The Big Tech AI: "NVIDIA Fake AI Platform": Beware of the AI Mass Delusion
Nvidia is to Burst The Big Tech AI Bubble in a few Months
[Less than a year past of the posting date of our prediction article, and Nvidia has lost about $b00 billion in market value in one day, the largest one-day loss in U.S. corporate history.
And the root cause is NOT DeepSeek, but the fundamentally wrong AI/AGI human-mimicking paradigm.
Now, wonder how could we be so existentially dumb and dull!?
It was clear from the very beginning of the Nvidia Fake AI Bubble, and only ONE reader, Dr.Thirupathi Ramanathan, was smart enough to see it]
In the allegory "The Cave", Plato describes a group of people who have lived chained to the wall of a cave all their lives, facing a blank wall. The people watch shadows projected on the wall from objects passing in front of a fire behind them and give names to these shadows. The shadows are the prisoners' reality, but are not accurate representations of the real world. The shadows represent the fragment of reality that we can normally perceive through our senses, while the objects under the sun represent the true forms of objects that we can only perceive through reason...
Plato's shadows are collective mass delusions, man-made by diverse influences to promote financial, ideological, political or psychosocial goals, and the prisoners are all humanity.
Machines are machines, Humans are humans....
In the age of AI and ML and GenAI and LLMs, the real danger isn’t that computers are smarter than humans. It’s that we think they are, thus falling into delusions, individual and collective.
Machines NEVER could think or reason or feel or perceive or learn or plan or understand or create as humans.
Humans are humans, machines are machines, they only could complement each other acting in their specific ways.
Computers are getting better, faster and more powerful, but computer algorithms are still designed to have the very narrow capabilities needed to perform well-defined jobs, like NL tasks, translation, spell checking, searching the internet or data (text, image, audio, video, code) cross-transformation.
This is a far cry from the human general intelligence needed to deal with the world, humans, machines, environments, physical or virtual, unfamiliar situations by assessing what is happening, why it is happening, and what the consequences are of taking action.
Computers cannot formulate ideas or rules or theories, do inductive reasoning or make plans. Computers cannot estimate if the patterns they find are meaningful or meaningless, only logic, wisdom, and common sense can.
Computers do not have the emotions, feelings, inspiration or imagination to write poems, novels, articles, or movie script. Computers do not know, in any meaningful sense, what things/concepts/data/words mean. Computers do not have the knowledge/experience/wisdom humans accumulate by studying and living life.
Human intelligence is fundamentally different from artificial intelligence, completing, not competing, each other, which is why it is needed more than ever.
NVIDIA Fake AI Platform
The Big Tech AI is a blatant example of mass delusions, which is mislabeled and misbranded, a fake and false AI (FF/AI), AI washing, like greenwashing, being a threat to progress due to the illusion of knowledge and induced mass delusion.
A leading case is the NVIDIA GPU/AI washing, the hottest stock in the market world, due to the misbranding GPU as AI/DL chips for training and inference and mislabeling NVIDIA DGX systems as AI supercomputers.
NVIDIA GPU deep learning is misbranding as the AI hardware for the AI services from Amazon, Google, IBM, Microsoft, and many others.
Now, NVIDIA Blackwell Platform Arrives to Power a New Stage of Fake AI Computing.
It has announced that the NVIDIA Blackwell platform is enabling organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.
Among the many organizations expected to adopt Blackwell are Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla and xAI.
After being valued at around a $350 billion market cap in 2023, Nvidia is now worth about $2.2 trillion, due to the misbranding its flagship GPU as AI products, AI accelerators, deep learning processors, or neural processing units.
Traded at 33 times of the ratio of price to revenue (UBS is on a ratio of 1.7 times and Siemens 1.3), Nvidia is a trillion AI bubble stock to be busted before soon together with all the big tech fake AI companies. They are rushing to buy Nvidia’s chips to support their fake AI efforts, and Microsoft, Meta, Google, Amazon and Tesla are some of Nvidia’s biggest customers.
