaches beyond humans. Testable predictions follow. ## ValuSpec: Plug-and-Play Candidate Valuation before Target Verification for Tree-Based Speculative Decoding DevFeed: [ValuSpec: Plug-and-Play Candidate Valuation before Target Verification for Tree-Based Speculative Decoding]() Original publisher: [Read original article]() Author: Liang He, SiYuan Ma, Qishi Zhan, Yongqi Fan, Mingyu Cao, Qizhen Lan, Zhaolu Kang, Xilu Wang Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [LLM optimization](), [Model Optimizer](), [Decoding]() Tags: [cs-ai](), [efficiency](), [evaluation](), [inference](), [speculative-decoding](), [valuation](), [verification]() ### AI overview ValuSpec filters low-quality branches from a candidate tree before costly target-model verification, aiming to reduce wasted computation as retrieval-based trees grow. It uses transfer-based supervision and estimates a filtering threshold from the target model's greedy trajectories on a calibration split, without parameter training. The target then verifies the pruned tree using an output-identical greedy rule, which the authors say guarantees accepted tokens match standard autoregressive decoding. On HumanEval, GSM8K, Dolly, and SpecBench, the abstract reports up to 2.38x end-to-end speedup in retrieval-based tree settings; integration with EAGLE-3's dynamic tree framework reached up to 2.78x without changing its tree construction strategy. ### Source excerpt arXiv:2610.31338v1 Announce Type: new Abstract: Tree-based speculative decoding accelerates large language model inference by verifying multiple candidate branches in parallel. However, in practical serving scenarios, similar requests repeatedly induce overlapping candidate branches. While retrieval-based methods mitigate this by reusing historical drafts, the resulting candidate trees often contain many low-quality branches as the candidate tree grows, which reduces verification efficiency and wastes target-side computation. To address this, we propose ValuSpec, a plug-and-play candidate valuation mechanism that filters low-quality branches from a supplied candidate tree before expensive target-side verification. ValuSpec introduces transfer-based supervision to align filtering with the target model's behavior. A filtering threshold is estimated from the target model's greedy trajectories on a calibration split, without any parameter training. The target model then verifies the filtered tree under the output-identical greedy rule, so accepted tokens are guaranteed to match standard autoregressive decoding exactly. Experimental results on HumanEval, GSM8K, Dolly, and SpecBench show that, in our main evaluation, ValuSpec achieves up to 2.38x end-to-end speedup in retrieval-based tree settings. Furthermore, integrating ValuSpec into the EAGLE-3 dynamic tree framework yields up to 2.78x end-to-end speedup without modifying its tree construction strategy, demonstrating that ValuSpec serves as a generally applicable and plug-and-play candidate valuation module. ## Report: OpenAI closing on $30B 'bridge' funding round to tide it over before IPO DevFeed: [Report: OpenAI closing on $30B 'bridge' funding round to tide it over before IPO]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-10-23T00:47:28Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Finance](), [Artificial Intelligence](), [OpenAI]() Tags: [ai](), [anthropic](), [artificial-intelligence](), [bloomberg](), [bridge](), [bridge-round](), [claude](), [coatue](), [commercial-deal](), [compute](), [funding](), [funding-round](), [investment](), [investors](), [ipo](), [news](), [openai](), [openai-ipo](), [public-launch](), [public-markets](), [sam-altman](), [the-latest](), [top-story-5](), [top-story-6](), [uae-investors](), [valuation]() ### AI overview CNBC reports that OpenAI is in advanced talks for a roughly $30 billion funding round led by investors from the UAE, with Coatue expected to participate. The proposed investment is described as an opportunistic bridge to fund the business while IPO plans are finalized; its size is unsettled, and OpenAI reportedly hopes for commitments by the end of the following week. The text says the round would follow OpenAI's $122 billion raise in March and notes that its IPO timing has shifted: Sarah Friar told employees it would be public in 2027, while Sam Altman called going public this year ill-advised. OpenAI is also reportedly discussing a separate UAE deal for Middle East compute capacity. These arrangements are described as talks, not completed agreements. ### Source excerpt Artificial intelligence leader OpenAI Group PBC might have plans to go public in the near future, but it isn't done with private funding rounds. Indeed, it's currently believed to be negotiating with investors an additional $30 billion investment that would bring its valuation to a staggering $1.25 trillion, which would make it the most valuable [...] The post Report: OpenAI closing on $30B 'bridge' funding round to tide it over before IPO appeared first on SiliconANGLE. ## Year-old AI research lab Flapping Airplanes raises $1B at a reported $6B valuation DevFeed: [Year-old AI research lab Flapping Airplanes raises $1B at a reported $6B valuation]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-21T22:18:30Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [AI Research](), [Artificial Intelligence](), [AI Development](), [Frontier AI]() Tags: [ai](), [ai-models](), [ai-research](), [ai-training](), [aidan-smith](), [algorithms](), [artificial-intelligence](), [asher-spector](), [atreides-management](), [ben-spector](), [bloomberg](), [board](), [chip](), [company](), [compute](), [computer-science](), [data-efficiency](), [flapping-airplanes](), [funding](), [gpus](), [gv](), [index-ventures](), [inference](), [jump-trading](), [kleiner-perkins](), [lab](), [lux-capital](), [mark-xu](), [megakernels](), [neuralink](), [news](), [nvidia](), [prod](), [raises](), [sequoia-capital](), [series-a](), [shardul-shah](), [startup](), [the-latest](), [thunderkittens](), [top-story-6](), [valor-equity-partners](), [valuation](), [xtx-ventures]() ### AI overview Flapping Airplanes raised $1 billion at a reported $6 billion valuation about a year after its founding, though it has no commercial product yet. The lab aims to build AI models that use far less data and spent much of its first year developing GPU primitives. It describes its megakernel framework as able to run a full training run, inference serving, and a video game within one GPU kernel, allowing memory-bound inference to overlap with compute-bound training. The company says this work could reshape AI's cost structure; these are company claims, not independently established results in the excerpt. Index Ventures and Lux Capital co-led the Series A. Investors have put $1.18 billion into the company, and Bloomberg reported the valuation had roughly quadrupled since the seed round. ### Source excerpt Artificial intelligence research lab Flapping Airplanes Inc. today announced it has raised $1 billion in new funding, about a year after it was founded. Investors valued the startup at $6 billion in the deal, Bloomberg reported. No commercial product has come out of the lab yet. Flapping Airplanes is trying to build AI models that [...] The post Year-old AI research lab Flapping Airplanes raises $1B at a reported $6B valuation appeared first on SiliconANGLE. ## Blacksmith hits unicorn valuation after Sequoia leads Series C DevFeed: [Blacksmith hits unicorn valuation after Sequoia leads Series C]() Original publisher: [Read original article]() Author: Dominic-Madori Davis Published: 2026-10-21T17:18:27Z Content type: news Language: en Sources: [TechCrunch | AI News & Artificial Intelligence]() Topics: [CI/CD]() Tags: [ai](), [blacksmith](), [build](), [ci](), [code](), [in-brief](), [sequoia](), [series-c](), [software](), [startups](), [unicorn](), [unicorn-valuation](), [valuation](), [venture]() ### AI overview Blacksmith raised $66 million in a Sequoia-led Series C at a $1 billion valuation, two months after its $45 million Series B valued it at $550 million. The company provides continuous integration (CI) tools to build, test, merge, and validate code, including AI-written software, addressing rising demand for automated checks. It reports 17x annual recurring revenue growth and more than 90 million CI jobs run. Customers named include Mercury and Granola; total funding is $125 million. ### Source excerpt Blacksmith, a platform that lets companies build and test AI code more effectively, just announced a $66 million Series C round led by Sequoia that values the company at $1 billion. ## Legal AI startup Legora raises $550M at $8.8B to change the face of the law DevFeed: [Legal AI startup Legora raises $550M at $8.8B to change the face of the law]() Original publisher: [Read original article]() Author: Kyt Dotson Published: 2026-10-21T15:55:35Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [AI Strategy]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-startup](), [ai-startup-funding](), [annual-recurring-revenue](), [apps](), [artificial-intelligence](), [atreides-management](), [automation](), [baron-capital](), [funding](), [gavin-baker](), [investors](), [law](), [legal](), [legal-ai](), [legal-ai-startup](), [legora](), [max-junestrand](), [news](), [raises](), [series](), [startup-funding](), [the-latest](), [top-story-6](), [valuation]() ### AI overview Legora raised $550 million in a Series E at an $8.8 billion post-money valuation, led by Atreides Management, with new and existing investors participating. The company reports more than $200 million in annual recurring revenue, up from $1 million in under 18 months, and says its platform supports over 140,000 legal professionals at more than 2,400 firms and in-house teams across 80 markets. Legora offers legal AI agents for multi-step work and plans to use the funding for global expansion and further development of its agentic AI legal operating system, aOS. In-house teams represented 40% of its new customers in the third quarter. The article cites a Deloitte survey in which 61% of legal departments were deploying AI but only 10% had fully embedded it in workflows; 84% had not redesigned roles around AI. ### Source excerpt Stockholm-based legal artificial intelligence startup Legora AB today announced it raised a notable $550 million in late-stage funding at an $8.8 billion post-money valuation. Atreides Management led the Series E round, which also brought participation from new investors Baron Capital, Capital Group, Fidelity Management & Research Company, Glade Brook Capital Partners and J.P. Morgan. Every [...] The post Legal AI startup Legora raises $550M at $8.8B to change the face of the law appeared first on SiliconANGLE. ## CAVA: Complementary-Label-Aware Data Valuation DevFeed: [CAVA: Complementary-Label-Aware Data Valuation]() Original publisher: [Read original article]() Author: Tan-Ha Mai, Hsuan-Tien Lin, Vu Nguyen Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning]() Tags: [annotation](), [benchmarks](), [cost](), [cs-cv](), [cs-lg](), [learning](), [margin](), [noise](), [optimal-transport](), [precision](), [random](), [supervision](), [synthetic](), [training](), [validation](), [valuation](), [vision-language-models-vlms]() ### AI overview CAVA addresses annotation errors in complementary-label learning (CLL), where mistakenly naming the true class as the class an instance does not belong to can train a model to suppress the correct answer. Directly transferring optimal-transport data valuation from ordinary labels fails because training and clean validation labels have opposite meanings: its score reverses below the 1/M noise crossover, a range the abstract says is typical in practice. CAVA adds a complementary-label cost and label margin to isolate each label's contribution to the transport potential. The margin retains a consistent direction across noise rates, supports diagonal-noise detection with a parameter-free threshold, and lets detected samples be reused as ordinary-label supervision. On synthetic and human-annotated benchmarks, the authors report near-perfect separation of diagonal noise where transferred OT performs worse than random; with a fraction of clean validation data, CAVA improves every evaluated CLL objective. ### Source excerpt arXiv:2610.26734v1 Announce Type: new Abstract: Complementary-label learning (CLL) trains a classifier from labels that name a class an instance does not belong to. Such labels are cheap to collect from crowdworkers or vision-language models (VLMs), making them attractive for large-scale annotation. However, both sources can mistakenly assign the true class as the complementary label. Such annotation errors are particularly harmful in CLL because the model is explicitly trained to suppress the correct answer. Optimal transport (OT) data valuation identifies harmful samples by comparing training data with a clean validation set, but transferring it directly to CLL creates a semantic mismatch: label $c$ means not class $c$ in the training but class $c$ in the validation. We show that this makes the OT label groups noisy mixtures whose behavior depends on the noise rate. The transferred score is informative only at sufficiently high noise, becomes uninformative at the crossover rate $1/M$ for $M$ classes, and reverses below it, where practical CLL annotation typically operates. We propose CAVA, which introduces a complementary label cost and a label margin that isolates the contribution of each complementary label to the transport potential. The margin keeps a consistent direction across noise rates, detects diagonal noise with a parameter-free threshold of closed-form precision, and allows detected samples to be recycled as ordinary-label supervision. On synthetic and human-annotated benchmarks, CAVA separates diagonal noise almost perfectly where directly transferred OT valuation performs worse than random. With only a fraction of the clean validation data, it also improves every CLL objective we evaluate, showing that OT-based valuation becomes reliable once the semantics of complementary supervision are modeled explicitly. ## Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation DevFeed: [Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation]() Original publisher: [Read original article]() Author: Mingyu Guo, Jiayuan Liu, Vincent Conitzer Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Operations research and optimization](), [Neural Network](), [Automated reasoning]() Tags: [adversarial](), [consumer](), [cs-gt](), [cs-lg](), [datasets](), [distributions](), [econ-th](), [formal](), [greedy](), [independent](), [mechanism](), [mechanism-design](), [neural-network](), [np-hard](), [pipeline](), [revenue](), [theoretical](), [valuation](), [verification]() ### AI overview For correlated bidders, extending Myerson-style allocation rules can produce non-monotonic allocations that are not strategy-proof, and optimal allocation is NP-hard to approximate. The paper proposes interpolating Myerson's greedy allocation based on marginal profits with neural networks, formally verifying exact strategy-proofness, and constructively repairing violations. The authors report near-optimal revenue across synthetic, adversarial, and real-world datasets, often reducing the revenue gap versus manual and neural baselines by an order of magnitude. They introduce greedy revenue as an empirical upper bound when the true optimum is elusive, extend the approach to multi-unit auctions with unit demand, and apply the verification techniques to make RegretNet exactly strategy-proof. ### Source excerpt arXiv:2610.24240v1 Announce Type: cross Abstract: We aim to design revenue-maximizing single-item auctions that are deterministic, strategy-proof, and ex post individually rational --- the quintessential fundamental model for optimal mechanism design. While Myerson's seminal work solved this model for independent bidders, straightforward extensions to correlated settings often result in non-monotonic, and therefore non-strategy-proof allocations. Critically, optimal allocation design for correlated bidders is NP-hard to approximate, rendering theoretical optimality guarantees in this domain computationally intractable. We establish a new standard of empirical rigor for this theoretically hard model by proposing an empirical pipeline. We synthesize neural interpolation of Myerson's greedy allocation based on marginal profits (the correlated variant of virtual valuation), formal neural verification