Watch out, they are comming for you

The cost of innovation is going down, barriers to entry are falling

Keeping it special

If you work in heavy industry and are near technology, you will know that there are some very robust pieces of kit out there. What I’ve always been surprised at is:

1. how simple many of the devices are in terms of functionality; and

2. how “special” they are in terms of obfuscating the obvious.

The effects of these two factors has been, for years, to reduce competition. By making it difficult to get hold of units (via price) and creating a jargon around the obvious configuration/deployment it has promoted a closed shop approach.

Keeping up standards

In some ways keeping out the riff-raff can be promoted as a good thing – it provides assurances around quality and safety. But it slows down innovation. You might say that perhaps this is good. Maybe you don’t want to be too innovative around safety and compliance systems. Afterall making mistakes is expensive and dangerous.

Keep up!

One of the aspects of the 4th industrial revolution that will challenge that thinking is simulation. I used to think that digital twins, virtual worlds and simulation would help reduce the cost of maintenance, let the experts create new ways to work and basically bring down the operating costs for the incumbents.

What if it leads to a whole new raft of competitors? What if anyone can have low-cost access to a virtual oil rig, or virtual power station, or virtual chemical plant? Not only will they learn how it’s supposed to work, they can try things and see what happens – learn by doing, learn by breaking, but do it virtually. Perhaps this will lead to:  

  1. they might come up with much better ways to operate it that you do; and
  2. train themselves to operate it before you hired them

Result: Better ways of working, access to more talent, incumbents get beaten.

If you have ever witnessed teenagers playing fortnite, you will know how fast their thinking can become and how fast their brain-hand connetion is. Imagine how quickly they will be able to react to real-world situations and think through the information being thrown at them.

Examples

I’ll provide two examples of where “public access” and “new ways of working” are already influencing established hierarchies. It won’t be long before these mechanisms appear in heavy industry.

Don’t expect today’s engineers to enter the workforce unprepared nor unwilling to take on the establishment. Watch out for competition from smart people who are not part of the established hierarchy. Don’t think the way you work today, will be the way you work tomorrow.

Example 1: Team Huub-Watt bike

I was lucky enough to see this cycle team win gold at the Track Cycling World Cup in December 2019. The team is comprised soley of amateur racers and they ran a completely novel strategy calculated using simulations and software. Their budget is £15,000 per year. They beat Team GB who have the best coaches, facilities and trainers available – and a budget this year of £26m. That’s over 1,000 fold decrease in cost and substatially BETTER performance.

Response from the establishment was to change the rules, enforce the status quo. This may not work forever. It probably won’t work for you.

https://www.tri247.com/triathlon-features/interviews/huub-wattbike-uci-interview

They were not, however, afraid to make use of the technology for their own ends. Zwift is a cycle simulator that people can use at home and join in real-time cycle events and ride-outs while collecting performance statistics. It is now being used by pro-teams to identify and recruit talent.

https://www.cyclingweekly.com/news/latest-news/i-want-to-ride-in-the-worldtour-how-british-cycling-are-using-zwift-to-help-identify-young-talent-454806

Example 2: British Touring Car Championship

In the gentleman’s toilet at the Royal Automobile Club in Pall Mall – in the heart of establilshment London – there are a series of framed caricatures of some of motor racing’s greats from the last 100 years. These include W.O. Bentley and Mike Hawthorn. Motor racing is glamourous. And costly. The money needed to race in formula 1 are legendary, but even the karting in a 125cc class will likely cost you the best part of £50K a season. Developing cars, tracks and drivers costs money.

So what do you think will be the outcome of last weekends win for James Baldwin in the first of the British GT Touring Car championship races? It’s a pretty big series, and winning a race is not easy.

Especially if it’s your first race you’ve ever competed in.

James honed his skill as a driver in a simulator he set up at home for under £1,000. And his talent was found when he entered a competition in an “E-Sports” event.

Turns out that the simulation prepared him surprisingly well.

https://www.goodwood.com/grr/race/modern/2020/8/worlds-fastest-gamer-wins-on-british-gt-debut/

https://www.bbc.co.uk/news/newsbeat-53554245

Responding to the Crisis: Leader’s Handbooks

What should we be doing right now?

