Learning

Nora Petrova, Machine Learning Engineer & AI Consultant at Prolific – Interview Series

Nora Petrova, is a Machine Learning Engineer & AI Consultant at Prolific. Prolific was founded in 2014 and already counts organizations like Google, Stanford University, the University of Oxford, King’s College London and the European...

How Robots Are Learning to Ask for Help

Within the evolving world of robotics, a groundbreaking collaboration between Princeton University and Google stands out. Engineers from these prestigious institutions have developed an revolutionary method that teaches robots an important skill: recognizing after...

Technique enables AI on edge devices to continue to learn over time

Personalized deep-learning models can enable artificial intelligence chatbots that adapt to grasp...

Unlocking the Power of Big Data: The Fascinating World of Graph Learning

Harnessing Deep Learning to Transform Untapped Data right into a Strategic Asset for Long-Term Competitiveness.Our goal was to predict business data based on physical data (and we did it). I'm pleased to tell you...

Bridging the expectation-reality gap in machine learning

There isn't a quick-fix to closing this expectation-reality gap, but step one is to foster honest dialogue between teams. Then, business leaders can begin to democratize ML across the organization. Democratization means each...

Revolutionizing Robot Learning: NVIDIA’s Eureka Aces Complex Tasks

In a world where technology is ever-evolving, NVIDIA once more demonstrates its prowess with a groundbreaking advancement: the Eureka AI agent. This cutting-edge tool is not only any AI model – it’s transforming the...

Dynamic Pricing with Contextual Bandits: Learning by Doing

Adding context to your dynamic pricing problem can increase opportunities in addition to challengesIn my previous article, I conducted a radical evaluation of the most well-liked strategies for tackling the dynamic pricing problem using...

Bolstering enterprise LLMs with machine learning operations foundations

Once these components are in place, more complex LLM challenges would require nuanced approaches and considerations—from infrastructure to capabilities, risk mitigation, and talent. Deploying LLMs as a backend Inferencing...

Recent posts

Popular categories

ASK ANA