Adaptive AI transforms the landscape of ML with its complex system approach, allowing the AI to seek its goals while simultaneously learning the optimum path and adapting its learning methods to align with real-world scenarios in the most effective way.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
As an AI language model, ChatGPT is a program consisting of a large neural network that has been trained on vast amounts of textual data. Specifically, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) family of models developed by OpenAI.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
Global Azure Bootcamp Pune 2023 - Lead the AI era with Microsoft Azure.pdfAroh Shukla
In the era of AI, you can lead and empower your users with the latest innovation of Azure. In this keynote, we will cover
1. Microsoft and OpenAI partnership
2. Azure OpenAI Service
3. Azure AI stack
4. Azure OpenAI Service Capabilities
5. Top Capabilities and Use Cases
6. Power Platform and Azure OpenAI Integration
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
As an AI language model, ChatGPT is a program consisting of a large neural network that has been trained on vast amounts of textual data. Specifically, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) family of models developed by OpenAI.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
Global Azure Bootcamp Pune 2023 - Lead the AI era with Microsoft Azure.pdfAroh Shukla
In the era of AI, you can lead and empower your users with the latest innovation of Azure. In this keynote, we will cover
1. Microsoft and OpenAI partnership
2. Azure OpenAI Service
3. Azure AI stack
4. Azure OpenAI Service Capabilities
5. Top Capabilities and Use Cases
6. Power Platform and Azure OpenAI Integration
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
Unleashing the Google Bard Discover the Revolutionary New Tool How does it Co...i-engage
Bard is a cutting-edge Chatbot Powered by LaMDA Large Language Model for Limitless Text-Based Creativity and Exploration.
This experimental AI by Google offers endless possibilities, from answering queries and generating summaries to crafting diverse forms of content with a simple prompt.
Moreover, Bard serves as your ultimate guide to exploring any topic by condensing information from the internet and suggesting links for further exploration.
“Bard’s an early experiment, it’s not perfect, and it’s gonna get things wrong occasionally,” says Eli Collins, the vice president of research at Google working on Bard.
ChatGPT and Brad AI are AI language models. ChatGPT, based on GPT-3.5, provides engaging conversation and responses. It's trained on vast internet text data. Brad AI, however, is unspecified and lacks specific information for comparison. Both aim to facilitate conversation and deliver meaningful interactions, but their capabilities depend on their respective architectures and training data.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
I gave a talk on the basics of Artificial Intelligence and Machine Learning in Android Developers Meetup in Gurgaon, India.
In this session I explained the basics of AI/ML, how ML is different from AI and also give brief introduction to Deep learning. I explained how ML works and what are basic types of ML - Supervised Learning, unsupervised learning and Reinforcement learning. What are the applications and most recent prominent examples of ML. And then I moved on to introduce the major frameworks for developers in this field. I gave brief introduction to Google Cloud vision, OpenCV, Pytorch, AWS Rekognition and finally ML Kit from Google. I explained ML kit in detail and how developers can use it. I also gave a demo on ML kit.
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Python tools to deploy your machine learning models fasterJeff Hale
Comparing Gradio, Streamlit, and FastAPI (with a little discussion of flask)
Jeff Hale's presentation for Data Science DC March 8, 2022
Repository with code at https://github.com/discdiver/dsdc-deploy-models
Keynote at Advantech's AI+Smart Manufacturing event. Shared the AI trend in smart manufacturing as well as a demo regarding how to use Azure Cognitive Services to empower employees and customers.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
Unleashing the Google Bard Discover the Revolutionary New Tool How does it Co...i-engage
Bard is a cutting-edge Chatbot Powered by LaMDA Large Language Model for Limitless Text-Based Creativity and Exploration.
This experimental AI by Google offers endless possibilities, from answering queries and generating summaries to crafting diverse forms of content with a simple prompt.
Moreover, Bard serves as your ultimate guide to exploring any topic by condensing information from the internet and suggesting links for further exploration.
