HEMANTH LINGAMGUNTA’s Post

Helping AI Communicate with Graphs for Systems Biology 🧠🔬🕸️ Researchers explored how to help powerful AI language models better understand graphs - those interconnected networks of nodes found everywhere, including systems biology. The key? Translating graphs into text formats AI can comprehend. How nodes, edges, and structures are represented makes a huge difference in AI performance. Bridging this gap between AI and the interconnected systems in biology could unlock new frontiers in research and healthcare. An inspiring step toward making AI fluent in the language of life's complexity.

From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on a large body of research in the two fields. We explore the potential of LLMs as tools for ALife research, for example, as operators for evolutionary computation or the generation of open-ended environments. Reciprocally, principles of ALife, such as self-organization, collective intelligence and evolvability can provide an opportunity for shaping the development and functionalities of LLMs, leading to more adaptive and responsive models. By investigating this dynamic interplay, the paper aims to inspire innovative crossover approaches for both ALife and LLM research. Along the way, we examine the extent to which LLMs appear to increasingly exhibit properties such as emergence or collective intelligence, expanding beyond their original goal of generating text, and potentially redefining our perception of lifelike intelligence in artificial systems. https://buff.ly/3WNP9Nb Join the agents community to learn, discuss, and collaborate with 8,000 agent engineers! https://buff.ly/4eufyqf

Very excited to share our recent article in Advanced Science, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials", published open access! I am very enthusiastic about this and future work in harnessing Generative AI tools for scientific and engineering aims. Read our article here! #generativeai #materialsscience

From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on a large body of research in the two fields. We explore the potential of LLMs as tools for ALife research, for example, as operators for evolutionary computation or the generation of open-ended environments. Reciprocally, principles of ALife, such as self-organization, collective intelligence and evolvability can provide an opportunity for shaping the development and functionalities of LLMs, leading to more adaptive and responsive models. By investigating this dynamic interplay, the paper aims to inspire innovative crossover approaches for both ALife and LLM research. Along the way, we examine the extent to which LLMs appear to increasingly exhibit properties such as emergence or collective intelligence, expanding beyond their original goal of generating text, and potentially redefining our perception of lifelike intelligence in artificial systems. https://buff.ly/3WNP9Nb Join the agents community to learn, discuss, and collaborate with 8,000 agent engineers! https://buff.ly/4eufyqf

https://lnkd.in/gPy6q-FS Westerners seem to value mathematics, coding, language and other so-called “cognitive abilities” more than they do “non-cognitive abilities”: plumbing, cooking, playing a saxophone solo. But ironically, these last three examples are the kinds of tasks that AI systems struggle with the most. Robots fall flat – sometimes literally – when trying to capture the physical and emotional dimensions of the human experience. Could these be the truly intelligent components of the human psyche? After all, our bodies (and the minds within) had been busy crawling around, interacting with other people, feeling emotions and building world models well before language and mathematics entered the scene. The fundamental reason that AGI will never be realized is that intelligence is not a scientific concept – it’s a cultural one, a loosely defined cluster of hopes and fears about the power of the human mind. #AI #Humanmind #Artificialintelligence

🔍 ELIZA: The First Chatbot (1964–1967) https://lnkd.in/gDtv8unn ELIZA, developed by Joseph Weizenbaum at MIT, was a groundbreaking natural language processing program. It pioneered the way humans and machines interact, making it one of the first chatterbots in history. Key features: - Pattern Matching: ELIZA used pattern-matching and substitution techniques to simulate conversation, creating the illusion of understanding without true comprehension. - DOCTOR Script: Its most famous script mimicked a Rogerian psychotherapist by reflecting user inputs back as non-directional questions. - Early Turing Test: ELIZA was among the first programs capable of attempting Alan Turing's famous test for machine intelligence. Impact and Insights: Weizenbaum originally designed ELIZA to explore human-machine communication but was stunned by the emotional responses it evoked in users. Some even attributed human-like feelings to the program. This sparked debates about the psychological impact of AI and its potential in therapeutic applications. Historical Rediscovery: For decades, the original source code was thought lost. Recently, the MAD-SLIP source code was rediscovered and published, offering fascinating insights into early software development and programming abstractions. #ArtificialIntelligence #HistoryOfAI #ELIZA #Chatbots #Innovation

🌟 Innovating with Large Language Models: Unveiling Patterns Like a Brain! In a fascinating new approach, researchers have discovered structural patterns in large language models (LLMs) using sparse autoencoders (SAEs), revealing an organization reminiscent of the human brain. By examining concept clusters at various scales, from the “atomic” (small-scale crystal-like structures) to the “brain” (modular functional lobes), and even the “galaxy” scale, this work highlights how LLMs organize semantic information spatially, similar to neural regions with specific functions in human brains. Through geometric analysis, the study finds that concepts in LLMs aren’t just scattered randomly. Instead, they form meaningful clusters: math and code concepts group into “lobes,” echoing how brain regions specialize in tasks. This kind of modularity in LLMs, along with patterns like power-law scaling, gives us a peek into how machine learning could evolve towards more brain-like architecture. Such insights don’t just advance AI—they reshape how we approach machine learning, hinting at a future where artificial models mimic human cognition even more closely. #AI #MachineLearning #Innovation #LLM https://lnkd.in/d-aJ6Fxu

