reasonit.app

#Reason It | Applications

#American Rare Earths (ASX: ARR) | United States reducing its dependence on China | The US government seeking to onshore supply of all critical materials for supply chain and national security purposes | USA needs mines to secure onshore supply of rare earths | Maiden JORC Resource | Halleck Creek | Government supported R&D to develop a domestic critical minerals supply chain

#Stockhead (ASX:) | Investor Business Daily | CAN SLIM | Identifying stocks with strong momentum | Strong current and annual earnings growth | Track record of strong annual earnings growth | Recently introduced new products or services | Supply and demand | Leading stocks in leading industries | Stocks with increasing institutional ownership | Stocks in sync with current market trend or momentum | 52-week high effect

#Polygon Labs | Blockchain technology creating a completely new type of media | Next generation content experiences

#Apple | Stock buyback | Slipping stock price | Berkshire Hathaway: Apple bet $135.4 billion, 790 million shares

#Alphabet (GOOG, GOOGL) | Megacap tech

#Amazon (AMZN) | Megacap tech

#Meta Platforms (META) | Megacap tech

#Microsoft (MSFT) | Megacap tech

#Nvidia (NVDA) | Megacap tech | AI computing remains strong | Second-quarter revenue $28 billion | AI accelerators a hot commodity

#Tesla (TSLA) | Megacap tech

#Advanced Micro Devices (AMD) | Megacap tech

#Amgen (AMGN) | BIO tech | Obesity drug

#S&P 500 companies | 79% beaten expectations

#Disney (DIS) | Star Wars

#Dropbox (DBX) | Dropbox

#Uber (UBER) | Ride-hailing

#Arm (ARM) | ICT

#Beyond Meat (BYND) | Vege

#Errawarra Resources (ASX:ERW) | Gold, nickel and lithium explorer | Large, stacked pegmatite swarm at 100kkm2+ Andover West project

#Azure Minerals ((ASX:AZS) | Western Australian-focused mineral exploration company

#Magnum Mining (ASX:MGU) | Green pig iron project in Saudi Arabia

#Anax Metals (ASX:ANX) | Copper mining

#Power Minerals (ASX:PNN) | Salta Lithium brine project in Argentina | Niobium-rare earths project in WA’s West Arunta region

#Latrobe Magnesium (ASX:LMG) | Producing magnesium oxide (MgO) from brown coal fly ash, waste product from brown coal power generation

#Latin Resources’ (ASX:LRS) | Salinas lithium project in Brazil

#Sigma Lithium | Grota do Cirilo mining complex in Brazil

#Pan Asia Metals (ASX:PAM) | Tama Atacama lithium brine project in Chile

#Lanthanein Resources’ (ASX:LNR) | Lithium mining

#Congatec | Autonomous Robotic Planetary Exploration

#Feier University Berlin | Tier Scalable Mission Architecture

#Harness Developer Hub | Flagging Targets Of Interest

#Hyundai Motor Group | Investing heavily in R&D | Developing platform for next-generation EV | Expanding product lineups | Developing core parts and advanced technologies ,| Establishing research facilities

#TVO | Olkiluoto 3 plant unit helps in achieving carbon neutrality goals for Finland | Third most powerful nuclear power unit in the world | Nuclear power is domestic electricity production kept in stable operation, in all weathers | 100 MW reserve power plant built

#Panasonic | Seeking to expand production of electric vehicle batteries in USA | Considering Kansas, Oklahoma and Nevada

#BHP (ASX:BHP) | Demand for nickel to 2050 to increase 200-300% on the previous three decades | Nickel is a key component of the cathode of NCM chemistry lithium ion batteries.| It makes EV batteries more energy dense which translates to longer driving range

#Woodman | Nickel output needs to increase three fold by 2050

#Benchmark Minerals Intelligence | 72 new nickel projects needed by 2035

#Wood Mackenzie | To reach Paris Agreement targets of zero carbon by 2050 nickel output needs to rise three fold

