Week 11

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The Meaning of AlphaGo

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Following the heights scaled by AlphaGo last week has illustrated the opposite, the depths of which human beings are capable in the indiscriminate attacks in Belgium with its own horrible and twisted logic:

Merely killing passers-by serves no warlike purpose in itself. The explosive force derives from our reaction to it, from the public attention awarded to it and from the response of the political community.

There’s little to be gained by adding further to the mass of articles on why it was done and what can be done about it.  It is however worth reflecting upon the events and considering whether the concerns that some commentators have about the ability for artificial intelligence to impact the course of human evolution are overplaying the human hand.  Perhaps there are some aspects to human behaviour we could do with a greater sense of urgency on addressing and which we are in danger of bestowing upon our creations as this week’s unfortunate incident with Microsoft’s “racist” AI revealed only too plainly:

This blog is going to continue to focus on our collective achievements with science and technology. Following on from the famous victory over Lee Sedol, Demis Hassabis published a blog post on “what we learned in Seoul with AlphaGo” .  Relatedly, another must-read post included an interview with the legendary Geoff Hinton, the “godfather of neural networks” and creator of backpropagation also now at Google (of course) musing on progress, timescales and obstacles:

My belief is that we’re not going to get human-level abilities until we have systems that have the same number of parameters in them as the brain. So in the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses. You have about 1,000-trillion synapses—10 to the 15, it’s a very big number. So that’s quite unlike the neural networks we have right now. They’re far, far smaller, the biggest ones we have right now have about a billion synapses. That’s about a million times smaller than the brain.

The kind of Deep Learning technology that helped turn AlphaGo into a grandmaster albeit using hypercharged training and huge compute capacity is still rather obtruse for the layman to access but that is likely to change in the coming years.  Key steps to mainstreaming the tech were arguably made this week with the announcement by Google of a user-friendly set of “Cloud Learning” APIs and (crucially) pre-trained models for speech recognition etc are to be made available for general purpose use within Google’s Cloud Platform (GCP):

Further evidence that Google are taking their Machine Learning responsibilities seriously is clearly evident in the decision to stop pursuing humanoid robot development and stick to software instead.

The consequence of all this advance is that the universal basic income (UBI) argument is going to become an ever more important in the coming years.  This Medium post suggests it will emerge as mainstream once we have learned the “lesson of our lives”:

No nation is yet ready for the changes ahead. High labor force non-participation leads to social instability, and a lack of consumers within consumer economies leads to economic instability. So let’s ask ourselves, what’s the purpose of the technologies we’re creating? What’s the purpose of a car that can drive for us, or artificial intelligence that can shoulder 60% of our workload? Is it to allow us to work more hours for even less pay? Or is it to enable us to choose how we work, and to decline any pay/hours we deem insufficient because we’re already earning the incomes that machines aren’t? … What’s the big lesson to learn, in a century when machines can learn?  I offer it’s that jobs are for machines, and life is for people.

Life in the post-work economy will need to address those who wish to cling to an increasingly shaky capitalist hegemony that will likely remain implacably opposed to anything approaching UBI which could end up framing the primary political battleground of the next generation:

The fact is that capitalism—with its tendency to income inequality, information monopolies, and financial power—is running out of steam. It’s time to start thinking about something new.

The Internet of Things and Robots


Now Fitbit users can hear praise for their daily activity from a human voice (and in full sentences), though a new integration with Amazon Echo’s digital assistant Alexa.

Android Wear is poised to become the only viable OS not just for the fashion industry’s smartwatches, but for the entire fashion industry at large.

Apps and Services

  • How the Dead Live on in Facebook and will inevitably outnumber the living one day.  When they do it will have profound and disturbing consequences for those left behind who risk mental confusion attempting to distinguish between reality and fiction:

The numbers of the dead on Facebook are growing fast. By 2012, just eight years after the platform was launched, 30 million users with Facebook accounts had died. That number has only gone up since. Some estimates claim more than 8,000 users die each day.

In Facebook, all places are present, all times are now. [The dead exist] in this medium just as I do. … There’s no moving on without any of the millions of dead Facebook users.  ,,, As of yet, there’s no good solution to the problem of dead data, of digital ghosts. The only hope is that the internet’s memory will at some point begin to fade.

The social network is changing how we experience death (Credit:Getty Images)

  • TechCrunch have released a new AI-powered newsbot on Telegram powered by the Chatfuel platform.  It comes across as a little limited in terms of scope since it only takes articles from one source.  A more catholic version would be interesting to see and would make this blog easier to pull together:

TechCrunch Telegram bot in action

Cloud Computing

Cloud Shell session

Devices and Manufacturers

We aren’t done with the mobile revolution. But we are mostly done with it in the developed world.  … the thing that is particularly exciting about new services in the developing world is that they may come with fundamentally new business models. And, it turns out, new business models are even more disruptive than new technologies.


Autonomous Vehicles

  • And right on cue, an excellent blog post on hacking a Tesla S electric car reveals it to be an “enterprise computing system on wheels” which incorporates some really good design decisions notably the inclusion of a first class FOTA update mechanism as well as separation of security domains:

Some reverse engineering of this service showed that the Tesla Model S does NOT seem to send raw CAN frames from the infotainment system to the vehicle. Instead, there is a Vehicle API (VAPI) whereby the CID asks the gateway to perform any one of an “allowed” set of actions.





Software Engineering

  • The Stack Overflow 2016 developer survey is essential reading for anyone who works in tech. In amongst the surfeit of coding language breakdowns is this shocking stat demonstrating the “other” technological Singularity the tech industry faces, one based on gender:



  • Data structures in C seems to betray the influence and impact of of dynamically-typed languages.


  • The Makers of Modern China:

Startups and Work

none of those companies had signed contracts, merely “letters of intent”, which did not commit them to anything. One senior figure in the company told me that young inexperienced sales staff were rewarded with a £2,000 bonus every time one of these letters was signed “so they weren’t particularly concerned about the quality of the deal”.


systemic, global, and amorphous problems … are solvable, but not easily, and not immediately. It is much easier to find someone to blame, which is why we are seeing the return to acceptability of xenophobia and jingoism. Trump on Muslims and Hispanics. Putin on Ukrainians. Erdoğan on Kurds. And the list goes on. The slope from here to scapegoating all dissenters as “enemies of the state” is proving frighteningly slippery.

  • The WashPo transcript of Donald Trump’s meeting with their editorial board is something of a car crash.  It reveals Trump to be extraordinarily fixated with perceived slights and negative reporting at the hands of journalists.  He appears to be more interested in focussing on that than ISIS.  He also seems to change his mind and blow hot, cold and everything in between (or more charitably be very unclear about the detail of his position) multiple times within the interview sometimes within the same response.

In the episode, Bart Simpson gets a glimpse of his future only to find that he’s pretty much a loser, while his sister Lisa has become the president of the US. She calls a meeting in the Oval Office to assess the damage done by her predecessor, Trump; an advisor holds up an plummeting line chart and explains, “We’re broke!”  … The idea of a Trump presidency came about when the writers needed to invent a world in which “everything went as bad as it possibly could”.


Society and Culture

Last year, frustrated, sixth-grader Madeline Messer analyzed 50 popular video games and found that 98% came with built-in boy characters, compared to only 46% that offered girl characters.

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