“Bard’s an early experiment, it’s not perfect, and it’s gonna get things wrong occasionally,” says Eli Collins, the vice president of research at Google working on Bard.
ChatGPT and Brad AI are AI language models. ChatGPT, based on GPT-3.5, provides engaging conversation and responses. It's trained on vast internet text data. Brad AI, however, is unspecified and lacks specific information for comparison. Both aim to facilitate conversation and deliver meaningful interactions, but their capabilities depend on their respective architectures and training data.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
I gave a talk on the basics of Artificial Intelligence and Machine Learning in Android Developers Meetup in Gurgaon, India.
In this session I explained the basics of AI/ML, how ML is different from AI and also give brief introduction to Deep learning. I explained how ML works and what are basic types of ML - Supervised Learning, unsupervised learning and Reinforcement learning. What are the applications and most recent prominent examples of ML. And then I moved on to introduce the major frameworks for developers in this field. I gave brief introduction to Google Cloud vision, OpenCV, Pytorch, AWS Rekognition and finally ML Kit from Google. I explained ML kit in detail and how developers can use it. I also gave a demo on ML kit.
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Python tools to deploy your machine learning models fasterJeff Hale
Comparing Gradio, Streamlit, and FastAPI (with a little discussion of flask)
Jeff Hale's presentation for Data Science DC March 8, 2022
Repository with code at https://github.com/discdiver/dsdc-deploy-models
Keynote at Advantech's AI+Smart Manufacturing event. Shared the AI trend in smart manufacturing as well as a demo regarding how to use Azure Cognitive Services to empower employees and customers.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
Machine Learning is the scientific practice of teaching computers to mimic human intelligence and actions. These computers also have to improve their learning in an autonomous manner. It is done by learning from observations and real-world interactions. Sets of data are studied by the machines to derive meaningful conclusions.
These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...Agile Testing Alliance
Interactive Session on "Use of AI and ML in Performance Testing" by Adolf Patel Performance Test Architect Cognizant at #ATAGTR2021.
#ATAGTR2021 was the 6th Edition of Global Testing Retreat.
The video recording of the session is now available on the following link: https://www.youtube.com/watch?v=ajyPSmmswpM
To know more about #ATAGTR2021, please visit:https://gtr.agiletestingalliance.org/
Machine Learning in Business What It Is and How to Use ItKashish Trivedi
Machine learning revolutionizes business by offering effective suggestions, accurate predictions, and advanced analytics, streamlining operations without extensive human effort. It's a process where AI learns autonomously, akin to human cognition, as demonstrated by DeepMind, learning from images and sounds without explicit labeling. This article delves into the essence of machine learning, showcasing its benefits, diverse business applications, various types, and real-world examples. Understanding these facets is key to harnessing its power in optimizing businesses and enhancing customer experiences.
Machine learning and artificial intelligence are two of the most rapidly growing and transformative technologies of our time. These technologies are revolutionizing the way businesses operate, improving healthcare outcomes, and transforming the way we live our daily lives. Learn more about it in the PPT below!
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
AI for workflow automation Use cases applications benefits and development.pdfmahaffeycheryld
AI for workflow automation optimizes business operations by automating repetitive tasks, improving efficiency, and reducing errors. It is used in various sectors for automating data entry, enhancing customer service with chatbots, and performing predictive maintenance. Key benefits include increased productivity, cost reduction, and improved accuracy. Implementing AI-driven workflows involves integrating machine learning, natural language processing, and robotic process automation tools. This enables businesses to focus on strategic activities, drive innovation, and maintain a competitive edge.
https://www.leewayhertz.com/ai-for-workflow-automation/
In today's tech-driven world, the integration of artificial intelligence (AI) into applications has become increasingly prevalent. From personalized recommendations to intelligent chatbots, AI enhances user experiences and optimizes processes. However, building an AI app can seem daunting to those unfamiliar with the process. Fear not! This guide aims to demystify the journey, offering step-by-step insights into how to build an AI app from scratch.