#Ecograf (ASX:EGR) | Nickel in the US critical minerals list, underpinned by the Inflation Reduction Act | US supporting nickel processing factory in Tanzania delivering battery grade nickel for EVs

#Larvotto Resources(ASX:LRV) | Drilled into super high grade clay rare earths | Mineralisation hosted within ionic clays, making it suitable for simple, cost-effective extraction | Mineralisation is ionic i.e. can be mined and processed economically | Mineralisation is open in all directions, LRV has not found edges yet

#Charger Metals(ASX:CHR) | Modelling fast to define priority targets for follow up drilling to test for extensions to the mineralisation

#Microsoft | Bugs to manoeuvre | Dropped Twitter from its advertising platform after Twitter started charging minimum of $42,000 per month to users of its API | $2.15 trillion market cap | $100 billion cash on hand

#Consensus | AI-powered search engine | Scientific research | Improving scientific web search quality | Solving problems of biased and inaccurate search results | Delivering expert knowledge from 200 million scientific and academic research papers

#Microsoft | ChatGPT | Based on a probabilistic methodology | Randomness plays a role in predicting future events | LLM machine learning requires vast amounts of data to train models | Vast amount of computing power needed to process data | Costs multi millions every time the dataset is updated | It takes months to retrain models | No way simply to add a new section or subset | You have to retrain the LLM on the whole dataset | The limitation of NLP or LLM is that more data given them to train on does not result in better performance | Datasets may become unclean due to an averaging effect over all data | Throwing more data is not the answer as it is a path of diminishing returns reached very quickly | Prompt engineering is to spend time refining questions given to the LLM in such a way as to guide model in correct direction | Distillation Model gathered 52,000 question-answering examples from OPenAI GPT-3.5 and used this to retrain a LLaMA model into an instruction-following form | One can get 80% of the performance for a lot less than the cost of ChatGPT| Unintended Bias | If data is biased, AI system can perpetuate that bias, leading to discrimination or unfair treatment | ChatGPT Plus stores information about how user wants it to respond and behave, retaining this perspective even when user closes one chat and begins another

#Obsess | Virtual shopping platform | Enabling brands and retailers to set up 3D 360 digital storefronts on their websites | Creating the next-generation online shopping interface

#Mujin | Platform for robotics integrators | Integrating sensors, vision, motion planning | Industrial and collaborative robots for pick-and-place and other logistics applications | Machine intelligence and real-time digital twins to enable robots to operate autonomously and reliably in production | Infusing robot with digital twin to understand its surroundings, and only do motion planning | Allowing machine to reason about its surroundings in predictable way and to complete tasks in optimal way | Decreasing time to create a new behavior or deploy a new system

#Pactum | Automating routine supplier negotiations | 3VC | Maersk Growth | NordicNinja VC | Atomico | Project A | Metaplanet | Taavet+Sten.| Volatility in transportation markets | Freight carriers and their customers often want to rewrite contracts | Some deals better handled by algorithms than humans

#Anybotics | Workforce App | Operate ANYmal robot from device | Set up and review robot missions | Industrial Inspection

#Marley Spoon (ASX:MMM) | Subscription-based weekly meal kit service in Australia, United States and Europe

#Melodiol Global Health (ASX:ME1) | Develops cannabis, hemp-derived and other plant based therapeutic, nutraceutical, and lifestyle products

#Power Minerals (ASX:PNN) | Direct Lithium Extraction (DLE)

#National Technical University of Athens | MariNeXt deep-learning framework detecting and identifying marine pollution | Sentinel-2 imagery | Detecting marine debris and oil spills on sea surface | Automated data collection and analysis across large spatial and temporal scales | Deep learning framework | Data augmentation techniques | Multi-scale convolutional attention network | Marine Debris and Oil Spill (MADOS) dataset | cuDNN-accelerated PyTorch framework | NVIDIA RTX A5000 GPUs | NVIDIA Academic Hardware Grant Program | AI framework produced promising predictive maps | Shortcomings: unbalanced dataset, marine water and oil spills are abundant, foam and natural organic material are less represented

