research-questions

1.How could we use computer vision for earth?

2.What are the things/patters that we want to detect?

3.Which problem are we solving?

4.Do we have the data for that?

5.How energy-intense are training and predictions?

6.Deep Learning or classic ml?

7.Is somebody already doing these?

8.How will we fund it? how will it be financially-self-sustainable?

9. Self-driving cars
Cities in China are experimenting with a different approach. 
Rather than training autonomous vehicles to navigate existing urban settings, 
they’re retrofitting cities to facilitate the technology.
Features include roadside sensors that pass along navigational cues, like lane changes and speed limits.

Traditional automakers are focusing on assisted driving features like Ford's Driver Assist and Mercedes’ Parking Assist.
Meanwhile, Waymo continues to work on fully autonomous vehicles, and smaller companies such as 
May Mobility and Voyage are deploying full autonomy in limited scenarios that they aim to expand over time. 
In parallel, companies such as TuSimple, Embark, and Starsky are concentrating on fully autonomous interstate trucking.

10. Marcus' incessant tweets reignited an old dispute between so-called symbolists,
who insist that rule-based algorithms are crucial to cognition, 
and connectionists, who believe 
that wiring enough neurons together 
with the right loss function 
is the best available path to machine intelligence.

11. Deep learning works like magic with enough high-quality data. 
When examples are scarce, though, researchers are using simulation to fill the gap.

12. using-neural-networks-to-solve-advanced-mathematics-equations
https://ai.facebook.com/blog/using-neural-networks-to-solve-advanced-mathematics-equations/

What’s next for equation-solving AI

Our model currently works on problems with a single variable, and we plan to expand it to multiple-variable equations. 
This approach could also be applied to other mathematics- and logic-based fields, such as physics,
potentially leading to software that assists scientists in a broad range of work.

But our system has broader implications for the study and use of neural networks. 
By discovering a way to use deep learning where it was previously seen as unfeasible,
this work suggests that other tasks could benefit from AI. 

Whether through the further application of NLP techniques to domains that haven’t traditionally been associated with languages, 
or through even more open-ended explorations of pattern recognition in new or seemingly unrelated tasks, 
the perceived limitations of neural networks may be limitations of imagination, not technology.



--
Combinatorial generalization
https://arxiv.org/pdf/1806.01261.pdf

A key signature of human intelligence 
is the ability to make “infinite use of finite means” (Humboldt,1836;  Chomsky,  1965), 
in  which  a  small  set  of  elements  (such  as  words)  can  be  productively composed in limitless ways (such as into new sentences).
This reflects the principle ofcombinatorial generalization,
that is, constructing new inferences, predictions, and behaviors from known building blocks.