While machine learning is an exciting subject, it is wrong to assume that it will solve all your problems. Scroll down to take a look at some myths in the machine learning field and how to overcome them.
Technology Driven Process Powerpoint Presentation SlidesSlideTeam
Introducing Technology Driven Process PowerPoint Presentation Slides to maximize your business efficiency and improve your business operations. Use this business intelligence PowerPoint presentation to analyze and present actionable data. Being an executive, manager or any other corporate user it is very important to make informed business decisions which is what this technological innovation PPT slideshow can help you with. Taking the assistance of this process technology PowerPoint slideshow you can state your company’s objectives such as boost organizational performance, make the company first in the field, overcome challenges, etc. You can also mention various components, key statistics, reasons and other vital aspects of machine intelligence using this robotic process automation PPT visual. Therefore, without any hesitation download this innovative and extremely useful process technology PowerPoint presentation now so stand ahead of your competitors by tapping the right technology at the right time. https://bit.ly/3gc30XQ
AI in supplier management - An Overview.pdfStephenAmell4
AI is instrumental in automating and optimizing various aspects of supplier management, starting with the streamlined onboarding of new suppliers. Automated AI-powered processes extract and validate crucial information from documents, expediting onboarding timelines and minimizing the risk of manual errors. AI’s predictive analytics capabilities enable organizations to assess supplier performance based on historical data, identifying patterns and trends that inform strategic decisions on supplier engagement.
AI for customer success - An Overview.pdfStephenAmell4
Customer success is a strategic approach where businesses proactively guide customers through a product journey to ensure they achieve their desired outcomes, thereby enhancing customer satisfaction, loyalty, and advocacy. It involves dedicated teams or individuals focusing on customer objectives from the initial purchasing phase through onboarding, usage optimization, and renewal, often utilizing data-driven methods to predict and respond to customer needs.
AI in financial planning - Your ultimate knowledge guide.pdfStephenAmell4
AI in financial planning is a game-changer in how businesses approach their financial analysis and decision-making processes. Traditionally, financial planning teams delve into substantial amounts of data to gauge a company’s performance, forecast future trends, and plan for success. This task, often labor-intensive due to the vast data volumes and ever-changing market dynamics, is now being transformed by AI.
AI in anomaly detection - An Overview.pdfStephenAmell4
Anomaly detection, also known as outlier detection, is a vital aspect of data science that centers on identifying unusual patterns that do not conform to expected behavior.
AI for sentiment analysis - An Overview.pdfStephenAmell4
Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
AI integration - Transforming businesses with intelligent solutions.pdfStephenAmell4
AI integration refers to the process of embedding artificial intelligence technologies into existing systems, processes, or applications, thereby enhancing their functionality and performance. This integration can introduce capabilities like machine learning, natural language processing, facial recognition, and speech processing into products or services, enabling them to perform tasks that typically require human intelligence.
AI in visual quality control - An Overview.pdfStephenAmell4
AI is reshaping various industries, and one area where its transformative power is particularly evident is in Visual Quality Control. By leveraging AI technologies like Machine Learning(ML) and computer vision, enterprises can enhance the accuracy, efficiency, and effectiveness of their quality control processes.
AI-based credit scoring - An Overview.pdfStephenAmell4
AI-based credit scoring is a contemporary method for evaluating a borrower’s creditworthiness. In contrast to the conventional approach that hinges on static variables and historical information, AI-based credit scoring harnesses the power of machine learning algorithms to scrutinize an extensive array of data from various sources.
AI in marketing - A detailed insight.pdfStephenAmell4
AI in marketing refers to the integration of artificial intelligence technologies, such as machine learning and natural language processing, into marketing operations to optimize strategies, enhance customer experiences and more.
