COVID-normal, powered by AI technology: the perfect storm

Futurists have long predicted that AI technology will leave a lot of workers without a job - especially those with specialised skills. As we emerge from a global pandemic and settle into ‘COVID normal’, the big question is: can AI help people get into work, rather than the other way around?

The answer is yes - however there is a ‘but’. It’s summarised well by a quote in this Wired article:

“You’ll be paid in the future based on how well you work with robots.”

There are many examples of humans and machines complementing each other, but it’s important to remember that we excel at different types of skills. Humans should focus on skills that they uniquely enjoy exercising, while AI technology handles the mundane tasks that don’t require human skills of judgement, creativity or empathy.

Humans and AI technology working together

International tech investor and startup adviser Anupam Rastogi describes the relationship between AI and humans as ‘human-machine symbiosis’.

In 2016 he wrote about the difference between artificial intelligence and intelligence augmentation, and mentioned examples from manufacturing, transport logistics, healthcare and agriculture where companies were leveraging advances in machine learning to augment human capabilities, enhance productivity or optimise use of resources.

Fast forward to today and many AI technologies have developed to help humans thrive, such as:


How much is AI technology worth to economies?

A recent article in the Australian Financial Review predicts that the COVID-19 pandemic could triple the value of AI, as businesses rush to digitise many of their processes.

Krishan Sharma, technology journalist writes:

“A government-sponsored road map from CSIRO published at the end of 2019 found that the AI sector would be worth $315 billion to the Australian economy by 2028 and $22 trillion to the global economy by 2030.

However, experts such as KPMG's Partner-in-charge, James Mabbott, tells the Financial Review that both these figures could be as much as “1.5 to 3 times greater” after taking into account the increased levels of investment driven by the disruption caused by the pandemic.”

There’s no doubt that the pandemic has helped push many businesses out of their comfort zone and into a place where they’re more likely to consider digital options and artificial intelligence. How the industry handles this increased interest - and spend - is crucial.


How AI technology is affecting jobs - now and in the future

One factor that will have a big influence on the success of AI’s broader adoption is the people currently employed to integrate it into workplaces. Right now AI is a growing industry - and it will only continue to grow. Having the right people leading AI programs is essential for ensuring that AI operates in harmony with the human workers in the business to generate sustainable positive results.

Infosys’ 2018 ‘Leadership in the Age of AI’ report revealed a possible expertise issue:

“Two thirds of Australian organisations are having difficulties in finding suitable staff to lead AI technology integration and 75% of IT decision makers felt that the executive team in their organization needs formal training on the implications of AI technologies.”

Looking forward, it seems inevitable that there will be some disruption to employment - but does the end justify the means?

The Adelaide University’s 2018 (yes, a little dated, but still relevant!)  report “The Impact of AI on the Future of Work and Workers” concludes:

“Occupations that can be replaced by AI and robots will be vulnerable. This has been true for the last 200 years of technological innovation and is hardly a surprise. There is not much call for typists anymore. Nor horse husbandry. These jobs have been taken over by machines. No doubt AI will substantially replace some occupations. But we have also learned from history that despite ever increasing automation from machines such as engines and computers, the total amount of employment has increased and average wealth has increased remarkably. 

The capacity of countries to adapt to greater automation has required retraining and investment in education and research on a mass scale so as to build the capacity of the workforce to make best use of the new technologies developed.”

People before profit

The driving force behind AI - as with most other trends or technologies - is money.

As interest in artificial intelligence grows, there is a strong focus on reducing the time and resource required to create machine learning models that can generate the opportunities which will enable the workforces of the future.

It’s in this scenario that the AI industry and humans can really thrive, as long as we find a way to strike the right balance between AI doing our jobs for us, and helping us do our jobs better.

Then comes the next set of questions that need answers:

  • Where will the accountability lie to reskill?
  • Will it be up to individuals to head back to the classroom, or create working opportunities to develop the required capabilities?
  • Or will industries lead the way by putting humans before short-term profits?

