AI Times Soumyadip Nandi

In this episode  I talk about how YOU can get started off in the field of Machine Learning without having a Computer Science or IT background. I am assuming the fact that you are familiar with data structures, algorithms because you have experienced some of it at the high school level. Even if you haven't , the resources that I have described in the episode will definitely help you out.  This episode aims to get your foot in through the door in Machine Learning and sets the tone for further aspirations, whether you wish to work as a developer or as a researcher. I have talked about the resources that has helped me when I was getting started with python and ML. Also, this is entirely my approach and framework and I am giving it to you as I believe it can be helpful for you as well.  Give it a listen and share with your tech circle if you wish to. 

In this episode, I talk about a few factors that are going to be necessary  if you want to stand out from your competition in Machine Learning. I talk about courses, projects, the marketplace as well as my own experiences that will help you. I also  provide tips about your resume and how you can effectively use LinkedIn and GitHub for your courses and your projects. I mention the  current market situation and  talk about why the market does not care about anyone and also why execution is necessary to stay ahead of your competition in the coming years.  In this episode I provide you with a framework that will help you stand out  even in the face of adversity. Hopefully, you will be pumped after listening. 

In this episode, I talk about a new idea known as the Ramanujan Machine. Ramanujan Machine was made to mimic the methods using which the famous Indian Mathematician Srinivasa Ramanujan would come up with solutions to Mathematical problems using only his intuition. In the episode, I have also talked about a few novel ways in which the Ramanujan Machine can be applied in the field of Artificial Intelligence. I have also discussed in brief about a recent paper that was released by DeepMind and how it can be connected with the Ramanujan Machine as well my thoughts on it. I hope, you will enjoy listening to this episode.

In this episode, I talk about Meta- Learning in details. Meta Learning aims towards basically Learning how to Learn  I have also discussed  the various steps involved in Meta-Learning as well as the types of Meta-Learning Algorithms that are in use today using examples of a number of Research papers from the field of Meta-Learning. The episode also includes a couple of novel ideas which can be applied in by combining sub fields of AI with Meta-Learning along with my thoughts on its applications.

  In this episode, I talk about a concept which is known as Artificial Curiosity. Curiosity is framed as the interest of an agent to learn unknown regularities. The idea is to reward the system if it finds something unexpected. On the other hand, such agent should get bored when facing patterns that are either predictable or inherently unpredictable. I discuss Artificial Curiosity at length, including Curiosity driven learning. The discussion also includes examples from a few research papers as well as my own insights on Artificial Curiosity. 

In this episode, I talk about Neuralink in depth. The discussions  range from Brain Machine Interface (BMI) to the technical details of Neuralink and is on the basis of Elon Musk's July, 2019 presentation on Neuralink and its paper.  I have also included my thoughts about Neuralink and its future.