Get me outta here!

5 Lessons on AI from the Tesla Autopilot Fatality


Unfortunately it takes extreme repercussions for us to feel in our bones, the limitations of our technologies.

Three points :

  1. I have included relevant links about this incident at the end of the blog. Informative read
  2. One of my parent died in an automobile accident; so I do know, first hand, the human toll – I do not take this lightly; in fact the reverse is true
  3. And the views expressed in my writing are my own and do not reflect any organization I am part of … now or in the future …

 

Lesson 1 : Our machines inherit our faults (so far …)

As I pointed out in one of my AI blogs:

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Lesson 2 : Many domains are not forgiving to byzantine failures

We are learning that painful lesson whether they are rockets, airplanes or cars. Even though we freak out of snapchat is down for an hour, we can survive that, but not these. The drivers need to understand the downside of technologies and be alert.

Lesson 3 : Mission Critical Systems should have redundancy, over coverage & independency

For example multiple sensor sources & probably independent situational interpretation. I saw the following from somewhere where the Japanese Ministry talks about “correcting the wrong train of thoughts”:

Lesson 4 : Swarm Intelligence

Lesson 5 : This might lead to some level of Standardization & Legalization

  • Standardization of components & protocols
  • Legislation/Standardization of algorithms or semantic behaviors incl image recognition, policies and pragmas …
  • Even driver education and certification to dive autonomous vehicles !

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Reference:

  1. http://fortune.com/2016/07/03/teslas-fatal-crash-implications/
  2. http://www.latimes.com/business/technology/la-fi-hy-tesla-google-20160701-snap-story.html
  3. https://cleantechnica.com/2016/07/02/tesla-autopilot-fatality-timeline-facts/
  4. https://www.teslamotors.com/blog/your-autopilot-has-arrived
  5. https://www.quora.com/How-does-Teslas-Autopilot-work-What-are-the-sensors-that-power-it
  6. http://www.gocomics.com/pcandpixel/2016/07/01
  7. Tesla’s Response to fortune’s Article http://fortune.com/2016/07/06/tesla-fortune-response-autopilot/
  8. http://www.greencarreports.com/news/1104892_tesla-autopilot-crash-what-one-model-s-owner-has-to-say
  9. http://money.cnn.com/2016/07/07/technology/tesla-autopilot-name/
  10. http://fortune.com/2016/07/11/elon-musk-tesla-self-driving-cars/
  11. http://fortune.com/self-driving-cars-silicon-valley-detroit/
  12. http://in.reuters.com/article/us-autos-selfdriving-investment-idINKCN0ZS0CQ
  13. http://www.latimes.com/opinion/editorials/la-ed-self-driving-cars-20160710-snap-story.html
  14. http://gizmodo.com/teslas-autopilot-driving-mode-is-a-legal-nightmare-1783280289
  15. http://www.bbc.com/news/technology-36783345
  16. http://www.freep.com/story/money/cars/2016/07/14/consumer-reports-tesla-disable-autopilot/87074826/
  17. http://www.usatoday.com/story/money/cars/2016/07/17/what-tesla-autopilot-crash-means-self-driving-cars/87219126/
  18. http://fortune.com/2016/07/17/tesla-rethinking-radar-system/
  19. https://www.wired.com/2016/08/hackers-fool-tesla-ss-autopilot-hide-spoof-obstacles/
  20. https://www.autoindustrylawblog.com/2016/08/02/connected-and-autonomous-vehicles-full-speed-ahead-or-tapping-the-brakes/
  21. http://www.usatoday.com/story/money/cars/2016/08/10/tesla-model-s-autopilot-china-crash/88510532/

 

Robot’s Rules of Order


As a designer of AI & autonomous behaviors, this week was pretty interesting:

Both are must read & discuss for the AI community. The abstracted 8 rules are :

This could beg the question, what exactly is an AI ? Let me make an attempt from an autonomous vehicle (cars, drones et al) perspective, which might not be complete or sufficient for other situations ….

What says thee ?

Yann LeCun @deeplearningcdf Collège de France


I am spending this weekend with Yann LeCun (virtually, of course) studying the excellent video Lectures and slides at the College de France. A set of 8 lectures by Yann LeCun (BTW pronounced as LuCaan) and 6 guest lectures. The translator does an excellent job – especially as it involves technical terms and concepts !

(I will post a few essential slides for each video …)

Inaugural Reading – Deep Learning: a Revolution in Artificial Intelligence

LeCunn-01

My favorite slide – of course !!! And the DGX-1 !!

Missing Pieces of AI – interesting …

The reasoning, attention,episodic memory and  a rational behavior based on a value system are my focus for autonomous vehicles (cars & drones!)

