DevOps and AI means that Lean Data has another Principle

In an earlier post I expounded on the principles of Lean Data:

• Organisations know what data they hold and manage;
• Data is classified according to subject area and criticality;
• Only the minimum data necessary to Add Value to the business is held;
• Data replication is kept to the minimum level necessary to optimise business performance;
• Data Value is determined by its utility in Serving the Customer, Supporting Essential Capability, Protecting the Organisation, Providing Insight for Business Decision Making.

I also covered Data Portfolio Management. However, what I missed was the emerging issues around database management systems and Machine Learning. It appears that as people start to scale their Lean efforts (Strategic Alignment, Design Thinking, Agile & DevOps) they are beginning to realise that this includes data too.

What becomes increasingly important is having a handle on configuration management over data base design (i.e. schemas) and data sets themselves, ensuring that it is co-ordinated with code configuration management and integration across multiple deployments. This should have been implicit, but apparently it was not.

Therefore I suggest adding the principle that:

• Data configuration management of schemas, test data conditions and machine learning training data is co-ordinated with applications and code configuration management.

Another Week In AI & Machine Learning

Driveless

So the hot news this week is that people keep crashing into driverless cars. Wired magazine discusses the issue that the way in which the current generation of prototypes being driven on America's roads are experiencing a high level of accidents, apparently because they don't behave is the same way that ones driven by humans do. 86% of collisions in California this year have been due to being rearended or side wiped! Autonomous cars appear to be over cautious and annoy human drivers, not only by stopping unexpectedly but also because they are over compliant with the letter of the law in interpreting situations. So why don't they have signs on the car, just like a learner does to warn drivers that they need to keep clear? It's a pretty similar situation and people do tend to steer as clear as practicable from learners who they know to be unpredictable.

AWS Capability Continues to Grow

AWS also held its AWS Innovate Online Conference. In his "state of the nation" talk, Boaz Ziniman outline current capabilities available Off the Cloud (OTC) rather than Off the Shelf (OTS). Basically, AWS still provides impressive capabilities which allow a developer, data scientist or organisation to just start using AI and scale rapidly. Though they have been adding to this capability with impressively powerful capabilities around visual recognition and voice processing as well as bundled environments which are pretty much deploy and go. this is very liberating, because it provides a readily used Pay as You Go (PAYG) capability, which avoids much of the traditional issues of continuously having to research, select, implement, integrate and tune the suite of tools and infrastructure needed, as well as continuously returning to CAPEX approval and purchase processes for scaling to deal with increasing volumes of performance issues. 

There's a caveat though. Some of the capabilities, e.g. recognising a face in a crowd, which may be useful for security solutions, might also be used as the tools for enforcing a police state or merciless pursuit by paparazzi. Enabling such massive AI at scale, will to some extent be another nail in the coffin of personal privacy.

Early Adopters Surge Ahead

Finally, MIT Sloan and Boston Consulting Group released a report "Artificial Intelligence in Business Gets Real" which surveyed the application of AI in business across the globe. Whilst there were the usual scare stories about China being ahead of the West in adoption and that the gap between pioneers and laggards is growing larger there were some interesting points. The Chinese are deploying to meet efficiency and cost needs. Everyone else is deploying, because AI helps them do more and there was a message that AI will not replace jobs, but it will change the nature of work and the skills needed.

The second key message, was to experiment with something simple and demonstrate the benefits, because business leaders who had seen AI in practice, get it and are prepared to invest more, to the point that AI is almost addictive in the way in which it influences investment appetite once a business has tried it.

The final message was really around AI at scale. If you are planning on building your  future business model around AI, then you need to plan, prioritise long term investments and to get effective data governance in place. This does not just mean establishing once source of the truth, with valid, complete and coherent data. It also means having a good handle on version control. Since, if multiple applications of AI depend on the same data, it needs to be synchronised to avoid unintended problems. So Lean Data practices are fundamental to adopting AI at scale.

Finally, it was interesting to hear how paranoid Chinese companies, adopting AI, are about cyber security. Protecting their data from competitors and the continuing availability of data and AI based solutions becomes increasingly important once a company's business model has evolved to adopt AI. So the principle "Cherish your Data" (see The Way of DAU) is key to successful exploitation.

Conclusion

Human issues, ethics and common sense remain central to AI adoption, an Agile mindset encourages adoption and Data Governance is a key enabler.

Digital Revolution for the Hospitality Industry?

How would Digital affect a traditional industry such as the hotel business? Well there's certainly potential when looking at what happens today and it goes beyond just a clean up of the myriad of decrepit and unintegrated applications used today.

