as of today I am not sure there are any “autonomous vehicles” at scale (excluding monorails etc). If you need a checkin once an hour that’s “vast majority” but not truly autonomous. and if you can’t run without guaranteed cell service, that’s not autonomous, either.
RESEARCH
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Building Your First Recurrent Neural Network
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Building Your First Recurrent Neural Network! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/First-RNN -
Recommended Machine Learning Books for Experts and Beginners
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Recommended Machine Learning Books for Experts and Newbies. #BigData #Analytics #DataScience #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Experts-Newbie
s-2022
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Accelerating Timelines: Impressive Execution Speed in AI Development
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Yeah I agree timelines are accelerating (though my tweet was more about the speed of their execution in particular, which I think is impressive)
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xAI Announces Grok-0: 33B Parameter LLM Approaching LLaMA 2
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Announcing #Grok by @xai @elonmusk v/ @_akhaliq Blog https://
x.ai ***************
trained a prototype LLM (Grok-0) with 33 billion parameters. This early model approaches LLaMA 2 (70B) capabilities on standard LM benchmarks but uses only half of its -
Algorithmic engagement optimization versus truth in content
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It’s designed to make you feel something, not designed to reflect the world. Causing you to feel disgust, disbelief, or pity is a victory condition; if it can accomplish that will outcompete a less evocative video of a family eating dinner. “Solve for equilibrium.” as they say.
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ARPA-H and PostLive Host Cancer Innovation Event
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This week, join @ARPA_HDirector and me @PostLive for innovations vs. cancer Register: http://
ccnov2023.splashthat.com -
FP8 Low-Precision Training Improves LLM Efficiency Without Hyperparameter Changes
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10/ FP8-LM – finds that when training FP8 LLMs most variables, such as gradients and optimizer states, in LLM training, can employ low-precision data formats without compromising model accuracy and requiring no changes to hyper-parameter.
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Enhancing LLMs Understanding through Emotional Stimuli Research
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9/ Enhancing LLMs by Emotion Stimuli – explores the ability of LLMs to understand emotional stimuli; conducts automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4.