Convnets are everywhere !

Probably the most important slides of the entire talk – the future of AI.

Parse it couple of times, it is loaded with things that we should pay attention to …

Can AI beat hardwired bad behaviors ?

LeCunn-06

I agree here, here, here and here – we don’t want AI to imitate us, but take us to higher levels !

Stay tuned for rest of the video summaries …..

Room With A View to the Thames


Copy of my post in Linkedin

RoomWIthAView

View From My Room

London has changed a lot since I visited last ! Interesting constructions – at least this part of the town.

Am at the Aloft London Excel, ready for our tutorial at the StrataHadoop London “Building machine-learning apps with Spark“. I will be talking aboutApache Spark/GraphX along with my esteemed colleagues Jayant & Vartika.

Got a good room with a view to the Thames !

Took the Norwegian flight OAK-GTW. This is a good alternative to London. Am also flying by Norwegian to Gothenberg.

The OAK-GTW fight was a 787-8 ! Good plane – I really like the window – a lot bigger … and it has the hi-tech ElectroChromatic Dimming System (or Sun Glasses as they are colloquially called ! ) that replaces the window shutter – you can always see the outside.

What would you want AI to do, if it could do whatever you wanted it to do ?


P.S: Copy of my blog in Linkedin

Note : I am capturing interesting updates at the end of the blog.

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Exponential Advances:

An interesting article in Nature points out that exponential advanced in technological growth can result is a very alternate world very soon.

IBM X Prize:

 

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And the IBM AI X Prize is offering a chance to showcase powerful ideas that tackle challenges.

Got me thinking … What do would we want our machines/AI to do ?

I am interested in your thoughts. Pl comment on what you would like AI to do.

Earlier I had written about us not wanting our machines to be like us; understand us – may be, help us – definitely, but imitate us – absolutely not  …

So what does that mean ?

  • Driving cars ? – Definitely
  • Image recognition, translation and similar tasks ? – Absolutely
  • Write like Shakespeare just by feeding all the plays to a neural network like the LSTM ? – Definitely not !

I see folks training deep Learning systems by feeding them Shakespeare plays and see what the AI can write. Good exercise, but is that something we would get an X Prize for ? Of course, that is putting the cart before the horse !

We don’t write just by memorizing the dictionary and Elements of Style !!

  • We write because we have a story to tell.
  • The story comes before writing;
  • Experience & imagination comes before a story …
  • A good story requires both the narrative power as well as a powerful content with it’s own anti-climax, and of course the hanging chads ;o)
  • Which the current AI systems do not possess …
  • Already we have robots (Google Atlas) that can walk like a human – leaving aside the the goofy gait – which, of course, is mainly a mechanical/balance problem than an AI challenge
  • Robots can drive way better than a human
  • They translate a lot better than humans can (Of course language semantics is a lot more mechanical than storytelling)
  • Robots and AI machines do all kinds of stuff (Even though Mercedes Assembly plant found that they cannot handle the versatile customization!)

Is there anything remaining for an AI prize One wonders …

In the article “How Google’s impressive new robot demo will fuel your nightmares” , at 2:09, the human (very rudely) pushes the robot to the ground and the robot gets up on it’s own ! That proves that we have solved the mechanical aspects of human anatomy w.r.t movements & balance.

[Update 3/17/16] Looks like Google is pushing Boston Dynamics out of the fold !

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But a meta question remains.

  • Would the robot be upset at the human ?
  • Would it know the difference – if it was pushed to keep it away from harm’s way (say a falling object) vs. out of spite ?
  • And, if we later hug the robot (as the author suggests we do) would it feel better ?
  • Will it forget the insult ?

So there is something to be done after all !

Impart into our AI – the capability to imagine, the ability to understand what life is;  feel sadness & joy; understand what it is to struggle through a loss,…

This is important – for example, if we want robots to act as companions for the sick, the elderly and the disabled, may be even the occasional lonely, the desolate and for that matter even the joyous!

If the AI cannot comprehend sadness, how can it offer condolences as a companion ? Wouldn’t understanding our state of mind help it to help us better? 

AI-09

 In many ways, by helping AI to understand us, the ultimate utility might not be whether AI really comprehends us or not, but whether we get to understand us better, in the process !! And that might be the best outcome out of all of these innovations.

As H2O-ai Chief SriSatish points out,

Over the past 100 years, we’ve been training humans to be as punctual and predictable as machines; … we’re so used to being machines at work—AI frees us up to be humans again ! – Well said SriSatish

With these points in mind, it is interesting to speculate what the AI X-Prize TED talks would look like in 2017; in 2018. And what better way to predict the future than to invent it ? I am planning on working on one or two submissions …

And what says thee ?

