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5 Machine Learning trends businesses should be aware of in 2023

Written by Brendan Walsh on . Posted in Machine Learning. No Comments on 5 Machine Learning trends businesses should be aware of in 2023

Find out more about Calligo’s Machine Learning as a Service (MLaaS) capability here

One thing’s for sure: in 2023, Machine Learning will penetrate even more businesses in even more sectors, following tremendous growth last year. But when it comes to ML, what should organizations have their eyes on, to stay ahead of their competitors? Here Calligo’s Chief Data Scientist, Tessa Jones, reveals her five Machine Learning predictions for the year ahead, based on her experience at the front line of helping businesses use ML and AI to unleash the value of their data and boost ROI.

Top five Machine Learning trends

1. Predictive systems

The evolution of Machine Learning and Artificial Intelligence has reached a point where businesses need to adopt intelligent solutions to remain competitive – and therefore, I anticipate a significant rise in the use of predictive systems this year.


Predictive analytics encompasses a variety of statistical techniques – including Machine Learning, statistical modelling, and data mining. This combined power gives organizations the ability to extract precise, forward-looking intelligence from their data efficiently. Predictions can become rooted in business decisions, giving deep insight into the future. It’s about moving from being ‘data inspired’ – where decision-makers use data to feel better about doing what they were going to do anyway – to obtaining new information that allows you to make data-driven steps forward.


A competitor that’s adopting dynamic, sophisticated ML solutions is going to have an edge over a business that’s still relying on legacy systems – with a dose of gut feel.


Business consideration: How do we truly get value out of our data? Do we want to validate a decision we already know we want to make, or apply intelligence onto it and discover fresh actionable insight?

2. ML & AI regulation

More regulation and formalized audit processes will continue to emerge in response to increased attention to data privacy and ethical AI. Dip into Calligo’s latest ‘Data Privacy Periodic Table’ to see the constantly moving pieces of the world’s data legislation jigsaw. Not to mention whether data privacy and AI is even possible. For more on that, do read a thought-provoking blog on the topic by my colleague, ethics and governance expert, Sophie Chase Borthwick.


Clearly, ML and AI regulation will continue to be a minefield; one with shifting parameters depending on geographical location. On the face of it, the EU is further advanced in that it has regulation in place; this will continue to evolve. For example, how should businesses carry out ML and AI audits and risk assessments? Importantly, in a way that doesn’t become too much of a hindrance for organizations to adhere to.


Whereas the US is still grappling with what ML and AI regulation might look like. There has been some headway in the privacy space and there’s a new law in New York that requires recruiters using AI tools to go through a robust AI audit to rule out bias. But that’s just one niche, in just one state. It’s a step in the right direction, but there’s clearly a long way to go. And of course, in our global world, businesses don’t operate just within state borders or even countries – nor does technology. If there’s a deluge of different ethical AI regulations being introduced in different parts of the world, things could get even more foggy than they are now.


Business consideration: Have you considered how your ML and AI could be audited? And do you have the right privacy, governance, and ethical AI expertise in place to manage your risk?

3. Real-time vs batch

I predict a surge in the use of real-time prediction models this year, that are integrated into live systems, so they deliver immediate results. This can often be more valuable than batch predictions – and that’s why I anticipate greater adoption this year. In my mind, the businesses that are investing in real-time ML will be the movers and shakers.


Batch predictions have certainly played a significant role. A model gives businesses insights on a weekly or daily basis. This is useful when you want to generate predictions for a set of observations all at once. But many use cases suffer from lagging predictions; data will be left waiting until the next batch to be processed. And it involves a fair amount of human graft.


In contrast, real-time inference allows the model to make predictions at any time and trigger an immediate response. Historically, this has been the domain of the big tech companies, but I see this becoming more mainstream in the near future.


Business consideration: Is your organization ready to optimize ML systems when real-time predictions are superior to batch predictions? The natural progression from batch to real-time predictions can unlock huge rewards.

4. MLOps improvements

This brings me seamlessly on to my next (not real-time or batch) prediction. Businesses can’t start harnessing the power of these live systems until they have robust ML operations (MLOps) in place – and by that I mean processes and people. The uncomfortable truth is Machine Learning is difficult to make useful, carries a high risk of failure and is hugely costly when things go wrong.


Tech is all well and good, but to deploy predictive real-time models effectively, I believe there are at least six distinct roles needed on top of the heavy lifting technology. This can surprise businesses who think (hope) that investing in one data scientist will suffice. Read more here about the different MLOps roles needed – and how this de-risks ML for businesses, while boosting productivity and profitability.


There’s an exciting new trend emerging of employing an army of MLOps experts only for the time they’re needed. And it’s called Machine Learning as a Service – like a timeshare apartment that’s rented only for the weeks you want it, rather than paying for the luxury all year round.


