Develop Your Public Speaking and Debating Skills

Debating

Gain the confidence to express yourself effectively. Explore our comprehensive guides on different motion types, public speaking techniques, and debating strategies.

What is AI?

Traditional Artificial Intelligence – also known as Narrow or Weak AI, focuses on performing specific tasks intelligently. It refers to systems that are designed to respond to a particular set of inputs. These systems possess the capacity to learn from data, and make decisions or predictions based on that data.

For example – imagine you’re playing computer chess. The computer knows all the rules, it can predict your moves, and make its own, based on the strategies it’s been programmed with. The computer is not inventing new ways to play chess – it’s taking action based on the data it has been programmed with. Traditional AI can make decisions within a specific set of rules. Examples of traditional AIs are voice assistants like Siri or Alexa, recommendation engines on Netflix or Amazon, or Google’s search algorithm. These AIs have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new.

Generative AI is the new generation of artificial intelligence. It can create something new. If you give generative AI the starting line for a story, say, “Once, there was a forest, and in that forest lived a dragon…”, the AI can take that line, and create a whole adventure, complete with characters, plot twists, and a thrilling conclusion. Generative AI can create something new from the piece of information given to it. What’s more, today’s generative AI can not only create text outputs, but also images, music and even computer code. Generative AI models are trained on a set of data and learn the underlying patterns to generate new data that mirrors the training set.

Consider GPT-4, OpenAI’s language prediction model, a prime example of generative AI. Trained on vast swathes of the internet, it can produce human-like text that is almost indistinguishable from a text written by a person.

People who oppose AI-produced art aren’t angry about the existence of all types of AI. Art generation tools in particular are the subject of people’s fury, and that’s because they’re fundamentally different from a lot of other AIs.

Something like, for example, a call center AI exists to support the work of human employees. It flags up keywords to show call center agents helpful information during their calls, and can alert a manager if an ongoing call needs intervention. That’s highly useful to these employees, and instead of threatening their job security, it makes their work lives more comfortable.

AI art generators don’t have the same impact on human artists. Instead of supporting them in their creation process, AI art tools use pattern recognition tools to ‘remix’ existing work into something new. They don’t help artists figure out how best to shade that tree, for example; they can only create a new facsimile of a tree out of data from other artists’ work featuring trees.

One of the biggest problems digital artists cite with AI art generators is the issue of how they got the data they use to create new works. Or rather, the pieces they splice together, since the works in question aren’t actually created from scratch. Despite the fact that AI art doesn’t rely on breaking copyright laws, many artists feel cheated by the fact that that’s even true. Imagine, for context, that you’re part of the team behind a popular piece of software. You put in hard work to make sure the code was just right, and that all major bugs were ironed out. You’re proud of the finished product. A week later, an imitation of your work shows up on the market, retailing for half the price. The code they use looks suspiciously similar to yours … but not quite similar enough for you to be able to take them to court over it.

Did they violate copyright laws to create the imitation? No, not quite. But you’ll likely be left feeling frustrated about the lack of legal protection, instead of relieved that no rules were broken. The fact that it’s technically fair play is the problem, which is the same for digital artists.

A common theme among artists’ complaints about AI art generators is that they’re unhappy about their work being used without their permission. This is true of the vast majority, if not all, of the art generator tools available. They were trained using data scraping, without consulting the people whose work they were trained on. And since AI tools can’t generate art out of nothing, that training wasn’t passive either. It’s not the same as a human learning from Da Vinci by studying his work and trying to paint like him. If anything, it’s more like a human tracing the Mona Lisa and then claiming they made it themselves.

Society faces an urgent and complex artificial intelligence (AI) data scraping challenge. Left unsolved, it could threaten responsible AI innovation. The automated process of gathering data from targeted sources using AI-based tools and techniques is known as AI data scraping. AI web scraping uses artificial intelligence algorithms that can automatically adjust to manage varying websites, unlike traditional web scraping, which depends on pre-defined selectors that isolate the data you wish to collect. The drawbacks of manual or no code-based scraping methods are addressed by this method.
An artificial intelligence (AI) web scraping tool is far more efficient. Artificial intelligence (AI) scraping technologies are made to browse web pages, find and retrieve data, and adjust layout changes without human assistance.
The privacy concerns of AI web data scraping are a major ethical issue to be considered. AI-powered data scraping tools can gather vast amounts of data, some containing personally identifiable information (PII).
This data, when used ineffectively, opens organizations to legal repercussions. Privacy regulations such as the General Data Protection Regulations (GDPR) enforce strict rules about how companies manage personal data.
In ethical terms, consent to data scraping is compulsory. Businesses and clients must know when their data is collected and how it will be used. Unfortunately, various AI scraping practices occur without the consent and knowledge of the owner. This lack of transparency can build up trust issues between businesses and consumers. Ethical AI data scraping practice includes precise data gathering and disclosure of usage, especially for particular fields.
AI data scraping can risk Intellectual Property (IP) rights, mainly when gathering proprietary data from several secured websites. Copyright laws protect original content, whereas unauthorized data scraping results in legal issues.

