Master Any Language M(A)L’s Post

Very interesting conversation with Christopher Manning regarding LLMs and NLP. So many insightful takeaways – a discussion not to be missed! https://lnkd.in/gMaTdu3Z #artificialintelligence #LLM #NLP #AGI #ChatGPT

Q What are effective methods for learning Japanese? How much time should be dedicated to studying it each day? It is about learning what Japan is, what the Japanese are and what the Japanese language is. It means learning about the differences with other countries, other peoples and other languages. Learning vocabulary, letters and grammar blindly will not lead to deep learning.

Definition of Language Language is a manifestation of the nature of an entity, the core of an entity - one of an atom, cell, organism (subconscious, glial meshwork), planet, star system, galaxy, universe. => Root -> Model -> Word -> Context => => Intuition -> Thinking -> Sensing -> Feeling => - Carl Jung => Knowledge -> Consciousness -> Understanding -> Memory => => Glial Meshwork -> Neural Network -> Neuromediators -> Blood Vessels => => DNA -> RNA -> Protein -> Signal Pathways => Francis Creek => Protons/Neutrons -> Photons -> Electrons -> Chemical Bonds => Niels Bohr => Earth -> Air -> Fire -> Water => => Basis -> Process -> Result -> Way => References: Noam Chomsky - The Structure of Language https://lnkd.in/djHDvs6G Noam Chomsky on AI, Neural Networks, and the Future of Linguistics https://lnkd.in/dd76VTdm A Unified Theory - Universal Language - https://lnkd.in/dtXc_N2X

Teachings of Jesus GPT In-depth guide on Jesus's life, encouraging exploration and discussion. https://lnkd.in/dYuHihA2

An excellent new paper on LLMs and Human Language Understanding.

Follow Hai Huang and Christopher Manning, if you're interested in AI. Related to the post below, one thing we know is that no five year old child had to read the entirety of Wikipedia and Twitter in order to learn to speak. However, there's also some evidence that exposure to more spoken words (the child's training dataset) seems to lead to a "smarter" child in some way (exposure to more spoken language before the age of five may be correlated with larger vocabulary, higher IQ, and more). So, a relatively larger training data set (google: "million word gap, reading, children") is important for children, too, it's just the a far, far smaller dataset (a few million words, rather than a trillion or so) is able to train a human mind for a language. The concept of a token (which isn't even exactly a phoneme) may be part of the problem. Transformer-based LLM systems stand as evidence that token prediction works great at one layer of abstraction, but on the way up the tree of abstractions toward meaning and understanding, the phoneme, then the word, then the phrase, become more relevant. In the high dimensional vector embeddings those abstractions exist as "features" and clearly these systems emit well-formed (grammatical) responses rather than streams polluted by random word fragments. However, the challenges such as: 🟣 frequent hallucinations, 🟣 the inability to learn simple maths, and 🟣 the inability of LLMs to comprehend negation (draw a scene without any elephants), might be showing us that token predictions aren't enough. Similarly, Google's recent work connecting Gemini to Google Search might be showing us that RAG (retrieval augmented generation) isn't enough, either. We might need another tool, something that operates not at the level of the predicted token, but at the level of the abstraction. Do pigs fly? If you put them on an airplane, or in the basket of a hot air balloon, or in a helicopter, or in a rocket… Anyway, just a bit of random brain stretching for the day. #ai #agi #transformers #llms #Gemini #GPT4 #Claude #tokens #neuroscience #IQ #vocabulary #reading #missingmillionwords

The 2nd point is very important You need to know the limits of LLMs to know how to use them

Stole a few points from Prof Christopher Manning, who talked about LLMs and language modeling in general in the latest TWIML AI Podcast: 📌 Humans acquire language skills in a way very different from LLMs. We need millions of words, compared to billions or even trillions of tokens for LLMs. LLM researchers may want to investigate and learn from how humans acquire language skills. 📌 LLMs cannot reason. However, there are other deep learning models, such as AlphaGo, that can. LLM researchers may want to look into how to integrate that type of reasoning/searching/planning capability into LLMs. 📌 LLMs' world models should enable search and discovery. Although Prof. Manning didn’t call this out explicitly, my understanding is more similar to a knowledge graph type of structure. 📌 Next-gen LLM idea: a soft form of locality and hierarchy. Transformers attend every token to every other token, which is very inefficient, while human language can be modeled by n-grams most of the time. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/euzwMQ6p

Great examples of the power of language.

Unlocking the Power of Language Models: Merging Math and Japanese for a Supermodel Discover how merging language models can create a powerful hybrid that excels in math and Japanese language, culture, and history. Dive into incredible word problems and witness the enhanced math skills of this amalgamated model. #LanguageModel #MathSkills #JapaneseLanguage #CulturalFusion #HybridModel #WordProblems #ModelingJapan #MathInJapanese #AIAdvancements #LanguageMerging