elian_fotografo has added a photo to the pool:
Incluso en los cielos más cubiertos, la luz siempre encuentra un camino para aparecer.
iain.davidson100 has added a photo to the pool:
iain.davidson100 has added a photo to the pool:
iain.davidson100 has added a photo to the pool:
Last year 2025 I was approached by art agency, Pistachio Kernel company limited https://www.pistachio-k.com/ to create 6 artworks for Cullinan Harbour (Kai Tak) property in Hong Kong, build by Sun Hung Kai Real Estate Development Company Limited. For this particular project, I was invited to create a series of artworks centered around the theme of water, with the Lotus plant as the central subject. When asked about the desired atmosphere, the response was clear and intentional: calm and relaxing. The choice of the Lotus, deeply rooted?both symbolically and literally?in slow-moving rivers and deltas, felt like a natural extension of the project.
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Buitenlandse media zijn onder de indruk van de 5-1 overwinning van Oranje op Zweden van zaterdagavond.
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This week OpenAI announced a 750-task test to to measure "whether AI systems can support realistic life science research tasks, not just answer biology questions."
But while OpenAI's top-performing GPT-Rosalind model led the rankings, Slashdot reader BrianFagioli notes that "it achieved a pass rate of just 36.1 percent, failing nearly two-thirds of benchmark tasks." Nerds.xyz points out that means "the best-performing model failed nearly two-thirds of the benchmark's tasks."
The benchmark also revealed a familiar weakness. AI systems generally perform better when everything is presented as text. Once they are forced to work with supporting documents, figures, or complex datasets, performance drops noticeably. GPT-Rosalind's pass rate fell from 45.1 percent on text-only tasks to 28.1 percent on tasks involving artifacts or URLs.
To be fair, the benchmark is not intended to suggest AI is useless in research. Quite the opposite. OpenAI found that models are becoming increasingly capable of scientific communication, evidence synthesis, and translating research findings into practical explanations. Those are valuable skills, particularly for researchers drowning in information. But LifeSciBench serves as a useful reminder that today's AI systems are still far from autonomous scientists. They can help. They can assist. They can sometimes provide surprisingly useful insights. What they cannot reliably do, however, is replace the expertise, judgment, and skepticism that real scientific research requires.
But while OpenAI's top-performing GPT-Rosalind model led the rankings, Slashdot reader BrianFagioli notes that "it achieved a pass rate of just 36.1 percent, failing nearly two-thirds of benchmark tasks." Nerds.xyz points out that means "the best-performing model failed nearly two-thirds of the benchmark's tasks."
The benchmark also revealed a familiar weakness. AI systems generally perform better when everything is presented as text. Once they are forced to work with supporting documents, figures, or complex datasets, performance drops noticeably. GPT-Rosalind's pass rate fell from 45.1 percent on text-only tasks to 28.1 percent on tasks involving artifacts or URLs.
To be fair, the benchmark is not intended to suggest AI is useless in research. Quite the opposite. OpenAI found that models are becoming increasingly capable of scientific communication, evidence synthesis, and translating research findings into practical explanations. Those are valuable skills, particularly for researchers drowning in information. But LifeSciBench serves as a useful reminder that today's AI systems are still far from autonomous scientists. They can help. They can assist. They can sometimes provide surprisingly useful insights. What they cannot reliably do, however, is replace the expertise, judgment, and skepticism that real scientific research requires.
Read more of this story at Slashdot.