Emily Jiang gave a presentation on the future of Java developers and AI. She discussed how AI tools like IBM's WatsonX can help with tasks like code generation and debugging to improve developer experience. While some jobs may be at risk of replacement by AI, such as data entry clerks, new jobs will be created like AI model trainers. Developers should embrace AI, stay up to date on new technologies, learn new skills focused on areas like architecture and innovation, and not worry about being replaced by AI. The talk concluded with Emily thanking the audience and providing her contact information.
Develop your career in the field of software development. Want to learn programming and develop your own applications, the presentation helps you to understanding the technology and the training methodologies required for that.
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
Continuous accuracy and efficiency of Large Language Models (LLM) is key to successfully building out your next AI-infused automation, regardless of business use case.
For our next Connector Corner webinar, we’ll explore how using a seamless AI integration process provides access to industry leading models, curated activities, and embeddings that help achieve operational efficiency.
Join us on March 26 to learn about:
Accessing large language models, hosted by UiPath
Reducing complexities of prompt-engineering, by using curated sets of activities
Assuring accuracy and safety, by building an AI Trust Layer to moderate the output of AI models, and their generated results.
Discovering what’s new in embeddings connectivity
Cultivating your AI knowledgebase using Vector Databases
Expect to see these use cases in action:
Leveraging UiPath hosted LLMs and activities
Document comparison using our LLM framework
Please stay tuned for additional use cases
Speakers:
Charlie Greenberg, host
George Roth, Technology Evangelist
Scott Schoenberger, Senior Product Manager
Koji Takimoto, Director Product Support
The document provides an introduction to generative AI and discusses its capabilities. It outlines the agenda which includes an introduction to AI, the current state of AI, types of AI, popular AI tools, an overview of the Azure OpenAI service, responsible AI, uses and capabilities of generative AI, and a demo. It defines generative AI as AI that can generate new content like text, images, audio or video based on a given input or prompt. The document discusses how generative AI works by learning patterns from large datasets to produce new content that fits within those patterns.
What's AGI? How is it different from an Agent or an AI Assistant? If you're looking to understand how AI Agents/AGI can help your company, check this out.
Defend against adversarial AI using Adversarial Robustness Toolbox Animesh Singh
With great power comes great responsibility. Adversarial examples in AI pose an asymmetrical challenge with respect to attackers and defenders. AI developers must be empowered to defend deep neural networks against adversarial attacks and allow rapid crafting and analysis of attack and defense methods for machine learning models.
Animesh Singh and Tommy Li explain how to implement state-of-the-art methods for attacking and defending classifiers using the open source Adversarial Robustness Toolbox. The library provides AI developers with interfaces that support the composition of comprehensive defense systems using individual methods as building blocks. Animesh and Tommy then demonstrate how to use a Jupyter notebook to leverage attack methods from the Adversarial Robustness Toolbox (ART) into a model training pipeline. This notebook trains a CNN model on the Fashion MNIST dataset, and the generated adversarial samples are used to evaluate the robustness of the trained model.
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
Python engineers are introduced to the transformative potential of Large Language Models (LLMs) in the realm of advanced data analysis and the application of Semantic Kernel techniques. We will talk about how LLMs like ChatGPT can be integrated into Python environments to automate data processing, enhance predictive modeling, and unlock deeper insights from complex datasets. The session will delve into practical strategies for embedding Semantic Kernel methods within Python projects, illustrating how these advanced techniques can refine the accuracy of machine learning models by embedding domain-specific knowledge directly into the analysis process. Attendees will leave with a clear roadmap for leveraging the combined power of LLMs and Semantic Kernels, equipped with actionable knowledge to drive innovation in their data analysis projects and beyond, marking a significant leap forward in the evolution of Python engineering practices.
Open Source Security and ChatGPT-Published.pdfJavier Perez
1) ChatGPT and other AI tools allow developers to produce code more quickly and efficiently but the validity and security of generated code must still be verified by developers.
2) While AI can introduce vulnerabilities if misused, it can also help find vulnerabilities when used properly under a developer's guidance.
3) Open source security involves continuously monitoring libraries and dependencies for vulnerabilities and applying patches through practices like software bill of materials and regular scans.
Emily Jiang gave a presentation on the future of Java developers and AI. She discussed how AI tools like IBM's WatsonX can help with tasks like code generation and debugging to improve developer experience. While some jobs may be at risk of replacement by AI, such as data entry clerks, new jobs will be created like AI model trainers. Developers should embrace AI, stay up to date on new technologies, learn new skills focused on areas like architecture and innovation, and not worry about being replaced by AI. The talk concluded with Emily thanking the audience and providing her contact information.
