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Recommended
Session 02: Draw (Graphical Abstract)
Workshop to Enhance Research Visibility by IES Research.
Speakers: Woei Fuh, Wong
For National Institutes of Health, Malaysia
Generative AI Masterclass - Model Risk Management.pptx
Here are some key points about benchmarking and evaluating generative AI models like large language models:
- Foundation models require large, diverse datasets to be trained on in order to learn broad language skills and knowledge. Fine-tuning can then improve performance on specific tasks.
- Popular benchmarks evaluate models on tasks involving things like commonsense reasoning, mathematics, science questions, generating truthful vs false responses, and more. This helps identify model capabilities and limitations.
- Custom benchmarks can also be designed using tools like Eval Studio to systematically test models on specific applications or scenarios. Both automated and human evaluations are important.
- Leaderboards like HELM aggregate benchmark results to compare how different models perform across a wide range of tests and metrics.
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to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
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1. The average salary for a professional with an AI certification is $110k a year in the USA according to Indeed.com. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt
2. In India, an Engineer with AI certification and minimal experience in the field commands a salary of Rs.17 lacs - Rs. 25 lacs, while it can go up to Rs. 50 lacs - Rs.1 crore per annum for a professional with 8-10 years of experience
3. The scarcity of people with artificial intelligence training is such that one report says there are only around 10000 such experts and companies like Google and Facebook are paying a salary of over $5,00,000 per annum
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Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
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The document provides an overview of explainable AI (XAI). It discusses how XAI helps interpret machine learning models by explaining why and how they work. It outlines different categories of ML models and challenges around accuracy vs interpretability. The document then describes various XAI techniques like LIME, SHAP and global/local explanations that help address issues like trust, bias and fairness. Examples show how XAI tools can explain predictions for tasks like image classification.
UKSG 2023 - Will artificial intelligence change how readers use the research ...
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Huge advances in machine learning and natural language processing are set to upend how researchers search and consume research articles as well as change how articles are written. These new approaches are becoming adept at summarising and rewriting text, answering questions about it and extracting key information. These abilities will enable humans to search for information in new ways, such as the new ChatGPT system. They are valuable tools for researchers who curate the research literature to build knowledge bases particularly in biomedicine. Nevertheless, these approaches suffer from large problems including their computational cost and that they can confidently output incorrect information. This session will provide background on how these new methods work and discuss their benefits, challenges and potential impact.The Future of AI is Generative not Discriminative 5/26/2021
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
List of Generative AI Tools
The document lists various AI tools across different categories including chat/speech tools, artwork generators, writing tools, speech-to-text transcription, visual editors, and video tools. Some of the tools listed include ChatGPT, DALL-E, Stable Diffusion, IBM Watson, Google Cloud, Microsoft Azure, Adobe Sensei, and Synthesia. The tools cover a wide range of applications from chatbots, image generation from text, writing assistance, speech recognition, image editing, and automated video creation.
Managing Supervisee-Supervisor Relationship
Supervision of graduate students is a challenging journey that the supervisor (advisor) and the student embark on together. It includes not only academic guidance, but also prolonged nurturing of the student’s personal, scholarly, and professional development. This presentation discusses various issues about the delicate relationship between graduate student (supervisee) and the supervisor, the do's and don'ts and ways to manage the relationship successfully.
Revised api pbas as per ugc
Revised API PBAS as per UGC
For Recruitments and Promotion Under CAS as per UGC’s 4th Amendment Dated 11th July, 2016
As per Forth Ammedment
How Reviewers Review : Wiley
Follow our LinkedIn Page for future events - linkedin.com/company/manuscriptpedia
Follow our Facebook Page for Future updates - https://fb.me/manuscriptpediaindia
Follow our Facebook Group for Future updates - www.facebook.com/groups/manuscriptpedia
Join our Telegram Group - https://t.me/Manuscriptpedia
Publons
The document discusses Publons, a website that allows researchers to track peer reviews and service, build profiles to highlight their review work, and integrate reviews with their profiles on ResearcherID and ORCID. It mentions features like the Publons CV, publication metrics, pending review records, and editing review records. It also provides contact information for Manuscriptpedia and encourages joining their social media pages and groups for research updates.
