What matters to you when you think about hiring an IT Consultant?

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Hiring an outside IT consultant is obviously a common business decision, but the experience can be complicated, costly and frustrating. How has it worked for you? We’ve turned to the monkey that surveys to learn a little bit more. Please take our brief survey and share your experience.

The (Human) Ethics of Artificial Intelligence

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Remember those three laws of robotics penned by Isaac Asimov?

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

  2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. 

As far back as 1942, we were wrestling with the ethical implications of thinking machines. We are rapidly approaching the intersection of silicon and cognition. It is more important than ever that we update Asimov’s laws to ensure that humans are the beneficiaries rather than the victims of artificial intelligence,: a concept that is no longer in the domain of science fiction. 

Our first brush with these concerns at a practical level surfaced a few decades ago when wide scale automation became a reality. The fear was that hard-working humans would lose their jobs to less expensive and more efficient robot workers. Companies were focused more on short-term profits than the long-term effects on of millions of newly unemployed factory workers who were unprepared to reenter the labor force.

This ethical dilemma has not been resolved. And with the advent of AI, it might well have been exacerbated. Now that we are that much closer to producing machines that can think for themselves, we must consider ethical implications that even Asimov couldn’t fathom. Before jumping on the AI bandwagon, consider the following:

A Proper Education

Intelligence without ethics is dangerous. Human intelligence is tempered with human empathy. This is not a bug, but a feature. We do not want people making laws that affect us who demonstrate a lack of human empathy. And we should be just as wary of machines that wield intelligence sans empathy.

Part of the way humans learn empathy is via the process of education. From the time a baby is placed in a mother’s arms, she is engaged in the process of education. Something very similar happens with AI.

Ethical AI begins with ethical training. AI is not merely programmed, it is trained like a human. It learns like a human.

To learn the right lessons, AI has to be trained in the right way, or ethical problems will inevitably arise. They already are.. Recently, facial recognition systems for law enforcement have come under fire because it had the tendency to misidentify people of colour as criminals based on mugshots.

This is a training issue. If AI is predominantly trained in Euro-American white faces, it will disadvantage ethnic groups and minorities. As a startup, you cannot settle for the first off-the-shelf solutions that give you a short-term advantage. You have to vet your AI solutions like you do employees, ensuring to the best of your ability that they have received a proper, ethical training.

When Better Is Worse

Every company wants the best, most efficient, most productive processes possible. But there are times when better is worse. One example is customer service. There is a good chance that in time, an AI solution such as Google Assistant will do a better job at making appointments, answering questions, and making outgoing sales calls. When that time comes, AI still might not be the right solution.

The simple matter is that humans want to talk to other humans. They do not want to talk to technology when they are stressed. If a person is calling for customer service, that means something has gone awry. And they are experiencing a higher state of stress.

What they need is human contact. They want their problem resolved. But they also want to feel like they have been heard. That is something AI cannot, and possibly should not attempt to do. The decision to eliminate humans from call centres has ethical implications.

You have to consider the ethical implication for every AI deployment in your business. If there is one thing we have learned about customer behaviour, it is that they repeatedly bypass better systems for more human friendly systems.

The Final Analysis

AI is inevitable. You might already be using it right now without being aware of it. There is no doubt that the proper application of AI will make you more efficient and save you money. It will even help you avoid blunders that would have put an end to your venture without it.

That presents us with the temptation to rely on AI as the final arbiter of all matters relating to our business ventures. What we have to remember is that all business is a human to human enterprise. AI can make it better. But you should always reserve the final analysis for yourself: the human with everything on the line.


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ERP is dead . . . Long live ERP

Dinosaurs. Edsels. Consumer-grade Betamax players. All long gone. Is ERP about to wind up on the dust heap of history?

For executives disillusioned with the problems created by cumbersome enterprise resource planning (ERP) systems, it certainly seems that these platforms – once hailed as work-saving technology must-haves by the IT world – are on their way out.

What will replace the traditional ERP model? Nothing less than a completely reimagined ERP – one that takes full advantage of advances in cloud services and artificial intelligence to maximise resources, provide actionable data, and restore confidence in the ability of ERP to truly smooth out business processes across departments.

Customisation: ERP’s double-edged sword

ERP’s biggest benefit lies in providing integrated applications for common back office functions such as technology, human resources, and finance, as well as for production processes and manufacturing. All the facets of a business’ operations, such as project planning, product development, sales and marketing  are part of a single database tied to an ERP application accessible through a user interface. Best of all, the enterprise has total governance of the system, rather than entrusting its critical data to servers on the public internet.

The ability to customise ERP to a specific enterprise’s operational needs is its most attractive feature, and one on which the biggest ERP vendors  have built billion-dollar businesses.

That customisation, however, is also the traditional ERP system’s biggest weakness. Making sure an implementation meets a company’s requirements takes careful planning and a measured approach, which increases the turnaround time on new implementations, maintenance and updates.  Most legacy ERP systems average just two updates per year. A decade ago, that pace may have been fine for most enterprises. In 2018, however, it’s far too slow to keep up with advances in technology.

That makes many traditional ERP systems nothing more than “hostageware” – software that holds a company hostage because a lot of money has already been sunk into it. An ERP system that can’t be updated quickly or cost-effectively can become a bottleneck, making it difficult to update the entire process and endangering future implementations.

What’s at stake?

Faced with the stark cost of current ERP systems, enterprises may be tempted to abandon or forego implementation and turn to a patchwork of third-party back-office management software, where they may have less control of their data and only surface-level business insights.

That in turn can put the enterprise at risk of more costly scenarios: misuse of customer data or data breaches, issues with government-sponsored contracts, and more.

For SMEs especially, making the right choice is important. That realisation can paralyse many small business owners, leaving them indecisive about which ERP to go with. And businesses need to be prepared to take the leap, or they’ll land short of their goal.

The future of ERP

Knowing what’s at stake has led a number of ERP providers to use the most promising advances in cloud technology and AI to solve many of the issues dogging traditional ERP.

These new generation platforms can reside securely on remote servers and take advantage of the increased computing power offered by dedicated facilities to give enterprises a real-time look at their data, along with AI-powered business intelligence.

And it's these next-gen ERP systems to which businesses are turning. A 2016 study by Panorama Consulting Solutions found 46 per cent of organisations were implementing new ERP systems to replace out-of-date ERP software, and 20 per cent were implementing ERP for the first time.