Develop your career in the field of software development. Want to learn programming and develop your own applications, the presentation helps you to understanding the technology and the training methodologies required for that.
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
Continuous accuracy and efficiency of Large Language Models (LLM) is key to successfully building out your next AI-infused automation, regardless of business use case.
For our next Connector Corner webinar, we’ll explore how using a seamless AI integration process provides access to industry leading models, curated activities, and embeddings that help achieve operational efficiency.
Join us on March 26 to learn about:
Accessing large language models, hosted by UiPath
Reducing complexities of prompt-engineering, by using curated sets of activities
Assuring accuracy and safety, by building an AI Trust Layer to moderate the output of AI models, and their generated results.
Discovering what’s new in embeddings connectivity
Cultivating your AI knowledgebase using Vector Databases
Expect to see these use cases in action:
Leveraging UiPath hosted LLMs and activities
Document comparison using our LLM framework
Please stay tuned for additional use cases
Speakers:
Charlie Greenberg, host
George Roth, Technology Evangelist
Scott Schoenberger, Senior Product Manager
Koji Takimoto, Director Product Support
The document provides an introduction to generative AI and discusses its capabilities. It outlines the agenda which includes an introduction to AI, the current state of AI, types of AI, popular AI tools, an overview of the Azure OpenAI service, responsible AI, uses and capabilities of generative AI, and a demo. It defines generative AI as AI that can generate new content like text, images, audio or video based on a given input or prompt. The document discusses how generative AI works by learning patterns from large datasets to produce new content that fits within those patterns.
What's AGI? How is it different from an Agent or an AI Assistant? If you're looking to understand how AI Agents/AGI can help your company, check this out.
Defend against adversarial AI using Adversarial Robustness Toolbox Animesh Singh
With great power comes great responsibility. Adversarial examples in AI pose an asymmetrical challenge with respect to attackers and defenders. AI developers must be empowered to defend deep neural networks against adversarial attacks and allow rapid crafting and analysis of attack and defense methods for machine learning models.
Animesh Singh and Tommy Li explain how to implement state-of-the-art methods for attacking and defending classifiers using the open source Adversarial Robustness Toolbox. The library provides AI developers with interfaces that support the composition of comprehensive defense systems using individual methods as building blocks. Animesh and Tommy then demonstrate how to use a Jupyter notebook to leverage attack methods from the Adversarial Robustness Toolbox (ART) into a model training pipeline. This notebook trains a CNN model on the Fashion MNIST dataset, and the generated adversarial samples are used to evaluate the robustness of the trained model.
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
Python engineers are introduced to the transformative potential of Large Language Models (LLMs) in the realm of advanced data analysis and the application of Semantic Kernel techniques. We will talk about how LLMs like ChatGPT can be integrated into Python environments to automate data processing, enhance predictive modeling, and unlock deeper insights from complex datasets. The session will delve into practical strategies for embedding Semantic Kernel methods within Python projects, illustrating how these advanced techniques can refine the accuracy of machine learning models by embedding domain-specific knowledge directly into the analysis process. Attendees will leave with a clear roadmap for leveraging the combined power of LLMs and Semantic Kernels, equipped with actionable knowledge to drive innovation in their data analysis projects and beyond, marking a significant leap forward in the evolution of Python engineering practices.
Open Source Security and ChatGPT-Published.pdfJavier Perez
1) ChatGPT and other AI tools allow developers to produce code more quickly and efficiently but the validity and security of generated code must still be verified by developers.
2) While AI can introduce vulnerabilities if misused, it can also help find vulnerabilities when used properly under a developer's guidance.
3) Open source security involves continuously monitoring libraries and dependencies for vulnerabilities and applying patches through practices like software bill of materials and regular scans.
This document discusses IBM's OpenTechAI initiative and the state of open source AI projects on GitHub. It provides statistics on the most popular open source AI projects on GitHub, including the number of code stars and forks. TensorFlow, Keras, and Sci-kit Learn are among the most popular. The document also shows that IBM's own open source AI projects like INTU and FfDL have fewer stars and forks compared to projects from other large companies. It outlines steps for individuals to get involved in OpenTechAI like getting a GitHub account, learning through reading, replicating, and reporting on projects. Finally, it presents a potential framework for benchmarking and measuring progress in artificial intelligence.