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The document summarizes a seminar on how to read a research paper. It outlines a three-pass approach: the first pass involves skimming key sections to get an overview; the second pass involves carefully reading the full paper; and the third pass involves attempting to recreate the paper to fully understand it. It also provides tips for selecting papers, resources for finding papers, and time management strategies for reading papers.
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Juan Cruz-Benito
GRIAL Research Group, Department of Computers and Automatics
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The document provides information about research funding opportunities in India. It discusses the objective of research and lists various international and national funding agencies for government and private organizations. It also describes the Science and Engineering Research Board (SERB) and its schemes for basic research funding. One of SERB's schemes discussed in detail is the Teachers Associateship for Research Excellence (TARE) scheme, which provides funding and fellowships for faculty to conduct research in other established institutions. The document concludes with information on how to apply for SERB funding online and get research papers free of cost.
Thesis Format
This document outlines the typical parts and structure of a thesis. It includes initial pages like the title page, abstract, acknowledgments and table of contents. It describes the typical chapters such as an introduction providing context, a literature review of previous studies, methodology, presentation of findings, and conclusions. Each chapter is described in 1-2 sentences. For example, the introduction provides context for the research topic and the significance and problem statements. The literature review summarizes previous related unpublished studies and how they inform the current research.
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This document provides guidance on scientific writing. It outlines the key characteristics of good scientific writing, including being clear, simple, structured logically, neutral, and accurate. The document then describes the typical elements of a scientific paper, such as the title, abstract, introduction, methods, results, discussion, and references. It also discusses writing tips, such as using active voice and appropriate verb tenses. Finally, the document provides guidance on effectively presenting information in tables, figures, and posters for scientific communication.
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI...
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will help you understand the differences between Machine Learning, Deep Learning and Artificial Intelligence, and how they are related to each other. The presentation will also cover what Machine Learning, Deep Learning, and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will explain the topics listed below:
1. Artificial Intelligence example
2. Machine Learning example
3. Deep Learning example
4. Human Vs Artificial Intelligence
5. How Machine Learning works
6. How Deep Learning works
7. AI Vs Machine Learning Vs Deep Learning
8. AI with Machine Learning and Deep Learning
9. Real-life examples
10. Types of Artificial Intelligence
11. Types of Machine Learning
12. Comparing Machine Learning and Deep Learning
13. A glimpse into the future
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About Simplilearn Artificial Intelligence Engineer course:
What are the learning objectives of this Artificial Intelligence Course?
By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
- - - - - -
Why be an Artificial Intelligence Engineer?
1. The average salary for a professional with an AI certification is $110k a year in the USA according to Indeed.com. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt
2. In India, an Engineer with AI certification and minimal experience in the field commands a salary of Rs.17 lacs - Rs. 25 lacs, while it can go up to Rs. 50 lacs - Rs.1 crore per annum for a professional with 8-10 years of experience
3. The scarcity of people with artificial intelligence training is such that one report says there are only around 10000 such experts and companies like Google and Facebook are paying a salary of over $5,00,000 per annum
Using the power of Generative AI at scale
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Research & Innovation
The document discusses research, development, and innovation. It defines innovation as a process of obtaining something new that solves a specific problem and can be utilized. Innovation is distinguished from invention, artistic creativity, problem-solving, and incremental improvement. Basic research aims to expand knowledge through observation and theory, while applied research focuses on developing technology and techniques to solve practical problems. Innovation management oversees the entire process from basic research through development, production, and marketing.
Artificial Intelligence for Product Managers by former Yahoo! PM
Jobs requiring artificial intelligence skills in the US has grown 450% in the last five years. Corporations are seeking relentlessly for product leaders who can utilize AI technologies on their products and services to improve the company’s bottom line or top line. It's called the Fourth Industrial Revolution, and it is happening right here, right now.
