AI or Artificial intelligence
AI or Artificial intelligence is the technology that enables machines to be just as smart and active as human beings, therefore granting it a type of intelligence from experience, problem-solving abilities, and decision-making.
Like, you may know about voice assistants Siri or Google Assistant, you may receive movie suggestions on streaming services, and you may also heard about self-driving cars, these all are belongs to AI.
AI has its own capabilities to perceive and identify objects. They can understand human language and respond accordingly. They learn experiences and new pieces of information. They may also send profound recommendations to the users as well as experts.
In short, They can act independently, replacing the need for human intelligence or intervention.
Evolution of AI
1940s – 1950s: Early Years of AI
In the 1940s, Mathematician Alan Turing started working on the idea of thinking machines. In 1950, he wrote a paper titled “Can Machines Think?” He designed the Turing Test, defined as the ability of a machine to demonstrate during a text-based conversation that it is a living being.
1956: AI is Born
It was named officially in 1956 at a conference known as the Dartmouth Summer Research Project. Representatives from linguistics and computer science were brought together to discuss whether computers could think like humans. It was here that computer scientist John McCarthy coined the term “artificial intelligence.”
1961: The First Chatbot
There was ELIZA, by Joseph Weizenbaum, in 1961-the first chatbot that could simulate a psychiatrist conversation. This took place among the first types of generative AI, where responses were furnished on the basis of input given.
1970s – 1980s: AI Winter
AI interest peaked down during early and late 1970s and 1980s, along with related funding, the phenomenon was called an AI winter. It was due to the reasons that people expected too much too soon and there was not much progress made.
2000s: The Era of Deep Learning
Deep learning burst into the limelight during the years of the 2000s. A technique by which machines can learn from large sets of data. Recurrent Neural Networks(RNNs )were being used for sequence-related tasks, such as languages. Some of the earliest early generative models developed at these times include Variational Autoencoders(VAEs) and Restricted Boltzmann Machines (RBMs).
2014: GANs: Generative Adversarial Networks
Then, in 2014, GANs were discovered, and that was a big deal in generative AI-they could generate high-quality images by making two neural networks race against each other.
2015: Diffusion Models
Diffusion models were discovered in 2015. They work by adding noise to data and then reversing the process that restores it, thus enhancing techniques of generating data.
2017: Transformers
In 2017, the new deep learning architecture known as transformers was discovered. This model enhanced the machine’s ability to process and generate human language.
2022: ChatGPT
The open AI developed an AI called ChatGPT, a generative AI, powered by a massive language model that creates text. It represented a significant step toward the advancement of AI capabilities.
2023: GPT-4
In March 2023, GPT-4 emerged that can generate even longer texts-even longer than the previous version could: up to 25,000 words! This showed that the thing is becoming more evolved and powerful.
AI has developed significantly from the small-scale conceptions back in the 1940s to the powerful machine like ChatGPT that we have today. Along this journey lies a mix of extreme advance and intense struggles. Among all these developments, generative AI is the sector that is going to change our life and work in distinct ways. As these models keep improving further, generative AI is going to shape us in more powerful ways through their influence on life.