Update modules/ui/ui.py
Browse files- modules/ui/ui.py +14 -30
modules/ui/ui.py
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@@ -639,39 +639,23 @@ def display_videos_and_info(lang_code, landing_t):
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<div class="section-title">Kaggle/Google - $100K</div>
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<strong>Explainable AI Models</strong><br>
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<em>Google
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<div class="section-title">Learning Tools Competition - $100K</div>
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<strong>
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<em>
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</div>
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""",
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'es': """
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<div class="content-text">
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<div class="highlight-box">
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Competimos por <strong>$950K USD</strong> en premios en múltiples competencias prestigiosas.
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</div>
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<div class="section-title">Learning Tools Competition - $600K</div>
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<strong>Fase II - Top 50 Global</strong><br>
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<em>International Society for Technology in Education (ISTE)</em><br>
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Compitiendo con 1,200 proyectos de 75 países
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<div class="section-title">NVIDIA Inception - $250K</div>
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<strong>Programa de Capital de Riesgo</strong><br>
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<em>NVIDIA Corporation</em><br>
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Programa exclusivo para startups de Deep Tech
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<div class="section-title">Kaggle/Google - $100K</div>
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<strong>Modelos Explicables de IA</strong><br>
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<em>Google Research</em><br>
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Investigación en IA interpretable para educación
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</div>
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"""
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}
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<div class="section-title">Kaggle/Google - $100K</div>
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<strong>Explainable AI Models</strong><br>
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<em>Google Tunix Hack - Train a model to show its work</em><br>
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<p>Most open-source or open-weight language models can give you an answer. But they typically don’t ‘show their work’
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- the steps they went through to arrive at that conclusion in a consistent manner. Here, you’ll use Tunix,
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Google’s new JAX-native library for LLM post-training, to train a model to show its work by laying out a
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reasoning trace before landing on an answer.</p>
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More info: https://kaggle.com/competitions/google-tunix-hackathon
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<div class="section-title">Learning Tools Competition - $100K</div>
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<strong>Datasets for Education Innovation</strong><br>
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<em> Phase II </em><br>
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<p>The 2026 Tools Competition launches at a time of rapid change, rising urgency,
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and widespread demand for understanding and direction amidst evolving technology.
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Learners of all ages face new demands driven by AI,
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shifting labor markets, and persistent inequities. From early learning to workforce development,
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they need tools that are more effective, inclusive, and future-ready.</p>
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More info: https://tools-competition.org/26-overview/
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</div>
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"""
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}
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