Bharat Rakshak

Seems like Nanha mujahids are meeting obl

https://www.bemlindia.in/Current_job.aspx
Engagement of Expert / Specialist as Consultant - Artificial Intelligence by BEML
https://www.bemlindia.in/Writereaddata/ ... igence.pdf

^^that is interesting

We need you in the Working Group. Unfortunately the first word there tends to empty the room fast, as you know.... But this thread is already a gold-mine: UBCN's AI Division should be able to do a writeup that looks as expert as Shekhar Gupta in Indian Express already.

UlanBatori wrote:We need you in the Working Group. Unfortunately the first word there tends to empty the room fast, as you know.... But this thread is already a gold-mine: UBCN's AI Division should be able to do a writeup that looks as expert as Shekhar Gupta in Indian Express already.

in the truest tradition of "machine learning" can you give the machine something to learn about the working group papers? point me..i can search but it has to meet the mongol yakdung

That has a few problems of coming out of caves on to the Steppe where unfriendly Pterodactyls might c... PM bliss?

UlanBatori wrote:That has a few problems of coming out of caves on to the Steppe where unfriendly Pterodactyls might c... PM bliss?

oh dont have that
bhoookhabhedia

I took a few pics of one of the US/UK aviation mags today. Had an article on the role of AI in combat aviation. Bliss advise how the mujahid can upload fotus?

ArjunPandit wrote:

UlanBatori wrote:APji: FYI: I know nothing.

UBji I think you should be banned for masquerading as a what you are . Before I joined brf i used to read posts, there were 5 posters whose posts were on a diff league. You're one. Your post above tells a lot about nothing. (-1)chaiM. Nevertheless, in the spirit of no gravity waves:
https://barandbench.com/rbi-mandates-da ... -services/

UBji, what was your old handle prior to yak herder avatar? Was it Daulat by any chance?

Al Qaeda #3. Pest-e-sha'eed many times.

UlanBatori wrote:Al Qaeda #3. Pest-e-sha'eed many times.

mods allowing punarjanm...wish sanku had that luxury. Rohitvats and his fights during tank days were folks of legend and good fun

titash wrote:I took a few pics of one of the US/UK aviation mags today. Had an article on the role of AI in combat aviation. Bliss advise how the mujahid can upload fotus?

twitter or google photos and then share

L&T announces new strategic initiative to focus on AI, cybersecurity

https://www.livemint.com/companies/news ... 21000.html
I think this will be interesting. Unlike other IT companies, L&T actually manufactures a wide range of products. This is where their other businesses will come handy.

Any ideas how to contact any L&T ppl on this?

UlanBatori wrote:Any ideas how to contact any L&T ppl on this?

dont have any direct senior contact. Can try to get contacts from my network on this. more context needed..

UlanBatori wrote:That has a few problems of coming out of caves on to the Steppe where unfriendly Pterodactyls might c... PM bliss?

bhoookhabhediya AT G Mard. Anyhow i can reach out to you?

ARTIFICIAL INTELLIGENCE, Machine Learning AND BLOCK CHAIN TECHNOLOGY IN PRODUCTION AND SUPPLY CHAIN MANAGEMENT
(25th April – 30th April, 2019)
https://www.nitw.ac.in/eict/files/EICT_AIMLPSC_NIT2.pdf

While these are good initiatives, I am wondering why in the age of conferences and mobile free conferencing tools, people have to go in person. There are many folks in city locations who cant make it to the colleges. While there is an advantage to it, but in my experience most of networking is for chai biskut

NOW u r using MAH Brain, as Laurel said to Hardy. Also, every International Conference on Climate Change should be required to compute their own Carbon Footprint. 2000 carnivores flying 10,000km each way, baggage, booze, food, taxis, rental cars.... paper used for brochures, bottled water, ice...

Like.. c this one. Y don't u emijjile the dumbass organizer there and ask what eej the cost oph this conpherenj? IITM in July!!! Just the airconditioning will cost more than any carbon they plan to save!

P.S. I c that "Private Message" eej not available to aam Oldies any more like in days of dinosaurs. Good thing too.

