brycedotvc:

Bear with me while I connect the dots from a few of the things rattling around the web today.

The guy with the most data wins.

That’s from my Partner Tim in his interview at the Where conference that was posted today. It’s a fantastic interview that covers a wide range of topics, well…

I could not agree with this more. The team that can collect, manage and most importantly understand big data wins.

Personally, I think a startup should include a big data strategy. Without a honest way to collect, grow and understand that data companies will be as companies who ignored the PC, Internet, and mobile revolutions.

Great post on data science.

The video aligns well with how I think of computer science. I break computer science into 3 key attributes:

  1. storage (disk size, memory size, data access & distribution)
  2. understanding (functions, data science and machine learning)
  3. visualization (UI, data visualization, presentation)

More data and more computer science makes for better models and greater understanding.

A well thought out perspective on personalization.

caterpillarcowboy:

TechCrunch published an article yesterday about the challenges of personalization and why no one has been able to innovate beyond what Amazon did 10 years ago. Leena Rao makes a good effort in trying to understand the challenges, mentioning the need for intent-based data, making sense of social, and privacy concerns. All are true. But the framework with which she’s approaching the problem is wrong.

The right way to look at this is by splitting the world of products into two: products that age and products that don’t.

  • Books retain value over time. A book you wanted to read last year is something you’d still consider buying today (hence, the existence of airport bookstores). Same goes for movies, which is why Netflix beat Blockbuster.
  • Fashion items (shoes, clothing, accessories) do not. Softlines (the retail term for fashion items) are extremely seasonal; items go out of style within months and unsold ones end up on the discount rack.

You’ll notice that successful personalization tech is tightly focused around items in the first category. Books, music, video, kitchen appliances, gardening equipment, (to a lesser extent) electronics - all things that Amazon’s recommendation algorithms are good at. (I would know, I was the product manager for that team). That’s because these products have a long enough shelf life to reach a critical mass of purchase data. You need dense datasets to do personalization right.

Where does personalization suck? The second category. To make it even more difficult, items in this category tend to be ones that you can look at and within half a second decide if you like it or not. They are visual, tactile, sensual. They are also highly individual - a watch that I love is also something you might hate, even if we share the same taste in movies. Hell, I might even love one watch but hate another that almost looks exactly the same. People shop in this category by gut feel and emotion, not by attempting to maximize a list of requirements and system specs. The result is a very sparse dataset with items going out of style too fast for the algorithms to become useful. What you end up with is least common denominator recs (like white socks and undershirts) that completely lack joy and delight.

The solution, like Leena points at, is social, although she gets it slightly wrong. I’ll follow up this post with my thoughts on how social can really make personalization work.

A presentation my brother give on using hadoop as a computational environment with focus on genetic sequencing.

Richard Resnick: Welcome to the genomic revolution

Interesting that computational biology is all about computers doing what we have been doing on the web for years. Finding patterns.

Now it’s about the betterment of mankind vs. ad targeting.

brycedotvc:

Algorithms terraforming the earth? You know it must be @slavin_fpo

A fantastic TED talk and great food for thought to start the week.

Ok that is one of the better TED talks I have watched. 

The whole machine learning emergence is really interesting and his Kevin’s talk described and educated well.  Having played in the Netflix challenge and seen how BellKor’s Pragmatic Chaos works is the tip of what will come. 

Some may say “Hello Skynet” others like myself think smart application of this math is hugely transformative, even revolutionary.