[Update 3/12/16] Very interesting article in GoGaneGuru about AlphaGo’s 3rd win.

AI-11

  • AlphaGo’s strength was simply remarkable and it was hard not to feel Lee’s pain
  • Having answered many questions about AlphaGo’s strengths and weakness, and exhausting every reasonable possibility of reversing the game, Lee was tired and defeated. He resigned after 176 moves.
  • It’s time for broader discussion about how human society will adapt to this emerging technology !!

And Jason Millar @guardian agrees.

AI-12

Maybe all is not lost after all, WSJ says … !

AI-13.png

[Update 3/9/16] Rolling Stone has a 2-part report – Inside the Artificial Intelligence Revolution. They end the report with a very ominous statement.

AI-10

 

[Update 3/4/16] Baidu Chief Scientist Andrew Ng has insightful observations

  • “What I see today is that computer-driven cars are a fundamentally different thing than human-driven cars and we should not treat them the same”- so true !

[Update 3/6/16] An interesting post from Tom Devenport about Cognitive Computing.

  • Good insights into what Cognitive Computing is, as a combination of Intelligence(Algorithms), Inference(Knowledge) and Interface (Visualization, Recommendation, Prediction,…)
  • IMHO, Cognitive Computing is more than Analytics over unstructured data, it also has touches of AI in there.
  • Reason being, Cognitive Computing understands humans – whether it is about buying patterns or the way different bodies reacts to drugs or the various forms of diseases or even the way humans work and interact
  • And that knowledge is the difference between Analytics and Cognitive Computing !

I like Cognitive Computing as an important part of AI, probably that is where most of the applications are … again understanding humans rather than being humans !

Reference & Thanks:

My thanks to the following links from which I created the collage:

  1. http://www.nature.com/news/a-world-where-everyone-has-a-robot-why-2040-could-blow-your-mind-1.19431
  2. http://lifehacker.com/the-three-most-common-types-of-dumb-mistakes-we-all-mak-1760826426
  3. https://e27.co/know-artificial-intelligence-business-20160223/
  4. http://spectrum.ieee.org/tech-talk/computing/software/digital-baby-project-aims-for-computers-to-see-like-humans
  5. http://www.techrepublic.com/article/10-artificial-intelligence-researchers-to-follow-on-twitter/
  6. http://www.lifehack.org/309644/book-lovers-alert-8-the-most-spectacular-libraries-the-world
  7. http://www.headlines-news.com/2016/02/18/890838/can-ai-fix-the-world-ibm-ted-and-x-prize-will-give-you-5-million-to-prove-it
  8. http://www.lifehack.org/366158/10-truly-amazing-places-you-should-visit-india?ref=tp&n=1
  9. http://www.lifehack.org/articles/lifestyle/20-most-magnificent-places-read-books.html

The Master Algorithm (Book Review) a.k.a Data the Final Frontier


P.S: Copy of my blog in Linkedin

Book Review of “The Master Algorithm”  MasterAlg-01

Prof.Pedro Domingos has done a masterful job of unboxing Machine Learning – and unboxing is the right word!

A very insightful book that would bring tears (of joy, not misery) to the eyes of Data Scientists and Data Engineers; not to mention the C-Suite execs who would acquire deep wisdom of the data kind (am not sure if they would shed tears, they would if they could….)

And for those who haven’t read the book yet you should run – not walk, to the nearest store (or to the nearest Amazon web site with a speedy DNS) and buy one (or more!)

While you are waiting for the book to arrive (by second day shipping – you’all have prime shipping don’t you ?) you could prime yourself for the intellectual feast by reading the two resources :

Prologue:

The book can be consumed at least at two levels – first an insight into the domain of algorithms, data and machine learning; but a more exciting level is as an inspiration and a guide post into techniques and mechanisms that augment current models one is working on – a natural extension to Prof.Domingos’ call for action …

I’d like to give you a parting gift …  the great undiscovered ocean stretches into the distance, the gift is a boat-Machine Learning- and it’s time to set sail

My trek through the book – the latter, and what an incredible journey it was ! As Prof.Domingos says

Before we can learn deep truths withmachine learning, we have to discover deep truths about machine learning …

and the book does the latter – in spades!

