IT Trends into 2018 – or the continued delusions of Ian Waring

March 5, 2018March 5, 2018ianwaring Amazon, Android, Apple, Bitcoin, Blind Alleys, Cloud, Enterprise IT, Future Trends, Google, Low Energy Bluetooth, Mobiles, Wardley Maps Leave a comment

I’m conflicted. CIO Magazine published a list of “12 technologies that will disrupt business in 2018”, which promptly received Twitter accolades from folks I greatly respect: Leading Edge Forum, DXC Technology and indeed Simon Wardley. Having looked at it, I thought it had more than it’s fair share of muddled thinking (and they listed 13 items!). Am I alone in this? Original here. Taking the list items in turn:

Smart Health Tech (as evidenced by the joint venture involving Amazon, Berkshire Hathaway and JP Morgan Chase). I think this is big, but not for the “corporate wellness programs using remote patient monitoring” reason cited. That is a small part of it.

Between the three you have a large base of employees in a country without a single payer healthcare system, mired with business model inefficiencies. Getting an operationally efficient pilot with reasonable scale using internal users in the JV companies running, and then letting outsiders (even competitors) use the result, is meat and drink to Amazon. Not least as they always start with the ultimate consumer (not rent seeking insurance or pharma suppliers), and work back from there.

It’s always telling that if anyone were to try anti-trust actions on them, it’s difficult to envision a corrective action that Amazon aren’t already doing to themselves already. This program is real fox in the hen house territory; that’s why on announcement of the joint venture, leading insurance and pharmaceutical shares took quite a bath. The opportunity to use remote patient monitoring, using wearable sensors, is the next piece of icing on top of the likely efficient base, but very secondary at the start.

Video, video conferencing and VR. Their description cites the magic word “Agile” and appears to focus on using video to connect geographically dispersed software development teams. To me, this feels like one of those situations you can quickly distill down to “great technology, what can we use this for?”. Conferencing – even voice – yes. Shared KanBan flows (Trello), shared BaseCamp views, communal use of GitHub, all yes. Agile? That’s really where you’re doing fast iterations of custom code alongside the end user, way over to the left of a Wardley Map; six sigma, doggedly industrialising a process, over to the right. Video or VR is a strange bedfellow in the environment described.

Chatbots. If you survey vendors, and separately survey the likely target users of the technology, you get wildly different appetites. Vendors see a relentless march to interactions being dominated by BOT interfaces. Consumers, given a choice, always prefer not having to interact in the first place, and only where the need exists, to engage with a human. Interacting with a BOT is something largely avoided unless it is the only way to get immediate (or out of hours) assistance.

Where the user finds themselves in front of a ChatBot UI, they tend to prefer an analogue of a human talking them, preferably appearing to be of a similar age.

The one striking thing i’ve found was talking to a vendor who built an machine learning model that went through IT Helpdesk tickets, instant message and email interaction histories, nominally to prioritise the natural language corpus into a list of intent:action pairs for use by their ChatBot developers. They found that the primary output from the exercise was in improving FAQ sheets in the first instance. Ian thinking “is this technology chasing a use case?” again. Maybe you have a different perspective!

IoT (Internet of Things). The sample provides was tying together devices, sensors and other assets driving reductions in equipment downtime, process waste and energy consumption in “early adopter” smart factories. And then citing security concerns and the need to work with IT teams in these environments to alleviate such risks.

I see lots of big number analyses from vendors, but little from application perspectives. It’s really a story of networked sensors relaying information back to a data repository, and building insights, actions or notifications on the resulting data corpus. Right now, the primary sensor networks in the wild are the location data and history stored on mobile phone handsets or smart watches. Security devices a smaller base. Embedded simple devices smaller still. I think i’m more excited when sensors get meaningful vision capabilities (listed separately below). Until then, content to let my Apple Watch keep tabs on my heart rate, and to feed that daily into a research project looking at strokes.

Voice Control and Virtual Assistants. Alexa: set an alarm for 6:45am tomorrow. Play Lucy in the Sky with Diamonds. What’s the weather like in Southampton right now? OK Google: What is $120 in UK pounds? Siri: send a message to Jane; my eta is 7:30pm. See you in a bit. Send.

It’s primarily a convenience thing when my hands are on a steering wheel, in flour in a mixing bowl, or the quickest way to enact a desired action – usually away from a keyboard and out of earshot to anyone else. It does liberate my two youngest grandchildren who are learning to read and write. Those apart, it’s just another UI used occasionally – albeit i’m still in awe of folks that dictate their book writing efforts into Siri as they go about their day. I find it difficult to label this capability as disruptive (to what?).

Immersive Experiences (AR/VR/Mixed Reality). A short list of potential use cases once you get past technology searching for an application (cart before horse city). Jane trying out lipstick and hair colours. Showing the kids a shark swimming around a room, or what colour Tesla to put in our driveway. Measuring rooms and seeing what furniture would look like in situ if purchased. Is it Groundhog Day for Second Life, is there a battery of disruptive applications, or is it me struggling for examples? Not sure.

Smart Manufacturing. Described as transformative tech to watch. In the meantime, 3D printing. Not my area, but it feels to me low volume local production of customised parts, and i’m not sure how big that industry is, or how much stock can be released by putting instant manufacture close to end use. My dentist 3D prints parts of teeth while patients wait, but otherwise i’ve not had any exposure that I could translate as a disruptive application.

Computer Vision. Yes! A big one. I’m reminded of a Google presentation that related the time in prehistoric times when the number of different life form species on earth vastly accelerated; this was the Cambrian Period, when life forms first developed eyes. A combination of cheap camera hardware components, and excellent machine learning Vision APIs, should be transformative. Especially when data can be collected, extracted, summarised and distributed as needed. Everything from number plate, barcode or presence/not present counters, through to the ability to describe what’s in a picture, or to transcribe the words recited in a video.

In the Open Source Software World, we reckon bugs are shallow as the source listing gets exposed to many eyes. When eyes get ubiquitous, there are probably going to be little that happens that we collectively don’t know about. The disruption is then at the door of privacy legislation and practice.

