autonomoussystem.ai

#Autonomous System AI Meta

#Agentic AI | Artificial intelligence systems with a degree of autonomy, enabling them to make decisions, take actions, and learn from experiences to achieve specific goals, often with minimal human intervention | Agentic AI systems are designed to operate independently, unlike traditional AI models that rely on predefined instructions or prompts | Reinforcement learning (RL) | Deep neural network (DNN) | Multi-agent system (MAS) | Goal-setting algorithm | Adaptive learning algorithm | Agentic agents focus on autonomy and real-time decision-making in complex scenarios | Ability to determine intent and outcome of processes | Planning and adapting to changes | Ability to self-refine and update instructions without outside intervention | Full autonomy requires creativity and ability to anticipate changing needs before they occur proactively | Agentic AI benefits Industry 4.0 facilities monitoring machinery in real time, predicting failures, scheduling maintenance, reducing downtime, and optimizing asset availability, enabling continuous process optimization, minimizing waste, and enhancing operational efficiency

#Traditional AI | Models focus on tasks like classification or prediction

#Generative AI | designed to create new content, like text, images, or videos, based on patterns learned from existing data

#Quantum AI algoritm | Quantum speed up in kernel-based machine learning | Method relying on quantum photonic circuit and bespoke machine learning algorithm | Increased speed, accuracy and efficiency over standard classical computing methods | Relying on photon injection.| Femtosecond laser | Extremely short pulses measured to write on borosilicate glass substrate to classify data points from a dataset | Kernel-based machine learning can have myriad applications across data sorting | Kernel-based systems pose relative simplicity and advantages when working with small datasets

#Antagonistic AI system | Behaving in disagreeable, confrontational or challenging ways | Forcing to confront assumptions | Building resilience | Developing healthier boundaries

#Throughput: Tokens per second

#Inference Speed Performance

#Autonomous Industry | Unbound Factory | Robots are getting closer to taking on a wide range of labor-intensive tasks | Humans are generalists—adaptable, quick to learn, and effortlessly retaskable | Chasing generalization in manipulation—both in hardware and behavior— is necessary

#Generative AI Stack

#Generative AI Ecosystem

#Perceptual AI-based systems

#Edge AI-based systems

#AI hardware accelerator

#Latency: Time to first tokens chunk received

#Automated Guided Vehicle (AGV)

#Positioning accuracy

#Roboticist

#AI models deployed in embedded systems at edge | Brushless DC motors | Hall effect sensors | Optical encoders | Sensorless motor control | Field-oriented control | Artificial intelligence at edge | Three fundamental modalities: vision, sound, and motion | Using AI models to infer information about device environment | Linear algorithms | Software and hardware combination | Deploying multiple AI models in embedded devices requires edge processors designed to run AI | Embedded systems using AI can be considered open | Sensor fusion utilizes combined data from multiple sensors | AI-based vision systems are more adaptable to natural variations inherent in object inspection | Objects can be identified and inspected more quickly with greater flexibility | Strong multimodal AI, a single model will process multiple types of data | Control algorithms will use inputs generated by AI, inferred from multiple sources of data | AI inferencing in data flow | AI-enabled image sensors are perfect for gesture detection | Event detection based on sound is an active area of development | On device learning in real time

#Natural feature navigation

#Simultaneous Localization and Mapping (SLAM)

#Safety scanner

#Odometry

#Fleet management

#Autonomous Navigation Technology (ANT)

#Vehicle Control

#Kinematic

#Optimized Path

#Obstacle Avoidance

#Mission Control

#GPU architecture for AI

#Vehicle automation

#Autonomous Mobile Robot (AMR)

#Robotics engineering

#System design

#AMR platform

#Mobile robot

#Precision agriculture

#Environmental sustainability

#Artificial Intelligence for Aviation business

#AI factory

#Dynamic sensing

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

#Industrial inspection robot

#Measurement sensor

#Navigating facilities built for humans

#Autonomous mobile inspection robot

#Asset-intensive industry

#Determining Jobs to be Done

#Detecting equipment failures

#Visual optical zoom camera

#Directional ultrasonic microphone

#High quality thermal camera

#Gas sensor

#360° Lidar scanner

#Updating 3D models on-demand

#Data contextualization

#AI-based inspection algorithm

#Object recognition

#Depth camera

#Lidar

#Robot control

#Universal Scene Description (OpenUSD)

