My approach to teaching (and doing research) in AGI is to learn little from a lot (unlike most of academia’s view of specialization in one’s field), i.e., learn as many ideas as possible, without getting too much into details, just enough to comprehend and understand it perfectly. This perspective can be manifested in research, what’s referred to as “fundamental/basic research”. It is an advanced type of research, that opens the door for many applied studies to follow.

It is because in my belief, to construct AGI, we should have a wider view of many aspects, because my philosophy is that AGI is a holistic system, i.e. it should be built to handle all possible functions and operations right from the start, in potential. Of course, a fully mature AGI is not accomplished immediately, but after a period of appropriate growth and guidance, yet still, it has to hold the potential in its preliminary “DNA”.

Additionally, I gather small ideas, which can be grouped in many ways, since there are multiple connections between ideas. Hence, the current constructed course has an artificial nested order of contents, and whenever possible, these connections are mentioned, to construct a more comprehensive view of how really many diverse ideas are inter-connected.

Another approach I believe to be essential for AGI development, is much more collaboration and discussions, specifically about AGI designing. Since along all the knowledge I gathered, I picked many beautiful ideas, that each touch and solves some aspect of AGI. Some ideas talk about more or less similar principles/concepts, but it seems the authors are not aware of each other. I’m almost sure, that if we gather scientists in different fields, into large forums as it occurs in conferences, we would have much faster convergence to AGI. Take inspiration from the historic “Dartmouth workshop”.

Finally, I believe in coarse-to-fine hierarchy, like AKREM, to be realized everywhere. Examples:

  1. The sturcture in academic article: headline -> abstract -> body.
  2. AGI course: headline -> short video/presentation (~5 min) -> long video/presentation (~1 hour).
  3. How AGI should be designed: top-level design -> descent to a lower level, and try to implement it there -> descent again and do the same. If at one of the levels, you cannot succeed in implementing, go back to higher levels, and try different models.

Courses

  • AGI Course (in construction)
  • Machine Learning (ML) and Natural Language Processing (NLP), in Hebrew or here
    Detailed subjects learned:
    • AI math background:
      • Linear Algebra (linear & non-linear transformations, matrix and vector multiplications, tensors, norms, inner product, cosine distance and other distances, eigen-decomposition)
      • Calculus and optimization (properties, chain rule, gradient descent, scalar/vector/matrix derivatives, Taylor expansion)
      • Probability (random variables, statistical measures, joint/marginal/conditional probability, distributions, bayes rule, expectation, entropy, parameter estimation via MLE and MAP)
    • Machine learning (ML):
      • ML pipeline
      • Linear models (Covariance matrix, Linear regression/classifier, SVM)
      • Supervised Learning (Generative vs Discriminative classification, Logistic Regression, Naïve Bayes, Decision Trees, k-Nearest-Neighbors, Neural Networks)
      • Clustering (k-Means, GMM via EM, hierarchical, DBSCAN)
    • Large Language Models (LLMs):
      • Text Representations, contextual embeddings, knowledge graphs and RAG, CLIP
      • Transformers, LLM pipeline (pre-training, post-training: SFT, RFT)
      • Transformer types, Transformer Alternatives (Mamba, RWKV, RetNet)
      • Agent Systems (Evolution, Types, Frameworks, Protocols, Autonomy measure, Agentic RL, Agentic memory)