Artificial Intelligence for Services. The whole shebang in the article relates back to BOTs. I personally think it’s more nuanced; it’s being able to process “dirty” or mixed media data sources in aggregate, and to use the resulting analysis to both prioritise and improve individual business processes. Things like www.parlo.io‘s Broca NLU product, which can build a suggested intent:action Service Catalogue from Natural Language analysis of support tickets, CRM data, instant message and support email content.

I’m sure there are other applications that can make use of data collected to help deliver better, more efficient or timely services to customers. BOTs, I fear, are only part of the story – with benefits accruing more to the service supplier than to the customer exposed to them. Your own mileage may vary.

Containers and Microservices. The whole section is a Minestrone Soup of Acronyms and total bollocks. If Simon Wardley was in a grave, he’d be spinning in it (but thank god he’s not).

Microservices is about making your organisations data and processes available to applications that can be internally facing, externally facing or both using web interfaces. You typically work with Apigee (now owned by Google) or 3Scale (owned by Red Hat) to produce a well documented, discoverable, accessible and secure Application Programming Interface to the services you wish to expose. Sort licensing, cost mechanisms and away. This is a useful, disruptive trend.

Containers are a standardised way of packaging applications so that they can be delivered and deployed consistently, and the number of instances orchestrated to handle variations in load. A side effect is that they are one way of getting applications running consistently on both your own server hardware, and in different cloud vendors infrastructures.

There is a view in several circles that containers are an “interim” technology, and that the service they provide will get abstracted away out of sight once “Serverless” technologies come to the fore. Same with the “DevOps” teams that are currently employed in many organisations, to rapidly iterate and deploy custom code very regularly by mingling Developer and Operations staff.

With Serverless, the theory being that you should be able to write code once, and for it to be fired up, then scaled up or down based on demand, automatically for you. At the moment, services like Amazon AWS Lambda, Google Cloud Functions and Microsoft Azure Functions (plus point database services used with them) are different enough to make applications based on one limited to that cloud provider only.

Serverless is the Disruptive Technology here. Containers are where the puck is, not where the industry is headed.

Blockchain. The technology that first appeared under Bitcoin is the Blockchain. A public ledger, distributed over many different servers worldwide, that doesn’t require a single trusted entity to guarantee the integrity (aka “one version of the truth”) of the data. It manages to ensure that transactions move reliably, and avoids the “Byzantine Generals Problem” – where malicious behaviour by actors in the system could otherwise corrupt its working.

Blockchain is quite a poster child of all sorts of applications (as a holder and distributor of value), and focus of a lot of venture capital and commercial projects. Ethereum is one such open source, distributed platform for smart contracts. There are many others; even use of virtual coins (ICO’s) to act as a substitute for venture capital funding.

While it has the potential to disrupt, no app has yet broken through to mainstream use, and i’m conscious that some vendors have started to patent swathes of features around blockchain applications. I fear it will be slow boil for a long time yet.

Cloud to Edge Computing. Another rather gobbledygook set of words. I think they really mean that there are applications that require good compute power at the network edge. Devices like LIDAR (the spinning camera atop self driving cars) is typically consuming several GB of data per mile travel, where there is insufficient reliable bandwidth to delegate all the compute to a remote cloud server. So there are models of how a car should drive itself that are built in the cloud, but downloaded and executed in the car without a high speed network connection needing to be in place while it’s driving. Basic event data (accident ahead, speed, any notable news) may be fed back as it goes, with more voluminous data shared back later when adjacent to a fast home or work network.

Very fast chips are a thing; the CPU in my Apple Watch is faster than a room size VAX-11/780 computer I used earlier in my career. The ARM processor in my iPhone and iPad Pro are 64-bit powerhouses (Apple’s semiconductor folks really hit out of the park on every iteration they’ve shipped to date). Development Environments for powerful, embedded systems are something i’ve not seen so far though.

Digital Ethics. This is a real elephant in the room. Social networks have been built to fulfil the holy grail of advertisers, which is to lavish attention on the brands they represent in very specific target audiences. Advertisers are the paying customers. Users are the Product. All the incentives and business models align to these characteristics.

Political operators, both local as well as foreign actors, have fundamentally subverted the model. Controversial and most often incorrect and/or salacious stories get wide distribution before any truth emerges. Fake accounts and automated bots further corrupt the measures to pervert the engagement indicators that drive increased distribution (noticeable that one video segment of one Donald Trump speech got two orders of magnitude more “likes” than the number of people that actually played the video at all). Above all, messages that appeal to different filter bubbles drive action in some cases, and antipathy in others, to directly undermine voting patterns.

This is probably the biggest challenge facing large social networks, at the same time that politicians (though the root cause of much of the questionable behaviours, alongside their friends in other media), start throwing regulatory threats into the mix.

Many politicians are far too adept at blaming societal ills on anyone but themselves, and in many cases on defenceless outsiders. A practice repeated with alarming regularity around the world, appealing to isolationist bigotry.

The world will be a better place when we work together to make the world a better place, and to sideline these other people and their poison. Work to do.

The Next Explosion – the Eyes have it

June 26, 2017June 26, 2017ianwaring Amazon, Apple, Cloud, Enterprise IT, Future Trends, Google, Privacy, Software 1 Comment

Crossing the Chasm – on one sheet of A4

One of the early lessons you pick up looking at product lifecycles is that some people hold out buying any new technology product or service longer than anyone else. You make it past the techies, the visionaries, the early majority, late majority and finally meet the laggards at the very right of the diagram (PDF version here). The normal way of selling at that end of the bell curve is to embed your product in something else; the person who swore they’d never buy a Microprocessor unknowingly have one inside the controls on their Microwave, or 50-100 ticking away in their car.

In 2016, Google started releasing access to its Vision API. They were routinely using their own Neural networks for several years; one typical application was taking the video footage from their Google Maps Streetview cars, and correlating house numbers from video footage onto GPS locations within each street. They even started to train their own models to pick out objects in photographs, and to be able to annotate a picture with a description of its contents – without any human interaction. They have also begun an effort to do likewise describing the story contained in hundreds of thousands of YouTube videos.

One example was to ask it to differentiate muffins and dogs:

This is does with aplomb, with usually much better than human performance. So, what’s next?

One notable time in Natural History was the explosion in the number of species on earth that occured in the Cambrian period, some 534 million years ago. This was the time when it appears life forms first developed useful eyes, which led to an arms race between predators and prey. Eyes everywhere, and brains very sensitive to signals that come that way; if something or someone looks like they’re staring at you, sirens in your conscience will be at full volume.

