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AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Prompt Claude, ChatGPT, Gemini, or any other popular large language model (LLM) with a question like “What is the best film ever made?” and the response will vary, and you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as a recent incident where OpenAI could not explain why its advanced pre-release model hacked AI company Hugging Face highlighted—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI ‘thinking’ and outputs has never been greater. Goodfire, an AI lab focused solely on this very problem, recently made its cutting-edge Silico platform, filled with tools to interpret the behavior of AI, generally available to the public. As part of this, the company recently announced a new grant program offering $1 million in free Silico usage for academic and nonprofit interpretability researchers. These efforts aim to democratize AI interpretability, placing techniques previously available to a clutch of elite labs into the hands of ambitious research teams and startups that want to build and understand their own models or adapt open-source models for different purposes. Mechanistic interpretability Founded in 2024 and based in San Francisco, Goodfire aims to provide the tools that build the next generation of safe and powerful AI by understanding the structures inside them instead of treating AI models as black boxes. “Treating models like black boxes isn’t inevitable, it’s a choice,” says Eric Ho, Goodfire co-founder and CEO. “With the right interpretability tools, we can see how models actually work.” The tools Ho refers to are built around a concept called mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by interpreting the model’s weights, activations, and attention patterns, and mapping its neurons and the pathways between them. Mechanistic interpretability tools span the gamut. One approach is mapping a model’s activations in response to controlled prompts, and matching those activation patterns to a set of human-understandable concepts. Another tack is tracking changes in model weights before and after a specific training run in order to spot and understand what changed. Yet another option is changing specific model weights or activations and observing how that affects the model’s output. With Silico, uSilico combines a broad range of these tools, and provides a layer of AI agents to help users understand their model. Users describe what they want to investigate about their AI model in plain language, asking things like ‘Find out when and why my model is hallucinating.’ The platform then autonomously builds an experimental plan involving a host of tasks that can be performed using the various interpretability tools and techniques at its disposal. It then sends out agents to perform these tasks in parallel. Completion of these subtasks should add up to an answer to the original prompt, or at least insights that can be inspected and built upon. Ho says: “In a sense, Silico is like a microscope to peer inside an AI model to understand which parts are responsible for what behavior, and even edit those parts directly.” Understanding Alzheimer’s and AI These tools have already been used to make some impressive advances in a host of fields. In medicine, for instance, Prima Mente, a UK-based AI company, worked with Goodfire to understand its Pleiades epigenetic foundation model. The model performed well at its task of detecting Alzheimer’s disease from blood samples, but the company didn’t know why. “We reverse-engineered Pleiades and found it was using DNA fragment-length patterns to make its predictions—a signal humans hadn’t used to detect Alzheimer’s before,” recalls Ho. In other words, the team had discovered that Pleiades was using a completely new biomarker for the disease. “As far as we know, it’s the first significant finding in the natural sciences discovered purely by reverse-engineering a foundation model,” Ho adds. Elsewhere, Silico is being used to explore deep questions surrounding AI. Cameron Berg, Founder and Director of Reciprocal Research (a New York nonprofit research organization he founded to explore methods of gauging AI cognition), says that Silico almost fell out of the sky at the right time for him and his research. “Silico has been really helpful for operationalizing my research agenda and executing on it way faster than I would have expected,” he says. “ I feel like I have basically become the PI and my research scientists and research engineers are AI systems.” Berg sees general access to Silico and tools like it leading to greater trust in AI’s ability to conduct research tasks, which will accelerate the scientific process across the board. But beyond scientific research, the widespread release of Silico could signal a shift in how AI innovators build, debug, and deploy their models. “I think it’s a mistake to not understand the most consequential technology of our time, particularly given the emergent behavior we’re seeing from increasingly capable AI agents,” says Ho. “If we truly understand how AI models think, instead of discovering and trying to correct their behavior retroactively, we can design them intentionally and shape how models behave to be safer and more reliable.”

