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Month: September 2022

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

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 ’.