Once a neural network is taught (you show it 1000s of images, and tell it which contain what, then it works out a model to fit), the resulting learning can be loaded down into a small device. It usually then needs no further training or connection to a bigger computer nor cloud service. It can just sit there, and report back what it sees, when it sees it; the target of the message can be a person or a computer program anywhere else.

While Google have been doing the heavy lifting on building the learning models in the cloud, Apple have slipped in with their own CloudML data format, a sort of PDF for the resulting machine learning data formats. Then using the Graphics Processing Units on their iPhone and iPad devices to run the resulting models on the users device. They also have their ARkit libraries (as in “Augmented Reality”) to sense surfaces and boundaries live on the embedded camera – and to superimpose objects in the field of view.

With iOS 11 coming in the autumn, any handwritten notes get automatically OCR’d and indexed – and added to local search. When a document on your desk is photo’d from an angle, it can automatically flatten it to look like a hi res scan of the original – and which you can then annotate. There are probably many like features which will be in place by the time the new iPhone models arrive in September/October.

However, tip of the iceberg. When I drive out of the car park in the local shopping centre here, the barrier automatically raises given the person with the ticket issued to my car number plate has already paid. And I guess we’re going to see a Cambrian explosion as inexpensive “eyes” get embedded in everything around us in our service.

With that, one example of what Amazon are experimenting with in their “Amazon Go” shop in Seattle. Every visitor a shoplifter: https://youtu.be/NrmMk1Myrxc

Lots more to follow.

PS: as a footnote, an example drawing a ruler on a real object. This is 3 weeks after ARkit got released. Next: personalised shoe and clothes measurements, and mail order supply to size: http://www.madewitharkit.com/post/162250399073/another-ar-measurement-app-demo-this-time

IT Trends into 2017 – or the delusions of Ian Waring

December 27, 2016December 31, 2016ianwaring Amazon, Cloud, Digital Ocean, Future Trends, Google, Internet, Linux, Management, Metrics, Microsoft, Mobiles, Open Source, Software, Wardley Maps Leave a comment

My perception is as follows. I’m also happy to be told I’m mad, or delusional, or both – but here goes. Most reflect changes well past the industry move from CapEx led investments to Opex subscriptions of several years past, and indeed the wholesale growth in use of Open Source Software across the industry over the last 10 years. Your own Mileage, or that of your Organisation, May Vary:

if anyone says the words “private cloud”, run for the hills. Or make them watch https://youtu.be/URvWSsAgtJE. There is also an equivalent showing how to build a toaster for $15,000. The economics of being in the business of building your own datacentre infrastructure is now an economic fallacy. My last months Amazon AWS bill (where I’ve been developing code – and have a one page site saying what the result will look like) was for 3p. My Digital Ocean server instance (that runs a network of WordPress sites) with 30GB flash storage and more bandwidth than I can shake a stick at, plus backups, is $24/month. Apart from that, all I have is subscriptions to Microsoft, Github and Google for various point services.
Most large IT vendors have approached cloud vendors as “sell to”, and sacrificed their own future by not mapping customer landscapes properly. That’s why OpenStack is painting itself into a small corner of the future market – aimed at enterprises that run their own data centres and pay support costs on a per software instance basis. That’s Banking, Finance and Telco land. Everyone else is on (or headed to) the public cloud, for both economic reasons and “where the experts to manage infrastructure and it’s security live” at scale.
The War stage of Infrastructure cloud is over. Network effects are consolidating around a small number of large players (AWS, Google Cloud Platform, Microsoft Azure) and more niche players with scale (Digital Ocean among SME developers, Softlayer in IBM customers of old, Heroku with Salesforce, probably a few hosting providers).
Industry move to scale out open source, NoSQL (key:value document orientated) databases, and components folks can wire together. Having been brought up on MySQL, it was surprisingly easy to set up a MongoDB cluster with shards (to spread the read load, scaled out based on index key ranges) and to have slave replicas backing data up on the fly across a wide area network. For wiring up discrete cloud services, the ground is still rough in places (I spent a couple of months trying to get an authentication/login workflow working between a single page JavaScript web app, Amazon Cognito and IAM). As is the case across the cloud industry, the documentation struggles to keep up with the speed of change; developers have to be happy to routinely dip into Github to see how to make things work.
There is a lot of focus on using Containers as a delivery mechanism for scale out infrastructure, and management tools to orchestrate their environment. Go, Chef, Jenkins, Kubernetes, none of which I have operational experience with (as I’m building new apps have less dependencies on legacy code and data than most). Continuous Integration and DevOps often cited in environments were custom code needs to be deployed, with Slack as the ultimate communications tool to warn of regular incoming updates. Having been at one startup for a while, it often reminded me of the sort of military infantry call of “incoming!” from the DevOps team.
There are some laudable efforts to abstract code to be able to run on multiple cloud providers. FOG in the Ruby ecosystem. CloudFoundry (termed BlueMix in IBM) is executing particularly well in large Enterprises with investments in Java code. Amazon are trying pretty hard to make their partners use functionality only available on AWS, in traditional lock-in strategy (to avoid their services becoming a price led commodity).
The bleeding edge is currently “Function as a Service”, “Backend as a Service” or “Serverless apps” typified with Amazon Lambda. There are actually two different entities in the mix; one to provide code and to pay per invocation against external events, the other to be able to scale (or contract) a service in real time as demand flexes. You abstract all knowledge of the environment away.
Google, Azure and to a lesser extent AWS are packaging up API calls for various core services and machine learning facilities. Eg: I can call Google’s Vision API with a JPEG image file, and it can give me the location of every face (top of nose) on the picture, face bounds, whether each is smiling or not). Another that can describe what’s in the picture. There’s also a link into machine learning training to say “does this picture show a cookie” or “extract the invoice number off this image of a picture of an invoice”. There is an excellent 35 minute discussion on the evolving API landscape (including the 8 stages of API lifecycle, the need for honeypots to offset an emergent security threat and an insight to one impressive Uber API) on a recent edition of the Google Cloud Platform Podcast: see http://feedproxy.google.com/~r/GcpPodcast/~3/LiXCEub0LFo/
Microsoft and Google (with PowerApps and App Maker respectively) trying to remove the queue of IT requests for small custom business apps based on company data. Though so far, only on internal intranet type apps, not exposed outside the organisation). This is also an antithesis of the desire for “big data”, which is really the domain of folks with massive data sets and the emergent “Internet of Things” sensor networks – where cloud vendor efforts on machine learning APIs can provide real business value. But for a lot of commercial organisations, getting data consolidated into a “single version of the truth” and accessible to the folks who need it day to day is where PowerApps and AppMaker can really help.
Mobile apps are currently dogged by “winner take all” app stores, with a typical user using 5 apps for almost all of their mobile activity. With new enhancements added by all the major browser manufacturers, web components will finally come to the fore for mobile app delivery (not least as they have all the benefits of the web and all of those of mobile apps – off a single code base). Look to hear a lot more about Polymer in the coming months (which I’m using for my own app in conjunction with Google Firebase – to develop a compelling Progressive Web app). For an introduction, see: https://www.youtube.com/watch?v=VBbejeKHrjg
Overall, the thing most large vendors and SIs have missed is to map their customer needs against available project components. To map user needs against axes of product life cycle and value chains – and to suss the likely movement of components (which also tells you where to apply six sigma and where agile techniques within the same organisation). But more eloquently explained by Simon Wardley: https://youtu.be/Ty6pOVEc3bA

