Safe Artificial Intelligence Development
How you can contribute to developing Artificial Intelligence
Creating solutions based on technological developments has been our practice as a graduate of a technical field. The unending pursuit of convenience, efficiency, and optimization drives technological development faster than imagined. So today, I will share the advantages and disadvantages of Artificial Intelligence (AI) through Simsimi. Furthermore, I will break down the different methodologies and techniques of AI to shed light on how you can contribute to developing AI safely.
Simsimi
Artificial Intelligence (AI) is a technology that has been around since the 1940s, developing mathematical and computer models. The latest advancements in computing power and access to data from computers led to more widespread use of AI in 2010. An example of the widespread adoption of AI is Simsimi, onboarding a user base of 400 million users worldwide.
SimSimi is one of the longest-running AI chatbots that engage in small talks or emotional talks with users using playful language. Originally developed to engage and extend emotional support to users, this product took an unfortunate turn as the AI model was corrupted with offensive language and sexual content.
Development Does Not Stop There!
Ultimately, developing an AI product for mass adoption should follow strict guidelines centered on human rights. Nevertheless, the capability of AI to transform various industries is limitless. Industries including healthcare, manufacturing, agriculture, transportation, finance, education, entertainment, and many more would benefit from the technology.
AI integration in blockchain technology is being explored with SingularityNET, a decentralized AI marketplace built on Ethereum. The technology interacts with and provides AI services such as facilitating payments and translation. The project has a long-term vision of open-sourcing Artificial General Intelligence (AGI).
Understanding Artificial Intelligence
Unlocking the endless possibilities with AI starts with understanding the methodologies and techniques used to develop it.
Optimization-based techniques are used in many areas of study to find solutions that maximize or minimize parameters.
Machine learning leverages data to improve performance and build a model based on sample data to make predictions or decisions.
Deep learning is a class of machine learning algorithms that uses artificial neural networks, which are multiple layers in the network to obtain a higher-level output from the raw input.
Artificial neural networking is a computational model or algorithm that mimics the way nerve cells work in the human brain.
A prediction model is a mathematical process used that predicts future outcomes by analyzing patterns in a given set of input data.
Heuristic-based technique is a problem-solving approach that produces a practical-level output for a limited timeframe.
Bayesian learning framework is similar to deep learning but a smaller dataset is used.
A multi-agent system solves problems with multiple agents in contrast to a single-agent approach.
Conclusion
Technology is great but, this does not mean it's fail-proof. Just like humans, technology is prone to mistakes. And this is the reason why safe and continuous AI development should take place to onboard mass adoption. With your understanding of the different methodologies and approaches of AI, you can help developers pinpoint problems and debug them.