Communications of the ACM
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From BLOG@CACM
The Sparks of AGI? Or the End of Science?
Marching into the future with an obstructed view.
From BLOG@CACM
Teaching Data Science Research Methods to Human Resources Practitioners: Part Three
How we took advantage of students' expertise in the application domain component of data science.
From BLOG@CACM
Artificial Intelligence Without Real Intelligence?
Do the programs understand what they write?
From BLOG@CACM
AI in the Public Interest: Education and Democracy
Now is a critical time to vigorously pursue public interests in education and to avoid a slippery slide into yielding public governance to an entirely private market...
From BLOG@CACM
Why Businesses Must Untether Deep Learning
Deep learning only took a few years to make the leap from another theoretical piece of future-tech to a very real tool that's already making an impact on the business...
From BLOG@CACM
GPT-4's Successes, and GPT-4's Failures
How GPT-4 fits into the larger tapestry of the quest for artificial general intelligence.
From BLOG@CACM
Teaching Data Science Research Methods to Human Resources Practitioners: Part Two
"When I heard we will be learning Python, I became anxious."
From BLOG@CACM
Cybersecurity Mistakes to Avoid at All Costs
Preventing unauthorized access or misuse of confidential data.
From BLOG@CACM
Twiddler: Configurability for Me, but Not for Thee
Online platform businesses have a distinctly more abusive and sinister character than brick-and-mortar retail chains.
From BLOG@CACM
The Urgency of the Technological, Geopolitical, and Sustainability Races
The HiPEAC Vision 2023.
From BLOG@CACM
Enabling AI Projects In An Enterprise
Identifying the development behaviors that have the highest chance of success for artificial intelligence efforts.
From BLOG@CACM
Serendipity: When Research in One Area Leads to a Positive Result in Another
A problem of the extremely optimal family of circulant graphs: when research in one scientific field gives an unexpected result in another.
From BLOG@CACM
Teaching Data Science Research Methods to Human Resources Practitioners: Part One
The challenge was to teach data science concepts to students who have gaps in their computer science background and are experts in human resources management and...
From BLOG@CACM
How Not to Test GPT-3
Why doing psychology on large language models is harder than you might think.
From BLOG@CACM
Why *is* Bing So Reckless?
And how did some prominent journalists utterly miss this initially?
From BLOG@CACM
Using GitHub CoPilot is Like Having a Superpower
With the advent of Large Language Models (LLMs) and tools like GitHub CoPilot, I think we’re a step closer towards a world where software can be created by anyone...
From BLOG@CACM
ChatGPT for Provost!
Three to five years from now, we won’t be talking about what ChatGPT can or can’t do, but we could be talking about how ChatGPT brought us all together, and the...
From BLOG@CACM
What Google Should Really Be Worried About
How sewers of lies could spell the end of Web search.
From BLOG@CACM
