The Big Tech Fraud: Why the Tech Giants as Google, Amazon, Facebook must be disbanded
High tech companies are increasingly at the center of political and social issues in the US and across the globe, and the past 10 years saw a wave of frauds and abuses of power, failed business ventures, and disastrous gadget rollouts.
Big Tech, as Facebook, Apple, and Google — some of the most powerful tech companies in the digital world — were the most frequent sites of scandal, as for massive illegal manipulation of personal data, fake news, monopolistic behavior, etc.
High technology (high tech) or frontier technology (frontier tech) is technology that is at the leading or cutting edge (the state of the art): the highest level of general development, as of a device, technique, or scientific field.
AI technology, ML techniques and Deep Neural Networks are monopolized by Big Tech as the most advanced technology available.
AI is liable to a massive fraud and a green-washing-like socially harmful mispractice, as being the most attractive brand for human minds.
AI-fraud and ML-washing describes how the big tech companies, not mentioning “AI-startups”, falsely label technology as “artificial intelligence” when it is automated “data analytics” at best, or in the worst case, the same software algorithms they have always used, just rebranded. This has led many organization leaders to become skeptics of all technologies labeled as AI or ML or DL.
As a result, today's Big Tech AI and ML and DL make one of the most sophisticated fraud, a high-tech innovation fraud, with all the consequences, technological, legal, social, economic, political or environmental. The Big Tech system wide AI & ML fraud is compromising the integrity of the whole high-tech industry, not mentioning other fraud impacts on people, governments, industries, services, privacy and security, and the environment.
The problem is much aggravated by the fact that the real AI with true ML is to make the best ever human strategic technology and the topmost general purpose integrative technology.
Big Tech as a major source of new high-tech fraud in the digital world
Big Tech, also known as the Tech Giants or colloquially the The American G-MAFIA and the Chinese of Gang of Four (GF), are the largest and most dominant companies in the information technology industry of the United States, especially Google, Microsoft, Amazon, Facebook, IBM, and Apple and China, Baidu, Alibaba, Tencent, and Huawei.
These ten make, besides IBM and Huawei, the most valuable public companies globally, with each having a maximum market capitalization ranging from around $500 billion to around $2.0 trillion USD.
Concerns over monopolistic practices have led to antitrust investigations from the Department of Justice and Federal Trade Commission in the United States, and the European Commission. The impact of these companies on privacy, market power, free speech and censorship (including inappropriate content), free and fair competition, and national security and law enforcement is counterbalanced by providing, seemingly, cheap or even free services to consumers, to stay popular.
Five characteristics were key in the emergence of the the Tech Giants oligopoly: media and information technology convergence, financialization, economic deregulation, globalization and high-tech fraud. The difficulty of politicians to understand a new high-technology and software issues made governmental intervention against monopolies ineffective. Financial deregulation led to Big Tech's big profit margins.
High Tech Fraud is one of the most serious, unrecognized and unchecked problem of the new digital world. As any big fraud, it "impacts on people, industries, public bodies, services and the environment and all of these can be irreversibly harmed".
The High Tech Fraud as an Industry Fraud against consumers and "public bodies can result in distorted markets where fraudsters obtain a competitive advantage and drive legitimate business out. It can affect services delivered by business and expose other sectors to further instances of fraud".
International Public Sector Fraud Forum Guide to Understanding the Total Impact of Fraud
"Consumer fraud is a product is sold, purporting to be something that it is not. Product counterfeiting is typically an organized group activity, because the manufacturing of goods takes people and time, and the goal is invariably profit. Many jurisdictions take the offence quite seriously, for reasons described below. As a result, most product counterfeiting would be considered organized crime under the Convention.
https://www.unodc.org/documents/data-and-analysis/tocta/8.Counterfeit_products.pdf
[The Justice Department took action against Google, alleging the company abused its market power and stifled competition. Google responded in a blog post by stating that DOJ's case is "deeply flawed," and if successful, would raise phone prices and prop up lower-quality search alternatives].
The U.S. Department of Justice has filed an antitrust lawsuit against Google, alleging that the online giant engaged in anticompetitive conduct to preserve monopolies in search and search-advertising.
The lawsuit, according to multiple reports, is the first of many long-anticipated actions by the Justice Dept. over massive tech companies like Alphabet, the parent of Google, Apple, Amazon and Facebook.
More specifically, the Complaint maintains that Google has unlawfully maintained monopolies in search and search advertising by:
Entering into exclusivity agreements that forbid preinstallation of any competing search service.
Entering into tying and other arrangements that force preinstallation of its search applications in prime locations on mobile devices and make them un-deletable, regardless of consumer preference.