0. wasnt there one on aviation which had some random topic and someone mentioned flight of icarus by iron maiden.
1. That said, who funds them. I have serious concerns when i see funding for such stuff
2. How does bhediya meet yak?

Yak herder I've some points ready, want to send them to you. Before elaborating I want to send you

UB,

Something to think about.

Estonia plans to launch an artificial intelligence (AI) judge to settle small claims disputes

Link has no info, just the headline.

why do you got to british bullshit corporation?
https://www.wired.com/story/can-ai-be-f ... thinks-so/

fanne wrote:Arjun, thanks for the input (particularly what course to take, otherwise it was an ocean and even with that link, one would not have made any progress. Where do you start? You cannot do all - nearly 1000-5000 hours of learning, mostly repeating and perhaps confusing). Follow up question - I see most classes have videos, it looks free. Is it all complete? Like if I needed an engineering degree in computer science, there are perhaps 10,000 colleges offering it, is the Oxford one complete in itself (to the extent it can be, the field is evolving every day) and fully available in that link?

Just pitching in, doing some POCs, weekend type projects might help. A hacker style taking a git repo and modifying it to a particular use case may be helpful.

NRao wrote:UB,
Something to think about.
Estonia plans to launch an artificial intelligence (AI) judge to settle small claims disputes
Link has no info, just the headline.

That does sound very interesting.
A great start.

while it is robotics, but robotics has AI at its heart...just saw a news item calling for treaty/ban on military developments of AI..i think chinese are going to mass produce these weapons. They are going the russia way, producing cheap items at a mass scale making it impossible to invade them (assuming no nukes) and overwhleming others while in conventional realms

http://en.worldrobotconference.com/
https://www.youtube.com/watch?v=bjcAUi6SZSI

gunnvant wrote:

fanne wrote:Arjun, thanks for the input (particularly what course to take, otherwise it was an ocean and even with that link, one would not have made any progress. Where do you start? You cannot do all - nearly 1000-5000 hours of learning, mostly repeating and perhaps confusing). Follow up question - I see most classes have videos, it looks free. Is it all complete? Like if I needed an engineering degree in computer science, there are perhaps 10,000 colleges offering it, is the Oxford one complete in itself (to the extent it can be, the field is evolving every day) and fully available in that link?

Just pitching in, doing some POCs, weekend type projects might help. A hacker style taking a git repo and modifying it to a particular use case may be helpful.

++1

Anyone interested in TensorFlow (Keras too) can get their feet wet at:
https://colab.research.google.com/noteb ... come.ipynb

Thanks guys!! I may need more help

fanne wrote:Thanks guys!! I may need more help

Sir jee, will be more than happy to pitch in. If you live in blr, can work collaboratively.

One can start with keras, its clean and simple api then move on to more low level api. This can be a good starting point https://github.com/ChristosChristofidis ... p-learning

Fanne sir my two cents ; it's too vast a field , depending on what exactly you wish to accomplish the path you take will vary . Generally speaking if it's say consulting then I would imagine we are not talking about learning the science in itself but actually learning how to identify and classify problems and map them to corresponding models (i.e. application of the technique and not learning the technique in itself), however if you wish to learn the area itself to be able to not just say for solving a problem 'x' you need to apply 'y' framework/technique then you will have to brush up everything right from linear algebra , probability , statistics and even neural networks . Once the method itself is understood learning to program these in python or etc would be the next step.