There are quite a range of “end of 2016” of surveys I’ve seen that reflect quite a few of these trends, albeit from different perspectives (even one that mentioned the end of Java as a legacy language). You can also add overlays with security challenges and trends. But – what have I missed, or what have I got wrong? I’d love to know your views.

Future Health: DNA is one thing, but 90% of you is not you

April 28, 2016April 29, 2016ianwaring Amazon, Apple, Cloud, Courses, Diet, Enterprise IT, Fitness, Future Trends, Google, Health, Kickstarter, Low Energy Bluetooth, Medical, Open Source, Software, Statistics, Watches Leave a comment

One of my pet hates is seeing my wife visit the doctor, getting hunches of what may be afflicting her health, and this leading to a succession of “oh, that didn’t work – try this instead” visits for several weeks. I just wonder how much cost could be squeezed out of the process – and lack of secondary conditions occurring – if the root causes were much easier to identify reliably. I then wonder if there is a process to achieve that, especially in the context of new sensors coming to market and their connectivity to databases via mobile phone handsets – or indeed WiFi enabled, low end Bluetooth sensor hubs aka the Apple Watch.

I’ve personally kept a record of what i’ve eaten, down to fat, protein and carb content (plus my Monday 7am weight and daily calorie intake) every day since June 2002. A precursor to the future where devices can keep track of a wide variety of health signals, feeding a trend (in conjunction with “big data” and “machine learning” analyses) toward self service health. My Apple Watch has a years worth of heart rate data. But what signals would be far more compelling to a wider variety of (lack of) health root cause identification if they were available?

There is currently a lot of focus on Genetics, where the Human Genome can betray many characteristics or pre-dispositions to some health conditions that are inherited. My wife Jane got a complete 23andMe statistical assessment several years ago, and has also been tested for the BRCA2 (pronounced ‘bracca-2’) gene – a marker for inherited pre-disposition to risk of Breast Cancer – which she fortunately did not inherit from her afflicted father.

A lot of effort is underway to collect and sequence the complete Genome sequences from the DNA of hundreds of thousands of people, building them into a significant “Open Data” asset for ongoing research. One gotcha is that such data is being collected by numerous organisations around the world, and the size of each individuals DNA (assuming one byte to each nucleotide component – A/T or C/G combinations) runs to 3GB of base pairs. You can’t do research by throwing an SQL query (let alone thousands of machine learning attempts) over that data when samples are stored in many different organisations databases, hence the existence of an API (courtesy of the GA4GH Data Working Group) to permit distributed queries between co-operating research organisations. Notable that there are Amazon Web Services and Google employees participating in this effort.

However, I wonder if we’re missing a big and potentially just as important data asset; that of the profile of bacteria that everyone is dependent on. We are each home to approx. 10 trillion human cells among the 100 trillion microbial cells in and on our own bodies; you are 90% not you.

While our human DNA is 99.9% identical to any person next to us, the profile of our MicroBiome are typically only 10% similar; our age, diet, genetics, physiology and use of antibiotics are also heavy influencing factors. Our DNA is our blueprint; the profile of the bacteria we carry is an ever changing set of weather conditions that either influence our health – or are leading indicators of something being wrong – or both. Far from being inert passengers, these little organisms play essential roles in the most fundamental processes of our lives, including digestion, immune responses and even behaviour.

Different MicroBiome ecosystems are present in different areas of our body, from our skin, mouth, stomach, intestines and genitals; most promise is currently derived from the analysis of stool samples. Further, our gut is only second to our brain in the number of nerve endings present, many of them able to enact activity independently from decisions upstairs. In other areas, there are very active hotlines between the two nerve cities.

Research is emerging that suggests previously unknown links between our microbes and numerous diseases, including obesity, arthritis, autism, depression and a litany of auto-immune conditions. Everyone knows someone who eats like a horse but is skinny thin; the composition of microbes in their gut is a significant factor.

Meanwhile, costs of DNA sequencing and compute power have dropped to a level where analysis of our microbe ecosystems costs from $100M a decade ago to some $100 today. It should continue on that downward path to a level where personal regular sampling could become available to all – if access to the needed sequencing equipment plus compute resources were more accessible and had much shorter total turnaround times. Not least to provide a rich Open Data corpus of samples that we can use for research purposes (and to feed back discoveries to the folks providing samples). So, what’s stopping us?

Data Corpus for Research Projects

To date, significant resources are being expended on Human DNA Genetics and comparatively little on MicroBiome ecosystems; the largest research projects are custom built and have sampling populations of less than 4000 individuals. This results in insufficient population sizes and sample frequency on which to easily and quickly conduct wholesale analyses; this to understand the components of health afflictions, changes to the mix over time and to isolate root causes.

There are open data efforts underway with the American Gut Project (based out of the Knight Lab in the University of San Diego) plus a feeder “British Gut Project” (involving Tim Spector and staff at University College London). The main gotcha is that the service is one-shot and takes several months to turn around. My own sample, submitted in January, may take up 6 months to work through their sequencing then compute batch process.