It’s an economic emergency. Every company is having to rethink what they do and how they operate. Together with AGM Transitions we’ve asked our networks to share their recent experiences. We’ve written three guides:

COVID – Responding to the crisis – Leaders Handbook

COVID – The Transformation Handbook

COVID – Remote Working Handbook

What happened?

Since I published my post on March 9th the world turned upside down. Covid-19 is a “big one”, certainly when considering the economic impact of the measures taken to stop its spread.

Couple that with the shocks to both supply and demand in the oil world and members of the Bestem Network have been left slightly shell shocked.

What will happen next?

We are starting to understand where we are – but we’re battling to understand where we will need to go.

As Gordon Ballard said in the FT on Saturday: “In the past, activity decreased then picked up again — each time, we saw it come back,” he said. “Now it’s not entirely clear if things just come back as normal. Everything has changed.” [Link]

For some context however I should point out that even with 30% drop in oil demand we are now only at the level that was normal in 1996 [Link]

What have I been up to?

Alongside my hour’s cycling, home cooking, housework and playing with electronics:

  • Looking after my clients
  • Contributing my skills to my community to innovate systems to support neighbours in need; and
  • Working out what we have to do to come out of this ready for the next phase.

Stay Safe, together we will get through this.

 

Sell now while stocks last

Who’d have thought it?

In December and January, I was writing about what we might face this year. The world looked very different than it does this morning.

As I write the London market is off 8%, the Oil Price has dived to $35/BBl and Energy stocks are off 20-30%.

Continued shocks

The world seemed a rosy place in 2013. Since 2014 we’ve experienced a series of shocks – 2014 Oil Price crash, Brexit, Trump, refugee crisis, Syrian wars, trade wars, climate strikes, energy transition, Covid-19 and now Saudi & Russia are playing poker. None of this was predicted widely.

As we head deeper into the 4th Industrial revolution we will see more “externalities” that will further disrupt our best laid plans.

What about Covid-19?

Maybe Covid-19 isn’t “THE ONE” maybe it is. But it has certainly exposed how susceptible our current end-of-3rd Industrial Age, free-trade, globalised and business-case-obsessed economy is.

We have not priced risk correctly and we have not built in contingency. Workers on zero hours contracts can’t self-isolate, just-in-time imports from China are not working. To address this will require changes in policy and macro-rules to make a response possible in the face of short-run economic competitive pressure.

For more information on Covid-19 McKinsey has an excellent primer here [link]

Will business need to change

It seems clear that changed business practices will be needed if we are to become more resilient in an era where travel can be minimised, whole communities quarantined and trade in physical products localised.

Perhaps we will quickly switch to business that makes more use of information-rich scenarios (video conferencing, designs for 3D printers, remote controlled operations)?

We also now have another example of what can happen when information travels wider and quicker than knowledge. In this case panic buying of toilet roll. As we become more information-reactive in our business processes we need to bear this in mind.

Innovation is the answer, now what’s the question?

The only strategy I can see that will help is to learn to innovate quickly and be ready to react with purpose and knowledge as the future reveals itself to us.

It will never be this slow again!

Mood music changes

So BP have gone back to the future. Beyond Petroleum all over again.

When I started the Bestem Network 7 years ago I focussed it on issues surrounding the Oil and Gas industry – specifically how to use technology and reconfigure operations to develop and produce projects at lower cost and risk.

Last drop or leave it in the ground?

The Wood report was flavour of the month and much of my work centred around MER-UK (Maximum Economic Recovery). One of the categories of posts on this site was (and still is) labelled “Last Drop”; it focussed around the changes that would be required to make it possible to cooperate economically to achieve the maximum aggregate profit for the industry. It tackled things like tying together infrastructure, developing small pools and draining the basin over the long-haul and not to optimise short-term or locally.

While I never expected that the industry would return to 2012 levels, I did expect that it would come back and stabilise at a more “normal level”. I was concerned that the “big-crew-change” would mean that young people would not have the knowledge to operate our much-needed oil and gas infrastructure. I had no idea that they would reject oil and gas completely. That thought occurred to me in 2019 when I visited London Tech Week.

In 2017 I wrote that exploration was really of waning interest [Link] but I didn’t expect one of the primary reasons was that we didn’t want any more hydrocarbons.

Contrast this recommendation from Wood in 2014: “Government and Industry to commit to a new strategy for maximising the recovery [of oil reserves] in UK Continental Shelf] with the growing idea that we might leave reserves in the ground.

