AI development directions according toO'reilly

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Interest in AI is not limited to NLP, however. Areas such as deep learning and reinforcement learning continue to grow. The PyTorch library has gained popularity, emphasizing the trend toward more efficient AI programming. An equally important aspect is MLOps, reflecting the growing need to manage and implement AI technologies.

New technologies like LangChain and vector databases point to future developments, while open-source projects like LLaMA democratize access to advanced AI tools. It’s not just the technological advances, but also the way AI is impacting sectors from healthcare to finance to government, that show how generative AI can be a catalyst for broad innovation.

In summary, the story of AI, especially from the perspective of GPT and generative AI models, is a story of rapid innovation and broad impact that reaches beyond the boundaries of technology, shaping the future of human-machine interactions and opening new possibilities in all areas of human endeavor.

Outstanding AI experts
Here are some of the most well-known artificial intelligence (AI) experts in the world who are active in both research and practical AI development:

Geoffrey Hinton : He is one of the most renowned researchers in the field of deep learning, which is a key area of ​​artificial intelligence. His work on neural networks was fundamental to the development of AI.
Yann LeCun : He is a French computer scientist best known for his work on convolutional neural networks (CNNs) and deep learning. He has been the Director of Artificial Intelligence at Facebook since 2013.
Andrew Ng : He is the co-founder of Google Brain, the hungary whatsapp data former head of Baidu AI Group, and the co-founder and leader of Coursera. Ng is one of the most influential educators in the field of artificial intelligence.
Demis Hassabis : He is the co-founder and CEO of DeepMind, an artificial intelligence research company that was acquired by Google. He is known for developing AlphaGo, the first program to beat the world champion in the game of Go.
Fei-Fei Li : She is a professor at Stanford University, former director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. She co-created ImageNet, a massive dataset for training AI systems for image recognition.
Elon Musk : Despite not being a scientist, Elon Musk is known for investing in and running artificial intelligence companies such as Tesla and OpenAI.
This is just a small sample of the many experts in AI, and the list is much longer and constantly growing as the field is very dynamic.

History of AI(Artificial Intelligence)
The development of artificial intelligence (AI) has gone through several key stages in history. Below is a general outline of these stages:

Early Concepts and Theories (before 1950) : Many of the concepts that later became the basis for AI were developed by logicians and philosophers many years earlier. For example, in 1843, Ada Lovelace suggested that machines could not only compute, but also create.
The Beginning of Artificial Intelligence (1950-1956) : The term “artificial intelligence” was first used in 1956 at the Dartmouth Conference. Before this conference, in 1950, Alan Turing published his famous work “Computing Machinery and Intelligence” in which he introduced the concept of the Turing Test.
The Golden Age of AI (1956-1974) : This period was characterized by optimism and significant financial support. Many scientists claimed that machines would be able to imitate human intelligence within a few decades.
First AI Winter (1974-1980) : This period was associated with the financial crisis and the realization that many problems that initially seemed easy to solve were in fact very difficult.
The expert systems boom (1980–1987) : During this time, expert AI systems that simulated the reasoning of experts in specific domains gained popularity.
The Second AI Winter (1987-1993) : After the explosion of expert systems, another crisis came, related to the limitations of these systems.
The rise of the internet (1994-2000) : The internet helped to collect large amounts of data that were necessary for the development of modern AI techniques.
The Era of “Big Data” and Machine Learning (2001-Present) : With the advancement of computer technology and data collection, machine learning algorithms have gained importance. This is the era of “deep learning” that has enabled significant progress in many areas of AI.