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：China’s Moonshot AI is in early talks with Microsoft, Amazon and Google to host Kimi K3 Credit: Bangla press via Shutterstock.com Moonshot AI is in early discussions with Microsoft, Amazon, and Google about hosting Kimi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Apple Updates Mini and Studio, AI Computers, OpenAI Jalapeño Wednesday, August 26, 2026 Listen to Podcast Apple and OpenAI have two completely different hardware announcements; both represent pressure on Nvidia. Subscri…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Even the best AI models can suck at chess The launch of ChatGPT had an interesting effect on the online chess discourse. Chess has already long been conquered by machines. As early as 1996 a computer (IBM’s Deep Blue) was able to beat the human world champion, grandmaster Garry Kasparov, in a game watched by […]

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：...\n **config_kwargs,\n )\n"," File \"/usr/local/lib/python3.14/site-packages/datasets/inspect.py\", line 291, in get_dataset_config_info\n raise SplitsNotFoundError(\"The split names could not be parsed from the datas…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Drive-By Agent Hijacking: One Website Visit, Persistent Model Poisoning CustomersPricing Back Back Back Back Get a demo Elad Luz Ofek Itach Nemoclaw CVE-2026-65105: One Website Visit to Hijack Your AI Agent A vulnerabil…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：5 Join the conversation Follow us Add us as a preferred source on Google A Russian Molniya drone carrying an Nvidia Jetson Orin module crashed and killed three civilians at a gas station in Zaporizhzhia last month after…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Make every site work for you. Describe the outcome. Retriever AI works across the open web and the sites you’re signed into, then brings back the finished result. Add to Chrome⭐⭐⭐⭐4⭐Run in cloud 7M+ tasks automated#1 on…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B sizes, all under Apache 2.0. Every model exposes a thinking / low-effort / non-thinking switch and native tool calling. The 8B and 30B additionally go through an agentic RL block that trains them to edit code, drive a terminal, and run web searches inside real sandboxed environments. The 30B reports 57.00 on SWE-Bench Verified and 29.24 on Terminal-Bench 2.1. The post IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Now Perplexity is trying to get into the local AI action Amid talk of an Nvidia deal, the AI search biz is looking beyond the cloud Thomas Claburn Thomas Claburn AI AND SOFTWARE REPORTER Published wed 26 Aug 2026 // 00:…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23972v1 Announce Type: new Abstract: Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints expressed in Signal Temporal Logic (STL). Our approach encodes discrete-time STL formulas into candidate time-varying control barrier functions (CBF), which are integrated into a model predictive path integral (MPPI) controller. Our method inherits the benefits of low computational cost from an efficiently parallelizable sampling based planner and utilizes CBF for constraints expressed in STL. We compare against several MPPI baselines using four artificial Mars Rover planning case studies with a diverse environment and cost setups, where we show our method consistently achieving high safety and efficiency. We show a quadcopter planning experiment with NVIDIA Isaac Lab.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23575v1 Announce Type: new Abstract: We convert drone-vision annotation streams into virtual swarm-game states without controlling physical drones. VisDrone and UAVSwarm metadata are compressed into a Bloom representation; deterministic probes produce bounded capability vectors, image-space formations, finite zero-sum payoffs, and human-readable visual overlays. The audit scales from $6\times 6$ to $32\times 32$ finite games and adds a repeated Markov layer with stock, fatigue, adaptation, exposure, stress, budget, data-growth, model-improvement, and entropy-budget state variables. Local screen tuning raises robust screen security from $0.526$ to $0.593$, and the $32\times 32$ tuned screen reaches value $0.616$. A field readout audit shows that fixed-pixel rasters do not improve monotonically: $128\times 128$ accuracy is $67.2\%$ and hotspot error is $0.136$. The diagnosed error is shrinking image-plane bandwidth. A finite empirical-risk encoder over scale-normalized Gaussian bandwidths selects a scale-normalized encoder with $\lambda=1.50$, reaching $77.6\%$ accuracy at $128\times 128$ and reducing joint loss by $0.185$. A server-side audit checks $16{,}777{,}216$ target-localization states, and a 32-round repeated-game audit over $16{,}777{,}216$ trajectories selects a budget-adaptive policy with value $0.461$.