Enhancing Generative AI with Human in the Loop: the beginning of an unlimited collaboration between AI and Humans

I. Introduction: Enhanced Humans and Generative AI – A win-win Collaboration

At isahit, we often refer to our workforce as "enhanced" by Generative AI, where the individuals collaborating with us are "Augmented Humans" consistently delivering near-perfect quality in all their digital tasks.

However, it's essential to acknowledge that they also play a crucial role in supporting Generative AI tools, whether it's through training AI models or participating in a human-in-the-loop system.

Generative AI has gained significant attention in recent months, unquestionably emerging as a game-changing technology across various industries. We are undeniably witnessing the early stages of this AI revolution.

Generative AI has a plethora of capabilities, from generating texts and images to effectively classifying, extracting, and tagging content. All technological tools incorporating Generative AI significantly enhance the speed and efficiency of content creation and modification made by humans. Currently facing to this solid and promising revolution, at isahit, we recognize that our core business is deeply evolving.

Nevertheless, we strongly believe that humans will continue to play a crucial role in the Generative AI production process. What we call the Human-in-the-Loop in our Data Labeling/Processing industry. Humans possess unique qualities, including precision, contextual understanding, judgment, creativity, and background knowledge, which machines cannot fully replace but rather complement and enhance... The key lies in strategically integrating Generative AI into our daily operations, leveraging its potential to assist us in producing relevant content, developing outstanding products, and making informed decisions.

Generative AI, much like jurisprudence, is an evolving discipline. Drawing from the past as a foundation, it embraces the present as a new perspective and anticipates the future as an ongoing process of correction and adaptation.

II. The recent evolution of Generative AI capabilities

In recent years, Generative AI, a part of Learning Management System (LLMS) technology, has made significant strides, becoming proficient in generating content across a wide array of domains. A decade ago, it was primarily text generation that saw advancements, with a limited connection to the realm of robotics. Today, however, Generative AI has killed these boundaries, demonstrating remarkable precision and quality in generating images and videos, marking a visible expansion in its capabilities. This technology can easily switch between different types of formats, and as more people use it, it's expected to become even better, creating even better content in the future.

Moreover, Generative AI, which previously focused on few knowledge domains, has evolved to generate content and provide information across a multitude of subjects. This ensures that all industries can benefit from the potential of Generative AI, always remembering of the indispensable role of humans in ensuring security and maintaining content quality.

This evolution recalls the growing necessity for humans to be involved in Generative AI training to continually enhance its capabilities. Training Generative AI models for tasks such as text classification is relatively straightforward, but challenges arise when tackling complex subjects like medical image analysis, such as brain scans.

As Generative AI keeps evolving, the demand for highly skilled and specialized labellers is expected to grow significantly.

III. Detailing the place of Human in the Loop in Generative AI Workflows.

In the rapidly evolving world of artificial intelligence, one thing is clear: the synergy between humans and machines is necessary to reach the full potential of Generative AI.

The best of AI : Speed, combined with the best of Humans : Expertise.

Generative AI has the capacity to generate content and code at a pace that seems almost supernatural for human-being. However, it is compromised, and subject to errors, inconsistencies, and biases. This is where the eye of humans becomes necessary. Ilya Sutskever, OpenAI's chief scientist, acknowledges that AI models will continue to evolve, but the human touch remains irreplaceable. It's the fusion of AI's computational process with human decision-making, ethical judgment, and critical choices that elevates the quality of AI-generated content.

Human-in-the-loop and Generative AI: the back and forth of Feedbacks and Improvements.

Human-in-the-loop isn't a one-time interaction; it's an ongoing, iterative process between the annotators and the content annotated automatically. Basically; Human-in-the-loop involves human experts in the training and adjustments of AI systems. Humans meticulously data (preprocessed by GenAI for instance), cleansing it of inconsistencies, cleaning errors and biases. In order to create high-quality training data for future models. They actively participate in the training of AI models, interact with them for refinement and optimization.

Three types of Human involvement: HITL, HIC and HOTL.

