Daniel Berger
Daniel Berger is a journalist with a focus on technology and its impact on society. He has written extensively about artificial intelligence, machine learning, and their applications in various industries. His work often explores the intersection of technology and healthcare, as seen in his article about Google's AI-assisted 3D mapping of the human brain. Through his reporting, Berger aims to provide readers with accurate and insightful information on emerging technologies and their potential implications.
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The Daily's Verdict
This author is known for its high journalistic standards. The author strives to maintain neutrality and transparency in its reporting, and avoids conflicts of interest. The author has a reputation for accuracy and rarely gets contradicted on major discrepancies in its reporting.
Bias
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Examples:
No current examples available.
Conflicts of Interest
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Contradictions
85%
Examples:
- Researchers found clusters of cells that tended to occur in mirror-image orientation to one another.
- Some neuron pairs had the surprising property of being connected to each other extremely strongly through as many as 50 synapses.
- The sample came from a part of the cortex called the anterior temporal lobe.
- This 3D mapping requires a monumental 1.4 petabytes (1.4 million gigabytes) to encode.
Deceptions
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No current examples available.
Recent Articles
Google and Harvard Unveil Detailed Digital Map of Human Brain, Revealing Previously Unknown Structures
Broke On: Thursday, 09 May 2024Google and Harvard scientists unveil the most detailed digital map of the human brain yet, revealing new structures and connections that may explain how habits are formed. The collaboration, led by Jeff Lichtman, used advanced imaging techniques and AI algorithms to reconstruct a cubic millimeter of temporal cortex in 3D, discovering rare axon connections with up to 50 synapses. Strange structures like oppositely-pointing nerve cells and detouring axons were found, shedding light on how learned habits are etched into the brain's physical architecture. The ultimate goal is to map a mouse's neural wiring using significantly more data.