Contextual Literature-Based Discovery (C-LBD) - Revision history

BPeat at 20:43, 28 April 2024

2024-04-28T20:43:12Z

BPeat at 14:00, 23 March 2024

2024-03-23T14:00:37Z

BPeat at 12:03, 5 July 2023

2023-07-05T12:03:55Z

BPeat at 01:45, 17 June 2023

2023-06-17T01:45:33Z

BPeat: /* Literature-Based Discovery (LBD) in Healthcare */

2023-05-31T11:21:37Z

‎Literature-Based Discovery (LBD) in Healthcare

BPeat at 11:12, 31 May 2023

2023-05-31T11:12:47Z

BPeat at 01:15, 31 May 2023

2023-05-31T01:15:43Z

BPeat at 01:11, 31 May 2023

2023-05-31T01:11:02Z

BPeat at 01:10, 31 May 2023

2023-05-31T01:10:14Z

BPeat at 01:05, 31 May 2023

2023-05-31T01:05:48Z

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=C-LBD+~Contextual+Literature-Based+Literature-Based+LBD&qft=interval%3d%228%22 ...Bing News]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University]

@@ Line 21: / Line 21: @@
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
-* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
+* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University]
 * [https://github.com/eaglew/clbd C-LBD | GitHub]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=C-LBD+~Contextual+Literature-Based+Literature-Based+LBD YouTube]

@@ Line 12: / Line 12: @@
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
-* [[Assistants]]  ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
+* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
 * [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University]
 * [https://github.com/eaglew/clbd C-LBD | GitHub]

@@ Line 33: / Line 33: @@
 = <span id="Literature-Based Discovery (LBD) in Healthcare"></span>Literature-Based Discovery (LBD) in Healthcare =
 <youtube>JXYoI-7xQBo</youtube>
 <youtube>eY-pQBKZvi0</youtube>

← Older revision		Revision as of 11:12, 31 May 2023
Line 31:		Line 31:
	* one that generates a full phrase explaining an idea		* one that generates a full phrase explaining an idea
	* and another that generates only a salient component of the idea.		* and another that generates only a salient component of the idea.
		+
		+	= <span id="Literature-Based Discovery (LBD) in Healthcare"></span>Literature-Based Discovery (LBD) in Healthcare =
		+	<youtube>JXYoI-7xQBo</youtube>
		+	<youtube>eY-pQBKZvi0</youtube>

@@ Line 28: / Line 28: @@
 <img src="https://i.redd.it/doao39n1es2b1.png" width="800">
-The assistant accepts as input (1) relevant information, such as present challenges, motives, and constraints, and (2) a seed phrase that should be the primary focus of the developed scientific concept. Given this information, the team investigates two forms of C-LBD:
+The assistant accepts as input (1) relevant information, such as present challenges, motives, and constraints, and (2) a seed phrase that should be the primary focus of the developed scientific concept. Given this information, C-LBD generates scientific hypotheses in natural language while grounding them in context that controls the hypothesis search space. C-LBD also takes into account critical context like experimental settings and background knowledge and motivations that human scientists. Two forms of C-LBD were investigated:
 * one that generates a full phrase explaining an idea
 * and another that generates only a salient component of the idea.

@@ Line 14: / Line 14: @@
 * [[Assistants]]  ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
 * [https://arxiv.org/abs/2305.14259 Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery | Q. Wang, D. Downey, H. Ji, & T. Hope - arXiv - Cornell University]
@@ Line 27: / Line 28: @@
 Contextual Literature-Based Discovery (C-LBD) developed by researchers at the University of Illinois at Urbana-Champaign, the Hebrew University of Jerusalem, and the Allen Institute for Artificial Intelligence (AI2). C-LBD aims to address the limitations of traditional literature-based discovery (LBD) by using a natural language setting to constrain the generation space for LBD and generate sentences. The researchers introduce a novel modeling framework for C-LBD that can gather inspiration from disparate sources and use them to form novel hypotheses. They also introduce an in-context contrastive model to promote creative thinking.  The team believes that expanding C-LBD to include a multimodal analysis of formulas, tables, and figures to provide a more comprehensive and enriched background context is an intriguing direction to investigate in the future. The use of advanced LLMs like GPT-4, which is currently in development, is another avenue to investigate. [https://www.marktechpost.com/2023/05/29/can-language-models-generate-new-scientific-ideas-meet-contextualized-literature-based-discovery-c-lbd/ Can Language Models Generate New Scientific Ideas? Meet Contextualized Literature-Based Discovery (C-LBD) | Tanushree Shenwai - MarkTechPost]