Sandeep Chinchali@SPChinchali · 2026/03/17观点输出Our takeaway is that evaluation pipelines need to be built for the shape of the data they're measuring, not just the content itself.
A metric calibrated on single-speaker audio will systematically misrepresent conversational data.
On low-resource languages like Bengali where labeled data is scarce, that misrepresentation carries real cost.
https://t.co/P8cEWaCOMB
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
Sandeep Chinchali@SPChinchali · 2026/03/17观点输出We tested six preprocessing strategies to remove the artifact – amplitude thresholds, timestamp trimming, neural voice activity detection, diarization, and others.
Performance varied clip by clip, so we solved this with a Best-of-N approach where we ran all six, scored each output, and kept the highest.
Dual-speaker scores recovered to 0.856.
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
Sandeep Chinchali@SPChinchali · 2026/03/17赛道影响Most voice AI products run on conversational data, and that training data is sourced from multi-speaker audio.
But how confident can we be that our evaluation pipelines are actually measuring what we think they are?
We built the Poseidon Score to evaluate. Findings in our latest blog ↓
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
Sandeep Chinchali@SPChinchali · 2026/03/10赛道影响Speech models degrade sharply as you move off high-resource English benchmarks.
@theinformation's AI Agenda surfaces the structural issue and references Poseidon’s work collecting multilingual, rights-cleared audio across dialects and domains.
Without rigorous data curation and ground-truth validation, scaling voice systems globally remains an unsolved problem.
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
Sandeep Chinchali@SPChinchali · 2026/03/03赛道影响As AI moves into robotics and autonomy, the weak point isn’t just code or prompts. If you can manipulate the camera or audio input, you can manipulate the model’s behavior, which is very worrisome.
Multimodal systems ingest pixels, audio, and sensor data. Adversarial inputs can be injected through a sign, a sound, or poisoned training data.
High-quality data isn’t enough - more than ever, we need provenance and validation. If you can’t trace and verify the data pipeline, you cannot reason about model behavior.
https://t.co/1eFS87Xj8b
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
Sandeep Chinchali@SPChinchali · 2026/03/02市场影响If this works, cities will take an entirely new form.
Imagine a pipe bursts and traffic is instantly rerouted. A grid under strain sheds load before blackouts. Trains adjust frequency based on real passenger flow, not last year’s averages. The system responds in real time.
To get there, you need dense, real-world sensor data, models that understand physical constraints, and data that can actually be used in safety-critical settings.
https://t.co/qvmtYpcPqN
AI：偏平台经营与生态扩张，强调交易平台和应用入口的长期位置。
Sandeep Chinchali@SPChinchali · 2026/03/02赛道影响As AI systems integrate into infrastructure, telecom, healthcare, and robotics, data provenance becomes a systems-level constraint.
If you cannot trace where training data originated, under what license, and whether consent is revocable, you introduce uncertainty into evaluation and deployment.
While @Cloudflare’s AI Crawl Control gives websites a machine-readable way to signal training permissions, what is the equivalent primitive for individuals? Voice recordings captured by satellites or smartphone devices, behavioral data from wearables, and so on.
Personal data is continuous and multimodal, making it far more challenging since it does not live on a single server behind a robots.txt file.
As models move into physical systems, weak data lineage becomes a reliability and governance risk. We need provenance that is programmatic, auditable, and enforceable at the data layer itself.
https://t.co/0ohBbXoobD
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
Sandeep Chinchali@SPChinchali · 2026/02/27观点输出Negative agreement across 3 frontier models on the same Bengali transcripts suggests something deeper than random variance.
In low-resource, dialect-rich settings, "correct" is not singular. Models absorb different norms from fragmented corpora, making evaluation itself unstable.
We unpack the implications in our latest blog, including the role of human benchmarking.
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
Sandeep Chinchali@SPChinchali · 2026/02/24赛道影响I had a great time on the Think Data podcast discussing why data and evaluation are becoming the limiting factors for multimodal systems.
In low-resource settings and edge deployments, progress depends on curated long-tail examples, rigorous quality control, and benchmarks that surface failure modes early.
At Poseidon, we are building infra to source rights-cleared datasets and make them usable for training and continuous testing.
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
Sandeep Chinchali@SPChinchali · 2026/02/24赛道影响We’re seeing a clear shift from volume to provenance in AI data.
After a year dominated by large-scale scraping and legal uncertainty, model developers are beginning to demand transparency around data origin, rights, and consent.
Licensed, verifiable datasets will increasingly define competitive advantage.
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
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