Human intervention in the development of AI, the concept of Human-in-the-Loop.

According to roboticist and writer Rodney Brooks, current artificial intelligence technologies are "still far from being able to lead us to true general artificial intelligence (AGI)." He argues that none of the current models will reach the AGI stage because they lack a model for representing the real world. "What these models do is correlation in the context of language," he asserts.

In this article, we wanted to delve into this topic and explore the role of human intervention in the development of AI.

I. Human Intervention is Essential in AI Development

a) Limits of AI without Human Interventions

AI models have their limitations. Without human intervention, they can have shortcomings in understanding the real world, unintended biases, and errors in judgment. Here are some examples:

1. Natural language understanding: AI models can struggle to grasp the complexity and nuances of human language. For example, machine translation can produce incorrect or inconsistent translations without human intervention to correct them.
2. Algorithmic bias: AI models are trained on datasets that may reflect existing biases in society, such as racial or gender prejudices. Without human interventions to detect and mitigate these biases, algorithms can replicate and amplify these inequalities.
3. Contextual understanding: AI models can have difficulty grasping the context and intention behind statements. For instance, speech recognition systems can misinterpret statements without considering the conversational context, leading to errors in interpretation.

b) What does HITL mean and why is it important in machine learning?

The term HITL, or Human-in-the-Loop, refers to the systematic integration of human intervention in machine learning processes. This approach allows for close collaboration between AI and humans, harnessing the strengths of each party to achieve superior results. Here are some concrete examples of the importance of HITL in machine learning:

1. Supervised learning: Human experts provide precise annotations to label training data, enabling AI to accurately classify new data (e.g., providing recommendations to customers on marketplaces).
2. Validation and correction of predictions: Professionals verify and correct uncertain or erroneous predictions made by AI, improving the accuracy of medical diagnosis or fraud detection, for example.
3. Active learning: Humans label the most informative examples to optimize the performance of the AI model, maximizing the use of human resources.
4. Solving complex cases: Human expertise is essential for making informed decisions in ambiguous situations, adjusting AI-generated recommendations based on the context (e.g., in medicine or law).

c) An Evolving Data Labeling Market

A key element of human intervention in AI development is the data labeling process. High-quality and accurately labeled data are essential for training high-performing AI models. This demand has resulted in a thriving data labeling market that allows human experts to collaborate with AI systems to annotate, verify, and enhance the quality of data used in machine learning.

Different players in the data labeling market, such as crowdsourcing platforms and business process outsourcing (BPO) companies, offer varied approaches to address labeling needs. However, they face challenges related to the quality and accuracy of annotations, data privacy, and scalability management to ensure reliable and efficient results. Ethical challenges, such as the working conditions of human annotators, fair compensation, and protection of their rights as workers, also need to be addressed.

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II. ChatGPT: An Example Illustrating Human Training

One example of human intervention in the development of AI is the ChatGPT (Generative Pre-trained Transformer) language model. This advanced model has gained significant attention due to its ability to generate fluent and coherent text. However, the achievement is a result of significant human effort in preprocessing and training the model.

a) Data Preprocessing by Humans

Textual data used to train ChatGPT must undergo preprocessing to remove inconsistencies, errors, and potential biases. Humans are responsible for cleaning and formatting this data to create a high-quality dataset that the model can rely on.

b) Humans Train and Improve It

After preprocessing the data, humans are essential in training ChatGPT. They provide text examples, correct generation errors, and adjust model parameters to improve its performance. The constant interaction between humans and the model allows for refinement and optimization of results. Additionally, by interacting with ChatGPT and providing feedback or corrections, users contribute to its continuous improvement and help refine its performance.

c) Humans Correct and Mitigate Biases

Human intervention in the development of ChatGPT is also crucial in correcting potential biases present in the data and model generations. Humans identify biases, flag them, and propose appropriate corrections. This step helps minimize distortions and promote greater fairness and neutrality in the responses generated by the model.

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III. Isahit's Expertise: mastering AI tools and knowing when to incorporate human input

a) Tools, models and scripts we use

Isahit uses a variety of tools, models, and scripts to meet the specific needs of its clients. In addition to OpenAI, isahit also employs other resources such as Google Vision API, which provides image recognition and analysis features, and Tesseract OCR, an open-source engine by Google for converting images to editable text. By integrating these different tools into its workflows, isahit offers comprehensive and high-performing solutions for natural language and visual processing.

Isahit's expertise lies in its ability to integrate these various tools and models into customized workflows. The goal is to maximize the use of artificial intelligence at the right time and in an optimal manner, combining the strengths of AI and human intervention. Isahit designs workflows that allow for seamless collaboration between AI capabilities and the skills and expertise of human annotators. This approach ensures high-quality results while providing the flexibility and adaptability required to meet specific project requirements.

b) The power of our Workforce

Isahit's workforce is its greatest asset, as we firmly believe in the importance of Human-in-the-Loop (HITL) in AI development. We recruit and train women worldwide, supporting them in their professional endeavors through free multidisciplinary training. By working closely with our community of women, we can address the challenges faced by numerous industries and cater to diverse use cases.

It is through the acquired expertise of these women and their dedication that we can truly maximize the impact of artificial intelligence in various domains and contribute to creating a more inclusive and equitable future. At isahit, we believe in a fairer world where businesses play a central role in social and environmental transformation. By embracing the "business for good" model, we are committed to integrating social and environmental objectives at the core of our mission. This approach allows us to create lasting positive impact by empowering women worldwide while contributing to the progress of society.

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