I wonder what the report on maximising the economic recovery from the whaling industry said.

Could the oil industry just disappear?

Despite sounding the drum for the 4th Industrial Revolution and arguing (nicely) with Patrick Von Pattay ( I was the more conservative because I thought that oil and gas really wouldn’t change fundamentally). It appears I may have underestimated things.

A very successful (and foresighted) businessman recently told me that the plastic-straw industry had simply ceased to exist within six months of the revelations of the damage it did to the oceans in the TV programme the Blue Planet. This chap now takes into account environmental position before bidding for work from a company – not for ecological reasons. He wants to direct effort to customers that will remain in business!

Surely we can’t do without oil?

Of course, there are oilmen who will tell you that the world economy cannot work without hydrocarbons – their case has always been that growth will come from renewables, and that demand would be flat. I tend to agree. But what if we’re wrong?

Here are a couple of thoughts for this (exceptionally) rainy Feb morning.

  • Solar is the cheapest form of energy production already. It’s getting cheaper and more efficient at a blistering rate.
  • Petroleum products might become classified as a dangerous substance – think asbestos or CFCs, what would that do to demand and price when supply, licensing, permitted uses and public perception of the product changes.
  • Microeconomics – which is what many businessmen optimise for – operates within Macro economic boundaries. Macro economics are formed by policy, are political and by nature are ideological. Think about: Soviet Russia, China, Thomas Pickety, Trade Wars, Sanctions. Things you think are “real” business decisions can be usurped by political will in an instant.
  • The IPCC report on climate change was issued in 2007, the Paris agreement was 2015 we seem likely to go beyond this and as a world embrace Net Zero sooner rather than later. For insight listen to Myles Allen on the life scientific (BBC https://www.bbc.co.uk/programmes/m000fgcn )

Engineering will still be important

With all this doom and gloom around it’s easy to get despondent. But, here’s the good news: if the world decides it wants to change then this will call for difficult and complex engineering, delivered in remote locations across political divides on an unprecedented scale over a mulit-decade period.

Not only will we need to invent all sorts of new technology for carbon reduction, energy efficiency, generation, storage etc. etc. We will need to deploy them all and decommission all the legacy assets.

There are not many companies that can muster the amount of engineering talent, capital control processes, large scale international project management, logistics construction that will be required. In fact, I can think of two that could – Energy and Shipping. And of course, if the world doesn’t change, oil and gas will have a renaissance.

Under all circumstances the people inside the oil industry will have skills that are needed and which are hard to replicated at scale. The only loss of value will come from those who can no longer exploit their control of underground deposits of oil in the future, and those that must pay for legacy assets and impact from the past.

Fundamental engineering practice still matters

With all the digital wizz (which I fully support) it is important not to lose sight of the practical situational requirements, human organisation and civil society that we need to enable the “platform” in which the innovative start-ups, electric cars and energy transition can happen.

Basic engineering discipline still matters, and is sometimes overlooked by hand-waving innovators and wet-behind-the ears management consultants.

You probably know about the 737-Max flight-stability software and instrumentation scandal. Recently, I read an article on Boeing where it says they are now re-inspecting new plane fuel tanks because they have found rags and tools left in them by construction workers: https://www.flightglobal.com/air-transport/boeing-orders-737-max-inspections-after-fuel-tank-fod/136819.article

It’s a sobering thought when flying :- if the wrong culture takes hold and introspective and solid processes are overtaken by gregarious and extroverted leadership.

The world still needs good engineering.

Ubique & Quo Fas Et Gloria Ducunt

2020 Vision

Sorry for the title. It’s not very original. Everyone’s been using that for the last decade, but still it seems appropriate. Every January I’ve made a post predicting the year ahead. I normally write this in December and publish it at the beginning of the year. It normally makes a few tongue in cheek exaggerations to in order to raise a smile. I stole this idea from Old Knights Almanac that used to appear each year in the RETRA magazine [Link ]

Today is the day we leave the European Union. My advice is to ignore this and go and buy today’s FT. It has many stories that summarise the transition we’ve witnessed and sets out the stall for next year. Below I’ve taken extracts and headlines and they tell the story. The one thing not mentioned is the UK Government’s industrial strategy, more on that in another post. Oh, and my watch phrase for this decade is “Society 5.0” – I think we’ll be hearing more about this in the comming while.

