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This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a \u201csniff-and-seek\u201d mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus\u2013molybdenum disulfide (bP-MoS\n                    <jats:sub>2<\/jats:sub>\n                    ) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.\n                  <\/jats:p>","DOI":"10.1126\/science.ady6571","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T19:01:31Z","timestamp":1764270091000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":17,"title":["Spectral kernel machines with electrically tunable photodetectors"],"prefix":"10.1126","volume":"390","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7077-0639","authenticated-orcid":true,"given":"Dehui","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA."},{"name":"Materials Sciences Division, Lawrence Berkeley National 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