#Synthetic Data Generation

#Robotics simulation

#Regression Testing

#Changes to prompt

#Retrieval strategy

#Model choice

#Granularity of information

#Annotation Queue

#Template language

#Making informed tradeoffs amongst latency, cost, and quality

#Evaluating response quality

#Multi-actor applications

#In-context (few-shot) learning

#Flow Engineering

#Iterative process

#Vector Retrieval

#Graph-based Metadata Technique

#Vector similarity search

#Robot set-up

#Initial training of robot

#Refresher training of robot

#Field certified robot

#Importing CAD models to perform realistic simulations

#Guiding robot through facilities

#Planning robot mission on-site

#Neural network

#Autonomous robots

#Automatic emergency braking (AEB)

#Warehouse automation

#Internet of Things (IoT)

#Autonomous mobile robots (AMRs)

#Autonomous forklifts

#Additive manufacturing

#Cold spray

#White Hydrogen

#Integrating LTE-M cellular connectivity into EV chargers to enable them to connect to Cloud to respond to dynamic electricity price changes

#Extracting interpretable features from LLMs

#Sparse autoencoder

#Controlling the sparsity level

#Activation shrinkage

#Dead latent

#Scaling laws

#Evaluation metrics

#Mitigating biases in AI systems

#Scaling monosemanticity

#Mixture of Experts (MoE) models in LLMs

#Expert Slimming

#Expert Trimming

#Structured State Space Duality (SSD)

#State Space Model (SSM)

#Transformer Architectur

#Retrieval-Augmented Generation (RAG) | Vector search

#Semantic ranker for search

#Hybrid search with re-ranking | Uutperforming vector search alone, which may struggle to find exact matches for proper names, IDs and numbers | Improving the relevance and accuracy of the AI generated responses

#MLOps platform

#Industrial digitalization

#Autonomous facility

#Reference workflow

#Physically based rendering

#AI robot development

#AI robot deployment

#Digital twin

#Modifying digital design in sensor feedback loop

#Robotics platform

#AI processor

#Building digital twins for real-time simulation of different factory layouts

#AI for manufacturing

#Transformational impact of generative AI and digital twin technologies

#Autonomous technology

#Digital twin of factory

#Virtual plant

#Training robots in virtual environment

#Situating sensors and networked video cameras in matrix to show plant operators right details

#Robot work cell design

#Robotics simulation platform

#Generating physically accurate, photorealistic synthetic data for training computer vision models

#Automatic Optical Inspection

#Autonomy algorithm

#Infrared sensor

#Integrated motion capture system

#Reflective tag

#System generating GPS signals

#Marine Autonomous System (MAS)

#Ocean observation

#Explainable AI (XAI): methods allowing humans to comprehend and trust the results of machine learning algorithms

#AI patents: digital product manufacturing sector accounting for 61.8 percent in China

#Cognitive robotics

#Humans in the loop

#Vision Language Model (VLM)

#Robot workcell

#Industrial robot programming

#Autonomous homing of robots

#Linear actuator | Device converting rotational motion into linear motion

#Disaggregation | Hardware and software components are separated to enhance flexibility and efficiency in network management

#Coherent optical transceiver | Utilizing advanced modulation techniques, including amplitude and phase modulation | Enhancing data transmission over fiber optics | Enabling higher bandwidth and longer reach by employing digital signal processors to manage dispersion and optimize spectral efficiency | Supporing various applications, including Dense Wavelength Division Multiplexing (DWDM), allowing multiple data streams on a single fiber | Essential for modern high-speed networks, facilitating capacities of 100G to 400G and beyond, crucial for data-intensive applications like cloud computing and 5G networks

