EIS Encyclopedic Intelligent Systems developed the first real model of the Universal AI, including the following elements of the Universal Intelligent Platform I-World:
Machine World Model;
World Data Framework World Data;
Global Knowledge Base World.Net
Domain Knowledge Base Domain.Net
EIS is to propose the first real model and system architecture of the Universal AI to all the interested parties, including the EU and Russia and the key players of the global AI market, as G(oogle)-M(icrosoft)A(pple)F(acebook)I(BM)A(mazon) and B(aidu)A(libaba)T(encent), to become the first license-holder of the truly disruptive and sustainable innovation of Real Artificial Intelligence Model.
The cost of the Universal AI Model is Euro1bn ONLY. What is more attractive than Microsoft’s investment of $1bn just for some prospective AGI R&D.
We all, governments and institutions, corporations and customers, are in urgent need of real machine intelligence now, which is capable to integrate a multitude of specialized AI technologies and AutoML platforms, with an increasing number of fragmented products and services and special applications, from self-driving transportation to digital assistants.
If we don’t have such a Universal and General and Encyclopedic AI, we will not have a Real and True and Trustful and Friendly Machine Intelligence, with its disruptive technologies and revolutionary applications.
For True and Real and Universal AI is all about deep understanding of the world, while the today’s AI is largely about predictive analytics, mathematical programming and statistical learning/ML/DL directed ONLY at specific tasks, from chess playing to image recognition.
The BigTech AI is about AML, Automated Machine Learning and Deeply Layered Neural Networks.
“Deep learning” systems are numerical neural networks running numerical functional models, numbers, weights, vectors, matrices, and predictive scores, with no meanings, and any intellectual processes or understanding.
To become really Deep AI, “Deep learning” systems are in need to include deep causal understanding and symbolic reasoning, and not just as iterative formal logical manipulation of symbolic information at the highest computing speed.
Broadly, Deep AI embraces understanding and interacting the world by means of abstractions and conception, symbols and models, cognition, learning and thinking, intelligent decision and acting.
Or technically, ‘the word embedding produced by word2vec or similar mechanisms’ should be topped by “the world embedding produced by world2vecand similar mechanisms”.
And what is most critical, real AI is not about some mathematical and statistical, logical or cognitive models of human intelligence, based on the cybernetic theory that the AI of the computer’s “brain” parallels the mechanism of human intellect.
True AI is not about automated pattern/object/image/speech/face recognition, automated NL processing or automated decision making or automated predictions.
Real AI is not about general models of the world being represented by interacting machine, while underpinning mathematical and statistical, logical or cognitive models of intelligent processes, based on the assumptions that the AI of the computer’s “brain” is not after imitating the mechanism of human intellect.
Combining ML and AI, Real Deep AI is about autonomous and intelligent pattern/object/image/speech/face recognition, NL understanding, autonomous and intelligent decision making or autonomous and intelligent predictions.
And such an integrated approach is to be followed by INDEPENDENT HIGH-LEVEL EXPERT GROUP ON ARTIFICIAL INTELLIGENCE SET UP BY THE EUROPEAN COMMISSION. https://ec.europa.eu/digital-single-market/en/high-level-expert-group-a…
It is looking for “a learning rational system is a rational system that, after taking an action, evaluates the new state of the environment (through perception) to determine how successful its action was, and then adapts its reasoning rules and decision making methods”.
And AI is broadly defined as including “machine learning (of which deep learning and reinforcement learning are specific examples), machine reasoning (which includes planning, scheduling, knowledge representation and reasoning, search, and optimization), and robotics (which includes control, perception, sensors and actuators, as well as the integration of all other techniques into cyber-physical systems).”
This simple truth is also motivated an investment of $1 billion from Microsoft tol focus on building a platform that OpenAI will use to create new AI technologies and deliver on the promise of artificial general intelligence.
The first real model and system architecture of encyclopedic machine intelligence
Now EIS offers to all the interested parties, including the key players on the global AI market, as G-MAFIA and BAT, an opportunity to become the first license-holder of the truly disruptive and sustainable innovation of Universal Artificial Intelligence.