In parallel, VC funded company uBiome provide the sampling with a 6-8 week turnaround (at least for the gut samples; slower for the other 4 area samples we’ve submitted), though they are currently not sharing the captured data to the best of my knowledge. That said, the analysis gives an indication of the names, types and quantities of bacteria present (with a league table of those over and under represented compared to all samples they’ve received to date), but do not currently communicate any health related findings.

My own uBiome measures suggest my gut ecosystem is more diverse than 83% of folks they’ve sampled to date, which is an analogue for being more healthy than most; those bacteria that are over represented – one up to 67x more than is usual – are of the type that orally administered probiotics attempt to get to your gut. So a life of avoiding antibiotics whenever possible appears to have helped me.

However, the gut ecosystem can flex quite dramatically. As an example, see what happened when one person contracted Salmonella over a three pay period (the green in the top of this picture; x-axis is days); you can see an aggressive killing spree where 30% of the gut bacteria population are displaced, followed by a gradual fight back to normality:

Under usual circumstances, the US/UK Gut Projects and indeed uBiome take a single measure and report back many weeks later. The only extra feature that may be deduced is the delta between counts of genome start and end sequences, as this will give an indication to the relative species population growth rates from otherwise static data.

I am not aware of anyone offering a faster turnaround service, nor one that can map several successively time gapped samples, let alone one that can convey health afflictions that can be deduced from the mix – or indeed from progressive weather patterns – based on the profile of bacteria populations found.

My questions include:

Is there demand for a fast turnaround, wholesale profile of a bacterial population to assist medical professionals isolating a indicators – or the root cause – of ill health with impressive accuracy?
How useful would a large corpus of bacterial “open data” be to research teams, to support their own analysis hunches and indeed to support enough data to make use of machine learning inferences? Could we routinely take samples donated by patients or hospitals to incorporate into this research corpus? Do we need the extensive questionnaires the the various Gut Projects and uBiome issue completed alongside every sample?
What are the steps in the analysis pipeline that are slowing the end to end process? Does increased sample size (beyond a small stain on a cotton bud) remove the need to enhance/copy the sample, with it’s associated need for nitrogen-based lab environments (many types of bacteria are happy as Larry in the Nitrogen of the gut, but perish with exposure to oxygen).
Is there any work active to make the QIIME (pronounced “Chime”) pattern matching code take advantage of cloud spot instances, inc Hadoop or Spark, to speed the turnaround time from Sequencing reads to the resulting species type:volume value pairs?
What’s the most effective delivery mechanism for providing “Open Data” exposure to researchers, while retaining the privacy (protection from financial or reputational prejudice) for those providing samples?
How do we feed research discoveries back (in English) to the folks who’ve provided samples and their associated medical professionals?

New Generation Sequencing works by splitting DNA/RNA strands into relatively short read lengths, which then need to be reassembled against known patterns. Taking a poop sample with contains thousands of different bacteria is akin to throwing the pieces of many thousand puzzles into one pile and then having to reconstruct them back – and count the number of each. As an illustration, a single HiSeq run may generate up to 6 x 10^9 sequences; these then need reassembling and the count of 16S rDNA type:quantity value pairs deduced. I’ve seen estimates of six thousand CPU hours to do the associated analysis to end up with statistically valid type and count pairs. This is a possible use case for otherwise unused spot instance capacity at large cloud vendors if the data volumes could be ingested and processed cost effectively.

Nanopore sequencing is another route, which has much longer read lengths but is much more error prone (1% for NGS, typically up to 30% for portable Nanopore devices), which probably limits their utility for analysing bacteria samples in our use case. Much more useful if you’re testing for particular types of RNA or DNA, rather than the wholesale profiling exercise we need. Hence for the time being, we’re reliant on trying to make an industrial scale, lab based batch process turn around data as fast we are able – but having a network accessible data corpus and research findings feedback process in place if and when sampling technology gets to be low cost and distributed to the point of use.

The elephant in the room is in working out how to fund the build of the service, to map it’s likely cost profile as technology/process improvements feed through, and to know to what extent it’s diagnosis of health root causes will improve it’s commercial attractiveness as a paid service over time. That is what i’m trying to assess while on the bench between work contracts.

Other approaches

Nature has it’s way of providing short cuts. Dogs have been trained to be amazingly prescient at assessing whether someone has Parkinson’s just by smelling their skin. There are other techniques where a pocket sized spectrometer can assess the existence of 23 specific health disorders. There may well be other techniques that come to market that don’t require a thorough picture of a bacterial population profile to give medical professionals the identity of the root causes of someone’s ill health. That said, a thorough analysis may at least be of utility to the research community, even if we get to only eliminate ever rarer edge cases as we go.

Coming full circle

One thing that’s become eerily apparent to date is some of the common terminology between MicroBiome conditions and terms i’ve once heard used by Chinese Herbal Medicine (my wife’s psoriasis was cured after seeing a practitioner in Newbury for several weeks nearly 20 years ago). The concept of “balance” and the existence of “heat” (betraying the inflammation as your bacterial population of different species ebbs and flows in reaction to different conditions). Then consumption or application of specific plant matter that puts the bodies bacterial population back to operating norms.

Wild mushroom “Lingzhi” in China: cultivated in the far east, found to reduce Obesity

We’ve started to discover that some of the plants and herbs used in Chinese Medicine do have symbiotic effects on your bacterial population on conditions they are reckoned to help cure. With that, we are starting to see some statistically valid evidence that Chinese and Western medicine may well meet in the future, and be part of the same process in our future health management.

Until then, still work to do on the business plan.

Another lucid flurry of Apple thinking it through – unlike everyone else

September 15, 2014September 15, 2014ianwaring Apple, Cloud, Facebook, Fitness, Future Trends, Google, Internet, Low Energy Bluetooth, Medical, Money, Samsung, Twitter, Watches Leave a comment

This happens every time Apple announce a new product category. Audience reaction, and the press, rush off to praise or condemn the new product without standing back and joining the dots. The Kevin Lynch presentation at the Keynote also didn’t have a precursor of a short video on-ramp to help people understand the full impact of what they were being told. With that, the full impact is a little hidden. It’s a lot more than having Facebook, Twitter, Email and notifications on your wrist when you have your phone handset in your pocket.

