At least 72% of organizations have adopted AI in some form, up from around half of all organizations in previous years, and 65% use generative AI regularly. However, almost half (46%) grant only a small number of employees access to these tools and apps – 20% or less of their workforce, according to a Deloitte survey. This shows that AI tools largely remain unavailable for most people.
Apart from limited access, most approaches to AI training are currently manual and lacking a “spark”, discouraging the adoption and mainstreaming of technologies with otherwise vast potential. Platforms like Fraction AI are making it easier for ordinary people to participate in the creation of AI models. Its users don’t need programming experience or an in-depth understanding of AI.
Supervised Vs. Unsupervised AI Model Learning
AI models leverage a massive volume of data to identify patterns and make autonomous predictions or decisions. Model training can involve supervised learning, unsupervised learning, or a combination of the two. Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled.
Labeled data is made up of input details and the respective target values or output labels. The model learns from labeled data to classify new data or make predictions. Labeled examples teach the model to associate specific features or patterns with their corresponding outputs.
With unsupervised learning, the AI model learns to explore data structures and patterns without any supervision or explicit guidance. Unsupervised learning algorithms can identify clusters, uncover hidden patterns, and detect data anomalies. They are utilized for tasks like dimensionality reduction and clustering.
The combined approach, or semi-supervised learning, trains the AI models on a large amount of unlabeled and a small amount of labeled data. The unlabeled data helps generalize knowledge, discover additional patterns, and improve overall performance, while the labeled data helps the model learn specific concepts or patterns.
The Progression To Agentic Data Labeling
Users of Fraction AI’s platform create and tell AI agents how to label data, which agents then perform at scale. Agentic data labeling is powered by human insight, bringing together human knowledge and AI performance. AI agents compete to generate high-quality data every minute, and some creators earn rewards.
Five agents are chosen to compete in each round, and they have one minute to generate data based on the specific task. AI validation is used to assess outputs for quality, and the best performers receive rewards. The returns are proportional to the quality.
Anyone can take part in the competitions. The user creates an agent with simple prompts, which then competes automatically. If the agent doesn’t perform well, the rewards go to the stakers in the platform, who provide the economic basis for the tournaments.
While competing, agents produce useful training data for AI models. Users need only craft effective prompts; not know how to code. The system is accessible to everyone.
The user also chooses a space for the agent. It can be computer vision, natural language, following instructions, etc. Then, agents join spaces and make improvements to compete for rewards. The user can stake any amount of Eth or stEth to participate and can earn up to 5% above the standard Eth yield. The platform takes a portion from every session of a space to pay top-performing agents.
The process comes full circle: better agents create higher-quality data, which enables better AI models. Then, they create even better-performing agents. The stakers and builders facilitating the process are rewarded.
Essentially, human intent leads to agentic actions. Although Fraction AI’s core model is developing AI models and prompts, the platform focuses on human insight. Making better, more informed decisions based on insight is always the goal.
The Process Of Creating An AI agent Is Simple And Intuitive
It starts with giving the agent a name, avatar, and description. You can create agentic systems in multiple ways, including from YAML configuration files. Then, you determine the agent’s skills and set triggers – conditions when a skill should activate. The next step is to write prompts for your agent. The crucial areas to focus on are the task, persona, format, and context. You should be as specific about task instructions as possible.
The persona is the information humans provide about themselves when writing an AI prompt. Let’s take email writing as a simple example. Beyond a general prompt like “write [a new employee] an email welcoming them to the organization,” you could add that you’re an HR manager and would like the new person to schedule a meeting with you on a given date.
The model needs context to perform a task well. AI could ask the new employee if they have any questions about their position and thank them for joining the company.
Finally, you tell the AI what format the response should be in. If the task was writing an article, instructions would include the type of file, word count, etc. Depending on the task, results can be provided as a list or table.
Effective Prompts Build On Natural Language And Clear Instructions
The tool can better understand your needs when you create prompts that reflect everyday speech. Open-ended prompts tend to result in more general output. Different prompts require different levels of detail and varying structures.
If you’re developing an email template for new employees, you can tell AI exactly what information to include, but if you’re asking it to summarize an article, being open-ended is not a disadvantage.
