Michael

i do ai and space research

[AssemblyAI @AssemblyAI]

Announcing Universal-2, our most advanced speech-to-text model to date.

Building on Universal-1's industry-leading performance, we've pushed Speech AI even further. Here's what's new:

[SpaceX @SpaceX]

Flight 4 liftoff from Starbase

[SpaceX @SpaceX]

Watch Starship’s fourth flight test → https://t.co/bJFjLCiTbK https://t.co/SjpjscHoUB

[SpaceX @SpaceX]

Targeting Thursday, June 6 for Starship’s fourth flight test.

A 120-minute launch window opens at 7:00 a.m. CT → https://t.co/bJFjLCiTbK

[SpaceX @SpaceX]

The Starship team is go for prop load

[Michael @michaelliangau]

Excited to share my first 2 contributions to the scientific community with @AssemblyAI 's Universal-1 and Conformer-1 ASR system papers!

Conformer-1: https://t.co/0YGh22kbD9
Universal-1: https://t.co/3fpF1UxNuP

[AssemblyAI @AssemblyAI]

Introducing Universal-1, our most powerful speech recognition model to date.

Trained on over 12.5 million hours of multilingual audio data, Universal-1 achieves best-in-class speech-to-text accuracy across English, Spanish, French, and German.

https://t.co/xgrE6EhmJu

[AssemblyAI @AssemblyAI]

We’re excited to announce that AssemblyAI has raised $50M in Series C funding to help further our vision of building superhuman Speech AI models that unlock the power of voice data for every organization.

The round, led by @Accel with participation from @KeithBlock and Smith Point Capital, @insightpartners, @danielgross and @natfriedman, and @ycombinator brings our total funding to $115M.

https://t.co/IrIMrmd20H

[Accel @Accel]

.@AssemblyAI was founded to help unlock a new class of AI applications leveraging voice data. We first welcomed the team to the #AccelFamily for their Series A in '22 – it has been inspiring to see their swift progress since. Learn about today's milestone: https://t.co/unYEz5FYJk

[AssemblyAI @AssemblyAI]

Introducing Conformer-1: our latest state-of-the-art speech recognition model.

Built on top of the Conformer architecture and trained on 650K hours of audio data, it achieves near-human-level performance, making up to 43% fewer errors on noisy data than other ASR models.

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