5 Real-world Applications of Large AI Models

Written by Damanpreet Kaur Vohra | May 29, 2024 8:30:00 AM

Large AI models, also known as foundation models or language models, are a new class of artificial intelligence systems capable of processing and generating human-like text, images, and other data. These models are trained on vast amounts of data, often comprising billions or even trillions of parametres. This allows them to capture intricate patterns and relationships within the training data. Large AI models can perform a wide range of tasks with few or no task-specific modifications, their capacity to generate coherent and contextually relevant outputs, and their ability to transfer knowledge learned from one domain to another.

What’s interesting here is how large AI models differ from traditional models. Traditional machine learning models require extensive feature engineering while large AI models are trained in a more generalised manner. This allows them to adapt to various tasks with minimal fine-tuning. Some popular examples of large AI models include Large Language Models like GPT-3 (Generative Pre-trained Transformer 3) developed by OpenAI, which has demonstrated impressive language generation capabilities with over 175 billion parametres. BERT (Bidirectional Encoder Representations from Transformers), created by Google, is another influential model for natural language processing tasks, with various versions ranging from 110 million to 3.8 billion parametres. DALL-E, also developed by OpenAI, is an AI model that can generate realistic images from textual descriptions.

Also Read: Phi-3: Microsoft's Latest Open AI Small Language Model

5 Real World Applications of Large AI Models

Large AI Models have shown various benchmarks and real-world applications such as natural language processing, computer vision, and content generation. Here’s how different industries are employing Large AI Models to stay ahead of the curve:

Natural Language Processing (NLP)

NLP is the field of AI that deals with understanding, processing, and generating human language.

Content generation: These models can generate human-like text for various purposes, such as creative writing, article generation, and content creation for marketing or educational materials. For example, GPT-3 can generate coherent and contextually relevant text on various topics.
Language translation: Large AI models can be trained on multilingual data to perform language translation tasks, potentially reducing the need for human translators in certain scenarios. For instance, Google's Transformer model has shown promising results in translating between multiple languages.
Sentiment analysis: By analysing the sentiment and emotion expressed in text data, large AI models can be used for tasks like customer feedback analysis, social media monitoring, and market research. For example, BERT has been effectively utilised for sentiment analysis tasks in various industries.
Chatbots and virtual assistants: Large AI models can power conversational AI systems, enabling more natural and contextual user interactions. For instance, GPT-3 has been used to create sophisticated chatbots and virtual assistants for customer service, healthcare, and other domains. You may read our documentation on running a Chatbot on our platform.

Computer Vision

Computer vision is the field of AI that deals with understanding and analysing visual data, such as images and videos.

Image recognition and classification: These models can accurately identify and classify objects, scenes, and activities in images, with applications in fields like healthcare (medical imaging analysis), retail (product recognition), and security (surveillance systems). For example, Convolutional Neural Networks (CNNs) like VGGNet and ResNet have achieved state-of-the-art performance in image classification tasks.
Object detection: Large AI models can localise and identify multiple objects within an image, which is crucial for applications like autonomous vehicles, robotics, and augmented reality. For instance, the YOLO (You Only Look Once) and Faster R-CNN models have been widely adopted for object detection tasks.
Facial recognition: By analysing facial features and patterns, large AI models can be used for facial recognition and verification systems, with applications in security, law enforcement, and social media platforms. For example, FaceNet, developed by Google, has shown impressive results in facial recognition tasks.
Autonomous vehicles: Computer vision is a critical component of autonomous vehicles, enabling them to perceive and understand their surroundings. Large AI models are used for tasks like object detection, lane detection, and pedestrian recognition, ensuring safe and efficient navigation. For instance, Tesla's Autopilot system utilises deep learning models for various computer vision tasks.

Healthcare

Large AI Models have upgraded medical practices through AI-powered solutions including:

Medical imaging analysis: Large AI models can be trained on vast datasets of medical images, such as X-rays, MRI scans, and CT scans, to assist radiologists in detecting and diagnosing various conditions. For example, deep learning models have shown promising results in detecting tumours, fractures, and other abnormalities, potentially improving diagnostic accuracy and efficiency.
Drug discovery and development: These models can be utilised in drug discovery and development processes by analysing vast amounts of data, including molecular structures, biological pathways, and clinical trial data. This can help identify potential drug candidates, predict their efficacy and safety, and optimise drug design and development processes.
Personalised treatment: By analysing patient data, including medical records, genomic data, and lifestyle factors, large AI models can provide personalised treatment recommendations tailored to individual patients' needs. This approach can improve patient outcomes and reduce the risk of adverse reactions or ineffective treatments.
Predictive analytics: Large AI models can be used for predictive analytics in healthcare, such as forecasting disease outbreaks, predicting patient readmissions, and identifying high-risk populations. This information can help healthcare providers allocate resources more effectively and implement preventive measures.

Finance

Organisations are leveraging AI to drive financial decisions and mitigate risks including:

Fraud detection: By analysing patterns in financial transactions, large AI models can identify potential fraudulent activities, such as credit card fraud, money laundering, and cyber attacks. This can help financial institutions prevent losses and maintain the integrity of their systems.
Risk assessment: These models can assess various financial risks, such as credit risk, market risk, and operational risk, by analysing vast amounts of data, including financial statements, market trends, and economic indicators. This information can aid in better risk management and decision-making processes.
Stock market prediction: Large AI models can analyse historical stock market data, news articles, social media sentiment, and other relevant information to predict stock prices and market trends. While not entirely accurate, these predictions can provide valuable insights for investment decisions and portfolio management.
Automated trading: AI-powered trading systems can use large AI models to analyse market data in real time and execute trades based on predefined strategies and algorithms. This can lead to faster and more efficient trading decisions, potentially increasing profitability.

