An Interview with…

Sunmbleena Meghany

What led you to start the Asian Arab Network? - Essentially, the main thing that drove this project of mine, which is now the well known Asian Arab Network, is the realisation of the lack of attention and support I had been receiving, as well as having faced racial discrimination at previous part time roles. I asked around at the time, of what was my precious network of 700 people, where do Asians and Arabs sit on the chart of the support system. And I heard of BAME - Black, Asian, Minority & Ethnic. And I questioned why don’t we have a sole system in place for the Asian and Arab community?

September 2023 arrived, regardless of my offers I was set to take a gap year because this was something I saw crazy potential with. I started monitoring Government statistics; asians have had the highest apprenticeship rates since July 2021….and yet there is NO community or support system in place for us? I saw the gap in the market and took my chance.

I then created a pitch deck with all my thoughts and I was ready to execute but then I thought: a great leader needs a great team. So I reached out to 2 women who are apprentices but also part of my network whom I’ve noticed have a strong voice and opinion when it came to this. As a founder of the community I proposed my idea to them, explaining my vision thoroughly. We went over a few logo designs, finalised some small details and, in February, The Asian Arab Network was born. Within 2 weeks, we hit the 1k following mark on LinkedIn!

What did you find to be the biggest challenge in setting up the network? - The biggest challenge in setting up the network was definitely putting boundaries in place. When you have a community of professionals, you can’t have the members on there swearing or insulting each other even if they are joking around because, ultimately, it is a professional group of people. But, at the same time, we didn’t want to bore everyone out otherwise what’s the point of having something engaging?
So, setting boundaries to what extent and lengths members could take their verbal or physical actions in relation to the network but also keeping in mind that its a platform of over 1000 people, including current apprentices, grade, interns and aspiring apprentices.

Where do you see the Asian Arab Network going in the future? - In the future I do strongly see the asian arab network as a national network with definitely many partnerships, there are many more plans but I don’t like to promise unfinished plans :) so all in due time!

Why did you decide to go down the route of an apprenticeship? - Apprenticeships in general have this purpose where you hit 3 birds with one stone: qualification, salary, experience. 3 things I feel that you just cannot get whilst studying at university.

How did you get into public speaking? - With public speaking, I was head girl at my 6th form so the natural element of presentation and speaking in a large audience was always something I practiced since I was young. When it came to opportunities to publicly speak I would automatically put myself forward. I do believe I’ve developed a lot of interpersonal and speaking skills from these opportunities.

Who is your biggest inspiration? - myself

Got something else you’d like to ask?

If you have burning questions you’d like me to pose to future Girl Economics Interviewees, do reach out on LinkedIn!

A deep dive into…

What exactly is generative AI?

Since the very public launch of Chat-GPT in late 2022, “generative ai” has been something of a buzzword. News articles have been filled with speculation over what may happen to our jobs, our lifestyles, and our economies as this technology continues to develop towards ‘Artificial General Intelligence’. But what exactly is generative ai, how does it work, and why do people care so much about it?

What is Generative AI?

Very simply, generative AI is a form of Artificial Intelligence which is able to create new content based on example data that the model has been trained on. More traditional AI models will be trained on a large dataset to learn to identify patterns and relationships between inputs and outputs with the goal of making accurate predictions or classifications. For example, you might have an app in which you can take a photograph of a leaf and, through the use of an AI system, the app tells you what type of tree that leaf comes from. In this case, the model will have been trained on lots of different leaves from different trees and ‘learned’ to match the two. How is this different to a generative model? Well, generative AI is about creating new data that resembles (rather than matches) the training data. Taking an image of a leaf may result in the generation of information about that leaf specifically- how big is it, what features does it have, is it characteristic for leaves of that type? This information about your specific leaf won’t have been in the initial data set meaning that the information you are being told has been generated by the model. Hence the name generative AI!

That might be cool, but how does it actually work?

You’ll already know that all AI works through the training of a model. What this means is that a large dataset of examples in a specific domain (usually either text or images) is fed into the model as inputs. In processing these inputs, the model learns the underlying structures and patterns of the data to produce a given output. This could be responding to a conversation or generating an image or video. Once trained on this data, the model can then generate responses to new stimuli (which were not included in the initial data set) by sampling the patterns that it learned from the initial dataset.

I think I get it, but an example might help!

You’re creating a piece of generative AI software to create captions for your instagram posts. The first thing to do is create a dataset on which to train your model and, in this case, that is likely to be a large number of photos that you might post to instagram alongside appropriate captions. Now, you feed this model into the AI system so it becomes familiar with the type of caption that corresponds to a certain type of photograph. After training, you should be able to give this model a new photograph and, using the photo-caption pairs it was trained on, the model can suggest a new caption. Importantly, for this model to be an example of generative ai, this caption must be different from all of the captions in the dataset it was trained on.

That’s interesting, but why has everyone become so obsessed?

Generative AI represents a significant advancement in our technological capabilities, allowing us to simulate complex natural phenomena, streamline production processes through increased automation, and even replicate some elements of human creativity. It is touching every industry in different ways: in healthcare it is accelerating research efforts and allowing us to discover new drugs and treatments much faster than previously; in materials science it is creating new compounds for structures that could have transformational uses; in marketing it is allowing for more targeted adverts and campaigns to be deployed. However, these benefits are also met with some ethical implications, which really add to the position that Generative AI occupies in the public psyche. There is a real risk of the misuse of generative AI, particularly in the context of 2024 being a year in which the majority of the democratic world holds elections.

Opportunity Corner

That is all for this issue, remember to share Girl Economics with anyone you think would enjoy reading it! See you in the next issue,

Erin McGurk