Only Dead Fish

Pattern Interrupts and Creativity

Thanks to Faris for highlighting this extract from Tim Harford's Messy:

Coincidentally, several days later Tim himself shared a link to this fascinating article on how Charles Darwin decided on the books he was going to read. The piece reports on some extensive analysis that researchers have conducted on 665 of the English non-fiction books that Darwin read, looking in particular at his information gathering approach and whether he stayed focused on one subject in order to delve deeper into it, or whether he jumped around to different topics.

Interestingly, the analysis showed that early on Darwin exploited singular subjects but over time (and particularly as it drew near to the writing of his masterpiece On the Origin of the Species) his reading was increasingly characterised by exploration and sporadic jumps to new topics. As Faris notes, pattern interrupts trigger your attention and can enhance creativity.

The Limitations of Machine Learning

I don't claim to be an expert on machine learning and AI but there is certainly no shortage of hyperbole about it right now. But in this case for good reason I think. It's on every trends/prediction list you read but it is surely the comprehensiveness in which it will be integrated into organisational capability, customer experience (and so competitive advantage) that makes this a true mega-trend. As Fred Wilson describes it 'AI will be the new mobile' (and he should know).

So it's both refreshing and insightful to actually find something that talks about the challenges and limitations that machine learning still faces. Just after Christmas Azeem Azhar (whose Exponential View is an excellent regular read on all things AI) tweeted a photo of a slide that neatly captured some of the current limitations of deep learning. Sadly he wasn't able to attribute the source, but the list focused on aspects such as how data-hungry it can be (requiring millions of examples), and how computationally intensive it can be to train and deploy, the fact that it can be limited in its representation of uncertainty and easily fooled by adversarial examples, the complexity in optimisation (needing high degrees of skill and experimentation) and notably the lack of trust that can come from poor transparency and 'uninterpretable black boxes'. 

One of the respondents to Azeem's tweet pointed at this piece by Peter Voss which expands further on current limitations. Whilst current mainstream techniques can be very powerful in narrow domains, they will typically have some or all of a list of constraints that he sets out and which I'll quote in full here:

• Each narrow application needs to be specially trained
• Require large amounts of hand-crafted, structured training data
• Learning must generally be supervised: Training data must be tagged
• Require lengthy offline/ batch training
• Do not learn incrementally or interactively, in real time
• Poor transfer learning ability, re-usability of modules, and integration
• Systems are opaque, making them very hard to debug
• Performance cannot be audited or guaranteed at the ‘long tail’
• They encode correlation, not causation or ontological relationships
• Do not encode entities, or spatial relationships between entities
• Only handle very narrow aspects of natural language
• Not well suited for high-level, symbolic reasoning or planning

Peter usefully goes on to detail a machine learning intelligence checklist of key features.

Reading about the challenges as well as the opportunities with machine learning gives a far more rounded perspective of where we are at, and brings to life the reality of the hurdles we still need to overcome. I've no doubt that many of these challenges will be surmounted in time (perhaps some in short order given the degree of focus on the area) but my overwhelming feeling about this is that it will result in a huge demand for people who can design, build and make sense of this kind of capability. Even if, as an organisation, you can plug into API-accessible machine learning capability or access open-source libraries of machine intelligence (like Tensorflow), you still need to be able to understand where the value is, and design elegant solutions and applications. The past few years has seen a huge growth in demand for data/analytics talent at a time when supply cannot keep up. I think we'll see the same for those skilled in not only the technical side of machine learning, but particularly those who can combine technical expertise with the kind of strategic and empathetic intelligence that can effectively mine the real value that is inherent in this new capability.

Post of the Month - December 2016 - The Winner

A somewhat evenly split vote this month but happy to say that Faris Yakob won through with his excellent take on How Toxic Nostalgia Built the Foundation for 2016. Well done Faris - you join the hall of fame. My thanks to all who took part

Post of the Month - December 2016 - The Vote

Thanks for the nominations. So our vote this month is between:

Imagining other people's lives is still a crucial marketing skill by Craig Mawdsley

The State of Technology at the end of 2016 by Ben Thompson

Just because you think you're a 'native' doesn't mean you're smarter from Martin Weigel

Young Creatives believe they have nothing to learn from older generation - they couldn't be more wrong by Gerry Farrell

‘Member? How Toxic Nostalgia Built the Foundation for 2016 from Faris Yakob

My Love-Hate Relationship with Digital Transformation by Lucia Adams

And you can vote below:

Genius Hesitates

In the brilliant Seven Brief Lessons on Physics which I'm reading right now (thanks to Inaki for the tip) there's a (very accessible) section explaining quantum theory. Building on the work of Max Planck, Einstein wrote a paper showing that light is made of 'packets' of energy. The introduction to the article (essentially the birth of his work on quantum theory for which Einstein would subsequently receive the Nobel Prize) is quoted,  and it begins with the humble words 'It seems to me that...'. This is noted by the 'Seven Brief Lessons' author, Carlo Rovelli:

'Note the wonderful initial "It seems to me...", which recalls the "I think...' with which Darwin introduces in his notebooks the great idea that species evolve, or the 'hesitation' spoken of by Faraday when introducing for the first time the revolutionary idea of magnetic fields.'

Often, it seems, genius hesitates.