If it sounds overwhelming what I suggest is this ; identify the area which interests you for example in ITVTY generally speaking the areas where we see ML or it's constructs applied are text parsing/processing (could be NLP) or say media (images/audio/video) if you have an area narrowed down it will make your pursuit more organised and easier to plan for . You can further refine your target area of study if you can reduce it down to an industry problem e.g. customer segmentation , sentiment analysis, general predictive use cases (revenue or churn prediction) or say if it's as simple as extracting text from physical documents (just throwing examples here) . If you have that list you can then find out what would be list of probable models to approximate these problems and then go all the way up the lineage to begin from fundamentals this will keep the scope relatively narrow.

negi wrote:Fanne sir my two cents ; it's too vast a field , depending on what exactly you wish to accomplish the path you take will vary . Generally speaking if it's say consulting then I would imagine we are not talking about learning the science in itself but actually learning how to identify and classify problems and map them to corresponding models (i.e. application of the technique and not learning the technique in itself), however if you wish to learn the area itself to be able to not just say for solving a problem 'x' you need to apply 'y' framework/technique then you will have to brush up everything right from linear algebra , probability , statistics and even neural networks . Once the method itself is understood learning to program these in python or etc would be the next step.

If it sounds overwhelming what I suggest is this ; identify the area which interests you for example in ITVTY generally speaking the areas where we see ML or it's constructs applied are text parsing/processing (could be NLP) or say media (images/audio/video) if you have an area narrowed down it will make your pursuit more organised and easier to plan for . You can further refine your target area of study if

you can reduce it down to an industry problem

e.g. customer segmentation , sentiment analysis, general predictive use cases (revenue or churn prediction) or say if it's as simple as extracting text from physical documents (just throwing examples here) . If you have that list you can then find out what would be list of probable models to approximate these problems and then go all the way up the lineage to begin from fundamentals this will keep the scope relatively narrow.

made the imp parts bold

25-July-2018 15:38 IST
Finalisation of National Artificial Intelligence Mission
http://pib.nic.in/newsite/PrintRelease. ... lid=181007

01-August-2018 16:39 IST
Artificial Intelligence
http://pib.nic.in/newsite/PrintRelease. ... lid=181324

21 OCT 2018 12:50PM by PIB Delhi
AI for ALL: Leveraging Artificial Intelligence for Inclusive Growth
http://pib.nic.in/PressReleseDetail.aspx?PRID=1550115

02 JAN 2019 3:14PM by PIB Delhi
Artificial Intelligence
http://www.pib.nic.in/PressReleseDetail ... ID=1558146

19 JAN 2019 7:49PM by PIB Delhi
NSTL - ACADEMIA MEET (NAM 2019) @ NSTL on 19.01.2019

07-February-2019 18:41 IST
Setting of New Centres for Artificial Intelligence in Higher Educational Institutions
http://pib.nic.in/newsite/PrintRelease. ... lid=188221

While all others have shared a lot of important information on the topic, I would like to share my views on applicability of AI/ML in the context of aviation particularly for systematization of airspace.

As I understand, civil aviation in India is growing at an alarming pace with more than 100 airports added to the network and more planned for in the next 5 years. This ultimately leads to more traffic in the civilian airspace in India particularly in the terminal airspace areas of Mumbai, Bangalore - Chennai, Kolkata and Delhi - NCR. Simply adding more infrastructure on the ground like additional runways or greenfield airports will not solve the problem of crowded airspace. Therefore we need to optimize the terminal airspace and routes by efficient planning. Further, with more traffic there are implications like noise, air quality emissions, economic impact for airlines etc. With a controller (ATC) led approach, chances are always high that any small change to initial conditions (could be a rogue drone in flight paths, cows in the runways, delayed departures etc.) could lead to prolonged holding patterns and stacks - therefore contributing to more fuel burn, noise levels etc. other than the economic impact of lost time.

Such combinatorial optimization problems can be tackled if systematization of airspace happens. That is gradual removal of ATC (human in the loop). This is where AI/ML comes into picture. Performance Based Navigation (PBN) is a global concept driven by ICAO. The idea is to accommodate more flights without compromising safety or regulatory requirements. ML models can be trained for climb prediction, curved approaches and turns, trajectory planning, probabilistic distribution of flight paths in a spatio-temporal context, 4D (alt/lat/long/time) analytics and predictions etc. Indian skies have rich sources of data. Coupled with the fact that powerful weather models are readily available from the India Meteorological Department, GAGAN - space based navigation overlay service using SBAS, free to use aggregated satellite ADS-B data plus time-series data from AAI can be used to implement continuous descent on arrival scheme, gateways and letterboxes in the skies, airport ground movement optimization, airport collaborative decision making and ultimately plug it to the land transport modes of travel like airport train/metro transfers - dynamic timetables, taxi fleet optimization etc.