First here is an extract from this story (https://www.ft.com/content/b64b692e-4387-11ea-abea-0c7a29cd66fe).

This caught my eye because it illustrates the emerging tech leadership that is flowing from a very entrepreneurial and exceedingly smart China, the comming tech trade-wars and how there is a shift in earnings among tech players reflective of the shift in tech approaches – showing even when you are the innovator you have to keep innovating!

BT has said the cost of implementing the UK government’s cap on the use of Huawei equipment will cost it £500m over the next five years as it reported its third quarter figures.

[…]

There’s a bumper crop of earnings to report: Microsoft reported a 14 per cent advance in revenues, to $36.9bn, helped by cloud revenues which grew 39 per cent to $12.5bn, Tesla has notched up its first-ever back-to-back quarterly net profits. The electric car pioneer called 2019 “a turning point”. AT&T’s entertainment business WarnerMedia revealed a $1.2bn hit due to costly investments in its upcoming streaming service to rival Netflix. Nintendo’s quarterly operating profit rose 6 per cent to $1.5bn, missing expectations. Samsung Electronics confirmed its fifth straight quarterly decline in profits but said it expected memory market conditions to improve in 2020.

To avoid the risk of plagiarism I am going to direct you to today’s FT (go buy a copy or have Amazon deliver you one). The headlines from these stories paint the picture and tell the story all by themselves.

Why Microsoft and Tesla are the decade’s big disrupters

https://www.ft.com/content/b3e659fc-4380-11ea-a43a-c4b328d9061c

Ginni Rometty steps down as IBM tackles cloud era

https://www.ft.com/content/aabee59a-43aa-11ea-abea-0c7a29cd66fe

Rich and famous turn to ‘personal cyber security’ to protect phones

https://www.ft.com/content/96c79040-40ea-11ea-bdb5-169ba7be433d 

The Apple effect: Germany fears being left behind by Big Tech

https://www.ft.com/content/6f69433a-40f0-11ea-a047-eae9bd51ceba

Elon Musk jolted by German protests over Tesla factory plan

https://www.ft.com/content/8b10555e-4345-11ea-abea-0c7a29cd66fe 

The UK’s employment and productivity puzzle

https://www.ft.com/content/a470b09a-4276-11ea-a43a-c4b328d9061c 

For today’s oil market the real threat is to demand, not supply

https://www.ft.com/content/5bf49cb0-41cb-11ea-bdb5-169ba7be433d 

Shell to slow investor payouts after earnings fall 50%

https://www.ft.com/content/4e1fa700-4334-11ea-a43a-c4b328d9061

Orsted/offshore wind: Go-Greta:

(Henrik Poulsen has turned a national oil company into the world’s largest offshore wind builder and green energy champion)

https://www.ft.com/content/719dd81d-2527-4b83-8aed-e6624476c191

Competition rules stymie co-operation on climate goals

https://www.ft.com/content/b3e0da9c-3eba-11ea-b84f-a62c46f39bc2 

I wish you a healthy, hearty,happy and prosperous 2020.

While we were sleeping – Oil 1.4 and Solar

It’s been very busy since the Network Dinner in September. I will post an update on the discussion later this month.

In the mean time I’ve been busy working on innovation – more of that later – but I recently came across two interesting items that I think might be worth sharing.

Firstly the FT ran a special issue talking about Oil and Gas 4.0 [Link]. It’s good to see that this term is being widely applied – and a big change from when I started to talk about it a few years ago.

I wrote an article in March 2016 when I claimed that Oil and Gas were really at 2.5 while industry was going 4.0 [Link] I was concerned about the lack of urgency and technology progress. I also called out the contribution of Collette Cohen as being one of the few that seemed to get technology. She is now director of the Oil and Gas Technology Centre.

The OGTC were referred to in this article [Link]

In October, the non-profit Oil & Gas Technology Centre (OGTC) in Aberdeen in Scotland, announced the next phase in its autonomous robots project with Total of France, which is developing what it calls the world’s first offshore work-class robot. The first phase of the work saw Austrian firm Taurob create a robot to conduct visual inspections at Total’s Shetland gas plant and the Alwyn gas platform in the North Sea. A second-generation version will have a stronger chassis and a heavy-duty arm that will lift objects and turn valves. It will be tested by Total and Equinor of Norway, the research initiative’s new partner.

