Real-world applications of Artificial Intelligence

AI is applied across various sectors, demonstrating its versatility and adaptability. Here are some real-world applications of AI:

Prediction

AI excels at making predictions based on patterns it learns from data, and its predictive ability has led to significant advancements in various fields:

Cancer recognition:

AI models have been shown to accurately identify cancerous tumors. For instance, an AI model developed at the Beckman Institute for Advanced Science and Technology could identify early signs of tumors in mammograms, retinal OCT images, and chest X-rays with high accuracy rates.

The model performed comparably to existing black-box AI systems, offering accuracy rates of 77.8% for mammograms, 99.1% for retinal images, and 83% for chest X-rays.

In some cases, AI systems can surpass human experts in diagnosing cancer. For example, a deep learning model created by researchers at UT Southwestern Medical Center was more accurate than traditional methods in predicting whether bladder cancer had spread to nearby lymph nodes​.

A study involving a deep learning radiomics model showed that it had high accuracy and an area under the curve (AUC) significantly better than conventional techniques for predicting LNM in bladder cancer patients.​

AI's ability to accurately analyze medical images and identify cancerous tumors has significant potential to enhance early detection and improve patient outcomes.

Financial market prediction

AI models are transforming how businesses and investors approach financial markets. For example, BlackRock's Aladdin platform uses AI to analyze historical stock prices, market sentiment, and economic indicators, helping portfolio managers identify trends and forecast stock price movements with precision.

J.P. Morgan and Bloomberg are at the forefront of using AI to enhance financial decision-making. J.P. Morgan employs advanced AI systems like LOXM to optimize trade execution by analyzing vast market data, improving efficiency and outcomes in equity trading.

Bloomberg, on the other hand, integrates AI within its platforms, including the Bloomberg Terminal, to process large-scale financial data, analyze trends, and provide actionable insights, aiding investors and policymakers in making informed decisions.

AI's impact extends to risk assessment as well. Kensho Technologies, acquired by S&P Global, uses AI models to predict market risks and optimize portfolios, modeling the effects of global events like geopolitical instability on financial markets.

AI-driven predictive models in finance not only refine their accuracy over time but also enhance investment strategies and portfolio performance, enabling financial institutions to respond more effectively to market dynamics.

Automation

Narrow AI is revolutionizing industries by automating repetitive and time-consuming tasks, leading to increased efficiency and productivity:

• Document automation:

AI technologies are being used to enhance efficiency and reduce errors associated with manual processes across multiple industries. In the legal sector, AI tools are increasingly integrated into practices such as eDiscovery, legal research, document management, and due diligence. These AI-driven tools help legal professionals handle large volumes of data with greater accuracy and speed, improving job satisfaction and operational efficiency​.

Bloomberg Law, for instance, uses machine learning to enhance legal research and streamline the brief analysis process, which indicates a significant application of AI in automating complex documentation tasks​.

• Customer service chatbots:

AI-powered chatbots have been increasingly adopted across various industries due to their efficiency and ability to handle customer service tasks. These chatbots utilize natural language processing (NLP), machine learning, and large language models to understand and respond to customer queries accurately and effectively.

AI chatbots can manage repetitive and straightforward customer queries, such as answering FAQs, processing orders, and resetting passwords, allowing human agents to concentrate on more complex and nuanced customer issues.

Unlike human agents, chatbots can operate round the clock, providing immediate assistance to customers anytime, improving customer satisfaction by reducing wait times and ensuring support is always available.

Through machine learning, AI chatbots continuously improve their responses and adapt to new queries based on past interactions. This ensures that they become more effective in providing accurate and relevant support over time.

• Manufacturing robots:

AI-powered robots have improved manufacturing by enhancing various tasks through increased precision, speed, and efficiency. AI-driven robots are employed on assembly lines to accurately handle repetitive and intricate tasks. These robots can adapt to new tasks quickly and optimize their movements to increase production output while maintaining consistency in quality.

AI and machine vision technologies have significantly advanced robotic welding. AI-powered welding robots can adjust their parameters in real-time to ensure high-quality welds, even in complex or variable conditions, leading to consistent, high-quality welds and reducing the need for rework.

Furthermore, these robots can address the shortage of skilled welders by performing tasks traditionally requiring highly skilled human labor. Robots equipped with AI and advanced vision systems can perform detailed inspections of products to ensure they meet quality standards.

