small8smart.org

#Small & Smart Org | AI powered Small & Smart Org

#Edge AI

#Continuously operating, interacting with and learning from its environment on its own and in real time

#AI Accelerator | AI Processing Unit (AIPU) | Digital in-memory (D-IMC) technology | SRAM (Static Random-Access Memory) memory densely interleaved with digital computation, each memory cell effectively becoming matrix-vector compute element | Increasing the number of operations per computer cycle (one multiplication and one accumulation per cycle per memory cell | 214 Tera-Operations per Second (TOPS) of AI processing | Power efficiency of 50 TOPS per Watt at 50% input and data sparsity | Metis AIPU run ResNet50v1 neural network processing 3,200 frames per second, with a relative accuracy of 99.9% | High performance AI acceleration at the edge | Energy efficient AI processing unit | Proprietary digital in-memory computing and RISC-V technology | Architecture minimizes data movement between memory and compute elements | Metis AI Platform

#Ability to change resource usage over time

#Off road Autonomy

#Physical AI

#Autonomous Inspection

#Robotic AI

#Robotic Autonomy

#Uncertainty aware AI

#Handling tasks without human intervention

#Spontaneously learning and improving from experiences

#Delivery robot

#Equip On Your Existing Device

#Precision application

#Manual use switch

#Targeted Efficiency

#Optimizing resources

#Reducing operating cost

#Sustainability

#Smart Insights

#Smart Decisions

#Autonomous technology

#Autonomous Platform

#Reducing labor costs

#Reducing maintenance costs

#Autonomous system

#Remotely monitoring

#Autonomous tractor

#Equipment availability

#Equipment runtime

#Dynamic sensing

#Bringing sensors to assets

#Dispatching agile mobile robots equipped with sensors to collect data on site

#More accurate data models

#Virtual Wall Functionality

#Automatic Movement Control

#Camera operating as an Edge Device

#Compressed video stream (H264)

#RTSP protocol

#MJPEG stream

#Live image with AI overlay

#Intelligent camera

#Digital Manufacturing

#Autonomous routes

#Digital Twin

#Points of Interest (POI)

#Augmented reality

#3D camera

#Z-accuracy

#Stereo camera

#Field of view

#RGB sensor

#AI-in-a-box

#Neuromorphic processor

#Sustainable AI technology

#Low-shot learning

#Biometric recognition

#Image classification model

#Temporal Event Neural Network

#Edge AI model

#Intelligent sensor technology

#Natural hydrogen: < $1/kg

#White hydrogen: carbon intensity with 75% hydrogen and 22% methane, rises to 1.5 kg CO2e per kg H2

#Gold hydrogen: highest production tax credits (PTC) because the lifecycle carbon intensity below 4 kg CO2e per kg H2

#Grey hydrogen: produced from fossil fuels, costs less than $2 per kilogram (kg) of hydrogen on average

#Green hydrogen: > $6/kg

#Machine learning (ML) to reliably distinguish between a significant and insignificant event on the power grid

#Generative physical AI

#Robotics stack

#Humanoid foundation model

#Autonomous machine development

#Cognitive AI-driven capabilities

#Robot-agnostic software platform

#Robotic-grasping

#Synthetic data generation

#Autonomous mobile robot

#Digital twin technology

#Reference workflow

#Training robots in virtual environment

#Robotic arm

#Robot work cell

#IoT data

#Digital twin workflow

#AI-enabled autonomous machine

#Software frameworks and robot model

#Human coworker

#Intelligent assistant

#Edge AI solution

#Robotics skills

#Electric Vehicle (EV) charging

#Open Smart Charging Protocol (OSCP)

#Cell-to-pack (CTP) design: integrating battery cells directly into battery pack

#Cell-to-chassis (CTC) design: incorporating battery cells directly into vehicle chassis

#Skateboard platforms pre-equipped for self-driving capabilities

#10-minute EV charging benchmark

#Perceptual AI hardware

#Perceptual AI software

#Perceptual AI algorithm

#Neural network architecture

#AI vision system

#Device-agnostic AI system

#Transformers: class of neural network models originally designed for natural language processing

#Polynomial-based continuous convolutions

#Neuromorphic sensing and computing

#Spiking neural network algorithms

#AI Processing Unit (AIPU)

#Vision AI in (biometric) access control

#8-bit integer inference arithmetic vs 32-bit floating-point full-precision

#False positives

#False negatives

#Temporal Event Neural Networks (TENNs)

#Capturing thermal, acoustic, and visual data consistently

#Cardiac ablation: medical procedure used to treat irregular heart rhythms (arrhythmias) by creating small scars in heart tissue

#Atrial flutter: abnormal heart rhythm

#Fluoroscopy: medical imaging technique using X-rays to create real-time moving images of internal structures such as heart

