摘要: 吳恩達近期在 Deeplearning.ai 的一場論壇,說明了自己對於機器學習(Machine Learning)和 MLOps 的最新看法。 在過去,機器學習社群在意的是建模、參數調整、架構選擇,來達到對性能的監控。現在產業運用 AI 的幅度大增,開始轉向了新的階段,一大面臨問題是,當模型投入了生產,許多無法預期的因素,會影響模型性能。

摘要: Since machine learning is a relatively new field, the limits of its application are constantly pushed outward. Virtual personal assistants were the stuff of dreams a few years ago, and now they’re seen in every other household. While some examples are conspicuous, here are some ways ML is changing our lives that you may not have thought of.

摘要: Automated machine learning, or AutoML, has generated plenty of excitement as a pathway to “democratizing data science,” and has also encountered its fair share of skepticism from data science’s gatekeepers. Complicating the conversation even further is that there is no standard definition of AutoML, which can make the debate incredibly difficult to follow, even for those well-versed.

摘要: The predictive prowess of machine learning is widely hailed as the summit of statistical Artificial Intelligence. Vaunted for its ability to enhance everything from customer service to operations, its numerous neural networks, multiple models, and deep learning deployments are considered an enterprise surety for profiting from data.

摘要: Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like “eye is to seeing what ear is to hearing,” sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge.