Resources -- 240616
Symposium on Explainable AI (XAI)
A Research and Industry Symposium on Explainable AI (RISXAI) is being organised in hybrid mode on 27th Sep 2024 at S. A. Engineering College with the support of IEEE CS, CSI and ACM.
Call for Papers: https://drive.google.com/file/d/1iAVRP4-PPJOkzK2IJULm113hAppcrBVz/view?usp=drivesdk
The last date to submit the intention & abstract is 20th Jun 2024. Use the registration link to provide details. https://forms.gle/VG7sSW1K597Eeg1y8
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Free eBook: "Writing AI Prompts For Dummies ($15.00 Value)
Free for a limited time till 25th jun 2024.
You may need a work email id (ieee.org mail id is considered as work mail id)
https://solo.tradepub.com/free/w_wile565/
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AI Tool Blends Programming and Language for Better Problem Solving
Researchers have developed natural language embedded programs (NLEPs), enabling AI models to solve complex tasks by generating and executing Python programs.
This method boosts accuracy in reasoning tasks and improves transparency by allowing users to inspect and correct code. NLEPs also enhance data privacy by processing information locally.
1. NLEPs prompt AI to create Python programs to solve complex tasks.
2. The method improves accuracy and transparency, allowing for code inspection.
3. NLEPs enhance data privacy by processing information locally.
https://neurosciencenews.com/nlep-ai-language-26329/
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What Is an NPU? How to Compare Its Specs Like Other PC Hardware
1. NPUs like Apple's ANE and Google's TPU are integrated into their smartphones and laptops for efficient AI processing.
2. NPUs are specialized processors for AI tasks, different from CPUs and GPUs, enhancing performance with lower power consumption.
3. Key NPU specs like TOPS, power efficiency, precision, memory bandwidth, and compatibility impact performance and user experience.
https://www.makeuseof.com/what-is-npu-how-compare-specs
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AI Algorithms Explained to Kids -- Part 1
Here’s What’s Included :
- Linear Regression: Predict future based on past data.
- Decision Trees: Make choices with yes/no questions.
- K-Means Clustering: Group similar items together.
- Neural Networks: Computers learning from examples.
- Naive Bayes: Predict outcomes using past information.
- Support Vector Machines (SVM): Separate items with a perfect line.
- Random Forests: Combine answers from multiple decision trees.
- Gradient Boosting: Improve predictions by learning from mistakes
(Explore more in the post)
https://www.linkedin.com/feed/update/urn:li:activity:7201773473520627713
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AI Algorithms Explained to Kids - Part 2
Here’s What’s Included :
- Logistic Regression: Predicting outcomes using past data.
- PCA: Simplifying data like packing a suitcase.
- Reinforcement Learning: Training computers with rewards.
- k-NN: Making predictions based on nearest neighbors.
- Genetic Algorithms: Evolving solutions by combining the best traits.
- CNNs: Helping computers recognize images.
- RNNs: Understanding sequences like stories.
- Autoencoders: Compressing and reconstructing data.
- Q-Learning: Finding the best path through exploration.
- Bayesian Networks: Making predictions with probabilities.
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Visualizations to better Understand AI
Here’s What’s Included :-`
- Roadmap to learn AI
- Mathematics In AI
- AI Algorithms
- Common Problems In AI
- The AI Workflow
- AI Expectation vs Reality
- AI Tools and Libraries
- AI Jobs Ecosystem
- Bias and Fairness in AI
- Use-Cases of AI
https://www.linkedin.com/posts/denis-panjuta_understand-ai-activity-7205218491493171200-AMvF
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Components of Prompt Engineering
Here’s what’s Included in the post :
- Tone: Specify the desired tone (e.g., formal, casual, informative, persuasive).
- Format: Define the format or structure (e.g., essay, bullet points, outline).
- Act as: Indicate a role or perspective to adopt (e.g., expert, critic, enthusiast).
- Objective: State the goal or purpose of the response (e.g., inform, persuade).
- Context: Provide background information, data, or context for content generation.
- Scope: Define the scope or range of the topic.
- Keywords: List important keywords or phrases to be included.
- Limitations: Specify constraints, such as word or character count.
- Examples: Provide examples of desired style, structure, or content.
- Deadline: Mention deadlines or time frames for time-sensitive responses.
(Explore more in the post)
https://www.linkedin.com/feed/update/urn:li:activity:7202253187427225601