#Google DeepMind Technologies Limited | Creating advanced AI models and applications | Artificial intelligence systems ALOHA Unleashed and DemoStart | Helping robots perform complex tasks that require dexterous movement | Two-armed manipulation tasks | Simulations to improve real-world performance on multi-fingered robotic hand | Helping robots learn from human demonstrations | Translating images to action | High level of dexterity in bi-arm manipulation | Robot has two hands that can be teleoperated for training and data-collection | Allowing robots to learn how to perform new tasks with fewer demonstrations | Collectung demonstration data by remotely operating robot behavior | Applying diffusion method | Predicting robot actions from random noise | Helpung robot learn from data | Collaborating with DemoStart | DemoStart is helping new robots acquire dexterous behaviors in simulation | Google collaborating with Shadow Robot

#Linux Foundation | LF AI & Data | Fostering open source innovation in artificial intelligence and data | Open Platform for Enterprise AI (OPEA) | Creating flexible, scalable Generative AI systems | Promoting sustainable ecosystem for open source AI solutions | Simplifying the deployment of generative AI (GenAI) systems | Standardization of Retrieval-Augmented Generation (RAG) | Supporting Linux development and open-source software projects | Linux kernel | Linus Torvalds

#Allen Institute for Artifical Intelligence | Robot planning precise action points to perform tasks accurately and reliably | Vision Language Model (VLM) controlling robot behavior | Introducing automatic synthetic data generation pipeline | Instruction-tuning VLM to robotic domains and needs | Predicting image keypoint affordances given language instructions | RGB image rendered from procedurally generated 3D scene | Computing spatial relations from camera perspective | Generating affordances by sampling points within object masks and object-surface intersections | Instruction-point pairs fine-tune language model | RoboPoint predicts 2D action points from image and instruction, which are projected into 3D using depth map | Robot navigates to these 3D targets with motion planner | Combining object and space reference data with VQA and object detection data | Leveraging spatial reasoning, object detection, and affordance prediction from diverse sources | Enabling to generalize combinatorially.| Synthetic dataset used to teach RoboPoint relational object reference and free space reference | Red and ground boxes as visual prompts to indicate reference objects | Cyan dots as visualized ground truth | NVIDIA | | Universidad Catolica San Pablo | University of Washington

#Ouster | BlueCity | Powering Lidar-Enabled Smart Traffic Solution | Traffic management solution in Chattanooga, Tennessee | Improvung roadway safety | Reducing congestion.| BlueCity solution to over 120 intersections | Combining digital lidar sensors and edge AI at each intersection | Managing traffic flow | Detecting and analyzing safety incidents | Providing detection for vehicle-to-everything (V2X) communications | Advanced perception software from Ouster | Lidar-powered smart traffic network | Optimizing traffic signal management on roads and intersections | Providing data to improve pedestrian safety | Intelligent signal actuation at intersections | Generating analytics data stream to traffic operators | Creating real-time 3D digital traffic twin of an intersection or road | Automating data collection in the cloud | Monitoring road events more accurately for vehicles, pedestrians and cyclists | Quick safety interventions | Long-term planning optimizations | Deep learning AI perception | Object classification | Object detection | Traffic actuation | Near-miss detection | Outside of crosswalk events | Red light running | Wrong-way driving | Southern Lighting & Traffic Systems | Center for Urban Informatics & Progress (CUIP) | University of Tennessee Chattanooga Research Institute (UTCRI) | Certified lidar traffic solution with Buy America(n) lidar