#YANG (Yet Another Next Generation) data modeling language | Designed for network management | Enables the definition of configuration and state data for network devices | Facilitates automation through protocols like NETCONF, RESTCONF, and gNMI | Human-readable and machine-processable | Simplifies network configuration and management across different vendors | Standardized by IETF | Supports various built-in data types | Allowing for extensibility and compatibility with existing management protocols like SNMP

#3D depth sensing

#Time Of Flght (TOF)

#Active stereo vision

#Reality capture workflows

#Dual polarization

#Prompt adherence

#Vector database

#Learning Management System (LMS)

#Prompt caching | AI reusing of large text across multiple API calls without reprocessing it each time | AI allowing to ask various questions about book while utilizing cached content | AI prompts with many examples | AI repetitive tasks with consistent instructions | AI cache elements: Tools, System messages, Messages, Images | AI conversational agents | AI coding assistants | AI large document processing | AI detailed instruction sets | AI agentic tool use | AI talking to books | AI talking to papers | AI talking to documentation | AI talking to podcast transcript | Python | Curl

#Integrating AI model with organization knowledge

#Scaling expertise across projects

#Scaling expertise across decisions

#Scaling expertise across teams

#AI powered software engineering

#AI powered computer use

#Syncing GitHub repositories with AI model

#Building Information Modeling (BIM) | Creating and managing 3D representations of physical and functional characteristics of buildings and infrastructure projects

#Edge AI

#Large Language Model (LLM)

#Retrieval-Augmented Generation (RAG)

#Conversational AI

#Custom machine learning model

#25 million tokens per second mark

#1 billion tokens per second

#Llama model

#CUDA

#Inference

#API Latency | Time to first token)

#API output speed (output tokens per second)

#Climate reporting requirements | Scope 3 emissions (generated from company supply chain) | Climate statement | Financial opportunities tied to climate change | Financial risks tied to climate change | Climate metrics & targets: emissions from Scope 1 (direct emissions), Scope 2 (indirect emissions from power use), and Scope 3 (everything else, like supply chain emissions) | Governance & risk management: how company is managing climate risks and opportunities) | 2°C scenario | 1.5°C scenario | Risks companies are exposed | Strategy for achieving net-zero emissions | Leadership engagement with climate change issues

#Environmental, Social, and Governance (ESG)

#Agentic AI | Adapting dynamically to its environment | Learning from interactions | Integrating machine learning, reasoning, and workflow optimization | Managing multi-agent orchestration for seamless collaboration

#California wildfire | Challenges | Access roads too steep for fire department equipment | Brush fires | Dangerously strong winds for fire fighting planes | Drone interfering with wildfire response hit plane | Dry conditions fueled fires | Dry vegetation primed to burn | Faults on the power grid | Fires fueled by hurricane-force winds | Fire hydrants gone dry | Fast moving flames | Hilly areas | Increasing fire size, frequency, and susceptibility to beetle outbreaks and drought driven mortality | Keeping native biodiversity | Looting | Low water pressure | Managing forests, woodlands, shrublands, and grasslands for broad ecological and societal benefits | Power shutoffs | Ramping up security in areas that have been evacuated | Recoving the remains of people killed | Retardant drop pointless due to heavy winds | Smoke filled canyons | Santa Ana winds | Time it takes for water-dropping helicopter to arrive | Tree limbs hitting electrical wires | Use of air tankers is costly and increasingly ineffective | Utilities sensor network outdated | Water supply systems not built for wildfires on large scale | Wire fault causes a spark | Wires hitting one another | Assets | California National Guard | Curfews | Evacuation bags | Firefighters | Firefighting helicopter | Fire maps | Evacuation zones | Feeding centers | Heavy-lift helicopter | LiDAR technology to create detailed 3D maps of high-risk areas | LAFD (Los Angeles Fire Department) | Los Angeles County Sheriff Department | Los Angeles County Medical Examiner | National Oceanic and Atmospheric Administration | Recycled water irrigation reservoirs | Satellites for wildfire detection | Sensor network of LAFD | Smoke forecast | Statistics | Beachfront properties destroyed | Death tol | Damage | Economic losses | Expansion of non-native, invasive species | Loss of native vegetation | Structures (home, multifamily residence, outbuilding, vehicle) damaged | California wildfire actions | Animals relocated | Financial recovery programs | Efforts toward wildfire resilience | Evacuation orders | Evacuation warnings | Helicopters dropped water on evacuation routes to help residents escape | Reevaluating wildfire risk management | Schools closed | Schools to be inspected and cleaned outside and in, and their filters must be changed

