Ashish Pawar

Hackernoon (Chosen as Top Story)

Learn why GenAI models fail with new information, the 'One-Shot Generalization Paradox,' and what future AI research holds for overcoming this challenge.

Hackernoon (Chosen as Top Story)

Discover how AI assistants like Ella are transforming customer interactions with human-like precision, the tech behind them, and what we may lose in this shift.

Hackernoon (Chosen as Top Story)

Should AI-generated speech enjoy the same legal protections we extend to human expression? Or should AI-generated content fall into its own category altogether, with separate rules?

Hackernoon

As businesses recognize the value of optimized AI prompts, a new debate emerges: can prompts become trade secrets, and what does that mean for innovation?

VentureBeat

Let me break down why over-reliance on gen AI in R&D could be innovation’s Achilles’ heel.

Hackernoon

Transformers are limited by sequence length due to quadratic scaling. Explore solutions like sparse attention, low-rank approximations, and spectral methods.

AI Advances

Let’s face it — when it comes to AI, it often feels like small businesses are part of a lopsided fight. How can small business compete?

Hackernoon

Can we really quantify that bias? Is there some magic metric that can definitively tell us how biased an AI is?

TowardsAI.net

Why can’t we have one cohesive AI system that flips seamlessly between different modes of understanding and generation, like we humans do?

DZone

Mode collapse makes GANs produce repetitive outputs. Solutions exist, but none fully solve it. Curious how researchers are tackling this? Read on!

Medium

A nerdy debate between character-level and word-level RNNs.

Data Science Central

Explores the core principles, practical steps, and common pitfalls to watch out for when structuring large datasets to get the most out of RAG.

Medium

Delves into the differences between these methods and provides a concise answer to the question at hand with a comprehensive analysis.

Arxiv

This paper analyzes different acquistion functions like Maximum Probability of Improvement and Expected Improvement and various optimizers like L-BFGS and TNC to optimize the acquisitions functions for finding the next sampling point.

Arxiv

After studying and analyzing various previous works in automating the tuning of these parameters, this paper proposes two algorithms - Grid Search with Finer Tuning and Controlled Random Search.

Arxiv

This paper provides the conclusion that the proposed portfolio manager with actions centered on weight allocations can surpass the risk-adjusted returns of conventional portfolio managers.