Business consideration: Are you aware that hiring one or two data scientists is not enough if you want ML solutions to boost productivity and profitability? You need an end-to-end data science team, consisting of at least six unique skill sets.

5. Edge AI & Federated Learning

As touched on in trend 2, the issue of privacy will become more and more prominent, as predictive models become more widespread – and this is where Edge AI and Federated Learning will increasingly take center stage.


As a quick reminder, Federated Learning is an ML technique that involves training an algorithm across several decentralized edge devices. Each trained model is then pooled centrally, modified (based on the specifications of the other models), then returned back to its local system. This effectively allows models to be informed by data that it never had access to, thereby supporting data privacy.


Edge AI meanwhile is the execution of already trained AI applications on decentralized devices, like a phone or off-line machine. This approach helps overcome issues related to latency, bandwidth, and privacy.


Business consideration: While Edge AI and Federated Learning are only relevant for select use cases and industries, it’s important for all organizations to be aware of new emerging tech that allows sophisticated systems to be built with higher levels of privacy.


Find out more about Calligo’s Machine Learning as a Service (MLaaS) capability.

ai bias lgbtq+

AI bias is frequently failing the LGBTQ+ community

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Privacy, Glossary, Machine Learning, Machine Learning. No Comments on AI bias is frequently failing the LGBTQ+ community

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In our latest Beyond Data podcast, co-hosts Sophie Chase Borthwick (our Data Ethics & Governance Lead) and Tessa Jones (our Chief Data Scientist) invited Tomer Elias, Director of Product Management at BigID, to discuss how AI bias affects the LGBTQ+ community.

Here we explore some of the episode’s highlights – although you can also watch the full episode here.

Why is there bias?

When building an AI algorithm or AI solution, it is crucial to make sure it’s based on data sets that are both unbiased and diverse and, in terms of the LGBTQ+ community, this often falls short. Whatever the sector – work, health, entertainment – all will be subject to bias if the LGBTQ+ community is not taken into consideration when an AI solution is being created.

For Tessa Jones, one of the barriers to collecting sufficient data is that people might be reluctant to share information about their sexual orientation or their gender journey – particularly if they don’t know how this personal data will be used. Sophie Chase-Borthwick agrees that it quickly becomes a catch-22 situation:

“The biases that make you nervous of disclosing information are the very reason that you need to disclose said personal information in order to prevent bias and improve.

Knock-on effects

Drawing on his experience as a board member of an organization that supports LGBTQ+ employees, Tomer Elias explains how candidates are being let down by recruitment AI solutions and that the consequences are significant.

“A lot of people in the LGBTQ+ community are unemployed and that’s not because they’re lacking the professionalism and passion.”

Meanwhile, medical advances in the LGBTQ+ community are constantly evolving, and many algorithms do not take these changes into account.

“People who are transitioning are not getting the right treatments because the treatment providers are not well educated about it and the data is not diverse enough,” explains Tomer.

Tessa also raises the issue of health apps that require a user to state whether they are male or female.

“Even though the equations could be written differently to how you use different input, they’re just not and that means, you either have to pretend you’re something different or just not use that tool.”

Potential of AI to help overcome bias

While AI bias is clearly affecting the LGBTQ+ community, there are innovative ways it can be used to overcome it, too. Such as in recruitment.

“At the initial interview stage, AI could be used to scramble the voice so you would not know if the candidate was male or female or someone who has transitioned,” says Tomer.

He also poses the possibility for AI to help with the retention of LGBTQ+ employees.

“Technology could help employers know that the employee is happy and feels a part of the organization.”

Time to step it up… 

There are already many AI forces for good – including recommendation systems which can help LGBTQ+ people feel more emotionally supported and The Trevor Project that uses AI to predict which callers are more likely to commit suicide to ensure they get help.

Much more needs to be done. But the fact that people are starting to think about AI bias and the LGBTQ+ community is a step in the right direction.

“Now we’re talking about it and people are realizing the actual real-world implications, hopefully more people will feel comfortable expressing themselves and we can close some of that data gap so there is more information for the models to work off,” according to our Data Ethics & Governance Lead, Sophie Chase-Borthwick.

“It’s also super critical that we have diverse AI developers who are knowledgeable about people and bias,” adds Calligo’s Tessa Jones.

To hear more of our fascinating discussion on AI bias and how it affects the LGBTQ+ community, tune in to our latest Beyond Data podcast episode below.

 

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How Machine Learning as a Service improves organizational productivity and reduces costs

Written by Brendan Walsh on . Posted in Machine Learning. No Comments on How Machine Learning as a Service improves organizational productivity and reduces costs

By Tessa Jones, Calligo’s VP of Data Science, Research & Development & Peter Matson, Calligo’s Data Science Practice Lead.

