Did you know that by 2030, the global artificial intelligence (AI) market is predicted to reach a staggering $2 trillion? This figure underscores not only the rapid growth of AI but also its widespread application across industries.
Yet, while the mention of AI often makes you think of super-intelligent machines and high-tech innovations, the real magic is far subtler and lies in the fine-tuning of these systems.
Contrary to the popular belief that AIs are omnipotent out of the box, they often start as generalists, much like a jack of all trades. It's through the process of fine-tuning that these systems are sculpted into industry-specific maestros.
So, instead of viewing AI as a one-size-fits-all solution, it's more accurate to see it as a versatile tool that needs a craftsman’s touch to reach its full potential in a given field.
Sounds interesting? Let’s delve into what this fine-tuning process is and how it’s revolutionizing multiple industries, from healthcare to finance to retail.
Imagine you've just been introduced to a new chef in town who recently graduated from a world-renowned culinary school. At school, they were exposed to various techniques – from chopping to sautéing, from marinating to baking. They learned about seasonings from every corner of the globe and how to handle various ingredients. With this vast knowledge, they have a foundational understanding of creating dishes from diverse cuisines. However, they might not have perfected the art of combining these techniques to create a unique, signature dish that stands out.
Think of this like an AI's initial training. It starts by learning from heaps of data. This gives it broad understanding and abilities, similar to our chef's diverse training.
But for both our chef and the AI, mastery in a specific niche requires further refinement.
Enter fine-tuning. If our chef accepts a position at a Mediterranean restaurant, they would then take the foundational techniques and knowledge they have, and start focusing on Mediterranean-specific recipes, ingredients, and cooking methods. Over time, they would not just recreate Mediterranean dishes, but master them, tweaking and perfecting each recipe until they’re exquisite.
An AI trained broadly on medical data might have a basic understanding of various diseases. But if we want it to excel in identifying skin cancer from images, we'd fine-tune it using only skin cancer images.
Consider ChatGPT. Normally, if you gave it customer issues to prioritize, it'd rely on its broad internet knowledge. But in a niche industry? It might not always get it right. Fine-tune it with specific company data, though, and it nails it every time. Plus, with this specialized training, you can just feed it issues. No need for extra context.
Just as a chef specializes in a particular cuisine at the restaurant, fine-tuning focuses the AI's broad knowledge to excel at specialized business tasks. It's the process of taking the general and making it exceptional, ensuring businesses harness AI's power most effectively for their unique challenges.
AI fine-tuning is disrupting various industries, here are just a few we will look at:
Entertainment and media
Now let's dive into each industry.
Some of the most meaningful and exciting AI fine-tuning applications are related to healthcare. Here are five interesting examples:
Google's DeepMind introduced an AI system trained to recognize eye diseases in diabetic patients. While its general training allowed it to identify a range of diseases, it was through fine-tuning with specific retinal scans that the model achieved a staggering 94% accuracy, rivaling the capabilities of expert ophthalmologists.
PathAI assists pathologists in diagnosing diseases by analyzing pathology slides. It uses data from more than 15 million annotations provided by its network of 450 pathologists to fine-tune its AI models. Not only do these models improve the accuracy of diagnosis, but they also allow pharmaceutical companies to accelerate drug development and come up with life-saving therapies.
Developed by DeepMind, AlphaFold is an AI model for predicting protein structures. It has achieved unprecedented accuracy in the Critical Assessment of Structure Prediction (CASP) competitions, enabling a better understanding of protein folding. This not only helps understanding diseases at a molecular level, but it also lets researchers develop drugs that target proteins.
DeepScribe revolutionizes the documentation process for medical professionals using AI-powered transcription. Fine-tuned to seamlessly capture doctor-patient interactions, the platform automatically transcribes and structures these dialogues into clinical notes. The result is an efficient tool that reduces administrative burdens, allowing physicians to redirect their focus to patient care and improving the overall healthcare experience.
IBM's Watson is personalizing cancer treatments. After being fine-tuned with oncological data, it can now suggest tailored cancer treatment options based on an individual's unique genetic makeup.
The bottom line? Fine-tuned AI models are an excellent way to reduce the burden on overworked doctors around the globe. They might not be ready to replace doctors just yet, but they’re certainly ready to assist them.
The finance sector, marked by vast data sets and complex operations, is another industry ripe for AI intervention. Here are three examples of how AI fine-tuning is disrupting this industry:
JPMorgan Chase introduced CoIN in 2017. It stands for “Contract Intelligence” and is an AI system designed to review legal documents like commercial-loan agreements. While initially trained on vast legal datasets, it was fine-tuned for specific financial agreements. The result? Completion of 360,000 hours of work in just a few seconds.
Mastercard uses Decision Intelligence, a system that examines cardholder behavior to detect fraudulent activities. By fine-tuning the AI model with transaction-specific data, they've greatly enhanced the accuracy of fraud alerts.
Robo-advisors, like Betterment and Wealthfront, leverage fine-tuned AI to provide personalized investment advice. By narrowing down AI's focus on an individual's financial history and goals, these platforms offer tailored investment strategies, making financial planning accessible to everyone.
Of course, retail giants like Amazon and Walmart could never ignore the profit-boosting technology that AI fine-tuned models can prove to be. When it comes to retail and AI, here are two interesting examples to be aware of:
H&M uses an AI chatbot to allow customers to see, share, and buy products from its catalog. But before that, it asks the user questions to determine what they look like. Chatbots like this are trained on general conversational data but are often fine-tuned using specific queries and feedback from the retailer's customer base to better address unique customer inquiries and issues.
Sephora, the beauty retail giant, offers a 'Virtual Artist' feature. This AI tool, refined with diverse facial data, allows customers to virtually try on makeup, tailoring recommendations based on individual features and preferences.
Last but certainly not the least is the entertainment and media industry. The confluence of AI and the vast data generated in the entertainment and media sector has led to unprecedented personalization and enhancements. Here's how AI fine-tuning is making waves:
Netflix’s Tailored Viewing: Netflix isn't just streaming — it's curating. Through a complex algorithm fine-tuned by individual viewing habits, Netflix provides users with a personalized list of show and movie recommendations. This degree of content personalization helps keep users engaged and less likely to switch to competing platforms.
Spotify’s Evolving Playlists: Music lovers rave about Spotify’s "Discover Weekly." This feature, powered by an AI model fine-tuned by each user's listening habits and preferences, curates a weekly playlist introducing users to thirty new yet familiar-sounding tracks.
NVIDIA’s Gaming Revolution: Gamers experience heightened realism thanks to NVIDIA's Deep Learning Super Sampling (DLSS). Through AI fine-tuned by countless gaming graphics and environments, DLSS provides high-resolution game visuals without demanding high-end hardware.
You don’t have to be at the top of the Fortune 500 to leverage the power of fine-tuned AI models. At Entry Point, we make it extremely simple to fine tune your own AI models — no coding knowledge needed.
Some uses that you can get out of Entry Point fine-tuned AI models include:
Lead Qualifier: Categorize leads into "Qualified" or "Unqualified" based on their submission details. Score the qualified leads and distribute them evenly among sales representatives.
Information Protector: Facilitate the automated removal of sensitive data in legal texts, guaranteeing compliance and maintaining client confidentiality.
Content Inspector: An adept model can swiftly and accurately detect spam, unsuitable or offensive material, and highlight it for subsequent steps.
Scam Spotter: Fine-tuned AI models can discern intricate data patterns that hint at potential fraud, aiding in minimizing hazards and defending your enterprise.
These are just a few examples. The possibilities are endless — try Entry Point today to discover more!