ERP providers are continually adding new features to make their systems easier and more attractive to use:

  • More device integration: ERP systems will be accessible by smartphone

  • Better business intelligence: Modules will not only store data, but provide deeper insights into that data

  • Internet of Things integration: The addition of IoT sensors will bring a raft of new data into ERP systems

  • Better automation: Repetitive, time-consuming tasks can be automated more quickly with next-generation ERP

  • Fragmented implementation: Multiple-point ERP solutions can be implemented in a shorter time, at lower cost and offer lower risk because of their modular nature

Many next-gen ERP system providers make the transition less painful by migrating the system as separate components – for example, separating finance, HR, and sales and marketing functions – shifting each component into a cloud-based system over time. This can cut CAPEX and allow the enterprise to amortise the cost of the shift over several months, if they desire.

To top it off, the best talent in the industry is bringing innovation to cloud-based ERP systems. Many are part of smaller teams where they can use precision skills to tackle difficult problems in just one component of cloud ERP, rather than general solutions at the enterprise level. All in all, the next generation of ERP may indeed deliver on its predecessors’ glowing promise, and that’s great news, but implementation and the technical competency around ERP will be more critical than ever.

 

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ENGINEERING STABILITY INTO KOMBUSTION'S PLUGIN SYSTEM

Kombustion is a CloudFormation tool that uses plugins to pre-process templates. It enables DevOps to re-use CloudFormation best practices, and standardise deployment patterns.

Kombustion's intent is to provide a reliable enhancement to CloudFormation. Starting with native CloudFormation templates, Kombustion uses plugins to enable offline reliable preprocessing transformations.

When you start using a plugin in your template, Kombustion relies on the following formula to guarantee stability of the generated template.

(SourceTemplate, Plugins) => Generated Template

Given the same SourceTemplate, and the same Plugins you will always get the same Generated Template.

To get this stability you need to commit kombustion.yaml, kombustion.lock and .kombustion to your source control. These files and folder are created when you initialise Kombustion, and install a plugin.

It's best practice for Plugins to be pure functions without side-effects. That is, with the same input they will always have the same output.

Kombustion makes an effort to prevent lock in, providing a way to "eject" from using it with kombustion generate. This will save the template after it has been processed with plugins. With the generated template, you can use the aws cli to upsert it.

You don't need to though, as Kombustion has a built in upsert function, with carefully chosen exit codes to make CI integration much easier.

In general when calling upsert if the changes requested (for example: Create Stack, or Update Stack) and are not cleanly applied, an error is returned.

And when calling Delete Stack if the stack is not fully deleted, an error is returned.

In addition Kombustion prints out the stack event logs inline so you have all the information you need to debug a failed upsert or delete, from within your CI log.

Without using any plugins, Kombustion will happily upsert a CloudFormation template. So you can start using it with your existing templates, and add plugins when you need to.

Download Kombustion from kombustion.io.

Follow our guide to writing your first plugin.

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A BUSINESS-PRACTICAL WAY TO THINK ABOUT AI

A salesperson representing a company that deploys artificial intelligence solutions walks into a CTO’s office one day and starts talking about the company’s product. You’re the executive assistant observing the conversation. What do you see? Can you see an animated individual talking incessantly about how great his company and its products are, and another one nodding and looking knowledgeable while not seeing the relevance of any of that to his business?

This scenario plays out everyday somewhere in the world, and it’s not uncommon for such meetings to end with the prospect holding an even bigger bag of questions than before. Questions like “How appropriate is this product or service to my business, specifically?” are often left unanswered. Nothing like “It will reduce your manpower requirement by 32 FTEs” or “This will speed up your average response time by up to 23%” ever breaches the surface.

If you’re the business owner or executive responsible for the decision, questions like that might leave you wondering whether it’s worth the effort at all. More often than not, the salesperson will gloss over many of the challenges, which only makes the decision even harder.

To help put your train of thought on the right track, we’ve identified some key elements that any business eyeing AI deployment needs to think about no matter what its size. Hopefully, these points will help clarify your position on AI and whether it’s really viable or even necessary for your organization.

First things first.

Quantify It

A lot of businesses fail to calculate the benefits of new, AI tech at work in a tangible way.

For example, if you’re responsible for customer support at your company, you need to ask how much AI chatbots will help reduce your issue resolution time.

If you operate an online store like Amazon.com, you need to know if a machine-learning-based inventory management system bring down your backorder levels or prevent the system from displaying out-of-stock items. Will customer ratings go up as a result? That’s the sort of tangible measurement that will help you develop your digital work.

It’s the same when adopting any new technology, like moving to a cloud computing environment. Moving to the cloud is a good thing for the most part, but unless you know exactly what workloads should be moved and how that will materially impact your revenue or other key metrics, it’s only going to be a trial and error exercise.

Ask metrics-related questions to help you pinpoint the areas that can positively impact your business. If you’re measuring a specific metric like backorder levels, the AI system you’re considering should ideally move the needle for that metric in the right direction, and considerably so.

Just as you quantified the benefits, you also need to consider the cons.

Understand the Downside

AI is a sensitive topic because of the perceived threat to jobs. What’s good for your business metrics might not be too great for your company when it comes to attracting new talent. If a lot of your business depends on bringing in the right people and your company is known for rapidly deploying efficient automated systems that result in job cuts, you might end up facing an HR crunch or labor unrest at some point. Here are some examples:

In early June 2018, casino workers in Las Vegas threatened a city-wide strike against companies like MGM Resorts International. One of their sticking points was the increased level of automation threatening their jobs.

Dock workers around the world regularly call for strikes because of the rampant automation efforts by freight companies and port authorities. In reference to the labor strike in Spain on June 29, 2017, the following comment was made by a leading port technology portal:

“Given the contemporary state of globalised business, it also means we inhabit a world of globalised unions, and with Spain seeking to gain Europe-wide support for its tangle with the EU, it is not impossible to imagine a much larger response to the burgeoning trend of AI automation in the near future.”

After a conversation with several human resources department heads, Deloitte vice chairman and London senior partner Angus Knowles-Cutler said,“The general conclusion was that people have got to (come to) grips with what the technology might do but not the implications for workforces.”

You need hard data to help you make the final decision, especially when facing strong opposition from the company’s stakeholders. It makes it easier to filter out unwanted investments that could hurt you versus those that can take your business to the next level in a positive way. In a future post, we'll explore an example of how one company implemented automation too quickly and too widely, and finally called the whole thing off.

 

Effective AWS IAM

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Know, Don't Slow, Your Users

Ian Mckay, DevOps Engineer for Kablamo, weighs in on the effective use of AWS IAM:

When creating a secure environment in AWS, IAM is a critical part of the security provided in the solution. It's used for controlling how users compute resources and other services interact with each other, however it can also be the critical hole if you don't properly secure your policies.

For users (and sometimes administrators), security is hard. It can slow you down with authentication and provide roadblocks when you are denied access to the resources you need. Many customer environments have experienced this and create dangerous holes for users. This can be an overzealous firewall rule, a shared credential or excessive privileges to perform the actions they need to do. It's worth considering what the risks are when a business creates these shortcuts, as more and more companies are having big public security failures that crush reputations.