Introducción al Machine Learning AutomáticoSri Ambati
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia.
H2O Driverless AI hace:
* Visualización automática de datos
* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
* Ajuste y capacitación automáticos del modelo
* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
This document summarizes a presentation about the future of AI and Fabric for Deep Learning (FfDL). It discusses how deep learning has advanced due to increased data and computing power, but that commonsense reasoning will require more research. FfDL is introduced as an open source project that aims to make deep learning accessible and scalable across frameworks. It uses a microservices architecture on Kubernetes to manage training jobs efficiently. Research is ongoing to further develop explainable and robust AI capabilities.
This document provides an overview of machine learning and data science using Python. It introduces machine learning and data science, the Python programming language, popular integrated development environments for Python, and Google Colab. It also discusses types of machine learning algorithms, the machine learning process, important Python libraries, the data science life cycle, data visualization techniques, and the differences between machine learning and data science. The document outlines how to use Google Colab for machine learning and data science projects and provides information on the scope and applications of machine learning and data science.
The victory of AlphaGo against Lee Sedol in March 2016 is a new milestone in Artificial Intelligence history, next one might be the mass adoption of self-driving cars or the generalized use of chatbots. These successes both raise expectations and increase the fear of an Artificial Intelligence getting out of control, which is now addressed in books and scientific papers.
Even though some other human capabilities like natural language are still way beyond machine's reach, the fear of this "control problem" is sometimes intensified by the assumption that human intelligence will be at some point surpassed in all domains by machines even if it is a controverse whether human elementary abilities like subjective experience, consciousness, moral values are even theoretically transferable to computers.
https://tech.rakuten.co.jp/
IBM provided an update to the Linux Foundation Artificial Intelligence Governance Board meeting in Lyon, France on October 31, 2019. The update covered antitrust policy, an introduction to IBM's Cognitive OpenTech group which works on open source AI projects, and a discussion of IBM's involvement in projects like MAX, DAX, AI toolkits, and Kubeflow to help build trusted and fair AI systems. IBM expressed its pleasure in starting its journey with LFAI and looked forward to contributing more open source projects.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
Software Analytics:Towards Software Mining that Matters (2014)Tao Xie
This document discusses software analytics and summarizes several related papers and projects. It introduces Software Analytics, which aims to enable software practitioners to perform data exploration and analysis to obtain useful insights. It then summarizes papers on techniques for performance debugging by mining stack traces, scalable code clone analysis, incident management for online services, and using games to teach programming.
1) An AI professor is urging people to focus on creativity and emotional intelligence as strengths that humans have over AI.
2) Several AI-powered code writing tools are described that can automate coding tasks and suggest code completions to improve productivity.
3) AI tools for exploratory data analysis are presented that allow data exploration and manipulation without writing code.
Major Project Presentation (7th Sem) - Code Detection.pptxsohanmahanta1
The document describes a tool called DE-AI that can determine if a submitted code was written by a human or an AI generator. It discusses:
- The objective of verifying code authorship and addressing issues like plagiarism.
- How existing generative AI models work and how they generate code.
- The functional requirements, tech stack, and system architecture of the proposed DE-AI tool.
- The implementation process including developing a website, hosting on Joomla, and using neural networks to analyze code and determine the probability of AI authorship.
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
20240411 QFM009 Machine Intelligence Reading List March 2024Matthew Sinclair
The document provides a summary of topics related to machine intelligence that were discussed in March 2024, including NVIDIA's Project GR00T which aims to create a general-purpose foundation model for humanoid robots, DeepMind's SIMA which explores using generative AI in 3D virtual environments, Meta's development of large AI clusters to support advanced model training, and an open-source desktop tool for interacting with large language models. The summary also mentions articles on understanding the abilities of large language models, security concerns regarding AI metacognition, and innovative defense strategies against AI attacks.
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTrivadis
This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.
SearchLove San Diego 2019 - Britney Muller - Machine Learning: Know Enough To...Distilled
What is Machine Learning and how can we apply it to digital marketing? After this session, you'll understand machine learning basics, what ML can be used for, examples of ML solving SEO tasks and executable programs you start using immediately.
Ai progress = leaderboards compute data algorithms 20180817 v3ISSIP
1) AI progress relies on leaderboards, computing power, data, and algorithms.
2) Computing power is increasing exponentially over time, lowering the costs of digital tools.
3) The amount of labeled data available for training models is a key factor and is growing significantly.