“A lot of our work on hazardous environments focuses on whether we can avoid sending people into those areas in the first place,” says Stephen Ashley, head of OGTC’s digital transformation solution centre.

Another article coined the phrase Oil and Gas 1.4 which is a clever take on the combination of an old-age industrial organisation embracing new digital technologies within its core business. I think I like this term better than my 2.5 one.

This article [link] makes the point that the new technology is prevalent in some areas of the business, but that the new frontier for production might be the application of technology to find economic ways of enabling enhanced oil recovery. 

Unmanned rigs are now commonplace, complex operations are monitored from a single control room, leaks and emissions of greenhouse gases can be identified by drones and satellites, removing much of the need for direct human inspection. Numerous technologies are being applied in ways that can reduce cost and improve productivity.

The key question, however, is whether the digital revolution can answer the sector’s biggest challenge: how to secure future production. Oil demand is not falling. There may be 7m electric vehicles on the world’s roads but there are also 1.2bn vehicles with internal combustion engines.

[…]

One answer must be for companies to make the most of assets they already hold. Across the world the typical recovery rate from a conventional oil or gasfield is only 35 per cent.Even after decades of production giant fields such as Prudhoe Bay in Alaska or Ghawar in Saudi Arabia still contain billions of barrels of oil. Recovery rates have slowly risen and provinces such as the North Sea, originally expected to close at the end of the last century, continue to produce oil and gas. In Norway recovery rates are typically 50 per cent — well above the world average but still leaving half the resource base undeveloped.

The point at which recovery becomes uneconomic, ie when the cost of enhanced recovery is greater than the value of the oil, is a serious constraint.

What I’ve found really interesting this year is how irrelevant the oil and gas industry seems to have become down here in London. What I mean by that is that Oil and Gas seemed to be at the crux of things in a way that, say, copper mining and concrete production wasn’t. It used to be a cool place to play with technology, travel the world and to make a bunch of money. I think those days may be over (though some predict a spike in prices around 2025). Now no-one here cares about Oil and Gas at all.

Where I am seeing a lot of action and excitement is around Solar and Wind. I thought for a while it was just me becoming aware, but now I’m onvinced that it was a sea change and it really is picking up. And the cost-curve of Solar is particularly striking.

I urge you to have a look at Tim Harford’s article on Solar [link]. As always he has an ability to grasp the implications of what he sees in ways that other’s don’t. He looks a PV cells – how in 1980 Solar was about $100 per watt ($10.000 to light a light bulb). It is now already below $0.25 / watt and falling. Utility scale production is now looking to provide generation at below $0.015 per KW/h. [link]

The thing about Solar Panels is that they are a pure manufacturing play. Once created they just sit there and make energy. No moving parts, no plat to really operated as such. We have been, and continue to be, very good at manufacturing standard products in standard factories.

Sometimes the learning curve is shallow and sometimes it is steep, but it always seems to be there.

In the case of PV cells, it’s quite steep: for every doubling of output, cost falls by over 20%.

And this matters because output is increasing so fast: between 2010 and 2016 the world produced 100 times more solar cells than it had before 2010.

Batteries – an important parallel technology for solar PV – are also marching along a steep learning curve.

The learning curve creates a feedback loop that makes it harder to predict technological change. Popular products become cheap and cheaper products become popular.

And any new product needs somehow to get through the expensive early stages. Solar PV cells needed to be heavily subsidised at first – as they were in Germany for environmental reasons.

More recently China seems to have been willing to manufacture large quantities in order to master the technology.

Watch this space, it’s just getting cheaper, better and faster. This is where the action is – I just don’t know how to play the opportunity yet.

 

Machine Learning – more learning….

As an addendum to last week’s post about machine learning – here is an article by the BBC : https://www.bbc.co.uk/news/technology-48825761. This describes a story about an amazon employee who built a cat-flap. Fed up with receiving “presents”, he made this clever device to recognise whether his cat has come home with prey in its jaws. If it has, it refuses to open and the cat must stay outside.

I thought it neatly encapsulated some problems of machine learning I am finding, and also pointed to some possible features where this technology could be applied to generate more value.