How IAM policies are evaluated

For developers that are new to working with IAM, the logic can be confusing at first, especially since the Amazon documentation is massive.

Here's how I explain to others how IAM policy rules are applied:

1.  If a permission isn't mentioned, it's not given

2.  If a permission is given, that action is allowed UNLESS any other policy explicitly denies that permission.

With the way rules are evaluated, the order of how the rules are applied does not matter.

Open IAM Roles

Let's look at an Amazon-provided role, ReadOnlyAccess.

This role gives the ability for a user to have read-only access to all resources within an AWS account, which is a privilege often given to users in order for them to view the resources within an AWS account. Though they have no access to perform any modifications directly, the scope of this role can unintentionally reveal information (unless explicitly denied elsewhere).

For example, this role grants permission to download all objects in every bucket in the account. Often, S3 objects may contain configuration information or even credentials that the developer may have thought secure. The role can also allow users to intercept SQS messages, get EC2 console output or get items from Kinesis or DynamoDB.

If you're looking for a role which further restricts users' access to the above resource, the ViewOnlyAccess role can alternatively be used, though you may find this to be too restrictive in some environments.

Conditional IAM Policies

One of the more powerful features of IAM policies is its ability to conditionally provide access to resources. This can help teams seperate themselves from other workloads or prevent unwanted actions. Here are some examples:

Tag-based access

The below policy grants access to perform all actions, so long as it has the "Department" tag set to "Finance". This is an easy way to segregate different parts of the business within the same account. Remember, not all services support tagging and account-wide limits still apply to everyone.

{
    "Version": "2012-10-17",
    "Statement": [ {
        "Effect": "Allow",
        "Action": [
            "*"
        ],
        "Resource": "*",
        "Condition": {
            "StringEquals": {
                "aws:RequestTag/Department": "Finance"
            }
        }
    } ]
}

TIMEFRAME RESTRICTION

The below policy grants access to perform all actions only within the timeframe shown. This is useful when users are only permitted to have access during certain periods, such as contractors.

{
    "Version": "2012-10-17",
    "Statement": [ {
        "Effect": "Allow",
        "Action": [
            "*"
        ],
        "Resource": "*",
        "Condition": {
            "DateGreaterThan": {"aws:CurrentTime": "2018-01-01T00:00:00Z"},
            "DateLessThan": {"aws:CurrentTime": "2018-02-31T23:59:59Z"}
        }
    } ]
}

IP-BASED RESTRICTION

The below policy grants access to perform all actions only when the request is made from the IP addresses specified. This can help restrict calls to only occur from within a corporate network, as an extra layer of security. Note that calls made by AWS services, such as CloudFormation when it creates resources, cannot be restricted in this way - however the call to create the stack could be.

{
    "Version": "2012-10-17",
    "Statement": [ {
        "Effect": "Allow",
        "Action": [
            "*"
        ],
        "Resource": "*",
        "Condition": {
            "IpAddress": {
                "aws:SourceIp": [
                    "1.2.3.4/24"
                ]
            }
        }
    } ]
}

Using Permission Boundaries

As of July 2018, IAM permission boundaries may be used to restrict the maximum permissions a user (or in some cases, a resource) can be assigned. If a permission boundary is set on an IAM user, the effective permissions that user has will always be the intersection of the permission boundary and their IAM policies. Here's an example of how this works in practice:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "SimpleUserPermissions",
            "Effect": "Allow",
            "Action": [
                "s3:*"
            ],
            "Resource": "*"
        }
    ]
}

The above policy is a simple example of what permissions a user might have. In this case, users can only perform S3 actions. Let's say this policy was created with the name SimpleUserPolicy. Within this account, there is a person assigned to administer the creation of users. The policy assigned to their IAM user is as follows:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "SimpleUserPermissions",
            "Effect": "Allow",
            "Action": [
                "s3:*"
            ],
            "Resource": "*"
        },
        {
            "Sid": "CreateorChangeUser",
            "Effect": "Allow",
            "Action": [
                "iam:CreateUser",
                "iam:DeleteUserPolicy",
                "iam:AttachUserPolicy",
                "iam:DetachUserPolicy",
                "iam:PutUserPermissionsBoundary"
            ],
            "Resource": "*",
            "Condition": {"StringEquals": 
                {"iam:PermissionsBoundary": "arn:aws:iam::111122223333:policy/SimpleUserPolicy"}
            }
        },
        {
            "Sid": "IAMPermissions",
            "Effect": "Allow",
            "Action": [
                "iam:Get*",
                "iam:List*",
                "iam:Simulate*",
                "iam:DeleteUser",
                "iam:UpdateUser",
                "iam:CreateAccessKey",
                "iam:CreateLoginProfile"
            ],
            "Resource": "*"
        }
    ]
}

This policy grants the IAM user the same permissions as the other users (with the SimpleUserPermissions statement) as well as the ability to browse through and update user details with the IAMPermissions statement. Also granted, is the ability to create users or change their assigned policies with the CreateorChangeUser statement. Crucially, this statement has a condition that applies a permission boundary on the create/update process. The created user must be assigned the SimpleUserPolicy permission boundary or the create user call will fail.

With this, we can ensure that the created users permissions will never be escalated past the permission boundary set by the IAM user administrator.

Summary

IAM Roles and Policies are an important piece of every AWS environments security and when done correctly, can be a very powerful tool. However, these policies can very easily get out of control and can have unexpected consequences. If you are having trouble managing IAM, get in touch with us to find out how we can help you master your AWS environment security.


AI - The Outer Reaches

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We'll be the first to admit that AI is scary.  

Recently Ibrahim Diallo, a software developer, was fired by AI.  He wrote a blog post about the experience.  It's an eyeopener, and a reminder of something that we talk about alot --AI without the right implementation doesn't instantly solve problems and can easily create more.

But there's a larger story worth exploring here and it's about the kind of world AI may eventually bring us.  From visions of super-intelligent machines seizing control of the internet, to robot overlords with little regard to human life, to complete human obsolescence, there are an unthinkable number of ways this whole AI thing could go horribly, horribly wrong.  And yet, many scientists, futurists, cyberneticists, and transhumanists are downright excited for the coming dawn of the post-information age lying just beyond the invention of the first superhuman, general AI.  To get in on the hype (and maybe quell some worries), let’s take a look at what exactly has all these smart folks giddy.  What are the possibilities of a world with supergenius silicon titans?  Here’s some possibilities, roughly in order of future development, although general AI technology is so powerful, there certainly are many more mind-bending possibilities than these. 

