Voice gets human. CRISPR rewrites immune destiny. And OpenAIās latest AI doesnāt just solve math ā it feels its way through it.
ChatGPT Finds Its Voice ā and Some Sarcasm
CRISPRās First Human Trial: Immunity Restored
Math with Vibes? o3-mini Thinks Like a Grad Student
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OpenAI
OpenAI just leveled up ChatGPTās voice mode ā and it sounds real. Think: expressive pauses, shifting tones, regional accents, empathy, even a hint of sarcasm. Itās not just voice synthesis anymore ā itās voice presence.
Also dropping: a streamlined live translation mode. Tell ChatGPT to start translating, and itāll keep doing so across your entire conversation ā until you say otherwise. No need to re-prompt.
Key Details:
Voice now adapts to emotions, nuance, and real conversational pacing.
Auto-translation mode supports fluid multilingual chats.
Available now for all paid ChatGPT users across platforms.
This is OpenAIās closest push toward making AI feel like a co-pilot ā not just a tool.
Nature
Gene-editing startup Prime Medicine just pulled off a medical first: editing an immune system disorder in a living person using a new version of CRISPR ā more precise than Cas-9, and far more adaptable.
What happened:
An 18-year-old with chronic granulomatous disease ā a rare immune disorder ā received gene-edited blood cells grown outside the body.
After reintroduction post-chemo, 66% of the patientās neutrophils started producing the enzyme they were previously missing.
One month in: No serious side effects.
Big Picture:
This new CRISPR system could treat a wider range of genetic diseases.
But: The process is expensive and technically complex. Prime Medicine is actively looking for partners to scale clinical trials.
This isnāt just about treating a disease ā itās proof of concept for rewriting immune systems from scratch.
Epoch AI
Forget benchmark scores ā how do AI models really think through math? Epoch AI just ran a unique study where 14 mathematicians dissected how OpenAIās o3-mini-high handles complex reasoning.
Spoiler: Itās weirdly human.
What they found:
The model tries multiple strategies, runs mini-experiments, and even writes code ā showing inductive, intuitive reasoning.
It rarely offers polished proofs, opting for messy, approximation-heavy problem-solving.
In 66% of cases, it matched the right theorem to the task ā but often failed to prove why.
75% of its reasoning paths had hallucinations: made-up terms, incorrect formulas, even fake links.
One expert said it feels like āa bright grad student ā well-read but not yet original.ā
Why this matters:
Models like o3-mini-high are not just solving ā theyāre navigating mathematical terrain like explorers, not calculators.
But until they learn to slow down, explain, and create new methods, they may remain powerful yet shallow assistants ā not true math innovators.
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