#Active MR tracking: real-time localization of catheters during MRI-guided interventions, utilizing microcoils or antennas to provide precise positional information, enhancing visualization within MR images, reducing the need for manual adjustments and improving efficiency compared to passive methods, integrates tracking sequences with imaging, allowing for dynamic updates of the imaging plane as the device moves

#Microcoils: enhancing MR tracking accuracy by providing improved sensitivity and localization of devices within MRI environment, enabling high frame rates, allowing real time tracking of moving instruments

#Micro transmit tracking

#Centroid pixel method

#Phase field Dithering

#Active microcoils

#Automatic registration of tracked devices

#IO-Link: an open-standard communication protocol (IEC 61131-9) designed for connecting sensors and actuators in industrial automation

#Detecting anomalies

#Active stereo vision

#Time Of Flght (TOF)

#3D depth sensing

#Smart home device

#Miltimodal

#Perceptual mode

#1550nm LiDAR | Advantages: safety, range, and performance in various environmental conditions | Enhanced Eye Safety: absorbed more efficiently by cornea and lens of eye, preventing light from reaching sensitive retina | Longer Detection Range | Improved Performance in Adverse Weather Conditions such as as fog, rain, or dust | Reduced Interference from Sunlight and Other Light Sources | More expensive due to complexity and lower production volumes of their components

#SLAM | Simultaneous Localization and Mapping

#Building Information Modeling (BIM)

#Architecture, Engineering, and Construction (AEC)

#3D modeling

#4D modeling (time scheduling)

#5D modeling (cost estimation)

#SLice Integration by Vision Transformer (SLIViT)

#Retinal scan

#Ultrasound video

#CT

#MRI

#Disease-risk biomarker

#Pre-training method

#Fine-tuning method

#Disease trajectory

#Tailoring treatment

#Fine-tuning 2D model on 3D scans

#Downstream learning

#Deep-learning framework

#Agentic workflow

#Vector database

#Learning Management System (LMS)

#Time To First Token (TTFT)

#Multimodal AI

#Robotic embodiment

#Humanoid robot

#Universal Scene Description (OpenUSD)

#Cognitive robotics

#Vertical movement

#Actuated linear guide system

#Actuator

#Field Foundation Models (FFM) | Physical system intelligence as the first risk-aware model for robots | Enabling any embodiment to autonomously operate in highly dynamic environments without GPS, pre-defined maps, or pre-programmed routes | Allowing robots to be deployed at scale and without human intervention for nearly any application

#Deployment of neural networks near sensors | Low-power FPGAs | Edge implementation of models

#Office of United States Trade Presentative (USTR) | Tariffs | Angola: 15% | Bangladesh: 20% | Bosnia and Herzegovina: 30% | Botswana: 15% | Brunei: 25% | Cambodia: 19% | Cameroon: 15% | Chad: 15% | Democratic Republic of the Congo: 15% | Equatorial Guinea: 15% | European Union: 15% | (for most goods)Falkland Islands: 10% | Fiji: 15% | Guyana: 15% | India: 25% | Indonesia: 19% | Iraq: 35% | Israel: 15% | Japan: 15% | Jordan: 15% | Kazakhstan: 25% | Laos: 40% | Lesotho: 15% | Libya: 30% | Leichtenstein: 15% | Madagascar: 15% | Malawi: 15% | Malaysia: 19% | Mauritius: 15% | Moldova: 25% | Mozambique: 15% | Myanmar: 40% | Namibia: 15% | Nauru: 15% | Nigeria: 15% | North Macedonia: 15% | Pakistan: 19% | Philippines: 19% | Serbia: 35% | South Korea: 15% | Sri Lanka: 20% | Switzerland: 39% | Taiwan: 20% | Thailand: 19% | Tunisia: 25% | Vanuatu: 15% | Vietnam: 20% | Zambia: 15% | Zimbabwe: 15%

#Large Language Model (LLM) | Foundational LLM: ex Wikipedia in all its languages fed to LLM one word at a time | LLM is trained to predict the next word most likely to appear in that context | LLM intellugence is based on its ability to predict what comes next in a sentence | LLMs are amazing artifacts, containing a model of all of language, on a scale no human could conceive or visualize | LLMs do not apply any value to information, or truthfulness of sentences and paragraphs they have learned to produce | LLMs are powerful pattern-matching machines but lack human-like understanding, common sense, or ethical reasoning | LLMs produce merely a statistically probable sequence of words based on their training | LLMs are very good at summarizing | Inappropriate use of LLMs as search engines has produced lots of unhappy results | LLM output follows path of most likely words and assembles them into sentences | Pathological liars as a source for information | Incredibly good at turning pre-existing information into words | Give them facts and let them explain or impart them

#Retrieval Augmented Generation. (RAG LLM) | Designed for answering queries in a specific subject, for example, how to operate a particular appliance, tool, or type of machinery | LLM takes as much textual information about subject, user manuals and then pre-process it into small chunks containing few specific facts | When user asks question, software system identifies chunk of text which is most likely to contain answer | Question and answer are then fed to LLM, which generates human-language answer in response to query | Enforcing factualness on LLMs