Filtering noise, making connections, and expanding knowledge (5-10 years away)

What do vitamins, horse racing, and the price of bread have in common?  Possibly, nothing.  But a general AI can figure it out for sure, and also find the other hidden connections between, essentially, everything.  After all, as was put by naturalist John Muir: “When we try to pick out anything by itself, we find it hitched to everything else in the Universe.”  This sentiment is often repeated, but harnessing the power of a general AI offers a real, viable path to discovering the depth of connections present in the world.  

Today, machine-learning algorithms often work by looking at data, making some tweaks, and charting responses to determine how one variable affects another.  Interpreting these changes allows the AI to predict outcomes, determine how to act, or simply classify information.  And when a general AI gets to work on the huge amounts of data that already exist (and will continue to be generated), we as people will be able to learn so much.  A growing percentage of scientific research centers on secondary data analysis, or searching for new trends in data that have already been collected.  Secondary data-driven research is extremely low-cost, efficient, and accessible, in fields ranging from sociology to medicine.  A sophisticated general AI could conduct millions of these studies, 24 hours a day, every day, in every field, at astounding speeds.  And, with advancements in natural language processing, the AI could publish the research for people to look at and understand.  Of course, this sort of connection-searching is problematic; after all, correlation does not equal causation (here’s a website that does a great job of pointing out examples of false-causation trends).  However, the benefit of a true general AI is that it will be able to discern whether or not the correlations it spots are true connections or merely coincidences, at least as well as a human researcher, and much, much faster.  Because testing connections and processing data are the current uses for artificial intelligences, you can be sure there will be rapid development in this field in the coming years.

The end of work (10-20 years away)

Yes, this has been promised before, and is often promised again after new scientific breakthroughs, but it could really be it this time.  Automation in factories didn’t free us from work, exactly, but it did take over jobs within its domain, such as mass-production and precision-assembly positions.  Self-driving cars are on the verge of swooping up 5 million transportation jobs in the United States alone.  And for a general AI, its domain bridges both computation and creative thought.  So, just as automation has taken over factory positions, general AI could replace skilled cognitive workers like programmers, engineers, mathematicians, and even artists like poets, painters and musicians, leaving a whole lot of nothing for people to do all day (for some cool examples of AI-created art, look here, or here, or here).

Even if work doesn’t end entirely, though, count on your workload and life changing for the better.  Have you ever wished for a great personal assistant, someone who can scan through your email and shoot back answers to basic questions automatically, someone who will schedule and manage your appointment schedule, someone who can pick up a little slack when you’re feeling slow?  Google is on it.  Already, Google is rolling out a “basic” reservation-making AI which can call restaurants, make reservations, ask for hours, and the restaurants don’t even know they’re talking to a machine.  Seriously, natural language processing has become sophisticated enough to trick humans in some standard cases, like asking for a table for 5 at your favorite Chinese restaurant (you can see a video of the announcement here).  Soon enough, the AI will function as a full-on secretary, available for everyone, and some of your daily work headache will be alleviated by a tireless computer assistant.  

Intelligence upgrades (and AI camouflage, too?) (20-30 years away)

People have been trying to directly interface with computers since the 1970s, when the first human-computer brain-machine (BMI) interface was invented.  So far, the development has been therapeutic, alleviating symptoms from neurological disorders like epilepsy and ALS, restoring hearing with cochlear implants, and helping quadriplegics move mouse cursors and robotic arms directly with their thoughts.  It’s only a matter of time before enhancements are developed for neurologically-healthy people.  Elon Musk has already thrown his hat in the ring with Neuralink, a company aiming to develop the first surgically-implanted, whole-brain computer interface, for the express purposes of enhancing human computational limits and, secondarily, connecting human intelligence with artificial intelligence (a great, really long write-up for the interested here).  Not only does Musk hope that such a system could allow for offloading mental tasks to computers (would you like to be able compute 196003x3313 in your head?), he also hopes it’ll give us a lifeline when the AIs rise up.  From Musk’s perspective, if you can’t beat them, why not become so intricately intertwined that destroying one would destroy the other?  It’s a pretty neat survival strategy, blurring the line between us and the machine so any self-preservation instincts in the new machine-consciousnesses would automatically extend to us people, too.  A hard pill to swallow, sure, but if we really become second best in the face of general AI, mutually-assured destruction can be a good deal for the little guy (us).

Human immortality (30-??? years away)

Here’s a biggie: general AI could offer a viable path to massive extensions in human life, depending on how far one is willing to stretch the concept of “life.”  Is a mind (and presumably, a consciousness) without a body “alive”?  If you say yes, you could be the first in line to get your brain uploaded.  By encoding your specific neural pathways into a general AI, it’s possible you could continue life, free from physical ailments, disease, and accidents, snugly inside a computer.  And if your new computer architecture allows connections to form within itself, and disconnects old connections no longer considered useful, well, have you lost much besides your squishy, meaty body and brain?  Many techies say no, and amazingly, the first companies promising brain uploads are already starting to crop up.  A particularly grisly startup called Nectome has developed an embalming procedure so advanced that every synapse in your brain is identifiable after under an electron microscope, and will remain perfectly preserved for hundreds of years.  The kicker?  The process is 100 percent fatal.  In order to embalm the brain so efficiently, their preservation fluids need to be pumped in while you’re still alive, euthanizing you but preserving the brain.  Then your perfect brain can sit around for any amount of time until brain-uploading technology is developed, and Nectome will resurrect you in a computer.  Not surprisingly, their target market is terminally-ill patients.  And who knows?  It just might work.

Not only could life be extended by physical protections and material upgrades, it could also be extended, at least perceptually, by upgrading processing speeds (Warning: far-fetched sci-fi logic incoming).  The human brain has a few fundamental frequencies, called brain waves, that seem to dictate perceived consciousness.  These waves range in frequency from 0.5 Hz when sleeping deeply to 30 Hz when absolutely alert, and other estimates put the maximum possible neuron firing rate at about 1000 Hz.  Now, consider that the 8th generation Intel i7 processor released last year is capable of pulling 4.7 gigaHz (that’s 4,700,000,000 Hz!), and try to imagine what it would be like to live in one of those.  Would you think 4 billion times faster?  Would you perceive time as passing 4 billion times slower?  And if you, a mere mortal human, could pack 4 billion seconds (that’s nearly 127 years) into every second, would you?  Even if you lived a normal 70 years, by using a little back-of-the-envelope math, we’re talking about a perceived lifespan of 8.8 exa-years (8,800,000,000,000,000,000 years!).  It’s been 13.8 billion years since the Big Bang, which is .0000138% of one single exa-year.  And all this has been calculated using processor speeds that already exist.  Who knows what our processors will be capable of in 2045 (Ray Kurzweil’s estimation for the first human-computer merger)?