The training problem and need for clean data

It took 23,000 photos to train the algorithm. Each had to be hand sorted to determine whether there was a cat, a cat with prey etc. etc.

This is like the oil and gas industry in that it needs a lot of clean training data that may not be available without a lot of manual input.

The Bayesian stats can work against you

The frequency of event-occurrence in this case is once every 10 days. The maker ran a trial for 5 weeks (so should have seen 3-4 instances in that trial, though in this case it’s stated as seven). There was one false positive and one false negative – giving an error rate which may be around 20% (though there are not enough samples to have a lot of confidence). If the algorithm continues to learn and say that the cat uses the flap 5 times a day on average. This means, in a year it will have 1,825 additional samples from which 35 will be positives of which 7 will be false and 1790 negatives of which 358 will be false-positives. Manual correction will be required 365 times (i.e. per day on average) and the learning rate will take about 12 years to duplicate the original training set. I don’t know, but I suspect there are diminishing returns on adding new data so how much smarter it will be in 12 years I’ve no idea.

Disclaimer – a good statistician will know my maths above is not quite right, but the principle is.

Do you trust the alarm? Do you take notice?

So in this example, quite like oil and gas operations, the issue was getting hold of clean training data. The event being detected was comparatively rare meaning that a lot of false positives are likely. In the example above an “alarm” would have sounded 365 times and 28 occasions it would have been real. With the sparsity of events the this means that the algorithm will not learn very fast and I think the alarms will be ignored.

So where can the applications be better?

Distributed learning

Parallel learning helps build better predictive models. if we had the same cat-flap for every cat, and every owner corrected the false signals and right algorithm could learn quickly and disseminate the results to all, this would speed the learning process. Self-driving cars are a good example of where this is possible, and google-search is great example of the power of parallel experience.

Products and services impossible for a human

Situations where there is so much data that manual processing is impossible. Here I don’t mean that you can collect so much data on a manual operation that is ongoing that you cannot analyse all the extra information. What I do mean is that there is intrinsically so much information it would be impossible to analyse by hand and never has been. So an ML approach is the only one possible. For instance looking at real-time changes patterns in data networks.

Simple situations where it’s expensive and boring for a human

Automated first-line help systems, call screening, password resetting etc. These are all tasks where humans can do them, but they are simple tasks which are too boring for smart people to do, where automated help can often provide a better experience. And where “sorry I didn’t get that, did you say yes?” is only mildly irritating not the cause of major corporate loss.

Conclusion

There are places that machine learning will be revolutionary, but I suspect that much of ML will be either embedded to make normal tasks a bit easier – such as auto-spell checking, voice recognition etc. Or they will tackle classes of problem such as IT security, or on-line shopping behaviour where there is inherently a lot of fast-moving data and manual monitoring is simply not feasible nor fast enough to work.

Machine Learning — Data First

With all the attention on Machine Learning (ML) that I encountered at London Tech Week, I thought I better find out a bit more about it. I wanted to verify my view that it won’t have a dramatic impact on the Oil and Gas industry and see if this was actually true.

My findings, so far, is that ML might:

  • Speed up some analysis
  • Change the spread-sheet paradigm (for better or for worse)
  • Enable people with the right level of expertise to create predictive analysis (whether this will be at all valuable is a different matter)
  • In a myriad of small ways, change minor tasks (removing annoyance, reduce low-value add data & reporting work, change interfaces)

None of these applications are dramatic in themselves, but over time they may add incremental benefits that provide a moving improvement-front. A bit like a six-sigma or Kanban.

What will you need in order to drive broad-value from ML

You’ll only be able to take advantage of that if four things are true:

  1. You have clean, historical data available
  2. You can access and combine quality-controlled, time-dependant data in near real-time (ideally from multiple sources)
  3. You have wide-spread knowledge of how to apply the new analysis tools – like those based on “R”. (think how many people can use Excel today for collecting, analysing, querying and reporting on data – varying degrees of proficiency, but who do you know that can use “R”)
  4. You are prepared to reorganise the way work is performed to take advantage of the new possibilities created by: data analysis leading to demonstrated-fact-based / probability-assessed management decisions & employee actions.

Low cost hardware is the trigger

The interest in machine learning is spawned from the dramatic drop in the cost of hardware and software required to perform the number crunching required. Because of this, not only has the complexity of the addressable problem increased but also the inefficiency of code that can be supported increases the usability of tools and techniques leading to their application by practitioners outside pure decision sciences.

