introduction to machine learning and deep learning of map with edges labeled with distances and about of traffic (which is being constantly updated)allowing program to reason about the shortest path between points such knowledge-based systemswherein the knowledge was compiled by experts and represented in way that allowed algorithms/programs to reason about itrepresented the second generation of ai at the heart of such approaches were increasingly sophisticated approaches for representing and reasoning about knowledge to solve tasks/problems that required such knowledge examples of such sophistication include the use of first-order logic to encode knowledge and probabilistic representations to capture and reason where uncertainty is inherent to the domain one of the key challenges that such systems facedand addressed to some extentwas the uncertainty inherent in many domains human beings are relatively good at reasoning in environments with unknowns and uncertainty one key observation here is that even the knowledge we hold about domain is not black or white but grey lot of progress was made in this era on representing and reasoning about unknowns and uncertainty there were some limited successes in tasks like diagnosing disease that relied on leveraging and reasoning using knowledge base in the presence of unknowns and uncertainty the key limitation of such systems was the need to hand-compile the knowledge about the domain from experts collectingcompilingand maintaining such knowledge bases rendered such systems impractical in certain domainsit was extremely hard to even collect and compile such knowledge--for exampletranscribing speech to text or translating documents from one language to another while human beings can easily learn to do such tasksit' extremely challenging to hand-compile and encode the knowledge related to the tasks--for instancethe knowledge of the english language and grammaraccentsand subject matter to address these challengesmachine learning is the way forward