The takeaway

Obviously, the possibilities of generally-intelligent AI are enormous.  As AI technology rapidly progresses, the future is looking more and more like a wild ride.  No matter who you are, AI will both have something tempting to offer and something appalling that will make your skin crawl.  Whether general AI will provide lightning-speed research or human-computer cyborgs is still unclear, but we can be sure the artificial intelligence future holds some drastic changes to human work, health, and the world as we know it.  And look out; it’s all coming sooner than you think.  One things certain, though, especially if you are an enterprise in 2018 --you still need to get your own IT house in order,, AI won't be up to that job for a while.

Security at the Centre

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The following was adapted from a sit down chat recently with our team.  Security is a big challenge in enterprise. You have legacy security teams that have traditionally worked in a check post type model where we have a six month product plan, and these dates we're going to do penetration tests, and at this date we'll do the security architecture review. That kind of all just flies out the window when you start having safe digital teams that are deployed into production multiple times a day.

We've worked pretty closely with some enterprise security teams in helping change their work flow and disseminate ownership of security back into the teams that are actually deploying these services. So you no longer have this ivory tower on a security team saying, "This is bad." How you should be securing the Cloud is by asking how, not saying no.

You need to enable teams to have ownership of their own, or a significant part of their own security model. It is the only way it can really scale. You can take what was traditionally, say, a security orbit and write automation around that and have compliance checks that are doing what we like to call continuance compliance.  We're running these checks --you can be running them every five minutes if you like--and you have a dashboard of high risk items in your Cloud.

But that only goes so far. You need to start again, instilling teams to think about security when they're designing systems. 

 

Introducing Kombustion: Our Open Source AWS Developer Tool

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The team is proud to announce the launch of Kombustion.  Here's the media release, want to give Kombustion a try?  Visit: www.kombustion.io

Australia, August 15, 2018 – Kablamo has released its most significant open source software project to date, Kombustion. The AWS plugin provides an additional layer of intelligence for AWS CloudFormation, reducing the time and complexity of managing thousands of lines of code across AWS environments of any size. 

The tool provides benefits for developers and engineers who use AWS, as tasks that previously took days or weeks can now be completed in minutes or a few hours.  For example, setting up a new Virtual Private Cloud in an AWS cloud account has typically required significant work to define and manage up to 30 different AWS resources. With Kombustion, a best practice AWS Virtual Private Cloud can be set up with a small amount of configuration to an existing plugin.

“We developed Kombustion to help solve a common challenge for all AWS CloudFormation users. It was built in-house, and we’d been using it ourselves, but after seeing the benefits Kombustion delivered to our team, we decided to open source the project and share it with everyone,” said Allan Waddell, Founder and Co-CEO of Kablamo. “Our Kablamo values align strongly with the open source software community and we are proud to play our part in making AWS an even better experience for its users.”

CloudFormation is a native AWS service, which provides the ability to manage infrastructure as code. Kombustion is a CloudFormation pre-processor, which enables AWS users to codify and re-use best practices, while maintaining backwards compatibility with CloudFormation itself.

Kombustion is especially useful where multiple CloudFormation templates are required. It enables developers, DevOps engineers and IT operations teams to reduce rework and improve reusability in the management of CloudFormation templates, whilst also enabling access to best practices via freely available Kombustion plugins.

Liam Dixon, Kablamo Cloud Lead and Kombustion contributor, said while the core functionality has been built, it was essentially a foundation and he hoped the wider AWS community would help make the tool even better.

“Different AWS users have different ways of pre-processing CloudFormation templates, but we saw the opportunity to develop a freely available tool with the potential to become widely used in Australia and overseas,” Dixon said. “Kombustion’s publicly available, plugin-based approach, means that the AWS developer community can reduce rework and share best practices in areas such as security, network design and deploying serverless architectures.”

As well as reducing the time and complexity of managing multiple AWS instances, other Kombustion benefits include:

  • Adoption can be incremental so there is no need to completely rewrite current CloudFormation templates;
  • Kombustion plugins can be installed from a Github repository;
  • Cross-platform functionality means Kombustion works on Linux, FreeBSD and MacOS; and,
  • Kombustion is completely free to use, for both personal and commercial use   

The first release of Kombustion is available for download today at: www.kombustion.io  Kablamo is calling for the AWS community to test and provide feedback on Kombustion, and to contribute towards future iterations of the project.

Buzzword: DevOps

Liam: DevOps does get thrown around a lot, and I think there's an aspect where...it is actually one of the phrases in modern-day IT that does get misused or probably misappropriated in terms of everyone has their own opinion on it.

And when we think about it, people define it as a job title. So for example, site reliability engineering, or even traditional operations is literally just going, "You're a DevOps engineer," when it's like, " Mm, not really." It's meant to be an ethos around development and operation collaborating.

Allan: Collaborating. Culture.

Liam: And again, if you can go folding into the security space, where DevSecOps is a role. How do you feel about this? I mean, you've sort of gone into more DevSecOps space...

Marley: Look, it definitely is just a, a chaining of words together, like you said. I mean DevOps, and...you have that, that crossover between developers and operators working closer together, but then you could also take it as, say, operations done through development, where you're just talking about infrastructural automation and not even talking about cross-team cooperation. I mean, look, it is relevant in the security space.

Allan: Mm-hmm. I think it's relevant in the transformation space. And transformation is probably another word, but for an organization that doesn't blend those teams and still have really rigid silos, it doesn't make sense to say DevOps as a center of excellence or, as what AWS would call it. Which obviously it never is in the first go, center of excellence, but there it kind of makes sense, but it's become not a culture word. It's become a role, a DevOps person, which almost in of itself defeats the purpose of why DevOps was created.  You're meant to have the culture of DevOps across your business, not be a DevOps person.

Liam: It’s not hire the DevOps, and then that problem is solved.  

Marley: Yeah, I feel like automation engineer is a better term. Because no matter whether you're doing software development or infrastructure or security...automating any of those workflows is what you're trying to choose as an outcome, right?
 

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DevOps is overused.

 

 

What does it really mean, and what's a better word for it?

 

 

 

Read or listen here: 

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PODCAST: Allan on Walk the Tech Talk

Kablamo's Allan Waddell recently appeared on industry podcast Walk the Tech Talk.  He was grateful for the opportunity and was especially grateful that the host, Harvey Nash's Anna Frazzetto, was just as interested in tackling some of the biggest ideas in tech as he was.  Here's the blurb from the show and audio below.  Enjoy!  If the button below doesn't work, please click here.

