As the pace of AI technology continues to accelerate, more tools have become available to researchers to solve longstanding problems, Hybrid approaches available today continue to push the computational limits of efficiency and precision. One of such problems is the inverse kinematics of redundant systems. This paper explores the complexities of a 7 degree of freedom manipulator and explores 13 optimization techniques to solve it. Additionally, a novel approach is proposed to contribute to the field of algorithmic research. This was found to be over 200 times faster than the well-known traditional Particle Swarm Optimization technique. This new method may serve as a new field of search that combines the explorative capabilities of Machine Learning with the exploitative capabilities of numerical methods.

ADAS (Advanced Driving Assistance Systems) and AD (Autonomous Driving) systems are the next big frontier for automotive companies. The challenge lays in finding the right balance between minimizing the number of accidents and casualties while maximizing the comfort of traveling in complex conditions. ADAS/AD functions combine a number of components, including sensors (hardware and software processing), the algorithm fusing the data coming from multiple sensors, the algorithm deciding to act upon those inputs (braking, steering, accelerating), and finally, the actuators that will be implementing the decision. ADAS/AD functions are also divided into a number of "levels", each dictating who is responsible for the action, the car or the driver. From level-0 to level-2, the systems are the "eyes-on and hands-on" type, meaning that the function is there to support the driver in supplying more information or automating some parts of the driving. From level-3 to level-5, the vehicle is in charge and can eventually (in the case of level-3 and level-4) give the controls back to the driver if the driving condition is too complex.

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.