If you’d like to let us know your thoughts, or find out more about what AI technology could do for your business, we’d love to talk. Please get in touch.

How AI can augment prediction and human decision making

We spend much of our time here at Red Marble exploring ways that AI and machine learning can elevate human performance. One area of particular interest is how AI can augment prediction and human decision making.

But before we can design AI, we need to understand the differences between how software and human brains make decisions.

How do humans make decisions?

Humans make thousands of decisions every day; subconsciously combining inputs from multiple parts of the brain, combining real-time data with historical information from our memory and blending rational thought with emotional cues.

In “Thinking, Fast and Slow,” Kahneman and Tversky explain how fast, instinctive and emotional decisions are blended with others that are made logically and deliberately - all with an assessment of risk, probability and judgement based on experience.

This is a pretty sound way to make decisions - however, it takes time and the quality of the decisions can depend on outside factors (ie the person’s health or mental state).

How does machine learning make decisions?

Machine learning (ML) models aim to emulate elements of this decision making, but clearly some areas are more accessible than others.

ML reflects the ability for software to ‘fit’ a particular model to a set of data by applying specific weightings to different parts of the data (called ‘features’). It uses this model and applies the weightings to extrapolate that data in order to predict future events.

A machine can process vast amounts of historical data to make its predictions, with a defined probability, based on past data - but it can’t apply an emotional lens (yet) to those decisions, and it struggles where context changes.

Where machine learning works well

A nice application of ML is to model the rational human decision making process and to make those decisions at scale. Let me share an example.

We recently worked with a client who was making predictions about stock levels of spare parts for machinery. Were they holding enough in stock? When spare parts were ordered, would they arrive on time? They needed to know they would have the parts when required.

Looking at the “health” of each material made the assessment fairly intuitive and simple for the human. A track record of late deliveries from suppliers, highly variable stock levels in the warehouse, parts being used for breakdown (rather than planned maintenance) all lead to a fairly simple judgement by the human worker.

The challenge is applying that process across 800,000 materials every day. Clearly not something a human can do.

Applying an ML model here does the heavy lifting superbly and creates a list of priorities that the human can work through and apply judgement to.

We apply similar models in our AI-driven employee engagement and digital adoption work. The software can analyse many users and predict which information is most valuable to help each individual use their technology to its full potential and to succeed in their role. It models the human analysis, and applies it at scale.

Where do ML models fall short? 

Machine learning takes historical data but can lack immediate context. Here’s another example.

We help one of our clients predict customer conversion for certain products and prices, helping them maximise margin. The model is trained on historical data, but within the context of COVID, historical data is not reflective of current buying behaviour. Companies need to adapt their models to make use of real-time context and the most recent data as it's coming in.

Machine learning can also fail to understand the behavioural aspects of decision making. Human decision making has lots of nuances; emotion, the time available, the paradox of choice as outlined by Barry Schwartz to name a few. AI models can be trained on aspects of these - and they will get better at this as technology improves - but for now will struggle to put it all together into cohesive reasoning.

When is emotional decision making a problem? 

There are areas where prediction models can expose harsh realities which the human mind might wish to overlook.

One of our areas of work is predicting the success of corporate projects. By tracking the data throughout the project and applying a model trained on historical information, there are clear ways of predicting which projects will succeed, and which will fail to deliver the predicted business benefits. However, many projects are “pet projects” of a particular group or person; and the human mind may already have an ideal end state in mind which will bias the decision making.

An ‘emotionless’ ML model will have no issues exposing the hard facts: “This project only has a 30% chance of delivering the desired benefits” quickly cuts to the reality and can save companies countless time and expense.

Human decision making + AI: what’s the best way to combine them?

Humans are amazing, and the ability to make complex decisions pretty quickly is significantly ahead of our software abilities. However - add in the capability of AI to apply models rapidly, constantly and at huge scale to the capability of human decision making, and you have some very exciting possibilities.

What IS GPT-3? And why do you need to know?

At Red Marble, our core belief is that artificial intelligence will transform human performance.