Generative AI in insurance- A comprehensive guide.pdfStephenAmell4
Generative AI introduces a new paradigm in the insurance landscape, offering unparalleled opportunities for innovation and growth. The ability of generative AI to create original content and derive insights from data opens doors to novel applications pertinent to this industry.
AI IN INFORMATION TECHNOLOGY: REDEFINING OPERATIONS AND RESHAPING STRATEGIES.pdfStephenAmell4
AI has become a disruptive force within the IT industry, offering a wide array of applications and opportunities. It has gained attention for its capacity to optimize operations, foster innovation, and enhance decision-making processes. AI is making significant strides in IT, empowering organizations to streamline processes, extract valuable insights from vast data sets, and bolster cybersecurity.
AI IN THE WORKPLACE: TRANSFORMING TODAY’S WORK DYNAMICS.pdfStephenAmell4
AI is transforming workplaces, marking a significant shift towards automation and intelligent decision-making in various industries. In the modern business realm, AI’s role extends from automating mundane tasks to optimizing complex operations, thereby augmenting human capabilities. This integration results in significant productivity gains and more efficient business processes.
AI IN REAL ESTATE: IMPACTING THE DYNAMICS OF THE MODERN PROPERTY MARKET.pdfStephenAmell4
The real estate industry has always been a significant pillar of the global economy, connecting buyers and sellers in the pursuit of properties for residential, commercial, or investment purposes. Traditionally, the process of buying, selling, and managing real estate has been largely manual, relying on human expertise and effort.
How AI in business process automation is changing the game.pdfStephenAmell4
Business Process Automation (BPA) stands as an essential paradigm shift in modern business operations. By melding technological advancements with strategic objectives, BPA offers a pathway to a streamlined, efficient, and strategically aligned business model. Its multifaceted applications, ranging from HR to marketing, exemplify the transformative potential of automation, setting a benchmark for the future of business innovation.
Generative AI in supply chain management.pdfStephenAmell4
Generative AI in the supply chain leverages advanced algorithms to autonomously create and optimize processes, enhancing efficiency and adaptability. This technology generates intelligent solutions, forecasts demand, and streamlines logistics, ultimately revolutionizing how businesses manage their supply chains by fostering agility and cost-effectiveness through data-driven decision-making.
AI in telemedicine: Shaping a new era of virtual healthcare.pdfStephenAmell4
In a rapidly evolving healthcare landscape, telemedicine has emerged as a transformative force, transforming the way healthcare is delivered and received. Telemedicine, also known as telehealth, is a mode of healthcare delivery that leverages modern communication technology to provide medical services and consultations remotely.
AI in business management: An Overview.pdfStephenAmell4
Business management involves overseeing and coordinating an organization’s various functions to effectively achieve its objectives and goals. It includes planning, organizing, leading, staffing, and controlling an organization’s human, financial, and technological resources to ensure efficient operation and the achievement of intended outcomes. Business management encompasses a wide range of responsibilities, from setting strategic goals and making high-level decisions to supervising employees, managing finances, and optimizing operations. Effective business management is crucial for the success of businesses across various industries.
AI in fleet management : An Overview.pdfStephenAmell4
Fleet management is the process of organizing, coordinating, and facilitating the operation and maintenance of a fleet of vehicles within a company or organization. It’s a procedural necessity and a strategic function vital for businesses and agencies where transportation is at the heart of service or product delivery. Its primary objective is to control costs, enhance productivity, and mitigate risks associated with operating a fleet of vehicles.
AI in fuel distribution control Exploring the use cases.pdfStephenAmell4
Fuel distribution control is the administration and supervision of the procedures used to transport different fuels, such as petrol, diesel, and aviation fuel, from production facilities to end-users, which might include consumers, companies, and industries. It includes all actions involved in the extraction, refinement, transportation, storage, and distribution of fuels, as well as its planning, coordination, and optimization.