Starting with the first point in the Customer Journey Map, selecting a hotel, most hotels have their own website and are marketed through a number of web sites or via companies who have block booked groups of rooms within a hotel. (These websites and companies are often referred to as OTAs or on-line travel agents). At least one company, XcelTrip, is deploying Etherium Blockchain to disintermediate the middle men and provide hotels with a greater throw weight, whilst retaining a larger share of revenue for themselves, via a Distributed Travel Environment (DTE). 

Also, Virtual Reality offers a much improved way to experience what the hotel is like before you get there so, not only can you understand what the room and facilities are like, you can also see what the view is like and understand the location in which you would be staying. This should be a great draw, especially for premium hotels offering on site experience or prime locations as a special feature.

Secondly the arrival and process can be greatly improved, especially for frequent travellers and participants in hotel loyalty schemes. If the hotel room has been booked and assigned, there really is no reason for someone to queue at a desk to receive a key. Payment details and identification (e.g. photo or passport) details should already have been captured as part of booking or from stored information within the loyalty scheme database. So it should be more than easy to provide the room details and an access code via text, email or an app used by loyalty scheme members. In this case a traveller can go straight to his or her room, unless a porter is needed. Any final identity checks or validations could easily be carried out by facial recognition as the traveller enters the room.

IoT and the current generation of home automation technology show how much the room iteself can be automated from smart locks, through smart controls for HVAC (Heating, Ventilation, Air Conditioning) provision of voice control for curtains, lights, temperature, locks, TV and entertainment systems and support for AI based translation, so visitors can understand all television channels irrespective of the languages that they use. They also should be able to order room service in the local language via AI translation too. 

Hotels could also consider providing some of the range of digital devices now available to assist in sleep augmentation. (see previous posts). As it has been shown that people do not sleep well in strange environments. Typically on the first night anywhere new, one hemisphere of the brain does not turn off and remains on alert through this first night, leading to less restful sleep.

Other features such as instrumenting the plumbing should make it easy to centrally monitor rooms for problems with leaks etc. and IoT enablement coupled with presence detection should allow for efficiencies to be made in room heating and cooling. Other automation devices such as robot hoovers are a natural application within a building which has a high requirement for daily cleaning and a fixed layout. Operationally, they should contribute to greater room performance and availability. 

Given machine learning's capabilities, it should also be possible to match guest lists with people's cultural backgrounds, previous ordering habits and stated preferences (when booking or entering their details into loyalty scheme systems) to ensure that menus are tailored on a daily basis to what is not only seasonal, but also meets the needs of guests, increasing the likelihood that they will spend on meals within the hotel, rather than going out to dine. Similar principles apply to the stocking of bars and menus for room service.

Delivery of room service meals, drinks and other supplies such as spare tooth brushes can also be effected using RPVs (remote piloted vehicles) for a significant proportion of such transactions. Typically this could shave 15-30 minutes off the wait for a room service request to be fulfilled, doing away with those annoying waits when staff are over stretched.

Checkout, just like check in, could be greatly streamlined. Almost everyone pre-authorises a payment card these days. So the only real issue is dealing with any queries about a billing or  with any complaints as typically 4 out of 5 checkouts are just confirmation of what everyone already knows and receipts can be provided electronically. So if a busy guest just wants to check out, all they should do is enter a swipe out code via an app, and house keeping can immediately add the vacated room to their list.

Finally, a guest's activity during his or her stay, provides a trail of data rich events which can be mined for insight into overall guest behaviour as well as individual tastes and needs, leading to optimisation of future marketing efforts and incentives for repeat business aimed at all customers, specific cohorts or just the individual. This includes increased flexibility in pricing to match capacity, demand and financial yield. Although there are also context based insights for planning refurbishments and room maintenance to fit better into periods where demand is either less intense or less profitable.

So the new hotel is coming, more automated, less frustrating and increasingly tailored to the traveller's needs.

Artificial Intelligence and Our 5 Senses

Artificial Intelligence (AI) is very much the thing of the moment. A lot of new enterprise models appear to be built on a combination of AI, IoT and Big Data to collect and process massive amounts of data and to apply human like skills of recognition and decision making with a level of consistency, speed and volume that was previously unattainable with conventional processes or systems. This leads to greater insight, improved performance, better use of scarce skills and consequently other benefits such as improved customer experience, better security and higher cost efficiency. It also means that some tasks which would never have been practicable before are readily delivered via automation.

However when we look at what a human can do, AI capabilities are still lacking. The human capability to use our 5 senses, think abstractly, innovate and test hypotheses, to develop new learning are still a challenge to AI.

