AI and Data Science
Data Science covers many topics including machine learning, statistics, artificial intelligence, and other techniques that attempt to learn from data.
- Details
- Written by Douglas Eadline
- Hits: 4126
from the long, but worthwhile article department
I have studied and watched artificial intelligence grow over the last forty years. Like many, back in 1968, I was inspired by HAL 9000 in Stanley Kubrick's 2001: A Space Odyssey. The question on my mind at the time was, "Is HAL 9000 possible?" Thus began my interest in learning all I could about computers and something called Artificial Intelligence.
By 1970, my excitement grew when I read a quote by AI pioneer Marvin Minsky (in Life magazine), "In three to eight years, we will have a machine with the general intelligence of an average human being." The future was so bright I was beyond inspired.
- Details
- Written by Douglas Eadline
- Hits: 146
From the well if you think about it department
Something about building GenAI LLMs bugs me. Before I begin, let me be clear: I am a supporter of AI technologies, particularly in science. Lately, however, a question keeps surfacing that I find hard to understand. GenAI promoters and sellers like to talk about "AI for Business" as a way to reduce costs and replace workers. Well, okay, all new technologies have that effect to some extent, but the GenAI industry seems to be touting GenAI as a replacement for the people who created the genesis content on which their technology was created. On the surface, this seems absurd; on closer inspection, it seems almost self-destructive. Talk about burning the ships while still at sea. There are many sectors where this question hits hard, but two specific topics of interest to me as a technical writer and sometimes programmer are content creation and programming.
- Details
- Written by Douglas Eadline
- Hits: 21821
From the this article was written by a puny human department
There is a notion floating about that suggests machine learning with deep learning is a GPU focused application. While GPUs excel at deep learning, they are not exclusively required to teach your servers some smarts. There are many free and open tools for machine learning that use good old fashion CPUs (some can also use GPUs). Before we point out some of the tools, however, a few background comments about machine learning are in order.
Machine learning can take many forms and cover many methods one of which is deep learning. Back in the 1980's machine learning was called Artificial Intelligence (AI). As with many technologies, AI was oversold and lost credibility because promised breakthroughs never materialized. Beyond the hype, there has been steady progress with machine learning. In commercial contexts, machine learning methods may be referred to as data science (statistics), predictive analytics, or predictive modeling.
- Details
- Written by Douglas Eadline
- Hits: 4276
From the machine-overlord department
Previously, we highlighted several open (and GPU optional) machine learning packages. Recently Baidu has released a beta version of their ML tools. According to the git hub page,
"The software will be released on Sept. 30 with full documentation and installation support. A pre-release version is available now for those who are eager to take a look. PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
One of the most interesting components of this release is a step-by-step quick start in Python!
Search
Login And Newsletter
