Beberapa model AI diklaim mampu melakukan proses berpikir secara penalaran (reasoning), misalnya Claude, DeepSeek dan sebagainya. Penelitian oleh Apple menunjukkan sebenarnya model-model tersebut tidak melakukan penalaran.
Berikut ini beberapa studi tentang efek AI terhadap produktivitas manusia
Large Language Models, Small Labor Market Effects
Penelitian di Denmark menggunakan survey menunjukkan bahwa AI chatbot menaikkan produktivitas 3%
“We examine the labor market effects of AI chatbots using two large-scale adoption surveys (late 2023 and 2024) covering 11 exposed occupations (25,000 workers, 7,000 workplaces), linked to matched employer-employee data in Denmark. AI chatbots are now widespread—most employers encourage their use, many deploy in-house models, and training initiatives are common. These firm-led investments boost adoption, narrow demographic gaps in take-up, enhance workplace utility, and create new job tasks. Yet, despite substantial investments, economic impacts remain minimal. Using difference-in-differences and employer policies as quasi-experimental variation, we estimate precise zeros: AI chatbots have had no significant impact on earnings or recorded hours in any occupation, with confidence intervals ruling out effects larger than 1%. Modest productivity gains (average time savings of 3%), combined with weak wage pass-through, help explain these limited labor market effects. Our findings challenge narratives of imminent labor market transformation due to Generative AI.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5219933
In a new paper, “The Simple Macroeconomics of AI,” MIT Institute Professor has a more conservative estimate of how AI will affect the U.S. economy over the next 10 years. Estimating that only about 5% of tasks will be able to be profitably performed by AI within that time frame, the GDP boost would likely be closer to 1% over that period, Acemoglu suggests. This is a “nontrivial, but modest effect, and certainly much less than both the revolutionary changes some are predicting and the less hyperbolic but still substantial improvements forecast by Goldman Sachs and the McKinsey Global Institute,” he writes.
Generative artificial intelligence (AI) tools have been selectively adopted across the academic community to help researchers complete tasks in a more efficient manner. The widespread release of the Chat Generative Pre-trained Transformer (ChatGPT) platform in 2022 has made these tools more accessible to scholars around the world. Despite their tremendous potential, studies have uncovered that large language model (LLM)-based generative AI tools have issues with plagiarism, AI hallucinations, and inaccurate or fabricated references. This raises legitimate concern about the utility, accuracy, and integrity of AI when used to write academic manuscripts. Currently, there is little clear guidance for healthcare simulation scholars outlining the ways that generative AI could be used to legitimately support the production of academic literature. In this paper, we discuss how widely available, LLM-powered generative AI tools (e.g. ChatGPT) can help in the academic writing process. We first explore how academic publishers are positioning the use of generative AI tools and then describe potential issues with using these tools in the academic writing process. Finally, we discuss three categories of specific ways generative AI tools can be used in an ethically sound manner and offer four key principles that can help guide researchers to produce high-quality research outputs with the highest of academic integrity.
Belajar deep learning nggak terlalu rumit. Modal dasarnya mengerti matematika SMA, komputer juga nggak harus yang mahal-mahal. Alamat kursusnya: “Practical Deep Learning“
Ada beberapa bahan yang biasa dipakai untuk membuat alat masak, yaitu:
Stainless steel (baja tahan karat)
Gelas
Besi cor
Teflon
Aluminium
Tembaga
Keramik
Stainless steel kualitasnya sangat baik. Panas dari kompor mudah menyebar merata. Dapat dipakai untuk berbagai macam cara. Tidak lengket jika sudah di’seasoning’. Perawatan mudah. Material dari wajan relatif tidak masuk ke makanan, kecuali chromium yang bisa bereaksi dengan makanan kalau dipakai memasak yang asam.
Wajan besi ada yang menggunakan besi saja, ada yang dilapis enamel. Besi bereaksi jika dipakai memasak makanan yang mengandung asam. Besi dari wajan dapat masuk ke makanan.
Wajan enamel adalah wajan besi yang dilapis dengan enamel.
Gelas bagus, namun mudah pecah. Konduktivitas panas kurang bagus dibandingkan wajan logam.
Tembaga sangat mudah menghantar panas. Kelemahannya adalah tembaga cukup reaktif dengan makanan.
Keramik tidak lengket. Kurang baik untuk panas tinggi. Tidak reaktif. Namun kadang diberi lapisan anti lengket teflon.
Teflon berpotensi melepas mikroplastik ke alam
Aluminium bersifat racun.
Carbon steel: lebih mudah berkarat dibandingkan baja stainless steel
Titanium lebih kuat dari baja dan lebih ringan. Namun harganya mahal sekali.