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If you’re looking for tools to track trends beyond Google Trends, here are some great alternatives:

1. Exploding Topics: Helps discover emerging trends before they become mainstream. It’s ideal for marketers and entrepreneurs.
2. SEMrush Trends: A comprehensive tool for analyzing industry trends and competitive insights.
3. BuzzSumo: Focuses on social media trends and content performance.
4. Pinterest Trends: Tracks trending topics and ideas on Pinterest.
5. Trend Hunter: A platform for discovering consumer insights and innovation trends.
6. Treendly: Offers trend data across various platforms like Google, YouTube, and Amazon.
7. Keyword Tool: Provides keyword trends and search volume data.
8. TrendWatchers: Specializes in YouTube trend tracking.
9. SparkToro: Focuses on audience insights and trending topics.
10. Muck Rack: Tracks trends in journalism and media.

Each tool has its unique strengths, so the best choice depends on your specific needs. Let me know if you’d like more details about any of these!

Ethical Intelligence in the Era of Al: Navigating the Post-Turing Landscape

The rapid advancement of artificial intelligence (Al) has ignited a global conversation about its potential benefits and inherent risks. The unease expressed by authors in London regarding the alleged unauthorized use of their work to train Al models underscores a growing concern within the creative ecosystem. This is not an isolated incident, but rather a symptom of a larger challenge: how to ethically integrate increasingly sophisticated Al into the fabric of our society, particularly within creative and political spheres where human values and rights are paramount. The deployment of Al in support of regimes committing atrocities further amplifies the urgency of establishing ethical boundaries for this powerful technology. It is no longer a question of whether unchecked Al will significantly impact these ecosystems, but rather how quickly and with what consequences. This paper will delve into the concept of “Ethical Intelligence” in the context of Al that is reaching, and in some interpretations, surpassing human-level conversational abilities, as symbolized by the Turing Test.

https://www.phephim.lol/phim-bo/cuoc-doi-duc-phat

https://www.msn.com/en-xl/news/other/the-best-movies-about-scientists/ss-BB1r0ilz?ocid=msedgdhp&pc=U531&cvid=9dc942cad2b0487787189f611a1c7a72&ei=15#image=19

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Continual Learning: A Review of Variational Dropout, Mixture of Experts with Prompting, and Backdoor Attacks

1. Introduction

The field of machine learning has witnessed significant advancements in recent years, enabling models to achieve remarkable performance on a wide array of tasks. However, a fundamental challenge arises when these models are deployed in dynamic environments where new data or tasks are encountered sequentially. This paradigm, known as continual learning, necessitates the ability of a model to learn from a continuous stream of information without forgetting previously acquired knowledge.1 A major impediment to achieving this goal is catastrophic forgetting, a phenomenon where the learning of new information leads to a drastic decline in performance on previously learned tasks.4 Overcoming this challenge requires specialized techniques that can maintain a delicate balance between the model’s capacity to learn new tasks (plasticity) and its ability to retain old knowledge (stability).4

https://rss.orbit13.synology.me

ai_news_generator

https://github.com/patchy631/ai-engineering-hub/tree/main/ai_news_generator

To stay updated on the latest news about AI and neural networks, here are some effective strategies:

1. Google Alerts:

   • Set up Google Alerts for keywords like “AI news” or “neural networks.” You can choose to receive updates via email or create an RSS feed for your alerts.

2. RSS Feeds:

   • Use RSS readers like Feedly or Inoreader to subscribe to AI-related blogs, news websites, and research publications. Many platforms, including Google Alerts, allow you to convert alerts into RSS feeds.

3. Tech News Websites:

make a markdown code about the following content:

Comparative Analysis of Advanced AI Architectures: Fourier Analysis Networks, Google Titan Transformer 2.0, and MoE-JEPA World Models

The field of artificial intelligence has experienced remarkable evolution with several novel architectures emerging to address the limitations of conventional deep learning approaches. This research provides a comprehensive comparative analysis of three cutting-edge AI architectures: Fourier Analysis Networks (FANs), Google Titan Transformer 2.0, and Mixture of Experts Joint Embedding Predictive Architecture (MoE-JEPA) World Models. Each model employs distinct approaches to overcome current AI limitations, particularly in handling periodic structures, long-term dependencies, and context understanding. Through detailed examination of their architectures, operational mechanisms, advantages, limitations, and empirical performance, this study offers insights into their potential impact on the future trajectory of artificial intelligence research and applications.

Research Proposal: MoE-JEPA World Models for Efficient Reinforcement Learning and Planning

Abstract

Current AI research emphasizes the development of sophisticated world models capable of understanding complex dynamics, particularly from video data, often leveraging self-supervised learning (SSL) for representation extraction. Predictive models in abstract spaces (like JEPA) are gaining prominence over generative ones. Simultaneously, Mixture of Experts (MoE) offers a way to scale neural network capacity efficiently. This proposal outlines a research approach combining these trends: developing an Action-Conditioned Mixture-of-Experts Joint-Embedding Predictive Architecture (MoE-JEPA) world model. This model will be pre-trained using self-supervision on large video datasets to learn robust visual representations and environment dynamics. The MoE structure will allow the model to efficiently capture diverse or multi-modal dynamics within an environment by routing inputs to specialized expert sub-networks. This sophisticated world model will then be integrated into a model-based Reinforcement Learning (RL) framework to enable efficient planning and decision-making for agents (e.g., robots) interacting with complex environments. We hypothesize that this approach will lead to more accurate world models, improved sample efficiency in RL, and better generalization across tasks compared to monolithic world models.

https://aistudio.google.com/apps

https://huggingface.co/datasets/PleIAs/common_corpus

 Kaggle or Data Commons, but here are some sample data and prompts to try:

• Stack Overflow Annual Developer Survey: try asking “Visualize most popular programming languages”

• Iris Species: try asking “Calculate and visualize the Pearson, Spearman, and Kendall correlations in this data”

• Glass Classification: try asking “Train a random forest classifier on this dataset”

One of the largest free datasets available for training large language models (LLMs) is the Common Corpus. It contains approximately 500 billion words and is multilingual, covering languages like English, French, German, Spanish, Dutch, and Italian. This dataset is designed to be open and free of copyright concerns, making it ideal for training open and reproducible LLMs.

AI benchmarks assess the performance and capabilities of models in standardized tasks.