• Ram Dulari
• Richa Nehra

AI research has focused on improving the decision-making capabilities of computers, i.e., the ability to select high-quality actions in pursuit of a given objective. When the objective is aligned with the values of the human race, this can lead to tremendous benefits. When the objective is misaligned, improving the AI system’s decision-making may lead to worse outcomes for the human race. The objectives of the proposed research are (1) to create a mathematical framework in which fundamental questions of value alignment can be investigated; (2) to develop and experiment with methods for aligning the values of a machine (whether explicitly or implicitly represented) with those of humans; (3) to understand the relationships among the degree of value alignment, the decision-making capability of the machine, and the potential loss to the human; and (4) to understand in particular the implications of the computational limitations of humans and machines for value alignment. The core of our technical approach will be a cooperative, game-theoretic extension of inverse reinforcement learning, allowing for the different action spaces of humans and machines and the varying motivations of humans; the concepts of rational meta reasoning and bounded optimality will inform our investigation of the effects of computational limitations.

Linear Regression Algorithm for Data Science, Machine Learning

"Linear Regression Algorithm for Data Science", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.e625-e630, August-2021, Available :http://www.jetir.org/papers/JETIR2108563.pdf

"Linear Regression Algorithm for Data Science", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppe625-e630, August-2021, Available at : http://www.jetir.org/papers/JETIR2108563.pdf