And the Nvidia-inflated AI bubble is much ‘bigger than the 1990s tech bubble.’
Again, the Federal Trade Commission issued a strong warning to vendors: stop lying about your AI....
Gary Gensler, Chair of the Securities and Exchange Commission (SEC), has warned recently that some companies were engaging in AI washing, which can break U.S. securities law, mislead consumers and harm investors.
[Vendors can avoid AI washing by being truthful when labeling a product, avoiding exaggeration and preparing a strong compliance strategy with the in-house legal team to shield against future lawsuits].
Computers are getting powerful, faster, and cheaper, but computer algorithms are still designed to have the very narrow capabilities needed to perform well-defined tasks, like NL tasks, as translation, spell checking, searching the internet or data transformation. This is a far cry from the real intelligence needed to deal with the world, physical or digital, with unfamiliar situations by assessing the what and who, where and when, why and how, as what is happening, why it is happening, and what the consequences are of taking action.
That's why we argue for the General Real Intelligence Rule (RIR) for Human Intelligence and Artificial Intelligence:
If natural intelligence or machine intelligence, be it human intelligence, AI hardware/software, AI models, ML, DL, LLMs, Chatbots, humanoid robots and automation, is unable to interact with the world or compute cause-and-effect, ruling all realities, physical or virtual, then they are effectively unintelligent, fake and false, with all the consequences.
The Big Tech Seven AI as a Fake and False AI (ff/AI)
There are Real and True Intelligence and False and Fake Intelligence, with some intermediaries, as true negatives or false positives.
The Examples of Real and True Intelligence
Real Human Intelligence, which is about knowing and categorizing, interpreting and understanding, qualifying and interacting with the world, at all levels and scopes, in the most effective and sustainable ways. Its general intelligence consists in the mental world models (World Model Engine).
Real Machine Intelligence [AI, ML, DL and NLP/NLU, LLMs, Chatbots, Robotics and Automation], which is about knowing and classifying, quantifying and interacting with the world, at all levels and scopes, in the most effective and sustainable ways. Its general intelligence consists in the world modeling and reality simulating engine (World Model Engine) programmed as a hypergraph causal networks (HGCN), a mathematical representation of reality and its data, consisting of hypernodes (representing causal variables, entities and changes) and hyperedges (representing all the causal relationships or interactions among and between the variables). It reifies all the valuable scientific models and theories, laws and facts, including statistical classifiers, AI models and ML algorithms, as DL neural networks, graph NNs, knowledge graphs, etc.
The Examples of False and Fake Intelligence
Fake and False Intelligence [AI, ML, DL and NLP/NLU, LLMs, Chatbots, Robotics and Automation] is about
fake knowing and classifying,
fake learning and understanding,
fake inference and predictions,
all with false outcomes.
Its fake and false intelligence consists statistical models and correlations and patterns matching in big data, in False input-False output (FIFO, like GIGO or RIRO).
All the Big Tech AI is a mislabeled and misbranded AI, a fake and false AI (FF/AI), AI washing, the like greenwashing, being a threat to progress due to the illusion of knowledge and induced mass delusion:
Alphabet FF/AI
Amazon FF/AI
Apple FF/AI
Meta FF/AI
Microsoft & OpenAI FF/AI
Nvidia FF/AI,
Tesla FF/AI
Nvidia FF/AI
NVIDIA AI Platform
NVIDIA FFAI is commercialized as "the world’s most advanced platform with full stack innovation across accelerated infrastructure, enterprise-grade software, and AI models. By accelerating the entire AI workflow, projects reach production faster, with higher accuracy, efficiency, and infrastructure performance at a lower overall cost".
