The field of Machine Learning and Artificial Intelligence is under ever accelerating technology change as no anything else. Recently, classical Machine Learning with Statistic Deep Learning was the hottest trend; now it’s like an Old Stone Age or iPhone 6S — outdated, and to be disrupted by the new, modern, future-looking AI, a Real-World, Transdisciplinary AI (Trans-AI).
The Real-World AI, Causal ML and Deep Causal Learning are the latest market trend these days, opening the New Trans-AI Age. Unlike the unscientific anthropocentric and anthropomorphic AI, the Real AI is a Scientific AI strictly relying on Science as the sum of universal knowledge about the world, Scientific Methodology and Scientific Principles of Unity of Knowledge (Transdisciplinarity).
“The Stone Age didn't end because they ran out of stones-it ended because better technologies were developed to meet humanity's changing needs”. The stone age ended with discovery of the new techniques and technologies of metalworking
Likewise, the age of fossil fuel won't end because we run out of oil, gas, and coal. The fossil fuel sources will be replaced by renewable sources as new technologies make them more cost-effective than non-renewable sources.
Again, the digital age of narrow and weak, specialized and human-mimicking AI/ML/DL is ending due to new Trans-AI technologies and techniques, models and algorithms making the current AI old and outdated, as the Stone Age tools.
The Harmful Ubiquity of the Old-Style AI/ML/DL
Let’s first see a global scale of sticking with the old-style, obsolete, and antiquated, weak, narrow and specialized AI and ML and its aged descriptions, as in:
- Machine Learning;
- Deep Learning
- Natural Language processing
- Expert System
- Machine Vision
- Speech Recognition, etc.
The Old-Style AI/ML/DL of Institutions, Countries, Companies, Universities and Researchers:
The OECD Old-Style AI Principles:
- Inclusive growth, sustainable development, and well-being
- Human-centred values and fairness
- Transparency and explainability
- Robustness, security, and safety
Suggestions for national policy priorities, including: Investing in responsible AI research and development; fostering a digital ecosystem for AI; shaping an enabling policy environment for AI; building human capacity and preparing for labour market transformation; and international cooperation for trustworthy AI.
National Old-Style AI Strategies
China Old-Style AI: n July 2017, The State Council of China released the “New Generation Artificial Intelligence Development Plan” (新一代人工智能发展规划). This policy outlines China’s strategy to build a domestic old-style AI industry worth nearly US$150 billion in the next few years and to become the leading old AI power by 2030.
The US Old-Style AI: Much of the current progress in the field has been in specialized, well-defined tasks often driven by statistical ML, such as classification, recognition, and regression (i.e., “narrow AI systems”). Surveys of the field have noted that long-term investments in fundamental research are needed to continue building on these advances in ML.
THE NATIONAL ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT STRATEGIC PLAN: 2019 UPDATE: https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf
Some Big-Tech Old-Style AI Blogs:
DeepMind Research Blog, https://deepmind.com/blog
Microsoft Research AI Blog: https://blogs.microsoft.com/ai/
Facebook AI: Open-Source Old-Style AI Tools, “We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment”. https://ai.facebook.com/blog/
Amazon ML/AI: AWS offers the most complete set of machine learning (ML) and artificial intelligence (AI) services to meet your business needs. https://aws.amazon.com/ai/
Apple ML/AI: Machine Learning Research at Apple: https://machinelearning.apple.com/; Areas of work include Back-End Engineering, Data Science, Platform Engineering, and Systems Engineering. https://www.apple.com/careers/us/machine-learning-and-ai.html
Leading Old-Style AI/ML/DL conferences
- NeurIPS: accepted papers; RecSys: 2020 list of accepted papers; KDD: 2020 list of accepted papers; ICLR: 2020 list of accepted papers
Old-Style AI Courses:
Coursera (4+ million to be mis-educated): https://www.coursera.org/learn/machine-learning
All Universities AI/ML/DL courses: from Cambridge AI: https://www.cam.ac.uk/topics/artificial-intelligence to Mohamed bin Zayed University of Artificial Intelligence: https://mbzuai.ac.ae/
Old-Style AI on arXiv
One of methods to find the latest old-style, narrow and weak AI/ML/DL papers, regardless of authors, is to use arXiv’s Advanced Search
Old-Style Statistic Language Models (interpreting data through statistical and probabilistic techniques)
OpenAI Generative Pre-trained Transformer 3 (GPT-3), the third-generation language model in the GPT-n series has a capacity of 175 billion machine learning parameters
China’s Wu Dao 2.0 of the Beijing Academy of Artificial Intelligence (BAAI) has been trained on 1.75 trillion ML parameters. It is reached and surpassed the nine benchmarks:
1- ImageNet (zero-shot): SOTA, surpassing OpenAI CLIP.
2- LAMA (factual and commonsense knowledge): Surpassed AutoPrompt.
3- LAMBADA (cloze tasks): Surpassed Microsoft Turing NLG.
4- SuperGLUE (few-shot): SOTA, surpassing OpenAI GPT-3.
5- UC Merced Land Use (zero-shot): SOTA, surpassing OpenAI CLIP.
6- MS COCO (text generation diagram): Surpassed OpenAI DALL·E.
7- MS COCO (English graphic retrieval): Surpassed OpenAI CLIP and Google ALIGN.
8- MS COCO (multilingual graphic retrieval): Surpassed UC (best multilingual and multimodal pre-trained model).
9- Multi 30K (multilingual graphic retrieval): Surpassed UC.
