We see a lot of companies and developers, scientists and researchers, vendors, professional services firms, end users and even politicians jumping on the AI bandwagon labeling their policies and strategies, technologies, products and service offerings, platforms and projects as AI products, projects, or offerings.
The only good explanation is that there isn’t a well-accepted distinction between what is definitely AI and what is definitely not AI, and how it differs from advanced data analytics, as machine learning, deep learning and neural network algorithms. Excluding ulterior commercial intentions, this is largely because there isn’t a well-accepted and standard definition of what is really AI.
If briefly, ML is not AI, as AI is not ML.
ML is commonly used alongside AI but they are different as truth and falsehood.
Today, mostly due to the big tech, dubbed as G-MAFIA and BAT-triada, ML is wrongly impersonated as AI; for its hyping, publicity and promotion, to hype us all up.
99% of American, European, Chinese or Russian startups claimed to use AI don’t use the technology.
There is a lot of ML examples and no one example of AI, due to its factual non-existence at this moment.
ML applications you may be familiar with:
- Smart phone virtual assistants, Alexa, Siri.
- OpenAI GPT-X.
- Medical diagnosis algorithms.
- Image processing software.
- The heavily hyped, self-driving Google car.
- Online recommendation offers such as those from Amazon and Netflix.
- Knowing what customers are saying about you on Twitter.
- Fraud detection.
- LAWS, Lethal Autonomous Weapon Systems, as killing robots and military drones
AI is the study of intellectual principles, phenomena and functions, as cognition, intelligence, intellect, reasoning, learning and understanding, in machines and computer systems, to effectively interact with the world.
Artificial Intelligence as the big tech fraud and high-tech scam or final human solution
The field of Machine Learning was supposed to seek to answer the question:
“How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”
In fact, Machine learning is the study of computer algorithms building mathematical models from input data based on sample data, known as "training data", in order to curve-fit, interpolate or extrapolate.
ML is simply a "method of data analysis that automates analytical model building" (SAS). Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
Machine learning is automation and iterative processes, statistical learning and statistical computing or predictive analytics. It has nothing with real learning, as acquiring new understanding, knowledge, skills, values, and behaviors.
You need to master some applied maths – statistics, probability, linear algebra, and calculus, together with high-level programming language (Python or R), and data analysis/ modeling, to go as a ML researcher, developer or engineer.
Now, deep learning sets up basic parameters about the data and trains the computer to curve-fit automatically by interpolating data patterns using many layers of processing as well as the backpropagation and optimization algorithms.
Neural networks are computing systems with interconnected nodes loosely mimicking neurons in the human brain. Using ML/DL algorithms, they can compute some patterns and correlations in raw data, cluster and classify it.
Again, ML/DL/NN is not AI, as AI is not ML
ML is the road to the massive state surveillance or global arms race in LAWS, as military drones, robotic weapons, killer robots or slaughterbots or UCAV.
With ZERO INTELLIGENCE, such advanced data processing automata can mindlessly/mechanically select (i.e. search for or detect, identify, track) and attack (i.e. intercept, use force against, neutralise, damage or destroy) targets without human intervention.
The Navy is developing offensive and defensive tactics for “Super Swarms” of up to a million drones.
Simply automating machines doesn’t make them intelligent. Training a computer to understand the difference between an image of a cat and an image of a dog doesn’t mean that the system can understand what it is looking at, learn from its own experiences, and make decisions based on that understanding.
Training and data-intensive computing systems might show some rudiments of ML capabilities, but that does not make them AI capabilities.
So, what is currently being branded as AI in the government, market and media is not AI at all, but rather just different versions of weak/narrow ML where the systems are being trained to do a specific, narrow task, using different approaches to ML, of which Deep Learning is currently the most promoted.
If you’re trying to make a computer to recognize an image just feed it enough data and with the tricks of math, statistics and neural nets weighing different connections over time, you’ll get the results you would expect.
But what you’re really doing is using your human understanding of what the image is, to create a large data set that can then be mathematically matched against inputs to verify what you as human understand.
In good old times it was called a fakery.
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