Discussions We Will Have in 2016
The future is complex. It arrives quickly like strong wave or slowly like a gradual tide, dependent on existing contexts. The pace of successful, new applications and businesses is dependent not only on established infrastructures, install bases, and technological capabilities, but cultural settings as well.
While considering what 2016 might have in store for us technologically, I am struck by the number of cultural live-wires awaiting us. There is always a gap between what we can do, what we understand, and what we’re comfortable with, but today gap seems wider than usual.
Over the last decade, people have acclimitized to the mobile/social norm. But this mass adoption — a Great Digital Onboarding—has staged the population into a network ready for computation and analysis. As this network has matured into a stable, more stagnant ecosystem dominated by Google, Facebook and Apple, we will begin to see new companies not building applications for people to join, but applications for analyzing the people already present. This is a shocking shift. The apparatus of technology will not be presented as a space for people, but as a mechanism pointed directly at them.
This change is why I believe the gap between capability and comprehension will snap back. As successful applications for processing our digital selves emerge, conversations will be kicked off in an attempt to negotiate a comfortable future.
Here are two conversations I believe we will have in 2016, due to the shift described above:
Two major, technical themes of 2015 were the ubiquity of cameras and deep learning. The impact of having cameras everywhere (especially on police officers and in cars) is already a major cultural conversation, but it will mutate in 2016 when mixed with deep learning capabilities.
In a nutshell, deep learning is human recognition at computer scale. The first step to create an algorithm is providing a program with lots and lots of data which has been organized by humans, like tagged photos. The programs then analyzes the bits of the raw data and notes patterns which correlate with the human organized data. These program then looks for these known patterns in the wild. This is how Facebook suggests friends to tag in photos and Google Photos searches by people.
So far, most of the deep learning applications people use are essentially toys: smarter photo albums and better speech recognition. These early applications are forgiving. If a learning algorithm misses a face or forces you edit a tricky word, it’s okay (usually). But as our investment continues and these algorithms become more dependable we’ll see them deployed in more interesting environments, with more interesting use cases.
Like police body cameras, which can recognize faces and license plates. Or security cameras in LA which note license plates seen near known prostitution hotspots, then main their owners threatening letters.
We will see many more of these morally tricky applications in 2016. The cameras are already deployed and image and facial recognition algorithms run easily on old, commodity hardware. The gap between what we can do and our understanding is massive in this arena.
Portable Reputation Systems
One story we didn’t discuss enough in 2015 was Peeple, the much maligned “Yelp, but for humans.” If you were offline in late September, here’s the Washington Post with the recap:
When the app does launch, probably in late November, you will be able to assign reviews and one- to five-star ratings to everyone you know: your exes, your co-workers, the old guy who lives next door. You can’t opt out — once someone puts your name in the Peeple system, it’s there unless you violate the site’s terms of service. And you can’t delete bad or biased reviews — that would defeat the whole purpose.
Imagine every interaction you’ve ever had suddenly open to the scrutiny of the Internet public.
The backlash was fast, furious, and the app was pulled. But sadly, our discussion almost exclusively focused on Peeple itself and it’s tone deaf founders. We barely touched on the idea of a open, network-powered, portable reputation system. An idea which seems to be inevitable.
Here are some reputation systems you likely already use:
- Product and service review systems, like Amazon and Yelp. These systems let us rank reviewers by helpfulness.
- Dating services, like Tinder. These systems use Facebook (friends of friends) and other networks as filter inputs.
- Peer-to-peer marketplaces, like Uber and AirBNB. These systems provide ratings on sellers and customers.
These are reputation systems which rank people and have impacts on our lives. When buying something used of Amazon, I almost always pay a premium to the seller with the highest satisfaction rating. On AirBNB, hosts have the opportunity to reject customers based on their previous behavior. Uber drivers are essentially fired because of your low ratings. We already live in an age where reputation systems affect the value of your money and your labor.
And like the issues we wrung our hands raw over when discussing Peeple are present here as well. Deleting a bad review on Amazon or AirBNB is akin to tilting at windmills. And while we can opt-out of such systems, their increasing ubiquity makes this problematic.
But none of the above systems are discussed with the same malice as Peeple. That is because they’re confined to their ecosystem. They’re not portable. They cannot yet hold a candle to credit score systems, our current ubiquitous reputation system.
But the foundation has been laid for such a networked system. As mentioned, the stability of the current Google, Facebook, Apple identity system online is the first step. As you log in with Google and Facebook in more places, the ability for reputation systems to flow back from their individual domains into a centralized system becomes more feasible. There are governers on this progression (specifically the fact that companies value their reputation data very highly and aren’t going to share it easily) but as we rank each other and new algorithms begin to rank us on their own, the likelihood of these individual pools joining into an ocean becomes significant.
And when a critical mass of data begins to form, it is also likely it will be run by more savvy people than those which launched Peeple. They will highlight the significant benefits that come with such a system. It will be quite a conversation.
Unlike programmatic cameras, this conversation stands to actually influence the future because a portable reputation network stands to replace an existing, flawed system: credit scores. A complex conversation, guided by strong leaders, could usher in a more humanistic system.