The integration of artificial intelligence (AI) into video lecture production has the potential to transform higher education by streamlining content creation and enhancing accessibility. This paper investigates a semi -automated workflow that combines Google Gemini for script generation, Amazon Polly for voice synthesis, and Microsoft PowerPoint for video assembly. Unlike fully automated text -to -video platforms, this hybrid approach preserves pedagogical intent while ensuring script -slide synchronization, narrative coherence, and customization. Case studies demonstrate the effectiveness of Gemini in generating accurate and context - sensitive scripts for visually rich academic presentations, while Polly provides natural - sounding narration with controllable pac ing. A two-course pilot study was conducted to evaluate AI -generated instructional videos (AIIV) against human instructional videos (HIV). Both qualitative and quantitative results indicate that AIIVs are comparable to HIVs in terms of learning outcomes, w ith students reporting high levels of clarity, coherence, and usability. However, limitations remain, particularly regarding audio quality and the absence of human - like avatars. The findings suggest that AI - assisted video production can reduce instructor workload, improve scalability, and deliver effective learning resources, while future improvements in synthetic voices and avatars may further enhance learner engagement.

Wolfram Alpha has been a huge hit with students. Whether in college or high school, Wolfram Alpha has become a ubiquitous way for students to get answers. But it's a one-shot process: a student enters the question they want to ask (say in math) and Wolfram Alpha gives them the (usually richly contextualized) answer. It's incredibly useful--especially when coupled with its step-by-step solution capabilities. But what if one doesn't want just a one-shot answer? What if one wants to build up (or work through) a whole computation?

Yesterday, AMD showed off the first real-time benchmarks of the Radeon Vega graphics card against the NVIDIA Pascal based Tesla P100 in deep learning benchmarks. In its first attempt, the RTG developed GPU was able to give NVIDIA's best compute card from last year a good beating but there's more to the benchmarks. NVIDIA launched the Tesla P100 based on Pascal GP100 back in early 2016. Since then, it has been the fastest compute solution available to date. NVIDIA kicked off 2017 with the announcement of the next chapter in graphics deep learning.

I recently returned from a vacation to find that Google's algorithms had created a customized slide show of my trip. I hadn't asked for one. But the company's software robots apparently noticed I'd traveled somewhere and taken a flurry of photos, which likely indicated I'd been vacationing. Now, I actually enjoy some of Google's simpler customization tools, like autocomplete. But this unbidden slide-show curation seemed too humanlike.

Robots don't see the world like humans do, at least not yet. They don't recognize discrete objects and have little common sense--that it's better to drive over a bush than a rock, for instance. They use a variety of sensors--cameras, laser range finders, radar--to gauge the shape, slope, and smoothness of the terrain ahead. They then use these data to figure out how to stay on the road and avoid obstacles. At least that's the idea, but the DARPA Grand Challenge showed it's a lot harder than it sounds.

"Excuse me," the former vice president says, dabbing a tissue at his nose before offering up an explanation. Outside Gore's New York City office, spring has certainly sprung--early too. This March was the hottest one ever, beating the prior record set in March 2015. The same goes for February and January of this year, and, oh, the eight consecutive months before. Gore knows these statistics by heart. The fact that you might know them too is likely because of him.