Re-tell Lecture — Philosophy of Science

Read the lecture ONCE, then re-tell it in your own words in 40 seconds without looking back. How scientific revolutions happen In his influential nineteen sixty-two book The Structure of Scientific Revolutions, philosopher Thomas Kuhn argued that science does not progress in a smooth, linear fashion. Instead, it alternates between periods of normal science, during which researchers work within an accepted framework called a paradigm, and brief periods of revolutionary science, during which the old paradigm is overthrown and replaced. During normal science, scientists solve puzzles within the existing framework without questioning its fundamental assumptions. Anomalies — results that the paradigm cannot explain — accumulate gradually. Eventually, enough anomalies build up to create a crisis, and a new paradigm emerges that explains both the old evidence and the anomalies. Kuhn called this shift a paradigm shift. The transition is not purely logical: it involves social and psychological factors, including resistance from established scientists who have built their careers within the old framework. The classic example is the Copernican revolution, which replaced the Earth-centred model of the solar system with a sun-centred one after decades of resistance.

Read the lecture ONCE, then re-tell it in your own words in 40 seconds without looking back. The problem of induction The problem of induction, first raised by the Scottish philosopher David Hume in the eighteenth century, questions whether we are ever justified in drawing general conclusions from specific observations. Science relies heavily on induction: we observe that the sun has risen every morning throughout recorded history and conclude that it will rise tomorrow. But Hume pointed out that no number of past observations can logically guarantee a future outcome. The sun could fail to rise tomorrow, and nothing in our past experience rules this out with absolute certainty. This does not mean scientific predictions are worthless — they work remarkably well in practice — but it does mean their justification is ultimately circular: we trust induction because it has worked before, which is itself an inductive argument. Philosopher Karl Popper attempted to solve this problem by arguing that science should not try to prove theories true through induction but instead should try to prove them false through decisive experiments. A theory that survives repeated attempts at falsification earns provisional acceptance, but can never be considered definitively proven.

Read the lecture ONCE, then re-tell it in your own words in 40 seconds without looking back. Ethics of artificial intelligence The rapid development of artificial intelligence raises philosophical questions that were previously confined to science fiction. One central issue is the alignment problem: how do we ensure that an AI system's goals remain aligned with human values, especially as systems become more capable and autonomous? A system optimising for a narrowly defined objective might pursue it in ways that humans find harmful or absurd. A more immediate concern is algorithmic bias. AI systems trained on historical data inherit the biases present in that data. Hiring algorithms have been shown to discriminate against women when trained on decades of male-dominated hiring decisions. Criminal justice algorithms have produced higher risk scores for certain racial groups, not because of any deliberate programming but because the training data reflected existing disparities in policing. Philosophers argue that deploying such systems without addressing these biases is ethically equivalent to endorsing discrimination. The question of moral responsibility is equally challenging: when an autonomous vehicle causes an accident, who is responsible — the manufacturer, the programmer, or the owner? Current legal frameworks were not designed for situations in which decision-making authority is delegated to machines.