We understand how hard it is to get a good accuracy and compromise with accuracy on compression.
That’s why, SHER DeepAI provides lossless AI model compression.
Compromise with Objective
The primary goal of AI models is to deliver accurate predictions or decisions. Compromising accuracy defeats the purpose of deploying the model.
Mistrust
High accuracy builds trust in the AI system. Users rely on the model's outputs, and inaccuracies can lead to mistrust and reduced adoption.
Negative Impact On Business
Inaccurate predictions can lead to poor decision-making, financial losses, or missed opportunities, especially in critical applications like healthcare, finance, or autonomous systems.
Ethical Issues
Inaccurate models can cause harm, such as biased decisions or unsafe outcomes, raising ethical and legal issues.
Reputation Damage
Poor accuracy can damage the reputation of the organization or product, making it harder to regain credibility.
Re-Training Cost
If accuracy is compromised, the model may need to be re-trained or fine-tuned, leading to additional time and resource costs.
Competitive Disadvantage
Inaccurate models put you behind competitors who prioritize accuracy, reducing your market edge.
Long-term Viability
Models with high accuracy are more sustainable and adaptable to future challenges, ensuring long-term relevance.
Wasted Resources
Deploying an inaccurate model wastes the resources invested in data collection, training, and deployment.
Customer Dissatisfaction
Deploying an inaccurate model wastes the resources invested in data collection, training, and deployment.
Scalability Issues
Inaccurate models may fail when scaled to larger datasets or new environments, limiting their applicability.
Foundational Principle
Accuracy is a cornerstone of AI development. Sacrificing it undermines the integrity of the entire system.
We understand the importance of the precise understanding about your AI model
That’s why, SHER DeepAI provides precise explanation of AI models
Precise explanation of AI models builds
Trust
Understanding why a model makes a certain prediction builds trust. A "black box" model can be difficult to trust, even if it performs well. Especially in critical applications like healthcare or finance.
Confidence
If you can see the factors influencing the outcome, you're more likely to believe and rely on the model, especially in critical applications like healthcare or finance.
Compliance and Regulation
In many industries, regulations require explainability for automated decision-making. For example, financial institutions need to be able to explain why a loan application was rejected.
Ethical Considerations
XAI helps uncover and mitigate potential biases in AI systems. By understanding how a model arrives at its conclusions, you can identify and address discriminatory or unfair outcomes.
Scientific Discovery
In some domains, AI is used to analyze complex data and discover new insights. XAI can help scientists understand why a particular pattern is observed, leading to new scientific discoveries.
Feature Engineering or Model Refinement
You might discover that the model is missing an important feature or that a particular input is being misinterpreted.
Security & Robustness
Understanding a model helps in identifying vulnerabilities to adversarial attacks. Ensuring robustness is easier when the model's decision-making is well-documented.
XAI
Caring about the precise explanation of an AI model is not just a technical concern; it’s a multifaceted issue that touches on ethics, legality, trust, and societal impact.
