Making sense of artificial intelligence


Making sense of artificial intelligence

Phil Whitehouse

When new trends and technologies burst onto the marketing scene, there’s always a frantic effort to either keep up or provide guidance, especially when serious amounts of money are involved. It happened with social media, it happened with personalisation and big data, and it’s happening now with artificial intelligence.

We’re approaching the top of the hype cycle where, like teenage sex, everyone is talking about it but very few are actually doing it. Conditions are perfect for the snake oil salesmen to move in. But there’s real substance behind some of work being done in this field, and in this post I’ll try to navigate through the fog of rhetoric to understand what’s required to make the most of the significant opportunities.

Back to the future

To go forward, let’s first look backwards. AI has been a popular subject in science fiction for decades, often running alongside robotics to create some of the most original storylines of all time. These stretch from the utopian (Star TrekHer and I, Robot*) to the dystopian (Blade Runner2001The Matrix and The Terminator), and everywhere in between. As a result, the term has become attached to the future, framing expectations around the art of the possible in the present.

As with all science fiction, it serves as inspiration for the people working on real-life applications. We still have a way to go before we catch up with fiction, but actual developments have, in their own way, been no less notable.

The Bombe that helped to decipher Enigma in WW2 meets the definition of AI, and it wasn’t the first of its kind. Machine learning has been around since the 1950s, growing alongside the development of computers, and held back mainly by computational power. More recently, as processor growth has followed Moore’s Law, the high-profile milestones have come thicker and faster, from Deep Blue beating Garry Kasparow in 1997 to Watson winning Jeopardy (and now trying to cure cancer), to Google’s DeepMind beating Lee Sedol at Go, through to Facebook’s Bots unveiled at F8 .

When developments in the real world start to mimic science fiction, this gets a lot of attention. Apocryphal headline grabbers such as the Microsoft Barbie Bot will gradually be replaced by the exceptions-that-prove-the-rule, such as the recent incident with Google’s self driving car (a.k.a. Johnny Cabs).

But the really big advances will come when we crack something called general artificial intelligence. Experts concur we’re many years away from that, but in the meantime we can expect an array of intriguing developments.

We’re approaching the top of the hype cycle where, like teenage sex, everyone is talking about it but very few are actually doing it. 

There’s a clamour of investment taking place, usually a good sign of fire behind the smoke. From 2 per cent of VC investment in 2013 to 5 per cent in 2015, interest is significant and growing. Google, Facebook, IBM, Apple, Salesforce, Cisco, Intel and many more have invested hundreds of millions in this space over several years, particularly in the adtech and finance fields. Our parent group, Publicis.Sapient, has bought a minority stake in Wired luminary and all round good guy, Kevin Kelly, says: “The business plans of the next 10,000 startups are easy to forecast: Take X and add AI”.

What makes it AI

So let’s take a step back and ask: What is AI? The dictionary definition suggests it’s a broad church. Arguably, a simple calculator qualifies as artificial intelligence. It’s not surprising then startups, vendors and suppliers claim their products and services include an element of AI. Look out for wagons being hitched to that horse.

One way to start conversations with vendors, partners and potential recruits and employers is by asking them what they mean by the term artificial intelligence. As with big data and innovation, it means different things to different people, and time can be wasted assuming you’re on the same page. We’ll need to develop our lexicon before we can sort the wheat from the chaff.

In the marketing and technology sector, this territory is currently dominated by the data scientists. The challenge of extracting value from huge data sets is in some ways fuelling the interest in AI. The goal here is to make even better use of data to support strategic planning and drive real-time decision making, reducing our dependency on expensive, fallible data scientists and customer support staff, and increasingly automating the next steps without human intervention.

There is already a wide range of examples in this space, from automated pricing to predictive customer care, personalisation of ad targeting, and more. This list from econsultancy of 15 examples is worth a scan.

When people like Elon Musk and Stephen Hawking says there’s a significant danger, it’s time for all of us to sit up and take notice.

One of the more interesting developments is, a service being developed by one of the ambitious Siri co-creators, Dag Kittlaus. Not only does he want to create a cloud-based platform for finding connections between disparate data sets, he wants to put a universally recognised voice interface on it. He’d like his ‘V’ logo to be as ubiquitous as the bluetooth logo, so we know how to engage with the system. Apparently we speak 3-4 times faster than we write, so this makes sense so long as the system isn’t plagued by the same challenges as Siri or Google Now.

When it comes to voice interaction in general, the early signs from Alexa (from Amazon) are very promising, so maybe we’re on the cusp of a breakthrough here.

AI and unexpected problem solving

The broad consensus is that we shouldn’t think of AI as simply improving the tasks we currently perform, either. We should also think about approaching problems in completely different and unexpected ways to achieve much greater outcomes. AlphaGo’s behaviour is a great early example of this.

Reinforcement learning”, or teaching computers how to learn for themselves, appears to be a more fruitful approach than “human teaching” or decision tree mapping, as it’s less constrained by our own human shortcomings. This is also where neural networks, such as this interesting experiment from TensorFlow, come in. In their own words, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. It’s a general principle that applies to one of the most interesting areas of AI.

What all of this says is we don’t know exactly where the emergence of artificial intelligence will take us, which makes the whole venture so exciting. Certain aspects, such as those relating to general artificial intelligence, will be exponential by their very nature. That’s when things get scary very quickly. For a taste of that I highly recommend this Wait But Why article. When people like Elon Musk and Stephen Hawking says there’s a significant danger, it’s time for all of us to sit up and take notice.

In the short term though, we can start looking for opportunities to exploit AI technologies as they mature and generate new forms of value. It’s important to get an early, solid understanding of how the opportunity can be exploited and, as with the technology waves that came before, a few well-chosen bets may pay off handsomely. She who dares wins, but let the buyer beware.

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