In the 'Online Video' section he calls out podcasts as an emerging/trending medium. I'd love to hear more about where people in HN community feel it's moving. Of course there's the obvious Serial momentum, and as a passionate consumer of podcasts I'd love to think through with you guys a little more where we think it'll go. For example a YouTube-like podcast portal seems like a potential option (i.e. moving away from…
The subscription/download orientation of podcasting is so ridiculous. With music and video you just find what you want and consume. I don't want to subscribe and I don't want to download!
16 Things
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#32Re: 16 Things
#33Re: 16 Things
#34Maybe it's an attempt to influence entrepreneurs to create ideas in those 16 areas.
I mean, only those 16 areas? We are in the middle of some unprecedented cultural shifts, and those are the 16 areas to focus on?
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#35Re: 16 Things
#36Bitcoin is the interesting one, as I would love to know in 10 years time if we look back at that with a wry smile as a fad, or if it ends up being something everyone uses.
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#37Notably missing: drones. This is a seriously expanding new industry.
The majority of their portfolio companies are based in the US, where regulation will likely stifle the growth of commercial drones, at least during 2015.
Re: 16 Things
#381. Offline Big Data - This is mostly the ETL crowd - Scalding, Cascading, Spark & associated novel startups, who provide technology to run Map Reduce jobs on TBs & PBs of data. This isn't going away anytime soon. Investment Banks & enterprise, financial institutions are the big customers with risk analysis( Var, CVar) & large scale monte-carlo scenarios on diverse financial instruments being commonplace.
2. Online Big Data - Storm, Summingbird & friends - continually ingesting high volume realtime data streams to provide realtime insights, which can be substantiated by #1 later, as and when those jobs run. For eg. say you ingest tweets realtime via a Storm pipeline & give me a running time series of how many tweets were from which city. Meanwhile, you squirrel away these tweets in hdfs so the offline MR job runs later & gives you exact counts.
3. Small-data ML - The result of #1 is typically a dataset of modest size ( few MB - few GB ) that can be ingested into your favorite ML solution ( too numerous to mention) for predictive analysis & BI purposes.
4. Soft "AI" - Using #2 + #3 in intelligent ad serving, traffic routing, realtime pricing to match inventory ( eg. there are several hotels in Las Vegas who reprice rooms based on number of passengers from commercial flights arriving into Vegas, local weather (sunny,rainy etc.), industry convention dates & such - all the ML + AI done out of a tiny office in SF), electricity regulation (https://news.ycombinator.com/item?id=8280315) etc.
5. AI without the quotes - tiny startups using rnn's to predict time series, using cnn's for image captioning & other really nifty AI applications not currently commercially exploitable at scale but definitely primed for acqui-hire.
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#39Y Combinator breaks the two out as two parts of the same RFS [1]
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#40Considering how much is happening in the 3D printing space this year, I'm surprised that it isn't on this list. It is an enabler of IoT and Crowdfunding, drones, and even the "Sensorification of the Enterprise". Cdixon even retweeted this recently: "Holograms are like print preview for 3D printing." Also where is Drones on this list?
But at the end of the day, it's still a16z list and subject to their interpretations.