"Even the best AI large language models (LLMs) fail dramatically when it comes to simple logical questions. This is the conclusion of researchers from the Jülich Supercomputing Center (JSC), the School of Electrical and Electronic Engineering at the University of Bristol and the LAION AI laboratory. In their paper posted to the arXiv preprint server, titled "Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models," the scientists attest to a "dramatic breakdown of function and reasoning capabilities" in the tested state-of-the-art LLMs and suggest that although language models have the latent ability to perform basic reasoning, they cannot access it robustly and consistently." #ai #llm #logic #basicreasoning

🚀Research Paper Highlights: Here is an interesting research on Memory in LLMs, read on to explore further, 'Memory3 : Language Modeling with Explicit Memory' by Hongkang Yang et al. 🚀Inspired by the human brain's memory hierarchy, a groundbreaking method reduces the training and inference costs of large language models (LLMs) by equipping them with explicit memory, a more economical alternative to model parameters and retrieval-augmented generation (RAG). This leads to smaller parameter sizes and lower training and inference costs. 🚀Types of memories in LLMs: 1) Implicit memory - model parameters 2) working memory - context key-values 3) Explicit memory - ?? 🚀A proof-of-concept 2.4B LLM, named Memory3, was developed, outperforming larger LLMs and RAG models while maintaining higher decoding speeds. This model employs a memory circuitry theory and introduces advanced techniques like memory sparsification and a two-stage pretraining scheme to externalize knowledge efficiently. 🚀The memory hierarchy of LLMs mirrors that of humans, where explicit and implicit memories serve as long-term storage, consciously or unconsciously acquired and utilized. 🚀Comparing Human memory with LLMs: Plain LLMs, much like patients with impaired explicit memory, struggle with semantic knowledge but can acquire skills through repetitive practice. This inefficiency in training highlights a significant potential for improvement. Just as humans find it easier to discuss a book they just read than to memorize it effortlessly, LLM training is data and energy-intensive. Integrating an explicit memory mechanism into LLMs aims to mimic the efficiency of human memory, promising enhanced performance and efficiency. Further reading: https://lnkd.in/dumgEh5n Stay tuned for more updates on upcoming research and analysis in this rapidly evolving landscape of Generative AI. #AI #MachineLearning #Innovation #LLM #Memory3 #ExplicitMemory #ArtificialIntelligence

🚀 Overcoming Transformer Limitations with Innovative Solutions! 🤖✨ Transformers have revolutionized natural language processing, but they still face fundamental challenges in tasks like copying and counting. A recent study sheds light on these limitations and proposes simple yet effective solutions to enhance Transformer performance. 🔍 Key Findings: 1️⃣ Representational Collapse: Transformers struggle to distinguish between certain inputs due to representational collapse, where distinct sequences yield nearly identical representations in the final token. This issue is exacerbated by low-precision floating-point formats, leading to errors in tasks like counting and copying. 2️⃣ Over-Squashing: Transformers' architecture favors earlier tokens, causing over-squashing of information in later tokens. This imbalance makes it difficult for the model to accurately predict next tokens based on later input tokens, explaining the U-shaped performance in retrieval tasks. 🧠 Proposed Solutions: 1️⃣ Intermediate Tokens: Introducing intermediate tokens throughout sequences can help maintain distinct representations and mitigate representational collapse. 2️⃣ Modified Attention Mechanism: Adjusting the attention mechanism to balance the influence of earlier and later tokens can address the over-squashing problem, improving the model's overall performance. 📈 Performance Highlights: - Empirical Validation: Real-world experiments demonstrate the practical implications of representational collapse and over-squashing, highlighting the necessity of addressing these issues for improved Transformer performance. - Enhanced Model Capabilities: Simple modifications to the Transformer architecture significantly enhance its ability to handle fundamental tasks like copying and counting, leading to more robust and reliable performance. 💡 Why It Matters: - Improved Accuracy: By addressing these fundamental limitations, Transformers can achieve higher accuracy in a wider range of tasks. - Broader Applications: Enhanced performance in basic tasks like copying and counting opens up new possibilities for applying Transformers in more complex and diverse applications. - Continued Innovation: Understanding and overcoming these challenges drives further innovation in Transformer-based architectures, pushing the boundaries of what's possible in AI and machine learning. Stay tuned for more updates on this transformative AI development! 🚀 📊PAPER: https://lnkd.in/eih-unJG #AI #Transformers #MachineLearning #TechInnovation #LLM #DeepLearning #DataScience #InformationExtraction