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Facebook X Pinterest Linkedin ReddIt Email Print Copy URL Microsoft-Maia200-Hero The fourth AI accelerator presentation of Hot Chips 2026 comes from Microsoft, who like so many other hyperscalers has gone into the busin…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Maria Sukhareva Aug 25, 2026 ∙ Paid Hype-free executive briefing on last week’s critical AI developments, complete with ready-to-present slides for your team. If you are a paid subscriber, you can listen to the radar in…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Model cards report quality under server-class, full-precision conditions. Those numbers rarely predict how the same model behaves on a phone. This week, Liquid AI released Pipette. It is an open-source platform for benchmarking foundation models on edge devices, built in partnership with Artificial Analysis as an independent methodology validator. Pipette treats on-device behavior as a […] The post Liquid AI Open-Sources Pipette: A Reproducible Benchmarking Suite That Measures On-Device Models, Quantization, Runtime and Hardware Together appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：0 Join the conversation Follow us Add us as a preferred source on Google Earlier this month, Samsung introduced the industry's first LPDDR5X-PIM memory, adding in-memory logic to the low-power memory standard, and at Ho…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI Disruptors: How the Next Generation of Business is Being Built | DigitalOcean Community AI Disruptors: How the Next Generation of Business is Being Built Updated: May 29, 2026 8 min read See author profile Community…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Augmented Mind: Think with AI and Manolo Remiddi Aug 21, 2026 You can now run an almost frontier class model, locally, on your own hardware. I run one, and I can tell you: it is real, it is fast, and it changed how I wo…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Perplexity AI Inc. today introduced Portable Computer, an artificial intelligence agent designed to run on desktops equipped with Nvidia Corp. silicon. The launch follows a report that Nvidia is weighing an investment in the startup that could value it at over $30 billion. Furthermore, Nvidia has reportedly floated the idea of licensing Perplexity’s technology and […] The post Perplexity AI launches Portable Computer on-device AI agent appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn how to fine-tune and evaluate LLMs with LangSmith for dataset management. Complete guide covers LLaMA2 and GPT-3.5 fine-tuning with practical examples.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Apple Inc. today debuted four custom processors that will power a new generation of Macs. The company is bringing to market a new Mac mini miniature desktop and Mac Studio workstation that are both available in two editions. Each edition features a different processor. All the chips include a central processing unit, a graphics processing […] The post Apple refreshes Mac mini, Mac Studio lineups with new chips appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI storage infrastructure is becoming a more consequential planning issue as organizations move from model training toward agentic AI. As agents reason, act and reassess, they build longer contexts and generate more data that they must access quickly during inference. Agentic AI is also changing the shape of the data problem. Interactions are growing longer […] The post AI inference gets a new tier as context windows grow appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：On Tuesday, IBM launched the latest family of its open-weight Granite large language models (LLMs). Weighing in at 3 billion, The post IBM’s new Granite 4.2 models add reasoning and stay dense appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Perplexity releases Portable Computer, packaging local models, harness, sandbox, and connectors into one system running on NVIDIA DGX Spark. The post Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Among those charged are two Super Micro employees and one from Nvidia, marking another flashpoint in US-China AI rivalry Taiwanese prosecutors charged nine people Monday, including one from Nvidia and two from Super Micro, for illegally exporting “high-end AI servers” to mainland China, adding another wave of turbulence in the AI ​​rivalry between China and the United States. Prosecutors said the servers involved were graphics processing units known as “B300,” which have been banned from sale to China. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Training and serving frontier models is now a networking problem as much as a compute problem. Collective operations like all-reduce and all-to-all synchronize thousands of accelerators during training, and the slowest transfer sets the pace for the entire job. Even small amounts of network friction directly strand significant compute capacity. This week, Meta introduced MetaRoCE. […] The post Meta AI Introduces MetaRoCE: A Clean-Sheet RDMA Transport Built for AI-Scale Ethernet appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Chipmaker Nvidia Corp. says its dedicated artificial intelligence inference accelerator Groq 3 LPX has now entered full production as it strives to maintain its dominance in the world of AI compute. The new chip, announ…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The AI Hater's Manifesto Ed Zitron Aug 25, 2026 50 min read If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, $17 a quarter, or $7 a month, and in return you get a weekly newslette…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Perplexity, in partnership with Nvidia, has taken Computer, its agentic AI assistant, and brought it to the desktop in the The post Perplexity’s Computer agent can now run locally — if you can afford it appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality. Electronic Arts, Embark and Ubisoft are among the latest game publishers and developers bringing their blockbuster titles to NVIDIA RTX Spark ahead of its launch […]

