IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. The skills gap in gen AI development is a significant hurdle. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Neural Network 239

Neural Network LLM AI AI 239

Snorkel AI

JUNE 12, 2023

They used a very standard model and a decoding algorithm so simple they named it “Stupid Backoff” 1. Better data almost always has a greater impact than fancier models or algorithms in AI—and yet, data development has always been undersupported by AI formalisms and technology. The key differentiator?

Data Platform 145

Data Platform Large Language Models Software Development ChatGPT 145

Snorkel AI

JUNE 12, 2023

They used a very standard model and a decoding algorithm so simple they named it “Stupid Backoff” 1. Better data almost always has a greater impact than fancier models or algorithms in AI—and yet, data development has always been undersupported by AI formalisms and technology. The key differentiator?

Data Platform 141

Data Platform Large Language Models Software Development ChatGPT 141

AI Impacts

JULY 20, 2023

I collected my favorite public pieces of research on AI strategy, governance, and forecasting from 2023 so far. May) Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Perhaps the most important question in AI strategy is what should AI labs do?

AI Strategy 100

AI Strategy AI AI Neural Network 100

Kavita Ganesan

JUNE 28, 2022

In fact, Gartner’s research shows that only 53% of AI initiatives make it from prototype to production. 3 Accountable AI will gain steam With all the Facebook drama and regulations around AI still being limited and “in the talks”, more and more businesses are becoming aware of the problems with algorithms.

Data Scientist 52

Data Scientist AI AI Algorithm 52

AWS Machine Learning Blog

MAY 9, 2024

These encompass a holistic approach, covering data governance, model development, ethical deployment, and ongoing monitoring, reinforcing the organization’s commitment to responsible and ethical AI/ML practices. They need to partner with the procurement team to develop a selection, onboarding, management, and exit framework.

ML 106

ML Generative AI AI AI 106

ODSC - Open Data Science

MARCH 5, 2024

The Evolution of AI Investment The journey of AI from theoretical research to a core driver of technological innovation has been marked by significant milestones. Factors influencing VC decisions include the startup’s technological edge, market size, team expertise, scalability, and responsible AI practices.

Responsible AI 52

Responsible AI Machine Learning Data Science Automation 52

DataRobot Blog

FEBRUARY 15, 2023

DataRobot provides a leaderboard showing results from different experiments, including a diverse range of algorithms, preprocessing, and feature engineering. The algorithm blueprint, including all steps taken, can be viewed for each item on the leaderboard.

Data Scientist 52

Data Scientist Data Drift Automation AI 52

IBM Journey to AI blog

AUGUST 15, 2023

AI is being implemented in key workflows like talent acquisition and retention, customer service, and application modernization, especially paired with other technologies like virtual agents or chatbots. What are foundation models and how are they changing the game for AI?

Artificial Intelligence 203

Artificial Intelligence Artificial Intelligence Natural Language Processing AI 203

Marktechpost

MARCH 22, 2024

At the heart of these different software projects were algorithms based on Mathematical Programming, Simulation, and Heuristics, as well as AI models based on ML and generative AI. Despite all the hype around AI and Data, many organizations (outside of the software industry) struggle to implement a successful AI strategy.

Data Scientist 126

Data Scientist Python Algorithm AI 126

Kavita Ganesan

NOVEMBER 8, 2022

Years ago, we had to develop a single specialized model for every task that we were looking to solve with AI. We needed the training data, the appropriate ML algorithm, and a data scientist. So it essentially democratizes AI development and makes it less time-consuming. Is this too good to be true?

Large Language Models 52

Large Language Models ML Neural Network Data Scientist 52