I use this simple map to demonstrate expressions and temporary variables.

But there's a minor issue with it. The map provides an answer which is a number of people, but currently it does so with a decimal, e.g. 4.24 people, like this:

For this purpose 4.24 people isn't actually the number of people needed; it needs to be rounded up to 5. So how do I round a number up, down or off?

This demonstration map shows how Rainbird can help firms comply with regulatory and legislative requirements.

There are two main queries which you can test:

• Which regulation(s) must the company adhere to?
• Regulatory self-assessment tool

At the moment there are only a couple of regulations included, but it could easily be expanded to cover all applicable legislation.

You can take a look at the map by importing the attached file in the Rainbird platform.

Could you see this map being used by industries to make sure they are complying with their regulatory requirements? Do you think it would reduce breaches and decrease risk if your company used a tool like this?

Summer is here and the heat waves have arrived, so it's the perfect time for a cocktail! But which one? 

That's where our Cocktail recommendation tool comes in. Give it a go - does it give a recommendation that matches your preferences?

You can take a look at the map itself by downloading the attached file and opening it in the Rainbird platform.

Enjoy your weekend!

Happy Friday everyone!

Here's a nice map for you - a breakdown resource agent. It's designed to be used by a roadside breakdown and recovery service, advising  on the most suitable resource to despatch to you when you need assistance.

What's really cool about this one is the datasource. It links into a vehicle database and uses that to extract information about the vehicle: make, model, weight, gearbox, and so on. The info is then used to decide on the breakdown resource needed for the job. I've tried it on my car and a few friends' vehicles and it really does work! (Note that it is a datasource for UK registered vehicles)

Click here to give it a go yourself, or download the attached file to investigate the Rainbird map. 

What do you think of the map and datasource, and are there any other use cases where a datasource link this would be useful?

Hi all,

Here's an interesting Rainbird tool for you! The Hamilton Banking Demo recommends a bank account for you based on your requirements. 

It could potentially be used by an adviser at the bank when setting up a new customer's account, or by you when you're thinking of switching accounts.

You can try it out for yourself straight away by clicking here. A copy of the Rainbird map is also attached, which you can download and import into the Rainbird platform.

Would you find a tool like this helpful when you're comparing different bank account options?

We're pleased to announce that @victor armenta-valdes is the winner of our AI Saves Holidays competition!

We were really impressed by the thought Victor put into his tool. It takes into account multiple possible factors and integrates data from several different sources in order to provide the best recommendation possible. Victor has really picked up on the capabilities provided by Rainbird and run with them.

Victor has win himself an Oculus Quest 2 VR headset - congratulations for your well-deserved win!

You can take a look at Victor's map and and a blog post explaining his design here.

If you're puzzled by Intelligent Automation, and aren't really sure what Rainbird actually does, sign up for our fortnightly Introduction to Rainbird webinar! This takes a look at a number of Rainbird use cases - you'll even get an opportunity to try out some Rainbird tools yourself.

The webinar is tomorrow (8 July) at 2:00 BST.

Automated decision making is ubiquitous in today's world, even if it's not immediately obvious.

Like it or not, computers make decisions for us (and about us) all the time. These can be relatively trivial: your washing machine decides when to start its spin cycle based on feedback from its sensors. Or the decisions can be significant: your mortgage application is accepted or rejected by a computer program today, rather than your bank manager as would have been the case a few decades ago.

But there are some areas where people are worried about the risks of automation. Self-driving cars are a great example: companies developing the technology know that safety is the number one priority. This is because people will be understandably nervous about handing over (or giving up) control to a machine, especially when their own lives, or those of their family or other road users, are at stake. 

Strangely this imposes a double standard. Automated technology won't be accepted even if it's safer than a human doing the same job: it has to be a LOT safer. A car crash caused by human error, while potentially tragic, is nothing new. Meanwhile a car crash caused by a self-driving car's AI will be thoroughly investigated and probably make the news headlines! Even if you can statistically prove that self-driving cars are safer than human drivers overall, that doesn't mean that they will be accepted until users genuinely feel safer as a result.

There are also a host of legal implications raised as a result of automated decision making. Who is ultimately responsible for the decision made by a computer? The company behind the decision model? The company using it? The original programmers? It's really not clear how this will work out.

All of this emphasises the importance of effective decision making, especially when there are leal, financial and (especially) health and safety consequences. And the decisions don't just have to be good - they have to be fantastic, and they have to be shown to be fantastic.

Luckily this is definitely possible - aviation is a great example of a field where automation technology is widespread, widely accepted and probably safe. Even with AI problems such as the 737 Max crashes, there's no attempt to return to manual control systems - luckily so, given their far worse safety record!

How do you feel about AIs taking over safety-critical systems from you - would you trust a self-driving car? What reassurance would you need in order for you to accept technology like this?

Here's the recording of today's webinar, showing how you can integrate an external datasource into a Rainbird map, together with the map itself.

This is the API used in the demonstration: https://www.programmableweb.com/api/about-corona-covid-19-rest-api-v1

And here is the useful JSON pathfinder tool: https://jsonpathfinder.com/

If you have any additional questions amount the integration or map please ask!

Don't forget to dial in to the third demo webinar, showing how you can add to your entry for our AI Saves holiday competition! The webinar is at 12:00 BST this Friday  18 June - sign up here: 

Don't forget you can vote for what we add next to the demo map here.