Just focusing on the 5 senses: Sight, Hearing, Touch, Smell and Temperature, AI's progress is quite patchy.

In the past 30 years or so, AI's ability to deal with vision and speech has progressed from a miserable score of  about 1 to 2 out of 5, to a 3 to 3 and a half out of 5. Handwriting recognition works well with typed characters, but still has a significant error rate with hand writing. Face recognition systems work quite well with caucasian faces but apparently struggle to deal with black african faces. It is not clear to me  whether the latter is to do with poor training (i.e. not been given enough sample data for adequate deep learning), fundamental flaws in the assumptions made when designing the systems to recognise features or some problems with technical measurement of contrasts and camera sensitivity across the visual spectrum. Whatever the problems, error rates are still quite high.

Temperature is quite easy with thermo-couples and can be engineered to deal with temperatures that people could not with stand. So high marks of 5 out of 5 are to be expected.

Touch has been a major area of research ever since robotics became a hot topic in the late seventies. Most of the work so far has focused on sensing around grip, so that a robot could pick up an egg or some other delicate object without crushing it. But I have not seen anywhere the type of sensing that could differentiate between the touch of skin, rubber, silk, cotton and wool. So this definitely languishing in 2 out of 5 territory.

Smell has barely been touched. However some work is suggesting that techniques for smell processing, which mimic natural biological processes could be invaluable in extending AI capabilities to deal with situations where learning data is limited, ambiguous or masked with other "noise". Smell has potentially many applications in medecine, agriculture and food processing. The techniques however, may be useful for Autonomous Unmanned Vehicles which often have to deal with unforeseen situations or noisy environments.

So there still a long way to go for AI to reach science fiction like capabilities. Conquering the 5 senses represents the first step. The question is, how long will this take and will all humans be machine augmented, by the time we get there?

Where Does AI take Us Next

AI is at last beginning to make serious inroads into practicable automation. At its heart, practical applications are mainly based upon recognition and learning. So if we think back to the late 80s when IVR with a few key words was abaout as good as it got, followed by some voice dictation systems in the early 90s which never quite delivered what was wanted, things have moved on. Early attempts at biometric based acces control were also quite fraught with too many false positives. Japan's big push into AI with its 5th generation computing initiative ended with a wimper and similar lower key initiatives in Europe were never sustained.

Perhaps the first significant advance was the use of voice analysis to detect whether people were likely to be lying, used by some large insurance companies. However gradually, in many small incremental steps other applications have gradually become more practicable, e.g. facial recognition in crowds to find wanted fugitives or prevent entry into countries at passport checkpoints. Other more subtle approaches have also been used to try and detect intentions of people in crowded public spaces so that probable terrorists or pick pockets could be identified before they commit offences.

Additionally, many issues facing enterprises managing their IT / Digital estates involve monitoring and analysis of so many events in order to detect, analyse and prevent probable service failures or security breaches that only automation can work. Given the large combination of factors, behaviours and knowledge needed, only AI can do this. So Dark Trace is currently a favourite in the Cyber Security community for its ability to detect anomolies, not just by looking at known issues, but comparison of behaviour of assets to detect anomolies suggeting problems. Dark Trace is credted with helping stem the Wannacry ransome ware attack in the NHS last year. Oracle has also started to provide an AI based service for system management and security across heterogenerous platforms. Whilst others are building capabilities into their existing event analysis products such as Splunk.

Much of this is what might be called "defensive" application, but it is interesting that a number of new initiatives are being investigated in the health sector to help automate the bureucratic aspects of doctors consultations, freeing them up to spend more value added time actually dealling with patients. Also some new research areas are looking at things like whether speech analysis can be use to help diagnose specific conditions. For Example the Mayo clinic is looking at its application to Coronary Artery Disease, so that remote over the phone diagnosis may become practicable.

Speech bots have also advanced a little beyond the IVR stage to be capable of sustaining "human alike" interactions in fairly limited and constrained applications and some of the recent game based demonstrators for GO and Chess have evolved beyond brute force projection to strategy formulation, although again these are limited in scope and application.

The papers are still full of instances where researchers have shon how to fool facial recognition or facial recognition works poorly on people with dark coloured skin. So there is room for significant incremental improvement.

Putting these limitations aside, it is possible to see that we are on course for real digital companions such as HAL in Stanley Kubric's well know film or legal AI entities in Iain Banks's novels. However, what is difficult to project is how soon this will be attainable. We do however need to start thinking about the ethical and legal frameworks for such adoption, otherwise we risk regressive reactive legislation in the future which undoes the value to be gained from future adoption.

