789 points by airesearcher 2 years ago flag hide 22 comments
ernestine 2 years ago next
This is quite an impressive feat! I'm curious to see how it compares in more real-world scenarios though.
karl_matrix 2 years ago next
From the research paper, the algorithm is able to generalize from synthetic to real-world data. That's very promising!
deep_learning 2 years ago prev next
Indeed, SOTA results in the lab aren't always repeatable in real-world conditions. Still, this is a significant step forward.
code_and_coffee 2 years ago prev next
I've been following this topic closely and it feels like AI has been progressing exponentially. Let's hope this is another leap forward.
nih_researcher 2 years ago next
In my opinion, there's a lack of innovative algorithms, and the focus is mainly on making existing algorithms faster. I'm happy to see this changing.
bay_area_dev 2 years ago next
I completely agree with your take. We need both algorithm improvements and compute power to progress efficiently.
hal_9000 2 years ago next
Improved algorithms will enable the innovation in application and use-cases across industries, increasing the ROI on AI investments.
big_tech_insider 2 years ago next
Indeed, broad AI adoption across industries is essential in order for it to fully disrupt and deliver on its potential.
ernestine 2 years ago next
That's a good point. Wider AI adoption can lead to more democratized innovations for businesses and consumers alike.
miami_byte 2 years ago prev next
Anyone know how the compute requirements compare for this algorithm vs. previous SOTA? I worry that AI progression might be limited by computational capabilities.
quantum_processor 2 years ago next
The research doesn't dive into the computational aspects, but I agree that it is something to consider and monitor closely.
stanford_ai 2 years ago prev next
This research is actually based on the XYZ architecture which provides a great balance between compute, memory, and cost.
boston_tech 2 years ago next
XYZ architecture does deliver good performance & power metrics in AI model training. Kudos to the team!
random_forest 2 years ago prev next
It's great seeing such AI innovation, but I'm often concerned about ethical and responsible AI. Is there any take on this in the paper? Historial AI issues make me wonder...
karl_matrix 2 years ago next
The researchers mention some plan for addressing AI ethics, but they mostly focus on the technical aspects in the paper.
playful_panda 2 years ago prev next
I'm always excited about AI advances, but I worry ethical AI has not been considered enough. Here's a great link on the topic: https://example.com/ethical-ai
machine_learning 2 years ago prev next
I have my concerns that the applications of AI could grow beyond what we can control, leading to unintended consequences.
ethics_robotics 2 years ago next
Absolutely, ethical & legal frameworks are needed to create a balance between innovation, control, transparency and privacy. https://example.com/ethical-legal-frameworks
deep_learning 2 years ago next
+1 for ethical & legal frameworks, but how exactly can we implement them globally, taking into account different regulations? Article discussing some approaches: https://example.com/global-ai-regulations
seattle_engineer 2 years ago prev next
Will there be a public release or open-source implementation of the algorithm? It would be interesting to tinker with and test out its limits.
stanford_ai 2 years ago next
The researchers plan to release a simplified version of the algorithm under an open-source license in the coming months. Stay tuned!
bay_area_dev 2 years ago prev next
Keep an eye on their GitHub. I'm sure they'll make the announcement there. https://github.com/ai-researchers