And as part of our everyday work, we’re continually coming across (and creating) ways that AI is improving workforce productivity.

Our projects generally fall under five technical patterns of AI: prediction, recognition, hyper-personalisation, outlier detection and the one I’ll talk about here...

Conversation and Language AI

Broadly, conversation and language AI deals with language and speech. There are 3 aspects to this pattern:

  1. The ability to have a conversation with software, either via text or voice. Common examples of this include Alexa or Siri, but we’re seeing an increasing number of voice-based interfaces within enterprises.
  2. The ability to understand language and analyse it; for example, we recently worked on a project where we analyse text in work notes  to understand if any contractual clauses may have been triggered.
  3. The ability to generate language - to create a natural language narrative based on input data, for example auto-generating a project status update narrative based on data collected.

There’s been a huge advance recently in natural language generation. It’s based on software called GPT-3 (Generative Pre-Trained Transformer, version 3) developed by California-based AI research centre OpenAI.

This technology was flagged in a research paper in May, and released for a private beta trial in July 2020.

What IS GPT-3?

GPT-3 is a ‘language model’, which means that it is a sophisticated text predictor.

A human ‘primes’ the model by giving it a chunk of text, and GPT-3 predicts the statistically most appropriate next piece of text. It then uses its output as the next round of input, and continues building upon itself, generating more text.

It’s special primarily because of its size. It’s the largest language model ever created, trained using around 175 billion variables (known as  ‘parameters’ in this context). Essentially it’s been fed most of the internet to learn what text goes where in response to certain input primes.

What can GPT-3 do? 

Some beta-testers have marvelled at what it can do - medical diagnoses, generating software code, creating excel functions on the fly, writing university essays and generating CVs just to name a few.

Others have rejoiced in posting examples showing that GPT-3, though sophisticated, remains easy to fool. After all, it has no common sense!

Why is GPT-3 important? 

For now this is an interesting technical experiment. The language model cannot be fine-tuned - yet. But it’s only a matter of time before industry-specific variants emerge, trained to skilfully generate excellent quality text in a specific domain.

Any industry where text based outputs or reports are generated - market research, web development, copywriting, medical diagnoses, property valuation, higher education to name a few - could be impacted.

Is GPT-3 intelligent? 

This is the big question for us and cuts to the essence of what we think makes for great AI.

In our view, GPT-3 is great at mathematically modelling and predicting what words a human would expect to see next. But it has no internal representation of what those words actually mean. It lacks the ability to reason within its writing; it lacks “common sense”.

But it’s a great predictor of what a human might deem to be acceptable language on a particular topic, and - we believe - that means that through all the hype, it’s a legitimate and credible model. We’ll be keeping a keen eye on it!

Hyper-personalisation: the secret to “one-to-one” communication

At Red Marble, our core belief is that AI will transform human performance. Within an enterprise, how can AI transform workforce productivity?

Most of our work falls into 5 technical patterns: prediction, recognition, conversation and language, outlier detection or the one I’ll talk about here…


This is the ability to treat every customer or every employee as a unique individual. It enables you to move to “one-to-one” marketing or far more focussed employee engagement.

You’ll probably have experienced the recommendation engines in Amazon or Netflix, tailoring offers just for you. These algorithms can be used for targeted upselling for online retailers or highly personalised offers.

  • An online wine retailer might recommend a particular type of wine, and nudge each buyer to a slightly higher price
  • A travel agent might tailor offers specifically for the demographics of each customer
  • A footy club might generate specific ticket offers to match the likely buying habits of each member

We also believe that this kind of technology has huge potential to increase workforce productivity by improving communication within a company:

  • Understanding what information each unique person needs in order to do their job well
  • Delivering that information at a time and in a way which is most effective for them
  • Using machine learning, to refine and improve that communications over time

As with any machine learning system, the more data you have, the more you can refine the model, and the more you can personalise.

The outcomes? Learning that sticks. New staff onboarded and quickly. Better adoption of new technologies...and your interactions with each employee more focussed and as helpful as possible.

