From Concept to Reality: A Timeline of Generative AI's Evolution

Evolution of generative AI
From Concept to Reality: A Timeline of Generative AI's Evolution

Generative AI from a buzz word to a tangible reality is a journey of a technological transformation. Today, generative AI is more than a technology holding a great place in every industry be it healthcare or advertising, its application is huge. Here, we will delve into the evolution of generative AI:

Generative AI

Generative AI are deep-learning models that are capable of generating top-notch content and images. Generative AI is trained on vast datasets. Artificial Intelligence (AI) works to replicate human smarts in unconventional computing activities such as identifying images, processing natural language, and translating between languages.

Generative AI represents the subsequent phase in AI development. It can be taught to understand human languages, coding languages, art, chemistry, biology, or any intricate topic. It leverages previously learned data to deal with new challenges.

Generative AI timeline: 1940s to 1960s

Despite receiving significant focus in recent times, the origins of generative AI can be traced back to the beginning of AI in the middle of the 20th century.

The Turing test

In 1947, the mathematician Alan Turing first referred to "intelligent machinery" in a document investigating the possibility of a machine recognizing logical actions. 

In a 1950 document, he presented the idea of the Turing Test, where a person would assess written exchanges between a human and a computer programmed to mimic human-like reactions. Should the assessor consistently fail to distinguish the computer from the human, the computer would succeed in the test.

ELIZA

The ELIZA chatbot, developed by a British scientist named Joseph Weizenbaum in 1961, stands as one of the earliest working instances of AI that can generate responses. It was the initial program that mimicked the role of a psychotherapist through verbal interactions, allowing ELIZA to converse with a person through simple text exchanges.

Generative AI timeline: 1980s to 2010s

Advancements in machine learning algorithms drove the development of generative AI, allowing machines to acquire knowledge from data and enhance their capabilities as time goes on.

RNNs and LSTM networks

The advent of Recurrent Neural Networks (RNNs) in the late 1980s and Long Short-Term Memory (LSTM) networks in 1997 improved the capacity of AI systems to handle data in sequence. The LSTM's skill in grasping the importance of order was essential for tackling complex issues, such as speech recognition and machine translation.

Breakthroughs in generative models

Another historical event for the development in 2014’s Generative Adversarial Network (GANs) in the category of generative AI was another breakthrough. A GAN is a type of unsupervised ML, where two neural networks are in conflict with each other. 

One network is a generator which is to produce fake content, and the other is a discriminator which aims at identifying whether given content is real or fake.

Through numerous iterations, the generator will eventually succeed in creating high-quality images that the discriminator is unable to tell apart from actual images.

At roughly the same period, other techniques were also discovered and among them include VAEs, diffusion models and flow-based models which generally enhanced the algorithm of image production.

Transformer architecture and introduction of GPT models

In a way, transformer models, which were first unveiled in 2017, work on patterns found in natural language texts by recognizing how words relate to each other. While older machine learning systems process sequences one piece at a time, these transformers perform all the pieces at once, making their efficiency and capability greatly improved.

The design of transformers paved the way for LMs, such as GPT (Generative Pre-trained Transformer), initially developed by OpenAI in 2018. GPTs are networks that employ a deep learning structure for generating text, interacting with users, and accomplishing various language-based tasks.

Individuals can use GPTs to simplify and improve activities like programming, writing content, researching intricate subjects, and translating text. Its greatest advantage lies in its remarkable speed and capability to handle large amounts of data.

Generative AI timeline: 2020s

ChatGPT

OpenAI’s ChatGPT, which was released in November 2022 and for five days got over one million users. Initially equipped with GPT-3. 5, ChatGPT enables programs to engage in informative and contextual conversation with the computer. 

It also enables users to ask ChatGPT to generate written text and different other materials in the desired style and with the specified length, format, and degree of elaboration.

Llama from Meta

Meta's Llama (Large Language Model Meta AI) is a suite of cutting-edge base language models that marked a significant milestone in the advancement of open-source AI technology. 

While its basic models are more humble than those of GPT-3 and similar models, it is accurate and learns at the same level of proficiency, all while using far less energy.

In 2023, at the Snapdragon Summit, we set a new record for the fastest Llama 2-7B on a mobile device, showcasing a conversation with an AI assistant that operates entirely within the phone.

PaLM and Gemini from Google

In April 2022, Google introduced its Pathways Language Model (PaLM), which was kept confidential until March 2023, when the company made it available through an API. PaLM marked a significant advancement in natural language processing (NLP), boasting an impressive 540 billion parameters.

Among all the inventions by Google, Gemini, the newest one can be considered as one of the most significant where performance, as well as options, are concerned. 

It is intended for utilization in various tasks with equal ease and can effectively analyse multiple types of information, namely, text, code, spoken words, pictures and movies. Gemini is available in three distinct models: The three primary types are known as the Ultra, Pro as well as the Nano.

Generative AI text-to-image models

DALL-E, Midjourney, and Stable Diffusion are cutting-edge AI systems that generate and alter visual content using written instructions. OpenAI creates a software named DALL-E, which generates detailed and realistic pictures. 

Manageable Diffusion offers a premium license where you can access state-of-the-art visual quality in addition to its availability as an open-source program.

In February 2023, we presented the global debut of Stable Diffusion's demonstration on an Android device right on the device itself.

Generative AI has undergone huge evolution since its inception till date. The technology had a huge impact creating innovation across various industries.

Related Stories

No stories found.
logo
Analytics Insight
www.analyticsinsight.net