Twitter 2.0 = Curated Signals + Applied Intelligence + Stratified Inference

P.S: Copy of my blog in Linkedin

Exec Summary:

One possible trajectory and locus (“product cadence”) for Twitter 2.0 is to be a platform – to tell stories with different levels of abstraction – from basic curated signals to aggregated intelligence (ie trends, positions, sentiments and issues) & finally the higher order of exposing stratified inference built on the signals and intelligence.

For example CPC advertisers might want to know “Who is an NBA Fan” for personalized ad campaigns based on the interest graph (We did a similar project few years ago, based on Twitter data)

All without sacrificing the core Twitter consumption experience, but adding different dimensions to Twitter consumption …

Constituents like political campaigns (pardon the pun ;o)) can consume the platform at different levels and sophistication. All the (potential!) President’s Data Scientists can run multiple models over the signals while All the President’s Devops can build dashboards for the strategists to consume the curated inferences

Twitter Network != Facebook Network; Twitter Graph != LinkedIn graph ie. Twitter is an interest graph, not a social graph. If so, why can’t Twitter expose the interest graph as a first class entity, with appropriate intelligence?

Twitter is the right platform for ad-hoc,ephemeral spaces to exchange quick notes.

[Update : Julia Boorstin’s blog What’s Next for Twitter also echoes many of my recommendations below]


Due to various reasons I have been contemplating about Twitter in general and specifically Political Campaigns as an example of an eco system where Twitter has lots of potential

Twitter has been in the news recently with the CEO change as well as the stock dip. Time for Twitter 2.0 ? Definitely !

Interestingly I had written about Twitter 2.0 in 2011 and most of it is still true ! I will include relevant parts from that blog

For the technically minded who are into the gory details, pl refer to materials from my 2012 OSCON tutorial [Social Network Analysis with Twitter] 

What do campaigns want ?

They want curated inference (which they can directly consume for actionable outcomes) and curated intelligence (for overlaying specialized models over the exposed signals at different orders). All the President’s Data Scientists would have interesting data science models over the Twitter signals. A general model is like so:

Twitter 2.0 – Trajectory & Locus

Now Twitter is an agora for pure message-based interactions; but it has lot more potential – to be a platform (of course,without sacrificing the essential nature of the medium) ! To get there, it needs to be proactive, providing different levels of abstraction – from the basic curated signals to aggregated intelligence (ie trends, positions, sentiments and issues) and the higher level of stratified inference. It also should provide congruences on Twitter to Rest-Of-The-World ie how indicative are the twitter-verse of the general population.

Topic Streams a.k.a TweeTopics

I use Twitter for 3 things – to keep current with topics that interest me, keep in touch with friends & acquaintances and finally publish things that I am interested in – many times as a bookmark !

It is almost impossible to follow topics. The List functionality never worked for me. It should be as easy to follow & unfollow at the level of topics. In the day and age, it is not that hard to run the tweets through a set of analytics engines, cluster them by subjects and offer the topics, with the same semantics as people ! The current interaction semantics are very relevant – that is what makes Twitter Twitter.

There was some thoughts about tweet threading – I think that defeats the purpose; tweets are stateless and that attribute is very important

Twitter is different from facebook and linkedin, it is not a social graph but an interest graph. Many of the traditional network mechanisms & mechanics, like network diameter & degrees of separation, might not make sense. But, others like Cliques and Bipartite Graphs do

Why can’t Twitter expose the interest graph, with appropriate intelligence ?

Topic Spaces a.k.a. TweetSpaces a.k.a TweetRooms

Twitter is the right platform for ad-hoc,ephemeral spaces to exchange quick notes.

This was my observation in 2011, and still it is true.

IM is too heavy weight and not that easy for quick things like “Where is that meeting room” or “Which seat are you in” or “What should we discuss next” et al. A one-to-many exchange, between people who are spatially (and even temporally) in separate spaces. They might in a plane, on a call or even in a hallway! Should be easy to add  a “!” tag, and shout the info. Yep, folks need to know what the ! tag is. Actually come to think of it, we could have many types of tags using a lot of the ‘$’,’@’,’%’,’^’,’&’ and ‘*’ characters with different semantics!

Time for a “Tweet Mark-up Language” ?

These are some of my quick thoughts, what says thee ?

An excursion into ranking the NBA with Elo

P.S: Copy of my blog in Linkedin

Ranking and odd making are one of the oldest professions, probably dating to around AD 69 – the romans applying inferences on predicting gladiatorial shows! Fast forward, the recent NBA finals have become more interesting (from a Data Science perspective, of course) after the Cavalier’s Win !

Update (for those who were here before) : The Game 4 win by GSW (See the Update section at the end) shows how Elo adjusts for larger Margin Of Victory without oscillation!

One interesting algorithm is the Elo ranking, which has seen application in chess, computer games, NFL, NBA and Facebook ! In the movie Social Network, Eduardo Saverin writes the Elo on the glass, responding to Mark Zuckerberg’s call for the algorithm – the picture says it all !

