1. Social challenges: the future of AI
AI is already driving a profound transformation in key areas of society, and it will continue doing so.
2. How AI may shape our present (and future) society?
Deep learning and computer vision approaches are already revolutionizing healthcare, by joining forces with human professionals in improving diagnostic tasks like cardiograms and eye analysis, suggesting optimal treatments adapted to patient needs, and robots intervention in surgical procedures.
From a government policy and legal perspective, the unstoppable progress of techniques like Generative AI are not only transforming current regulations around responsible use of AI, but also creating new regulations to account for issues that previously did not even exist.
But AI is also contributing to build more secure infrastructures in high-risk scenarios like finance, providing real-time insight for risk management and detecting fraud.
Before looking further into the future of AI, let's make a quick stop at the global challenge of sustainability, concretely the Sustainable Development Goals.
3. AI and sustainability$^1$
AI is actively driving progress towards the 17 Sustainable Development Goals, with organizations harnessing its potential to tackle pressing global challenges.
From eradicating poverty and hunger through smart agriculture, to fostering inclusive equality under responsible AI, and building sustainable cities,
4. AI and sustainability$^1$
AI is at the forefront of addressing diverse economic, societal, and environmental issues, paving the way for a more prosperous future.
But AI can also become an obstacle to a sustainable future if concerns such as energy consumption, algorithmic biases, and ethics are not addressed. Let's discuss some final examples of this double-edged role AI can play in the future.
5. AI and the future of workforce
One of the future concerns where AI is triggering most polarizing discussions is in the workforce.
Mainly because of the fear of job displacement: the impact of AI in process automation is said to increasingly displace workers, and the rapid evolution of the field demands the acquisition of new human skills. The rise of LLMs like ChatGPT is a clear example of AI tools at the center of this debate, raising new skills like prompt engineering.
On the other hand, AI integration may also create new roles as part of the industry transformation.
6. AI and the future of education
The rapid changes in professional skills needed to work alongside AI are also a future challenge from the angle of education. Institutions need to constantly provide relevant training that develops the most demanded skills in the market. Besides, it is crucial to address the digital divide and ensure equal access to AI-powered educational tools.
On the bright side, AI can drive quality education through personalized learning experiences tailored to students' needs.
7. AI and the future of the environment
And finally, let's not underestimate the strong bond between AI and the natural environment.
Greater collaborative efforts are imperative to prevent AI from exacerbating ecological deterioration. AI systems and data centers require intensive computing requirements, leading to increased energy consumption and contributing to the carbon footprint. The production and waste of materials like semiconductors may also pose environmental risks and disproportionate resource consumption if not managed adequately.
But AI, in particular ML and Deep Learning solutions fed with large datasets, can be a powerful ally for understanding climate patterns and support new strategies to adapt to climate change.
AI can also optimize resource usage in domains like agriculture, and improve energy management to promote the adoption of renewable energy sources.
8. Let's practice!
Let's deepen your understanding through a few practical exercises.