Entering into long-term agreements with Apple that require Google to be the default – and de facto exclusive – general search engine on Apple’s popular Safari browser and other Apple search tools.
Generally using monopoly profits to buy preferential treatment for its search engine on devices, web browsers, and other search access points, creating a continuous and self-reinforcing cycle of monopolization.
William Barr, the U.S. Attorney General, said in a press release, “Today, millions of Americans rely on the Internet and online platforms for their daily lives. Competition in this industry is vitally important, which is why today’s challenge against Google — the gatekeeper of the Internet — for violating antitrust laws is a monumental case both for the Department of Justice and for the American people.” https://www.justice.gov/
Building the anti-Google, anti-Microsoft, anti-Apple, anti-Facebook, anti-IBM, anti-Amazon, anti-Baidu, anti-Alibaba, anti-Tencent and anti-Huawei
The American G-MAFIA and the Chinese of Gang of Four (GF) combine vertical levels of information and communications technology (ICT) power, infrastructure, data centers, internet connectivity, computer hardware including smartphones, operating systems, Web browsers and ML cloud platforms with user-level software and online services.
On July 29, the House Judiciary Committee conducted an extraordinary virtual hearing, tested the CEOs of Alphabet (ticker: GOOGL), Facebook (FB), Amazon.com (AMZN), and Apple (AAPL). In reaction, all four stocks rallied.
The horizontal concentration of power includes email, instant messaging, online searching, downloading and streaming, all combined internally within any of the G-MAFIA/GF members.
The tech giants has been routinely criticized for "avoid[ing] taxes, invad[ing] privacy, and destroy[ing] jobs", becoming an oligopoly to dominate the online market through anti-competitive practices, ever-increasing financial power, and intellectual property law. The current situation is the result of economic deregulation, globalization, and the failure of politicians to understand and respond to developments in new technology.
In June 2020, the European Union opened two new antitrust investigations into practices by Apple.
Fines are insufficient to deter anti-competitive practices by high tech giants, according to European Commissioner for Competition Margrethe Vestager. Commissioner Vestager explained, "fines are not doing the trick. And fines are not enough because fines are a punishment for illegal behaviour in the past. What is also in our decision is that you have to change for the future. You have to stop what you’re doing."
The only smart way to stop all the anti-competitive oligopolistic manipulative practices of the American G-MAFIA and the Chinese of Gang of Four is to disrupt the Big Tech fake AI/ICT technologies by building really intelligent, truthful, trustworthy, ethical ICT, like as projected by
Global Human AI Platform as the most reliable digital interface to the world
Artificial Intelligence for Everything and Everyone (AI4EE): a Global Scale Platform and General Purpose Technology
Disbanding the G-MAFIA and BAT-Triada oligopoly for a high-tech consumer fraud and continuous and self-reinforcing cycle of monopolization
Google’s Tensorflow, Keras, or Facebook’s Pytorch are typical examples of big tech AI counterfeit products and services, “consumer fraud: a product is sold, purporting to be something that it is not”, a system wide AI fraud compromising the integrity of the whole high-tech industry, not mentioning other fraud impacts on people, governments, industries, servicies, privacy and security, and the environment.
Product counterfeiting is typically an organized group activity, because the manufacturing of goods takes people and time, and the goal is invariably profit. Many jurisdictions take the offence quite seriously, for reasons described below. As a result, most product counterfeiting would be considered organized crime under the Convention.
https://www.unodc.org/documents/data-and-analysis/tocta/8.Counterfeit_products.pdf
They are not any AI if only the fake ML platforms dishonestly capitalizing on a widespread AI-fraud.
At the very best, all the big tech AI is about a so-called Narrow/Weak AI or Artificial Narrow Intelligence with ZERO perception, cognition, intelligence or understanding.
I suggested to the US GA a strong legal move in the DOJ vs Google antitrust case to highlight a massive “faking AI branding” by Google, Facebook, Apple and other big tech AI bubbles.
Today, only humans and animals are able to learn.
The Big Tech’s AI Machine Learning has nothing to do with a real learning, as acquiring new data/information/knowledge, perception, understanding, behaviors, skills, values, attitudes, and preferences, by studying, investigating, practicing, observing, teaching, or experiencing.
Today’s Machine learning running neural networks is mere “a method of data analysis that automates analytical model building”. It can NOT learn from data, or “recognize patterns” or “make decisions” with minimal human intervention.
Today’s machine learning (ML) is the study of computer algorithms that improve automatically through “training data” to fit some parameters; or the computing ability to automatically apply complex mathematical calculations to big data, the large volume of data – both structured and unstructured.
Today’s ML is a mix of computational statistics, or statistical computing, predictive analytics, encompassing statistical techniques from data analytics, mining, predictive modelling, to analyze (current and historical) facts to make estimations about future or otherwise unknown events.