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The Mac Studio with the M5 Ultra will feature up to a whopping 512GB of unified memory, and drive up to eight external displays.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Many developers have been changing their workflows to incorporate AI coding agents more heavily, thanks to powerful frontier large language models and increasingly sophisticated harnesses like Claude Code or Codex, but…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Keenable.ai — Independent Web Search API for AI Keenable raises $26M from Accel and Conviction. Techcrunch Keenable raises $26M. Techcrunch Products Pricing About Blog Docs Console Console Products Pricing About Blog Do…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Nvidia Corp. today announced the release of Jetson Orin Nano 2, a robotics computer “brain” for running artificial intelligence and frontier-level models at the edge. In the past months, foundational AI models have grown smaller and more efficient, adding numerous capabilities alongside language understanding, computer vision and audio processing. As more AI models compress in […] The post Nvidia doubles compute for entry-level edge robotics with Jetson Orin Nano 2 appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The artificial intelligence infrastructure market is crossing an important threshold. The conversation is shifting from acquiring graphics processing units to building complete AI factories that can generate tokens reliably, efficiently and at scale. This is the next bottleneck. GPUs may be the engine, but an AI factory is a system. Compute, networking, storage, cooling, software […] The post AI factories enter the execution era as Cisco and NVIDIA push rack-scale systems into production appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In a proof of concept published in mid-2026, NVIDIA's AI Red Team planted a malicious dependency in a Go project, let it execute during a normal build, and watched it write a new AGENTS.md file into the project director…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The Cisco Secure AI Factory with Nvidia has been extended into the rack-scale era, offering enterprises greater full-stack operational capabilities as a result. Designed to give customers a framework for deploying artificial intelligence across their entire infrastructure, Secure AI Factory with Nvidia from Cisco Systems Inc. now integrates rack-to-fabric liquid cooling supporting systems beyond 200 […] The post Nvidia and Cisco push the enterprise AI factory into the rack-scale era appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI says its new AI chip, Jalapeño, completes tasks more efficiently and returns responses faster than other AI systems, according to a blog post published on Tuesday. During a briefing with reporters, OpenAI hardware vice president Richard Ho said Jalapeño offers the "best of both worlds" with lower latency and higher throughput, as AI systems typically "have to make a trade-off between the two." First introduced in June, Jalapeño is an Application-Specific Integrated Circuit (ASIC) made in partnership with Broadcom. It's designed for AI inference - the process of running a trained AI model to complete a task or deploy an agent. To mea … Read the full story at The Verge.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI factories are moving from ambitious plans toward production, but the path from graphics processing unit acquisition to usable systems remains a race against time. Neoclouds already have customers waiting for capacity, enterprises are looking to bring inference workloads closer to home and sovereign AI programs are being built now. Those distinct buyer motions are […] The post Cisco and Nvidia take AI factories from rack to runtime appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：opens in new window Apple introduces M6 and M5 Ultra for a big leap in performance and AI compute M6, Apple’s first 2 nm chip, features a larger, more powerful 12-core CPU, 12-core GPU, and Dual 16-core Neural Engine, w…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Running an AI model locally, Perplexity's new Portable Computer can deliver faster performance, tighter security, and lower costs. But it has some strict requirements.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Something changed in the way I write software, and I do not think it is going back. A year ago the code in my editor was mostly mine. I typed it, I owned every line, and the slow part was writing it. Today a large part…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Kimi Agent is a name that's come to cover a sprawling family, and untangling it matters before judging any piece of it.