Henry Shevlin – a philosopher of mind and AI ethics from Cambridge, just got hired as an in-house philosopher at Google DeepMind. He'll be focusing on machine consciousness, human-AI interaction, and the ethical governance of increasingly autonomous systems. What's significant here: DeepMind is treating philosophy as a discipline on par with computer science and neuroscience, embedding it directly into core research rather than just keeping ethicists as external advisors. The labs are starting to think about the consciousness, agency, and moral reasoning question. Whereas, I am working at the applied human level – what happens when a mid-level manager doesn't trust the AI their company just deployed, or when a team's workflows break because no one designed the adoption path. That's not the philosophy angle but rather organisational and psychological infrastructure. Both matter. [Translated from EN to English]
The role of memorization and knowledge is to cache & reuse past cognitive work. It should be leveraged as a way to speed up cognition, not as a *replacement* for cognition.
Simply retrieving a reasoning trace looks a lot like human reasoning, until it's time to navigate uncharted territory. If you memorized all reasoning traces of humans from 10,000 BC, you could automate their lives but you could not invent modern civilization.
Centenarians who are sharp and still running things is already happening without any of the upgrades. The interesting question is what happens when that becomes the norm rather than the exception.
Grade Body Measurement with Machine Learning Grading a patient’s obesity level is a critical component of effective healthcare. Obesity is a significant risk factor for a range of serious diseases, including chronic conditions, type-2 diabetes, heart disease, and certain cancers. Understanding a person's obesity status can serve as a powerful catalyst for individuals to take control of their weight. Additionally, intentional weight management not only mitigates health risks but also offers the compelling benefit of reducing disease susceptibility. Despite its widespread use, the Body Mass Index (BMI) – the standard metric defined by the World Health Organization (WHO) – fails to capture the complexities of obesity, as it overlooks essential body-type variations. Furthermore, nutritional needs differ markedly across regions and body types, underscoring the necessity for a more nuanced approach to obesity assessment. Traditional anthropometric measurements, while effective, are often impractical due to the requirement for trained professionals to perform them accurately. In response to this challenge, innovative research utilizing 3D scanning technology is gaining momentum as a less-invasive and more accessible alternative. Unlike Computed Tomography (CT) or Dual-energy X-ray absorptiometry (DXA)—considered the gold standard for measuring body fat percentage (bf%)—3D scanners eliminate the risks associated with radiation exposure during frequent assessments. Moreover, evaluating health risks demands a multifaceted approach rather than relying solely on a singular measure. In this study, we collected paired data from 3D body scans and DXA for a Korean population, providing a more comprehensive understanding of obesity and paving the way for improved health management strategies. This pioneering research has the potential to transform how we assess and respond to obesity, ultimately leading to healthier outcomes for individuals and communities alike. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Jeon, S., Kim, M., Yoon, J., & et al. (2023). Machine learning-based obesity classification considering 3D body scanner measurements. Scientific Reports, 13, 3299. Retrieved February 27, 2025, from doi.org/10.1038/s41598-023-3…
