car stereo + internet

 Posted by on December 16, 2010 at 15:55  internet  1 Response »
Dec 162010

car-stereoI want a car stereo with a mobile broadband connection, receiver and a separate interface to customize my car audio experience from the web – customized algorithmically but with a layer of individual curation to bound the algorithms.

Traffic and weather “channels” will push content (broadcast signal or web streaming) to me based on my location (mobile triangulation is precise enough or GPS can provide) and future location (partially based on the Google Maps query I did last night). Algorithms will push “station one” to me if it is 8:00 and that station does traffic at the top of the hour, or push station two if their time better matches, or push the most recent cached update if none matches, or push me the station with the highest ratings for traffic. When Twitter and similar data is better curated, I can listen to tweets about an accident that just happened at an intersection that I’m due to hit in ten minutes.

My favorite music will be pulled in from Internet radio services like Pandora. If I want to surf randomly, but based on my preferences, other stations will pull in local broadcast stations that meet my criteria. Programmed, smart “seek”. Same for sports, news, weather etc. Similar model for all other content in the cloud – podcasts, audio books, MP3s etc. Ditto for other content that I may want in audio form – voicemails, texts converted to voice, etc.

The algorithms that build my channels will incorporate social networking and social graph, recommendation engines and crowdsourcing, along with my preferences and curation. Channels that feature content that is most in play in my social network. Or least in play if that’s my persuasion. Brand new content that I might not know about but is recommended based on my preferences, listening history and social graph. Etc. When my wife drives my car, she switches to her profile – as long as I’m not a passenger ; ). And that’s just from a non-professional driver’s perspective – there are more interesting use cases for professional drivers, from trucking to FedEx to taxis.

I think we’ll see this model of individual customized pushes of slices of content in many other areas too, e.g. TV, as a specific type of signal to noise solution via web services, but think specific use cases like car audio experience could be the first to develop with the least barriers in the way and the most opportunity for the various players.

Dec 152010

“We need to apply cognitive psychology, the principles of design, and tighter feedback loops to our own health. Health care needs to have its design Renaissance, where products and services are redesigned to be responsive to human needs and considerate of human frailties.”

That quote is from Aza Raskin’s blog, commenting on why he’s leaving Mozilla to start Massive Health. Very cool and extremely interesting to think about.

I never considered that side of the upcoming healthcare revolution, but it is critical as Aza lists. So layer the design Renaissance on top of medical research democratization, fuel it with the CPUs and all-to-all connectivity of the cloud, and sprinkle in some crowdsourcing and curation, and we have quite a recipe.

signal to noise

 Posted by on December 2, 2010 at 15:08  internet  No Responses »
Dec 022010

bat-signalShare this. Tweet that. Like everything. Noise screaming from every site, page, app and applet. Signal to noise ratio approaching zero.

Everyone is now a publisher. Blogs, tweets, videos, podcasts, wall updates, broadcasts, forums, magazines, movies, ebooks, reviews, comments, aggregations. Long-form to 140-character form. All goodness but lots of Noise.

Signal? Google. Google can get the first page of results “right” most of the time. PageRank has been brilliant but even today falls short and in the future it too will be a dinosaur.

David Segal did a nice job showing one example of a kink in the armor in his Bully Finds a Pulpit on the Web article. Google showed that they can act quickly like a small company while leveraging long-term signal to noise R&D efforts that their large company resources funds by adding some signal to noise algorithms for this specific use case as described by Amit Singhal in this blog post.

This is just one use case. The more general signal to noise development will be fascinating. More on that another time except to list few variables that need to be better developed in the signal to noise algorithms:

+ individual-level, granular, portable reputation
+ the interesection of algorithmic curation, human curation and crowdsourcing
+ social graph intelligence
+ use of presence and location to add metadata automatically
+ feedback loops amongst these