If you attend any of the IoT conferences – or speak to the large vendors of real-time industrial data, you’ll hear a lot about how edge-computing and Machine-Learning will change things for industry. “Edge” means placing computing power in the field with low-power and small costs.

Putting this alongside the sensor enables pre-processing information to send back only the results. This helps to reduce data bandwidths and increase responses.

For a view on how cheap this type of technology now is – and how undramatic the applications of ML really are I invite you to have a look at this video (from the hobbyist market) showing what can be done for less than $100. Listen out for the references to “TensorFlow” one day that will be important, there are also some passing references to cloud-based resources that may be of interest.

What I learned at London Tech Week

Big Data, Artificial intelligence, Machine Learning and Computer Vision

Turns out that Computer Vision requires Machine Learning, and Machine Learning requires Artificial intelligence. Artificial intelligence is of most use when there is large amounts of data to process. Hence in a way, AI relies on big-data (though not always). The cognoscenti use the abbreviations CV, ML, AI and err…. “big data” to refer to these technologies.

Some simple definitions

These are what are called HORIZONTAL TECHNOLOGIES, because they are general and can be applied to a range of problems across industries. They are already having an impact in some areas but they are not a panacea.

Big Data relates to the collection and storage of large quantities of data and being able to access and manipulate this quickly – using high speed networks, special search algorithms, parallel processing etc. It gives rise to a whole world of shared resources, cloud computing and specialised storage schemes. When real-time information is included (such as sensor data) then this is the world of IoT, time-series data and edge processing.

Ai is a way to analyse the information contained the big-data very quickly. Creating inferences between data signals and looking for patterns, sometimes used to predict outcomes and reduce uncertainty. AI is very narrow in its applicability, even Bill Gates says you wouldn’t trust it to order your inbox for you, so it’s ability to make judgements is limited. A lot of what we talk about being AI is a form of linear regression, mass computational power enabling the quick processing of data and crunching of large amounts of data. AI can give the illusion of being smart, when in fact it can be easily fooled.

Machine Learning is the ability for an algorithm/analysis to change over time by examining a changing stream of input information and comparing computed outcome with desired outcome and tuning. Neural networks. Machine learning is an application of AI.

Computer vision is an application of both AI and ML which is used to process images (still and moving), one of the most controversial applications of this is with facial recognition and the automatic tracking of people.

Some of the take-aways from London Tech-week

Big Data

Everyone that spoke (and I mean everyone) – said that their biggest issue of applying any form of advanced analysis fell down on the quality of the data. The meaning of information collected and the way it is labelled is so inconsistent. There were some semantics companies working with different ways of expressing ideas in language which may hold a key to explaining the differences between the labelling of data items. Until this takes off then 75%-90% of your AI budget is going to be spent cleaning up data and sorting out the meaning of feeds. Trouble is you will spend this 90% of your budget with no tangible change in outcome as you can’t get started until it’s sorted out.

AI

I saw what I thought was a brilliant example of this from a Swedish company called Spacemaker (https://spacemaker.ai/ ). This company works alongside architects to help them choose between the complex trade-offs required when selecting the layout of buildings. Trade-offs between natural light, housing density, noise exposure, energy efficiency etc. By providing optimisation inputs the computer very quickly generates possible layouts which works alongside the architect freeing them from the mundane, but complex, calculations and predictions of weather patterns, seasons etc. The result is much better buildings but not taking away the artistic judgements of the architects.

In Oil and Gas I can see a similar “advisor” system working alongside production engineers, economic planners, maintenance engineers, planners and schedulers helping to provide scenarios based on optimisation parameters enabling them to choose the best configuration to implement.

Machine Learning

I saw an example from a company called Dark Trace (https://www.darktrace.com/en/ ). As well as being a simply brilliant commercial success with enough financing to direct sufficient money dedicated to PR, marketing, sales and distribution (as well as well researched and implemented tech). Said quickly their system sits at the network hubs in your organisation and reads each packet of information (sometimes understanding the content, but often only it’s source and destination). It uses ML to work out what normal looks like for you (and evolves), and if it sees something abnormal start it can raise questions. It can also take action by IP spoofing to intercept traffic and block comms. One of their success stories is the NHS trusts that installed them and contained the WannaCry attack (https://www.bbc.co.uk/news/health-43795001 )

This might be applicable to Oil and Gas by monitoring all the signals in the real-time stack and learning what normal operation looks like and then being able to spot abnormalities as they start to occur. It would be more complex because the relationships are more complex than network traffic but still, got to be worth a shot.

