Category: Innovation

Artificial Intelligence (AI) has been just around the corner for a long time – but looks like we have now arrived.

Computers can beat us at Chess and Go and respond to voice commands.

Navigation systems are so good many of us now have never really learned to use a map.

There are so many ways of looking at and classifying the field of AI and machine learning that it’s almost impossible to get a sense of the field.

But we can start by looking at some broad domains – what do humans do a lot of the time?

We sense things – taking in vast quantities of visual, auditory and tactile information and responding to our environment.

We can detect the edges of things, work out which way is up or down and work out what is near us and if we are going to bang into them.

A particularly human thing to do is reason. Our brains are essentially prediction machines – we can think about what has happened and use reasoning to work out what we should do next.

But we don’t exist in isolation – as social creatures we interact with others – listening, speaking and responding.

We make plans – choose between alternatives or options – that range from what to eat to how to get somewhere.

We are also teleological – the conscious part of our brain helps us do things with purpose.

Our brains have evolved to be the way they are – but how would we go about creating an artificial one?

We could start by writing down all the rules we follow.

For example, doctors get to a diagnosis by considering and eliminating possibilities based on the symptoms they see and the measurements they take.

Rule based or expert systems take all this knowledge and use it to create if-then rules – if the temperature is above X, check Y next.

These systems are now pretty effective – and help us select the best flight, the cheapest online store for an item and schedule calendar entries from text in emails.

If there is too much data and variation to come up with rules, then we might use probabilistic approaches.

For example, we can run weather simulations that are probably accurate over hours or days but less so over weeks and months.

We can look at the distribution of a time series and use that to predict the range of probable future values – which then lets us pick out values and events that fall outside expected levels.

The rule based and probabilistic approaches are pretty easy to build and many systems in use now will be based on them.

A more complex approach is pattern matching, where a learning algorithm adjusts itself and learns from the data that goes into it.

For example, every time we type a search term into Google, we are training its AI engines. If we type in the word “eagle” and then click on a picture of an eagle, Google can learn what eagles look like and eventually predict that a picture contains an eagle.

With pattern matching, the more information we have the better our algorithm gets – and so it’s a winner take all situation where the systems we interact with most will learn the most and pull away from the rest.

But where can we use this technology now?

Three areas that are of interest in the energy sector are forecasting, scheduling and trading.

The energy system is all about balancing supply and demand, whether at the grid level or the domestic level.

If we know when the wind is going to blow, then we can make a call on the number of fossil fuelled power stations we need.

If we can see when demand or prices are high, we can schedule when we do work to avoid costs or take advantage of high prices.

We could even trade between ourselves – selling or buying electricity from the grid or a peer-to-peer network for a profit.

An interesting thing that happens with AI is that as it gets cleverer we tend to dismiss the things it does as simply something a machine can do.

As a result it is quietly augmenting how we do things without us really noticing. For example, how many of us now choose a different route based on Google’s recommendations first thing in the morning?

Many AI applications will be almost unnoticed, simply transforming the essential building blocks of our economic system.

Eventually, one hopes, AI will free humans up to do more creative fulfilling work and leave the mundane to the machines.

What happens to us if we see our planet from space?

The overview effect is a mind shift experienced by some astronauts when seeing the Earth firsthand from space.

They immediately get that we all live on the same small, fragile planet protected from the void by nothing more than thin air.

And the things we do to mess things up here – from conflict to resource exploitation – are simply stupid and what is needed is the united will to do something about it.

But – less than 600 people have travelled space and that probably isn’t enough to create a movement.

As a starting point, perhaps we can look at some pictures.

Benjamin Grant started a project called Daily Overview that curates stunning images of the Earth from space and our impact on it.

These images capture how we have completely taken over the planet and the scale of activity and building we carry out – from deforestation in Bolivia to iron ore mining in Australia.

A related set of images called juxtapose shows images of before and after – how Dutch tulip fields bloom or how a patch of desert turns into a refugee city.

Contrast “England’s green and pleasant land” with the Zaatari Refugee Camp in Jordan that holds refugees fleeing from conflict in Syria.

Grant was inspired by a film from the planetary collective, available on vimeo and now viewed nearly 8 million times that explores the overview effect.

We often think that solutions to problems like climate change have to be technological – we focus on things like reducing carbon through more efficient technology or processes.

To really get people engaged we should help expand their perspective and see things from a different point of view.

And you don’t get much more different from the moon – imagine seeing an Earthrise the way the first astronauts did.