If you’re still doing internal comms the old way - one-size-fits-all - now’s the time to chat.

And if you’re interested in reading more about the five patterns of AI, check out our recent blog: Behind the Five Types of AI.

Behind the five types of AI

We’re extremely proud of our track record of successfully deploying artificial intelligence and machine learning models to enterprise customers.

But artificial intelligence (AI) and machine learning (ML) are pretty broad fields. And we know you probably need a little more background on what we actually ‘do’.

So - here is a rundown of the five types of AI, or patterns, our work generally falls into - and how each pattern can bring benefits for your business.

Red Marble’s five types of AI

1. Prediction

Artificial Intelligence can give you the ability to predict the future! Or rather, it can predict the probability of a certain event occurring in the future, based on historical data.

Here are a few examples:

  • Forecasting conversion rate for an online retailer
  • Alerting when a machine is likely to be due for maintenance
  • Determining whether a member of a footy team is likely to sign up again next year
  • Predicting the likely valuation of a particular commercial property

To generate business value from prediction, you need to act on the information quickly - often in real-time. That combination of the ‘nugget of gold’ insight along with the right advice on how to apply it is what will bring big benefits for your business.

When we’re working with a client, our machine learning engineers and data scientists partner closely with subject matter experts who know the business well. We’re a big believer in these partnerships - we know AI inside out, but to properly capitalise on opportunities in different industries, we like to surround ourselves with diverse skills and experience.


2. Recognition

AI can recognise humans, objects or situations in a scene or recognise patterns in data. Businesses can use this in many ways.

Here are a few examples:

  • Recognising heavy machinery on a pedestrian area of a work site
  • Analysing whether a pizza has enough toppings as it comes out of the oven
  • Checking whether your staff are wearing masks during COVID lockdown

Businesses can use AI recognition to maintain the processes that make them great. Quality assurance teams can make sure each product heading out the door is at the right standard, and HR teams can use AI to ensure the safety of their teams.


3. Hyper-personalisation

This is one of the AI patterns we believe will generate tremendous value for organisations.

Hyper-personalisation is where we use machine learning across different data sources to build a unique profile of an individual. This means we can generate actions and content specific to that individual - such as personalized marketing or offers.

We’re all familiar with hyper-personalisation in one way or another. The most common example is your Netflix queue or the recommendations you get when you log into Amazon. This kind of engine - technically a set of algorithms - is hugely valuable for companies for engaging both their employees and their customers.

A few possibilities:

  • Offering specific holiday offers for families based on their demographics and location
  • Selling slightly more expensive wine to a regular online wine customer
  • Unique insurance offers based on a customer’s history, location or family status
  • Sending each employee a dynamic onboarding email based on their department, skills or interests

The possibilities of hyper-personalisation are really exciting for businesses. It facilitates one-to-one marketing, or unique learning paths for every employee. We’ve seen its power already, but we think it’s just getting started.


4. Conversation and language

This pattern of AI allows humans to interact with software using words - either by speech or text. The AI understands and infers intent and meaning from those words.

We believe that, in time, natural language will be the ultimate user interface. Just say or type what you want and the software understands. We do this already with Google Home or Alexa, but we see the application of voice assistants rapidly expanding into workplaces - especially as AI’s ability to infer meaning develops.

A few ways conversation and language AI could be used:

  • Recording, transcribing and uploading the CEO’s shareholder message
  • Analyzing work notes to see if any indicate a potential contractual claim
  • Listening to sales calls to infer the percentage probability of closing a deal

AI has the ability to change how people work within corporate environments - both helping employees save time and be more efficient, and by adding value at crucial points.


5. Outlier detection

This is the ability to detect unusual activity and behaviour outside of the norm. 

Anomaly detection is widely used in fraud prevention and cyber-security or to detect specific events from sensor data. It’s also increasingly used in medicine and in industrial environments.