More than tools & tech

85% of Machine Learning (ML) projects fail. This stark reminder from Gartner – despite more tools being available to businesses than ever. The thing is ML success is not just about tools and technology; it’s about how they’re put into production by experts. Plural. Machine Learning – that improves productivity and profitability by finding valuable insights buried deep in your company databases – needs a small army to leverage it. An entire MLOps collective – from platform engineers to software developers, business translators. And yes, Data Scientists.

The reason your ML endeavors are failing (or not thriving) and carry the risk of costing your business eye watering amounts is because one or two Data Science experts are not enough. You need a minimum of six unique skill sets.

ML overwhelm

If you’re reading this, you may be interested in building Machine Learning solutions, because you know they can reap huge rewards for your scaling business. But it can feel overwhelming – because you also know that ML is easy to get wrong, and can drain your budget. It’s a long-term commitment. And now we’ve told you will need a small army of capabilities.

But there is a new way – Machine Learning as a Service where you can hire an entire end-to-end Data Science team with the required technology, rather than recruiting one or two permanent positions and on-boarding new and expensive tech. Buying ‘people as a service’ essentially. Faster outcomes, decreased costs, greater value. More on this in a moment…

Machine Learning tools: offerings and shortcomings

There are more Machine Learning tools on the market than ever before. Suppliers like Google’s AutoML, Microsoft’s Azure, DataRobot, Dataiku and others have positioned themselves as off-the-shelf/‘out of the box’ solutions for all your Machine Learning needs. 

These kinds of solutions offer quick ways to gather insights from data. They can be excellent tools for building prototypes and frameworks to support deployment and ongoing management of models. They can also fully automate the model building process – which means organizations can have their hands on a deployable model within days. 

However…what is an optimal ML target? Is it statistically sound? How much compute do we need?’

Back in the box

Out of the box solutions can’t give you the answers. There’s no end-to-end service. And by that we mean, none of them allows you to buy ‘people as a service’. You load your data into their system, a model is built automatically and you’ll get access to the outputs. But you still need to do a lot to get those results  – build and maintain the model, integrate it into the business, keep it productionized and in a state that provides continuous value. These tools also can’t support your data security, privacy, or governance needs.

Cue the first Machine Learning as a Service of its kind

Now it’s time to introduce the six (people) roles and responsibilities you need to develop, deploy and monitor Machine Learning – to pave the way for long-term success:

1.      Business Translator: helps explain how to frame the business problem into an ML problem.

2.      Data Scientist: analyzes and processes the data, builds, and tests and monitors ML models.

3.      Data Engineer: manages how the data is collected, processed, and stored.

4.      Platform Engineer: builds and maintains specialized tools and infrastructure, integration with existing processes.

5.      ML Architect: develops blueprints and orchestration processes and identifies risks.

6.      Software Developer: builds and maintains robust and relatable interfaces for end users.

And then there’s a non-human no.7: the heavy-lifting technology. A full end-to-end tech stack to facilitate automation, optimize deployment – with ongoing monitoring.

Not ‘Jacks of all trades…’

These are all Masters of their own expertise. They are very purposefully not ‘Jacks of all trades’.  Rather, deep, unique skill sets that together form integral parts of the ML chain. Collectively this brings extraordinary business value.

And here’s the differentiator: rather than recruiting one, two, three Machine Learning experts – who can never possess all the skills listed above – you can hire six, for a fraction of the price. Like a timeshare apartment, you secure the property you want, for when you want it – without paying all year round for the luxury.

Minimal IT, no Data Scientists or ML technology

Let’s put Machine Learning as a Service (MLaaS) into an (anonymous) Calligo client context. An insurance provider uses ecommerce services to generate its customer base. And it allows end users to search for quoting estimates across many different providers. Initial quotes are generated based on the end user’s risk parameters – but the final pricing always needs to be dynamic and adaptable relative to their competitors.

The insurance provider had a small IT team and no Data Scientists. It had attempted using out of the box solutions that promised valuable price optimization modeling. But the output was too binary and did not recommend price changes. Put simply, it didn’t work. Cue Calligo’s MLaaS – that introduced a production ML model that continuously optimizes pricing, with minimal human requirements. Our client now has confidence in its pricing strategy and can see the end user characteristics that most impact price adjustments. The increased revenue potential was $3.1 million a year, generating great profit for the insurance specialists by targeting the right customers with the right price.

De-risking Machine Learning for businesses

Not only that, but its Machine Learning has now been de-risked. Making ML useful is a really hard task. There is a high risk of failure and if things go wrong, it’s expensive. Really expensive.

Machine Learning as a Service is the first of its kind because it simultaneously overcomes these six obstacles to adoption:

·      Cost

·      Usability

·      Technology

·      Security

·      Data privacy

·      Applied expertise

Human brains & smart machines

The blend of the human factor and machine is critical in delivering excellent Machine Learning solutions for your business. You no longer have to build your own (limited/ expensive) infrastructure or rely on off-the-shelf solutions that lack human input and monitoring.

