Apperceptive (moved to buttondown)

Apperceptive explores the ethical, technical, and societal complexities of artificial intelligence and autonomous vehicles, delving into issues like AI's value, the limitations of emotion recognition technology, the challenges faced by self-driving cars, and the impact of anthropomorphism in technology. It also highlights concerns over AI's historical and ongoing biases, the necessity of integrating psychology into AI, and the critiques of platforming harmful ideologies.

Artificial Intelligence Autonomous Vehicles Ethical and Societal Impacts of Technology Emotion Recognition Technology Anthropomorphism in Technology Machine Learning Technology and Bias Social and Psychological Aspects of AI Platform Ethics and Regulation

Top posts of the year

And their main takeaways

The value-destroying potential of AI

32 implied HN points • 31 Jul 23
🕹 Technology AI Software Metrics Chatbots Applications
  1. Many businesses struggle to measure the true value of implementing AI solutions.
  2. The key problem lies in defining and measuring what 'good driving' or 'good writing' actually means.
  3. Executives should be cautious about overly relying on AI like ChatGPT for creative tasks, as they may miss out on the unique perspectives and insights that humans offer.

I knew this was coming

32 implied HN points • 27 Oct 23
🕹 Technology Self-driving cars Tech industry Software Development Autonomous Vehicles
  1. The self-driving car industry had many startups aiming for a piece of the autonomous car market.
  2. Waymo and Cruise were seen as leading the race for self-driving vehicles, but had vastly different approaches and challenges.
  3. Cruise faced difficulties transitioning from testing to deploying revenue taxi service while still grappling with technical challenges.

How the field of "AI" got like this

20 implied HN points • 02 Nov 23
🕹 Technology AI Machine Learning Computer Science Deep Learning Models
  1. The field of AI can be hostile to individuals who are not white men, which hinders progress and innovation.
  2. The history of AI showcases past failures and the subsequent shift towards more practical, engineering-focused approaches like machine learning.
  3. Success in the AI field is heavily reliant on performance advancements on known benchmarks, emphasizing practical engineering solutions.
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The cost we bear

16 implied HN points • 22 Sep 23
🕹 Technology Risk management
  1. Autonomous cars struggle with handling left turns across traffic due to the difficulty in predicting oncoming vehicles' movements.
  2. Human drivers navigate left turns based on social interactions and a higher tolerance for risk compared to autonomous vehicles.
  3. Acceptance of the risks involved in traditional vehicles influences societal readiness for autonomous vehicles, with potential consequences.

Is "artificial intelligence" as a term per se racist?

8 implied HN points • 05 May 23
🕹 Technology AI Racism Intelligence Research Ethics
  1. Artificial intelligence term originates from a time of great optimism and confidence in modeling human intelligence.
  2. Early AI researchers focused on abilities like linguistic fluency and chess skills, which are not central to human intelligence.
  3. Historically, the measures of intelligence used in AI development have roots in racism and socioeconomic factors.

Understanding (and) psychology

8 implied HN points • 09 Aug 23
🔬 Science Psychology Machine Learning Cognition General Intelligence Human behavior
  1. Understanding what you're measuring is crucial in machine learning and can have implications on race issues.
  2. Machine learning involves supervised learning, which essentially teaches models to predict human responses, making it a form of human behavioral measurement at a large scale.
  3. Psychological experimentation in measuring human behavior and cognition is complex and requires meticulous control and understanding, which is often underestimated in various fields.