New Scientist Artificial Intelligence day – Session One; the Mainstream – Irina Higgins

Irina Higgins is a senior research scientist at DeepMind, and has a background in neuroscience.

The second presentation at this event largely focused on telling a story about DeepMind’s development of AlphaGo – using this as a vehicle to explain DeepMind’s approach and give insights into its culture.

She told us that DeepMind now has 300 scientists, and was keen to emphasise the high-minded aspirations of the organisation – from its mission statement;

Solve intelligence. Use it to make the world a better place.

to its ‘intentionally designed culture’, which aims to mesh the best aspects of industry and academia; the intense focus and resources of the former with the curiosity driven open-ended approach of the latter.

DeepMind’s operating definition of general intelligence is apparently; Continue reading “New Scientist Artificial Intelligence day – Session One; the Mainstream – Irina Higgins” →

New Scientist Artificial Intelligence day – Session One; the Mainstream – Simon Lucas

Simon Lucas is Professor of computer science at the University of Essex. His research is focused on the application of artificial intelligence and machine learning to games, evolutionary computation and pattern recognition.

This was the foundation-laying talk of this event, and it was excellent – a rapid-fire but followable overview of the history and principal themes of AI research and development, and more detail on the approach currently producing the results that have been making headlines – neural networks. There was nothing here that some general reading wouldn’t get you, but it was engagingly and thoroughly presented at speed.

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New Scientist Artificial Intelligence day – Overview

This New Scientist event was aimed at a general interest audience, rather than an expert one, but assumed a relatively high level of general understanding – the presentations were light on technicalities, but not shy of discussing complex ideas. I had booked without looking into the speaker’s details, trusting to New Scientist to deliver, and my trust was over-rewarded, as the presentations provided a wider range of views than I could have imagined.

These notes are provided mostly because a number of people I’ve spoken to since weren’t at the event, but were wishing they had been – they will be a poor substitute for having been there, but will hope to convey the key points and provide some links. I’ve split the event up into several posts – skim the headings and dip in to the parts that interest you – there is no grand overarching story here, folks!

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Startup Equity Distribution – an incremental approach

Equity distribution in early-stage startups is a slightly odd subject. Obviously at this point the startup is worth nothing – or less-than-nothing, if expenses are being recorded as debts on the future company – and who wants to argue about percentage points of nothing? Sometimes the whole subject is just ignored.
On the other hand, whatever the addressable market size of the idea at hand, the spectre of founders squabbling over enormous wealth is lurking somewhere in the subconscious of everyone involved, so it is equally possible to go the other way, and invoke complex calculation methods of one kind or another, however irrationally over-fussy.
While complex approaches are arguably better than failing to address the issue at all, a simpler method is more typically adopted: if there are two founders at the beginning, they are usually assumed to have 50% each, if three, 33 1/3%, etc – as in this Seedcamp agreement template.
If they add additional co-founders, there is a re-distribution by agreement, such that the original co-founders see their percentage ownership reduced, to ‘make room’ for the new partner. The process is repeated each time a new equity-holder is added (ignoring such things as special share types – usually considered as over-complicated at early stages).

I consider that there are several problems with this:

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Project for a Progressive Ethics

I’ve been going to quite a few events recently which broadly come under the heading of futurism – indeed many of them have been through a reliably high quality meetup group actually called London Futurists.

These meetings deal with more-or-less mind-boggling speculations and predictions of things like robots taking all the jobs, artificial intelligences surpassing human capacities, people hacking their own or their children’s biology through genetic or prosthetic modifications, and similar subjects. Sci-fi stuff, you might think …

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Security as an Overhead isn’t working

We’re building a medical app. Of course, Therapy-Smarter isn’t collecting deeply intimate data – just basic contact information, some physiotherapist’s notes, exercise prescriptions and exercise performance data – but nevertheless, medical data is medical data- it’s inherently sensitive, and any company that cares about its reputation needs to take data privacy – and thus data security – very seriously indeed.

So, we’ve been thinking about it fairly hard – but not in a technical way; it’s a specialist domain and we assume that we will need to pay people who know what they are doing to advise us on best practice and then get them to assess our implementation.

No, we’ve been thinking hard about security in terms of business culture, because it seems painfully clear that this is where security weaknesses really come from. That’s right – I’m saying that security weaknesses have much more to do with business culture than they have to do with engineering.

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Convergence product #1

I’ve written about convergence before, and here is NexDock, a slimline laptop which docks to all sorts of computing devices to provide screen real-estate and a physical keyboard.

It’s an IndieGogo project, 56% backed, with a month to go, so if you’re interested, check it out. It’s a cautious first project, but with large ambition. I’m going to back it, and I wish it well!

Developing a Digital idea without Developers

This is in many ways a companion piece to my previous post – it started out as a version for LinkedIn, but rapidly evolved into something with a different emphasis.

The internet revolution has changed the landscape of our lives, and yet the disruption has only just started. Existing ways of doing business are largely unchanged from the way they were 20 years ago. Hilarious disconnects exist all over, when ultra-slick digital-only processes crash into messy physical transactions.

There is a reason for this. Coders like to code – they like the safe, ordered, complicated-but-not-truly-complex world of programming. And coders are the ones who feel empowered to invent digital businesses. So, of course, the early digital businesses are the ones that can be achieved with purely digital workflows, and don’t require the startup team ever to leave their own world.

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Using FilemakerPro as a RAD platform just got more interesting

I’ve mentioned before that we’ve used FPro as a type of Rapid Application Development Platform. Building a fully functioning prototype using this high-level (largely graphical) database application has allowed us, as non-coders (albeit with a decent understanding of how a properly architected database should be structured), to develop our therapy-smarter tool in an iterative way, without spending large amounts of money.

Another benefit is that the product we are using remains fully accessible to us at the architecture and data level, and is largely self-documenting. FilemakerPro produces human-readable graphical representations of its database structure as a standard report and the functional scripts are basic-like in their use of real language, and thus easy to follow.

What this means is that, for a team like ours, without coders, the scary step of commissioning native code from people whose work we will ultimately not be able to judge in detail is made much less risky. When we judge that the time is right to take the system to the next level – when we need it to go faster and handle more clients, we will have to get the thing recoded – and this will mean bringing developers in.

For startup teams without coders, this is a terrifying point. How do you find the right person? How do you choose which framework, which language, which platform, which architecture? How can you even begin to judge the recommendations you are hearing? How can you describe the features you want?

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Semantic Scholar trawls unread scientific papers

New Scientist reports (pay barrier, sorry) that the Seattle based Allen Institute for Artificial Intelligence (AI2) has launched a tool called Semantic Scholar, which aims to;

…read, digest and categorise findings from the estimated 2 million papers published each year.

The article goes on to say;

Up to half of these are never read by more than three people.

That’s right. One million scientific papers a year, read by only three people each. That’s some sort of sad exemplar for futility.