#Robotics & AI Institute | Collaborates with Boston Dynamics | Developed jointly Reinforcement Learning Researcher Kit for Spot quadruped robot | Developing sim-to-real for mobility | Transferring simulation results to real robotic hardware | Bridging sim-to-reality gap | Training policies generating a variety of agile behavior on physical hardware | Trying to achieve novel, robust, and practical locomotion behavior | Improving whole body loco-manipulation | Developing robot capability to manipulate objects and fixtures, such as doors and levers, in conjunction with locomotion significantly enhancing its utility | Exploring new policies to improve robustness in scenarios | Exploring full-body contact strategies | Exploring high-performance, whole-body locomotion and tasks that require full-body contact strategies, such as dynamic running and full-body manipulation of heavy objects, necessitating close coordination between arms and legs | Aiming to utilize reinforcement learning to generate behavior during complex contact events without imposing strict requirements | Develop technology that enables future generations of intelligent machines | Streamlining processes for robots to achieve new skills | Developing perception, situational understanding, reasoning, cognitive functions underpinning robot abilities and combining them with advances in their physical capabilities | Conducting research in four core areas: cognitive AI, athletic AI, organic hardware design, and ethics related to robotics

#UC Berkeley, CA, USA | Professor Trevor Darrell | Advancing machine intelligence | Methods for training vision models | Enabling robots to determine appropriate actions in novel situations | Approaches to make VLMs smaller and more efficient while retaining accuracy | How LLMs can be used as visual reasoning coordinators, overseeing the use of multiple task-specific models | Utilizing visual intelligence at home while preserving privacy | Focused on advancements in object detection, semantic segmentation and feature extraction techniques | Researched advanced unsupervised learning techniques and adaptive models | Researched cross-modal methods that integrate various data types | Advised SafelyYou, Nexar, SuperAnnotate. Pinterest, Tyzx, IQ Engines, Koozoo, BotSquare/Flutter, MetaMind, Trendage, Center Stage, KiwiBot, WaveOne, DeepScale, Grabango | Co-founder and President of Prompt AI

#Thinking Machines Lab | thinkingmachines.ai | Building artificial intelligence models and products | Competing on high end of large language models | Human-AI collaboration | Building AI that can adapt to full spectrum of human expertise | Multimodal systems that work with people collaboratively | AI models that can work across text, audio, video | AI models designed to excel in science and programming | Publishing technical blog posts, papers, program code | Mira Murati: CEO | John Schulman: Chief Scientist | Barret Zoph: CTO | Alexander Kirillov: Multimodal Research Head | John Lachman: Head of Special Projects | Alex Gartrell: Linux kernel, networking, and containerization | Andrew Tulloch: ML systems research and engineering | Brydon Eastman: Human and synthetic data, model alignment and RL | Christian Gibson: Supercomputers used in training frontier models | Devendra Chaplot: VLMs, RL, & Robotics | Ian O Connell: Infrastructure engineering | Jacob Menick: ML researcher | Joshua Gross: Products and research | Kurt Shuster: Reasoning | Kyle Luther: ML researcher | Lilian Weng: Research | Luke Metz: Research scientist and engineer | Mario Saltarelli: IT and Security leader | Myle Ott: AI researcher | Nikki Sommer: HRBP | Noah Shpak: ML Engineer, GPUs | Pia Santos: Executive Operations Leader | Randall Lin: Algorithms | Rowan Zellers: Realtime multimodal posttraining | Sam Schoenholz: Scaling, optimization | Sam Shleifer: Inference | Stephen Chen: Infrastructure engineer | Stephen Roller: Full-stack pre-training | Yinghai Lu: ML system engineer

#MemryX | AI Accelerator Module | Install system software, MemryX SDK, MemryX board | Compile AI model(s) making executable file | Send data & receive results using APIs for AI processing | Up to 6 TFLOPs (1GHz) per chip | Up to 16-chips (96 TOPS/TFLOPs) can be interconnected | Activations: bfloat16 (high accuracy) | Weights: 4 / 8 / 16 bit | Batch = 1 | 10.5M 8-bit parameters (weights) per chip | PCIe Gen 3 I/O | USB 3 interface | 0.6-2W per chip av power | Smart compiler: optimized and automated AI mapping to MemryX hardware | Powerful APIs: Python and C/C++ low and mid-level APIs for AI integration | Runtime: driver and firmware to support Windows or Linux distributions | Bit Accurate simulator: accurately testing models even without MemryX hardware