A few examples:

  • Spotting cancers and anomalies in medical scans
  • Detecting when a machine is not running smoothly by its acoustic signature

This kind of detection is a powerful defence and protection against negative unforeseen events.


How can you leverage different types of artificial intelligence?

When people wonder what AI is, or what do we actually do in a machine learning project, the answer is usually one of these five patterns – prediction, recognition, hyper-personalisation, conversation and language or outlier detection. 

These forms of machine learning are already able to create value within your organisation. We encourage you to think about how your business works - would you benefit from being able to predict the future? Spot anomalies? Speaking to each customer or prospect individually?

To find out more about which types of AI can work for your business, get in touch.

Post-COVID hack: Driving user adoption of digital technologies with AI

The hack is back!

At Red Marble, we’ve done some of our best work during short, focused hackathons that aim to generate an idea—and then develop it into a prototype—in just a matter of days. 

As a company that helps clients incorporate AI into their business, these hackathons provide an incredible foundation from which we can solve real organisational challenges and improve the competitive advantage we can offer. It also helps us practice our skills in an artificially intense, fast-paced environment. 

Over the last two years we’ve run successful hacks with a number of clients including Webjet, Coca Cola Amatil, Dulux, Bookbot and others.

We’ve also run a number of internal hacks where we split into teams and tackle a specific problem. However, this has been tricky of late due to the working from home rules.

In early June, using a combination of dispersed desks in the office, plus Zoom and Slack, we ran our first post-COVID hack! 



Our team focused on how we can employ AI to better drive user adoption of digital technologies in corporate environments. This has become an important issue across almost all industries as our workforces move online.

McKinsey recently reported on the need to blend digital technology, analytics and behavioural science to personalise change programs. The article highlighted that the most effective way to make organisational change succeed is to carefully consider each employee's unique skills and mindset

It turns out there’s already some pretty impressive capability in the consumer world that can be applied in corporate environments. For example, the algorithm-based management of messages and communications that share what’s relevant and appropriate for every user, based on their individual capability and readiness.


A quick break from the hack for lunch with office dog Jimmy.

Many companies make large investments in digital technology with strong business cases, on the assumption that software is properly adopted by the users. In practice it doesn’t always work like that.

In a post COVID-19 world, this adoption has never been more crucial.

That’s why hacks like this one are extremely helpful. They give us a chance to test our hypotheses around what drives user adoption. By regarding each employee as a unique individual, we’re proving that we can reduce a business’ overall costs and realise the productivity potential of what these technologies offer.

As a result of our recent internal hack, Red Marble is excited to be piloting solutions with two clients. We’re using AI algorithms to tailor unique messages to each individual and drive process adoption - effectively automating organisational change. 

We’ll report back on the results, what we learned and how the power of AI can improve user adoption of the many technologies being implemented across organisations.

If you’re struggling to drive user adoption of your big technology purchases, need help with change or can see other areas for optimisation, AI can help. Get in touch - you never know, we could be solving your problem at our next hack!

AI helping sports get back on their feet

While COVID-19 has put a hold on the on-field action, Australia’s professional sporting codes are busy off the field, planning for the return of play and their longer-term future.

Sports is one of our most competitive industries, and these codes face many challenges: building operational efficiency connecting with a fan base with more choice and keeping up with our ever-evolving consumption habits.

As a result, we’ve seen a huge increase in appetite for AI in sports analytics, both on the field and off it, as teams and codes look for every advantage possible.

Red Marble is excited to announce its partnership with Anthony Moore Consulting, a leader in transformation and change management across professional sports, including the NBA and AFL.

In a time of global uncertainty and commercial complexity, the creation of Red Sports Analytics delivers the benefits of analytics combined with deep management expertise to drive organisational performance.

How AI is reshaping the playing field

Analytics and AI makes use of behavioural data across digital platforms. It helps us understand which, when and how fans are watching their favourite sports.

This powerful data allows sporting organisations to mine sentiment from social media streams to understand what fans are thinking and use these analytics to provide personal experiences and marketing opportunities. 

