Artificial intelligence (AI) is rapidly becoming commonplace in a wide range of industries, including finance, retail, healthcare, and more. Artificial general intelligence (AGI) is a type of AI that possesses human-level intelligence to perform any intellectual task that humans can. For General AI models to be accurate, they need to be trained and supervised by humans. Here, CloudFactory explores how human intelligence is utilized to advance the precision of general AI.
What is general intelligence AI?
General intelligence AI is also known as “strong AI,” as it helps create machines that have intelligence comparable to humans. These machines are used to understand, learn, and perform any intellectual task that humans can do. In 2025, General AI is only a theoretical concept and hasn’t yet been implemented in the real world.
General intelligence AI differs from artificial narrow intelligence (ANI), also referred to as “weak AI.” ANI is created to perform narrow, specific tasks with a limited range of abilities. It cannot adapt or learn beyond what it has been programmed to do. This is because it lacks general understanding and the ability to problem-solve creatively. ANI is the type of artificial intelligence that is currently used in real-world applications, such as voice assistants like Siri and Alexa, facial recognition, language translation tools, and the like.
The reason that AGI doesn’t exist in real-world applications yet is due to the challenges of creating advanced AI-powered systems that can think, reason, and adapt like humans. The main difficulty involves replicating humans’ cognitive abilities and adaptability across a vast range of tasks and domains. Common sense reasoning, generalization, and autonomous learning are challenging for AI-driven models to learn. AI frameworks are designed with specific data and narrow algorithms, which are different from the nuanced and context-dependent understanding of common sense. Other challenges include the massive amounts of energy, storage, and data processing resources that AGI will require, as well as concerns about the transparency and accountability of autonomous systems.
Why general AI still needs human intelligence
To implement general AI, human intelligence will be necessary to ensure accuracy, trustworthiness, and model validation. AI algorithms have limitations in understanding context, ambiguity, and real-world complexity, so humans are needed to overcome these limitations. Although AI is competent in many areas, the systems are still vulnerable to errors that require human intervention for correction. They include:
Intent misinterpretation
AI models use historical data to learn patterns and make predictions. However, if the data quality is inaccurate or incomplete, AI systems can make mistakes. They may misunderstand the nuances of language and user intent, resulting in incorrect responses or actions. This misinterpretation can also happen when the user input deviates from trained models, or the model has insufficient data training to cover all possible expressions of intent. Humans help correct this type of error by providing the model with feedback to clarify the error, as well as using more specific instructions.
AI hallucinations
Generative AI systems may experience “hallucinations,” which occur when plausible information is produced, but it is factually incorrect or irrelevant. This is usually due to the model’s training on large datasets where it unconsciously learns to generate content that, while coherent, does not reflect reality accurately or use the specific data it needs. To correct this error, humans can use high-quality training data and refine the system continually via optimization.
Entity recognition
AI tools can incorrectly identify and classify entities that a user requests, causing incorrect or irrelevant responses. To identify and correct these errors, humans must review the AI’s output and then manually annotate the text with the proper entity types and boundaries. This helps train AI models to be more accurate over time.
Context handling
AI systems, particularly virtual assistants, can fail to maintain the context of a conversation or remember all the previously discussed topics. This can result in answers that are irrelevant or disconnected from the interaction. To correct this, humans can provide human-in-the-loop (HITL) feedback, which is when humans are actively involved in the training, testing, and tuning of models.
How human-in-the-loop AI strengthens general AI models
Human in the loop is a concept in AI and machine learning where humans are used to train, test, and tune models to address biases and enhance accuracy. Essentially, human feedback is used to refine these machine-learning models and help them become more precise. Some of the AI use cases where human intelligence significantly improved AI accuracy include:
Healthcare
AI is becoming increasingly utilized in the healthcare field, especially in medical imaging. Using computer vision and AI-based imaging systems in conjunction with the expertise of radiologists has helped improve cancer detection rates. AI is used to rapidly analyze medical imaging scans in real-time and flag potential issues, while human doctors apply their judgment and experience to make a final diagnosis. Using AI image recognition and processing tools also helps radiologists streamline their workflows, allowing for efficiency and scalability.
Finance and accounting
In Financial Planning and Analysis (FP&A), AI combined with human intelligence has enhanced decision-making processes. Humans have the skills of contextual understanding, strategic thinking, problem-solving, and common sense, while AI has the skills of precision and efficiency in data processing. This results in more accurate financial forecasts, enhanced risk management, and more informed decisions. It also helps reduce human error that can occur when working with vast amounts of data.
Computer science
AI is often used in computer science to create intelligent systems that can learn, reason, and perform tasks that traditionally require human intelligence. These systems can encompass aspects like machine learning, natural language processing, and computer vision. Although these AI systems can learn and make decisions, they still require human intervention to design machine learning algorithms, ensure ethical operation, and prepare big data.
HITL AI also helps strengthen general AI models by ensuring decision-making takes ethical considerations into account and aligns with societal values. It also promotes transparency and accountability, boosting trust and confidence in AI technology.
CloudFactory’s approach to enhancing general AI with high-quality data
CloudFactory offers an AI data platform that helps teams deliver real results. The platform combines machine and human intelligence to improve AI model accuracy. CloudFactory also offers several AI data services, including data analysis, data annotation, and customer service automation. To enhance AI accuracy, we custom-train up to eight models simultaneously to automate annotation, retraining them as needed. We also help your customer service algorithms and chatbots become more conversational and robust through training and pattern recognition.
Our humans in the loop help ensure AI errors are identified and corrected, helping to improve explainability, enhance model performance, and speed up production. By providing human oversight on AI systems, CloudFactory’s workforce ensures accurate and ethical AI training data.
The future of general AI: the evolving role of human intelligence
Many people worry about whether artificial intelligence and the applications of AI will surpass human intelligence. However, despite recent advances, AI still relies on human intelligence and oversight to become more accurate, accountable, and ethical. As general AI continues to be developed, human intelligence will continue to be needed. By combining artificial and human intelligence, society gets access to the benefits of AI that can be used for various tasks. AI will likely not replace or surpass human intelligence, but rather supplement human capabilities by automating repetitive tasks and allowing humans to focus on more intensive tasks.
Turn to CloudFactory for HITL AI Solutions
If your business needs expert assistance to enhance general AI model accuracy, CloudFactory is here to help. Our human-in-the-loop AI solutions will help improve accuracy and promote better model performance. To consult with CloudFactory experts and see how our initiatives can help your business, request a demo today.
