We currently live in an era defined by unprecedented technological advancements. With this the integration of Artificial Intelligence (AI) into our daily lives has become an undeniable reality. From virtual assistants in our smartphones to the incredible precision of medical diagnostics, AI has infiltrated various aspects of our everyday applications and made science fiction into our new reality. From its little beginnings to now we are looking at the rise of AI in everyday Applications.
Generative AI is a subset of artificial intelligence techniques and models that are designed to generate new, often human-like, data samples or content based on patterns and structures by learning from existing data. This type of AI system is capable of creating original content rather than just making decisions or predictions based on input data. There are several types of generative AI including:
- Generative Adversarial Networks (GANs):GANs consist of two neural networks, a generator and a discriminator,the generator creates data, while the discriminator tries to distinguish between real and generated data becoming better at generating realistic data over time.
- Variational Autoencoders (VAEs): VAEs are generative models that use variational inference, encoding input data into a latent space and then decode it to generate new data.
- Recurrent Neural Networks (RNNs): RNNs are a type of neural network that can generate sequences of data. They are often used in natural language generation tasks, such as text generation and speech synthesis.
- Transformers: Transformers are models like GPT (Generative Pre-trained Transformer), and have gained popularity for their ability to generate natural language text.
- LSTM and GRU Networks: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are specialised RNN architectures that are used for sequence generation tasks, including text generation and music composition.
- Autoencoders: Autoencoders are neural networks that can learn a compressed representation of input data. By generating data from this compressed representation, they can be used for generative tasks, such as image denoising and anomaly detection.
The Timeline Of Generative AI
When Generative AI models first started they were primarily used for numerical data in statistics. After the introduction of VAEs in 2013, the use of deep-learning models for generating realistic images and speech was introduced as well. They also introduced the ability to generate novel data variations from generative adversarial networks (GANs) to diffusion models, capable of producing ever more realistic, but fake, images setting the stage for today’s generative AI.
In 2017 Google introduced Transformers which revolutionised language models with their attention-based encoder-decoder architecture. This new technology could process text in parallel, making training faster. Transformers learned word positions and relationships leading to the growth of foundation models. This led to the ubiquitous use of Transformers as foundation models, and later led to pretrained models on large unlabeled datasets, for both non-generative tasks like classification and generative tasks like translation and summarization.
Generative AI is an undeniable testament to the incredible progress we’ve made in the world of technology. Its applications are vast and continually expanding, from revolutionising content creation and personalising user experiences to accelerating scientific research and driving innovation across industries. Contact Eden AI at firstname.lastname@example.org and we will help you harness the power of AI to learn all the ways you can make your day-to-day activities easier.
This article was enhanced using information from these sources:
McKinsey Article (2023) What is generative AI? McKinsey & Company
Martineau, K. (2023) What is generative AI? IBM
Porter, A. (2023) Unveiling 6 Types of Generative AI Big ID