Foundation Models in amazon bedrock
What is a Foundation Model?
Data scientists now approach machine learning (ML) differently to foundation models (FMs), which are enormous deep learning neural networks trained on massive datasets. To create machine learning (ML) models that drive new applications more rapidly and economically, data scientists start with a foundation model rather than creating artificial intelligence (AI) from scratch.
Researchers created the phrase “foundation model” to refer to machine learning models that have been trained on a large range of generalized and unlabeled data and are able to carry out a wide range of general tasks, including language comprehension, text and picture generation, and natural language conversation.
What is unique about foundation models?
The versatility of foundation models is one of its special qualities. Based on input prompts, these models are capable of accurately completing a wide variety of diverse tasks. Among these tasks are picture categorization, question answering, and natural language processing (NLP). FMs differ from regular ML models, which usually carry out certain tasks like sentiment analysis of text, picture classification, and trend forecasting, because to their size and general-purpose nature.
Foundation models can serve as base models for more complex downstream applications. The scale and complexity of these models have grown over the course of more than ten years of work.
One of the earliest bidirectional foundation models, BERT, for instance, was published in 2018. A 16 GB training dataset and 340 million parameters were used in its training. Just five years later, in 2023, OpenAI used a 45 GB training dataset and 170 trillion parameters to train GPT-4.
Since 2012, the amount of computing power needed for foundation modelling has quadrupled every 3.4 months, according to OpenAI. Modern FMs, like the text-to-image model Stable Diffusion from Stability AI and the large language models (LLMs) Claude 2 and Llama 2, can do a variety of tasks right out of the box across multiple domains, including creating images, writing blog posts, solving math problems, having conversations, and responding to queries based on a document.
Why is foundation modeling important?
The machine learning lifecycle is about to undergo a major transformation to foundation models. They are helpful in the long run, even though creating a foundation model from scratch today costs millions of dollars. Instead of building new ML models from scratch, data scientists may create new ML applications more quickly and affordably by using pre-trained FMs.
Automating procedures and tasks, particularly those that call on reasoning skills, is one possible application. A few uses for foundation models are as follows:
- Customer service
- Translation of languages
- Creation of content
- Writing copy
- Classification of images
- Production and editing of high-resolution images
- Extraction of documents
- Robotics
- Autonomous vehicles for healthcare
How do foundation models work?
One type of generative artificial intelligence (generative AI) is foundation models. They use one or more inputs (prompts) in the form of instructions in human language to produce output. Complex neural networks, such as transformers, variationally encoders, and generative adversarial networks (GANs), serve as the foundation for the models.
Despite the differences in how each kind of network operates, the underlying ideas are the same. Generally speaking, an FM predicts the subsequent item in a sequence by using learnt patterns and correlations. In picture generation, for instance, the model examines the image and produces a clearer, more distinct version of the image. In a similar manner, the model uses the context and preceding words in a text string to predict the following word. following, it uses probability distribution algorithms to choose the following word.
Foundation models generate labels from input data through self-supervised learning. This indicates that no one has used labelled training data sets to instruct or train the model. This characteristic sets LLMs apart from earlier supervised or unsupervised learning ML frameworks.
What are the capabilities of foundation models?
Despite being pre-trained, foundation models can nevertheless pick up new information during inference from prompts or data inputs. This implies that with well chosen prompts, you can provide thorough outputs. Language processing, visual comprehension, code production, and human-centered engagement are among the tasks that FMs can complete.
Language processing
These models can create brief scripts or articles in response to cues, and they are remarkably good at answering queries in normal language. They can also use NLP technologies to translate languages.
Visual understanding
FMs are quite good at computer vision, particularly when it comes to recognising pictures and real objects. Applications like robotics and driverless cars might make use of these capabilities. The ability to edit photos and videos and create visuals from input text is an additional feature.
Code generation
Using plain language inputs, foundation models are able to produce computer code in a variety of programming languages. It is also possible to evaluate and debug code using FMs. Find out more about the creation of AI code.
Human-centered engagement
Human input is used by generative AI models to learn and enhance their predictions. The potential of these models to assist human decision-making is a significant and occasionally disregarded application. Clinical diagnosis, analytics, and decision support systems are among possible applications.
Developing new AI applications by optimising pre-existing foundation models is another capability.
Speech to text
FMs can be utilised for speech-to-text tasks like transcription and multilingual video captioning since they are linguistically aware.
Examples of foundation models
The market has seen a sharp increase in both the quantity and size of foundation models. Dozens of models are currently available. The following is a list of well-known foundation models that have been available since 2018.
BERT
Bidirectional Encoder Representations from Transformers (BERT), one of the earliest foundation models, was published in 2018. BERT is a bidirectional model that produces a prediction after analyzing the context of an entire sequence. It was trained with 340 million parameters and 3.3 billion tokens (words) from Wikipedia and a plain text corpus. BERT is capable of text translation, phrase prediction, and question answering.
GPT
In 2018, OpenAI created the Generative Pre-trained Transformer (GPT) model. It makes use of a self-attention technique in a 12-layer transformer decoder. Additionally, the BookCorpus dataset which contains more than 11,000 free novels was used to train it. Zero-shot learning is one of GPT-1’s noteworthy features.
In 2019, GPT-2 was released. Unlike GPT-1, which employed 117 million parameters, OpenAI trained it with 1.5 billion.GPT-3 trains its 175 billion-parameter 96-layer neural network using the 500 billion-word Common Crawl dataset. Popular ChatGPT chatbot uses GPT-3.5. The latest version, GPT-4, released in late 2022, passed the Uniform Bar Exam with a 297 (76%).
What are challenges with foundation models?
On topics they haven’t had explicit training on, foundation models are able to respond to cues in a logical manner. However, they have some shortcomings. Foundation models confront these challenges:
- Infrastructure needs: Training can take months, and creating a foundation model from the ground up is costly and resource-intensive.
- Front-end programming: Developers must include foundation models into a software stack that includes tools for pipeline engineering, rapid engineering, and fine-tuning in order to use them in real-world applications.
- Not understanding: While foundation models are capable of producing factually and grammatically accurate responses, they struggle to understand the meaning of a request. Furthermore, they lack psychological and social awareness.
- Untrustworthy responses: Responses to enquiries on particular topics may be untrustworthy and occasionally offensive, poisonous, or inaccurate.
- Bias: Given that models may detect offensive language and offensive undertones in training datasets, bias is a real risk. Developers should carefully filter training data and incorporate particular standards into their models to prevent this.
