Cultivating AI that is Both Efficient and Secure through the Adoption of a Growth Mentality
Artificial intelligence (AI) has gradually seeped its way into various industries, yet its full potential remains largely untapped. Financial benefits from AI-powered systems are scarce, despite many companies employing AI experts to spearhead these initiatives.
A significant factor hindering progress is the conventional implementation approach, which is not tailored to AI's unique characteristics. Unlike traditional technologies, AI systems learn from data and improve over time, even surpassing their intended capabilities. This necessitates a nurturing approach, as detailed below.
Cultivating AI for Real-World Success
Embrace Early Deployment and Learn from Imperfections
AI thrives in real-world scenarios, where it generates ample data for learning. Although it may seem risky, this approach enables the system to become more refined and adaptive, particularly for complex language corrections, as demonstrated by Grammarly.
Implement Safety Mechanisms
Developing safe and comprehensive testing environments for AI systems is crucial before they are deployed in the real world. Adversarial training, as employed by Open AI, exposes programs to hostile content to recognize and avoid malicious inputs.
Art for AI applications requires stricter guardrails to prevent biased and poor-quality data from influencing outcomes. Such data can lead to discriminatory results, so measures must be in place to prevent this.
Capture User Interactions
Logging user interactions is essential in the standard AI development process, enabling continuous program improvement. However, engaging users in real-world scenarios provides a more enriched source of data for AI learning. This data can be translated into valuable feedback for AI systems' learning process, facilitating improvements in teaching, correcting, and collecting data.
Design for Lifelong Learning at Scale
Educate AI for Long-term Growth
AI systems should be continually updated to adapt to technological advancements. A well-designed system should automate feedback collection and provide regular updates, training on historical and real-time data, and generate simulated training data for faster development.
Organisations must adopt a growth mindset and embrace continuous learning methods to create AI-based solutions suitable for practical applications. By nurturing AI systems with a continuous flow of data and feedback, companies can ensure their products remain adaptable and safe.
Reference:
Ojanperä, T., Vuori, T., & Huy, Q. (2023). Developing Effective and Safe AI With a Growth Mindset. Retrieved from https://knowledge.insead.edu/ai-digital-transformation/4-essential-strategies-for-ai-implementation-43828
Source:
Tero Ojanperä and Timo Vuori, Aalto University, and Quy Huy, INSEAD. (2023, June). Developing Effective and Safe AI With a Growth Mindset. INSEAD. https://knowledge.insead.edu/ai-digital-transformation/4-essential-strategies-for-ai-implementation-43828
- To unleash the full potential of artificial intelligence (AI), conventional implementation approaches should consider AI's unique learning characteristics, such as deploying systems early in real-world scenarios to gather ample data and refine the system.
- In the development of AI, it is essential to implement safety mechanisms, including adversarial training and stricter guardrails for art (AI applications) to avoid biased and poor-quality data, thus ensuring the AI systems' outputs are fair and reliable.