An AI-based price engine is a pricing tool or system that leverages artificial intelligence and machine learning techniques to make pricing decisions and recommendations based on various factors and variables. The pricing engine goes beyond traditional rule-based approaches and incorporates advanced algorithms to analyze complex data patterns, customer behavior, market trends, and other relevant factors in real-time.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Key Trends Shaping the Future of Infrastructure.pdf
How to implement adaptive AI.pdf
1. 1/17
How to implement adaptive AI?
leewayhertz.com/how-to-implement-adaptive-ai
Artificial intelligence holds the potential to be a transformative tool, capable of
continually learning and providing rich, actionable insights to drive the growth and
success of your business. Due to their rigid structure, traditional machine learning models
are ill-equipped to handle the dynamic nature of our rapidly changing world, particularly
with the influx of data generated by the IoT and autonomous vehicles. The inability of
these models to adapt to new data streams often limits the effectiveness of even cutting-
edge machine learning methods. But with the advent of adaptive AI, this issue is being
addressed. Adaptive AI, with its capability for continuous learning, provides a more
flexible approach to machine learning by enabling models to evolve and adapt to changing
data in real time. This helps to overcome the limitations of traditional machine learning
and effectively tackle the challenges posed by our constantly evolving data landscape.
Continuous learning is a powerful form of AI that continually builds knowledge and
improves over time. This makes it a game-changer for a multitude of industries, as it
represents a move away from static learning towards a more adaptive approach. Gartner
has identified adaptive AI as one of the top 10 emerging trends for 2023, poised to take AI
to new heights by enabling it to learn as it develops. It is predicted that businesses
utilizing adaptive AI will outperform their competitors by 25%. In a note, Gartner also
mentioned “Flexibility and adaptability are now vital, as many businesses have learned
during a recent health and climate crises,” says Gartner distinguished VP Analyst Erick
Brethenoux. “Adaptive AI systems aim to continuously retrain models or apply other
mechanisms to adapt and learn within runtime and development environments — making
them more adaptive and resilient to change.”
This article provides an overview of adaptive AI, possible business areas where you can
use it and the steps to implement it in real-life business.
What is adaptive AI?
Adaptive AI, which combines artificial intelligence with an advanced and responsive
autonomous learning method, is the next evolutionary step. Systems that use adaptive AI
can quickly adapt to changing circumstances, which was not possible during initial AI
development. This is achieved by continuously retraining and learning models in runtime
based on new information. This type of AI is designed to improve its performance over
time by modifying its algorithms, decision-making processes, and actions based on the
information it receives from its environment. This allows it to respond better to changes
and challenges and to achieve goals more effectively.
2. 2/17
Static ML model vs Adaptive AI model
Static ML Adaptive ML
Learn once
Data
Deploy once
Learn continually
Deploy continually
New Data
LeewayHertz
To understand the concept better, let’s compare adaptive AI with the traditional machine
learning model.
Consider an object detection system to drive a self-driving vehicle. The car must be able to
recognize pedestrians and cyclists to ensure safe operation. It does an amazing job by
training its neural network with large numbers of samples. However, as new object
categories are constantly emerging, including hoverboards and electric steps, the car is
also expected to detect hoverboards and electric steps if we update our detection system
with a sufficient number of representative images. However, there is a catch! With
traditional machine learning models, our system would forget pedestrians and cyclists
from the old task if we update the system with new data and leave them unidentified. This
phenomenon is known as catastrophic forgetting within neural networks.
There comes adaptive AI with continuous learning, a sequential method that does not
involve catastrophic forgetting. In this case, the neural network retains all concepts
learned over time, so it is easy to recall what you have learned using data. You can store
and revisit the information when you are ready to learn something new. Given that these
data streams may not have a definitive endpoint, it is crucial to remember that they may
never end, thereby avoiding the risk of catastrophic forgetting within our systems.
Here is a brief comparison between adaptive AI and traditional AI:
Feature Adaptive AI Traditional AI
3. 3/17
Feature Adaptive AI Traditional AI
Maintenance Requires ongoing monitoring
and maintenance to ensure the
system continues learning and
adapting to new information.