On this episode of Walk The Tech Talk, Anna interviews Allan Waddell, Founder of Kablamo, a human-centered, cloud-based software to make efficient end-to-end use of the cloud. Anna and Allan discuss how AI is becoming a key factor in digital transformation and break down what AI initiatives are having an impact and what are just hype. Allan also discusses the main drivers pushing businesses towards AI, his thoughts on AI actually affecting jobs, how AI, machine learning and neural networks all fit together and so much more. Join Anna and learn from the strategies and accomplishments of this episode’s tech trailblazer.

Buzzword: AI

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In this week's buzzword chat the team tackles how machine learning & AI aren't going to solve your IT woes -- robust discussion ensues.

Ben: Artificial intelligence, yeah?

Liam: Yeah, that and machine learning probably. The coupling of those two as a discrete entity. I need to do the AI.

Marley: A lot of businesses seem to wrap up that they need AI and machine learning when it’s really just about a bad process issue.

Liam: The amount of times we have companies that have a ridiculous amount of data, we've got so much data we don't know roughly how to use it.  And again, they look at some of the technologies that people are doing around machine learning models and sort of the idea of artificial intelligence... again because there are a lot of groups publishing it. When realistically what they really need to do is just start performing data transformation over the top of it and standard data science pieces on it.

Not every solution needs to be in the machine-learning model. A lot of the data points in terms of what they're actually looking to address and find in terms of the data and about their audience, or looking at some of their outcomes in terms of what they're not doing in terms of their product or in their marketing or in their pitch...you don't need to necessarily put that through copious amounts of machine learning or even the idea of creating an AI engine or compute layer to do that. They are probably the two words that get misused the most if we're honest.

Ben: It's not the future everyone thinks it is. Everyone thinks that you're going to ask AI "How do I..." I don't know, "talk to me in a natural way." And every chatbot that I've ever seen is, is utterly terrible. They're not very intuitive. And everyone's going, "Oh, we want that fantastic chatbot experience," but what they really should be aiming for in AI is something that's, you know, solving the customer problem. And yeah, I think it's just been oversold. It's really, really oversold.

Allan: Yeah, but it's rapidly changing. I mean, I think, I think we're rapidly moving towards a place where the Turing test is going to hold. And I think that is, we're talking about 44% of job replacement by, by AI in the next 20 years across Australia.

Liam: That’s more around robotics and machine learning…

Allan: Actually, no. It’s actually not.

Liam: Okay.

Allan: That's not the physical, it's not the white collar, sorry, it's not the blue collar workers that are going to get hit by AI role-replacement first, it's going to be all the white collar. It’s going to be banking, finance in general, and legal.

Liam: What, accountants, really?

Allan: Yes, effectively.  And software developers. Software development as the processes today is going to change rapidly. We deal a lot with the chat services that would apply at a call center or contact center. And I think you're right. It does need to solve the customer problem first, and I think that's where most companies are really behind is the detailed workflows, you know, consistent form that AI would need in order to be effective. And that's going to take some time just to map it out. To be fair, it's like training your replacement. People are going to resist that change. But yeah, in the background it's really that evolution as well of the humanization of those technologies. I can imagine a time, and it's inside our lifetimes, that you're going to pick up the phone and speak to someone and think you're speaking to somebody, and if it doesn't solve the problem it's moot. So yeah, I completely agree.

Ben: The classic example is I know a guy whose name is Paris, and the chatbot says, "What's your name?" He types in "Paris" and then it goes, "Oh, you want to go to France?" And he's like, "No, my name is Paris. You just asked me that." They can’t even do that right so I’m very skeptical.

Allan: It’s got a way to go.

Liam: I think that’s more an implementation issue there. In terms of the context of what it’s capturing or otherwise.

Ben: Ah, maybe. Maybe...

Liam: If you take Alexa and Google Home, I think there’s an element of their chatbot integration, and again some of their machine learning aspects in terms of NLP space. It has done reasonably well, not to say they’re like the be-all end-all.

Ben: Just saying, I’m yet to see any chatbot that impresses me. And I’ve tried them all.

You Talkin' To Me? Chatbots might not be here yet.

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Science fiction has had mixed results predicting the future. Star Trek called the smart phone (sort of), and Jules Verne hit a few targets with the submarine, helicopter, and moon landing (although his steampunk aesthetic predicted your laptop would probably have a chimney).

But recently, we’ve really dropped the ball. We’re only now unlocking our phones with our faces, we’re yet to send a single tourist to space, and we're still waiting for our hoverboards, jet packs and flying cars (the "flying cars" making the rounds now look like no one ever thought an open propeller at head height might be dangerous)

As if we didn’t have enough to mope about, another sci-fi disappointment arrived on our doorstep in mid-2016 - the chatbot. This time, it was silicon valley billionaires, not TV writers, making the promises, so consumers sat up and paid attention.

But instead of ushering-in a new era by simulating the elation of a great conversation (either auditory or textual), they only mimicked the disappointment of a New Year’s Eve party - we got all dressed up for a night of adventure only to end up crying in the bathroom.

The Fiction - 2018: A Chatbot Odyssey

There was a great big beautiful tomorrow, and it was just a chatbot away! 2018 really was supposed to be the golden age of chatbots - here’s how it was supposed to look.

The Turing test defeated, our mornings would start with a kind voice welcoming us to the day (the specific tone would be matched to our search history). If your hair was shabby, you’d ask her to book a haircut. She’d make the booking, then put it in your calendar, (she’d leave time to walk from the lunch date she booked you yesterday).

At work you’d get more done after welcoming our chatbot overlords, with customers looked after by Kelly, the customer service bot. She’d take no breaks, and expect no raises. She’ll also send your mum flowers, but remind you to take credit for it.

The Reality - Episode 1: The Chatbot Menace

Despite years of hype and a venture capital fuelled budget, the reality of chatbots hit us like a pile of midichlorian flavoured mud from a Naboo swamp.

Admittedly, that might be a little unfair to chatbots. But our Ben Boyter says most have failed to live up to expectations.  

“I find most chatbots to be fairly dumb”, Boyter says.

“The experience is so poor that a well designed web page could achieve the same result, for a fraction of the cost and frustration.”

Chatbots were promised to be the next big thing in tech, replacing sales teams, apps, and websites. But instead of iRobot, we got iNobot.

Here are a couple of reasons for that.

Chatbots aren’t different enough

When computers switched from text-based input to Graphic User Interfaces (or GUIs, those pretty clickable pictures), the way humans interacted with computers fundamentally changed.

We’re visual creatures, and seeing our options visually suits the way we work. In solving a basic problem, GUIs made computers at least ten times easier to use.

Chatbots don’t offer the same value proposition yet. As Ben explains, chatbots are usually just an alternative to a search engine. A worse one, mostly.

“Chatbots generally want you to follow a clear flowchart of question actions. A good search engine is still a better option,” Ben says.

