By: Ananya Dixit
As the world becomes increasingly data-driven and digitized, the ability to communicate effectively with machines has become a critical necessity. At the forefront of this endeavour are large language models, innovative AI systems that are redefining the way we interact with language itself. From chatbots to content generation, these models are revolutionizing various industries, and the latest developments are truly remarkable. These sophisticated AI systems, powered by state-of-the-art deep learning techniques, are capable of understanding and generating human-like text with accuracy and fluency. As the field of LLMs continues to evolve, the latest developments are poised to shape the future of language technology, opening up new frontiers in areas ranging from content creation to natural language understanding.
Let us look at some of the most recent developments in the field of AI and LLMs :
- ChatGPT-4
- Stable Diffusion
- Multimodal Models
- Bagging
- Stacking
- Ensemble Models
- Whisper by OpenAI
- AI in Genomics
- Utilizing facial analysis by AI systems to examine people's faces and reliably recognize hereditary illnesses.
- Using machine learning methods to determine the main type of cancer from a liquid sample.
- Estimating a patient's course of a specific type of cancer.
- Using machine learning to distinguish between benign and disease-causing genetic variations.
- Enhancing the functionality of gene editing instruments like CRISPR with deep learning.
- Alphafold 2
- LLM Based AI Assistants
OpenAI released this powerful language model in 2023, surpassing GPT-3 in capabilities across many benchmarks. It demonstrated improved reasoning, multimodal abilities, and better handling of instructions. GPT-4 is not only able to handle issues more precisely than its predecessor, but it can also generate writing that sounds more natural. Along with text, photos can also be processed by it. However, the AI is still susceptible to some of the same issues that beset previous GPT models: prejudice, stepping beyond boundaries meant to keep it from speaking inappropriate or harmful things, and "hallucinating," or boldly fabricating lies that aren't supported by its training data.
The fact that GPT-4 is "multimodal," or capable of handling both text and graphics, may be the biggest update. While generative AI models like DALL-E and Stable Diffusion are able to produce images, this model is unable to process or react to visual inputs.
Stable Diffusion is an AI system for generating images from text descriptions, released in 2022 by Stability AI and researchers. It utilizes a diffusion model, a type of generative AI that starts with pure noise and iteratively denoises and adjusts the input to produce an image matching the text prompt. What makes Stable Diffusion notable is that it matches the state-of-the-art image generation abilities of models like DALL-E 2 while being based on an open source codebase. This has allowed Stable Diffusion to gain rapid adoption and enable a new wave of AI-powered creative tools and apps. The model demonstrates an impressive capability to render highly detailed and complex scenes based on rich text descriptions.
One of the most important processes in stable diffusion is diffusion, which takes place in the "image information creator." Iteratively creating an information array entails extracting token embeddings from the input text and mixing them with a random initial picture information array (latent). The image decoder uses this information array to create the final image.
However, like other powerful generative AI models, Stable Diffusion also raises concerns around potential misuse for creating misinformation, biased outputs, and other harmful content that the research community continues grappling with.
The goal of the artificial intelligence subfield of multimodal learning is to efficiently process and interpret data from several modalities. To put it simply, this refers to merging data—text, image, audio, and video—from several sources to create a more thorough and precise understanding of the underlying information.
As the name implies, multimodal model is a machine learning approach that entails merging numerous models to enhance the performance of the primary model. The idea behind this approach is rather straightforward: if each model has distinct strengths and shortcomings, then combining numerous models may be able to overcome those weaknesses and produce forecasts that are more reliable and accurate.
Several frequently employed methods for merging models are as follows:
Systems like Google's Flamingo demonstrated strong multimodal capabilities combining vision, language, and other modalities. Applications for the idea of multimodal learning can be found in many fields, such as emotion recognition, autonomous vehicles, and voice recognition.
The Whisper models are trained for speech recognition and translation tasks, capable of transcribing speech audio into text in the language it is spoken (ASR) as well as translating it into English (speech translation). Whisper has been trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Whisper is an Encoder-Decoder model. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model to perform tasks such as language identification, phrase-level timestamps, multilingual speech transcription, and speech translation to English.
The study of human chromosomes and genes is known as genomics. There are normally 24,000 genes and 23 pairs of chromosomes in the human genome. Genome and DNA sequencing, which identify a DNA molecule's precise structure, are procedures used in medicine to gain additional insight into the molecular biology of a patient. While the field of genomics is still in its infancy regarding the application of AI/ML techniques, researchers have already benefited from creating customized programs. Some examples include:
DeepMind, a division of Alphabet, created the artificial intelligence (AI) program AlphaFold, which makes predictions about the structure of proteins. The software intends to be a deep learning system.
It is known that DeepMind used more than 170,000 protein sequences and structures from a public repository to train the software. The application uses a deep learning technique called attention networks, which focuses on letting AI recognize components of a larger problem and then putting the pieces together to find the solution as a whole. Between 100 and 200 GPUs were used for processing power during the entire training period. It took "a few weeks" to train the system on this hardware, and it would only take "a matter of days" for the algorithm to converge for every structure.
It solves the Protein Folding Problem, which basically means determining how a linear protein sequence folds into a unique 3D structure is key to understanding its function. This has huge implications for biology, disease research, and drug discovery.
LLMs are exceptionally proficient in two areas: language reading and writing, as well as question responding based on the training material. They do this through iteratively fine-tuning and training on enormous volumes of data. The AI picks up on linguistic quirks and patterns to forecast words and produce insightful answers.
Human interaction is involved in the fine-tuning process. Experts engage with the AI, rating its answers and offering constructive criticism. The synergy between human and AI improves the model's capacity to deliver precise responses.
Although LLMs seem to know something, they don't really understand. The distinction between knowledge and comprehension is blurred when they react in accordance with their feedback loop and training. To avoid that, we must introduce our own data and force the LLM to forget everything it previously understood. With the use of this methodology, businesses may respond in a safe, precise, and brand-specific manner without having to resort to hallucinations—answers the LLM made up that are not true. Thus, with the right AI platform and expert partner, you can harness the power of the LLM to provide accurate and safe answers to customers.
Conclusion.
In conclusion, the latest developments in Artificial Intelligence and language models have shown us a glimpse of the potential they hold. From revolutionizing industries to streamlining processes, their impact is undeniable.
As we navigate this ever-evolving landscape, it's crucial to maintain a balance between technological advancement and ethical considerations. By fostering a collaborative environment that prioritizes innovation while ensuring responsible implementation, we can harness the full power of AI and language models for the betterment of society.