4) Algorithm models are progressing from basic pattern recognition to more advanced cognition, relationships, and roles.
Past, Present and Future of Generative AIabhishek36461
Generative AI creates new content (images, text, music) based on learned patterns.
It learns from vast examples and can produce original, unseen works.
Capable of blending learned elements to generate unique outputs.
Can produce customized creations based on specific prompts.
Improves and refines its output over time with more data and feedback.
This document discusses IBM's OpenTechAI initiative and the state of open source AI projects on GitHub. It provides statistics on the most popular open source AI projects on GitHub, including the number of code stars and forks. TensorFlow, Keras, and Sci-kit Learn are among the most popular. The document also shows that IBM's own open source AI projects like INTU and FfDL have fewer stars and forks compared to projects from other large companies. It outlines steps for individuals to get involved in OpenTechAI like getting a GitHub account, learning through reading, replicating, and reporting on projects. Finally, it presents a potential framework for benchmarking and measuring progress in artificial intelligence.
Introducción al Machine Learning AutomáticoSri Ambati
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia.
H2O Driverless AI hace:
* Visualización automática de datos
* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
* Ajuste y capacitación automáticos del modelo
* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
This document summarizes a presentation about the future of AI and Fabric for Deep Learning (FfDL). It discusses how deep learning has advanced due to increased data and computing power, but that commonsense reasoning will require more research. FfDL is introduced as an open source project that aims to make deep learning accessible and scalable across frameworks. It uses a microservices architecture on Kubernetes to manage training jobs efficiently. Research is ongoing to further develop explainable and robust AI capabilities.
This document provides an overview of machine learning and data science using Python. It introduces machine learning and data science, the Python programming language, popular integrated development environments for Python, and Google Colab. It also discusses types of machine learning algorithms, the machine learning process, important Python libraries, the data science life cycle, data visualization techniques, and the differences between machine learning and data science. The document outlines how to use Google Colab for machine learning and data science projects and provides information on the scope and applications of machine learning and data science.
The victory of AlphaGo against Lee Sedol in March 2016 is a new milestone in Artificial Intelligence history, next one might be the mass adoption of self-driving cars or the generalized use of chatbots. These successes both raise expectations and increase the fear of an Artificial Intelligence getting out of control, which is now addressed in books and scientific papers.
Even though some other human capabilities like natural language are still way beyond machine's reach, the fear of this "control problem" is sometimes intensified by the assumption that human intelligence will be at some point surpassed in all domains by machines even if it is a controverse whether human elementary abilities like subjective experience, consciousness, moral values are even theoretically transferable to computers.
https://tech.rakuten.co.jp/
IBM provided an update to the Linux Foundation Artificial Intelligence Governance Board meeting in Lyon, France on October 31, 2019. The update covered antitrust policy, an introduction to IBM's Cognitive OpenTech group which works on open source AI projects, and a discussion of IBM's involvement in projects like MAX, DAX, AI toolkits, and Kubeflow to help build trusted and fair AI systems. IBM expressed its pleasure in starting its journey with LFAI and looked forward to contributing more open source projects.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
Software Analytics:Towards Software Mining that Matters (2014)Tao Xie
This document discusses software analytics and summarizes several related papers and projects. It introduces Software Analytics, which aims to enable software practitioners to perform data exploration and analysis to obtain useful insights. It then summarizes papers on techniques for performance debugging by mining stack traces, scalable code clone analysis, incident management for online services, and using games to teach programming.
1) An AI professor is urging people to focus on creativity and emotional intelligence as strengths that humans have over AI.
2) Several AI-powered code writing tools are described that can automate coding tasks and suggest code completions to improve productivity.
3) AI tools for exploratory data analysis are presented that allow data exploration and manipulation without writing code.
Major Project Presentation (7th Sem) - Code Detection.pptxsohanmahanta1
The document describes a tool called DE-AI that can determine if a submitted code was written by a human or an AI generator. It discusses:
- The objective of verifying code authorship and addressing issues like plagiarism.
- How existing generative AI models work and how they generate code.
- The functional requirements, tech stack, and system architecture of the proposed DE-AI tool.
- The implementation process including developing a website, hosting on Joomla, and using neural networks to analyze code and determine the probability of AI authorship.
