Real AGI Machines (RAGIM) = Interaction/Causality Engine (ICE) + Generative AI (GenAI) +LMLMs...
As an engine is the heart of physical machines, an interaction/causality engine is the mind/brain of intelligent machines, following the development algorithm of real artificial general intelligence (RAGI):
RAGIM = Real AI = AGI = Real/Causal AGI Machines = the World Modeling and Reality Simulating Machine [Interaction/Causality Engines] +
Software/Hardware Information Tools [ AI chips + Symbolic AI + Predictive AI + GenAI + LLMs + LMLMs + Knowledge Graphs + Digital Twins + Robotics]
If your machines are missing the Interaction/Causality Engines, they are simply information machines or data processing tools or advanced software systems.
Specifically, symbolic AI, predictive AI, gen AI, or large multimodal language models (LMLMs) are simply advanced statistical learning machines, software/hardware information tools, or quasi-AI, fake AI or false AI.
The examples of the quasi-AI models are OpenAI's GPT series of models, GPT-3.5 and GPT-4, ChatGPT and Microsoft Copilot, Google's PaLM and Gemini, xAI's Grok, Meta's LLaMA family of open-source models, Anthropic's Claude models, Mistral AI's open source models, Databricks' open source DBRX...
Here is a simple universal criterion (iron law) of intelligence: "something/somebody is rational or intelligent, a human, animal or machine, IFF IT KNOWS WHAT IT IS DOING.
Otherwise, it is simply advanced software/hardware tools, widely misrepresented as "Artificial intelligence, or AI, technology that enables computers and machines to simulate human intelligence and problem-solving capabilities".
Without understanding the cause and effect of interactions within the world, there is no True Learning and Intelligence or Understanding, or Autonomous Machine Intelligence, as Real AI or AGI, with various models and applications.
Interaction/Causality pervades all and everything, as the engine of the world, at all possible levels and scales, from the quantum microworld to the macroworld to the whole universe.
The same eights systems of Earth (atmosphere, hydrosphere, cryosphere, geosphere, pedosphere, lithosphere, biosphere, and the magnetosphere) interact and feedback through material and energy fluxes, between the Earth's sub-systems' cycles, processes and "spheres", to produce the environments we are familiar with.
Building Causal AI/AGI Machines
AA/AI Rule: "Without understanding the cause and effect of interactions within the world, no software or hardware, computers, AI models, ML algorithms, DL techniques, robotic agents, programming applications, language machines, digital technologies, or ICT tools are rational or intelligent", be it:
Natural language generation converting structured data into the native language
Speech recognition converting human speech into a useful and understandable format by computers
Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the IMB Watson
Biometrics, AI-powered facial recognition systems. fingerprint/iris/gait/speech/emotion/behavior recognition
Decision management systems for data conversion and interpretation into predictive models
Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models
Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data
Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server
Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do
Artificial neural networks of various structures and topologies
Generative AI tools, as LLMs, GPT-4, ChatGPT, AlphaCode, GitHub Copilot, Bard, Cohere Generate, Claude, Synthesia
AI optimized hardware support AI models, as neural networks, deep learning, and computer vision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.
Robotic machines and autonomous vehicles, humanoid robots
ICT, the internet, internet of things, metaverse, virtual reality and social media, cloud computing services, video conferencing and collaboration tools, unified communications systems, mobile communication networks, etc.
Again, "if generative AI and LLMs are to be transformed as true AGI, then a comprehensive rational world model is what that information tools or robotic machines or software agents need for real intelligence and true understanding, as of their identities and actions".
Or, bringing together computer programming and world modeling/reality simulating, not simply logical symbolic systems, mathematics, statistics, linguistics, training data, or mental models, is the only rational way to produce real AI or AGI:
AI = AGI = Real/Causal AGI Machines = the World Modeling and Reality Simulating Machine [Interaction/Causality Engines] +
Software/Hardware Information Tools [ AI chips + Symbolic AI + Predictive AI + GenAI + LLMs + LMLOMs + Knowledge Graphs + Digital Twins]
Causality Engines as Causality Software/Hardware Engines are the Reality AI Mechanism of the World Modeling Machine.
Causality Engines are causal machines transforming, translating or converting matter, energy or information, as material, forces, movement or data, into new forms of matter, energy or information, for changes and actions, interactions or effects, prescriptions or predictions.
"Causal AI is an artificial intelligence system that can explain the cause and the effect. You can use casual AI to interpret the solution given the AI Machine learning model and the algorithm. In different verticals, casual AI can help explain the decision making and the causes for a decision".
Why is a causality engine superior to a ML/AI engine?
Causality Software/Hardware Engine is an AGI technology platform that learns, understands, infers and interacts with the world based on reality simulation and causation of matter/energy/data input, not on statistical patterns and correlations.