#Context window | Amount of information LLM can handle in one input/output exchange, with words and concepts represented as numerical tokens, LLM own internal mathematical abstraction of data it was trained on

#Multipurpose commercial humanoid | Potential for useful and reliable and affordable humanoids | Difficult problem making highly technical piece of hardware and software compete effectively with humans in labor market | Robots are not hard to build; but they are hard to make useful and make money with | Whole perception pipeline running at the framerate of sensors nowadays | All the technology is here now | Starting with surrogate robot from someone else to get autonomy team going while building own robot in parallel | Giving out a significant chunk of the company to early joiners | Combined efforts of the research community enables commercialization | Building team is really important

#Multi-agent ecosystems | Shift toward multi-agent ecosystems designed for specific workflows

#Deep Research | OpenAI | Autonomous analyst | AI accelerates open knowledge sharing | Companies may lean harder into secrecy to maintain competitive edges

#Event-based imaging for machine vision

#Event-based cameras for highly efficient motion analysis

#A-list celebrity home protector | Burglaries targeting high-end items | Burglary report on Lime Orchard Road | Burglar had smashed glass door of residence | Ransacked home and fled | Couple were not home at the time | Unknown whether any items were taken | Lime Orchard Road is within Hidden Valley gated community of Los Angeles in Beverly Hills | Penelope Cruz, Cameron Diaz, Jennifer Lawrence, Adele and Katy Perry have purchased homes there, in addition to Kidman and Urban | Kidman and Urban bought their home for $4.7 million in 2008 | 4,100-square-foot, five-bedroom home built in 1965 and sits on 1¼-acre lot | Property large windows have views of the canyons | Theirs is one of several celebrity properties burglarized in Los Angeles and across country recently | Connected to South American organized-theft rings

#Professional athlete home protector | South American crime rings | Targeting wealthy Southern California neighborhoods for sophisticated home burglaries | Behind burglaries at homes of professional athletes and celebrities | Theft groups conduct extensive research before plotting burglaries | Monitoring target whereabouts and weekly routines via social media | Tracking travel and schedules | Conducting physical surveillance at homes | Attacks staged while targets and their families are away | Robbers aware of where valuables are stored in homes prior to staging break-ins | Burglaries conducted in short amount of time | Bypass alarm systems | Use Wi-Fi jammers to block Wi-Fi connections | Disable devices | Cover security cameras | Obfuscate identities

#Neural cascade | Scheme that allows division of computation across devices

#Self-learning infrared digital anti-masking system | System continuously monitors the sensor environment and adjusts accordingly, reducing the chances of false alarms while maintaining security performance | Unlike manually adjustable ones it adapts upon installation | Anti-masking | Designed to detect and respond when sensor is deliberately covered or obstructed | Anti-masking is critical feature in professional security solutions | Achieved by emitting infrared or microwave signals that continuously check for obstructions | Allowing user to fine-tune detection sensitivity to suit environment minitored

#Artificial intelligence | Accelerated growth in space and satellite industry | Enhancing data processing, automation and decision-making | Enabling faster image analysis | Real-time satellite adjustments | Predictive maintenance | Autonomous spacecraft operations | Making space technology more efficient, cost-effective and scalable

#Mobile robots

#AI in the warehouse

#Humanoid robots

#Field Foundation Model (FFMs) | Physical world model using sensor data as an input | Field AI robots can understand how to move in world, rather than just where to move | Very heavy probabilistic modeling | World modeling becomes by-product of Field AI.robots operating in the world rather than prerequisite for that operation | Aim is to just deploy robot, with no training time needed | Autonomous robotic systems applucations | Field AI is software company making sensor payloads that integrate with their autonomy software | Autonomous humanoid Field AI can do | Focus on platforms that are more affordable | Integrating mobility with high-level planning, decision making, and mission execution | Potential to take advantage of relatively inexpensive robots is what is going to make the biggest difference toward Field AI commercial success

