How to solve the UN Sustainable Goals utilising Neural Networks

The world is as complex or as simple as you want to make it. If you think of the world as business called Planet Holdings Inc, and the human inhabitants as employees of this large organisation we can simplify the problems into manageable and fixable situations, we all know it is not that simple, but bear with my logic for a few more paragraphs.

The operating model of Planet Holdings Inc is a flat structure of leaders who each run their own business within a business, which in turn have their own departments. Some businesses are flat out fighting with each other in open hostility, whilst other businesses support one side or another without any of the businesses thinking about the impact to Planet Holding Inc and its employees.

Most businesses I have worked with need to think of themselves as part of a whole. Their leaders need to think of themselves as leading Planet Holdings Inc together, not just looking at the profit margins of their own individual businesses. This however takes a vision.

Now, as we all know, consultancy practices love a good visioning challenge and so in steps the biggest consultancy practice of them all, the United Nations and works with these different businesses to get them all (well a lot of them) aligned to 17 sustainable goals, which in the interest of transparency results of their efforts are published.

The United Nations are a first class consultancy as they originally came up with the Millennium Development Goals in the year 2000, but pivoted in 2012 to bring in some additional revenue by adding other areas which were signed off by 193 businesses in Planet Holding Inc in 2015.

Roll forward 9 years and their latest introduction states “As we begin the second half of our journey to 2030, signs of a determined, sustained global comeback have yet to emerge. This year’s report reveals that only seventeen per cent of SDGs targets are on track to be achieved, nearly half are showing minimal or moderate progress, and progress on over a third has stalled or even regressed”

So 24 years on for some of these targets and almost a decade for the others and less than one fifth are on track. Could you imagine if anybody came to you as a leader and said they had delivered less than 20% of their goals, what would your response be…those are my thoughts exactly…I am afraid the United Nations are now on PiP…a Personal Improvement Plan where we need to have weekly meetings to ensure progress is back on track.

I have been brought in to Planet Holding Inc to offer some advice on how neural networks can help improve their results and deliver a better cycle time to value. Now for those of you still reading who are thinking, what on earth are neural networks then let me share. If you are as old as me, you will remember your mother stood on her doorstep talking to the different neighbours and would then come back in to her house and share all of that juicy gossip of what was going on in different parts of our local community. Those mothers were the nodes of that neural network, they collect, analyse, train and share information to relevant parties all the whilst learning to be more effective in their own domain.

“Sharon told me a great recipe for chocolate chip cookies, I think I will try them.” Your domain has now received the benefit of information from the neural net and Dad has gained weight, whilst your face is covered in chocolate, but you are better for it.

Well things have progressed from doorstep mother nodes to a technological age where mothers now live in our computers and on the internet and can learn, share and analyse at frightening speeds, so how can this benefit the 17 SDGs, Planet Holding Inc and get the UN off of their PiP.

I would normally do interviews with the heads of the businesses and put together clear proposals of how I (or internal teams) can help deliver the benefits of data and analytics, in this case neural networks, but as this has not happened (yet) I have prepared some examples that could lead to improved performance for the next report of the SDGs in 2025.

1. No Poverty

  • Economic Insights: Neural networks can analyse large datasets from economic transactions, employment statistics, and social programs to identify patterns, predict economic trends, and optimize poverty alleviation programs.

  • Targeted Assistance: Machine learning models can identify vulnerable populations who need assistance, helping governments allocate resources more effectively. Getting a better use out of the monies invested.

2. Zero Hunger

  • Agricultural Optimisation: Neural networks can enhance crop yield predictions, optimise planting schedules, and detect crop diseases early, improving food security.

  • Supply Chain Management: They can optimise supply chains, reducing waste and ensuring food reaches people in need more efficiently. There are several cases of this happening across the world that could be improved further with the connections of a neural net.

3. Good Health and Well-being

  • Healthcare Diagnostics: Neural networks excel in medical image analysis, allowing for early disease detection and personalized treatment plans.

  • Pandemic Response: They can analyse and predict virus spread patterns, optimize vaccine distribution, and help in tracking and predicting health crises.

4. Quality Education

  • Personalized Learning: AI can provide tailored learning experiences by analysing students’ progress and suggesting materials suited to individual needs.