#Smart electric vehicle technology | XPENG | AI-driven mobility company | Designs, develops, manufactures, and markets Smart EVs | Catering to tech-savvy consumers | Develops Full-stack advanced driver-assistance system (ADAS) technology | Intelligent in-car operating system | Xmart OS: from driving cockpit to intelligent space | XPILOT ASSIST: Intelligent driving assistance-Easy to drive, easy to park | Over the air software update (OTA) | AI-powered production car equipped with an L3-grade computing platform | Effective computing power exceeding 2000 TOPS | Onboard deployment of VLA (Vision-Language Action) + VLM (Vision-Language Motion) models | Autonomous driving research | Large-scale fleets | Vast real-world data | Data-driven era |

#NVIDIA.$500 billion initiative | Establishes independent compute financing platforms to turn AI hardware into a brand-new financial asset class | Announced via Memorandums of Understanding (MOUs) in August 2026 | NVIDIA has partnered with six of Wall Street premier asset managers | Apollo Global Management | BlackRock | Blackstone | Brookfield Asset Management | Goldman Sachs | KKR | Core objective is to treat graphics processing units (GPUs) and AI factories as income-generating infrastructure, similar to commercial real estate, toll roads, or aircraft leases | Third-Party Capital Mobilization | Wall Street firms will source, vet, and individually underwrite loan proposals for hyperscalers, frontier labs (like OpenAI and Anthropic), and enterprises | GPUs as Loan Collateral: borrowers secure massive loans using NVIDIA hardware itself as collateral, functioning on premise that compute has clear intrinsic resale and rental value | NVIDIA Financial Backstop: NVIDIA provides residual support, promising to backstop up to 25% (or $125 billion) of individual deals to stabilize hardware value if a borrower defaults | Secondary Liquidity Ecosystem: If a client defaults, NVIDIA and its partners plan to quickly re-rent or relocate affected chips to other waitlisted data centers, protecting enders from total capital loss | Wall Street financial engineering introduces massive benefits for NVIDIA corporate ecosystem and financial metrics | Handing credit analysis and capital pool over to independent institutional giants validates genuine market demand | Unlocks kong-duration revenue share: beyond selling silicon upfront, NVIDIA could capture up to a 35% revenue share above breakeven from these platforms, potentially adding a 10%+ upside to FY2029 earnings per share | Secures CUDA ecosystem: by subsidizing and simplifying financing hurdle for startups and enterprises, NVIDIA locks customers deeper into its proprietary CUDA software stack, keeping competitors out | BlackRoc CEO Larry Fink likened this initiative to 1970s creation of mortgage-backed securities, calling it the next era of financial engineering | If underlying economic demand for AI tokens and services keeps pace, this structure ensures NVIDIA remains undisputed gatekeeper of global infrastructure | Bringing independent long-term institutional capital to infrastructure market demand is genuine | 35% revenue share above breakeven could provide more than 10% upside Nvidia fiscal 2029 earning

#Unitree IPO in Shanghai | Unitree Robotics became the first humanoid robot maker listed on A-share market in Shanghai | The first humanoid company to go public in mainland China | Chinese robotics giant Unitree soars in stock market debut | Unitree Robotics stock soars 460% in Shanghai IPO debut | Shares of Unitree surged nearly 630% in China, before closing up 460% | Company raised $900 million in its debut | Strategic investors include Chinese AI startup DeepSeek, a group associated with tech giant Tencent, and several state-owned utility companies | Retail traders were 5,000x oversubscribed | China humanoid market is predicted to grow from $2 billion 2026 to $15 billion by 2030 | IPO price of 150.80 yuan with stock closing at 845 yuan represented a 460 per cent gain | Unitree move toward capital market sends important signal: humanoid robotics and embodied AI are moving beyond technology development, competition-based validation and product iteration toward industrialization, scalability and broader recognition from capital market | Hangzhou-based company offered ca. 40.45 million shares at 150.8 yuan each, representing a price-to-earnings ratio of 219.23 | Its cumulative quadruped robot shipments exceeded 33,000 units, with a global market share of nearly 60 percent | Unitree specializes in quadruped and humanoid robots | Unitree has fully self-developed core components, including motors, reducers, controllers, and LiDAR | Company posted revenue of about 1.15 billion yuan in the first half of 2026, up 48.54 percent year on year | Funds raised will be put toward intelligent robot model development, robot hardware R&D, new product development and manufacturing base construction | Business moves from robot manufacturing toward building a broader ecosystem for high-performance general-purpose robots | Unitree founder Wang Xingxing was quoted by Shanghai Securities News | Unitree unveiled its new humanoid robot Superman | Global humanoid robot shipments are projected to exceed 510,000 units by 2030