As a member of European AI Alliance, EIS Encyclopedic Intelligent Systems developed the first real model and system architecture of the Universal AI, including the following architectural components:
- Machine World Model;
- Master Algorithm;
- World Data Framework;
- Global Knowledge Base World.Net;
- Universal Intelligent Platform I-World (a mapping of UAI functionality onto hardware and software components, the software architecture onto the hardware architecture, and human interaction with these components)
The reality and quality of machine intelligence are decided by the reality and quality of AI data, models, algorithms, architectures and infrastructure, as the engine of any great UAI cloud systems.
The UAI ecosystem is emerging as the convergency/integration/synthesis/synergy of human collective intelligence and exponential technologies:
specific artificial intelligence, machine learning and deep learning systems,
advanced robotics, cognitive robots and drones,
the Internet, internet of things,
5G, mobile internet, smart phones,
virtual and augmented reality,
additive manufacturing and 3D printing,
alternative energy systems,
biotechnology, digital medicine, etc.
The USI could emerge as the universally distributed hybrid human-digital intelligence endowed with general digital ontology, common world model, master algorithm and global data classification platform, and implemented as the I-World Platform, the core of
Global AI Internet.
AI as Pansophic Technology/Networks/Platforms/Machines/Systems/Algorithms
The I-World Platform as a framework for Narrow AI & AutoML frameworks
The USI Platform is embrace all the meaningful Big-Tech ML/DL/AIs, as self-briefed below:
AI is the capability of a machine to imitate intelligent human behavior. Through AI, machines can analyze images, comprehend speech, interact in natural ways and make predictions using data.
Only Azure empowers you with the most advanced machine learning capabilities. Quickly and easily build, train, and deploy your machine learning models using Azure Machine Learning, Azure Databricks and ONNX. Use tools and frameworks of your choice without lock-in. Develop models faster using automated machine learning. Easily deploy and manage across the cloud and the edge.
At Google AI, we’re conducting research that advances the state-of-the-art in the field, applying AI to products and to new domains, and developing tools to ensure that everyone can access AI.
Google’s mission is to organize the world’s information and make it universally accessible and useful. AI is helping us do that in exciting new ways, solving problems for our users, our customers, and the world.
AI is making it easier for people to do things every day, whether it’s searching for photos of loved ones, breaking down language barriers in Google Translate, typing emails on the go, or getting things done with the Google Assistant. AI also provides new ways of looking at existing problems, from rethinking healthcare to advancing scientific discovery.
Bringing the world closer together by advancing artificial intelligence
At Facebook AI, we're connecting people to what they care about, powering new, meaningful experiences, and advancing the state-of-the-art through open research and accessible tooling.
Our teams accelerate research breakthroughs across both existing and new learning paradigms to develop state-of-the-art AI that has a positive impact on people and society.
Understanding the visual world around us
Creating personalized and meaningful interactions
Building AI solutions to keep people safe on social platforms
Natural Language Processing
Next generation text understanding and generation
Ranking & Recommendations
Connecting people to what's most meaningful
Developing novel algorithmic, software, and hardware techniques
AWS has the broadest and deepest set of machine learning and AI services for your business.
On behalf of our customers, we are focused on solving some of the toughest challenges that hold back machine learning from being in the hands of every developer.
You can choose from pre-trained AI services for computer vision, language, recommendations, and forecasting; Amazon SageMaker to quickly build, train and deploy machine learning models at scale; or build custom models with support for all the popular open-source frameworks.
Our capabilities are built on the most comprehensive cloud platform, optimized for machine learning with high-performance compute, and no compromises on security and analytics.
Machine-learning models are used across Twitter to enhance the product and serve the public conversation. The data that supports these models is often extremely large, complex, and constantly changing. At Twitter, we represent this information in the form of embeddings. Generating and sharing high-quality, up-to-date embeddings enables teams to effectively leverage various forms of data, improve the performance of ML models, and decrease redundant efforts.
Build realtime, personalized experiences with industry-leading, on-device machine learning using Core ML 3, Create ML, the powerful A-series chips, and the Neural Engine. Core ML 3 supports more advanced machine learning models than ever before. And with Create ML, you can now build machine learning models right on your Mac with zero code.