to enforce exact strategy-proofness, and a constructive repair procedure. Empirically, our method is highly effective, consistently achieves near-optimal revenue across a wide range of distributions, including synthetic, adversarial, and real-world (consumer, financial, and industrial) datasets. Compared to existing manual and neural baselines, our approach shows substantial improvement, often reducing the revenue gap by an order of magnitude. Our study is also the first to introduce (unattainable) greedy revenue as a rigorous upper bound for empirical benchmarking, providing a definitive quantitative performance measure where true optima remain theoretically elusive. We demonstrate the generality of our approach by extending it to multi-unit auctions with unit demand and integrating our verification techniques into RegretNet to achieve exact strategy-proofness. ## AI infrastructure management software startup ASI hits $3B valuation after raising $325M in funding DevFeed: [AI infrastructure management software startup ASI hits $3B valuation after raising $325M in funding]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-10-21T02:11:00Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [AI Infrastructure]() Tags: [ai](), [ai-infrastructure](), [ai-modeling](), [ai-models](), [ai-platform](), [ai-simulation](), [ai-systems](), [air](), [air-space-intelligence](), [air-traffic-simulation](), [artificial-intelligence](), [asi](), [citadel](), [craft](), [energy](), [energy-simulation](), [frontier-models](), [funding](), [infrastructure](), [management](), [military-command-and-control](), [models](), [news](), [phillip-buckendorf](), [power-grid-simulation](), [series-c](), [software](), [spark-capital](), [startup](), [the-latest](), [traffic](), [valuation]() ### AI overview ASI builds AI models that simulate air traffic, energy grids, and other physical operations so infrastructure operators can forecast scenarios and plan in real time. Its aviation system combines flight paths, live traffic, weather, and controller staffing to predict congestion; the FAA has deployed it at Dulles and plans broader airport expansion. ASI also supplies command-and-control software to the U.S. Army and Air Force and planning tools to National Grid. The company raised $325 million in Series C funding at a $3 billion valuation, following an $875 million multiyear FAA agreement and a separate five-year, $99 million Army deal. It plans to expand in aviation, defense, and energy, enter logistics, and grow research and engineering. ### Source excerpt An artificial intelligence startup called ASI Inc., which builds complex models of air traffic and energy grids, said today it has scored $325 million in a Series C funding, bringing its valuation to a cool $3 billion. The round was led by Spark Capital and joined by participants including Citadel Securities, Craft Ventures, Meritech Capital, [...] The post AI infrastructure management software startup ASI hits $3B valuation after raising $325M in funding appeared first on SiliconANGLE. ## Eka Robotics nets $150M at a $1.45B valuation for robot hands that sense force DevFeed: [Eka Robotics nets $150M at a $1.45B valuation for robot hands that sense force]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-21T00:13:47Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [robot sense of touch](), [Robot Manipulation](), [Physical AI]() Tags: [ai](), [andreessen-horowitz](), [artificial-intelligence](), [camera](), [dexterity](), [e14-fund](), [eka-robotics](), [emerging-tech](), [f-prime](), [fidelity-investments](), [funding](), [google-plus-deepmind](), [grippers](), [improbable-ai-lab](), [kris-hauser](), [manipulation](), [massachusetts-institute-of-technology](), [model](), [news](), [obvious-ventures](), [pitchbook](), [pulkit-agrawal](), [robot](), [robot-dexterity](), [robotic-manipulation](), [robotics](), [sensors](), [series-a](), [the-latest](), [tuomas-haarnoja](), [university-of-illinois-urbana-champaign](), [valuation](), [vision-force-action-model]() ### AI overview Eka Robotics raised $150 million at a reported $1.45 billion valuation to expand its robotic manipulation model, which combines camera images with force-sensor data. A custom gripper senses how firmly it holds an object, allowing the robot to adjust its grip if an item slips. The company reports about 1,500 operations per hour on objects outside its training data, more than 95% pick success, and handling of objects up to 1 kilogram; these figures are company claims. Its gripper costs under $1,000 and is rated for 5 million duty cycles, short of Eka's 20 million target for standard industrial arms. Unnamed industrial pilots are underway in pick-and-place and wire handling. A University of Illinois professor praised the system's adaptation to varied objects but questioned year-long reliability and generalization beyond pick-and-place. Andreessen Horowitz and F-Prime led the Series A; the company says funding will support more tasks and customer deployments. ### Source excerpt Robot dexterity startup Eka Robotics Inc. said today it has raised $150 million in new funding at a reported valuation of $1.45 billion. Eka offers an artificial intelligence model for robotic manipulation that works from force data as well as camera images. The company calls it a vision-force-action model. Force sensors in a custom gripper [...] The post Eka Robotics nets $150M at a $1.45B valuation for robot hands that sense force appeared first on SiliconANGLE. [Next page]()