What is horrible with today's AI, it makes a sort of AI alchemy, mixed with some black-box magic.

Instead of being advanced science and engineering, it is selling some math-techniques, statistic models, algorithms and black-box functions, curve-fitting, gradient descent, etc., as some surrogates of mind, intellect, intelligence or understanding and real learning.

Today's big-tech AI is a big fiction, a sort of high-tech alchemy, concerned with the transmutation of biased datasets/matter/base metals into gold or find a universal elixir or narrow/general/universal intelligence.

Some AI researchers getting an insight that machine learning algorithms, in which computers learn through trial and error, have become a form of "alchemy."

AI researchers allege that machine learning is alchemy

In fact, it is largely about statistic inductive inference machines relying on big data computing, algorithmic innovations and statistical learning theory and connectionist philosophy.

For most people it is mere building a machine learning (ML) model with an easy journey, going via data collection, curation, exploration, feature engineering, model training, evaluation, and finally, deployment.

EDA: Exploratory Data Analysis

AI Ops — Managing the End-to-End Lifecycle of AI

A few lines of R or Python code will suffice for such endeavor and there’s a plethora of resources and tutorials online to train your quaisi-neural networks, like all sorts of deepfake networks, manipulating image-video-audio-text, with zero knowledge of the world, as Generative Adversarial Networks, BigGAN, CycleGAN, StyleGAN, GauGAN, Artbreeder, DeOldify, etc.

They create and modify faces, landscapes, universal images, etc., with zero understanding what it is all about.

https://junyanz.github.io/CycleGAN/

"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks"

"14 Deep and Machine Learning Uses That Made 2019 a New AI Age".

There is a myriad of digital alchemy tools and frameworks going in their own ways:

Open languages — Python is the most popular, with R and Scala also in the mix.

Open frameworks — Scikit-learn, XGBoost, TensorFlow, etc.

Approaches and techniques — Classic ML techniques from regression all the way to state-of-the-art GANs and RL

Productivity-enhancing capabilities — Visual modeling, AutoAI to help with feature engineering, algorithm selection and hyperparameter optimization

Development tools — DataRobot, H2O, Watson Studio, Azure ML Studio, Sagemaker, Anaconda, etc.

As a sad result, a Data Scientist’s working environment: scikit-learn, R, SparkML, Jupyter, R, Python, XGboost, Hadoop, Spark, TensorFlow, Keras, PyTorch, Docker, Plumbr, and the list goes on and on, is providing everything for your alchemy of AI.

The First GLOBAL AI Company: EIS Encyclopedic Intelligent Systems ltd

https://www.slideshare.net/ashabook/eis-ltd

https://www.quora.com/What-sucks-about-AI/answer/Kiryl-Persianov

Universal Artificial Intelligence: AI for Everybody and Everything (AI4EE)

https://www.linkedin.com/pulse/ai-everybody-everything-ai4ee-universal-artificial-azamat-abdoullaev/