Brian Greenberg :verified:<p>🧠 Neural networks can ace short-horizon predictions — but quietly fail at long-term stability.</p><p>A new paper dives deep into the hidden chaos lurking in multi-step forecasts:<br>⚠️ Tiny weight changes (as small as 0.001) can derail predictions<br>📉 Near-zero Lyapunov exponents don’t guarantee system stability<br>🔁 Short-horizon validation may miss critical vulnerabilities<br>🧪 Tools from chaos theory — like bifurcation diagrams and Lyapunov analysis — offer clearer diagnostics<br>🛠️ The authors propose a “pinning” technique to constrain output and control instability</p><p>Bottom line: local performance is no proxy for global reliability. If you care about long-horizon trust in AI predictions — especially in time-series, control, or scientific models — structural stability matters.</p><p><a href="https://infosec.exchange/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://infosec.exchange/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://infosec.exchange/tags/NeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuralNetworks</span></a> <a href="https://infosec.exchange/tags/ChaosTheory" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ChaosTheory</span></a> <a href="https://infosec.exchange/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://infosec.exchange/tags/ModelRobustness" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelRobustness</span></a><br><a href="https://www.sciencedirect.com/science/article/abs/pii/S0893608025004514" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">sciencedirect.com/science/arti</span><span class="invisible">cle/abs/pii/S0893608025004514</span></a></p>