Requires less maintenance, as the
system does not change or adapt
once it has been deployed.
Human
Interaction
Required Optional or not required
Learning Online learning or continuous
learning involves feeding data
to the system and updating its
model in real-time.
Batch learning, where the system is
trained on a fixed dataset and then
deployed, with no further updates
to its model.
Performance Improves over time Fixed or degrades over time
Real-World
Application
Ideal for dynamic and rapidly
changing environments where
the system must evolve and
continuously provide value.
Suitable for environments with
stable and well-defined conditions,
where the system’s performance
does not need to change over time.
Adaptability High, able to adapt to new
information and changing
conditions.
Low, limited to the performance
capabilities determined by the fixed
training dataset.
Scalability High Low
Implementation Dynamic and flexible Static and inflexible
Definition AI systems that can adapt and
improve their performance over
time through continuous
learning.
AI systems that are trained on a
fixed dataset and do not adapt to
new information or changing
conditions.
How does adaptive AI work?
Adaptive AI works based on continuous learning (CL), an essential step toward achieving
AI. Continuous learning refers to the ability of a model to learn continuously using the
stream of data, which means that a model can autonomously learn from new data and
adapt to production. This is why it is also known as continuous AutoML or auto-adaptive
learning. CL is a method that mimics human’s ability to continuously acquire, refine and
transfer knowledge and skill throughout their lives. Machine learning aims to create
models in a production environment, whereas continuous learning allows us to use the
data coming into the production environment and retrain the model using the new
activity. For example, we are familiar with Netflix’s highly-acclaimed recommender
system for “Up Next”. The Netflix recommender system suggests a show right after the
last episode ends. This type of model is necessary to be retrained as new movies,
preferences and market trends change. Continuous learning is key to gaining high
4. 4/17
accuracy and retaining high-performing models. Data scientists find continuous learning
is a great way to improve models’ accuracy and performance. It also saves time in
retraining by making models auto-adaptive.
Adaptive AI working mechanism
Data validation
Cleaning & Labeling
AutoML & HPO
Monitoring
Model validations
Predictions
Model development
DATA
LeewayHertz
AutoML plays an essential component in the continuous learning process of adaptive
AI. AutoML (Automated Machine Learning) refers to the process of automating the entire
machine learning pipeline, from data preparation to model selection and deployment. The
goal of AutoML is to remove the need for manual intervention, reduce the time and
resources required for training models and improve the accuracy of machine learning
models. AutoML does not have to be complicated meta-learning; instead, it is easy to use
frameworks, hyperparameter optimization and open-source algorithms with AutoML.
Transfer-learning might be a good option if you are working on a computer vision
problem that allows you to use a variety of pre-trained models, which you can then use to
retain the last layer of your network and then have your model deployed. For continuous
learning, you can choose one of the pre-built models, such as VGG, Inception or ResNet.
Also, specifying parameters for each model is essential.
After the training, you will do model validations to verify that all models function
properly. You can also choose the best model and then deploy it to production. In this
case, the pipeline appears to be like a closed traditional machine learning pipeline where
monitoring is added to the pipeline to apply continuous learning while connecting it back
to the data.
Monitoring will take place for predictions collected within the model deployment area.
After the data has been monitored, you can clean it and label it as needed. However,
closing the loop with no human labeling for things like forecasting or recommender
systems will be possible. After clearing and labeling the data, we will move it back to the
data for validation and training. The loop is closed in this case.
Your models will learn continuously and adapt to new trends and data while improving
the model’s accuracy. Consequently, your application will perform better overall.
Why is adaptive AI critical for business growth?
5. 5/17
Adaptive AI combines agent-based design and AI techniques like reinforcement learning,
allowing systems to adapt their learning patterns and behaviors to real-world changes
while in production. It learns from past human and machine experiences and delivers
better results in runtime environments. For example, the U.S. Army has developed a
system that adapts to each learner’s strengths using its learning system. It can tell what
to teach, when to test, and how to measure progress while acting as a tutor and tailoring
the learning to each student.