What are the Demographics of People Above 60 and How Do They Impact the Economy across Nations?

The percentage of people above 60 varies significantly across developed, developing, and underdeveloped nations. Developed countries typically have a higher elderly population due to better healthcare, higher life expectancy, and lower birth rates. For example, around 28% of Japan’s population and 25% in Italy and Germany are over 60. In developing countries like China and India, the elderly population is growing but remains lower, with approximately 17% and 10% over 60, respectively. Underdeveloped countries, particularly in Sub-Saharan Africa, often have less than 5% over 60 due to higher birth rates and lower life expectancy.

The economic implications of an ageing population are profound. In developed nations, a growing elderly population can strain social security systems and healthcare services, as a smaller workforce must support an increasing number of retirees, leading to higher taxes and reduced economic growth. Countries like Germany and Japan are already facing these challenges, necessitating policy adjustments such as raising the retirement age or encouraging higher birth rates and immigration. Developing countries, on the other hand, have the potential to benefit from a “demographic dividend” if they invest in health, education, and job creation for their younger populations. However, they also face the need to expand social security and healthcare services for their growing elderly populations, as seen in China’s rapid healthcare infrastructure expansion. Underdeveloped countries, with smaller elderly populations, face challenges in providing healthcare and social support, often relying on family support systems.

The impact of an upper age limit for political office should be analysed considering the demographics and value systems of individuals above 60. This age group brings extensive experience and unique perspectives to governance, often having spent decades in public service, which can be invaluable for navigating complex policy issues. Older adults generally enjoy greater financial stability, influencing their policy preferences toward stability and wealth preservation.

The value systems of individuals above 60 often lean towards conservatism and tradition. Older adults typically value established systems and may be more resistant to rapid changes. This can manifest in their preference for policies that maintain traditional institutions and social norms. For instance, they might support initiatives that uphold long-standing community structures and practices. Additionally, given their stage in life, individuals in this age group are more likely to prioritise healthcare and social security. They are often strong advocates for programs like Medicare and pensions that provide financial and medical support in old age.

Additionally, they value community cohesion and stability, supporting policies that promote social order and well-being. With intergenerational concerns, they also back initiatives that ensure a stable future, such as education funding and environmental protection. In Japan, for instance, senior politicians emphasise social stability and robust social security systems to benefit both current and future generations.

Age plays a crucial role in politics, as seen in the discourse surrounding the candidates for the US Elections (81) like Joe Biden and Donald Trump (78), who are among the oldest individuals to hold the office of U.S. President. The extensive experience that older politicians bring can be a significant advantage. Biden’s long career in politics, including his time as Vice President and a U.S. Senator, provides him with valuable expertise in legislative processes and international diplomacy. Similarly, Trump’s background in business and media has given him considerable experience in deal-making and public communication. This depth of experience can help navigate complex political landscapes and make informed decisions.

Age also brings challenges, particularly regarding physical and cognitive health. At 81, Biden’s age raises concerns about his stamina and mental sharpness, crucial for the demanding role of President. Trump, at 78, faces similar scrutiny regarding his health and energy levels. Additionally, age can affect public perception and trust. Older leaders might be seen as wise and experienced, but they can also be viewed as disconnected from the concerns of younger generations. The challenge for both Biden and Trump is balancing their substantial experience with the need to stay relevant and responsive to a diverse and evolving electorate. Their ages influence not only how they are perceived but also their policy priorities and decision-making styles.

Template for a Debate

First Prop

What?

5-Minute Speech

Introduction

20 Seconds

Set-Up (Define Key Terms, Discuss your Model)

50-60 seconds

Give a roadmap for your speech and your case

10-15 seconds

Present your first constructive argument

1.5-2 minutes

Take a POI

20-30 seconds

Present your second constructive argument

1.5-2 minutes

First Opp

What?

5-Minute Speech

Introduction

20 Seconds

Set-Up (Define Key Terms, Discuss your Model)

50-60 seconds

Rebuttal

50-60 seconds

Present your first constructive argument

1.5-2 minutes

Take a POI

20-30 seconds

Present your second constructive argument

1.5-2 minutes

Second Prop

What?

5-Minute Speech

Introduction 

20 seconds

Rebuttal

2-3 minutes

Conclusion to Rebuttal 

15-20 seconds

POI

20-30 seconds

Constructive argument 

2-3 minutes

Second Opp

What?

5-Minute Speech

Introduction 

20 seconds

Rebuttal

2-3 minutes

Conclusion to Rebuttal 

15-20 seconds

POI

20-30 seconds

Constructive argument 

2-3 minutes

Third Prop

What?

5-Minute Speech

Introduction 

10-20 seconds

Theme 1

1-1.5 minutes

POI

20-30 seconds

Theme 2

1-1.5 minutes

Theme 3

1 minute

Third Opp

What?

5-Minute Speech

Introduction 

10-20 seconds

Theme 1

1-1.5 minutes

POI

20-30 seconds

Theme 2

1-1.5 minutes

Theme 3

1 minute