What Is Artificial Intelligence?
Artificial Intelligence (AI) is a field of computer science focused on creating machines capable of performing tasks that usually require human intelligence. This is done through learning, reasoning, understanding, and adapting. AI has been around for many years and has been used for specific, often narrow applications such as recommendation engines in online searches and computer vision, which is used to identify and react to objects in images or videos.
AI operates using complex software algorithms that follow patterns of instruction and logic to make decisions in a process called machine learning. There is also deep learning, which uses layers of machine learning processes arranged in a neural network (similar to the human brain) to allow AI to recognize and learn from patterns on its own and ultimately make more-sophisticated decisions.
Narrow AI and General AI
With the launch of large language models (LLMs) and generative AI (GenAI) tools such as ChatGPT, AI has become more prevalent and useful in daily life. This has also given rise to a new categorization of AI that anticipates future use cases:
- Narrow AI or weak AI: This type of AI is designed to perform specific tasks, such as facial recognition or driving a car. Most current AI applications fall into this category.
- General AI or strong AI: This type of AI is designed to use a broader range of cognitive abilities to perform any reasoning task that a human being can. General AI does not exist yet but is considered a long-term goal for AI research.
Artificial Intelligence Benefits
AI offers numerous advantages for end users and businesses across every industry. Depending on the use cases, AI is capable of identifying patterns and forecasting events, automating complex processes, and tailoring workflows to meet the individual needs of a project or person. AI can also play a critical role in monitoring and optimizing resource management. With advances such as natural language processing (NLP), AI can also power chatbots and interfaces to provide personalized interactions to users, helping make information more accessible. AI can have a transformational impact on the way people and organizations work, make decisions, and express creativity.
How Artificial Intelligence Works
At the core of AI technology lies the ability for machine learning and deep learning processes, called AI models, to analyze data, recognize patterns, and make decisions with minimal human intervention. On a technical level, AI models operate through sophisticated algorithms and neural networks that enable machines to process data, learn from it, and make informed decisions.
AI models are essentially software that data scientists and AI developers code and train using preprocessed data. Frameworks like TensorFlow and PyTorch play crucial roles in AI development by providing tools, libraries, and platforms to design, train, and deploy machine learning and deep learning AI models efficiently.
The AI Development Workflow
The AI development workflow involves three main stages. The first stage is data preprocessing in which the data that will be analyzed is cleaned and formatted. Next is AI modeling, where algorithms and frameworks are selected to build the model, and the model learns from the preprocessed data. Finally, the trained model is deployed and used for inferencing, making predictions or decisions based on new data to demonstrate its real-world applicability. From end to end, this entire process is often referred to as an AI pipeline.
The Scale of LLMs and Gen AI
Given widespread interest in LLMs, people may wonder what sets LLMs apart from traditional AI deployments. LLMs differ from typical deep learning AI models in scale and complexity. LLMs employ extensive neural networks, called transformers, to help understand the nuance of language and train on vast amounts of textual data across a diverse range of sources. This is what allows LLMs and tools like ChatGPT to be effective at generating human-like text that can be creative and capable of navigating different syntaxes, styles, and contexts. Comparatively, deep learning AI models usually focus on a specific task, such as image recognition or simple language translation.
How Artificial Intelligence Is Used
Owing to its adaptability and potential for yet-to-be-envisioned applications, AI is maturing as a fundamental component of digital transformation across several industries. Here are a few highlighted examples:
- AI in Automotive: AI is helping driverless vehicles become a reality, using computer vision to enable driver and passenger monitoring and bringing Gen AI assistants and AI-enabled gaming to vehicles.
- AI in Banking, AI in Financial Services: AI chatbots are personalizing customer interactions, while on the back end, AI is helping to detect and prevent fraud, automate risk assessment, and facilitate algorithmic stock trading.
- AI in Manufacturing: AI is driving robotics on the factory and warehouse floor, automating situational awareness with digital twins, helping reduce downtime with predictive maintenance, and helping improve output with automated defect detection.
- AI in Sustainability, AI in Energy: AI enhances smart grids that efficiently integrate renewable energy, enables predictive maintenance for energy infrastructure like power lines, helps optimize energy use in buildings, and analyzes environmental and emissions data to help combat climate change.
Challenges of Artificial Intelligence
There are some barriers to entry to deploying AI, including a high initial cost associated with setting up AI infrastructure and hiring skilled professionals to develop and maintain these systems. Business leaders may also find that integrating AI technologies into their existing workflows can be time-consuming and disruptive, as well as requiring continuous monitoring, analysis, and refinement of AI models for best results. Furthermore, organizations must be proactive about counteracting the potential bias in AI models when using AI to inform their decision-making.
Fortunately, there may be ways for organizations to leverage existing IT infrastructure for their AI processes, in addition to using hybrid cloud resources, to help offset initial costs. Gradual implementation and pilot programs can help ease the transition to AI prior to full-scale rollout, and continuous refinement of AI models can result in more-efficient AI over time. To help mitigate bias in AI models, organizations can regularly audit data and inference results for greater transparency, use diverse sets of training data, and facilitate diversity and equity within teams that design and develop AI systems.
Future of Artificial Intelligence
AI is evolving quickly, demonstrating remarkable progress that suggests a future brimming with potential. Advances in the already-established fields of machine learning and deep learning, combined with the ingenuity of LLMs, could potentially reshape industries, enhance efficiency, and unlock new realms of creativity. The pursuit of responsible AI that is transparent, fair, and accountable can help cultivate mindfulness about the potential impacts of AI on society and help ensure that advances in AI continue to uplift communities.
Conceptually, AI in the future may possess inherent abilities that far exceed what is possible using today’s compute infrastructure and neural networks. These possibilities include a General AI that fully realizes human-like cognitive abilities and AI that achieves theory of mind, which refers to the ability to understand and interpret emotions, beliefs, and intentions of other beings. AI with theory of mind could be much more empathetic in conversation compared to the personalized chatbots that users interact with today. Lastly, AI with self-awareness describes a system that could comprehend its own existence, which for now remains in the purview of theory and science fiction.