Author-Controlled AI (ACAI)

The difference between controlled and generative AI - it's all about risk.

When your message matters, you need to remain in control. Let's begin by understanding the core difference between generative and controlled AI and which is better suited for what type of tasks.
‍
Generative AI refers to a category of artificial intelligence techniques and models designed to generate new data that resembles or mimics existing data. Unlike traditional AI systems primarily used for classification or prediction tasks, generative AI focuses on creating novel outputs, such as images, text, audio, or video, based on patterns learned from training data. Generative AI encompasses various approaches, including:
‍
1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, trained simultaneously in a competitive manner. The generator creates synthetic data samples, while the discriminator distinguishes between real and fake data. GANs can generate highly realistic and diverse outputs through this adversarial training process.
‍
2. Variational Autoencoders (VAEs): VAEs are probabilistic generative models that learn to encode and decode data samples. They consist of an encoder network that maps input data to a latent space and a decoder network that reconstructs data samples from latent space representations. VAEs can generate new data samples by sampling from the learned latent space.
‍
3. Autoregressive Models: Autoregressive models, such as recurrent neural networks (RNNs) or transformer architectures like GPT, generate sequences of data one element at a time, conditioned on previous elements. These models are commonly used for generating text, music, or other sequential data. 

Generative AI has applications in various domains, including:

Image Generation: Generating realistic images of objects, scenes, or people for creative purposes, image editing, or data augmentation.
‍
Text Generation: Generating natural language text for language translation, dialogue systems, or content generation tasks.
‍
Content Creation: Creating artwork, music, or literature autonomously or collaboratively with human creators.

Data Augmentation: Generating synthetic data samples to augment training datasets and improve the performance of machine learning models.
‍
Overall, generative AI enables the creation of novel and diverse data samples, pushing the boundaries of creativity, imagination, and innovation in artificial intelligence. 
‍
Let's look at the top risks concerning GENAI today:

Bias / Misinformation
‍Generative AI, like any technology, can reflect biases present in the data it's trained on or the algorithms used to create it. The AI model may produce biased or skewed outputs if the training data is biased or incomplete. Bias can manifest in various ways, such as reinforcing stereotypes, favoring specific demographics, or misrepresenting underrepresented groups.

Hallucinations
‍"Generative AI hallucinations" are often used to describe unexpected or surreal outputs generated by AI models, particularly in the context of generative models like Generative Adversarial Networks (GANs) or autoregressive models like GPT. These "hallucinations" occur when the AI model generates unusual, imaginative, or divergent outputs from what it was explicitly trained on. They can range from distorted images to nonsensical text passages to new concepts that emerge from the model's creative processes. Hallucinations can sometimes be intriguing and lead to novel insights, but they can also be nonsensical or even disturbing, depending on the context and the quality of the model. In the case of text-based generative AI like GPT, hallucinations might appear as sentences or paragraphs that drift away from coherent discourse, producing unexpected associations or blending concepts in unusual ways.
‍
Ethical considerations
‍Several ethical considerations arise with generative AI (GENAI):
‍
1. Bias and Fairness: As mentioned earlier, generative models can inherit and propagate biases present in the training data. Ensuring fairness and mitigating biases in the training data and the model's outputs is critical to prevent perpetuating stereotypes or discriminating against certain groups.
‍
2. Misinformation and Manipulation: Generative AI can be used to create highly realistic fake content, including text, images, and videos. This raises concerns about the spread of misinformation, as such content can be used to deceive people or manipulate public opinion.
‍
3. Privacy: Generating AI may inadvertently expose sensitive information in the training data or leak private details when generating content. Protecting user privacy and data confidentiality is essential, especially when dealing with sensitive data.
‍
4. Intellectual Property: There are concerns about the potential infringement of intellectual property rights when generative models produce content that resembles existing copyrighted works. Clear guidelines and regulations are needed to navigate the legal implications of generating content that may resemble or replicate existing intellectual property.
‍
5. Security: Generative AI can also be exploited for malicious purposes, such as creating realistic fake identities for fraudulent activities or generating convincing phishing emails. Ensuring the security of AI systems and implementing safeguards against misuse is crucial to prevent harm.
‍
6. Accountability and Transparency: Understanding how generative AI models arrive at their outputs can be challenging, especially with complex neural network architectures. Ensuring transparency in AI decision-making processes and establishing mechanisms for accountability when errors or biases occur is essential for building trust in AI systems.
‍
7. Human Empowerment vs. Replacement: While generative AI has the potential to enhance human creativity and productivity, there are concerns about its impact on employment and the economy. Balancing the benefits of automation with the need to ensure equitable distribution of opportunities and resources is a significant ethical consideration. 
‍
Addressing these ethical considerations requires collaboration among stakeholders, including AI researchers, policymakers, ethicists, and industry leaders, to develop guidelines, regulations, and best practices that promote the responsible development and deployment of generative AI technologies.
‍
Controlled AI is an artificial intelligence system designed or programmed to operate within predefined constraints or guidelines. These constraints can include limitations on the actions the AI system can take, the types of decisions it can make, or the range of inputs it can process. Controlled AI aims to ensure that AI systems behave predictably and responsibly, particularly in sensitive or high-stakes applications where the potential for unintended consequences or misuse is significant. By imposing constraints on AI behavior, developers can mitigate risks related to safety, security, fairness, and ethical concerns. Examples of controlled AI include:
‍
‍Safety-Critical Systems: AI systems deployed in safety-critical domains, such as autonomous vehicles, medical devices, or industrial control systems, often operate under strict constraints to ensure the safety of users and the general public.

‍Content Moderation: AI algorithms used for content moderation on social media platforms are often designed to enforce community guidelines and prevent the disseminating of harmful or inappropriate content.

‍Compliance and Regulations: AI systems operating in regulated industries like finance or healthcare must comply with industry-specific regulations and standards. Controlled AI ensures that these systems adhere to legal and ethical requirements.

‍Human-in-the-Loop Systems: Some AI systems are designed to work in conjunction with human operators, who provide oversight and intervene when necessary to correct errors or ensure compliance with organizational policies.

‍Bias Mitigation: Controlled AI techniques can mitigate biases in AI models by constraining their behavior to avoid producing discriminatory outputs or reinforcing existing biases in the training data.
Overall, controlled AI approaches are essential for building trust in AI systems and ensuring responsible deployment across various domains. By defining clear boundaries and constraints, developers can mitigate risks and maximize the benefits of AI technology while minimizing potential negative impacts.
‍
Qvio®️ allows you to upscale your video content by adding Author-Controlled AI™️ to handle viewer questions in real-time. However, the human-in-the-loop approach requires the author to authenticate generated answers in the data set before publication. 

WHY WE USE BOTH
‍While Qvio leverages controlled AI for answer delivery, it does allow authors to jump-start their answer data with GenAI or by connecting to another trusted LLM. However- before those answers can be considered by the controlled AI model they must be validated by the author. This approach allows content creation that's quick and easy to scale and content delivery that is trustworthy and safe.
‍

• Improve model accuracy in response to gaps
  

• Build trust with valuable insights into how your models are performing

• Continually improve the fairness of model outcomes without re-deploying
  

• Comply with industry regulations governing use of AI

• Upload your video, generate your dataset, refine and share