In such a ML, a common task is the study and construction of algorithms that can INTERPOLATE from and make ESTIMATIONS on data. Such algorithms function by making data-driven APPROXIMATIONS or CALCULATIONS, through building a mathematical model from input data. The model is initially fit on a training dataset, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks of the model. The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method, using optimization methods such as gradient descent or stochastic gradient descent.
It is ONLY a REAL ML, which is “an application of artificial intelligence (AI) that provides systems the ability to automatically self-learning and self-improving from experience.
Specifically, what is said by their sites proves the non-AI status.
Keras
Keras, a cross-platform software, an open-source library that provides a Python interface for artificial neural networks.
Built on top of TensorFlow 2.0, “Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages”.
Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier to simplify the coding necessary for writing deep neural network code.
Keras allows users to productize deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. It also allows use of distributed training of deep-learning models on clusters of Graphics processing units (GPU) and tensor processing units (TPU).
Tensorflow/N-dimensional array data graphflow
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
It is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and (is also used for machine learning applications such as neural networks).
Pytorch
Pytorch, an open source ML framework that accelerates the path from research prototyping to production deployment.
It is free and open-source software, with the Python interface a C++ interface.
PyTorch used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab (FAIR).
A number of pieces of DL software are built on top of PyTorch, including Tesla Autopilot, Uber's Pyro, HuggingFace's Transformers, PyTorch Lightning, and Catalyst.
All in all, the Big Tech AI/ML massive fraud compromises a new general-purpose technology and an emerging strategic industry.
Narrow/Weak AI, ANI or Fake AI
Narrow/Weak AI or artificial narrow intelligence is a euphemism for a imitation, or fake AI, involving "artificial neural networks, machine learning and deep learning" and data science, with no ZERO perception, cognition, intelligence or understanding.
Narrow AI is as different as an idiot, imbecile, moron, autist, or specialist.
General AI is as different as a talent, polymath and genius.
Of all the programmable functions of AI systems, perception, cognition, planning, learning, reasoning, problem solving, understanding, intellect, volition, and decision making, Narrow/Weak AI operates under a narrow set of constraints and limitations. Narrow AI doesn’t mimic or replicate human intelligence, but mimics, fakes or simulates only specific human behaviour based on a narrow range of parameters and contexts.
It is to perform only singular tasks, be it facial recognition, or speech recognition/voice assistants, or driving a car, or searching the internet.
Examples of narrow or fake or imitation AI:
- Google’s Tensorflow, Keras, or Facebook’s Pytorch.
- Rankbrain by Google / Google Search.
- Siri by Apple, Alexa by Amazon, Cortana by Microsoft and other virtual assistants.
- IBM's Watson.
- Image / facial recognition software.
- Disease mapping and prediction tools.
- Manufacturing and drone robots.
- Email spam filters / social media monitoring tools for dangerous content
- Entertainment or marketing content recommendations based on watch/listen/purchase behaviour
- Self-driving cars, etc.
The real or general AI with a true ML is to make the most critical human strategic technology and the topmost general purpose integrative technology, as in:
the steam engine, railroad, interchangeable parts, electricity, nuclear power, electronics, mechanization, control theory (automation), the automobile, the computer, the Internet, space industries, and artificial intelligence.
And the real AI value creation by 2030 would be an order difference than what Mckinsey Global Institute projected, $ 13 x 10 trillion.
Resources
How far are we from achieving general artificial intelligence (GAI)?
How to Regulate the Big Tech fake AI: "AI and ML Falsification Act"
How to remedy Fake AIs and build Real AI World: True Trustworthy AI
https://www.dhirubhai.net/pulse/how-remedy-fake-ais-case-big-tech-azamat-abdoullaev/?published=t
What is the (true) nature of reality?
https://www.dhirubhai.net/pulse/what-true-nature-reality-azamat-abdoullaev/?published=t
If AI is the End of Humanity, or how to survive the intelligence revolution
https://www.dhirubhai.net/pulse/ai-end-humanity-azamat-abdoullaev/?published=t
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)
https://www.dhirubhai.net/pulse/artificial-global-intelligence-converging-general-big-abdoullaev/
Universal AI and how much might cost real AI model
Artificial Superintelligence as the next big thing
https://ec.europa.eu/digital-single-market/en/news/artificial-superintelligence-next-big-thing
AI as the Internet and the Internet as AI: Global Human AI Platform: Towards A Smart Future World
PhD Computer Science/Data Science/Python
4 年Overall, I agree with your opinion, but I?disagree that AI was established as a copycat game by G-MAFIA but rather by Alan Turing