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business models, strategies and technologies

Rethinking rethinking, in digital dimensions

Ideas – and the mindset behind them – are the driving force behind radically new business models. The non-digital idea has to be fully viable before it can be digitally implemented

Scalability and success

The digital revolution has brought us an exponential expansion of many capabilities – in industry, business and entertainment, and pretty much everywhere else (whether we realise it or not). For many digitally-anchored services, it costs just about the same to roll out to one customer and one million customers, and web-based geographical reach is normally only a marginal cost. In digitally-anchored manufacturing, item number one costs pretty much the same as item number one million.

Automation, robot manufacturing and artificial intelligence will probably strengthen and accelerate this entire process, paving the way to significant new business opportunities – if they’re conceived right.

The cognitive edge

Effective exploitation of digital capabilities – and their role in implementing innovative business ideas – hinges entirely on the viability of the ideas and the mindset at their core. Digitally based capabilities are fundamentally generic, replicable and scalable – whereas it’s the ideas behind them that mould, plan and refine these capabilities into the much-vaunted competitive edge that builds business difference that’s more than just turning a quick buck.

Our ability to tackle business challenges creatively, and to whack together multiple ideas and technologies to sculpt innovative solutions for often-complicated problems, is crucial – and has to be backed by people’s ability to actually get the ideas implemented. Our biggest challenge lies in how we deal with the relentless, accelerating pace of technological innovation.

That’s why applied brainpower and cognitive flexibility (fancy words for agile thinking, really) are crucial for business development, to keep ahead of the digital wavefront. This is the one field in which computers cannot yet match us mere mortals. We have to design strategies, processes, job functions and business models based on an awareness that every aspect of tomorrow’s business practices and technology capabilities can be dramatically different from those of today.

So it’s perplexing to note that clear renditions of pivotal business ideas – the ultimate differentiators – so rarely feature on corporate websites and statement materials, which tend to drown in descriptions and detail of mere implementation. Easy way out … Companies where the customer can’t see the wood for the trees, or the real benefits, aren’t much of a breakthrough.

Upping the AI ante

The one field in which computers cannot yet match us mere mortals? Or so it was thought.

But in March 2016 AlphaGo, an artificially intelligent computing system built by Google researchers, made a seemingly incredible 37th move in a Go match against between Lee Sedol, 1 8-time world champion and one of the absolute top players of this ancient Chinese board game. Go is played on a 19-by-19 grid and is exponentially more complex than chess, with more possible move configurations (10761 apparently) than there are atoms in the universe, and therefore long considered one of the last remaining redoubts of human supremacy over artificial intelligence. AlphaGo then went on to win the third straight game and take the Google DeepMind Challenge with a historic 3–0 victory over the human world champion.

The win itself was an epochal breakthrough for an artificial intelligence programme that has achieved unprecedented results using an advanced system based on deep neural networks and machine learning.

It’s not a human move. I’ve never seen a human play this move

said Fan Hui, the first top Go player to challenge AlphaGo, referring to the 37th move. Which was so unexpected and unprecedented that it left experienced commentators gasping for ways to even describe or evaluate it – an equally unprecedented situation in a game that has been played assiduously for more than 2,500 years. Some wondered whether it was a technical error, some (including Fan Hui) considered it “beautiful”. The computer’s opponent, Lee Sedol, had to leave the board in complete shock. AlphaGo had broken – nay, shattered – the mould of cognitive capabilities. So perhaps the cognitive edge is shrinking, too.

In the arcane world of AI, creating “general” or multi-purpose intelligence, rather than “narrow”, task-specific capabilities is the ultimate goal, corresponding to something resembling human reasoning based on inputs of different kinds and – a crucial consideration – self-learning.

“Because the methods we have used to build AlphaGo are general purpose, our hope is that in the long-run we will be able to use these techniques for many other problems,” said DeepMind CEO Demis Hassabis.

PS A delightful denouement to this story is that on 13 March 2016, Lee Sedol won a fourth follow-up after a dramatic match that lasted nearly five hours. There seems to be a glimmer of hope for the human je-ne-sais-quoi after all, even though AlphaGo ended by winning four games out of five in this landmark meeting of minds from different mothers.






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