Apple's new M3 silicon + Alibaba unveils GPT-4 competitor Tongyi Qianwen 2.0

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🎃 Quick News 🎃

🖼️ The copyright claims against AI image generators such as Midjourney, Stable Diffusion, and DeviantArt were dismissed by a California judge. The allegations of the three artists collapsed due to the absence of properly registered copyrights and the lack of conclusive evidence of infringement. This case will undoubtedly have profound implications on how AI intersects with copyright laws.

🍎 Everything Apple announced at its ‘Scary Fast’ event - the updated MacBook Pro and iMac models, both featuring the company's new M3 silicon chip. These new models are tailored for AI and machine learning use, offering significant improvements in performance and energy efficiency.

🏭 Siemens and Microsoft are partnering to promote AI across industries with the introduction of Siemens Industrial Copilot, an AI-powered assistant to enhance human-machine collaboration. They aim to create more such AI aides for manufacturing, infrastructure, transportation, and health industries. The approach is expected to significantly reduce simulation times from weeks to mere minutes.

🔮 Ireland's AI research centre, CeADAR, has been chosen as a European Digital Innovation Hub, leading to fresh recruitment in data science and AI. With €14m in funding, it plans to help SMEs and public service organisations enhance their digital proficiency through AI and data science.

☄️ Google Brain's founder, Andew Ng, criticizes big tech for using fear of AI leading to human extinction to push self-beneficial policies. He advocates for sound AI regulations over those proposed by various governments, emphasizing transparency in technology firms.

💸 Shield AI is making headway in defense technology with their autonomous drone systems supported by an encouraging $200M funding. Architected for the U.S. military and allies, this advancement heralds the dawn of safer operational frameworks, lessening human casualty in hostile environments.

👻 Nerd section 👻 

🗃️ RedPajama-Data-v2 is an impressive database created for advancing language model training. It houses 30 trillion tokens derived from over 100 trillion, sourced from 84 CommonCrawl data dumps in five languages. Furthermore, it provides 40+ pre-computed data quality annotations helpful for data filtering and weighting.

🦾 7 Steps to Mastering Large Language Models (LLMs) - this detailed guide walks you through everything you need to know about LLMs - their structure, capabilities, how they differ from other deep learning models, and the wide range of applications they offer. It closes with a step-by-step method to build and deploy an LLM app.

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