An Introduction to Elo:

Leaving Eduardo and Mark Zuckerberg aside for a moment and moving on to the world of LeBron James and Steph Curry, Elo ranks teams or individuals in chess, basketball, computer games et al. The rank goes up or down as one wins or loses.

If a team is expected to win and it wins, the Elo rank goes up by a small amount; the gains are higher when a lower ranking opponent wins against a stronger team, with adjustments made for the margin of victory.

After every season, the rank reverts to a norm of 1505 (for Basketball) – but basketball teams being stable Year-to-Year, the folks at 538 has a distribution of 75% carry over and 25% revert to norm – we won’t deal with this now, but I did check this in my R program

Back to the main feature … NBA

The current NBA is a dream series for Elo – the thrills and chills of Elo can be observed! viz. a good matchup, but definitely a seemingly strong team, winning 1st game as expected; and boom, games 2 & 3 won by the (not so) weaker team !

You can see (below) the Elo stats at it’s best viz. capturing the transition, giving credit (and higher ranking) where it is due

I had done Elo for NFL, but wasn’t going to try NBA after game 1, but now lit ooks like a good exercise in data algorithmics …

Fortunately Nate Silver & his team has curated the basketball data from 1946 and explained their methodology. Thanks Guys.

I downloaded the data and did some R programming.

An ugly graph plotting Elo rating for the 2015 season for GSW (black) & CLE (blue).

We can definitely see that GSW is the stronger team, but CLE (Cavaliers) is getting stronger recently – especially as it wins over stronger teams.

Let us trace the stats summary ie the Elo rating of the teams, the point spread predictions, the actual score and the response from the algorithm ….

Stuff that brings tears to the eyes of a Data Scientist !

  • Going to Game 1, Elo said – GSW : 1802; CLE : 1712 ; Point Spread : GSW by 6.78 points. Actual – GSW by 8 points
  • Nothing fancy; the Elo ranking of GSW goes up by a little, CLE goes down a little
  • Going to Game 2, Elo said – GSW : 1806; CLE : 1708 ;Point Spread : GSW by 7.07 points. Actual – CLE by 2 points
  • Now, Elo kicks in ! CLE gains higher Elo (because they won over a stronger team), GSW loses more
  • Going to Game 3, Elo said – GSW : 1798; CLE : 1716 ;Point Spread : GSW by 2.92 points. Actual – CLE by 5 points
  • GSW’s Elo goes down; CLE’s future brightens; GSW still has a slim lead
  • Going to Game 4, Elo says – GSW : 1791; CLE : 1723 ;Point Spread : GSW by 2.3 points ! <- We are here (June 10,2015)

I will update with more Elo stats after Games 4,5,6 & 7 … (am sure it has the possibility to go to 7!)

6/11/15 : See Updates below

Incidentally Nate Silver’s tweets have an unintended consequence ! They are motivating Steph ! I am hoping this is the beginning of GSW’s path to a title …


  • [Update 6/11/15 10:31 PM ] Actual : GSW by 21 Points !
  • Nate’s Tweets worked !
  • It is instructive to see the Elo graph. Even though the point spread (21 points) is much larger (than 2 & 5 points from earlier games) the Elo doesn’t go up by a huge amount. This is good, because we don’t want Elo to oscillate, but still should account for the larger than normal point spread. The Margin Of Victory multiplier adjusts that. Interesting to see the graph below, as Nate says it, in one game GSW regained their old position.
  • Going to Game 5, Elo says – GSW : 1810; CLE : 1704 ;Point Spread : GSW by 7.35 points (with home court advantage-refer to the formula (above) for details) ! <- Now, we are here (June 11,2015)
  • [Update 6/14/15] Game 5, GSW by 13 !
  • Going to Game 6, Elo says – GSW : 1814; CLE : 1701 ;Point Spread : GSW by 4.04 points (without home court advantage-refer to the formula (above) for details) ! <- Now, we are here (June 14,2015)



Of Byzantine Failures,unintended consequences & Architecture Heuristics

P.S: Copy of my blog in Linkedin

Way …. back in 2007, I gave a talk on Architecture Heuristics – we talked about Byzantine failures, systems with strong bones and the politics of systems architectures.

One would think that all this is way behind us ! Apparently not so ! There is a software bug in 787 GCU ! The root cause – yep you guessed it, integer overflow !

The plane’s electrical generators fall into a failsafe mode if kept continuously powered on for 248 days. The 787 has four such main generator-control units that, if powered on at the same time, could fail simultaneously and cause a complete electrical shutdown

And self-parking car hits pedestrians because …

Keeping the car safe is included as a standard feature, but keeping pedestrians safe isn’t. …

Interesting … whatever happened to the prime directive ? And Pedestrian Recognition – an option in self parking cars ? What next ? Steering wheel as an option ?

And, we keep on building machines that are software intensive ! Ford GT has more code than a 787 !

Back to Architecture Heuristics …

  1. Select technologies that you can dance with & Be flexible in scaling as you grow
  2. Embrace Failure & Influence Scalability
  3. Build systems with good bones (my slides from 2007 sill look relevant!)
  4. Solve the right problems
  5. While we build complex AI systems, remember that our ingenuity is hard to beat – even by the smart machines that we build !
  6. And, those who don’t learn from the history should read these recommendations, they are still valid !
  7. … Of course, pay that extra $3,000 and buy the Pedestrian Detection – you might drive the car in this world (where we humans reside – at least for now) not in Mars !

