IBM Journey to AI blog

JULY 6, 2023

IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won. On a broader level, it asks if machines can demonstrate human intelligence.

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Data Science Machine Learning Big Data Data Mining 175

IBM Journey to AI blog

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

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Neural Network LLM Algorithm NLP 256

Towards AI

FEBRUARY 15, 2023

Understanding Large Language Models — A Transformative Reading List In just five years, large language models (transformers) have revolutionized the field of natural language processing. This article explains why. […] Three 5-minute reads/videos to keep you learning 1.

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Large Language Models Prompt Engineer Prompt Engineering AI Chatbots 98

IBM Journey to AI blog

AUGUST 11, 2023

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data.

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Data Science DevOps Machine Learning Data Scientist 170

TheSequence

DECEMBER 13, 2023

He has previously built machine learning-powered applications for start-ups and enterprises in the domains of natural language processing, topological data analysis, and time series. You are the co-author of the Natural Language Processing with Transformers Book.

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LLM Natural Language Processing Machine Learning Deep Learning 64

Topbots

MARCH 2, 2023

Descartes is credited with developing algebra to explain geometry. A geometric shape could be explained by a series of equations (algebra), whereby coordinates located a point, points determined lines and lines determined planes and shape. Science could be understood by applying computer modeling to look for patterns in systems.

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Generative AI Explainability Algorithm ML 52

TheSequence

SEPTEMBER 11, 2024

He has previously built machine learning-powered applications for start-ups and enterprises in the domains of natural language processing, topological data analysis, and time series. Could you describe the various components of the NuminaMath recipe and explain how they work together?

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Deep Learning Python Natural Language Processing Machine Learning 59