Unless you travel to the edge of the solar system.

From 6 billion kilometres away the Earth is simply a pale blue dot, the size of a pixel on a screen.

Although most of us won’t get a chance to travel into space, perhaps technologies such as virtual reality and augmented reality can help us have the same experience right here on Earth.

Maybe the technologies we have have created to escape reality can also help us protect it.

Many of us assume that if we just give people the information they need, they will act in the right way.

This assumes that households and businesses are run by micro-resource managers who make decisions about how to act after looking at the costs and benefits of all their options.

To change behaviour all we need to do then is provide things like smart meters and everything will be better, won’t it?

It turns out that isn’t the case because there are a number of problems we face.

For starters, not everyone understands the language of energy or water as shown on a display or report.

It’s hard to visualise 140 litres of water, for example.

It may be easier to grasp when it is translated into something that people can relate to – for example a one litre bottle for every child in a small primary school or the amount of water that goes into making one unit of product.

Even when people understand it, they may not feel they have any choice about using energy.

No one wants cold tea – and we would rather wear clean clothes. That means using the kettle and washing machine.

We can’t turn off energy if we have to do things – we have no control really.

A related problem is focusing on visible things and forgetting the stuff running all the time in the background.

For example, using the kettle will turn displays red with a power spike – but it only lasts a small time.

Or many organisations focus on replacing lights, because that is a very visible way of showing that they are reducing energy – but miss out on larger savings.

Then there are traffic light systems – which have been shown to improve behaviour by getting people to try and stay in the green.

At the same time, they make it ok to do things as long as the display is green – consuming more power overall over time.

A major stumbling block is simply what we want. We’d like a bigger telly, even though the old one works.

Christmas is about shopping – and we’re not going to deprive the kids of stuff even if it’s mostly plastic.

All these problems and more mean that getting people to change behaviour is a bit of a challenge.

So, how should we look at solutions? We can take some lessons from the designers of better eco-feedback solutions.

First, we need to provide better information.

For information to work, it needs to be easy to understand, attract attention, have a social component and be provided at the right time – the EAST framework.

An example is making sure there are labels that say when and who something must happen – like a sticker on light switches saying the last person out should turn off the lights.

Then there are goals.

Companies that commit to targets like Science Based Targets will give people the responsibility and permission to take action.

Government targets can ripple through economies and cause changes in behaviour in order to comply.

There is some evidence that comparisons work – people like being on top of league tables, for example.

After a certain point, however, it may not be possible to eke out further savings, and so comparisons become less important as people feel they are impossible to achieve.

Incentives and rewards, even small ones, can affect behaviour.

Simply having a star after the names of people can change how the group views and engage with the activities required – either to get their own star or keep one they already have.

For eco-feedback to work, it needs to follow the Goldilocks principle.

Not too much, not too little, but just right.

We hear a lot about smart contracts these days.

But what exactly is one, how do we create it and when can we use it?

The term was invented by Nick Szabo, who applied his interests in computer science, law and cryptography to create a cyberspace equivalent of gold – and came up with the idea of decentralized digital currency.

The point of a currency, however, is that it acts as a store of value and a medium of exchange – but when do we actually need it?

Nick, in his original article, says a contract is really a set of promises.

The things that go into a contract have evolved over time – from a contract of marriage to a contract of employment, from a contract of sale to a contract of lease.

All these structures say that the people involved promise to do certain things as set out in the contract.

While the concept of a contract can encompass a blood oath sworn at midnight under a lightning blasted oak tree, normal contracts have four characteristics:

1. The participants can observe how each party is performing on the contract.
2. An arbitrator can verify that the contract is performing as set out or is being breached.
3. The only people involved are the people that need to be involved – their rights are secure and private.
4. The contract can be enforced – for which we need acceptance of the contract by the powers that be.

A smart contract takes these concepts and recreates them in the digital world.

The difference is that parties enter into a digital agreement instead of signing a paper contract.

This has been done several times before – every payment with a Visa debit card, Ebay purchase with Paypal and software contract we sign without reading are all digital contracts.

The idea of smart contracts when linked with Blockchain technology adds a few ideas to standard paper contract.

The first is that contracts can be expressed in code using an algorithm.

Common algorithms may be if-then or when-do.

For example, if you pay me X, then I will give you an hour of work.

Or when a shipment of copper is delivered in two months time I will pay you the price then that we agree now.

So this means that we need to identify the real life elements that the contract talks about like money and stuff.