Is AI a threat to the Rockstar Developer?

This week IoT TECHEXPO was the main event at Olympia in London, combined with BLOCKCHAIN EXPO and AI EXPO. This was an impressively large event, though still not quite at the critical mass of some of the Security Exhibitions held recently at the same location. Though it shows that converged application of the 3 different technologies is gaining traction.

It was impressive how many vendors were launching new services based on blockchain. An aweful lot of them were based around assuring identity and provenance of assets, people and devices. So there were universal "passport" service, property dealing services, fine art trading, new travel disintermediation services and so on. In addition to several crypto currencies that I had not heard of before, there were a lot of wallet and token services.

In fact at one point I got the impression that the suppliers there were providing the means for circumventing national authorities with their own private currencies and virtual passports. For if you imagine a consortium of multinational businesses using tokens to trade with some end of year settlement in real money, this could totally confuse any picture of their tax obligations based on monetary trading transactions.

Putting this aside, the most interesting talk was probably the one on using AI for programming by Kiki de Bruijn of Diffblue which was heavily oversubscribed. She described the challenges of trying to generate code without human input and advocated the use of AI and Machine Learning based tools as productivity and quality aids instead, as it is difficult to replace the creative aspect of development as well as deal with all the potential complexities of developing well integrated code to meet all eventualities,

UK Viewpoint on Digital Trends

At this time of year, Nimbus Ninety publishes its annual survey report on digital trends. What's nice about this is that not only do they provide a UK only perspective on the marketplace, but some of their commentary is contributed by leading practitioners, rather than consultants or journalists.

Anyway, this year shows strong themes around AI as the big disrupter and big data as the continuing big focus of spend. Predictably enough, Blockchain has moved into the picture as one of the future technologies of interest for disrupting business, just leapfrogging IoT by a single percentage point. Cyber defence technology remains one of the top disrupters too.

Culture is still cited as one of the big barriers to success identified by participants. This echoes a concern cited in this month's PM Today magazine that 51% of UK IT Leaders are struggling to secure boardroom consensus on digital transformation objectives (source: Interoute sponsored research).

Overall businesses are driven mainly by the need to meet customer expectations and improve customer interactions, whilst improving the speed and efficiency of processes. Only a third of businesses seem to be thinking about re-inventing their business model, which is still a large number but suggest that many businesses are still thinking short term about what Digital is about. Although half are pursuing Digital to enter new markets or develop new products. 

Put together this suggests that too many businesses are taking a departmental or functional approach to implementing digital and have not got into the One Team One Strategy approach needed for successful and sustainable Digital Evolution into Digital As Usual Businesses.

Interestingly enough though, the survey's respondents seem to rate fear of internet giants entering their markets much greater than fear of new entrants or startups. This mirrors the recently emerging consensus that digital markets tend to favour those who have already built scale, such as the big platform based businesses, e.g. Amazon.

Has Big Data Lost its Mojo?

A number of surveys were published during 2017 which suggested that Big Data has lost the CxO mind share that it had.

The overall impression gained was that business leaders are putting their emphasis on Artificial Intelligence (in particular Machine Learning) and IoT, whilst CIOs and Technical Leaders are worrying about Security and Lean (Agile and DevOps). 

At the same time the consumer and gadget end of things are focusing on wearables, voice and VR/AR based devices (implying other types of AI are getting important). 

Somewhere in the mix, people are worrying about culture, product management and marketing, and organisations designed around empowered product teams.

There almost appears to be an assumption that big data has been cracked and apps are easy. Also, data governance is not really getting the attention that it should do and vendors are pushing APIs for integration.

This suggests not just a gap between business vision and technical capability to deliver, but also that the consumer led boom in exploitation is going places which don't necessarily fit well with classical business environments. Open plan offices are not the place where people should all be talking to their digital assistants.

The lessons I draw from this are than business management teams need to start thinking holistically about what is needed to deliver their new business (i.e. digitally augmented) strategies and models, but also about what the future workplace looks like. Is the office finally dying?

They also need to get real about data. It needs to be managed using lean data principles. Integration and security are vital to get right. IoT and other exploitation approaches are going to emphasise Big Data's importance further. So it's vitally important to develop data specialists who understand your business and are networked with the right people. A recent article in the Harvard Business Review suggests that most organisations have focused on technicalities and need to look at a more immersive approach and organisational issues too.

Human AI Integration Gets Closer

In another mind-blowing breakthrough TNW (the next web) reported on an AI which reads and interprets human brainwaves to understand what you are seeing.

