AI In Internet Of Things (IoT)

AI and IoT have come a long way since their conception. Initially, IoT was seen as a network of 'dumb' devices, merely collecting and transmitting data. However, with AI's incorporation, these devices have evolved into 'smart' entities capable of intelligent decision-making.

One of the major breakthroughs in AI and IoT's history was the development of edge computing. It enabled devices to process data locally, reducing latency and improving efficiency. Another milestone was the advent of AI-based cybersecurity solutions for IoT networks, addressing one of the biggest concerns of IoT implementation - security.

Several technologies and methodologies have shaped the evolution of AI in IoT. Machine learning, a subset of AI, has been instrumental in enabling IoT devices to learn from their data and improve their performance. Deep learning, another AI technology, has further refined this capability by allowing devices to mimic human brain functions and learn without explicit programming.

Another significant technology is Natural Language Processing (NLP), which allows IoT devices to understand and respond to human language. For instance, smart home devices like Amazon's Alexa use NLP to interact with users.

AI and IoT have found applications across various sectors. For instance, in healthcare, AIoT can predict patient health risks by analyzing data from wearable devices. In agriculture, smart irrigation systems use AI to optimize water usage based on weather forecasts and soil data.

Smart cities are another testament to AI and IoT's power. For example, Barcelona uses AIoT for waste management, parking, and street lighting, leading to significant cost savings and improved citizen quality of life.

Despite its potential, AI in IoT faces several challenges. Data privacy is a major concern, as AIoT devices collect a lot of personal data. Additionally, integrating AI into IoT requires significant computational resources, which may not be feasible for all devices.

Ethically, there are concerns about AI's decision-making, especially in critical sectors like healthcare. There's also the fear of job displacement due to automation, which needs careful handling.

The future of AI and IoT looks bright, with several emerging trends on the horizon. AI is expected to become more autonomous, with devices capable of self-learning and self-healing. IoT devices are expected to become more pervasive, with predictions of over 75 billion connected devices by 2025.

Another trend is the rise of 5G technology, which will significantly enhance AI and IoT's capabilities by providing faster and more reliable connectivity.

AI in IoT has profound economic and societal implications. Economically, it is expected to generate $14.2 trillion of global economic value by 2030. It is also expected to create new jobs, albeit replacing some traditional ones.

Societally, while AI in IoT promises convenience and efficiency, it also raises questions about data privacy and security. Public perception towards these advancements is mixed, with excitement about the possibilities and apprehension about the potential risks.

The rapid advancement of AI in IoT necessitates robust regulatory frameworks. These regulations need to address data privacy, security, ethical use of AI, and more. However, creating such frameworks is challenging due to the dynamic nature of these technologies.

In terms of ethics, issues like informed consent for data collection, transparency in AI decision-making, and accountability are being hotly debated.

In conclusion, AI and IoT's synergy is a major technological revolution with far-reaching implications. Despite the challenges and limitations, their potential benefits are immense. As we move forward, a balanced approach towards their development and implementation is needed, ensuring that advancements in AI and IoT continue to serve humanity's best interests.