Retail and E-commerce

Organisations have particularly improved customer experiences with personalised AI applications including:

Customer service chatbots: Large AI models can power conversational AI chatbots and virtual assistants, enabling more natural and efficient customer service interactions. These chatbots can handle customer inquiries, provide product information, and assist with order tracking and returns, potentially reducing the workload on human customer service representatives.
Personalised recommendations: By analysing customer data, such as purchase history, browsing behaviour, and preferences, large AI models can provide personalised product recommendations tailored to individual customers' needs and interests. This can increase customer satisfaction and drive sales.
Demand forecasting: These models can analyse historical sales data, market trends, and other relevant information to forecast product demand accurately. This can help retailers optimise inventory levels, streamline supply chain operations, and reduce waste and overstocking.
Inventory management: By analysing sales patterns, customer behaviour, and other relevant data, large AI models can help optimise inventory levels across different locations, ensuring adequate stock levels and minimising inventory carrying costs.

Limitations of Large AI Models

To fully utilise the potential of large AI models, organisations must develop practices to mitigate associated risks that include:

Data Quality: Large AI Models require vast amounts of training data, raising issues around data quality, representativeness, and potential biases. Using personal or sensitive data necessitates strict data governance and anonymisation protocols.
Computational Demands: Training and deploying models with billions/trillions of parametres requires immense computational power. Hence, organisations require powerful and expensive GPUs designed for efficient parallel processing and matrix operations.
Interpretability: Despite impressive performance, these models often lack interpretability, making it difficult to understand their decision-making processes, and raising concerns for high-stakes applications requiring explainability.
Ethical Considerations: Large AI Models can perpetuate or amplify societal biases present in training data, leading to unfair or discriminatory outputs. Ensuring fairness, accountability, and ethical AI development/deployment is crucial through frameworks and guidelines.

Developments in Large AI Models

The development of large-scale AI models is linked to the availability of powerful hardware and computing resources. As these models grow in size and complexity, they require immense computational power to train and operate efficiently. A recent development could be the NVIDIA Blackwell Architecture announced on 18 March 2024 by NVIDIA CEO Jensen Huang at GTC 2024, designed to accelerate Generative AI. The NVIDIA Blackwell architecture features six transformative technologies for generative AI and accelerated computing, which will help in leading innovations in data processing, electronic design automation, computer-aided engineering and quantum computing. Hyperstack is one of the first providers in the world to offer reservation access. To secure early access, reserve your Blackwell GPU through Hyperstack here.

While many existing large AI models focus on single modalities like text or images, there is growing interest in developing multimodal models that can process and generate data across multiple modalities, such as combining vision, language, and audio. These models have the potential to enable more natural and intuitive human-machine interactions, with applications in areas like virtual assistants, multimedia content creation, and augmented reality. A recent example could be OpenAI’s Sora, a multimodal AI that can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt.

As concerns over data privacy and security continue to grow, there is a need to allow the training of large AI models without compromising sensitive data. These techniques can help address privacy concerns. NVIDIA’s Blackwell HGX B100 and DGX B200 GPUs come with advanced confidential computing capabilities to protect AI models and customer data with uncompromised performance, with support for new native interface encryption protocols critical for data-sensitive industries like healthcare and financial services.

With the increasing adoption and impact of large AI models, there is also a growing need for responsible AI practices and regulatory frameworks to ensure these technologies are developed and deployed ethically and transparently. For instance, Meta has adopted a comprehensive system-level approach that empowers developers to use their advanced language models, such as LLaMA 3, responsibly. Through iterative instruction fine-tuning and extensive red-teaming and adversarial testing efforts, Meta has focused on developing safe and robust models that mitigate potential risks. New tools and frameworks are being introduced to facilitate responsible deployment. The LLaMA Guard 2, which leverages the MLCommons taxonomy, provides a standardised means of evaluating and mitigating risks associated with language models. yberSecEval 2 is designed specifically for code security evaluation, while Code Shield helps filter out insecure or malicious code generated by AI systems.

Conclusion

Large AI Models have been transforming various industries with applications like natural language processing, computer vision and AI-powered cybersecurity. However, training these massive models requires immense computational power. At Hyperstack, we offer access to cutting-edge GPUs like the NVIDIA A100, H100 PCIe, H100 SXM, and the highly anticipated NVIDIA Blackwell GPUs, designed to tackle complex AI training workloads efficiently. Build Innovative AI Models with Hyperstack’s Powerful NVIDIA GPUs. Sign up now to get started!

FAQs

What are large AI models?

Large AI models, also known as foundation models or language models, are artificial neural networks with a large number of parameters, often ranging from hundreds of millions to trillions. They learn from vast amounts of data, capturing intricate patterns and relationships, enabling them to perform a wide range of tasks with minimal task-specific modifications.

What are some applications ofartificial intelligence in real world?

Some real world applications of AI are as mentioned:

Natural Language Processing (content generation, translation, sentiment analysis)
Computer Vision (image recognition, object detection, facial recognition)
Healthcare (medical imaging analysis, drug discovery, personalised treatment)
Finance (fraud detection, risk assessment, stock market prediction)
Retail/E-commerce (personalised recommendations, demand forecasting, chatbots)

What are the limitations of large AI models?

Key limitations of large AI models include data quality and privacy concerns, immense computational resource requirements, lack of interpretability and transparency, and ethical considerations such as perpetuating societal biases in training data.

What is the best GPU for training large AI models?

We recommend using the NVIDIA A100, H100 and Blackwell GPUs for training large AI Models effectively.