Although this is early days, as a proof of concept it is very powerful, showing that the ability to read and interpret human thoughts and senses reliably is not as far away as you would think.

Obviously there is still a lot to do to refine this particular example, as it focuses on sight and recall of images only, but it definitely puts Elon Musk's vision of direct HCI integration into the next twenty years of achievability (allowing for the need to deal with volumes of data and presentation, psychological and ergonomic issues, as well as security and miniturisation.

But it's definitely coming. So there's an ethical debate to be had too around privacy and safeguards.

Also, recently there have been reports on brain hacks to use AI to control electrical stimulation of the brain and improve its performance.

More prosaically, adoption of implantable chips in people is growing; at present, see Brian Jonhson's video, the chips are for single function usage, e.g. access control or train tickets, but the potential is there for multi-functional  implantable chips with flexible reprogramming. So the seeds are being being sown for full human computer integration, and the potential for embedded AI enhancement.

This leads to the question of how obsolescence and technical upgrades will be managed as current approaches to patching are likely to be inadequate.

Updated 9th Feb 2018

The Launch of Human 2.0

CES is America's annual gadget show. Whilst the tech giants such as Google and Amazon don't normally announce much here, the ecosystem of companies who exploit products such as Android and Alexa/Echo use this as a platform for launching new products. Apparently, this year's theme is Voice as a host of home automation products, exploiting voice commands and Alexa's AI capability, are using CES as the platform for their launch. Although you may be interested to know that Alexa will soon be available on Windows 10, challenging Microsoft's Cortana assistant.

Meanwhile, the latest cool device in the Wearables arena has been announced by Innomdle. This is the Sgnl which is a wristband cum watch strap which converts your fingers into a telephone receiver. When paired with a phone, it transmits vibrations down your hand to your fingers, so you can hear your phone by touching them to your ears. The device also has notification and fitness features. Initial reviews are positive and the shipping date has been announced as March.

At the same time the range of new Wearables announcements include: 

• The Eagle a wearable home theatre system from Kopin and Pico Interactive. A key feature of this is that you retain awareness of the outside world, lending it to scenarios such as automated plant control.

• Omron's forthcoming Heartguide which promises to deliver accurate blood pressure monitoring (countering reports that many smart watches are innacurate);

• Fitbit's hint that it will be delivering blood sugar monitoring technology.

So what does this mean to people looking at digital exploitation? my thoughts are this is the year when Human 2.0 gets launched. What do I mean by this, well Human 2.0 places people at the centre of automation and control of an extended network of digital assets and automated devices. So a person can control an automated production cell or a power plant with integrated feeds of information, slave AI bots to carry out standard operating procedures, and all relevant KPI information and alerts fed to him or her. At the same time, a raft of wearables will monitor and tend for the person, as it is only a short step from wearable monitoring to automatically administering nutrition, hydration and medecines.

This means that most current concepts around Industry 4.0 need to change.

Where Next for Wearables

I recently found this article https://www.wareable.com/wareable50/best-wearable-tech-2017 on wearable technology trends, which is worth a read.

The thing I really liked was the Thynk Relax device, which basically uses electrical neck stimulation to help you relax and improve sleep. It's a bit pricey but slots alongside Dreem's headset as something which is really focused on individual well-being and in the the nature of Zeitgheist addresses one of the major issues of the age, Stress.

However overall this article gives a snapshot of where investment is going. Out of 50 (or so items mentioned) Mainstream trends are:

• Watches and Wriststraps account for 14 of the main items, all with slightly different slants.
• Smart Clothing and shoes get 7 mentions.
• VR, AR and Mixed Reality get 6 with evolved approaches to smart glasses and weareable cameras counting for another 4.
• Hearables and earbuds count for another 3 with more mentions of possibilities as well.
• Home automation platforms and static digital assistants (which are not currently weareable, but this is an obvious evolutionary path)  garner 6 mentions.

Looking across the products, one gets a sense of evolution and convergence. Some things are being simplified to address user centric design concerns (make things simple). Single use items such as payment rings probably will not survive long as products in their own rights. Products are starting to address response to human emotions, e.g. straps which detect them and home management systems which adjust the home environment to fit your mood and music/media based output to clothing. There is also a slight shift away from just sensing to doing things to you as well.

The overall wearable eco system is evolving to sense everything to do with the wearer and his or her environment, to provide him or her with information input (visual, audio, heating and cooling of clothing, and bodily stimulation), to screen out unwanted stuff, e.g. background noise, as well as issue commands to your car, devices in a smart house etc. and to provide AI based advice and updates of information from the internet.

