Heard my 13 year old talking to his friends about #ChatGPT. I had to warn him to refrain from the temptation to use it for doing his “boring” homework. We are in dangerous territory!
ETHICS
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General Intelligence Theory: Pragmatic Patternist Perspective
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The General Theory of General Intelligence: A Pragmatic Patternist Perspective Ben Goertzel : https://
arxiv.org/abs/2103.15100 #AGI #ArtificialGeneralIntelligence #AGIDebate -
ChatGPT Makes Mediocre Essay Jobs Obsolete for Students
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Any jobs that require mediocre essays can now, *in real life*, be done with ChatGPT assistance! Students who use ChatGPT to generate essays are *realistically* showing they can handle the mediocre-essay-writing jobs for which modern universities are vigorously training them.
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ChatGPT milestone debate: AI evolution and comprehension limitations
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The advancement of ChatGPT is a significant milestone in the realm of artificial intelligence, but it is not the beginning of an AI revolution. AI technology has been in the process of evolution for many decades. ChatGPT lacks the capability to comprehend. #AGIDebate #AIDebate
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EHR-Safe Generates Privacy-Preserving Synthetic Electronic Health Records
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Analyzing electronic health records (EHRs) has great potential, e.g. to enhance patient care, but common anonymization methods can decrease the data’s utility. To that end, read how EHR-Safe generates high-fidelity & privacy-preserving synthetic EHR data→ https://
goo.gle/3HZfpxn -
AI Debate 2: Building Ethical and Trustworthy AI Systems
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AI DEBATE 2 "It takes a village to raise an AI that’s ethical, robust, and trustworthy" – Gary Marcus Official Video: https://
youtu.be/VOI3Bb3p4GM http://
MONTREAL.AI Debates Series #MontrealAI #AIDebate #AIDebate2 -
Citing Your Research Rival’s Prior Work Obligation
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When your research rival wrote a prior work that you're obligated to cite https://
clickhole.com/heartbreaking-
the-worst-person-you-know-just-made-a-gr-1825121606/
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Barriers to Enterprise Adoption of Advanced ML Models
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Tons. 1) the brilliant researchers working on these models don’t know how to run an ML business 2) cost 3) performance, guardrails, safety 4) infosec/org/legal approval for use in-house 5) power concentration/small talent pool of folks that know this tech
Etc.