For example, payments might be done using a decentralized digital currency like Bitcoin or Ethereum while each hour of work or shipment of copper is tagged with a digital identifier.

So, a second part of a smart contract is the ability to give things a digital ID.

Then there is the question of where to store the smart contract – and that’s where the idea of a ledger comes in – especially a blockchain based distributed ledger.

This keeps the information related to the contract secure by design.

Smart contracts are the way things are going to go – paper agreements have little place in the future.

Many other part of a contract – the promises we make, the reliance on the state to enforce agreements, and the lawyers that create and interpret them – are all still likely be around.

The key, however, is going to be to get the contract algorithm right.

Theodore Levitt, a Harvard economist and professor, said People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.

With larger purchases come greater expectations of performance, reliability and longevity.

An approach companies use to deliver these is servitization.

Servitization is a change in mindset from selling a product to selling a Product-Service System.

Professor Andy Neely from the University of Cambridge is a leading researcher in this area and says that the term has been around since the 1980s.

The classic example here is Rolls Royce.

It no longer sells jet engines. Instead, it sells flying hours.

Rolls Royce now delivers reliability – done through planned and predictive maintenance by analysing data from monitoring systems in the field.

Making a move from scheduled repairs to targeted repairs based on performance data enables fleet operators, from the military to councils, to improve things from combat readiness to bus times.

The way we get media has changed with this approach.

Netflix offers media as a service and Amazon offers ebooks.

Servitization has quietly transformed the software business.

It’s hard to buy a software product any more. It’s all software as a service (SAAS) now.

This is not always a good thing.

Netflix has lots of rubbish on it. The good stuff still needs to be paid for.

We get a million books with an Amazon Unlimited subscription – but the good authors still set their own prices and get committed readers.

Servitization in these cases gives the companies a revenue stream and a customer convenience – but the pressures of controlling costs means that the performance is not superior to just buying a really good collection of books and movies.

And software as a service really all-too-often means self-service software.

So, it’s still a product sell rather than a service when we think about it.

According to Professor Neely, servitization is something of a paradox.

Many firms offer product-service systems, but it makes less money and carries higher risks than we first think.

The key thing is a change in mindset – in how we connect a product, service and performance.

This shift starts with the customer going from wanting to own a product to being happy with a service.

Why should we try and use data better?

The short answer is because we can make money or save money using the insights we get from analysing data.

But a more useful answer might be because it helps us see more clearly.

That’s very Zen.

Zen teaches that we normally see the world through a haze – a fog made up of the assumptions, preconceptions and fixed ideas – that is created by our desires.

For example, many people are so involved in the way they do something that they fail to see that the something they do is no longer relevant. Innovation simply passes them by.

Or, if we really want something to happen or believe that an approach is right, we become blind to anything that contradicts that point of view.

The way to see clearly is to look at the world as it is.

This is pragmatic advice, but how do we go about doing it?

The Cross Industry Standard Process for Data Mining (CRISP-DM) is now over 20 years old – but still useful.

There is data everywhere these days, leaking out of sensors, meters, surveys, analysis and social media.

We could analyse everything, but we should probably start with an understanding of the business.

What is special about this particular industry, what is the competition doing, how do customers act and what would the business like to do?

We can then stick our heads in the fog and try to understand the data that’s available – going back to the business to ask questions or get some more.

Is it clean or patchy? Do we have lots of numbers or is it full of words? Is it in an open format or do we need to get it out of databases or proprietary formats?

For example, the way we treat energy metering data is different from twitter comment mining or newsfeed monitoring

A huge amount of work then often goes into data preparation.

The data needs to be bashed and cut and manipulated into a form that we can work with.

All too often, the data preparation takes so long and ends up in another messy form that there is little time left to do any modelling.

But – getting to the right kind of data structures can make the task of modelling much easier.

Models help us create and test theories.

Do we think there is a relationship between variables or a robust way to identify problems?

Then its time for evaluation.

We could do an infinite number of analyses, but we need to focus on the ones that are aligned with what the business needs to know.

Are we providing useful insights that can be used to make the business more effective?

If so, then we can head towards deployment.

There is no point doing analysis once and then forgetting about it.

The law of entropy – things decay over time – means that without management things will simply drift and become worse again.

Ideally, we’d automate the analysis and reports and let people know when things are going wrong so that they can get involved and fix things quickly.

The CRISP-DM is a process we can follow to create a data mining project.

The real value comes from the understanding we get when we see what is actually going on.