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Machine Learning (ML) and Artificial Intelligence (AI) have become essential tools in managing the massive amounts of data generated by today's digital landscape. Without ML and AI, platforms like social media would struggle to remain functional. Consider these staggering statistics:

• Instagram sees 95 million photos uploaded daily
• Twitter hosts 500 million tweets per day
• Snapchat processes 3 billion snaps daily
• Facebook handles 6 billion likes and comments per day
• WhatsApp manages 65 billion messages every day

For developers, this immense volume of data is overwhelming and cannot be effectively processed using traditional technologies, even with increased computational power. The real challenge lies in parsing unstructured content and deriving meaningful insights. Language carries cultural context, and a single word or sentence can have vastly different meanings depending on the user's location, background, and language mix. Multilingual users often blend languages, like Hinglish (Hindi + English), further complicating the context. Traditional technologies would struggle to keep up, often resulting in inefficient processing loops.

The Growing Threat of Malicious Entities

As the digital world expanded, so did the presence of malicious actors, including hackers, fakes, deep fakes, hate-mongers, trolls, and others with harmful intentions. Similar to how ethical hackers emerged to combat cyber threats, we now need ethical AI to counter the misuse of social media platforms. This problem cannot be addressed through legislation, breaking up social media companies, or increasing the number of human moderators.

The Need for Ethical AI

To effectively counter malicious AI, we need to develop ethical AI. Many social media companies and other organizations are already working on solutions or creating ethical guidelines for their platforms. However, a fragmented approach will lead to a multitude of specialized AIs and machine-learning algorithms tailored to specific platforms, resembling applied AI rather than achieving artificial general intelligence (AGI). A robust ethical AI requires industry-wide collaboration, forming a consortium to tackle malicious forces and enhance social media credibility.

Drawing Inspiration from Nature and Psychology

In designing solutions, systems, or even standalone functions (microservices in current terminology), we often seek inspiration from nature. The universe operates efficiently without oversight, offering valuable insights for ethical AI development. By leveraging psychological, statistical, and demographic profiling techniques, we can create an AI system that continuously learns, adapts, and counters malicious activities.

Hypothesis: Profiling Computing Units

Our hypothesis is that each computing unit (laptop, server, desktop, cell-phone, etc.) has a profile associated with an organization, individual, or a combination of both. Similar to how we profile individuals using various data and personality traits, we can profile computing units based on their activities and expected behaviors. Once profiled, ethical AI can be developed to learn, adapt, and counteract malicious forces effectively.

Conclusion

To address the growing threats in the digital world, we must use AI against AI. By developing ethical AI through industry collaboration and leveraging insights from nature and psychology, we can create robust systems to counter malicious entities and improve the credibility of social media platforms. For a deeper discussion on this topic, contact us.