As a reminder, Machine Learning:

-It is difficult to make useful.

-Carries a high risk of failure.

-Hugely costly if things go wrong.

Machine Learning as a Service is therefore the future. An extension of your team, your very own small army of ML experts – rented, not permanently recruited, deployed for the time you need to get your ML arsenal fired up and reeling in value and profitability. Learn more about our Machine Learning as a Service here, or get in touch to chat about how our ML experts can best support your business goals.

Global Food Waste – Can AI Offer a Solution?

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Glossary, Machine Learning, Machine Learning. No Comments on Global Food Waste – Can AI Offer a Solution?

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In our latest Beyond Data podcast ‘Global Food Waste – Can AI Offer a Solution?’, we invited Data Science leader Shawn Ramirez to help us explore the global issue of food waste and discuss how AI has the power to make a difference. Co-hosts Sophie Chase Borthwick (our Data Ethics & Governance Lead) and Tessa Jones (our VP of Data Science, Research & Development) steered Shawn to share her insight and examples of where AI is helping combat this prevalent ‘human’ problem. Here we explore some of the episode’s highlights. 

What a waste… 

To say global food waste is a huge problem seems like an understatement. Nearly one third of all food around the world is currently being wasted. Estimates also suggest that 8-10% of global greenhouse gas emissions are associated with food that’s not consumed. In Shawn’s own words, there are some stark facts that are hard to ignore:  

“In the United States about 40% of food is wasted while, at the same time, 40 million people in the US are suffering from hunger, including 12 million children. If we could reduce and redistribute food waste by 15%, we’d actually feed more than half of those hungry people.  And it’s a similar story in Europe where 153 million tons of food is being wasted.” 

Not just about hunger… 

In addition to hunger, food waste is directly connected to all kinds of additional concerns – such as resource conservation, carbon emissions, and climate change. Clearly hugely passionate about the subject, Shawn explains why we all need to commit to change.   

“With our rising population, the situation is only going to get worse and, if we could reduce or redistribute that food waste, we could have a massive global impact.” 

Where is it happening? 

In the US, 60% of food is wasted before it even reaches the consumer. In Europe, 55% to 60% occurs in consumer households. Whereas in developing countries, most waste happens during agricultural production. Where you are in the world’s supply chains can make a big difference. 

What part can AI play? 

It’s increasingly clear that AI can and is starting to play an important role in turning the tide on food wastage. Exciting hi-tech innovations are enabling more sustainable farming – such as AI-enabled monitors, computer vision, remote sensing, as well as robots. Shawn highlights how this technology is revolutionizing vertical farming. 

“We are now seeing single vertical farms that produce the same amount of fruit and vegetables as an 80-acre farm and they are using 97% less water.”   

A Swedish company is transforming disused office buildings into autonomously controlled greenhouses and a company in Singapore has created the world’s first low carbon hydraulic water-driven vertical farming system.  

Throughout the supply chain, AI is becoming an indispensable planning tool. And Shawn has seen this first-hand, thanks to her time at Shelf Engine – an end-to-end grocery ordering solution using advanced AI. 

“We worked with grocery stores, using inventory simulations to optimize the freshness of food by predicting customer demand…Connecting data across the supply chain facilitates better informed decisions.”   

Knowledge is power

Then there’s a need for efficient and effective monitoring of what’s actually being wasted – something AI now has the capability of doing in granular detail. 

“AI-powered garbage cans equipped with weight sensors, cameras and computer vision have the ability to recognize and track the amount and type of food we’re throwing away.”  

As well as in the home, these can be used in restaurants, hotels, and other businesses – enabling people to think carefully about their waste, while helping companies effectively monitor and understand what’s being thrown away and when.   

You may have heard of Ikea partnering with Winnow Vision AI to track kitchen waste using computer vision technologies. Well, Ikea then used this data to implement changes resulting in a saving of 20 million meals. That amounted to 40,000 tons of carbon dioxide.  

Food for thought

The US Department of Agriculture has set a target of reducing food waste by 10% within the next decade. To achieve this, Shawn believes education in the capability of AI is the next vital step. 

“We want to see more organizations thinking about the food that they waste and realizing how they can make a massive difference by adopting different AI technologies.” 

To hear more of our valuable discussion on how AI has the power to reduce food waste, tune in to our latest Beyond Data podcast episode now. 

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The Jersey Transform 2022 Event

Written by Brendan Walsh on . Posted in Cloud, Data Ethics, Data Insights, Data Privacy, Machine Learning, News. No Comments on The Jersey Transform 2022 Event

The Channel Islands’ Premier Data & Cloud Strategy Event

Join The Channel Islands’ Premier Data & Cloud Strategy Event – Transform 2022

Our speaker line-up includes Professor Hannah Fry, a Professor in the Mathematics of Cities, science broadcaster, and winner of the prestigious Zeeman Medal.

