Artificial Intelligence (AI): Breaking Down the Tech
The history of AI dates back to 1950, when Alan Turing published his groundbreaking paper, Computing Machinery and Intelligence. Fast forward to today, advancements in cloud processing and computing power have propelled AI from theory (or sci-fi) into the core of our everyday lives.
Now, AI permeates everything from personalized financial advice to automated trading strategies in crypto. Yet, due to its complex nature, grasping its full capabilities can be challenging. In this course, we'll break down the key components of AI and explore how this transformative technology is reshaping our world.
What is AI?
AI is a technology that enables machines—like computers and robots—to mimic human thought and behavior. This means AI can learn, solve problems, make decisions, and even generate creative ideas. At its core, AI embodies the drive to make machines smarter and more autonomous.
Central to AI are techniques like machine learning and deep learning. Machine learning empowers AI to improve its performance through experience, learning from data without needing specific instructions for every task. Deep learning takes this a step further by employing artificial neural networks that mimic the structure and function of the human brain. These networks consist of interconnected layers that process data in a way that enables AI to grasp complex patterns and relationships.
Beyond that, AI systems are capable of understanding and responding to human language (think chatbots), analyzing vast datasets to predict trends (such as market fluctuations or user behaviors), and offering personalized recommendations (like curating product suggestions based on past purchases). By operating independently, these systems can make decisions and take actions that often eliminate the need for human oversight.
How Does AI Work?
At the heart of AI lies a foundation model, a deep learning framework serving as the groundwork for various generative AI applications. Today, large language models (LLMs) are the most popular as they are primarily designed for text generation. However, there are also models tailored for creating images, videos, sounds, and music, along with multimodal versions that handle multiple content types.
Creating a foundation model involves training a deep learning algorithm on massive amounts of unstructured data—think terabytes or petabytes of raw text, images, or videos sourced from the internet. This process yields a neural network with billions of parameters that capture entities, patterns, and relationships within the data, allowing the model to autonomously generate content in response to prompts.
Once the foundation model is in place, it must be fine-tuned for specific tasks. This can involve supplying the model with application-specific labeled data, such as common questions and their corresponding answers. Another approach is employing reinforcement learning with human feedback, where users evaluate the model's outputs to enhance its accuracy.
Developers continuously assess and adjust the model's performance, often tuning it weekly for improved relevance. Meanwhile, updates to the foundation model occur less frequently, typically every year or 18 months. An effective strategy for further enhancing generative AI applications is retrieval-augmented generation (RAG, a mechanism that we use in Alva), which incorporates relevant external sources beyond the original training data, refining the model’s parameters to achieve greater accuracy.
How Has the World Changed with AI?
Since the public release of AI models like ChatGPT, our work, play, and daily lives have been transformed. AI has proven to perform many tasks with greater efficiency and accuracy than humans, paving the way for new business opportunities across various sectors. Let’s explore some of the key benefits AI brings.
Streamlining Data-Heavy Processes: By harnessing AI systems and automation tools, organizations can significantly reduce the time spent on data processing. This advantage is particularly evident in sectors such as finance, insurance, and healthcare, where real-time data can facilitate quicker, more informed decision-making.
Reliability in Results: One of AI’s standout features is its capacity to deliver consistent outcomes. Guided by algorithms, AI systems execute tasks with a level of precision that mitigates the inconsistencies often found in human performance.
Precision in Detail-Oriented Tasks: AI shines in identifying intricate patterns and relationships within data that often elude human detection. For instance, in oncology, AI systems have proven instrumental in early cancer detection, accurately pinpointing potential issues like breast cancer and melanoma. This capability empowers healthcare professionals to make more informed assessments.
Scalable Solutions: AI systems effortlessly scale to accommodate growing workloads and data volumes. This scalability is particularly beneficial in environments where demands can rapidly increase, such as online search services and business analytics.
However, as promising as AI is, it encounters several challenges stemming from its early development:
High Development Costs: Developing AI solutions can demand a substantial financial investment. Constructing an AI model requires significant upfront costs for infrastructure, computing power, and software necessary for training and data storage. Ongoing expenses for model inference and retraining can also accumulate quickly, especially for complex generative AI applications.
Potential for Algorithmic Bias: AI and machine learning systems can inherit biases present in their training data. When widely deployed, these biases can perpetuate and even amplify subtle inequities, leading to potentially skewed outcomes.
Challenges in Generalization: While AI models excel at specific tasks for which they are designed, they often struggle with new or unexpected scenarios. This limitation can hinder their overall effectiveness, necessitating the development of entirely new models for different tasks. For example, a natural language processing (NLP) model trained exclusively on English text may falter when applied to texts in other languages without significant retraining.