The foundations are really being laid to get rid of the smart phone, home computer and pad, and replace them with smart clothing, jewelry and even smart temporary tattoos. Interestingly as this wave hits, Google and Apple appear to be trying to keep up, but with slightly underwelming products, and Microsoft is barely visible in the race. It's time to try and work out who will be the next consumer and design driven giant in the tech field.

The Perils of Bad AI

Everyone by now will have read that they are about to be replaced by robots and AI bots at work. Many people have also heard how employment agencies and some large firms use AI based software to screen applications and most who do complain about it.

So, recently, I tried an experiment with CV parsing software. I used a "top 10" CV parser and took some copies of a friend's CV which had been tweeked to apply for slightly different roles and submitted it to the free demonstration site.

Within hours I had received back the analysis of the 3 different CVs. I almost fell off my chair in astonishment of the analysis produced. In all cases, it cited the wrong position as his most recent one. It ignored half of his work experience, reducing over 20 years experience to just over 10. It got his level of seniority wrong and in one case it totally missed what was his major skill. In fact it bore such little relationship to what the CVs said that it could be said to be "made up" or "fabricated". It certainly was not accurate and was misleading.

I then decided to change the presentation of the information slightly in the CV. I moved his most recent role to the second page and where he listed earlier previous experience, I changed the orger in which role, organisation and date was presented. The content itself remained the same. Guess what? his most recent role was identified as an even earlier one. But it now identified the missing 10 years or so of experience. It still however did not correct the level of seniority that it assigned.

The thing which strikes me is that the agencies are using the software to deal with a problem of volume. Each advert typically attracts 300 to 1,000 applications. But they are not advising people how to structure their applications so that the software reads the CVs (or resumes) accurately. 

In my opinion, any agency or recruiter using this type of software is not going to pick the right candidates and is opening itself to breaches of data protection legislation (at least in europe) as use of any incorrect, out of date or deficient data which causes loss, harm or embarrassment is a criminal offence. Arguably, failing to shortlist someone because you rely on erroneous information would be in that category.

This shows how fragile and potentially dangerous some of the existing AI and machine learning applications are. They are powerful when properly trained and tested, but treating them as a facile magic bullet which will automate all interpretative assessment out of processes is dangerous.

Artificial Intelligence and Other Tales

Engineers at Google recently published a paper outlining their work developing a custom AI chip which is so efficient that it saved them having to build new data centres to cope with the introduction of AI based services. The paper itself provided a great insight into the scale and nature of Google's operations. However, it was the catalyst for musing on where we are going with AI and what it all means.

In the 1980s there was a lot of hype around artificial intelligence resulting in thousands of university undergraduates learning languages such as LISP and Prolog, and the Japanese Government sinking hundreds of millions of dollars into its Fifth Generation Computing programme in an attempt to progress the technology further. More modestly the UK's DTI published a few pamphlets and books on state of the art Intelligent Knowledge Based Systems (IKBSs) and Neural Networks. A few rather expensive products hit the market. There were a few relatively trivial case stydies and then it all just seemed to fizzle out, gradually becoming forgotten as the Internet Bubble grabbed people's attention.

However, AI did not go away, its development just went into submarine mode. People were there working quietly away in the background on specific applications of AI and bringing them to a level of maturity where they can be adopted wholesale in real life situations. In the process there have been substantive strides, so for example voice recognition systems now require little training and accommodate regional and national accents. Additionally, there has been a little gentle re-branding to adopt the term "Machine Learning". Consequently many people use "personal assistant" agents on their phones or built into household automation devices. Self driving cars are reaching commercial aodption and the next generation of UAV aircraft will be capable of accepting a programmed mission, taking off and completing it without human intervention or interaction, unless some mission parameter is encountered which requires human decision making or to receive required intelligence.

I was literally blown away with incredulity recently, when someone showed me how easy it is to set up a simple machine learning application in Azure and train it to produce a useful output. But I have been reading recent alarmist claims that "AI robots" will replace millions of human professions with a level of scepticism, as this is not substantiated by previous experience.

In the 1980s, when I worked for a bank, there was an interesting article in the Financial Times on the adoption of IT by the banking industry. The gist of the story was that if the banking industry had not adopted IT to mechanism a lot of its work, then the explosion in consumer banking products in the UK which happened at that time would not have been possible without employing the whole of the UK workforce to support it.

