Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
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Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
Dolphins & DeepMind: Cracking the Code of Animal Language with Dr. Denise Herzing | 107
Sidecar Sync
39 minutes
2 months ago
Dolphins & DeepMind: Cracking the Code of Animal Language with Dr. Denise Herzing | 107
Send us a text In this episode of Sidecar Sync, Mallory Mejias is joined by marine biologist and behavioral researcher Dr. Denise Herzing for a one-of-a-kind conversation about dolphins, data, and deep learning. Dr. Herzing shares insights from her 40-year study of Atlantic spotted dolphins and how that lifetime of underwater research is now powering DolphinGemma—an open-source large language model trained on dolphin vocalizations. The two discuss what it means to label meaning in animal comm...
Sidecar Sync
Send us a text In this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias dig into the startling findings of a recent MIT study: 95% of enterprise AI initiatives are failing to deliver measurable impact. Why are so many organizations investing in AI only to watch their efforts fizzle? Mallory and Amith unpack 10 core reasons for these failures—ranging from building chat-first features instead of end-to-end workflows, to skipping the unglamorous data cleanup that powers real results. ...
