Sumerian Star Map Recorded the Impact of an Asteroid (2024)
from archaeologyworlds.com
83
by
griffzhowl
6h ago
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Article:
8 min
The discovery of a 5,500-year-old Sumerian star map reveals evidence of an ancient asteroid impact event at Köfel in Austria around 3100 BC.
- The star map was discovered in the 650 BC King Ashurbanipal’s underground library in Nineveh.
- It is believed to be the first astronomical instrument, an Astrolabe, with marked units of angle measure inscribed on its rim.
- The map records a massive asteroid strike that occurred around 3100 BC, which was later confirmed by modern computer simulations.
- The asteroid was over a kilometer in diameter and had an Aten type orbit close to Earth’s orbit.
- The impact caused a landslide at Köfel due to the enormous pressures generated but did not create a classic impact crater.
Quality:
The article presents a well-researched and balanced account of the discovery, with references to academic sources.
Discussion (29):
The comment thread discusses an unusual story involving an asteroid, with participants expressing amazement at the human ability to connect ancient artifacts and astronomical events.
- The story involves an asteroid with unusual behavior
Ancient History
Archaeology, Astronomy
Show HN: I trained a 9M speech model to fix my Mandarin tones
from simedw.com
300
by
simedw
13h ago
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Article:
11 min
The article discusses the creation of a Mandarin pronunciation training tool using deep learning techniques. The author trained a small CTC model to grade users' pronunciation, focusing on tones which are challenging for non-native speakers due to their relative nature and context-dependency.
- Initial plan was to build a pitch visualiser but found it brittle due to special cases.
- Decided on building a deep learning-based Computer-Assisted Pronunciation Training (CAPT) system.
- Treated the task as an Automatic Speech Recognition (ASR) with a Conformer encoder and CTC loss.
- CTC was chosen over sequence-to-sequence models because it outputs probabilities for every frame, forcing alignment without auto-correction.
- Tokenisation used Pinyin syllables + tone to explicitly show pronunciation errors.
- Training involved combining AISHELL-1 and Primewords datasets with SpecAugment augmentation.
- Model parameters were reduced from 75M to 9M while maintaining accuracy.
- Addressed alignment bug by ignoring frames where the model is confident it's seeing silence.
Discussion (102):
24 min
The comment thread discusses a tool designed to assist in pronunciation correction for Mandarin learners. Users appreciate its innovation, find it helpful for beginners, and suggest improvements such as better handling of tone transformations and speech speed. There is debate on the tool's effectiveness for advanced learners and concerns about accuracy issues.
- The tool is beneficial for language learning
- Feedback can be inaccurate or misleading
Counterarguments:
- The tool may not be sufficient for advanced learners
- Improvements are needed to enhance the user experience
Artificial Intelligence
Machine Learning, Natural Language Processing
NASA's WB-57 crash lands at Houston
from arstechnica.com
21
by
verzali
3d ago
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Article:
2 min
NASA's WB-57 aircraft made an emergency landing at Ellington Field in Houston due to a mechanical issue with its landing gear.
- Pilot maintained control during the landing.
- Crew was not harmed.
- NASA spokesperson confirmed all crew are safe.
Quality:
The article provides factual information without sensationalizing the incident.
Discussion (2):
More comments needed for analysis.
Aviation
Aircraft Incidents, Historical Aircraft
A Step Behind the Bleeding Edge: A Philosophy on AI in Dev
from somehowmanage.com
73
by
Ozzie_osman
2d ago
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Article:
15 min
This memo discusses the team's philosophy on adopting AI in software engineering, emphasizing a balanced approach that avoids being overly reliant on the bleeding edge while still staying informed about advancements. It stresses the importance of maintaining accountability for work quality and fostering creativity through thoughtful engagement with AI tools.
AI adoption in software engineering may lead to increased productivity and efficiency, but it also requires careful management of accountability, creativity, and the potential for skill atrophy.
- Avoiding the constant pursuit of the bleeding edge to prevent thrashing and security exposure.
- Exploring the frontier through dedicated time, resources, and safe individual initiatives.
- Continuing to own one's work by reviewing everything before asking for others' input or feedback.
- Using AI as a tool for thought-partnership, idea generation, editing, and synthesis rather than replacing deep thinking.
- Leaving room for inspiration in the face of increased productivity from AI.
Discussion (20):
Comment analysis in progress.
Software Development
AI/ML in Software Engineering, DevOps