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It s not aligned with the RIR, missing the intelligence core programmed knowledge about the causal world: GPU > GNNs > WholeGraph Storage >...Causal GNNs (missing)
"Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing intricate relationships in graphs, powering applications from social networks to chemistry. They shine particularly in scenarios like node classification, where they predict labels for graph nodes, and link prediction, where they determine the presence of edges between nodes.
Processing large graphs in a single forward or backward pass can be computationally expensive and memory-intensive. The workflow for large-scale GNN training typically starts with subgraph sampling to use mini-batch training. This entails feature gathering to capture needed contextual information in a subgraph. Following these, the extracted features and subgraphs are employed in neural network training. This stage is where GNNs showcase proficiency in aggregating information and enabling the iterative propagation of node knowledge. However, dealing with large graphs poses challenges. In scenarios like social networks or personalized recommendations, graphs can many nodes and edges, each carrying substantial feature data.
This post introduces WholeGraph, a new feature in the RAPIDS cuGraph library. WholeGraph is a kind of graph storage, which can work together with PyG, cuGraph-PyG, DGL, cuGraph-DGL, and cuGraph-Ops to accelerate large-scale GNN training".
NVIDIA has recently announced Project GR00T, Generalist Robot 00 Technology, a general-purpose foundation model for humanoid robots, acts as the mind of robots, making them capable of learning skills to solve a variety of tasks, designed to further its work driving breakthroughs in robotics and embodied AI.
“Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” “The enabling technologies are coming together for leading roboticists around the world to take giant leaps towards artificial general robotics.” Jensen Huang, founder and CEO of NVIDIA. NVIDIA GTC 2024 Keynote. Don’t Miss This Transformative Moment in AI.
NVIDIA is building a comprehensive AI platform for leading humanoid robot companies such as 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Figure AI, Fourier Intelligence, Sanctuary AI, Unitree Robotics and XPENG Robotics, among others.
All its AI solutions are effectively fake and false, with all the consequences:
Why the big tech fake and false AI must be disrupted by Real and True AI
The rationale of the big tech seven class action vs. [Alphabet, Amazon, Apple, Meta, Microsoft & OpenAI, Nvidia, Tesla] is documented in the LinkedIn postings which are supported from many parts of the world, from Africa to America.
The lawsuits are to seek damages, compensatory and punitive, up to 40% of market cap of the Big Seven; for the commercial fakery of artificial intelligence technology, deliberate misleading advertising and unethical business practices and massive commercial fraud, such a Deepfake Anti-Human Technology, massive IPRs infringements and personal data exploitation.
The settlement amount, up to 40% of the Big Seven market cap, >> $10T, will go to
European regulators trying to crack down on Big Tech with no big effects, the?EU's multi-billion antitrust activities fines and penalties?would hardly be ever recovered, considering all the loopholes.
The European Parliament has adopted the Artificial Intelligence Act, establishing the first AI regulatory framework to date. This act bans AI applications considered high-risk, including biometric and facial recognition for sensitive characteristics, social scoring systems, and AI that could manipulate or exploit vulnerabilities. AI systems in critical sectors like infrastructure, education, and employment will need to adhere to stringent requirements, including risk assessments, transparency, and human oversight.
Meanwhile, the Big Tech oligopolies are booming and blooming, which is mostly due to deliberate misleading advertising and unethical business practices such as massive IPRs infringements and personal data exploitation.
The?Big Seven's market capitalization exceeds $10 trillion?of all companies: 8,184?and?total market cap: $101.738 T.
Conclusion
Computers are getting powerful, faster, and cheaper, but computer algorithms are still designed to have the very narrow capabilities needed to perform well-defined tasks, like NL tasks, as translation, spell checking, searching the internet or data transformation. This is a far cry from the real intelligence needed to deal with the world, physical or digital, with unfamiliar situations by assessing the what and who, where and when, why and how, as what is happening, why it is happening, and what the consequences are of taking action.