“At least if I search for the wrong thing I’ll get no results rather than ‘I don't understand’. Or I might get a page of results to work through. There’s just more information and it lets me reach my goals faster.  Considering how far ahead of chatbots search engines are, I think this is a better use case for every chatbot I’ve ever seen.”

Chatbots didn’t get good enough fast enough

There might be a parallel dimension where chatbots did live up to the hype. There, they quickly improved enough to be genuinely useful, even if only for a few basic tasks.

But Ben says our universe wasn’t so lucky --at least not yet.

“The biggest heartbreak is that chatbots are mostly just a flow diagram with a horrible user interface”, he says.

“It’s frustrating for those who aren’t tech savvy - you should see my Dad two-finger-typing his questions. They’re also annoying for those who work with tech, because we know it’s a waste of time.

“I mean, some bots can’t even cope with spelling errors. What chance do they have with slang, or other technical questions? The technology just isn't there.”

Back to the future: Is there a sequel for the chatbot?

Chatbots aren’t going the way of Betamax or Google Glass just yet. The early success of voice assistance like Amazon Alexa and Google Home, which are essentially voice-chatbots, have given consumers and developers proof of worthy applications.

For a chatbot to be useful it needs to offer something more than we get from a webpage, search, or app - or something different at least. Natural Language Processing also has to be good enough not to frustrate users before resolving their query.

Machine Learning will help. Instead of building basic flowchart chatbots, developers will need to create bots that use the inputs of users to improve itself.

But the biggest wins will come from personalisation. A bot answering a question is great, but to be as game changing as the GUI, chatbots should know what you want before you realised you want it.

Chatbots might still be late-bloomers in the imagination-to-innovation product cycle. If so, they could still be a genuine game-changer.

They’d be the first interaction most consumers would have with advanced artificial intelligence. And that’s where some of the greatest potential for our society’s future is held.

I mean, that or Skynet.

 

Buzzword: Blockchain

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Listen to the team tackle Blockchain as a buzzword,  current industry uses, what blockchain might look like in the future and the effectiveness of distributed systems (or read the transcript)

Liam: I feel like we’ve missed the absolute elephant in the corner here, which is blockchain.

Allan: Ha. Yeah.

Liam: Let’s try and find something else we can use blockchain to solve...

Marley: I just get sick of seeing LinkedIn profiles that say "I'm a blockchain advocate".

Ben: Or a "blockchain expert." Like really? Did you invent blockchain?

Liam: I sort of feel like it's the thing that everyone attempts to try and find a problem to try and solve with it.

Ben: Solution looking for a problem.

Liam: Yeah. It's one of those things, like it works quite well in the cryptocurrency space, right? In terms of that transaction holding the ledger context of it, I think there's a real essence where banks have attempted to look at it for quite some time. You've got the ASX and a couple other organizations that are looking at how that fits in their platform space. You've got groups like VISA or MasterCard that have come out and been very anti-blockchain in terms of going from a power consumption space. .

Ben: It's one of the three big advancements that we've ever had in accounting. There was single-ledger accounting, a double-ledger, and then there’s blockchain. I think that’s going to have a big impact eventually.

Liam: The question I always wonder is, “Is this like the internet?” Is this the early days of the internet when people are just saying, "They can't necessarily see that far ahead". The idea of going, "Okay, if we could tell the future then we'd all be millionaires in certain aspects." But is it like the internet where it existed in like '98, to what it is today and what we do over it today? Were there that many people back in the '90s talking about eCommerce and web platforms and API-driven aspects? 

Allan: I’ve got very strong feelings about this…

Marley: If you follow the analogy, it's kind of concerning for the future of blockchain, right, because you're going to go again from a distributed system to things like siloed, walled gardens we have today on the internet, right?

Liam: Roughly speaking, yeah?

Marley: I mean it kind of defeats the purpose of technology in the first place.

Liam: How distributed does a distributed system need to be to still be effective? So if you treat the idea of a monopoly as a single ledger, and you say oligopoly is a controlled network. So let's say the banks all start to do blockchain between themselves as they're all the trusted ledgers. But they don't have untrusted ledgers in terms of, "Hey, you could just go and run the blockchain for the bank or be a node within the blockchain of the bank."

Ben: I might be okay with it if I could read the blockchain. At least to PenSpec what’s going on.

Liam: Yeah, you could still inspect it and look for fraud. Maybe with the Royal Commission that’s happening right now, maybe that’s one way to at least get visibility into a banking institution. But does blockchain always have to public? How distributed does a distributed network need to be?

Ben: Well, considering what Facebook's going through, I'd say distributed is a good thing.

Liam: Yeah...data sharing.

 

Architecting Culture at Scale

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Last week, we proudly welcomed Angus Dorney to the Kablamo family.  In his blog post, Angus did an awesome job expressing the significance of his joining, and the much-needed step-change that Kablamo brings to enterprise. We're on the precipice of something new, something great, it's a very exciting time for us.

But thanks to his inherent humility, Angus did a pretty darn average job of expressing one of the more outstanding gifts he brings to us.  Please allow me to explain. 

Angus is known industry-wide for his effectiveness as a tech leader, but what truly distinguishes him in my opinion is his ability to maintain culture at scale.  Angus knows how to grow a business, just Google his name to see a shopping list of distinguished achievements. But he also knows a much more profound thing, how to grow a great culture that matches business growth.  

"Company Culture” these days is 101 level HR marketing fodder, an afterthought. The ideal of company culture is too often tossed around as a concept that sounds great but doesn’t necessarily mean much. 

Kablamo is built around our humans.  We didn’t buy them.  We gravitated together because we share a single vision, we have a single purpose: Deliver cloud software in a way that absolutely knocks the socks off our loyal enterprise customers.  We don’t have dispensable people any more than we have dispensable ethics or values.  Our humans are exceptionally bright and deliver outcomes with unwavering humility. We don’t need or want undeserved monikers like “we’re the best”, “the biggest”, “the fastest”, “the most cloudiest/devopsiest/secopsiest” — we just get on with it. Our team builds really valuable software, we solve problems.  We work really hard, but we maintain balance —and we don’t just say it, we do it. And we enjoy it. 

Our new Co-CEO embodies the ideal of servant leadership. Angus is the safe pair of hands to grow something culturally unique like Kablamo into something much bigger. What’s so critical here is that scaling the Kablamo culture will mean huge benefits for organisations looking for a trusted accountable partner to drive truly valuable enterprise solutions at scale. (Just for fun , try saying that 5 x with a mouth full of marbles)

So, welcome, Angus.  Kablamo’s culture is on the launch pad for the next stage of our journey and the mission is clear.

— Allan Waddell, Co-CEO (Proudly) and Founder Kablamo

Why The Technology Behind Bitcoin May Someday Save Your Life

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Allan wrote the following piece for CSO.  You can also read it there.

