<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/3abd569af3b04335b8ad144b772e73e0&quot; frameborder=&quot;0&quot; width=&quot;1152&quot; height=&quot;864&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>864</height><width>1152</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>864</thumbnail_height><thumbnail_width>1152</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3abd569af3b04335b8ad144b772e73e0-7b0fc5e5c58bc0b0.gif</thumbnail_url><duration>126.548</duration><title>Essential Challenges in Software Engineering 🤖 - Part III (Future Directions)</title><description>In this video, I highlighted the limitations of current AI models in representing images symbolically, which contrasts with the systematic nature of language. I also touched on the fascinating areas of neurosymbolic AI and AGI research. While there is still much to learn, I believe there are opportunities to enhance example-based programming beyond what tools like Excel currently offer.

Part I - https://www.loom.com/share/0c9639885c244e7889bb11843aa948d6 
Part II - https://www.loom.com/share/b28dd8b897a749b3a96dda8cf29cc33f
Part III - https://www.loom.com/share/3abd569af3b04335b8ad144b772e73e0</description></oembed>