That's why we formulated the General Real Intelligence Rule (RIR):
If natural intelligence or artificial intelligence, be it human intelligence, AI models, ML, DL, LLMs, Chatbots, humanoid robots and automation, is unable to interact with the world or compute cause-and-effect, ruling all realities, physical or virtual, then they are deeply unintelligent, having fake learning, fake understanding, fake intelligence, fake inferences, fake language, with the false outcomes, predictions, decisions, recommendations, communication, or actions.
Resources
SUPPLEMENT, or why the big tech AI is threat to science and engineering
The reason is rather simple, the big tech AI threatens true, real or causal understanding, scientific progress and engineering innovations.
All today’s AI is transacting with big data in the digital environments iteratively trained “to predict the outcome you design your model to predict”, no more, no less.
It all depends on what and how you teach your prediction model using machine model algorithms, as neural networks machine programs, to extract properties and identify features, structure, functions or objects for your specific goals.
Say, to build ML models that predict the weather/policy/war, some applicable features would be environmental variables (temperature, cloud coverage, and humidity), socio-economic variables or geo-political variables.
Training data, a training set, training dataset or learning set, is the material through which the computer learns how to process information.
The ML algorithm develops confidence in its performance by rote learning, memorizing information based on repetition, the underlying statistical correlations, patterns, relationships, and structures within a training dataset. The reality of the training data defines the reality of machine intelligence and learning.
Engineering is the designing, testing and building of machines, structures and processes using maths and science to solve real world problems, to innovate new technologies, products and processes and services, increase efficiency and productivity, and improve legacy systems, from infrastructure to mechanical, electronic, chemical, bio-engineering and cyber-physical systems.
Engineers need to predict how well their designs will perform to their specifications prior to full-scale production, using prototypes, scale models, simulations, destructive or nondestructive tests, and stress tests.
Computers generate and analyze the models of designs through digital twin technologies without having to make expensive and time-consuming physical prototypes.
Product drawings and engineering specifications have progressed from handmade drafting to computer-aided drafting/computer-aided design to model-based systems engineering and the digital twin concept as the entire manufacturing process, involving extended reality and spatial computing. The last one is about teaching computers to better understand and interact with people more naturally in the human world using computer vision (AI/ML), interfaces and sensors.
In other words, you need to teach your engineering prediction models cause and effect, by meaningful or active learning, as far as all engineering systems are causal dynamic systems
Here comes the Real Intelligence Rule (RIR) for Human and Artificial Intelligence:
If natural intelligence or machine intelligence,
be it human intelligence, AI hardware/software, AI models, ML, DL, LLMs, Chatbots, humanoid robots and automation,
is unable to interact with the world or compute full causality, ruling all realities and its systems and networks, physical or digital or virtual or cyber-physical, then they are non intelligent, fake or false, with all the consequences.
SUPPLEMENT 2. About NVIDIA
Since its founding in 1993,?NVIDIA?(NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling industrial digitalization across markets. NVIDIA is now a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry.
Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, features, and availability of NVIDIA’s products, services, and technologies, including Project GR00T, NVIDIA Thor system-on-a-chip (SoC), NVIDIA Isaac robotics platform, including generative AI foundation models and tools for simulation and AI workflow infrastructure, NVIDIA Isaac Manipulator, NVIDIA Isaac Perceptor, NVIDIA Isaac Lab, and NVIDIA OSMO; enabling technologies coming together for leading roboticists around the world to take giant leaps towards artificial general robotics; GR00T-powered robots being able to understand natural language and emulate movements by observing human actions and quickly learning coordination, dexterity and other skills to navigate, adapt and interact with the real world; NVIDIA building a comprehensive platform for leading humanoid companies to support the growing robotics ecosystem; the belief that modern AI will accelerate development, paving the way for robots like Digit to help people in all aspects of daily life; and Embodied AI helping to address some of the biggest challenges facing humanity, like population shrinkage, climate change and disease, as well as create innovations which are currently beyond our reach or imagination are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.
Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein.
More information at?https://nvidianews.nvidia.com/.