🔮 The serious issue: after Trump, reforming Facebook, deep learning, dogs++ #87

The Exponential View

On Donald Trump’s victory. Facebook, media and discourse. The state of the global village. Future silicon architectures. Deep learning learns to rap. Robots and the Rubik’s cube. The origin of dogs

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Dept of the near future

🌍 EV subscriber, Saul Klein publishes his Majority World Report for 2016. EXCELLENT slide presentation on the macro-challenges and opportunities of the coming few years.

🌟 EV reader, Maciej Olpinski, presents Userfeeds.io, a content ranking and reputation system based on shared economic interests. Somewhat technical but some interesting early ideas. Think Pagerank meets blockchain vs Facebook. THOUGHT PROVOKING

🇪🇺 How to reform Europe:  why only a twin track approach can work. Very THOUGHTFUL, from Eric Beinhocker, one of my favourite thinkers. (Twenty minute read.)

🔮 Ryan Avent and I in conversation over the future of the political economy and the prospects for work and employment. Recorded before the election, the Exponential View podcast covers many of the issues that underlie this week’s result.  Repost from last week. MUST LISTEN

💻 How the system-on-a-chip will displace the CPU:  “Form-factor, cost and power-per-function are now critical drivers in the mobile market and that in turn has increased the importance of on-chip integration.” INSIGHTFUL

Dept of Facebook, social media & discourse

Mark Zuckerberg doesn’t believe that content on Facebook can influence how people vote. But at the same time, the company contends that advertising on Facebook does influence what people will buy.

That is a tightrope to walk. Or as a friend tweeted: “Awwwkwaaard!”

Read Zuckerberg’s statement here.  Doc Searls’s comment on the statement is also worth looking at.  Pew Research’s data disagrees with Zuckerberg too.

⭐️ Niemanlab: “The forces that drove this election’s media failure are likely to get worse.

🗣 Emily Bell: “Facebook can no longer be ‘I didn’t do it’ boy of global media

🤖 Tim O'Reilly on how Facebook’s chase for engagement puts the truth second, and what the firm needs to do about this.

The problem that Facebook faces, as we have discussed in many previous issues, is to reconcile its role as dominant media platform with its objective of serving engaged users to advertisers.

Engagement (which ultimately drives strategic financial value) is easy to enumerate and target.

Truth, diversity, serendipity, civil discourse, reason, empathy, all things we might want our civic discourse to manifest are just much harder to make explicit. And so they are much harder to build. And it’s not clear they lead to more dollars from Unilever, American Express or Nestle compared to the current strategy of optimising content to drive user engagement.

Short-term incentives are misaligned with the long-term benefits of civil society. Worse, the gains are taking by the producers, in this case, Facebook’s owners, while the costs are borne by society. (Seem familiar?)

Could Facebook tune the newsfeed another way? Yes, of course, it could. It could optimise its newsfeed by allocating more weight to a user reading a story from sources that had broad and diverse (and long-standing) trust signals. Or it could reward the algorithm for showing me some stories on topics read by people on the other side of the interest graph from me. Or liked by people who sometimes, but not always, disagreed with me. Or it could put greater weight on items read or promoted by people based on their Scientific h-index.

Facebook chooses not to. Is that the right decision or the wrong one? We can discuss that. But what seems certain is that these choices are Facebook’s to make.

Note: PeerIndex built an algorithmic curation engine back in 2009 to its acquisition in 2014 and we contended with many of the issues of trust, credibility, filter bubbles, serendipity and discovery. We learnt that sorting signals across 300m+ users per month was not an easy technical task, but that it was possible. But ultimately meta-editorial judgement was required to determine our specific technical implementations (what objective you target, what features you evaluate, how you weight them and what errors you allow.)

Dept of Trump

The recent US election is so important that I am sharing a few interesting perspectives below.

On what happens next

On voting dimensions and the campaign

On technology and economics:

Dept of Artificial Intelligence

DeepMind shows off a system that ‘learns the laws of physics’

Generating dope rhymes with deep learning. NSFW language.

🚗 Tesla’s own numbers may show Autopilot has a higher crash rate than human drivers.

Machine learning system can predict suicidal behaviour

Exponential View ❤️ San Francisco - Salon

Very excited about having my first event in San Francisco. I’m excited to have teamed up with our friends at Lux Capital and Silicon Valley Bank to hold the discussion.

I’ll be talking about technology, automation and society. All the more relevant since the election result.

The event is on December 1st at 1800 in WeWork, Taylor St.

There is huge demand so please apply for a ticket.

Short morsels to appear smart at dinner parties

How Amazon Web Services build an $11bn run rate business.

The sales during Alibaba’s Singles Day are astounding

A fifth of the world’s vacuums are now robots.

Why diverse teams are smarter: a greater fact orientation and more innovation

Wind power approaches 5c per kWh

A robot solves a Rubik’s cube in less than a second.

Finnish schools scrap subjects in favour of topics.

🐕 Our best friends: on the origin of dogs

End note

I want to give you heads up on our last Exponential View salon of the year.  It is in London on December 5th, on the Future of Longevity.

**Register early interest now. **

This week was heavier on the politics and lighter on exponential issues. I think the  election called for it.

Have a great week!