  • Venue: The Royal Yacht Hotel
  • Location: Weighbridge Pl, St Helier, Jersey
  • Date: 30th November 2022
  • Timings: Conference from 1.30 pm-5 pm, cocktails and canapes from 5.30-7 pm

Please Note: This event is for business leaders, and spaces are therefore limited. 

To secure your exclusive place, register here.

Join your peers from across the Channel Islands and get past the buzzwords to learn more about what Business intelligence (BI) really means in today’s modern businesses and why this is a strategic imperative for leadership teams and not just your IT teams.

You will learn about Data and how to unlock its true power, covering:

  • The trends in Cloud technology and the business advantages to be gained from them
  • Why the Cloud is the foundation to becoming a truly data-driven business
  • Best Data practices to help organisations make better decisions
  • Using accurate Data to drive change and grasp opportunities quicker
  • Eliminate risk and inefficiencies
  • Adapt quicker to market challenges

The dark side of AI energy consumption – and what to do about it

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Privacy, Data Strategy, Glossary, Machine Learning, Machine Learning. No Comments on The dark side of AI energy consumption – and what to do about it

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Artificial Intelligence’s ability to augment and support progress and development over the past few decades is inarguable. However, when does it become damaging, contradictory even? In our latest Beyond Data podcast AI’s Climate Jekyll & Hyde – friend and foe, Tessa Jones (our VP of Data Science, Research & Development) and Sophie Chase-Borthwick (our Data Ethics & Governance Lead) discuss exactly this with Joe Baguley, Vice President and Chief Technology Officer, EMEA, VMware.

Our speakers explore the multifaceted topic of energy consumption and AI – from whether all applications are equal for energy consumption (or reflecting if there are some ‘better’ than others), to creating visibility and responsibility of energy consumption for all stakeholders. Here we try to give clarity to some of the grey areas that were discussed.

Should we consider all applications equal?

“AI and machine learning are about huge things, huge data sets, huge computation actions … all of those have huge implications in terms of energy,” Joe observes, before dropping in hugely sobering stats such as the total annual energy consumption of bitcoin being the same as Norway. Even when considering the often-touted argument of 57% of the energy for bitcoin mining using renewables, Joe counters: “But those renewables could have been used for something else, right? Those solar panels… and those hydropower stations and those wind turbines, we could be using them for something else.”

This raises the ethical question of whether there should be stricter governance, standards, and precedent set on more ‘moral’ applications for their energy consumption. Should we be more closely considering the difference in energy consumption between server farms that support minimizing food waste versus those that are focused on mining digital currency, for example?

“Is there an opportunity for [greater] regulation?” Tessa ponders. Would this regulation help challenge the current status quo for all applications’ energy consumption being considered equal? While Sophie observes: “We’ve had certain European nations start to put rules around data center expansion, where you’re allowed and not allowed to build because of the capacity there, which isn’t regulating the use of it. But it does have that knock-on effect that if you literally can’t build the data center support, you have to start thinking about other ways to build [models].”

When considering Sophie’s point on alternative ways to build models, Joe notes: “We’re using AI to deal with the symptoms, but maybe there’s some better ways we could be using AI to deal with the cause as well”.

And this all raises the next question – who should ultimately be making these ongoing moral calls for the environment and energy usage?

Embedding Environmental, Social, and Governance (ESG) by design

Environmental, Social, and Governance (ESG) is shorthand for a framework that helps stakeholders understand how an organization is managing risks and opportunities related to environmental, social, and governance criteria. Our speakers untangle the idea of ESG and how companies could use it to help triage the different applications they use.

Joe asks: “Is there an ESG-led marketing opportunity here? Your AI might be the same as my AI, but my AI is better from an ESG perspective. They both get the same results at the same time for the same cost, but this one’s better from an ESG perspective, in terms of sustainability, in terms of social good, in terms of environmental.”

By placing more emphasis on ESG as the criterion for measuring impact and success, it could help with embedding sustainability in the heart of the application’s deployment, rather than a siloed approach. Sophie agrees: “We have privacy by design, we have security by design. Why not have ESG by design?”

Following on from this thought, our speakers consider the cost implications of AI and ESG with Joe observing, “There’s a lot of businesses right now that can’t afford AI because it’s expensive…but I believe they will come to a tipping point where they can’t afford not to”.

Are we over-prioritizing accuracy?

“There’s a hyper-focus on the accuracy,” according to Tessa. “It ends up not even being about the motivation for green, it’s a motivation for fast training, fast tuning. Unfortunately, it’s how most data scientists are motivated; be faster without having to compromise their accuracy.”

