Large language models: the AI systems clinicians are now encountering - Irish Medical Times
Large language models: the AI systems clinicians are now encountering Irish Medical Times
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AI offensive cyber capabilities are doubling every six months, safety researchers find
AI models are rapidly improving at exploiting security vulnerabilities. According to a new study, their offensive cyber capability has been doubling every 5.7 months since 2024, with Opus 4.6 and GPT-5.3 Codex now solving tasks that take human experts about three hours. The article AI offensive cyber capabilities are doubling every six months, safety researchers find appeared first on The Decoder .
Error While using langchain with huggingface models
from langchain_core.prompts import PromptTemplate from langchain_community.llms import HuggingFaceEndpoint import os os.environ[“HUGGINGFACEHUB_API_TOKEN”] = “hf_your_new_token_here” prompt = PromptTemplate( input_variables=[“product”], template=“What is a good name for a company that makes {product}?” ) llm = HuggingFaceEndpoint( repo_id=“mistralai/Mistral-7B-Instruct-v0.3”, temperature=0.7, timeout=300 ) chains = prompt | llm print(“LLM Initialized with Token!”) try: response = chains.invoke({“product”: “camera”}) print(“AI Suggestion:”, response) except Exception as e: print(f"Error details: {e}") when i run this i get Value error can anyone help me out? Its a basic prompt template and text gen code but still it doesnt work i used various models from Huggingface and its not working well
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AI offensive cyber capabilities are doubling every six months, safety researchers find
AI models are rapidly improving at exploiting security vulnerabilities. According to a new study, their offensive cyber capability has been doubling every 5.7 months since 2024, with Opus 4.6 and GPT-5.3 Codex now solving tasks that take human experts about three hours. The article AI offensive cyber capabilities are doubling every six months, safety researchers find appeared first on The Decoder .
AI benchmarks systematically ignore how humans disagree, Google study finds
A Google study finds that the standard three to five human raters per test example often aren't enough for reliable AI benchmarks, and that splitting your annotation budget the right way matters just as much as the budget itself. The article AI benchmarks systematically ignore how humans disagree, Google study finds appeared first on The Decoder .
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