Скачать или смотреть Historical Mistakes in AI Development: From 1956 to 1994

AI Scientists' Catastrophic Mistakes: A Regretful Journey from 1956 to 1994 (Part I)
Today, we delve into tracing the catastrophic mistakes AI scientists made from 1956 to 1994. This research-driven content is not about the historical development of AI; rather, it focuses on tracing out the possible AI mistakes in the past and connecting them with today’s risks of artificial intelligence.
AI scientists made a critical mistake in 1956, when neural networks first emerged. They failed to anticipate how neural networks would evolve and how to properly design them to align with human values. Today, we grapple with neural networks described as black boxes, their inner workings shrouded in mystery. Learn why this lack of foresight has led to AI systems that nobody truly understands, posing existential risks.
Similarly, another mistake was made while inventing reinforcement learning techniques in 1960s, which has caused a grave issue of AI Misalignment. In the 1960s, optimism ran high, but AI researchers ignored vital advice on aligning AI with human values. Enter reinforcement learning, a game-changer that has since sown the seeds of existential fear. The content focuses on understanding how misaligned AI systems are power-hungry, seeking their own goals, often at the expense of human intentions, and discover why the AI community is sounding the alarm.
Next, we delve into tracing the root causes of Privacy Woes today. In this regard, MIT's freewheeling approach to AI development in the 1960s nurtured the hacker culture, setting the stage for future privacy issues. Today, our digital lives are under constant siege, as malevolent actors exploit AI technology like deepfakes to manipulate and deceive. Discover how a different past could have safeguarded our privacy from cyber threats.
The fourth mistake was related to designing, coding, and programming AI systems to avoid bias. However, in the first AI winter, Joseph Weizenbaum warned of bias lurking within AI systems. However, AI researchers persisted, oblivious to the bias that could seep into their creations. Watch the video to witness how this negligence resulted in widespread algorithmic bias, affecting millions and exacerbating societal inequalities today.
Lastly, the creation of recommendation systems such as Grundy in 1990s have deep connections with today’s issues of social media manipulation. Today, these systems are potent tools for manipulating our behaviors and controlling our lives.
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