To understand the spectrum of human involvement in training of AI, we can divide their position in the workflow in three approaches:

1. ‍Human-in-the-Loop (HITL): In this approach, humans are engaged in the decision-making and learning process of the machine. AI makes decisions, but the results are controlled by a labeler who can correct if necessary. This ensures that AI continues to learn and improve under human(s) supervision.‍
2. Human-on-the-Loop (HOTL): HOTL leans more towards automation. AI makes decisions autonomously but seeks human intervention periodically when unexpected situations arise. Data Labelers step in to resolve exceptional cases rather than continuous control over the outputs.‍
3. Human-in-Command (HIC): In this approach, humans have the final words. AI provides suggestions and recommendations, but it's humans who make the ultimate decisions and have control over final actions taken. AI acts as a decision-support tool but cannot act autonomously without human validation.

In essence, HITL, HOTL, and HIC exemplify the spectrum of collaboration between humans and AI, each with its unique advantages and applications. As we navigate the intricate landscape of Generative AI, it becomes evident that the real magic lies not in the machine alone but in the symphony of human expertise guiding it towards excellence. With HITL as our compass, we embark on a journey where AI continues to learn, adapt, and improve, hand in hand with the guardians of wisdom—humankind.

IV. Human in the Loop and Generative AI: Examples and Benefits

Real-world examples show us how the remarkable impact of Human in the Loop can leverage the content quality produced by GenAI tools

1. ‍CoPilot's Coding Brilliance: In the coding world, Generative AI assistants like GitHub's CoPilot and Amazon's CodeWhisperer collaborate effectively with Developers, resulting in increased efficiency and clean code. Microsoft's integration of CoPilot into Microsoft 365, despite added costs, demonstrates how Humans are ready to deal with such technologies.‍
2. AI-Powered Analysis in Healthcare: AI (in pre-labelling or building from scratch Medical images, assists surgeons, enhancing surgical procedures and reducing errors.

The Advantage of Human-in-the-Loop

Here are some key benefits of Human-in-the-loop added in “development” of AI models.‍

• ‍Enhanced Content Quality: Humans are the assurance of quality. The result is automatically generated content adapted with creativity but also on a coherent, relevant, and accurate basis.‍
• Contextual Adjustments: Humans help AI tools producing content by adding the nuances of human understanding and context. This ensures that the content not only makes sense but has a fit to its intended audience.

At isahit, we stand at the forefront of this fast-moving revolution, building tools and workflows to get the most of Human-in-the-loop and Generative AI combined. The goal being to craft content that not only meet expectations, but a content that grab the best from robots and humans.

VI. HITL + Generative AI: Ethical challenges and future concerns.

When discussing "Human in the Loop" interaction with generative AI tools, some challenges come to the forefront. For example, implementing "Human in the Loop" can be complex, with scalability and cost emerging as major concerns. This is why, at isahit, we assist companies in constructing smart workflows and integrating the right workforce to maximize the benefits of these workflows.

Furthermore, the ethical dimension remains substantial as we manage human feedbacks, peristently working to identify and rectify potential biases while looking for the best relevance.

In our journey towards AI improvement, it is essential to underline the importance of responsible AI development and effective regulation, whether by companies or governments. These pillars ensure that the AI revolution aligns harmoniously with our values, guaranteeing that in our future, innovation coexists with ethics and integrity.

The future is full of possibilities when talking about the collaboration between AI and people. AI, with the guidance of human interactions, will learn and adapt at an unprecedented pace. The synergy between human expertise and AI algorithms holds the promise of generating more advanced and creative AI systems in the next decade.

While true autonomy in AI models may be a realistic in a near future, recent outputs generated by AI and demonstrate that the role of human is significant in producing a qualitative content. Despite concerns about job replacement, Generative AI is a work in progress, demanding human collaboration to reach its full potential.

As we look forward, the relationship between Labelers and Generative AI in content generation is a infinite one, and the journey is just beginning.