Computer vision

I found this very interesting. I spoke to a company Winnow which has applied computer vision to the problem of cutting food waste from restaurants. (https://venturebeat.com/2019/03/21/winnow-uses-computer-vision-to-help-commercial-kitchens-cut-food-waste/ ). By using a camera it captures images of what the chef is throwing away and through a series of algorithms works out the amount and value of the waste. For instance, perhaps a chef orders the same amount of broccoli every day, but really only uses most of it on Fridays, Wednesdays and Saturdays. Or maybe the ordering changes with the weather – but either way, if you can reduce the over ordering then everyone wins.

People are already applying technology like this in industrial settings to check that people use their safety equipment (like glasses, harnesses etc.), you could also start to put cameras covering manual controls to create records of change and current settings – it would be a cheap way to retro-fit instrumentation.

Conclusion

That’s all for now but some of the other things I found out about included “The Ostrich Problem”, the real-world applications of AR/VR, more on cyber security and what 5G will really mean. Other things that are hot right now are Commercial adoption, future of work, small-scale adaptive robotics, AI-Ethics and Decarbonisation. More of this later.

London Tech Week

Last week was London tech-week. I guess a bit like London fashion week, only much larger.

There were events all over London – this has grown from a week of borrowed conference rooms and underground gatherings into a large series of happenings. Monday-Wednesday saw the COG-X show near the google campus north of Kings Cross. At this event there were 10 stages giving parallel presentation sessions over the full three days, an Expo, start-up section and various corporate networking events.

Wednesday and Thursday (yes overlapping) saw the TechXLR8 exhibition at the Excel Centre, this was a large expo event with presentation 6 stages running all day.

Alongside these two there was also 5G Europe and Identity Management conferences both of substantial size.

Have a look at the website here https://londontechweek.com/events – there are 18 pages of events with 7 events per page. Tech-XLR8 above is only one of these.

This blog previously covered the launch of the UK’s industrial strategy (at Jodrell Bank) and the lack of coverage of this in the main stream media [Link]. Well, despite there being still no interest from the media. The UK industrial strategy was evident everywhere – with announcements from the various bodies, challenges, and funding opportunities. Have a look at this if you haven’t already : https://www.gov.uk/government/publications/industrial-strategy-the-grand-challenges

And did you know there is an “Office for Artificial Intelligence” ? https://www.gov.uk/government/organisations/office-for-artificial-intelligence

I’ll write more about some of the events in due course but here are the highlights:

  • There were 1000’s of under 40, very intelligent, eager advocates of tech everywhere. Very diverse in terms of sex, ethnicity, country of origin you name it, very much in contrast to this [Link]
  •  AI, IoT, ML, CV, AR, VR were the flavours of the moment (and I learnt some really interesting new insights here, more later)
  •  AI Ethics is a huge deal, and lots of people are thinking about this.
  •  Energy tracks focused on decarbonisation, distributed grid and combining sensor technology with predictive algorithms to reduce consumption. Oil and Gas didn’t feature once.
  •  Interesting to see the traditional tech players (with notable exceptions) were looking dated and pushing out platitudes about the new tech and the business impact it should have (but with no concrete examples). Meanwhile there were (really) hundreds of well-funded small companies that had real-world use-cases for niche solutions that had demonstrated value (though most had not had to pass a business-case hurdle to get going).

What struck me most was the vibrancy of the arenas, the buzz of conversation and the high-energy engagement between participants – problem solving and exchanging ideas. It was very refreshing to see. There was also a willingness by all sorts of industries to try new solutions and approaches – knowing that not everything will work but understanding the need to learn and push the envelope forward. The pace of change is amazing.

I was lucky enough to have the chance to try a VR simulation made by Linconshire Fire Brigade to train their officers in fire investigation. On with a VR head-set and into a virtual world. It was very, very realistic.

Oh and everyone was talking about “Digital Disruption”

Next year London Tech-Week should be one for your diary.