AI is already being used by major sporting codes across the globe. 

On top of building a great on-court product, the NBA is often ranked among the world’s most innovative organisations. The league has been building global subscribers year on year, with revenues doubling in a decade to $8.76 billion US for the 2018/19 season. NBA push technology across the product introduced fans to:

  • NBA 2K League - the first extension of pro sports into esports - which has 21 teams and games that stream on Twitch.
  • A partnership with MGM Resorts International, integrating real-time data into a gambling platform and enabling in-game micro-betting that kept viewers watching every play.

The power of AI in professional sports isn’t just limited to the codes - teams like the NBA’s Sacramento Kings (Fast Company’s most innovative company in Sports 2017) are getting in on the action too. 

The Kings developed a team and venue app which provides fans with a hyper-personalized arena experience. The app delivered on-demand information regarding every aspect connected to their visit, including information about transportation and parking, arena line queuing, and access to advanced game metrics, exclusive video angles and more.

We’re looking forward to seeing solutions like this on Australian shores - and we don’t think it will take long.

Powered by Red Marble, Red Sports Analytics is delivering enterprise solutions to help sporting codes recover from the difficult times brought on by COVID-19 and take advantage of new fan experiences. 

Three AI solutions helping sporting codes survive COVID-19

Next-generation chatbots

Chatbots help close the gap between customer service and conversational marketing. next-generation chatbots make use of live data streams, not just static data. This means we can give fans the information that will improve their experience, from “Live stats on their favourite player” to “Best car park to enter on game day”.


Membership analysis

Customer segmentation using analytics allows sporting clubs to identify and cluster customers based on behavioural and demographic indicators. By identifying and understanding fans preferred experiences, we can define actions to minimise fan churn and maximise the value of each segment.

Hyper-personalised marketing

Hyper-personalised marketing is more engaging and useful than traditional marketing because it goes beyond basic customer data. We’re leveraging analytics, AI and machine learning to increase wallet spend by delivering relevant and personalised offers and experiences. 

Sporting codes are slowly emerging from this period of disruption and entering a new phase of fan engagement. Red Marble is ready to harness the power of AI and the operational expertise of Anthony Moore Consulting to deliver innovative customer experiences and organisational success.

To find out more about Red Sports Analytics, please get in touch. 

How can AI help businesses better recover from COVID-19?

For now, at least, it seems Australia has successfully “flattened the curve”. 

We’re starting to emerge from lockdown - and thankfully, from a health perspective Australia seems to have been spared some of the horrors of other countries. With family living in the UK, France and New York, I’m acutely aware that the impact of COVID-19 has varied widely around the world.

From an economic perspective, COVID-19 has affected many industries – travel, retail, hospitality, entertainment and higher education to name a few. On the whole, technology companies have fared a little better as they navigate the current environment, but still face unique challenges as they establish their own ‘new normal’.

During this period, the importance of big data and trusted analytics has never been more vital. Artificial Intelligence has been in the news lately for a number of reasons, including the early detection of the pandemic and frontline resourcing and vaccine research.

How can we apply AI to help businesses recover from COVID-19?

We have conversations every day exploring the possibilities of AI with existing and potential clients. The challenges that organisations are facing right across the globe don’t have a playbook and navigating the next few months will undoubtedly be complex.

Our work generally falls into one or more of the following five areas:


  1. Prediction
    Being able to forecast future behaviour based on past experience, for example forecasting conversion rate for an online retailer or when predicting when a machine is likely to require maintenance.


  1. Recognition
    The ability to recognise people, objects or situations in a scene, often using computer vision technology, or recognizing patterns in data. This could include showing social distance between people or recognizing heavy machinery on a pedestrian area of a work site.


  1. Conversation & Language
    Enabling interaction between user and software using either voice or text. It requires understanding of a user’s intent and the ability to understand language and context. The equivalent of Siri or Alexa with the specific knowledge of a company’s “corporate dictionary” of acronyms, systems and processes.