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Designing AI and machine learning systems that systematically bring the human element into the process is good practice, if not a necessity in the AI industry. 

What is Human-in-the-loop?

The "human-in-the-loop" approach (the cooperation between AI and the Human) reframes and prioritizes the interactions between humans and machines in order to create smarter systems that integrate an intervention useful and relevant to humans. The AI ​​behaves like a student who is beginning to master a subject and is likely to make mistakes or not understand certain nuances. The human in the loop can then intervene to tell the system what distinguishing signs to look for and help it provide more accurate answers.

How does Human-in-the-loop work?

For example, a system that has learned to recognize different animals based on skin patterns will quickly learn to distinguish a zebra from other creatures due to its unique stripes but might have trouble identifying certain animals with similar shapes and colors. The human in the loop can then intervene to tell the system what distinguishing signs to look for and help it provide more accurate answers. By associating a human guide with a machine, we take advantage of two types of intelligence simultaneously. By providing the AI ​​system with data to study and validating their efforts during the trial-and-error process, the human can combine the knowledge they have acquired over their lifetime with the speed of the computer system. 

Difference between Human-in-the-loop and active learning

Human in the Loop Learning happens when a learning machine or computer system can assimilate selected human inputs into it's learning process which creates a feedback cycle or ‘loop’. HITL also includes active learning methods and the creation of data sets through human labeling.

In active learning, a learning machine model can interactively ask a user to label new data points with the desired outcome. The model is allowed to be “curious” and the human is there to shape that curiosity for better results.

How to install Human-in-the-loop systems in your company

Machine learning is frequently touted as a fantastic answer to a variety of issues. Yes, machine learning is currently the closest thing we have to magic, and it can solve or at least improve a wide range of issues. When integrating it into a business, there should be a distinct beginning and finish to the process, as well as logical phases in between. Someone or some system could start by looking at a set of input data. There may also be guidelines that assist a human in making a choice, or even automate it in specific situations. Machine learning isn't required if the process can be automated well with a few rules. Both approaches may be acceptable for human-in-the-loop machine learning, as described above, but the devil is in the details. Ascertain that the procedures are suitable for AI automation by consulting with both the business and your machine learning partner. Remember that machine learning can automate a wide range of processes and work with a wide range of data sources, including database fields, free-form text, pictures, and speech data.

Benefits of the Human-in-the-loop process in machine learning

1. Human-in-the-Loop avoid Bias:

Machine Learning models can easily become biased as a result of being trained on biased data. Having a human in the loop allows for early detection of bias.

2. Human-in-the-loop supplement rare data:

To produce accurate results, most popular machine learning algorithms require a large amount of labeled data. In many circumstances, though, there isn't even a big amount of unlabeled data to work with. If you're seeking for examples of fake news in a language with only a few thousand speakers, for example, you might not find any. As a result, the algorithm will have nothing from which to learn. Keeping humans in the loop in this scenario can provide the same level of accuracy even for rarer forms of data.

3. Human-in-the-loop maintain Human-Level Precision:

n many cases, you don't want the AI to perform below human-level precision. If you're making essential equipment for an airplane, for example, employing machine learning for inspections can increase safety, but you don't want to risk safety for the sake of automation. So, to ensure that you constantly achieve human-level precision, you'll need a system that can be overseen by humans.

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By providing the AI ​​system with data to study and validating their efforts during the trial-and-error process, the human can combine the knowledge they have acquired over their lifetime with the speed of the computer system. This dynamic of collaboration helps to overcome the shortcomings of humans and machines in order to obtain more precise results. This symbiotic relationship can guarantee the steady improvement that fuels future innovation. And this is a fulfillment of what many people will agree to be the future of artificial intelligence’s destiny, which is to be a natural augmentation of human intelligence, existing side by side with humans and helping them make wiser decisions and more amazing products.

At isahit, the Human In The Loop approach is at the heart of each of our solutions because we believe that human intelligence elevates artificial intelligence. We are the bridge that connects human intelligence and artificial intelligence. 

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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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