  • Access to Education: Language processing models enable the translation of educational content, making quality education accessible to non-native speakers globally.

5. Gender Equality

  • Bias Detection: Neural networks can analyse large datasets from media, workplaces, and organizations to identify gender bias and support policy creation for gender parity.

  • Violence Prevention: Neural networks in social platforms can identify signs of gender-based violence or harassment, helping authorities respond faster.

6. Clean Water and Sanitation

  • Water Quality Monitoring: Image and data analysis neural networks can monitor water quality in real-time, identifying contaminants and predicting water shortages.

  • Leak Detection: Neural networks can detect leaks and inefficiencies in water systems, conserving water and improving access.

7. Affordable and Clean Energy

  • Renewable Energy Optimization: Neural networks can optimize energy grid management, integrating renewable sources more effectively and predicting energy demand.

  • Energy Efficiency: They can improve building energy management systems, reducing energy waste and promoting sustainable use.

8. Decent Work and Economic Growth

  • Labour Market Insights: Neural networks can identify trends in labour markets, such as skills in demand, helping align education and training programs with job market needs.

  • Economic Forecasting: AI can analyse market data to provide economic forecasts, supporting policies that promote inclusive growth.

9. Industry, Innovation, and Infrastructure

  • Smart Infrastructure: Neural networks in sensor systems can monitor infrastructure health, predicting failures in bridges, roads, and buildings to reduce risks and optimize maintenance.

  • Research and Development Acceleration: Neural networks can speed up innovation processes, from drug discovery to materials science, supporting industrial growth.

10. Reduced Inequalities

  • Resource Allocation: AI can help identify regions and groups in need, optimizing resource distribution for fairer economic and social systems.

  • Financial Inclusion: Neural networks can support credit scoring models to provide financial services to those without a traditional credit history, enabling access to loans and financial services.

11. Sustainable Cities and Communities

  • Urban Planning: Neural networks can analyse traffic, pollution, and population data to improve urban planning, making cities more liveable and sustainable.

  • Disaster Response: Neural networks can predict natural disaster impacts, aiding in urban resilience planning and timely resource mobilization.

12. Responsible Consumption and Production

  • Waste Reduction: Neural networks in supply chain management can predict and optimize inventory, reducing overproduction and waste.

  • Product Lifecycle Management: They can analyse product lifecycles to identify recycling or reuse options, supporting a circular economy.

13. Climate Action

  • Climate Modelling: Neural networks can model climate change impacts, providing insights that help policymakers implement more effective climate action.

  • Carbon Footprint Reduction: AI algorithms can monitor and optimize industrial and individual carbon footprints, helping track and reduce emissions.

14. Life Below Water

  • Marine Ecosystem Monitoring: Neural networks analyse satellite images and sensor data to monitor ocean health, track illegal fishing, and detect marine pollution.

  • Sustainable Fishing: AI can help regulate fishing practices by tracking fish populations, ensuring sustainable quotas, and reducing overfishing.

15. Life on Land

  • Biodiversity Monitoring: Neural networks can classify images from drones and satellites to track endangered species, deforestation, and habitat loss.

  • Precision Agriculture: By analysing soil and crop data, neural networks support sustainable land use practices that reduce land degradation.

16. Peace, Justice, and Strong Institutions

  • Conflict Prediction: Neural networks can analyse social, economic, and environmental factors to identify early signs of conflict, helping prevent violence.

  • Corruption Detection: By analysing patterns in public spending and administrative records, neural networks can identify corruption risks, promoting transparency.

17. Partnerships for the Goals

  • Global Data Collaboration: Neural networks facilitate data-sharing platforms for cross-country partnerships, improving monitoring and transparency in SDG progress.

  • Resource Mobilization: AI can optimize fundraising efforts, identifying the best strategies to attract investments for sustainable projects.

Imagine all of these projects at a Plant Holdings Inc global enterprise level, shared, co-ordinated and using the addition of Agentic AI to manage flows, prompts and automations to move from debate to action.

So why do we settle for no action when the consequences of this are so dire. Start by collaborating with the countries in the 193 that can work together and test, validate and improve the neural networks.

We have 6 years to meet the targets that may already be out of reach, but we should use every thing available to try and move as close to the UN SDG’s as possible.

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