Top 20 AI Concepts You Should Know
1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.
Artificial intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Hope this helps you ☺️
1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.
Artificial intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Hope this helps you ☺️
❤4
How AI really works?
The OpenAI team created an interpretable model which is much more transparent than typical transformers, behave like a "black box."
This is important because such a model helps understand why AI hallucinates, makes mistakes, or acts unpredictably in critical situations.
The new LLM is a sparse transformer: much smaller-simpler than modern LLMs (at level of GPT-1). but goal is not to compete, but to be as explainable as possible.
🟢 How it works?
This is an important step toward true interpretability of large AI.
The OpenAI team created an interpretable model which is much more transparent than typical transformers, behave like a "black box."
This is important because such a model helps understand why AI hallucinates, makes mistakes, or acts unpredictably in critical situations.
The new LLM is a sparse transformer: much smaller-simpler than modern LLMs (at level of GPT-1). but goal is not to compete, but to be as explainable as possible.
🟢 How it works?
- the model is trained so that internal circuits become sparse,
- most weights are fixed at 0,
- each neuron has not thousands of connections, but only dozens,
- skills are separated from each other by cleaner and more readable paths.
In usual dense models, neurons are connected chaotically, features overlap, and understanding the logic is difficult.
Here, for each behavior, a small circuit can be identified:
sufficient, because it performs the required function itself,
and necessary, because its removal breaks the behavior.
The main goal is to study how simple mechanisms work to better understand large models.
The interpretability metric here is circuit size,
the capability metric is pretraining loss.
As sparsity increases, capability drops slightly, and circuits become much simpler.
Training "large but sparse" models improves both metrics: the model becomes stronger, and the mechanisms easier to analyze.
Some complex skills, such as variables in code, are still partially understood, but even these circuits allow predicting when the model correctly reads or writes a type.
The main contribution of the work is a training recipe that creates mechanisms
that can be *named, drawn, and tested with ablations*,
rather than trying to untangle chaotic features post hoc.
LIMITS: these are small models and simple behaviors, and much remains outside the mapped chains.
This is an important step toward true interpretability of large AI.
❤1