But today, a computer beating a human player at chess would not be considered AI SOTA anymore, because computers and the AI field have since surpassed that level.

There are many AI technologies in the market today, but the most advanced ones are based on Deep Learning and Machine Learning algorithms. Deep learning has been around for a few decades, but only recently has it had a tremendous breakthrough in accuracy and precision. One of the most fascinating aspects of Deep Learning is that it can process data in a more human-like way. The most advanced Deep Learning networks available today are:

- Convolutional Neural Networks (CNNs) are algorithmic architectures that have been used in everything in our lives, from facial recognition to image classification and even at the backend of simple object tracking technologies that can be found in our phones. Companies like Google and Facebook use it in many products to provide users (and themselves) more value.

- Generative Adversarial Networks (GANs) are algorithmic architectures that use two Neural Networks. They put the two networks against each other in order to generate new, synthetic instances of data that can pass for real data or as real data. The most popular example of its use is Deep-Fake, which can manipulate videos so that they look real and are hard to distinguish from reality.

- Recurrent Neural Networks (RNNs) and the sub-type Long-Short Term Memory Networks (LSTMs) are a special type of artificial Neural Network adapted to work for time series data or data that involves sequences. Those types of algorithms consider the missing dimension of time in CNNs and keep the data’s history. The most popular example of its use is in autonomous cars: as they use feeds of data from sensors to navigate through traffic and avoid obstacles on the road, Object Detection by itself is not enough.

Startups and big companies fine-tune those networks to build more specific models to be used by many sectors, such as the military, autonomous cars, smart cities, and more. Many previously unsolved tasks in the fields of Natural Language Understanding, Computer Vision, and Robotics are now solved by those algorithms.

Those technologies have been around for a while, and are available for free to anyone who needs them - thanks to open-source libraries and frameworks like:

- PyTorch - was created by Facebook and accessible on GitHub.
- Caffe - has been funded by Berkeley Vision and Learning Center (BVLC).
- TensorFlow - was developed by Google.
- Keras - was developed as part of the research project and maintained by François Chollet.
- Detectron2 - which was developed by Facebook with the help of the tech community, including the author of these lines.

The main limitation of AI is on the hardware side. Researching and developing new models takes a lot of processing power from GPUs and CPUs with TPU limitations.

There is new progress in Quantum Computing that may bring good news and it will have an enormous impact on the future of AI and other fields. The development of quantum computing will also help to advance Artificial Intelligence because it will allow for more complex simulations and algorithms to be run. Soon, quantum computers and AI will be used together to make breakthroughs in solving some of the world’s most pressing problems.

Technology is advancing at a rapid pace. The advancements in AI are making it possible for machines to learn, perceive, and understand the world around them. The future of technology will be amazing.