What's in this podcast?

In this episode Jason talks to Diana Goenaga, a strategist focused on Artificial Intelligence. Together, they discuss AI’s impact in every industry and how it is a game changer for many challenges the world faces today. 

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One big message

Artificial Intelligence (AI) has become a powerful tool in the world we live in. From smart technology, through to automated text and art generation AI has shown that the possibilities for it to learn and improve are limitless. When it comes to making decisions about the progression of humankind such as the UN’s Sustainability Goals, AI can assist by analysing and dissecting large amounts of data quickly. 

[00:30] How Diana transferred her skills from working in biology to data and AI in the general corporate sector

[02:50] Looking at the growth of both AI and humankind and how they occur in reverse

[05:13] The Evolution of AI: How Machines are Learning to replicate the Human Brain

[06:44] The Benefits and Challenges of Teaching AI about the World

[14:53] The Use of AI in Achieving Sustainable Development Goals

[19:14] How AI helps with the vast amount of information available

[21:42] The Impact of AI on Sustainability Initiatives

[28:14] The Benefits of Using AI to Solve Global Problems


The evolution of AI

AI has developed in the opposite way that a human brain does. A human brain starts with the ability to recognize faces and emotions and then develops to more complex tasks such as solving mathematical problems. AI, on the other hand, started with complex tasks such as solving mathematical problems and is now developing the ability to recognize faces and emotions.


Creating the perfect environment for AI to flourish

AI at present is not innately intelligent without external input. It needs to know how to connect different concepts in order to be truly intelligent. It is arguably difficult to do because it is hard to create an accurate model of the world. There is even an issue of whether our current state of the world is even something that AI should aspire to remodel. However, it is possible to formalise the concepts of what exists in the world in order to help machines understand the connections between different things.


Proving the benefits of AI 

There is a segment of the population that believes AI doesn’t have much value and worse, has the potential to do more harm than good. When it comes to evangelising the benefits of AI it is important to stick to facts. With a rapid amount of content and data being produced every single day, AI is important in helping humans by condensing information down in a way that is meaningful and easily digestible for humans to consume. The main point here is that AI has been created to assist humans make better decisions in a shorter amount of time. 


The ethics of AI in sustainability

It is difficult to drive global decisions in a world where there are many different economic and cultural regions with different perspectives. This is where AI seems to have some difficulty. One solution is to find a neutral party to conduct further research and collect data from all over the world, however from an ethical and logistical angle this may be hard to do due to things such as political agenda and laws like the GDPR. Until AI can be fed enough data, we can’t fully rely on it to give us all the answers to large global problems.


UN sustainability goals and AI

AI is better at some jobs than others. When it comes to the advancement of humankind there are different areas that impact the entire human race which need to be focused on. AI works best when it comes to solving factual and mathematical problems. For example, AI can help with food distribution by calculating how much of certain resources needs to be allocated. Another application AI can greatly help in is with the climate. It can help to predict weather patterns, track global warming and give warnings when an anticipated natural disaster is about to occur. 



By providing key concepts and raw data to AI, it can greatly help key decision makers. However, the data required to make global decisions requires a global amount of data which is logistically hard to acquire. In the absence of this, AI is forced to look at smaller samples of data and attempt to scale it up according to the concepts and prefabricated models fed to it. By doing this, there is hope that over time there will be enough smaller problems tackled by AI which will help to chip away at the bigger problems in the world. 

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