Customer reports that Tesla has halted deliveries for its futuristic Cybertruck amid allegedly dangerous safety issues with its gas pedal come as no surprise to one former insider. 'After I left, it got worse,' said Balan, who is suing her former boss Elon Musk's electric car company for defamation. 'I have quite a few people that are right now in Tesla,' Balan said. 'They brought some serious safety issues to my attention.' New Cybertruck owners have described its gas pedal as a'deathtrap,' demonstrating how the pedal cover can slide off the accelerator and become snagged on the carpet, locking it in place and spurring the car to accelerate at top speed.

Using artificial intelligence (AI) to enhance teaching and learning has been a kind of nirvana for education leaders for several years now – a place of perhaps unimagined power that has perpetually seemed just out of grasp. And though it may feel as if it's always just around the next corner, forever one tool or dataset away, one entrepreneur says we're getting closer. In fact, he says we may be close enough to say we've actually arrived at the place where AI products and systems are already showing the return they've promised for so long – personalizing learning for students, yielding rich and actionable data, simplifying teaching practices and, best of all, improving learning outcomes. That positive assessment comes from Ramesh Balan, the founder and CEO of Knomadix – the buzz-worthy AI education company he launched in 2015. And Balan may be worth listening to.

In this note we prove that reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem") can be performed using Lipschitz continuous maps. Specifically we show that when the nonlinear analysis map $\alpha:{\mathcal H}\rightarrow\mathbb{R}^m$ is injective, with $(\alpha(x))_k=||^2$, where $\{f_1,\ldots,f_m\}$ is a frame for the Hilbert space ${\mathcal H}$, then there exists a left inverse map $\omega:\mathbb{R}^m\rightarrow {\mathcal H}$ that is Lipschitz continuous. Additionally we obtain the Lipschitz constant of this inverse map in terms of the lower Lipschitz constant of $\alpha$. Surprisingly the increase in Lipschitz constant is independent of the space dimension or frame redundancy.