However, as a Product Manager, how do you gain the necessary knowledge to analyze, understand, plan, and design products based on artificial intelligence technologies? Since you cannot get a college degree in AI Product Management, how do you adapt to this rapid change? In this talk, Adnan helped to answer these questions.
Introduction to Data Visualization
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
How to build a generative AI solution From prototyping to production.pdf
This document provides an overview of how to build a generative AI solution from prototyping to production. It discusses key steps such as defining the problem, collecting and preprocessing data, selecting algorithms and models, training and deploying models. Generative AI can be applied to areas like software engineering, content generation, marketing, healthcare, product design. The document provides examples of companies applying generative AI and concludes with a detailed guide to prototyping, developing and deploying a generative AI solution.
Creating Seeding Visuals to Prompt Art-Making Generative AIs
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
An Introduction to XAI! Towards Trusting Your ML Models!
The document provides an overview of explainable AI (XAI). It discusses how XAI helps interpret machine learning models by explaining why and how they work. It outlines different categories of ML models and challenges around accuracy vs interpretability. The document then describes various XAI techniques like LIME, SHAP and global/local explanations that help address issues like trust, bias and fairness. Examples show how XAI tools can explain predictions for tasks like image classification.
UKSG 2023 - Will artificial intelligence change how readers use the research ...
UKSG 2023 - Will artificial intelligence change how readers use the research ...UKSG: connecting the knowledge community
Jake Lever - University of Glasgow
Will artificial intelligence change how readers use the research literature?
Huge advances in machine learning and natural language processing are set to upend how researchers search and consume research articles as well as change how articles are written. These new approaches are becoming adept at summarising and rewriting text, answering questions about it and extracting key information. These abilities will enable humans to search for information in new ways, such as the new ChatGPT system. They are valuable tools for researchers who curate the research literature to build knowledge bases particularly in biomedicine. Nevertheless, these approaches suffer from large problems including their computational cost and that they can confidently output incorrect information. This session will provide background on how these new methods work and discuss their benefits, challenges and potential impact.The Future of AI is Generative not Discriminative 5/26/2021
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
List of Generative AI Tools
The document lists various AI tools across different categories including chat/speech tools, artwork generators, writing tools, speech-to-text transcription, visual editors, and video tools. Some of the tools listed include ChatGPT, DALL-E, Stable Diffusion, IBM Watson, Google Cloud, Microsoft Azure, Adobe Sensei, and Synthesia. The tools cover a wide range of applications from chatbots, image generation from text, writing assistance, speech recognition, image editing, and automated video creation.
Managing Supervisee-Supervisor Relationship
Supervision of graduate students is a challenging journey that the supervisor (advisor) and the student embark on together. It includes not only academic guidance, but also prolonged nurturing of the student’s personal, scholarly, and professional development. This presentation discusses various issues about the delicate relationship between graduate student (supervisee) and the supervisor, the do's and don'ts and ways to manage the relationship successfully.
Revised api pbas as per ugc
Revised API PBAS as per UGC
For Recruitments and Promotion Under CAS as per UGC’s 4th Amendment Dated 11th July, 2016
As per Forth Ammedment
What's hot (20)
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI...
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI...
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How to build a generative AI solution From prototyping to production.pdf
How to build a generative AI solution From prototyping to production.pdf
Creating Seeding Visuals to Prompt Art-Making Generative AIs
Creating Seeding Visuals to Prompt Art-Making Generative AIs
An Introduction to XAI! Towards Trusting Your ML Models!
An Introduction to XAI! Towards Trusting Your ML Models!
UKSG 2023 - Will artificial intelligence change how readers use the research ...
UKSG 2023 - Will artificial intelligence change how readers use the research ...
The Future of AI is Generative not Discriminative 5/26/2021
The Future of AI is Generative not Discriminative 5/26/2021
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