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
20240411 QFM009 Machine Intelligence Reading List March 2024Matthew Sinclair
The document provides a summary of topics related to machine intelligence that were discussed in March 2024, including NVIDIA's Project GR00T which aims to create a general-purpose foundation model for humanoid robots, DeepMind's SIMA which explores using generative AI in 3D virtual environments, Meta's development of large AI clusters to support advanced model training, and an open-source desktop tool for interacting with large language models. The summary also mentions articles on understanding the abilities of large language models, security concerns regarding AI metacognition, and innovative defense strategies against AI attacks.
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTrivadis
This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.
SearchLove San Diego 2019 - Britney Muller - Machine Learning: Know Enough To...Distilled
What is Machine Learning and how can we apply it to digital marketing? After this session, you'll understand machine learning basics, what ML can be used for, examples of ML solving SEO tasks and executable programs you start using immediately.
Ai progress = leaderboards compute data algorithms 20180817 v3ISSIP
1) AI progress relies on leaderboards, computing power, data, and algorithms.
2) Computing power is increasing exponentially over time, lowering the costs of digital tools.
3) The amount of labeled data available for training models is a key factor and is growing significantly.
4) Algorithm models are progressing from basic pattern recognition to more advanced cognition, relationships, and roles.
Past, Present and Future of Generative AIabhishek36461
Generative AI creates new content (images, text, music) based on learned patterns.
It learns from vast examples and can produce original, unseen works.
Capable of blending learned elements to generate unique outputs.
Can produce customized creations based on specific prompts.
Improves and refines its output over time with more data and feedback.
Similar to What need to be mastered as AI-Powered Java Developers (20)
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Emily Jiang gave a presentation on MicroProfile, a set of lightweight, open source APIs for Java microservices. She began with an overview of MicroProfile's history and community-driven development process. She then provided a deep dive on various MicroProfile specifications, including Config, REST Client, OpenAPI, JWT Auth, Fault Tolerance, Health, Metrics, Telemetry, and more. Finally, she discussed the future of MicroProfile, including upcoming versions that will adopt OpenTelemetry Metrics and make other updates.
Hybrid Cloud Applications Built with Pure Openness
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This document discusses NoSQL databases and Jakarta Data, which aims to unify data access. It provides examples of using Jakarta Data annotations to define entities, repositories, and queries for NoSQL databases. Key features discussed include basic CRUD operations, named parameters, sorting, pagination, and keyset pagination to improve efficiency. A demo of Jakarta Data on Open Liberty is referenced that implements entities, repositories, and services for accessing crew member data.
- Microservices originated as an evolution from monolithic applications and technologies like EJBs and SOAP/REST, promoting principles like independent deployability and team ownership.
- While microservices are useful, adopting them fully comes with difficulties around domain-driven design, operations, and converting monoliths.
- Rather than debate microservices vs. monoliths, the focus should be on cloud native applications, which may incorporate properties of both along with containerization, APIs, and serverless architectures. The future lies in approaches like GraalVM native images and InstantOn that enhance applications for the cloud.
What is Augmented Reality Image Trackingpavan998932
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Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
What need to be mastered as AI-Powered Java Developers
1. Emily Jiang, Java Champion, FBCS
IBM, STSM, Cloud Native Architect and Advocate
16th May 2024
AI-Powered Java
Developers
2. 2
Java Developers
– You Build It, You Run It
– Dev and Ops together
– IDE
– Build (Jenkins, Containers)
– Deploy (CI/CD)
– Maintain (SBOM, Vulnerabilities)
6. AI-Powered Java Developer Checklist
1. What is AI, LLM?
2. What is RAG?
3. What code assistant tools available?
4. What can help with building AI-Infused apps?
7. AI
Artificial intelligence (AI) is the simulation of human intelligence in
machines that are programmed to think and act like humans.
Learning, reasoning, problem-solving, perception, and
language comprehension are all examples of cognitive abilities.
8. AI History 1956 John McCarthy held a
workshop at Dartmouth on
“artificial intelligence”
1957-1974 AI flourished
2011 IBM Watson won the
game Jeopardy!
Apple released Siri, the first
popular virtual assistant.
2015 OpenAI founded
2020, OpenAI announced
GPT-3
2021, OpenAI introduced
DALL-E
1950 Alan Turing “Computing
Machinery and Intelligence”
1997 IBM Deep Blue beat the
world chess champion, Gary
Kasparov.
8
– https://www.youtube.com/watch?v=056v4OxKwlI
9. GenAI
Generative AI (GenAI) refers to deep-learning models that can
generate high-quality text, images, and other content based on
the data they were trained on.