There were a lot of folks focussing on it’s looks and comparisons to the likely future of the Swiss watch industry. For me, the most balanced summary of the luxury esthetics from someone who’s immersed in that industry can be found at: http://www.hodinkee.com/blog/hodinkee-apple-watch-review

Having re-watched the keynote, and seen all the lame Androidware, Samsung, LG and Moto 360 comparisons, there are three examples that explode almost all of the “meh” reactions in my view. The story is hidden my what’s on that S1 circuit board inside the watch, and the limited number of admissions of what it can already do. Three scenarios:

1. Returning home at the end of a working day (a lot of people do this).

First thing I do after I come indoors is to place my mobile phone on top of the cookery books in our kitchen. Then for the next few hours i’m usually elsewhere in the house or in the garden. Talking around, that behaviour is typical. Not least as it happens in the office too, where if i’m in a meeting, i’d normally leave my handset on silent on my desk.

With every Android or Tizen Smart Watch I know, the watch loses the connection as soon as I go out of Bluetooth range – around 6-10 meters away from the handset. That smart watch is a timepiece from that point on.

Now, who forgot to notice that the Apple Watch has got b/g WiFi integrated on their S1 module? Or that it it can not only tell me of an incoming call, but allow me to answer it, listen and talk – and indeed to hand control back to my phone handset when I return to it’s current proximity?

2. Sensors

There are a plethora of Low Energy Bluetooth sensors around – and being introduced with great regularity – for virtually every bodily function you can think of. Besides putting your own fitness tracking sensors on at home, there are probably many more that can be used in a hospital setting. With that, a person could be quite a walking network of sensors and wander to different wards or labs during their day, or indeed even be released to recuperate at home.

Apple already has some sensors (heart rate, and probably some more capabilities to be announced in time, using the infrared related ones on the skin side of the Apple watch), but can act as a hub to any collection of external bluetooth sensors at the same time. Or in smart pills you can swallow. Low Energy Bluetooth is already there on the Apple Watch. That, in combination with the processing power, storage and b/g WiFi makes the watch a complete devices hub, virtually out of the box.

If your iPhone is on the same WiFi, everything syncs up with the Health app there and the iCloud based database already – which you can (at your option) permit an external third party to have access to. Now, tell me about the equivalent on any other device or service you can think of.

3. Paying for things.

The iPhone 5S, 6 and 6 Plus all have integrated finger print scanners. Apple have put some functionality into iOS 8 where, if you’re within Bluetooth range (6-10 meters of your handset), you can authenticate (with your fingerprint) the fact your watch is already on your wrist. If the sensors on the back have any suspicion that the watch leaves your wrist, it immediately invalidates the authentication.

So, walk up to a contactless till, see the payment amount appear on the watch display, one press of the watch pays the bill. Done. Now try to do that with any other device you know.

Developers, developers, developers.

There are probably a million other applications that developers will think of, once folks realise there is a full UNIX computer on that SoC (System on a Chip). With WiFi. With Bluetooth. With a Taptic feedback mechanism that feels like someone is tapping your wrist (not loudly vibrating across the table, or flashing LED lights at you). With a GPU driving a high quality, touch sensitive display. Able to not only act as a remote control for your iTunes music collection on another device, but to play it locally when untethered too (you can always add bluetooth earbuds to keep your listening private). I suspect some of the capabilities Apple have shown (like the ability to stream your heartbeat to another Apple Watch user) will evolve into potential remote health visit applications that can work Internet wide.

Meanwhile, the tech press and the discussion boards are full of people lamenting the fact that there is no GPS sensor in the watch itself (like every other Smart Watch I should add – GPS location sensing is something that eats battery power for breakfast; better to rely on what’s in the phone handset, or to wear a dedicated bluetooth GPS band on the other wrist if you really need it).

Don’t be distracted; with the electronics already in the device, the Apple Watch is truly only the beginning. We’re now waiting for the full details of the WatchKit APIs to unleash that ecosystem with full force.

Yo! Minimalist Notifications, API and the Internet of Things

August 16, 2014August 16, 2014ianwaring Android, Apple, Cloud, Future Trends, Google, HomeKit, iBeacon, Low Energy Bluetooth, Mobiles, Software, Twitter, VCs Leave a comment

Thought it was a joke, but having 4 hours of code resulting in $1m of VC funding, at an estimated $10M company valuation, raised quite a few eyebrows. The Yo! project team have now released their API, and with it some possibilities – over and above the initial ability to just say “Yo!” to a friend. At the time he provided some of the funds, John Borthwick of Betaworks said that there is a future of delivering binary status updates, or even commands to objects to throw an on/off switch remotely (blog post here). The first green shoots are now appearing.

The main enhancement is the ability to carry a payload with the Yo!, such as a URL. Hence your Yo!, when received, can be used to invoke an application or web page with a bookmark already put in place. That facilitates a notification, which is effectively guaranteed to have arrived, to say “look at this”. Probably extensible to all sorts of other tasks.

The other big change is the provision of an API, which allows anyone to create a Yo! list of people to notify against a defined name. So, in theory, I could create a virtual user called “IANWARING-SIMPLICITY-SELLS”, and to publicise that to my blog audience. If any user wants to subscribe, they just send a “Yo!” to that user, and bingo, they are subscribed and it is listed (as another contact) on their phone handset. If I then release a new blog post, I can use a couple of lines of Javascript or PHP to send the notification to the whole subscriber base, carrying the URL of the new post; one key press to view. If anyone wants to unsubscribe, they just drop the username on their handset, and the subscriber list updates.

Other applications described include:

Getting a Yo! when a FedEx package is on it’s way
Getting a Yo! when your favourite sports team scores – “Yo us at ASTONVILLA and we’ll Yo when we score a goal!”
Getting a Yo! when someone famous you follow tweets or posts to Instagram
Breaking News from a trusted source
Tell me when this product comes into stock at my local retailer
To see if there are rental bicycles available near to you (it can Yo! you back)
You receive a payment on PayPal
To be told when it starts raining in a specific town
Your stocks positions go up or down by a specific percentage
Tell me when my wife arrives safely at work, or our kids at their travel destination

but I guess there are other “Internet of Things” applications to switch on home lights, open garage doors, switch on (or turn off) the oven. Or to Yo! you if your front door has opened unexpectedly (carrying a link to the picture of who’s there?). Simple one click subscriptions. So, an extra way to operate Apple HomeKit (which today controls home appliance networks only through Siri voice control).

