Trump announces 'very big contract' for UAE to buy AI chips

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Welcome to TipSeason Trending AI Newsletter.

In today’s edition, we will cover:

  • Trending AI News

  • Trending AI Tools

  • Best AI Prompt pick for ChatGPT / Gemini / any AI

Also welcome to the new subscribers who joined us this week and staying on top of AI Trends.

Trending AI News:

  • Trump announces 'very big contract' for UAE to buy AI chips on last leg of Gulf trip Link

  • You Can Get a Google AI Certification for $99. Or Just Do the Training for Free Link

  • Apple’s AI partnership with Alibaba raises alarms in Washington Link

  • Google I/O 2025: What to expect, including updates to Gemini and Android 16 Link

  • AI Discovers Suspected Trigger of Alzheimer's, And Maybe a Treatment Link

  • AI models can't tell time or read a calendar, study reveals Link

  • AI Could Help Humans Understand Animals Link

  • AI Models Like ChatGPT Are Politically Biased: Stanford Study Link

  • It's not all ChatGPT — these are the 5 AI tools I use every day Link

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Best AI Prompt Pick:

For ChatGPT, Bard, Claude and other AI chatbots:

Topic:  Machine Learning vs Deep Learning: Key Differences

Prompt:

You are an expert technical analyst with deep experience in both Machine Learning (ML) and Deep Learning (DL). Your task is to produce a comprehensive and in-depth comparison between Machine Learning and Deep Learning based on the following variables:


1. [Use Case Focus]: Define the specific application domain (e.g., predictive maintenance, speech recognition, financial modeling).
2. [Audience Expertise Level]: Define the technical level of the reader (e.g., beginner, intermediate, expert).
3. [Comparison Emphasis]: Define the priority in the comparison (e.g., accuracy vs. efficiency, interpretability, training speed, deployment readiness).


Contextualize your response using the following principles:
- ML includes traditional models like SVM, Decision Trees, KNN, Logistic Regression, and Ensembles.
- DL consists of neural network architectures such as CNNs, RNNs, LSTMs, Transformers, with high performance on large unstructured datasets.
- Use frameworks and tools including Scikit-learn, XGBoost (ML), and PyTorch/TensorFlow (DL).
- Reference common metrics and practical use case decision criteria.


Structure your response in the following format:
1. Introduction: Overview and key principles tailored to [Use Case Focus]
2. Step-by-Step Breakdown:
   - Algorithmic architecture
   - Data needs
   - Performance and resource requirements
   - Typical implementation tools
3. Best Practices & Common Pitfalls:
   - Model selection strategies
   - Trade-offs and when to use which
   - Avoidable mistakes in training and deployment
4. Actionable Recommendations:
   - Expert suggestions based on [Comparison Emphasis] and [Audience Expertise Level]
   - Toolkits, learning resources, and evaluation tips


Ensure precision, clarity, and technical accuracy. All content should be logically reasoned with appropriate use of examples. Avoid vague generalizations and ensure all statements are technically grounded.


Tone: Educational and professional  
Style: In-depth, structured, bullet-points where helpful  
Length: Minimum 800 words  


Important: Don’t use AI words like: *whether*, *unleash*, *unlock*, *craft*, *leverage*, or phrases like: *are you looking*, *whether you are*, *tired of*, *struggling to*, or similar ones.

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