You can always find the latest and hottest topics in AI, ML, and Data Science research at conferences like this:

Open Data Science Conference

NIPS 2018 Call for Papers

Call for Papers – IEEE ICDM 2018

SIAM International Conference on Data Mining (SDM18)

KDD 2018

Artificial Intelligence Conference

My data science heroes include a majority of the folks on this list:

Top 100 Influencers, Brands and Publications

Thank you for that question. I have talked about and written about that topic many times. Here are some places where you can see and hear my story:

(1) Data Science in 30 Minutes: Kirk Borne – A Fortuitous Career in Data Science

(2) Meet the Experts

(3) Kirk Borne – Analytics Visionary, Space Scientist, and Chronic Learner

(4) A Growth Hacker's Journey – At the right place at the right time

(5) #4: Kirk Borne — NASA, Astrophysics, & The Evolution of Modern-Day Data Science – AJ Goldstein

(6) Data Mining at NASA to Teaching Data Science at GMU: Kirk Borne Interview

Thank you for your question. Check out this excellent article on Reinforcement Learning that covers concepts, examples, strategies, challenges, and application areas: http://bit.ly/2cvrsSJ

I also recommend the following readings to anybody seeking some interesting application areas in data science:

(1) Modernizing cybersecurity approaches

(2) https://www.datamakespossible.com/tag/kirk-borne/

Health and Healthcare are near the top of my list of the most significant areas in which data science and analytics can be applied. The top of my list is CYBERSECURITY, which should not surprise anyone in the modern world. There are so many places where you can apply analytics in healthcare, from healthcare administration to precision medicine, and everywhere(!!!) in between. Seek the application area that is most appealing to you — follow that passion first, and then you will find data science applications there. You can find some examples of healthcare applications in my presentation here (especially slide #40):

http://www.kirkborne.net/UVA2017/kirkborne-UVA_SIEDS-28April2017.pdf

I found this book to be really interesting and helpful:

Taylor & Francis Group

Thank you for your question. Every field, every domain, every business, every organization can use data and data science these days. Aerospace is no exception. You can imagine applications in logistics, design, scheduling, risk prediction, time series forecasting of interesting events, predictive / prescriptive maintenance of devices, autonomous systems (for exploration or for maintenance), knowledge discovery in research data sets, anomaly detection in all sorts of data (machines, network logs, science instruments, engines, launch systems, human activities, health & safety, etc.). The list can go on and on. Here is a short article from a person who is active in this field:

https://www.linkedin.com/pulse/data-science-space-health-redouane-boumghar-phd/

Thank you Adrianna for your question. There are many 'data for good' opportunities, including the datasciencebowl.com, datakind.org, bayesimpact.org, drivendata.org, data for climate action, UN Global Pulse data challenges, and Kaggle's Data Science For Social Good events:

Introducing Data Science for Good Events on Kaggle

Anyone can join these competitions. There are also hackathons in many communities throughout the year. You should search online, ask local data scientists, and attend local MEETUPS on data science — all of those sources can offer many opportunities to grow and apply your data science skills in many different application areas.

Thank you for your questions. I don't have a good answer since I am not actively studying those topics, though of course I read about them just like you. I believe that we are definitely moving rapidly toward a more general intelligence, with amazing advances appearing everyday. Pierre Pinna on Twitter posts a lot of stuff about this:

ipfconline (@ipfconline1)

Quantum computing (and quantum M.L.) are still in their infancy — a very long way to go from a few qubits to megaqubits or gigaqubits of computing power. So, don't expect to see any big implementations soon. There will be interesting (but very small) demonstration projects soon, but not enterprise-ready solutions.

Nevertheless, despite the progress of these things appearing either too slow (quantum computing) or too fast (AGI) for some people, these things are coming!!

Thank you Kavita for your kind remarks. Here is one on my first in-depth data science projects that I found interesting and was a real learning experience for me:

https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20050180456.pdf

Here is another project that I worked on for nearly 10 years (I am still a project team member, though I don't have much time to work on it anymore, which is okay since there many many hundreds of other people on the team already):

1) An infographic that describes my work on the LSST project: