Below are guest posts and opinions from Zac Cheah, co-founder of Pundi ai.
Through autonomous artificial intelligence (AI) agents, Brouhaha needs to fundamentally transform industries such as healthcare and finance. Autonomy is a spectrum, and even the most autonomous AI agents require some form of human intervention to function properly.
A fully autonomous AI agent is not possible. And rather than eating work, autonomous AI agents create new job opportunities where humans can help AI agents function throughout their lifecycle.
Diverse job options within the AI ​​industry
All autonomous AI agents in the production or deployment stage cannot operate independently, thus requiring human action to create jobs. While AI agents operating at large scales exceed one person’s cognitive abilities, each agent has multiple human-led teams in their development pipeline.
These agents need human developers to build the underlying infrastructure, code algorithms, prepare human-labeled datasets for training, and oversee audit procedures.
For example, the accuracy of an autonomous AI agent depends on high-quality data training and running repeated analytical tests. It’s no wonder that 67% of data engineers spend hours preparing datasets for AI model training.
Fragmented datasets lead to operational problems for autonomous agents, so project teams need to clean the data before training. Furthermore, because data gaps can produce incorrect output, developers must ensure AI agents integrity and market positioning through rigorous evaluations. Therefore, each AI company is requesting human data cleaners, labelers, and evaluators to run the model.
Additionally, human surveillance audits provide the necessary checks to prevent harm from autonomous AI agents who commit fraud after deployment. Such defense mechanisms consist of elaborate, hierarchical teams that include company management, policy workers, auditors and other skilled technicians. A village is required to build and maintain AI agents during the lifecycle. Therefore, fully autonomous AI agents generate multiple employment opportunities, as human expertise is required to create, deploy and evaluate these agents.
Autonomous AI agents create new human-driven employment opportunities
Human experiences help them develop subtle social understandings. This helps you make logical reasoning and rational decisions. However, autonomous AI agents cannot “experience” their surroundings and cannot always make healthy decisions without human assistance.
Therefore, humans need to meticulously prepare the dataset, evaluate the accuracy of the model, interpret the output generation, and ensure functional consistency and reliability. Human assessments are important to identify bias, to ease bias and to ensure that AI agents are in line with humanitarian values ​​and ethical standards.
A joint approach between human and machine intelligence is needed to prevent ambiguous output generation events, understand the nuances, and solve complex problems. With human contextual knowledge base, common sense reasoning and consistent deductions, AI agents work better in real life situations.
Therefore, autonomous AI agents create new job roles and job opportunities rather than assuming employment within the AI ​​industry. To this end, Pundi AI promotes AI innovation by empowering humans to contribute directly to the industry’s growth narrative.
In addition to computational power, AI models require high quality data accessibility for model training, and domain specialists need to fine-tune the data for efficient model performance. However, Megacorporations dominates control over human-generated data to construct AI-ML models.
Pundi AI offers a distributed data solution and provides an equitable opportunity for everyone to ensure that large companies do not misuse data producers. Therefore, humans can maintain control over their data and benefit directly from using it for AI model training and create new AI-related job options.
According to a Gartner study, companies will abandon more than 60% of their AI projects by 2026 due to unavailability of AI data. Solutions such as Pundi AI AIFX provide financial incentives for developers and users to create AI-ready data assets, exchange them on-chain, and curate robust data sets.
Beyond the preprocessing dataset, AI agents also require human assistance during the processing (inference) and postprocessing (development) stages. Several methods such as human feedback (RLHF) and reinforcement learning through human loops (HITL) are required to evaluate AI agents during training or during real-time operations for effective output generation and model optimization.
Similarly, interactive debugging helps human auditors scrutinize AI agents’ responses and assess them from social benchmarks of fair decision making. Sometimes sensitive agent applications need a hybrid method of combining expert human-level verification with machine-generated answers to remove uncertainty and build trust.
Human intuition and creativity are key to developing new AI agents that can function autonomously in society without causing any harm. In addition to enhancing the general intelligence of autonomous AI agents, human supervision ensures optimal performance for high-performance agents in an independent setting.
Thus, a decentralized approach to building and deploying AI agents will democratize the AI ​​industry by redistributing data and model training among people from diverse backgrounds, reducing structural bias and creating new jobs.