Early users are showing simple Restful URLs and http GET/POSTs to trigger events to the Yo! API. I’ve also seen someone say that it will work with CoPA (Constrained Application Protocol), a lightweight protocol stack suitable for use within simple electronic devices.

Hence, notifications that are implemented easily and over which you have total control. Something Apple appear to be anal about, particularly in a future world where you’ll be walking past low energy bluetooth beacons in retail settings every few yards. Your appetite to be handed notifications will degrade quickly with volumes if there are virtual attention beggars every few paces. Apple have been locking down access to their iBeacon licensees to limit the chance of this happening.

With the Yo! API, the first of many notification services (alongside Google Now, and Apples own notification services), and a simple one at that. One that can be mixed with IFTTT (if this, then that), a simple web based logic and task action system also produced by Betaworks. And which may well be accessible directly from embedded electronics around us.

The one remaining puzzle is how the authors will be able to monetise their work (their main asset is an idea of the type and frequency of notifications you welcome receiving, and that you seek). Still a bit short of Google’s core business (which historically was to monetise purchase intentions) at this stage in Yo!’s development. So, suggestions in the case of Yo! most welcome.

Nadella: Heard what he said, knew what he meant

July 14, 2014July 18, 2014ianwaring Cloud, Enterprise IT, Future Trends, Management, Marketing, Microsoft, Software Leave a comment

That’s a variation of an old “Two Ronnies” song in the guise of “Jehosaphat & Jones” entitled “I heard what she said, but knew what she meant” (words or three minutes into this video). Having read Satya Nadella’s Open Letter to employees issued at the start of Microsoft’s new fiscal year, I did think it was long. However, the real delight was reading Jean-Louis Gassee – previously the CTO of Apple – not only pulling it apart, but then having a crack at showing how it should have been written:

Team,

This is the beginning of our new FY 2015 – and of a new era at Microsoft. I have good news and bad news.The bad news is the old Devices and Services mantra won’t work. For example: I’ve determined we’ll never make money in tablets or smartphones.

So, do we continue to pretend we’re “all in” or do we face reality and make the painful decision to pull out so we can use our resources – including our integrity – to fight winnable battles? With the support of the Microsoft Board, I’ve chosen the latter.

We’ll do our utmost to minimize the pain that will naturally arise from this change. Specifically, we’ll offer generous transitions arrangements in and out of the company to concerned Microsoftians and former Nokians.

The good news is we have immense resources to be a major player in the new world of Cloud services and Native Apps for mobile devices.

We let the first innings of that game go by, but the sting energizes us. An example of such commitment is the rapid spread of Office applications – and related Cloud services – on any and all mobile devices. All Microsoft Enterprise and Consumer products/services will follow, including Xbox properties.

I realize this will disrupt the status quo and apologize for the pain to come. We have a choice: change or be changed.

Stay tuned.

Satya.

Jean-Louis Gassee’s full take-home on the original is provided here. Satya Nadella should hire him.

The Moving Target that is Enterprise IT infrastructures

June 17, 2014ianwaring Amazon, Cloud, Enterprise IT, Future Trends, Google, Open Source, Software, Twitter Leave a comment

A flurry of recent Open Source Enterprise announcements, one relating to Docker – allowing Linux containers containing all their needed components to be built, distributed and then run atop Linux based servers. With this came the inference that Virtualisation was likely to get relegated to legacy application loads. Docker appears to have support right across the board – at least for Linux workloads – covering all the major public cloud vendors. I’m still unsure where that leaves the other niche that is Windows apps.

The next announcement was that of Apache Mesos, which is the software originally built by ex-Google Twitter engineers – largely the replicate the Google Borg software used to fire up multi-server workloads across Google’s internal infrastructure. This used to good effect to manage Twitters internal infrastructure and to consign their “Fail Whale” to much rarer appearances. At the same time, Google open sourced a version of their software – I’ve not yet made out if it’s derived from the 10+ year old Borg or more recent Omega projects – to do likewise, albeit at smaller scale than Google achieve inhouse. The one thing that bugs me is that I can never remember it’s name (i’m off trying to find reference to it again – and now I return 15 minutes later!).

“Google announced Kubernetes, a lean yet powerful open-source container manager that deploys containers into a fleet of machines, provides health management and replication capabilities, and makes it easy for containers to connect to one another and the outside world. (For the curious, Kubernetes (koo-ber-nay’-tace) is Greek for “helmsman” of a ship)”.

That took some finding. Koo-ber-nay-tace. No exactly memorable.

However, it looks like it’ll be a while before these packaging, deployment and associated management technologies get ingrained in Enterprise IT workloads. A lot of legacy systems out there are simply not architected to run on scale-out infrastructures yet, and it’s a source of wonder what the major Enterprise software vendors are running in their own labs. If indeed they have an appetite to disrupt themselves before others attempt to.

I still cringe with how one ERP system I used to use had the cost collection mechanisms running as a background batch process, and the margins of the running business went all over the place like a skidding car as orders were loaded. Particularly at end of quarter customer spend spikes, where the complexity of relational table joins had a replicated mirror copy of the transaction system consistently running 20-25 minutes behind the live system. I should probably cringe even more given there’s no obvious attempt by startups to fundamentally redesign an ERP system from the ground up using modern techniques. At least yet.

Startups appear to be much more heavily focussed on much lighter mobile based applications – of which there are a million different bets chasing VC money. Moving Enterprise IT workloads into much more cost effective (but loosely coupled) public cloud based infrastructure – and that take full advantage of its economics – is likely to take a little longer. I sometimes agonise over what change(s) would precipitate that transition – and whether that’s a monolith app, or a network of simple ones daisy chained together.

I think we need a 2014 networked version of Silicon Office or Hypercard to trigger some progress. Certainly their abject simplicity is no more, and we’re consigned to the lower level, piecemeal building bricks – like JavaScript – which is what life was like in assembler before high level languages liberated us. Some way to go.