#Plus.ai | Autonomous driving software | Reasoning leverages Vision Language Model to interpret complex real‑world interactions and generate high‑level driving decisions for out-of-ODD edge cases

#LangChain | LangSmith | Platform purpose-built for LLM application development | Prompt engineering tools | Tools testing LLM applications | Set up real-time alerts on error rates | Run latency, and feedback scores to spot failures before your customers do | LangChain has standard interface for multimodal data across chat models, including support for images, PDF documents, and audio

#Weebitnano | AI inference is shifting from cloud to edge devices like phones and cars | Weebit ReRAM shaping up as memory tech that could quietly power billions of them | Artificial intelligence noot just living in cloud, it lives at edge | AI traiking happens in massive data centres, where models learn from huge datasets | AI inference happens on edge devices like smartphones, smartwatches, autonomous cars, drones, factory sensors, and wearables – right where action is | Shift to edge happening because latency, power, privacy, and bandwidth all matter | You can not afford 300-mis delay when your autonomous car needs to spot pedestrian | Edge inference is juggernaut in motion | Flash memory can not scale below 28nm when embedded | Most AI applications already require smaller geometries | Weebit Nano (ASX:WBT) Resistive RAM (ReRAM) technology comes into picture | ReRAM is non-volatile memory (NVM) that stores data faster, uses less power and offers stronger security than Flash | ReRAM can scale to the most advanced semiconductor manufacturing processes | NVM memory retains data even when power is switched off | ReRAM is less vulnerable to tampering, better suited for storing AI model weights, and easier to embed right onto the same chip as processor | Gesture recognition system developed with EMASS | Weights for detection model are stored in ReRAM for processor | South Korea DB HiTek | US semiconductor company onsemi

#Figure AI | Designing robots for the real world | Helix generalist humanoid Vision-Language-Action model reasoning like a human | Figure Exceeds $1B in Series C Funding at $39B Post-Money Valuation | Accelerating efforts to bring general-purpose humanoid robots into real-world environments at scale | Round led by Parkway Venture Capital | Significant investment from Brookfield Asset Management, NVIDIA, Macquarie Capital, Intel Capital, Align Ventures, Tamarack Global, LG Technology Ventures, Salesforce, T-Mobile Ventures, and Qualcomm Ventures | Unlocking the next stage of growth for humanoid robots | Scaling AI platform Helix and BotQ manufacturing | Scaling humanoid robots into homes and commercial operations | Building next-generation GPU infrastructure to accelerate training and simulation | Powering Helix core models for perception, reasoning, and control | Launching advanced data collection of human video and multimodal sensory inputs

#Sevensense | 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

#Export-Import Bank of the United States | The official export credit agency of the United States | Supporting American job creation, prosperity and security through exporting | Issuing letters of interest for over $2.2 billion in financing for critical mineral projects | Supply Chain Resiliency Initiative (SCRI) to help secure supply chains of critical minerals and rare earth elements for U.S. businesses | Maintaining access to critical materials to secure U.S. jobs in sectors like battery, automobile, and semiconductor manufacturing | SCRI provides financing for international projects with signed long-term off-take contracts with U.S. companies, providing these U.S. companies with access to critical minerals from partner countries | SCRI: EXIM financing is tied to import authority and the financed amount depends on the amount of the off-take contract between the foreign project and the U.S. importer | Off-take agreements ensure that EXIM financing for critical minerals projects benefits American companies and workers | For U.S. domestic production in critical minerals and rare earth elements, EXIM can provide financing through Make More in America Initiative (MMIA) | SCRI: project must have signed off-take contracts that will result in the critical minerals and rare earth elements output being utilized in the United States, for products that are manufactured in the United States