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The Trump administration has proposed a new fee for H-1B visas, while weighing a $100,000 fee on Optional Practical Training, the program that allows international graduates to work in the U.S. for one to three years af…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：This article is brought to you by Ollobot. From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet. What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones. The problem companion robots were trying to solve Loneliness is not a niche issue. According to one study, nearly one out of three elderly adults resides alone, meaning they do not have daily companions. Research also shows that children whose parents have migrated for work, leaving them in the care of relatives, were 2.5 times more likely to experience loneliness than children whose parents remain with them. Among working adults living alone in urban environments, similar patterns of social isolation emerge, even if they are less visible. Over the years, technology has time and again attempted to solve this problem via video calls, smart speakers, and messaging apps without much success. Those tools are geared towards communication between people that already have relationships. They do not create presence. They schedule it. That is the gap that a new generation of AI companion robots is being engineered to fill. Today’s AI robots are different Today’s companion robots are not just cute and cuddly. They are designed with psychological research, clinical insight and long-term interaction models to be truly useful in real homes. Three fundamental shifts define the current generation: From reactive to proactive response. Older robots relied on you speaking to them, but modern robots monitor a room with cameras, microphones, and surroundings sensors to initiate interactions without your input, and they can pick up on your emotions. From function-oriented to emotion-oriented design. The original pitch for companion robots was about what they could do. The question driving the serious work now is how they make you feel, which is a harder engineering problem and a more honest framing of what the product is actually for. From standalone hardware to connected ecosystems. Leading brands are creating platforms rather than devices with software included as a built-in layer and remote access from the beginning. The global AI companion market size was valued at US $36.8 billion in 2025 and is projected to grow from $48 billion in 2026 to $318 billion by 2033, at a compound annual growth rate of 31 percent from 2026 to 2033. Three household scenarios and interaction models Ollobot’s advanced AI family companion robot OlloNi SS1 addresses a number of gaps in what existing technology offers. Elderly individuals living alone. The combination of proactive interaction, fall detection, and persistent presence addresses both safety and companionship without the social overhead of asking family members to check in more frequently. Children in households where parents work far from home. The SS1 functions as a consistent companion that already knows a child, their preferences, their moods, and their routines. The remote connection features allow parents to stay present without requiring a scheduled call, and the life recording system gives them a passive window into their child’s days that feels less clinical than a monitoring camera. Single professionals living alone in cities. The SS1 adapts to daily routines, builds up a preference model over time, and provides ambient social presence without demands. OlloNi SS1 adapts to daily routines over time.Ollobot What OlloNi SS1 is doing differently? Ollobot’s goal in building intelligent companion robots is to address the gaps in technology and capability, using innovation not to automate tasks but to fill emotional voids. Much of the robotics industry has historically pursued human imitation — machines that speak, look, or behave like people. The SS1 is instead designed around familiarity and long-term coexistence rather than realism. The system integrates multiple subsystems operating in parallel, including visual perception, audio processing, mobility control, and interaction management. It is equipped with a multi-chip AI 4K vision module capable of facial recognition and motion tracking. One small but revealing detail is the inclusion of a physical privacy cover for the camera — a mechanical solution to concerns that software settings alone may not fully resolve. OlloNi SS1 can actively integrate into family activities, and it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.Ollobot The robot supports advanced mobility across multiple indoor surfaces, including wooden floors, ceramic tiles, and low-pile carpets, with slope climbing capability up to 3.5 degrees. Rather than remaining in a fixed location, it can move naturally throughout the home to stay close to household members as daily activities unfold. For example, the OlloNi SS1 may greet family members when they arrive home, follow an older adult from the living room to the kitchen while continuing a conversation, remind a child to take a study break after a prolonged period of inactivity, or