Scope of machine learning

The scope of machine learning are as follows-

1. Explaining Human Learning - A mentioned earlier, machine learning theories have been preceived fitting to comprehend features of learning in humans and animals. Reinforcement learning algorithms estimate the dopaminergic neurons induced activities in animals during reward-based learning with surprising accuracy. ML algorithms for uncovering sporadicdelineations of naturally appearing images predict visual features detected in animals' initial visual cortex. Nevertheless, the important drivers in human or animal learning like stimulation, horror, urgency, hunger, instinctive actions, and learning by trial and error over numerous time scales, are not yet taken into account in ML algorithms. This a potential Opportunity to discover a more generalized concept of learning that entails both animals and machines
2. Programming Languages Containing Machine Learning Primitives- In the majority of applications, ML algorithms are incorporated with manually coded programs as part of application software. The need for a new programming language that is self-sufficient to support manually written subroutines as well as those defined as "to be learned". Programming languages like Python (Skit-learn), R, etc. Already making use of this concept in a smaller scope. But a fascinating new question is raised as to developed a model to define relevant learning experience for each subroutine tagged as " to be learned", timing and security in case of any unforeseen modification to the program's function.
3. Perception- A generalised concept of computer perception that can link ML algorithms which are used in numerous forms of computer perception today including but not limited to highly advanced vision, speech recognition, etc., is another potential research area. one thought-provoking problem is the integration of different senses (e.g., sight, hear, touch, etc.)to prepare a system that employs self-supervised learning to estimate one sensory knowledge using the others. Researches in developmental psychology have noted more effective learning in humans whin various input modalities are supplied, and studies on co-training methods insinuate similar results.