It’s time to chin up and stop grousing that we’re all late to the crypto-currency party. Most of us may have missed our moment to make millions off of Bitcoin, but the technology behind it could eventually save our lives. 

This potential is likely behind the government’s recent decision to use the budget to set aside $700,000 for the Digital Transformation Agency (DTA) to “investigate areas where blockchain technology could offer the most value for government services.” It doesn’t sound like much, but it’s a start that other many other nations haven’t made.

While the early arrivals to Bitcoin may be having more fun financially than the rest of us, the cryptocurrency frenzy is rapidly changing from a gold rush-themed greed-fest to a surprise party: turns out that blockchain, the technology behind Bitcoin and dozens of other cryptocurrencies, has untold practical and even altruistic uses, from making food safer to boosting humanitarian aid; from improving electric grids to safeguarding workers’ rights.

Blockchain creates a shared digital ledger, an open book whose decentralised information can’t be altered or hacked.  This transparency and stability is where the potential lies.

In April, Blackmores, the natural food company, and Australia Post announced that they are teaming up with China’s Alibaba in a new Food Trust Network to develop a food tracing technology using blockchain. The world’s largest dairy exporter, New Zealand’s Fonterra, will also participate in the network, as will that country’s postal system. The project will start with Blackmores’ fish oil and Fonterra’s Anchor products.

In mere seconds, their system will be able to track food from the store shelf to the farm and shipment it came from, thanks to special codes on the food’s packaging.

The system has enormous potential to address health threats in our food supply in ways that could actually save lives.

For example, seven deaths and a miscarriage were linked in recent months to listeria-tainted rockmelon grown in New South Wales. After the first death, in mid-January, it took authorities working without benefit of blockchain about 40 days to find and contain the source; by then a total of 19 people would be affected.

Granted, even with blockchain, this particular outbreak would have been hard to trace. That’s because listeria symptoms can take months to emerge, shrinking the likelihood that victims will remember what they ate or where they bought it. Blockchain can’t work its rapid magic, remember, if the label is long gone or the store is forgotten.

 Even so, once the grower was known, the technology would have helped authorities instantly pinpoint which stores sold the grower’s melons and when; possibly even to whom. Given that the melons were sold throughout Australia and exported to nine  countries, that would have been a good thing.

The U.S. is experiencing a similar food health crisis with romaine lettuce. Dozens of  people have developed kidney failure from an E. coli outbreak tied to romaine lettuce. Authorities still haven’t found the source and are warning people against eating any romaine lettuce until they do. Twelve years ago, spinach with E. coli killed three Americans outright and authorities spent weeks afterward trying to figure out where the deadly stuff was grown and packed.

The technology has already proven to have profound benefits in humanitarian ways. The World Food Programme recently began using it to distribute food aid to Syrian refugees in a Jordan refugee camp.No digital middleman is needed to broker transactions, and there are no fees. For that reason, refugees shop at the camp’s ‘food market’ and at checkout, a biometric scanner charges the WFP for their selections. Meanwhile, the blockchain safeguards the refugees’ identities, eliminating paperwork. The Danish Foreign Ministry says it might soon disburse all of its humanitarian aid in this way. And when a British charity recently used blockchain to send aid to some Swaziland schools, Reuters reported that the savings paid a year’s tuition for three additional students.

It’s the decentralised and transparent aspect of the technology that has the potential to make power grids ‘smarter.’ By eliminating the need for a single centralised server, everyone on a network can share information simultaneously, which will make it possible to detect problems faster when power grids fail. One day blockchain may also be able to distinguish clean energy in the grid from that derived from fossil fuels. This will make it easy for governments to track how much of it is generated and used. And Western Australia’s PowerLedger is trying to use blockchain to disrupt energy retailing and permit the rise of the “prosumer” (the energy producer/consumer) by giving them the tools to trade power.

And just last month, the U.S. State Department and Coca-Cola Co. announced a plan to fight hidden forced labor in countries where the beverage giant gets its sugar cane. The notion is to rely on the tamper-proof aspect of blockchain to create a workers’ registry that also tracks workers’ contracts with employers. Such a registry can’t force companies to honour their promises, but at least there’d be evidence of what those promises were.

To be sure, there’s a downside to the technology, which is that it requires massive amounts of energy to work its magic. But computer scientists from the MIT and other universities are racing to develop greener varieties.

Too bad all this potential for good is so often obscured by all the hoopla over blockchain’s profit-making potential. As one U.S. enthusiast, Naval Ravikant, said, it’s truly a shame that this technology of trust burst on the scene “dressed up as a get-rich-quick scheme.”

So relax. You’re not late to the most important party. It’s only just getting started, and with the right initiatives and policy, it will benefit everyone in ways we can’t even imagine yet. 

Why I’m joining Kablamo

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Today, I join a special group of people who are creating a truly amazing company. In a short period of time, Allan Waddell and his team at Kablamo have built a strong reputation in the Australian cloud marketplace – they’re known for delivering high impact customer outcomes, making Agile more than just a buzzword, and for being some of Australia’s smartest cloud and application engineers.

In short, Kablamo is a leader in  a new generation of cloud application partners.

Most enterprise IT service providers say people are their most important asset, but very few practice what they preach. They talk about delivering outcomes for customers but end up only delivering big invoices. They claim to put the customer at the core of their delivery, but don’t take time to really understand their customers’ needs and match these needs with relevant and modern service offerings. In every case, Kablamo is different.

And people are taking notice.

It isn’t often that you get a chance to be a part of the next big thing. I believe Kablamo is that next big thing.  In less than two years, Kablamo already counts some of Australia’s largest organisations as customers. Kablamo’s team are execution engineers, “black ops” code warriors who deliver and educate, working on site with customers and enabling their organisations to move faster via the speed and quality of delivery.  This combination of getting important things done securely and rapidly, whilst investing their customers with lasting cloud knowledge, means  customer teams inherit the capabilities they need to make the most of cloud.  The reputation for delivering, for guiding customers along their cloud journey, is the reason such an impressive list of enterprises already work with Kablamo. 

When I spend time with Kablamo people, I see common traits of humility, accountability, curiosity, creativity and bravery. I see values that align with my own and values upon which we can build a truly world class business.

Today is another step forward in Kablamo’s journey to reshape what customers expect from their technology and cloud partners.  

I am honoured and grateful for the opportunity to partner with Allan, as Co-Chief Executive Officers of Kablamo, and to take this young organisation forward on an exciting mission. I look forward to working with many of the people I have met on my journey so far and I equally look forward to meeting new people and organisations. I have no doubt that today marks the beginning of a fulfilling new chapter in my story, and I look forward to sharing the ride with you all.

-- Angus Dorney, Co-CEO Kablamo