The society is changing, one learning algorithm at a time” – The prologue runs like a Bond movie (A Tron-esq Master Algorithm/MCP as the next head of Spectre, anyone ?) expanding this idea into various modern day successes, for example “The candidate with the best voter model wins” (Ref my blog All The President’s Data Scientists)

Main Ideas:

The main thesis of the book is around the Five Tribes of Machine learning and the Master Algorithm that unifies all (& more..) The central hypothesis of the book is like so :

 All knowledge – present, past & future – can be derived from data, by a single, universal learning algorithm – the Master Algorithm

Master_Algorithm_1

 

The language is poetic and picturesque, weaving through a lot of deep concepts, conveying the art of possible and the probable, tickling the imagination of the uninitiated as well as the practitioner.

The analogies are very real and reflect the fundamental principles of Machine Learning and Big data viz

  • Learning Algorithms are the seeds, Data the soil & Learned programs the grown plants
  • Machine Learning cartons in super market labelled ‘Just Add Data’
  • Every field needs data commensurate with the complexity of the phenomenon it studies
  • Perceptrons – mathematically impeachable, searing in it’s clarity and disastrous in it’s effects
  • ramblings of a drunkard, locally coherent even if globally meaningless
  • MCMC as drown our sorrows in alcohol, get punch drunk & stumble around all night
  • SVM as a fat snake slithering thru mine field or comparing dimensionality reduction and arranging books on a shelf  !

The book is full of nuggets of wisdom and insights, let me iterate a couple:

  • S-curve as the basis of evolving systems “the most important curve in the world”, quoting Hemingway’s The Sun Also Rises about how he went bankrupt “Two ways – Gradually & then Suddenly!” the S curve of course. Also the S-curve, not Singularity that will explain the evolution of AI
  • The progression from Hopfield’s deterministic spin glass, to work on probabilistic neurons by Hinton, et al.
  • Nature (the program) evolves for the nurture (the data) it gets, and the Baldwin evolution ie “behaviors that are first learned become genetically hardwired” – a strong case for the important step of model evolution after deployment (I had talked about it at The Best of the Worst in Big Data – see slide #7, video of pyata talk)
  • Power laws, where things get better with time, “except, of course, Windows, that gets slower with every version !
  • The jobs machines are good at “Credit applications and car assembly rather than stumbling around a construction site”. The key is, machines can’t be like us and vice versa; humans are good at tasks that require complex context & common sense and we don’t compete with the machines viz. “you don’t outrun a horse, you ride it!” – well said, Prof.Domingos. I also have similar thoughts about AI.

Epilogue:

Absolutely worth reading, in the genre of Stephen Bakers “Final Jeopardy” (my book review) & Stephen Levy’s “In The Plex” (my book review) to name a few. It is instructive to see how much the domain of Machine Learning has evolved in the span of ~4 years !

Trivia:

Works that blend multiple genres are hard to create but provide endless enjoyment. I enjoyed 3 in the last couple of weeks – Prof. Domingos’ The Master Algorithm, the movie Bahubali and the songs (a juxtaposition of Sanskrit/ vernacular) and of course, Spectre (the movie & the motion picture soundtrack)

And am planning on next set of book reviews – a somewhat orthogonal domain- FinTech – Actually am pursuing the MS-CFRM at UWA !

Illuminae (and S – I have both !) belong to a new meta genre – books that give you a multi-dimensional on-line experience; the inverse (or transpose – am watching MIT 18.06) of e-books, that is, you read them like an e-book, but in the physical form !

Fintech-01.png

The Art of NFL Ranking, the ELO Algorithm and FiveThirtyEight


In this blog, I will focus on the NFL Ranking based on the ELO algorithm that Nate Silver’s FiveThirtyeight uses. The guys at 538 have done a good job.The ELO and NFL ranking was part of my workshop at the Global Big Data Conference this Sunday. The full presentation is in slideshare


ELO – the algorithm made famous by Facebook & depicted in the movie Social Network

gbdc-r-04-P30


 Basic ELO

gbdc-r-05-P20

The k-Factor is the main leverage point to customize the algorithm for different domains.

  • For example Chess has no notion of a season; Soccer,Football & Basket ball are dependent on seasons – teams change during different seasons
  • Chess has no score to consider except WIn,Lose or Draw; but ball games have scores that need to be accommodated
  • For Chess k=10; for soccer it varies from 20 to 60; 20 for friendly matches to 60 for World Cup Finals
  • As we will see later, NFL adjusts k with the Margin Of Victory Multiplier
  • NFL also adjusts k to weigh recent games more heavily, w/ exponential decay
  • There are also mechanisms for weighing playoffs higher than regular season games (We will see this in Basketball)

538’s take on ELO

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gbdc-r-05-P22


NFL 2014 Predicts & Results

The R program ELO-538.R is in Github

2014 Ranking Table

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To Do

  1. Exponential decay with more weight for recent games – later in the season
  2. Calculate the rankings from 1940 to present, draw graphs like this from 538

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