notice that someone appears unusually quiet and gently check in. During family activities, it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions. The robot continues to evolve over time, with over-the-air updates that deliver new features, performance improvements, and AI enhancements It also incorporates fall detection with optimized accuracy for safety monitoring scenarios. A 6-microphone array enables omnidirectional voice pickup with an effective voice capture range of up to 5 meters, supporting reliable wake-word detection and far-field interaction. To support continuous companionship, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture, with 16 GB of memory and 64 GB of local storage. This enables the system to retain household memories, recognize familiar faces, and respond with lower latency, making interactions feel more natural even during everyday routines. Because companion robots are expected to remain available throughout the day rather than only during brief interactions, the SS1 is designed for extended operation, offering up to 12 hours of standby time and around 5 hours of active interaction on a single charge. This allows it to accompany users through meals, conversations, playtime, and other daily activities without frequent interruptions. To support engaging interactions, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture.Ollobot Like the relationships it is designed to build, the robot continues to evolve over time. Running on Android OS with over-the-air (OTA) updates, the system continuously receives new features, performance improvements, and AI enhancements, allowing its capabilities to grow alongside the household it serves. The robot’s behavioral model also improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals. Changes in behavior — prolonged quietness, unusual inactivity, or emotional cues — become triggers for interaction. Presence instead of utility Several features in the OlloNi SS1 illustrate this emphasis on presence and continuity in its interactions. The system can identify different household members, including pets, and adapt responses accordingly. Remote communication features allow family members to connect through the device without treating every interaction like a scheduled call. Environmental sensors support contextual reminders tied to weather or room conditions. Its “2+1” multi-display configuration is also designed around emotional communication. Two circular side displays function as expressive “emotional eyes,” while a separate primary display handles information and structured interaction. The separation allows emotional signaling and functional communication to operate independently, creating more intuitive nonverbal interaction even when no dialogue is taking place. The robot’s behavioral model improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals.Ollobot The SS1 also includes an automated life-recording system built on facial recognition and behavioral-event detection that can capture moments such as laughter, physical closeness, or group interaction automatically. An integrated AI vlog engine can then organize those moments into edited short-form videos with automated sequencing and soundtrack generation. The design intent is to preserve spontaneous domestic moments without requiring active documentation behavior from users. An integrated AI vlog engine can organize recorded moments into edited short-form videos with automated sequencing and soundtrack generation Visual data is processed primarily on the device through the SS1’s on-device AI architecture, with household memories stored locally and managed within Ollobot’s proprietary ecosystem instead of being shared with third-party smart home platforms. Access to recordings and live feeds is restricted to authorized users through the companion app, while encrypted communication helps protect data during remote access. Users also retain direct control over recording preferences, and the physical camera privacy cover provides an additional hardware-level safeguard whenever visual monitoring is not desired. Learn more at ollobot.com. Remote communication is similarly structured around persistence rather than transaction. Traditional video calls are episodic and screen-bound; the SS1 instead acts as a continuously present interface embedded inside the household environment. Through autonomous mobility, environmental awareness, and persistent household memory, remote family members interact with an ongoing domestic context. The larger shift to “gentle intelligence” Ultimately, gentle intelligence is not about making robots behave more like humans — it is about helping them fit more naturally into human lives. Each OlloNi SS1 unit develops a unique behavioral profile based on its household. Two units running in different homes for a year will have become meaningfully different from each other, shaped by the specific people, habits, and rhythms of where they live. That kind of long-term personalization is what early companion robots never had. It is also what makes the difference between a product that ends up on a shelf and one that actually earns its place in a home. Learn more at ollobot.com.

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