A recently touted robotic brick layer is unlikely to eliminate all brick laying jobs because there is a current shortage of bricklayers and robots will not be economic on small sized jobs. Furthermore, the reason that the United States has traditionally enjoyed higher productivity than Europe is not a result of American technical superiority, but the result of the fact that the US has always been resource rich and people poor. A shortage of labour and skills leads to new methods and investments in mechanisation and automation.

Furthermore, the UK Government Digital Strategy is promoting the investment into the development of AI skills, because the Government believes this will grow both the economy and jobs. The real impact will be in the change of the nature of the jobs. People will do less humdrum stuff and explore their curiosity more to invent new things, discover new things and do their jobs more creatively.

Modern Artistry - This Week's Digital Themes

Last night I went to a BCS (British Computer Society) event on DevOps with the title "Fast Forward - Catch the Buzz!". The speakers were mainly from a company called Infinityworks. As was to be expected with anything to do with DevOps, this was a popular event with a good turn out and a diverse audience.

They presented 2 case studies on assignments they are working on for clients. In the first, they had taken over the management of a legacy website and its associated app to support engagement with a sports betting site. This they moved from dedicated hardware to a cloud platform, upgraded and then modified to produce different versions for new european markets, all in a matter of months. It was a great exposition on how modern tooling and automation, combined with agile and DevOps approaches can be used to do things quickly, safely and at scale. They also had interesting things to say about how elastic a cloud platform really is, if your loading is massively peaky on, say, 1 day per week. A key message which came across is how the culture of modern professional service providers is aligned to working intimately in genuine partnership with their customers.

The other example, discussed an unloved legacy application within the NHS, which had been delivered under a controversial national programme, had been taken in house, re-platformed and co-developed with them to make it more economical, performent and fitter for purpose using DevOps style techniques and some automation. The interesting thing being that the application did not need to be moved to a cloud platform to get these benefits and that suitable investment in tooling could achieve identical cloning and continuous integration. One of the salient lessons coming out of  the exercise was that design decisions should favour the optimisation of operability over ease of development as applications spend more time in operation than they spend in development and this is where the value is delivered.

It was also good to hear afterwards, in discussion with some of the presenters that the NHS is genuinely engaging via patient workshops in understanding the "customer journey" and using insights to feed into product management of its key applications. This is huge sea change from the previous central government driven approach which mandated things for politically dogmatic reasons.

Anyway, today as I was still reflecting on last night, I opened my email Inbox to discover an invite from the iconoclastic people at "The Register" to a lecture enticingly called "Sex, AI, Robots and You". Tbe format being a talk, Q&A and networking event. As this obviously has some overlap with digital topics, wearable technology and (wo)man machine interfaces, I just had to investigate a little further. When I followed up on the speaker, it turns out that she is a lecturer at Goldsmiths College and this is one of her research areas. Interestingly, she has started to hold hackathons called the SexTechHack. There were links from a review ( see: https://www.girlonthenet.com/2016/12/21/amazing-inventions-from-the-goldsmiths-sex-tech-hack/ ) to Youtube posted videos on presentations from the various groups at the hackathon. Overall they were surprisingly innovative and wide ranging. The content, however, only served to reinforce my thoughts discussed in my last posting on Post Digitalism. The key issues to address in the next generation of post digital technology are cultural, psychological and legal.

Post Digital Dawn: The New Human Computer Interface

As an expression, La Interfaccia Uomo Macchina sounds a lot more sexy than HCI (or human computer interface), but then english speakers have often been suckers for Romantic Languages. Recently, however, the whole topic has become much more interesting as Elon Musk has started to hint about his interest in Neural Laces or direct brain to AI links.

He has now come into the open and announced his investment and interest in Neuralink, a startup specialising in brain to compute interfaces (see: http://bit.ly/2naV4sB ). This builds on progress in a number of areas around understanding the brain and development of a significant number of devices which interface with the central nervous system to enable things such as control of artificial limbs and to help manage the impacts of neurological conditions such as Parkinson's Disease. 

Basically we are seeing the start of the next wave of Information Science and the next big thing after Digital. Although Digital is powerful and will continue to change the way the world operates for quite some time to come, it is limited by the siloed nature of current technologies. True they operate together, with a little integration effort, but they are constrained by the limits of the internet and existing graphical interfaces.

Others have also come into the open with their plans for human computer interfaces, e.g. Brian Johnson of Braintree discussed this at the Recode code conference and Steve Hoffman at TED.

Musk has now lit the fuse for next revolution. Although to be honest this will take a long time to mature in terms of capability, safety and comfort in using the technologies involved. There are a lot of issues to resolve before Neural Laces become something that people are comfortable to adopt.