Often, the increase in accuracy can be mapped on a logarithmic graph. Good gains at first, but quickly tapering off to minimal increase. Is it useful to be that much more accurate, often by points of a decimal? “Some are good, more must be better … people just keep going, as opposed to saying actually good enough is good enough,” Joe summarizes.

Instead of chasing marginally better accuracy each time, we should be considering the application in a holistic view. The increase in accuracy might be 0.01%, but would cost heavily for energy consumption – is it worth it? Should we be better at exposing these costs more vigorously throughout a team so everyone can feel more empowered and have the visibility to interrogate more closely?

To hear about how our speakers untangle these controversial questions and more, tune in now to Beyond Data podcast episode 3: AI’s Climate Jekyll & Hyde – friend and foe.

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Putting the ping into office pong: Meet our table tennis score predictor – powered by Tableau

Written by Brendan Walsh on . Posted in Data Insights, Machine Learning. No Comments on Putting the ping into office pong: Meet our table tennis score predictor – powered by Tableau

A game of table tennis is always a good way to unwind – over a few drinks with colleagues after the to-do list is done or perhaps during lunch break. But, if you’re looking for a way to rev up competitive spirit and even take on teams in other ZIP codes or countries, we have created the perfect solution.

If your office has access to a ping pong table (essential) you’re eligible to climb the global ranks soon to see if you can beat other companies – and even the data.

Let us introduce you to our new Ping Pong leaderboard & Predictor dashboard, powered by Tableau. Our data analysts, data engineers and data scientists have been working hard behind the scenes to bring this new product to offices across the world – soon.

How does it work?

Put simply, you play a game of ping pong, input the scores – and then you can start seeing where you rank on the dashboard.

“Companies can sign up and get their own dashboards for their employees,” explains James Faure, Calligo Data Scientist and Ping Pong Predictor project champion. “It’s the perfect way to add some additional energy into work get-togethers. You can keep the competition internal, and just play against your colleagues. Or you can expand your competitive horizons to within your country, or even the whole world.”

The more you play, the better – the predictive model is hungry for data, to get an accurate understanding of your state of play. Through the app you’ll be able to see who’s predicted to win the next match. If you’ve decided to take on a rival business across the street, for example, previous data fed into our Ping Pong Predictor might lead it to say you’re going to lose by 18-21. But…will that ring true? Can you not only beat your human competitors, but also take on data analytics, engineering, and science?

A bit of geekery

On that note, while this no doubt sounds intriguing, you’re probably wondering how it’s even possible. Well, the brains of our data analysts, data engineers, and data scientists are constantly whirring, devising creations that are not yet out there – and not just for our clients. This is the latest example of their combined, extraordinary skills; the perfect trinity of Calligo’s disciplines – with (more than) a little help from React, Python, Snowflake and openFaaS. And, of course, Tableau – that not only powers the dashboards, but makes them highly intuitive to use and pleasing to the eye. A mighty partner, indeed.

Analytics + Engineering + Science

Firstly, data visualization and analytics expertise combined to create the dashboard in Tableau. (On a quick side-note, now seems an opportune moment to mention that a piece (Human Disease Network) by one of our data visualization experts – Anjushree B V  – was chosen last year by Tableau as its Viz of the Day (VOTD). You can read more about this here.)

Then came the data engineering – SQL database programming and designing the system. This ascertained how data would flow between the dashboards. As we get more match data, we will start to take it across to train the models – aka Machine Learning, that will be deployed back into the app.

“As well as being interactive entertainment for companies, this demonstrates the Calligo team’s exceptional skills,” says James. “Our Ping Pong Predictor is an awesome playground where we can test new technologies that we want to implement for our clients. I also love to teach people about data – and this is a diverse way to illustrate what it involves for younger professionals considering a career in this field.”

Coming soon…

Back to what are likely to be hotly contested corporate competitions… Ping Pong Leaderboard and Score Predictor Dashboard powered by Tableau is coming soon to offices near you. We are currently beta testing the platform, and by signing up to Calligo’s newsletter, we will provide an update as soon as the platform is released to the public. It’s time to put the ping back into office pong…

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A shout out to Calligo’s Ping Pong Predictor team

And those exceptional skills deserve a special mention. Thanks to Nick Mischko – our Senior Data Analytics Team Lead  who built the Tableau dashboard. Supporting James Faure with engineering insight was John Jackson, Calligo’s Director of Data Integration & Engineering, and internal tech help – such as deploying pieces of code – from Gary Bright. The Machine Learning element of this app will become more prevalent, thanks to Peter Matson, our Data Science Practice Lead, and Tessa Jones, our VP of Data Science Research & Development. And, last but not least, software engineer expertise came from Artur Kruell, showing how software engineering is still very important in the data field as the glue that sticks different pieces together.