Take Care of the Ball, Value every Possession & Protect the Rim

P.S: Copy of my blog in Linkedin

CurryWas watching the NBA Western Conference Finals; the Warriors Team, Coach Kerr & Stephen Curry all are inspiration not only for Sports but also for the startup world.

I picked up a few insightful quotations from the post-game conference … will let you fill-in the inferences & lessons to keep this blog short …

Agility & Nimbleness : What I like most about the Warriors, is the way they morph & raise to the occasion. They find ways to reorganize & adapt against different teams … time will tell how they will do against LeBron and the Cleveland Gang … but for now, they are very effective …

[Update 5/24/15] Interestingly, today Tim Kawakami expressed the same sentiment in his blog at San Jose Mercury News !

“Take Care of the ball, value every possession & protect the rim” – Steve Kerr. Lot of truth in this statement … for life and business …

Steve Kerr about Harden “He sees every angle and we try to close as many of them as we can …”. That is all what we need to do in business to get ahead. The talented will make the shots, under any circumstances, like Kobe says … (er, tweets)

So be comfortable in taking those difficult shots !

Lesson for the Rockets : “Don’t play around the edges, play in the paint” echoed by Kevin McHale “Win the paint & win the board” … So true in sports and in startup business …

Curry Flurry : “Stephen Curry is very patient & will let the offense come to him ! Then he starts !” – In game 3 he had 40 points but only one in 1st quarter ! Once he got the offense, he flawlessly executed his characteristic “confidence & smoothness of the shots” …

In short, “Steph”, Kerr said “was Steph” !

BTW, don’t count the Houston Rockets out yet ! Against all odds, they won against LA Cilppers; Harden & Kevin McHale have a way with adversities …

And on another note, I need to update my NFL/ELO blog to applying ELO in BasketBall …

Reference for material & pictures:


Data Science is the new Electronics

P.S: This is a copy of my blog in Linkedin.


A good friend of mine asked me “What exactly is this Data Science”?

That got me thinking – we have tons of blogs on “Who or What is a Data Scientist” including mine.

One can explain the intuition behind Data Science, the pragmas of the profession, but not the essence !

Then I remembered an engineer on a flight to Tokyo, who was at 61G, I was 61H. It was years ago, probably a lot more years than many (or most) of the readers would remember. I asked him what he was doing and his answer was “Helping companies to embed electronics in their products!”. I remember when autos had no electrical circuits except for the lights. Then came ignition electronics, engine electronics and now powerful computers that control almost all functions; except, of course, to roll where we still need old-fashioned wheels & tires !

We are at that stage with Data Science, where the three Amigos of Data Science(Intelligence, Inference and Interface) can be embedded in enterprise systems increasing their capabilities that far exceed the current ones !

We can really build adaptive systems .. not descriptive, not reactive but truly adaptive, that have malleable intelligence instead of the brittle newtonian rules !

As Sonny Elliot would say – Exactically!

Exactically similar to Electronics some years ago ! Now is the time to think Data Science as embeddable modules with Intelligence/Inference at the systems level and interesting Interfaces for the users …

And that, probably, is the mission of Data Scientists …

If they choose to accept … This blog could self-destruct in 5 seconds …5…4…3…2

Data Science with Spark on the Databricks Cloud – Training at SparkSummit (East)

DataSci-03-P24We had a good Data Science training session in Sheraton, Times Square, NY; second day of SparkSummit (East). It was my privilege to co-author and lead the Data Science track, along with Reza, Paco, Andy, Hossein, TD,Joseph and Xiangrui. I have shared the slideset at Slideshare as well as at the Databricks site.

[Update 4/12/15] : The video is posted at Youtube (5hrs!)

This was the second time I was involved with a training fully based off of the Databricks cloud and it worked out very well ! The Databricks cloud was very robust and resilient. Unfortunately we had problems with the wireless at the Sheraton Hotel !DataSci-03-P27
The training was a mixture of hands-on and lecture.We sterted out with a dataset of 30 records and then moved onto the titanic dataset (900) to the movielens medium (1,000,000) and finally with the RecSyschallenge dataset (33,000,000!). What a progression in a day !

You can see the details in the slides. Ping me if you have any questions.

DataSci-03-P28Data wrangling over the RecSysChallenge 2015 data captures the essence of the Databricks cloud. I will quickly cover the RecSys Challenge dataset as an illustration.

The training data consists of 33,003,944 clicks and 1,150,753 buys. Our mission, if we choose to accept is to predict the session-items bought from a test dataset of 8,251,791 clicks.

A quick data exploration workflowdbc-01:



All at scale, in an elastic cloud, seamlessly moving between dev, model, stage and prod ! The magic of Databricks Cloud !

BTW, we also explored the State Of the Union Speeches from Washington, Lincoln, FDR, Clinton, Bush & Obama. The graphs below show a succinct view of the mood of the nation at each periods …


And finally after 100 slides later …!