#Precision motion control system

#Robots-as-a-Service (RaaS) | Gives companies option to hire robots rather than purchase them outright, lowering financial risk while still providing full benefits of automation | Fixed monthly fee for fleet of robots to perform tasks | Robots are flexible workforce that can be hired on demand

#Artificial Intelligence and Spatial Computing for Enterprises | Integrating AI, XR, and 3D technologies | Virtual Reality visualization | Digital prototyping | Combining generative AI, Extended Reality (XR), and IoT | 3D model creation process to adopt Vrtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR) and 3D technologies

#Realistic data to autokomous systems | AI-generated data indistinguishable from real life | Generating suggestions based on business logic | Quality assurance [QA] layer built on application or use case | Statistical analysis to contextually understand data and build out data points | Building decision trees | Generating wider distribution of data | Augmenting client AI systems | Semantic segmentation, contextual and visual labeling, 2D and 3D bounding boxes to client basuc data | Finding data caps | Filling data caps with AI generated photorealistic imagery and advanced annotation | Experience with cars and drones | Targeting companies producing not only autonomous systems and industrial robots | Creating data engines

#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

#Vision-language model (VLM) | Training vision models when labeled data unavailable | Techniques enabling robots to determine appropriate actions in novel situations | LLMs used as visual reasoning coordinators | Using multiple task-specific models

#Robot dog | Equipped with a yellow methane detection probe | Sniffs out potential gas leaks | Poised to become a bodyguard | Enhancing community safety | Can be pre-programmed with specific routes and key inspection areas | Conducts regular patrols and safety checks within residential compounds

#Robot autonomy system combining the benefits of Visual SLAM positioning with advanced AI local perception and navigation tech | Visual Al technology | AI-based autonomy solutions | Visual SLAM | Dynamic obstacle avoidance | Constructing accurate 3D maps of the environment using sensors built into robots | Algorithms precisely localize robot by matching what it observes at any given time with 3D map | Using AI driven perception system robot learns what is around it and predicts people actions to react accordingly | Intelligent path planning makes robot move around static and dynamic obstacles to avoid unnecessary stops | Collaborating with each others robots share important information like their position and changes in mapped environment | Running indoors, outdoors, over ramps and on multiple levels without auxiliary systems | Repeatability of 4mm guarantees precise docking | Updates the map and shares it with the entire fleet | Edge AI: All intelligence is on the vehicle, eliminating any issue related to the loss of connectivity | VDA 5050 standardized interface for AGV communication | Alphasense Autonomy Evaluation Kit | Autonomous mobile robot (AMR) | Hybrid fleets: manual and autonomous systems work collaboratively | Equipping both autonomous and manually operated vehicles with advanced Visual SLAM and AI-powered perception | Workers and AMRs share the same map of the warehouse, with live position data of each of the vehicles | Turning every movement in warehouse into shared spatial awareness that serves operators, machines, and managers alike | Equiping AGVs and other types of wheeled vehicles with multi-camera, industrial-grade Visual SLAM, providing accurate 3D positioning | Combining Visual SLAM with AI-driven 3D perception and navigation | Extending visibility to manually operated vehicles, such as forklifts, tuggers, and other types of industrial trucks | Unifying spatial awareness across fleets | Unlocking operational visibility | Ensuring every movement generates usable data | Providing foundation for smarter, data-driven decision-making | Merging manual and autonomous workflows into a single connected ecosystem | Real-time vehicle tracking | Traffic heatmaps | Spaghetti diagrams | Predictive flow analytics | Redesigning layouts | Optimizing pick paths | Streamlining material handling | Accurate vehicle tracking | Safe-speed enforcement | Pedestrian proximity alerts | Lowerung insurance claims | Ensuring regulatory compliance | Making equipment smarter, scalable, interoperable, and differentiable | Predictive maintenance | Fleet optimization | Visual AI Ecosystem connecting machines, people, processes, and data | Autonomous robotic floor cleaning | Industry 5.0 by adding people-centric approach | Visual AI to providing real-time, people-centric decision-making capabilities as part of autonomous navigation solutions | Collaborative Navigation transforming Autonomous Mobile Robots (AMRs) into mobile cobots | Visual AI confering robots the ability to understand the context of the environment, distinguishing between unobstructed and obstructed paths, categorizing the types of obstacles they encounter, and adapting their behavior dynamically in real-time | Automatically generating complete and very accurate 3D digital twin of an elevator shaft | Autonomous eTrolleys tackling last-mile problem |Autonomous product delivery at airports