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Vehicle Autonomy; the good, the bad, and the complicated

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Insights, Data Privacy, Data Warehouse, Glossary, Machine Learning, Machine Learning. No Comments on Vehicle Autonomy; the good, the bad, and the complicated

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In our second Beyond Data podcast episode ‘Autonomous mass transportation and its impact on citizen privacy’, we will sit down with Beep’s Chief Technology Officer, Clayton Tino to explore the current landscape of autonomous vehicles (AVs), whether AVs truly can replace the human factor in public transportation, and how AV ethics can be holistically measured. Here we give you a snapshot of that fascinating discussion by digging into a few of the explored topics.

You can watch episode 1 here

When looking at AV ethics, there are two strands to consider:

1: The ethics programmed into the AV itself (e.g., how the AV ‘decides’ which course to take when it identifies a hazard, otherwise known as the ‘trolley car’ scenario).
2: The ethics surrounding embedding AVs into society (e.g., whether we can truly replace the human factor in AVs, or what level of surveillance AVs should have).

Going beyond the trolley car scenario

Often touted as the litmus test for AV ethics, the ‘trolley car’ or ‘trolley problem’ is a thought experiment where someone chooses between saving five people in danger of being hit by a runaway trolley by diverting the trolley to hit one person. This is extrapolated to AVs by using a scenario such as an AV traveling down the street when suddenly a group of pedestrians runs out. The AV must ‘choose’ between hitting the group or altering its course but by doing so, hitting a lone pedestrian.

The ‘Moral Machine’ experiment was an online survey of 2.3 million people worldwide that investigated the moral dilemmas faced by autonomous vehicles. The study found that moral principles guiding drivers’ decisions varied from country to country, and also women and men viewed ethical and moral situations differently. This made something like the trolley problem difficult to quantify and standardize worldwide.

Far from a simple ethics exercise…

On the surface, it seems a simple ethics exercise. But as Clayton Tino summises: “People like to think they have a preconceived notion of how they would behave, but I just don’t buy that. [A near miss] is a purely reactive response. We’re setting unrealistic expectations on the machine because we need to blame something when something goes wrong.” Tessa Jones (podcast co-host) agrees, observing: “AVs need some decision-making process, but I don’t have a decision making process myself.”

As Sophie Chase-Borthwick (podcast co-host) explains: “We expect our AVs to be guaranteed safe. But we know that any other vehicles are not 100% safe with a human behind them. So we have a higher expectation of what ‘safe’ looks like when it’s autonomous [as opposed to] to when it’s a human.”

In our opinion, the disproportionate emphasis placed on the trolley problem to solve the lion’s share of AV ethics is reductive and dangerous to advancing AV technology. It’s a useful piece of the puzzle but it’s a symptom when we should be focusing on fixing the cause.

In our podcast, we also explore the importance of accurate and timely hazard perception (both in humans and AVs). By improving hazard perception, it not only provides safety methods for AVs but can help reduce or mitigate entirely AVs even having to make the trolley problem decision in the first place. 

Can we ever truly replicate the human factor?

There are five levels in the maturity of autonomy of AVs – with Level 1 being no autonomy and Level 5 being a vehicle without a driver safely taking you to where you want to go.

For Clayton, Tessa and Sophie the debate centers on where the application of AVs could work best with the least blockers. They wonder whether public transportation seems an ideal choice, given how it could be geo-fenced, fixed route and hyper-local.

However, when considering AVs in the context of public transportation, they realize it’s important to look at the holistic service of public transportation, beyond just the driving. As Clayton pithily observes when considering AVs for school buses, “[Bus drivers] do a heck of a lot more than just drive the bus … they need to be aware of passenger safety and security, assistance…”.

For example, in London, there’s been some disputes between wheelchair users and pram users about who has first access to the space. Bus drivers (and others in charge of public transportation) are expected to act as mediators to settle these disputes. How would this be replicated in an AV with no human factor?

The answer could lie in more secure and closely governed surveillance. Having surveillance on public transport AVs could add a safety layer to minimize vandalism, protect the users and ensure the AVs remain a reliable and safe choice. Our podcasters observe the marked differences between privacy in the US and Europe but with the introduction of GDPR-style laws such as the California Consumer Protection Act (CPPA), there will inevitably be more scrutiny on how the surveillance data is used and stored.

However, as is often the case with autonomy when it comes to public transport there’s no easy decision. By removing the human factor, there need to be other allowances made to fill the gap. Companies and governments need to work hard to make sure both the users and their data are protected and that these allowances do not harm the end-users or misuse them for commercial purposes.

Our podcast delves more into the nuances and pitfalls when considering the commoditization of a public service, such as public transportation. Generally, the people who need it most are vulnerable, and unless there’s a significant level of transparency, can users be fully aware and able to consent to the wider implications of being surveilled?

To hear more about how we untangle and much more, watch our episode on ‘Autonomous mass transportation and its impact on citizen privacy ’. 

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how intelligent are AI tea-making robots

How intelligent are AI tea-making robots?

Written by Cynthia Hoza on . Posted in Beyond Data Podcast, Glossary, Machine Learning, Managed Cloud. No Comments on How intelligent are AI tea-making robots?

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When it comes to how truly intelligent Artificial Intelligence (AI) is, it’s a polarizing debate. Either AI will solve the world’s woes or robots will rule us all – Matrix-style. But it’s all a little more complicated than Hollywood makes it seem…

Watch podcast episode 2 here

For a deep dive, do listen to our Beyond the Data podcast hosted by Sophie Chase-Borthwick (Calligo’s Global Data & Governance Lead) and Tessa Jones (VP of Data Science Research & Development).

Meanwhile, in this blog we look at tea-making and social care robots to illustrate an otherwise very nuanced and arguably never-ending narrative on the ‘intelligence’ part of the AI equation.

It’s important first to consider the different types of AI:

  • The majority of AI is ‘narrow AI’ – a single task, building a system to perform a particular task. You can build lots of narrow AI systems to perform together.
  • General AI, in comparison, is a lot more broad – intelligent machines that can learn, perform, and comprehend intellectual tasks much like a human. This is the territory where it’s a lot less clear-cut.

Let’s unpick the gray area of ‘general AI’, by looking at what robots are capable of – and whether this makes them truly intelligent, yet…

Tea-making as a success criteria for intelligence?

A robot making a cup of tea isn’t something a lot of us think twice about and wouldn’t be the first example of proving intelligence in a typical setting. However, scientists are doing just this, typically by:
1. Coding in the tasks a robot has to complete first (boil kettle, get cup, put the teabag in and so on).

2. Using experience-based learning to demonstrate how to make a cup of tea. When the robot doesn’t do it well or something is not done correctly, then the robot is given more examples of how to do that task.

To successfully have the robot make a cup of tea, scientists are having to build in and prescribe a lot of the parameters and tasks a robot has to complete. However, if the environment changes (for example a robot has to make a cup of tea in a different room) it would likely struggle because it isn’t familiar with the environment and the parameters.

Intelligence can’t just be about managing to do a task correctly; it’s being able to use inference to adapt in a new environment and navigate unfamiliar parameters to complete a task.

However, this adaptation and re-learning is a lot slower for robots than it is for humans. As Tessa Jones highlights, it’s referred to as Moravec’s paradox and essentially means it’s easy to train robots to do things that humans find hard, like chess and logic-driven tasks. However, it’s hard to train robots to do things humans find easy, like walking and image recognition.

In the podcast Sophie Chase-Borthwick observes: “Playing a game of chess is very rule-based [and easy to code into a robot] whereas making a decent cup of tea is definitely an art”.

Using a Japanese concept to make robots more human

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When looking at robots comprehending tasks much like a human, what could be more human than caring for one another? Japan is leading the exploration of the use of social robotics for assisted care. However, rather than the robot just serving a functional task, Japanese scientists are building one step further…

“There’s a concept coming out of Japan – a concept called ‘kokoro’”, says Tessa. “For robots to actually be effective and useful, there needs to be a heart-to-heart connection between the human and the robot”. There’s typically three kinds of kokoro you can achieve:

1. How the robot affects the human. If the human is feeling sick, whether the robot can interact in a way that lifts their spirits – for example Paro, a soft baby seal robot designed for use in hospitals and nursing homes as a therapeutic tool.

2. Whether the robot understands a human’s emotions. The robot can conceptualize when the human is feeling sad or angry. But getting this right is very difficult, as it’s hard to detect between anger and happiness based on imagery and voice. Microsoft has even recently stopped a lot of its programs around emotion detection as it opens the door to racial biases, and different facial and voice features.

3. When the robot itself feels and has its own ‘kokoro’. Currently, this remains confined to science fiction as it maps to ‘super intelligence.’

However, it’s worth considering the spectrum of human diversity. For example, neurodiverse people don’t always recognise what some emotions are but they are still intelligent. So recognising emotions and responding to them on its own isn’t a demonstration of intelligence.

As Sophie poignantly puts it: “Are we re-defining intelligence to suit the machines – and in doing so, carving out some humans?”.


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Create an ethics-by-design approach for data

Written by Brendan Walsh on . Posted in Data Ethics, Data Privacy, Machine Learning. No Comments on Create an ethics-by-design approach for data

Our VP for Data Ethics & Governance, Sophie Chase-Borthwick, was recently part of a panel – the PICCASO Special Interest Group. Sophie joined William Malcolm (Privacy Legal Director at Google), Radha Gohil (Data Ethics Strategy Lead at Shell), and Anne Woodley (Security Specialist at Microsoft) in untangling what data ethics actually means and how best to support it. Here we look at this in more detail. 

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