Firstly, the operating systems of current technologies are just not robust enough. Anything that is intended to operate closely with the human brain should be as least as reliable as the human brain. Secondly, no only must it be safe to attach and integrate Neural Laces with the human brain, there needs to be a robust path for maintenance, upgrade and removal. As it is highly probable that some people's bodies or brains would reject such technology, no one would want to be locked into an inevitable obsolescence as the technology improves, and there are bound to be faults which need rectifying. So "graceful failure" must also be built in. Thirdly, there needs to be a high level of security and privacy built into the Neural Laces, otherwise the risk of being hacked, swamped with unwanted information or interrogated by thought police is too high. Fourthly, the Neural Lace needs to be able to take power from biological processes within the body and to operate at a temperature which will not damage the human brain. Finally, the Internet will need to be completely re-engineered to work safely and effectively with people. There already is a known challenge for the IoT, but this represents a complete step up for networking capability. This all assumes that the basic interface technology can be developed in the first place.

However, the biggest challenges are likely to lie in the human and societal arena. Putting aside the fact that some people would always oppose such technology as being against their religious beliefs, there is a need to address questions around ethics of use, legal issues around IPR, liability and things such as acceptability of evidence sourced via a Neural Lace. There may even be issues of disability if someone is not capable of adopting such technology. Finally, there is a need to develop psychological understanding of how to deal with the vast amounts of data (as opposed to information) associated with using a Neural Lace. If appropriate filtering of data and information feeds is not designed into a Neural Lace, the danger is that it will cripple its host or drive the person insane.

So it may be that the real beneficiaries of developing this technology will be the psychologists and lawyers needed to make it fit for people and society.

Platform Schizophrenia

This year I became aware that there are two definitions to Digital Platforms. Whilst I had been meandering around in IT Space thinking that digital platform meant services like AWS and Azure, our friends in Marketing Space had decided that digital market places were Digital Platforms. So to them ComparetheMarket.com, Deliveroo.com and Uber.com are platforms. 

Anyway moving on from this diversion, it has for some time been a surprise to me that amazon has dominated IT thoughtspace and the market for PaaS based Digital Platforms, whilst belatedly Microsoft pushed into the market with Azure. 

In recent projects I have been involved with both AWS and Azure as well as all sort of fun with the OSS tools which are available on them. To traditionalists coming across the database as a service offerings available is quite amazing. I was also blown away when a Solution Architect who had no experience of Neural Networks was able within 2 weeks to knock up a fully working and trained prototype of a Machine Learning application on Azure.

So it has become increasingly interesting to see that Google, one of the most born in the cloud companies going, has recently started promoting its services. One has to ask why did they wait so long, especially as they have always made much of the fact that their products are all architected around a SOA concept and the ability to expose themselves as services, both internally and externally.

Oracle and IBM have also appeared actively in the market place this year promoting their own special blends. 

The thing about this is that they all have really good stories to tell. You will note that I am not stating any preferences, as to be honest, anything I say about them today will already be wrong tomorrow as this is an ever faster moving situation. Today's facts will be obsolete tomorrow.

So what does it all mean to the average business trying to go Digital?

Firstly, the means are there. You have to be comfortable with the fact that terms and conditions are what they are. You need to examine the pricing and understand how this would play out in some key real world applications. However there is plenty available to "Free Your business from the Tyranny of Infrastructure" and Focus on Value. If you choose reasonable sensibly, you will be able to scale costs with business activity and exploit platforms which support Agile and DevOps so you can move quickly and lightly in the pursuit of opportunities. All the major vendors are investing significantly in security and if you dig deeper, most offer localisation options if data cannot move outside certain jurisdictions. Additionally there are industry certification schemes which many providers are signed up to. So a lot of inhibitors have been addressed.

The key issue is going to be how much do you insulate yourself from the risk that you may need to change platform provider. Business Performance, Legislation, Pricing etc. will change with time. So you may need an exit plan. Therefore, some thought needs to be given to insulating yourself from future supply threats. Where your application is going in for short term gains, e.g. a new financial instrument which will only be around for a a few months or perhaps a couple of years, this is not a problem. But if you are locking yourself into a platforms specific machine learning solution for years, you may need to think how you would deal with problems if the platform vendor ceases training.

In the end, however, we have always faced these problems. Finding a totally vendor agnostic solution has always been too complicated and too costly. So its time to get comfortable with not being in total control. The System of Systems concept of de-optimising components to integrate and optimise the overall performance of the Big System applies. You just need to understand your risk appetitie, your risks, how you want treat them, what you will accept, what you need to insure against and get on with it. The risks of not doing so are far greater.