^^esonmmuk
i fundamentally split this paradigm on three dimensions
1. Data
2. Processing technique
3. Application & Monitoring
Platforms (Component of all 3) and associated integration

Data: Data from traditional sources and alternative sources, including monitoring similar to the feedback loop in traditional control systems
Processing Techniques: This includes rules, NB, and other ML techniques which may include predictive parts
Application & Monitoring: Application is the front end that most users see. the depth varies with the technical grasp/depth of an individual. Brar might see at a different level than mine and so on. Monitoring is on key performance metrics and incidental ones which may not be that critical to the front end user but are imp to the overall schematics.

Now almost all the activities of human civilization have all 3 with varying degree for quite some time. What is happening now is that with availability of cheap sensors and data storage all three can be effectively integrated (read platform) and dealt in a unified fashion in a predictive way rather than the reactive way. Faster computation has enabled methodologies, which while being opaque, are more effective and faster.

Now what you mention is just one example. There are so many things
1. Agriculture
2. Transportation: all modes including railways, road transport
3. Civic services
4. Governance
5. Military

You name it and I can bring up use cases. The objective is not to discourage but to highlight why people say data is the new oil and why it is the electricity of our century. I am afraid we are missing the bus again.

Wow! With Pee Aref and Wikipedia and Google Translator hu needs college? And "AI" can do my thinking 4 me!

^^UB actually, US college model is under serious threat I dont feel like I am joking. I used to think that with ballooning costs the age of Taxila and Nalanda will return with students coming to India in droves and we wont have to do much to export the culture. however the MOOC revolution has turned that upside down. Now if you have internet you can read stuff and see videos and be an expert. With the python thingy you just need to import the right library and invoke the right set of commands. In few years perhaps you may just want to say waht you want and it will pop up on your mobile once done.

^ a very common example of AI doing thinking for you is Kinect, it automatically detects your head, brain and legs and then their motion. Many years back it used to work with decision tree then it moved to ensemble ones. Now that's a mass scale produced sub $1000 product that is supposed to work with friendlies. I am sure you have travelled enough and know what full body scanners can do. Combine them with image processing and your imagination. You have your imaginations running wild to imagine what can be done in other frequencies of EM spectrum.

ArjunPandit wrote:^^esonmmuk
i fundamentally split this paradigm on three dimensions
1. Data
2. Processing technique
3. Application & Monitoring
Platforms (Component of all 3) and associated integration

Data: Data from traditional sources and alternative sources, including monitoring similar to the feedback loop in traditional control systems
Processing Techniques: This includes rules, NB, and other ML techniques which may include predictive parts
Application & Monitoring: Application is the front end that most users see. the depth varies with the technical grasp/depth of an individual. Brar might see at a different level than mine and so on. Monitoring is on key performance metrics and incidental ones which may not be that critical to the front end user but are imp to the overall schematics.

Now almost all the activities of human civilization have all 3 with varying degree for quite some time. What is happening now is that with availability of cheap sensors and data storage all three can be effectively integrated (read platform) and dealt in a unified fashion in a predictive way rather than the reactive way. Faster computation has enabled methodologies, which while being opaque, are more effective and faster.

Now what you mention is just one example. There are so many things
1. Agriculture
2. Transportation: all modes including railways, road transport
3. Civic services
4. Governance
5. Military

You name it and I can bring up use cases. The objective is not to discourage but to highlight why people say data is the new oil and why it is the electricity of our century. I am afraid we are missing the bus again.

I would like to add no.4 to your list:

4. Algorithmic fairness/detecting bias - if your training data is biased, models (AI/ML is 'black box') turn out biased irrespective of your validation data .... therefore it is important from an algorithmic fairness perspective to address the bias. Here is a nice paper from IBM on metrics for algorithmic fairness.

https://arxiv.org/pdf/1810.01943