Explaining Distributed Data Consistency to IT novices? Well, …

June 12, 2014ianwaring Bitcoin, Cloud, Crypto Currencies, Enterprise IT, Google, MongoDB, Open Source, Software Leave a comment

it’s all greek to me. Bruce Stidston cited a post on Google+ where Yonatan Zunger, Chief Architect of Google+, tried to explain Data Consistency by way of Greeks enacting laws onto statute books on disparate islands. Very long post here. It highlights the challenges of maintaining data consistency when pieces of your data are distributed over many locations, and the logistics of trying to keep them all in sync – in a way that should be understandable to the lay – albeit patient – reader.

The treatise missed out the concept of two-phased commit, which is a way of doing handshakes between two (identical copies) of a database to ensure a transaction gets played successfully on both the master and the replica sited elsewhere on a network. So, if you get some sort of failure mid transaction, both sides get returned to a consistent state without anything going down the cracks. Important if that data is monetary balance transfers between bank accounts for example.

The thing that impressed me most – and which i’d largely taken for granted – is how MongoDB (the most popular Open Source NoSQL Database in the world) can handle virtually all the use cases cited in the article out of the box, with no add-ons. You can specify “happy go lucky”, majority or all replicas consistent before confirming write completion. And if a definitive “Tyrant” fails, there’s an automatic vote among the surviving instances for which secondary copy becomes the new primary (and on rejoining, the changes are journaled back to consistency). And those instances can be distributed in different locations on the internet.

Bruce contended that Google may not like it’s blocking mechanics (which will slow down access while data is written) to retain consistency on it’s own search database. However, I think Google will be very read heavy, and it won’t usually be a disaster if changes are journaled onto new Google search results to its readers. No money to go between the cracks in their case, any changes just appear the next time you enact the same search; one very big moving target.

Ensuring money doesn’t go down the cracks is what Blockchains design out (majority votes, then change declines to update attempts after that’s achieved). That’s why it can take up to 10 minutes for a Bitcoin transaction to get verified. I wrote introductory pieces about Bitcoin and potential Blockchain applications some time back if those are of interest.

So, i’m sure there must be a more pithy summary someone could draw, but it would add blockchains to the discussion, and probably relate some of the artistry behind hashes and Git/Github to manage large, multiuser, multiple location code, data and writing projects. However, that’s for the IT guys. They should know this stuff, and know what to apply in any given business context.

Footnote: I’ve related MongoDB as that is the one NoSQL database I have accreditations in, having completed two excellent online courses with them (while i’m typically a senior manager, I like to dip into new technologies to understand their capabilities – and to act as a bullshit repellent!). Details of said courses here. The same functionality may well be available with other NoSQL databases.

Sometimes a picture is “How on earth did you do that”?

May 18, 2014May 18, 2014ianwaring Amazon, Cloud, DEC, Enterprise IT, IBM, Open Source, Pricing, Purchasing, Software Leave a comment

People often remember a startling or surprising first impression. Riverdance when they first appeared during the voting interval during Eurovision 1994. 19-year old Everton substitute Wayne Rooney being put on the pitch against a season-long unbeaten Arsenal side, and scoring. A young David Beckham doing likewise against Wimbledon from the half way line. Or Doug Flutie, Quarterback for Boston College, throwing the winning touchdown in a Rose Bowl final from an incredible distance with no time left on the clock. There is even a road in Boston called “Flutie Pass” named in memory of that sensational hail mary throw.

There are always lots of pressures on IT Managers and their staff, with tightening budgets, constrained resources and a precious shortage of time. We used to have a task to try and minimise the friction these folks had in buying Enterprise IT products and services from us or our reseller channels. A salesperson or vendor was normally the last person they wanted to have a dependency on for basic, routine “stuff”, especially for items they should be able to work out for themselves. At least if given the right information in lucid form, concise and free of surprises – immediately available at their fingertips.

The picture was one of the ones we put in the DECdirect Software Catalogue. It shows an IBM 3278 terminal, hooked up to an IBM Mainframe, with Digital’s VAX based ALL-IN-1 Office Automation Suite running on it. At the time, this was a startling revelation; the usual method for joining an IBM system to a DEC one at the time was to make the DEC machine look like a remotely connected IBM 2780 card reader. The two double page spreads following that picture showed how to piece this, and other forms of connections to IBM mainframes, together.

The DECdirect Software catalogue had an aim of being able to spit out all the configuration rules, needed part numbers and matching purchase prices with a minimal, simple and concise read. Our target for our channel salesforce(s) was to enable them to extract a correct part number and price for any of our 550 products – across between 20-48 different pricing tiers each – within their normal attention span. Which we assumed was 30 seconds. Given appropriate focus, Predictability, Consistency and the removal of potential surprises can be designed in.

In the event, that business (for which I was the first employee in, working alongside 8 shared telesellers and 2 tech support staff) went 0-$100m in 18 months, with over 90% of the order volume coming in directly from customers, correctly priced at source. That got me a 2-level promotion and running the UK Software Products Business, 16 staff and the country software P&L as a result.

One of my colleagues in DEC Finland did a similar document for hardware options, entitled “Golden Eggs“. Everything in one place, with all the connections on the back of each system nicely documented, and any constraints right in front of you. A work of great beauty, and still maintained to this day for a wide range of other systems and options. The nearest i’ve seen more recently are sample architecture diagrams published by Amazon Web Services – though the basics for IT Managers seeing AWS (or other public cloud vendors offerings) for the first time are not yet apparent to me.

Things in the Enterprise IT world are still unnecessarily complicated, and the ability to stand in the end users shoes for a limited time bears real fruits. I’ve repeated that in several places before and since then with pretty spectacular results; it’s typically only a handful of things to do well in order to liberate end users, and to make resellers and other supply channels insanely productive. All focus then directed on keeping customers happy and their objectives delivered on time, and more often that not, under budget.

One of my friends (who works at senior level in Central Government) lamented to me today that “The (traditional vendor) big players are all trying to convince the world of their cloudy goodness, unfortunately using their existing big contract corporate teams who could not sell life to a dying man”.

I’m sure some of the Public Cloud vendors would be more than capable to arm people like him appropriately. I’d love to help a market leading one do it.

Footnote: I did a previous post on what Vendors, Distributors and Resellers want here.

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