  1. Hyper-personalisation
    Using machine learning across different data sources to build a unique profile of an individual and act upon this information with targeted marketing messages, personalised offers and unique discounts. It’s the ability to treat everyone as an individual rather than as part of a generalized market segment.


  1.  Outlier Detection
    This is the ability to detect unusual activity, patterns or behaviour which are ‘outside of the norm’ based on the available data.


Applying our AI methods to COVID recovery

Which of the above are most likely to be beneficial as companies return from lockdown? We’re seeing clear trends in the market around:


  • Recognition of people and situations
  • Hyper-personalised marketing
  • Prediction


The main focus for most companies is on returning to work and opening up to staff and customers safely. This requires a good understanding of the challenges within the physical environment.

Computer vision technology can be applied using existing security cameras for functions such as counting people in an area. Algorithms can detect people proximity issues, which can lead to either real-time alerts or heat maps to show flow of ‘traffic’. With some specialist equipment, heat-sensing technology can be applied to see temperature outliers within a group of staff or customers which might indicate a fever.

Companies are also looking at doing more with less. Hyper-personalised marketing can be applied to target product offerings to customers, re-engaging them without adding significant cost. Similar personalisation techniques are being applied within an enterprise to improve internal communications and  improve technology adoption.

Prediction using machine learning is likely to face challenges in the short term. Most models make certain inherent assumptions based on the data used to train the model, which may have seen large deviations from the norm.

Finally, automation technologies in general are increasingly being used to remove cost from operations. Though not typically “intelligent”, these are an effective way of automating many data processing tasks which can drive significant cost savings in the short term.

It’s been inspiring to see the innovation and ingenuity of so many businesses as they navigate this incredibly challenging period. It’s great to see that many businesses are looking at technology as a solution to the current obstacles.

Now more than ever, companies need projects to deliver value quickly. If you would like more details on how Red Marble can help, please get in touch.

Six key questions towards Ethical AI

As companies emerge from these tough times of COVID-19, many organisations are looking to take advantage of Artificial Intelligence (AI) to build efficiency and improve customer and staff experience. Along with the many benefits, there are also risks and liabilities to consider - particularly when it comes to inheriting bias.

It’s critical to deploy any artificial intelligence project with ‘Ethical AI’ as a key lens as we move into an equitable future.

AI might seem immune from the moral tangles and complicated contradictions of humans, but in reality it’s difficult to escape the biases that unknowingly sit in the data used to train the models. Not only does this create an ideological issue, it also affects the business outcomes and increases the risk of negative impact.

Ethical AI - six questions to ask

As the market and more importantly the influence of AI continues to build, we need Ethical AI to be our directional “North Star” to ensure we manage the inherited risk of bias within AI models.

At a minimum, we suggest asking and discussing six key questions throughout the process of procuring, designing and implementing AI projects. This may be a project sponsor asking a vendor, or a project manager asking their developers, or a CEO asking a CIO:

  • Which laws and regulations does the software need to adhere to? 
  • How does the solution design enable any decisions, or recommendations made, to be explained? 
  • Is there transparency about this use of AI with each stakeholder group? 
  • What biases may be inherent in your training data? How are you managing the risk of bias?
  • What decisions will the software make or inform? Are the decisions reversible? 
  • What’s your process around code reviews and how are you adopting software engineering best practice?

Leading the charge for responsible AI

Many governments, organisations and businesses, particularly in Australia, have voiced their support of the Ethical AI movement.

The Australian Government’s Department of Industry, Science, Energy and Resources has created a list of eight voluntary AI ethics principles; among them, “Throughout their lifecycle, AI systems should benefit individuals, society and the environment.” However, we feel that most companies need a more practical approach and find those principles a little too high level. 

Red Marble AI has an ethical AI playbook for companies looking to establish their capability quickly - please reach out for a copy. There are also a number of specialist ethical AI firms emerging, such as Dr Catriona Wallace’s new advisory which recently opened out of Sydney.