Know the Real World and Its AI Models
It is critical for leaders and all citizens alike to develop a firm understanding of the fundamental differences between the Real-World AI, ML, and DL and the Fake, Human-Imitating AL, ML, and DL.
The increasing levels of business insights that can be gained from a shared understanding of AI is evident when understanding exactly how these ever-growing, disruptive technologies can be harnessed by your organization.
Here are some key areas that rely on the real-world artificial intelligence:
- Autonomous driving
- Agricultural industry
- Safety and security
- Communication, including technology platforms, social media networks, bots and digital assistants
- Health care
- Real estate
- Augmented reality and virtual reality
- Banking, trading, and other financial services
- Public administration
- Space exploration and astronomy
Apple, Google, Amazon, Microsoft, and Facebook, enjoying the highest ever 1+ trillion dollar market cap, are still working on an old and obsolete statistics-based extrinsic AI/ML/DL that could potentially imitate some parts of the human brain, mind, or behavior.
Meantime, a global AI company, EIS Encyclopedic Intelligent Systems LTD, https://www.slideshare.net/ashabook/eis-ltd, has successfully completed an unprecedented R&D of the Transdisciplinary AI Model as a Real-World AI or a Causal Machine Intelligence and Learning. It is trademarked as Causal Artificial Superintelligence (CASI) GPT Platform complementing human intelligence, collective and individual. The company has spent zero public funding and private investment for the outstanding discovery, which is set up to change the ways the world works, relying only on its own resources.
EIS is aimed to engineer a Real-World, Trans- AI, deploying it as a Global Human-Machine Internet Platform. It is open to the civic science platforms and large public and private investors from the EU, USA, China, Russia, UK, and socially responsible big tech companies, to develop Proof-of-Concept/Mechanism/Principle Prototype that can demonstrate the CASI feasibility for a full-scale global deployment.
The Trans-AI will eat the world
Marc Andreessen famously said that “Software is eating the world”. And now we see how data is eating the world.
The point is everything surrounding us is data, generated by digital devices, internet of things or human beings.
We create a huge universe of data, a data hyperreality, which denizens are:
statistics, facts, recordings, observations, numbers,
data points, observations,
structured or unstructured data,
structured data in database rows,
unstructured data in text,
graphs, charts, plots, charts, statistic graphics,
text, data documents,
coded data, software, information, and knowledge.
By 2025, IDC predicts there will be 163 zettabytes of data.
The Trans-AI model is powerful to understand all real world and machine generated virtual data, their nature, data types, scales, levels, measurements, handling them with the speed of light.
The Trans-AI will eat the world.
The Trans-AI as the Most Sustainable Transdisciplinary Solutions of Global Threats
The Real-World AI is set up to change how the world works, being the engine of digital revolution, as well as all transdisciplinary science and technology, including community science and its projects.
The COVID-19 pandemic global crisis has accelerated the need for transdisciplinary solutions, of which one of the most disruptive innovations could be a Transdisciplinary AI (Trans-AI) or Real-World AI.
It is designed as the human-machine digital intelligent platforms facilitating integrated knowledge, competences, and workforce skills to meet massive technological unemployment. As mentioned, addressing societal challenges, as embedded in SDGs, using transdisciplinary research is considered a “mainstream modus operandi for research” by the OECD Global Science Forum (GSF).
In the AI and Robotics era, there is a high demand for the trans-disciplinary knowledge, competence, and high-technology training in a range of innovative areas of exponential technologies, such as artificial intelligence (AI), machine learning (ML) and robotics, data science and big data, cloud and edge computing, the Internet of Thing, 6G, cybersecurity and mixed reality.
The combined value – to society and industry – of digital transformation across industries could be greater than $100 trillion over the next 10 years. “Combinatorial” effects of AI and Robotics with mobile, cloud, sensors, and analytics among others – are accelerating progress exponentially, but the full potential will not be achieved without collaboration between humans and machines.
Given that, the Trans-AI is proposed to integrate disciplinary AIs, symbolic/logical or statistic/data, as ML Algorithms (Deep Learning (DL), Artificial Neural Networks (ANNs)), aiming to augment or substitute biological intelligence or intelligent actions with machine intelligence.
The Trans-AI is to be developed as a Man-Machine Global AI (GAI) Platform to integrate Human Intelligence with Narrow AI, ML, DL, Human-level AI, and Superhuman AI. It relies on fundamental scientific world’s knowledge, cybernetics, computer science, mathematics, statistics, data science, computing ontologies, robotics, psychology, linguistics, semantics, and philosophy.
Since it is widely recognized that the lack of reality with causality is the “root cause” of development problems of current machine learning systems, the Trans-AI is designed as a Causal Machine Intelligence and Learning Platform, to be served as Artificial Intelligence for Everybody and Everything, AI4EE.
The Trans-AI technology could make the most disruptive general-purpose technology of the 21st Century, given an effective transdisciplinary ecosystem of innovative business, government, policy-makers, NGOs, international organizations, civil society, academia, media and the arts. The Trans-AI Knowledge Graph covers ERC’s fields of research: Physical Sciences and Engineering (PE), Life Sciences (LS), and Social Sciences and Humanities (SH).
The Trans-AI is an advanced digital technology transdisciplinary project beyond discipline-specific approaches, involving all basic knowledge fields, as ontology, computer science, mathematics, statistics, data science, physics, cognitive sciences, psychology, linguistics, semantics, cybernetics, and general philosophy, among others, as well as an AI citizen science.