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It evaluates all the possible variables in a system, process or data—from trades, sales and marketing to human resources, from innovative R & D to digital twins—and finds causal patterns, insightful relationships, objective regularities. It operates on extremely large datasets to derive valuable knowledge about the combinations of factors that contribute most strongly to specific outcomes.
Causality AGI Engines automatically applies rational, ontological, mathematical, scientific, and computational methods to world's data to find causal models/regularities/patterns/correlations/associations to explain reasons, discover inventions, infer predictions, prescriptions and create rational interactions.
A Hierarchy of Causal Machines
Machine, device, instrument, tool, invention, unit, or physical system to augment or replace human or animal effort for the accomplishment of tasks, as the robotics field is to create intelligent machines that can assist or replace humans in all possible of ways.
Any machine is a causal machine that uses causal power/mechanism/forces to control changes and to perform an action.
The operation of a physical machine may involve the transformation of potential, chemical, thermal, electrical, or nuclear energy into mechanical energy, or vice versa, or simply to modify and transmit forces and motions.
All machines have a causal input, an output, and a transforming or modifying and transmitting causal mechanism.
All machines are either prime movers, or generators, or motors, or operators, or the combinations thereof, like robotic machines which ideally could combine all the categories in one unit:
Since the invention of simple basic machines, humanity has been dealing with material machines of increasing complexity.
All material machines marked with physical constraints, limitations and specifications, processing matter, energy or/and data, with all its parts working together for specific tasks or functions, classified, as in:
physical machines, from basic to complex hybrid machines
simple basic machines: the wedge, screw, lever, pulley, inclined plane and the wheel and axle
mechanical machines, motors, engines, mechanical automata
electrical machines, electro-mechanical automata, electrical propulsion systems, electrical automata
electromagnetic machines, electrostatic machines, motors, dynamo machines, generators, transformers, electromagnetic-rotor machines, EM automata
thermodynamic machines, heat engines, from Earth's heat engine to internal combustion engines to refrigerators and firearms
chemical machines, chemical engines, chemical propulsion systems, chemical reactors, chemical automata
physico-chemical machines, vehicle machines of all types, wagons, bicycles, motor vehicles (motorcycles, cars, trucks, buses, mobility scooters), electric vehicles (trolleybuses, solar machines), railed vehicles (trains, trams), watercraft (ships, boats, underwater vehicles), amphibious vehicles (screw-propelled vehicles, hovercraft), aircraft (airplanes, helicopters, aerostats) and spacecraft, physico-chemical automata
biological machines, bioreactors, nanites, molecular machines
computing machinery
computers, nano-computers, micro-computers, mini-computers, servers, mega-computers, supercomputers, quantum computing machines, the internet, "a global computer network connecting computers all over the world to provide a variety of information and communication facilities"
cyber-physical machines
robotic machines, mechanized, automatic, semi-autonomous, autonomous
drone machines, UAVs, swarm drones, trans-media machines, flying submarines, submersible aircraft
the internet of things
the internet of machines, the worldwide networking of machines via the Internet
complex living machines
animals
humans, human body machines
intelligent machines
human brain machines
ML machines (ANNs), AI models, LLMs, GPT-x, Chatbots, medical, educational, conversational, financial, voice, Q/A, self-driving, autonomous systems
AGI machines (humanoid robots)
Rational Cyber-Physical Systems
Hyperintelligent Man-Machine Technology
Superintelligent Transformers, Universal machines
Resources
Sounds like you need a deep understanding of cause and effect for true AI Azamat Abdoullaev
CEO at Cognitive.Ai | Building Next-Generation AI Services | Available for Podcast Interviews | Partnering with Top-Tier Brands to Shape the Future
7moThat's a fascinating perspective on true machine intelligence Understanding causality is key for genuine AI development. Keep exploring the depths of AI possibilities Azamat Abdoullaev
Associate Director | Market Research | Healthcare IT Consultant | Healthcare IT Transformation | Head of Information Technolgy | IoT | AI | BI
7moUnderstanding causality is key to true machine intelligence. Great point 🧠
Senior Financial Analyst at BlackRock | VP Finance at Kniru | ex-Goldman Sachs
7moInteresting take! Can I share a link to this post on the AI Finance Discord community which has industry professionals Sequoia, Citadel, BlackRock & more? I'm sure this would spark some interesting conversations. I'd also be happy to share an invite if you'd want to join in!
GEN AI Evangelist | #TechSherpa | #LiftOthersUp
7moUnderstanding causality is key to developing true Artificial General Intelligence. Without it, we're simply working with advanced software tools misrepresented as AI. Azamat Abdoullaev