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  1. en.wikipedia.org › wiki › TESCREALTESCREAL - Wikipedia

    Hace 5 días · TESCREAL is an acronym neologism, proposed and advocated by computer scientist Timnit Gebru and philosopher Émile P. Torres, standing for transhumanism, extropianism, singularitarianism, cosmism, rationalism, effective altruism, and longtermism.

  2. Hace 2 días · Un estudio de 2018 realizado por los investigadores especializados en inteligencia artificial Joy Buolamwini y Timnit Gebru, que evaluaron las herramientas de reconocimiento facial de Microsoft, IBM y Face++, constató que las mujeres de piel más oscura eran las más propensas a ser identificadas erróneamente por la tecnología de ...

  3. Hace 1 día · No último dia 6 de junho, a pesquisadora Timnit Gebru, referência mundial por peitar práticas antiéticas do Google no desenvolvimento de sistemas de IA, e fundadora da DAIR – Distributed AI Research Institute, esteve no Brasil para o debate “IA Para o Bem Comum: fortalecendo sistemas alternativos”, que organizamos entre Coding Rights, MediaLab, Lavits e […]

  4. Hace 3 días · For example, 2018 research from Joy Buolamwini, a computer scientist at MIT Media Lab, and Timnit Gebru, the founder and executive director of the Distributed Artificial Intelligence Research ...

  5. Hace 4 días · This is the first exhaustive review of the literature surrounding the risks that come with rapid growth of language-learning technologies, said Emily M. Bender, a University of Washington professor...

  6. Hace 1 día · Joy Buolamwini and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification". In: Conference on fairness, accountability and transparency. PMLR. 2018, pp. 77-- 91. Google Scholar [19] Laura Cabello, Anna Katrine Jørgensen, and Anders Søgaard.

  7. Hace 4 días · They were told not to put their names on the paper if it was published, and two departed Google under conflicting stories, including the head of ethics at the time Timnit Gebru. At the time, Dean said in an internal email that the paper "didn't meet our bar for publication" and failed to include recent findings on how models could be made more efficient.