What happens when you use the AI image engine DALL-E for a blog post?
I was just finishing a post on Medium titled "APIs — the best way to train AI on transport & mobility" and needed an image to exemplify this.
So I purchased a few credits on DALL-E as an experiment and gave it the prompt:
"a computer scientist on a train holding a bag containing data"
(Yes, I did use the word 'train' there in the transport context, rather than the education one - in an attempt to be slightly funny)
And this was what is came back with:
I then picked the second image for the post, as I thought it represented most closely what I wanted... even though the rendered face was slightly incorrect:
The concept of Open Data has been around for many years (the earliest I can find is around 2000, but there may well be much earlier usage). Yet some individuals still seem to be confused by the concept.
Here is the Open Data Handbook’s definition:
"Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike."
https://opendatahandbook.org/guide/en/what-is-open-data/
This clear and understood approach to data provides increased transparency, improved decision making, fosters innovation and can increase civic engagement.
But its range of use can perhaps make it difficult for people to understand what Open Data is.
Here’s some examples:
Research:
Researchers & academics can use Open Data to study impacts and changes across economies, governments and populations
Business:
Organisations can Open Data to develop new products and services, such as a map app that shows the location and availability of parking spaces
Public Sector:
Governments can use Open Data to demonstrate and improve transparency & accountability, such as by publishing data on Public Sector spending.
Individuals:
Citizens and journalists can use Open Data to hold governments & authorities accountable, such as tracking the performance of local schools.
However, in my experience some people have deliberately misunderstood the term Open Data. Why?
Open Data can lead to change, and some people may be afraid of this. They may worry that Open Data will subsequently make it more difficult for them to control information, perhaps leading to job losses. Others may have vested interests in keeping data closed. For example, businesses may not want their competitors to have access to their data, and governments may not want citizens to have access to data that could be used to hold them accountable.
Ultimately Open Data is a powerful concept. It is therefore up to all of us to ensure that we all share the same definition and push for Open Data's continued societal benefit, not just the few.
The rise and rise of Generative Artificial Intelligence (AI) has been the main technology talking point of the last 6 months. Text services like ChatGPT and Google Bard are now able to engage in dialogue that may evenpass the Turing Test. And image generation services such as DALL-E & Stable Diffusion can create realistic and almost life-like pictures just from a text description.
To perform these feats means training these large language
models (LLMs) on vast amounts of data that is computationally expensive
and therefore prohibitive for many organizations.
For example, OpenAI's GPT-3 LLM was trained on a dataset of
175 billion words. This dataset was collected from a variety of sources,
including books, articles, websites, and code repositories. Google's PaLM LLM
was trained on a dataset of 540 billion words,
But as the flurry of initial excitement about Generative AI now
dies down a bit, focus is turning to the sources of data used. With the worry
being that unless that data is reputable and trustworthy, these systems will have
the wrong inputs to base their machine learning algorithms upon.
Perhaps more than ever, the computing phrase of “Garbage in,
Garbage out” is worth more than a passing consideration.
Mobility-as-a-Service (MaaS) is more than just a multi-modal journey planner and ticketing app. Integrated digital transport platforms have the power to provide seamless & easier mobility options and incentivise users towards more sustainable means of transport. I personally believe that (interoperable and open standards-based) MaaS is the future of transport for those living in cities, towns and more rural communities.
I'm therefore incredibly happy that I have been asked to join the Steering Board of MaaS Scotland. To provide external strategic guidance towards the group's ambition to support the implementation and scaling-up of MaaS solutions across Scotland.