– https://research.ibm.com/blog/what-is-generative-AI
10. Generative AI
Anything
that creates
new content
Large language model
Great
at text
Foundation
model
Unlabeled
data
Transformer
ChatGPT
inspired interest…
But there is a
bigger concept,
e.g. GPT
Which will
change business
Building blocks of generative AI – Foundation Models
– BERT
– GPT
– Claude
– Cohere
– Stable Diffusion
13. Foundation Models
Foundation Model AI system Applications
LaMDA (Google) Bard (Google) AI chat
GPT-4 (OpenAI) ChatGPT (OpenAI) AI Chat
Codex (OpenAI) GitHub copilot (Microsoft) Code generation
AudioLM (Google) MusicLM (Google) Create Music
BLOOM (Hugging Face) Use directly Mutiple NLP tasks. Trained in 46
languages and 13 programming
languages.
LLaMA (Meta) Use directly AI research
DALL-E 2 (OpenAI) Use directly Image creation
– https://www.datacamp.com/blog/what-are-foundation-models
15. Comparison of LLMs
– https://research.ibm.com/blog/granite-code-models-open-source
16. Chat
Q&A
Summarization
Summarize info – meeting
minutes, etc
Content Generation
Create email, marketing
materials, etc.
Named Entity
Recognition
Produce audit data
Insight Extraction
Medical diagnose, etc.
Classification
Sort customer complainants,
security vulnerability
classification, etc.
The most common
generative AI tasks
implemented today
17. Issues related to AI
• License
• Audit
• Hallucinations
• Potentially generate bad code
• Security risk
• Lack of innovation
17
18. How to reduce LLM Hallucination
• Domain knowledge gaps
• Data out of date
20. Retrieval Augmented
generation (RAG)
An AI framework for improving the quality
of LLM-generated responses
Grounds a model on additional sources of
knowledge to supplement its internal
representation of information
33
RAG involves three basic steps:
Search for relevant content in your knowledge base
Pull the most relevant content into your model
prompt as context
Send the combined prompt text to the model to
generate output
1
2
3
Significantly elevates level of trust:
• Ensures that the model has access to the
most current and reliable facts
• System becomes "business-aware"
• Sources are known, ensuring output can be
checked for accuracy
• Less likely to make-up a factually inaccurate
responses, with ability to say, "I don't know."
21. RAG
components
Knowledge
Base
Can be any collection of information containing
artifacts such as:
• Internal procedural wiki pages
• Files in GitHub (various formats)
• Messages in a collaboration tool
• Topics in product documentation
• PDF files
• Customer support tickets
• more
Can be any combination of search and content
tools that reliably return relevant content from
a knowledge base (or bases):
• Search and content APIs like GitHub APIs
• Vector databases like Milvus
A generative LLM that suits your use case, prompt
format, and content being pulled in for context
Retriever
Generator
22. Typical RAG process
User Question
Search &
Retrieval
Prompt =
Instructions +
Search
Results +
Question
LLM
Generated
output with
sources
Top search
results
23. Data storage – using embedding and a vector database
Passages
of text
“Embeddings”
New step
Data storage process
(a) Original files to documents
(b) Documents to chunks
(c) Chunks to embeddings
(d) Embeddings to vector store
Vector
database
Semantic vs.
Syntactic match
24. Tasks AI will do for us
• Generate code snippet
• Create tests
• Debugging
• Code review
• Code summarization
• Refactoring
24
25. Some GenAI tools
Chatbot
– Anthropic’s Claude 2
– Google’s Bard
– Meta AI’s Hugging Face Llama 2 Chat
– Microsoft’s Bing Chat
– OpenAI’s ChatGPT
AI code assistant
Github Copilot
Amazon CodeWisperer
Divi AI
Tabnine
Replit
Sourcegraphy Cody
25
– https://www.elegantthemes.com/blog/wordpress/best-ai-coding-assistant#4-tabnine
– https://www.youtube.com/watch?v=TXtnFw9eDmM
30. AI-Powered Java Developer Checklist
1. What is AI, LLM? √
2. What is RAG? √
3. What code assistant tools available? √
4. What can help with building AI-Infused apps? √
31. 31
Join MicroProfile AI group to create a MicroProfile AI Spec
Monday weekly meeting
5pm CEST
Zoom: https://eclipse.zoom.us/j/83815795087
32. Crucial skills for Java
Developers
• Focus on the architecture
• Innovation
• Serviceability
• AI-infused apps
32