#AI laws in California | New laws signed by Gov. Gavin Newsom aim to make AI and social media landscape in California safer, especially for minors | Requiring AI companies to incorporate guardrails that prevent so-called companion chatbots from talking to users of any age about suicide or self-harm | Requiring all AI systems alert minors using chatbots that they are not human every three hours | Systems also are barred from promoting any sexually explicit conduct to users who are minors | Children committed suicide after being influenced by an AI chatbot companion | Removed a civil legal defense that some AI developers have been using to make the case that they are not responsible for any harm caused by their products | Forcing developers to vet their product better and ensure that they can be held to account if their product does cause harm to its users | Increasing civil penalties for AI developers who knowingly create nonconsensual deepfake AI pornography | Preventing students to create AI generated nude photos of classmates | Requiring large AI companies to publicly disclose certain safety and security protocols and report to CA state on critical safety incidents | CalCompute to provide resources to startups and researchers developing large AI systems

#Immediate.Measures to Increase American Mineral Production

#Critical minerals in Artificial Intelligence | At the core of AI transformation lies a complex ecosystem of critical minerals, each playing a distinct role | Boron: used to alter electrical properties of silicon | Silicon: fundamental material used in most semiconductors and integrated circuits | Phosphorus: helps establish the alternating p-n junctions necessary for creating transistors and integrated circuits | Cobalt: used in metallisation processes of semiconductor manufacturing | Copper: primary conductor in integrated circuits | Gallium: used in compound semiconductors such as gallium arsenide (GaAs) and gallium nitride (GaN) | Germanium: used in high-speed integrated circuits and fibre-optic technologies | Arsenic: employed as a dopant in silicon-based semiconductors | Indium phosphide: widely used in optical communications | Palladium: used in production of multi-layer ceramic capacitors (MLCCs) | Silver: the most conductive metal used in specialised integrated circuits and circuit boards | Tungsten: serves as a key material in transistors and as a contact metal in chip interconnects | Gold: used in bonding wires, connectors, and contact pads in chip packaging | Europium: enables improved performance in lasers, LEDs, and high-frequency electronics essential to AI systems and optical networks | Yttrium: improves the efficiency and stability of materials like GaN and InP, supporting advanced applications in photonics, high-speed computing, and communications technologies

#Critical minerals in Data Storage for Artificial Intelligence | Lithium: powers batteries that support portable storage devices, SSDs, and memory-rich electronics, in data centres, lithium-ion battery arrays ensure continuous power to storage systems | Silicon: the heart of solid-state drives (SSDs) and memory chips | Manganese: next-generation memory technologies, such as resistive RAM (ReRAM) and spintronic memory, also used in lithium-ion batteries for backup power in enterprise storage systems | Praseodymium: helps improve the efficiency and durability of motors in HDDs | Neodymium: key component in neodymium-iron-boron (NdFeB) permanent magnets used in spindle motors of hard disk drives (HDDs).| Samarium: used in samarium-cobalt (SmCo) magnets, which offer exceptional thermal stability, these magnets are favoured for mission-critical and defence-grade storage systems | Gadolinium: used in advanced memory storage systems. It plays a role in magneto-optical storage technologies | Dysprosium: important for data storage applications | Platinum: used in the manufacture of high-purity glass and crucibles required in the production of memory and storage devices | Gold: used in memory chips