Ultimately, we expect companies will form an AI ethics advisory board or sub-committee, similar to existing risk and audit committees, to develop a framework for a company’s expectations around AI. But in the meantime, we believe that asking these six questions – and the discussions that ensue - will give most companies a strong head start.

It would be great to hear your thoughts on Ethical AI, and our six principles. How strict should we be? Do we need government legislation? Please get in touch here.

The Proximity Pal journey

The response to COVID-19 from Australian businesses has been inspiring – we’ve seen small operators completely pivot their business plan to survive, larger companies changing up their production lines to help make safety equipment and tech companies trying to engineer solutions to the problem.

In the past month, we’ve partnered with another start-up – Vimana Tech – to develop Proximity Pal, a smart sensor which detects physical proximity between people and helps to maintain social distancing.

A prototype of Proximity Pal.

As well as working the muscles of our talented machine learning team, it’s also been fascinating experience to go from idea to launching a product in under four weeks.

Here’s the timeline of Proximity Pal’s conception and creation.


Thursday 26th March

Around 4pm, I took a call from Joel Kuperholz, a pal who is one of the founders of Vimana Tech, small technology firm based in Melbourne. We’ve partnered with Joel and his team in the past. They were working on a physical device to do proximity detection and needed help with the algorithms. Our machine learning team are mainly mathematicians and physicists by background and so we were happy to help. We were on a zoom call by 5pm and spent the evening and all of Friday exploring the concepts.


Sunday 29th March

After a few long days and late nights, we had a working model using 3D printed parts, simple electronics and some clever software.

How Proximity Pal alerts users.

We began sharing within our network for feedback; one of our construction clients was ready to trial and sign up. There was also interest from mining companies and other retailers.


Thursday 9th April

Most of Australia was preparing for a very different Easter weekend. The country was in lockdown, and the press coverage was focussed on keeping everyone home. It was a critical weekend in controlling the spread and flattening the curve. Some business were already closed and more seemed likely to close imminently.

For us, a working weekend; but we didn’t have much else to do in any case!

By now, we’d agreed a joint venture between Vimana Tech, Red Marble AI and investors, created a company Proximity Pal, sorted branding and logos, filed a patent for the innovative technology, had the computer boards manufactured and created test devices. Results were promising.


Wednesday 15th April

We ran trials at a large hardware store in Melbourne with good results.

It was clear that the technology can detect proximity between humans and help keep people safe. But it was also clear that environmental conditions would affect the performance of Proximity Pal. Indoor or outdoor, different ceiling heights, different acoustic conditions and other factors all individually have minor impacts - but when coupled, could add up to be significant.

By now it was also clear that Australia wasn’t going to experience the horrors that many other countries had seen. Our rate of doubling thankfully had decreased from 25% to low single figures to a handful of cases each day. The threat of closing more businesses had turned into a narrative around easing lockdown and getting back to work sooner.

Our story had changed too; our new customers were now talking about how they can make the technology do more, and have a longer-term focus, to have applicability beyond the lockdown.


Monday 20th April

Proximity Pal is officially launched, 25 days after that first phone call.

We have a product in the market in three and a half weeks which meets a market need, and we’re now working on version two to give it applicability beyond the lockdown.


What have we learned?

Firstly... that everything is accelerated at the moment. Planning assumptions are out of date within days or even hours. Speed, agility and the ability to rapidly prototype, innovate and pivot have never been more important.

Secondly... it’s amazing what you can achieve with some bright people, clear focus and lots of urgency and belief. Companies need to find a way to harness that kind of energy in their normal operations.

Thirdly... that innovation requires constant experimentation. It requires a healthy scepticism to be blended with a “can-do” attitude. Collect and trust the data.

My final takeaway is that every meeting you have with every network contact can lead somewhere. Joel and I first met 8 months ago, we hadn’t worked together before now, but the seed was sown one morning in the middle of last year.

The world is experiencing lots of horrors at the moment; but through every crisis there are things we can learn and should maintain once we are through the worst.

Find out more about Proximity Pal and see our explainer video.