MIT-IBM Watson AI Lab: A collaborative industrial-academic laboratory focused on advancing fundamental AI research
Founded in 2017, the MIT-IBM Watson AI Lab is a unique academic / corporate partnership to spur the evolution and universal adoption of AI. The MIT-IBM Watson AI Lab focuses research on healthcare, security, and finance using technologies such as the IBM Cloud, AI platform, blockchain and quantum to deliver the research to industries.
Project Debater: An AI system that can debate humans on complex topics
Project Debater digests massive texts, constructs a well-structured speech on a given topic, delivers it with clarity and purpose, and rebuts its opponent. Eventually, Project Debater will help people reason by providing compelling, evidence-based arguments and limiting the influence of emotion, bias, or ambiguity.
POWERING CHANGE WITH AI AND DEEP LEARNING
AI doesn’t stand still. It’s a living, changing entity that powers change throughout every industry across the globe. As it evolves, so do we all. From the visionaries, healers, and navigators to the creators, protectors, and teachers. It’s what drives us today. And what comes next.
SEE YOUR LIFE'S WORK REALIZED WITH AI
Preventing disease. Building smart cities. Revolutionizing analytics. These are just a few things happening today with AI, deep learning, and data science, as teams around the world started using NVIDIA GPUs. Today, these technologies are empowering organizations to transform moonshots into real results.
Baidi AI Baidu Research
Baidu Research brings together top talents from around the world to focus on future-looking fundamental research in artificial intelligence.
- Data Science and Data Mining
- Natural Language and Speech
- Business Intelligence
- Robotics and Autonomous Driving
- Computer Vision
- Machine Learning and Deep Learning
- Computational Biology and Bioinformatics
- High Performance Computing
- Quantum computing
Machine Learning Platform For AI
An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements.
Machine Learning Platform For AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine Learning Platform For AI combines all of these services to make AI more accessible than ever.
At Alibaba AI Labs, researchers advance the state-of-the-art in artificial intelligence, applying AI algorithms to empower machines and design innovative interface that enable collaborative and natural interactions between people and machines. We develop platforms that infuse computer, material and system technologies to enable a larger community can easily build AI-empowered products. We develop advanced products such as TmallGenie (personal voice assistant) to extend human ability and ensure everyone access the benefit of AI.
Tencent AI Lab is a corporate-level research and application lab of artificial intelligence.
Today we have 70 world-class AI research scientists and 300 engineers working to solve the world's biggest AI challenges. We focus on fundamental research in machine learning, computer vision, speech recognition, and natural language processing, and their applications in Game, Social, Content and Platform AI.
Our goal is to use our technology assets including computing power, massive data sets and diversified user scenarios, to improve AI's capabilities in understanding, decision-making and creation, and "Make AI Everywhere".
The UAI Road Map: From Narrow Artificial Intelligence (nAI) to General Artificial Intelligence (gAI) to Universal Artificial Intelligence (sAI) , or Real AI (rAI)
And the UAI Strategy:
The World=Reality > Data > Human >UAI > Intelligent Technology > I-World Platform
Following the strategy, EIS Encyclopedic Intelligent Systems developed the following critical elements of the Universal Intelligent Platform I-World:
Machine World Model;
World Data Framework World Data;
Global Knowledge Base World.Net
Domain Knowledge Base Domain.Net
Artificial Superintelligence, USA, 1999
Abdoullaev A. Reality, universal ontology, and knowledge systems: toward the intelligent world. - Hershey; New York: IGI Publishing, 2008. ISBN 978-159904966-3
AI as Pansophic Technology/Algorithms/Networks/Platforms/Machines/Systems/Applications
Global Artificial Intelligence (GAI): Narrow AI, Applied AI, ML&DL, Strong AI, Full AI, AGI, Global AI, Real AI, Superhuman Intelligence
Artificial Global Intelligence (AGI)
Artificial Superintelligence as the next big thing
A DEFINITION OF AI: MAIN CAPABILITIES AND DISCIPLINES
Definition developed for the purpose of the AI HLEG’s deliverables.