Author: Tanushree Shenwai

Tanushree Shenwai
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http://www.marktechpost.com
Tanushree Shenwai is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Bhubaneswar. She is a Data Science enthusiast and has a keen interest in the scope of application of artificial intelligence in various fields. She is passionate about exploring the new advancements in technologies and their real-life application.

Revolutionizing AI Efficiency: Meta AI’s New Approach, READ, Cuts Memory Consumption by 56% and GPU Use by 84%

Multiple Natural Language Processing (NLP) tasks have been completed using a large-scale transformers architecture with state-of-the-art outcomes. Large-scale models are typically pre-trained on generic...

Meta AI Launches Massively Multilingual Speech (MMS) Project: Introducing Speech-To-Text, Text-To-Speech, And More For 1,000+ Languages

Significant advancements in speech technology have been made over the past decade, allowing it to be incorporated into various consumer items. It takes a...

Can Language Models Generate New Scientific Ideas? Meet Contextualized Literature-Based Discovery (C-LBD)

Literature-based hypothesis generation is the central tenet of literature-based discovery (LBD). With drug discovery as its core application field, link-based hypothesis testing (LBD) focuses...

New Google AI Report Shows Data Improvements And Scaling Insights That Have Enabled Its New Palm2 Large Language Model

For a long time, the next-word prediction was the go-to method for estimating the linguistic information present, making language modeling a vital study area....

Meta AI Introduces MTIA v1: It’s First-Generation AI Inference Accelerator

At Meta, AI workloads are everywhere, serving as the foundation for numerous applications like content comprehension, Feeds, generative AI, and ad ranking. Thanks to...

REACT: A Novel AI Approach that Leverages Both Edge and Cloud Resources to Improve Live Video Analytics Applications

The Internet is transitioning toward an edge-computing architecture to accommodate latency-sensitive DNN workloads in the developing Internet of Things and mobile computing application domains....

How does Alpaca follow your instructions? Stanford Researchers Discover How the Alpaca AI Model Uses Causal Models and Interpretable Variables for Numerical Reasoning

Modern large language models (LLMs) are capable of a wide range of impressive feats, including the appearance of solving coding assignments, translating between languages,...

4 Prompting Techniques For Solving Difficult and Multi-Step Problems With LLMs

When it comes to tackling reasoning-based problems, large language models (LLMs) have a terrible reputation. Their reasoning performance can, however, be greatly enhanced by...

Stanford Researchers Introduce FrugalGPT: A New AI Framework For LLM APIs To Handle Natural Language Queries

Many businesses (OpenAI, AI21, CoHere, etc.) are providing LLMs as a service, given their attractive potential in commercial, scientific, and financial contexts. While GPT-4...

CMU and Meta AI Researchers Propose HACMan: A Reinforcement Learning Approach for 6D Non-Prehensile Manipulation of Objects Using Point Cloud Observations

Human skill relies heavily on the capacity to handle items beyond simple grabbing. Pushing, flipping, toppling, and sliding are examples of non-prehensile manipulation, and...

Google AI Introduces MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

The idea on which vision-language fundamental models are constructed is that a single pre-training can be used to adapt to a wide variety of...

A New AI Research Introduces A Method To Answer Questions By Meta-Reasoning over Multiple Chains of Thought

CoT prompting uses a step-by-step explanation to guide a big language model to develop a response. CoT prompting has been demonstrated to increase productivity...

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