These robots can detect defects and inconsistencies that might be missed by human inspectors, leading to higher product quality and reduced waste. For example, SCARA and Delta robots are used for their speed and precision in inspecting small, delicate parts and sorting products at high speeds​.

Integrating AI into robotic systems in manufacturing improves the precision and speed of operations and reduces costs by minimizing errors, enhancing product quality, and allowing human workers to focus on more complex tasks that require critical thinking and creativity​.

Self-driving cars

Self-driving cars represent one of the most ambitious and potentially transformative applications of AI. Self-driving cars, also known as autonomous vehicles, utilize a sophisticated combination of technologies to operate.

These include a variety of sensors like cameras, LiDAR, radar, and ultrasonic sensors that gather data about the vehicle’s environment. The data is then processed by advanced AI algorithms that make real-time driving decisions, ensuring safe navigation and obstacle avoidance.

Self-driving cars can provide mobility solutions for those unable to drive, such as the elderly or disabled, making transportation more inclusive. They can also optimize routes and driving patterns, leading to reduced fuel consumption and lower emissions, which can also alleviate traffic congestion through better traffic flow management.

Self-driving cars are a prime example of Narrow AI's potential to transform industries. While there are still challenges to overcome, including regulatory and safety concerns, the advancements in this field promise a future of safer, more efficient, and more accessible transportation.

AlphaGo

In March 2016, AlphaGo, an AI system developed by DeepMind, achieved a significant milestone by defeating Lee Sedol, a world champion Go player, in a five-game match. AlphaGo won four out of the five games, demonstrating the advanced capabilities of AI in mastering one of the most complex board games in existence.

The victory was remarkable because Go is known for its vast number of possible moves and required strategic depth, which had previously made it a challenging game for AI to master.

Generative AI

Generative AI is a rapidly growing field focused on creating new content, such as images, music, and even code. Some notable examples include:

• Text and image generation:

AI-powered applications are changing content creation in the media and entertainment industry. For instance, ChatGPT can quickly generate text-based content that aids journalism, creative writing, and marketing. This capability is particularly valued for its potential to inspire new ideas and overcome challenges like writer's block.

On the other hand, DALL-E specializes in creating images from textual descriptions. It is used to produce unique and engaging visual content. This tool enhances artistic creativity and reduces costs associated with traditional content production​.

In education, generative AI is reshaping teaching and learning processes. AIs are used to develop educational content and provide personalized learning experiences. They are also used for tasks such as automating feedback on student assignments and generating educational materials, thereby enriching the learning environment.

• Image generation:

AI models can generate realistic images from text descriptions, creating new possibilities for creative expression and design. One such model is DALL-E, an AI model developed by OpenAI that generates images from text prompts. It combines advanced natural language processing and image generation capabilities, allowing users to create highly detailed and realistic images.

Stable Diffusion is another AI model that produces high-quality images based on text inputs. It is noted for its flexibility and capacity to generate diverse styles, from photorealistic images to artistic interpretations, enhancing creative possibilities in art, graphic design, and other visual fields.

These AI models enable artists, designers, and everyday users to visualize concepts quickly and easily. They support rapid prototyping, inspiration for new designs, and even the creation of art that would be difficult to achieve manually, broadening the scope of digital creativity.

• Code generation:

AI tools like GitHub Copilot can assist developers by suggesting code snippets. GitHub Copilot is an AI-powered code completion tool that uses machine learning models trained on billions of lines of code. It suggests code snippets, entire functions, and even whole lines of code as developers type, significantly speeding up the coding process and reducing repetitive tasks.

Studies and user feedback suggest that GitHub Copilot can boost productivity by automating routine coding tasks, reducing the need to search for syntax or standard functions, and allowing developers to focus more on complex problem-solving and design. It serves as a coding assistant that makes development faster and smoother.

By suggesting standard and tested code snippets, Copilot helps reduce common programming errors. The tool can suggest best practices and typical patterns, reducing the chances of syntax errors or logic flaws in repetitive tasks.

These examples are just a glimpse into Narrow AI applications' vast and expanding landscape. As research progresses and technology advances, we can anticipate even more innovative and impactful uses of AI in the future. From healthcare to education, finance to entertainment, Narrow AI is poised to continue transforming our world profoundly.