Adaptive AI systems will ultimately open the door to new business models, products,
services, and channels, breaking down silos, and eventually, they will be able to enable
new ways of doing things.
In a nutshell, adaptive AI helps business growth in the following ways:
Increased efficiency and automation: Adaptive AI helps automate routine
tasks and processes, freeing up employees’ time and increasing productivity.
Improved decision-making: Adaptive AI provides real-time insights and data-
driven decision-making, eliminating the chances of human error.
Personalization and customization: Trained adaptive AI models understand
individual customer preferences, enabling companies to offer personalized
experiences and products.
Competitive advantage: Companies that adopt adaptive AI technology can gain a
competitive edge by staying ahead of their competitors in terms of efficiency and
innovation.
Increased customer satisfaction: Adaptive AI can help companies provide
faster and more effective customer service, increasing customer satisfaction and
loyalty.
Cost savings: Automation and improved decision-making can lead to cost savings,
freeing up resources that can be invested in other business areas.
Better risk management: Adaptive AI can analyze data and predict potential
risks, allowing companies to avoid potential problems proactively.
Decision-making is an essential but more complex task for any company, making it
necessary to have decision-intelligence systems that can exercise greater autonomy.
However, adaptive AI will require that decision-making processes be reengineered to
make them more efficient. This could have significant implications for existing process
architectures and will require business stakeholders to ensure the ethical use of AI for
compliance with regulations and laws.
Adaptive AI use cases
Adaptive AI has many potential applications across various industries. Here are some of
the most common use cases for adaptive AI:
Customer service
6. 6/17
Adaptive AI can be integrated into chatbots to provide customers with personalized and
efficient support as part of customer service. Let’s focus on how adaptive AI can be
integrated to fulfill the purpose of seamless customer service.
Use of natural language processing (NLP) and machine
learning: Adaptive AI chatbots use NLP and machine learning algorithms to
understand and interpret customer inquiries by analyzing and categorizing the text
into specific topics to determine the best response. This can also enable them to
personalize user preferences based on the customer’s history and preferences. For
example, they can recommend products or services based on previous purchases or
provide answers tailored to the customer’s specific needs.
Multi-channel integration: Adaptive AI enables chatbots to be integrated with
multiple channels, such as websites, mobile apps, and social media, to provide
customers with seamless support across all touchpoints.
Continuous improvement: Machine learning algorithms allow adaptive AI
chatbots to improve their performance over time continually by analyzing customer
interactions, identifying areas for improvement, and updating their knowledge base
to provide better support in the future.
Healthcare
Adaptive AI is a new dimension on the horizon of improving the diagnosis and treatment
of diseases. Here are the technical details of how adaptive AI can be applied in healthcare:
Medical image analysis: Adaptive AI algorithms can analyze medical images,
such as X-rays, MRIs, and CT scans, to assist with the diagnosis of diseases. For
example, it can detect patterns and anomalies that may indicate a specific condition
and provide doctors with more accurate diagnoses.
Predictive analytics: Adaptive AI algorithms can use patient data, such as
medical history, lab results, and demographic information, to predict future health
outcomes. For example, it can predict the likelihood of a patient developing a
certain disease or the probability of a patient’s condition improving or deteriorating.
Personalized treatment: Adaptive AI algorithms can personalize treatment
plans based on patient needs and progress. For example, it can recommend specific
medications or treatments based on a patient’s unique medical history or adjust the
plan as the patient’s condition changes.
Real-time monitoring: Adaptive AI algorithms can monitor patients in real time
and detect any changes in their condition. For example, it can monitor vital signs,
such as heart rate and blood pressure, to detect changes and alert medical staff to
potential problems.
Clinical decision support: Adaptive AI algorithms can provide doctors with
clinical decision support by analyzing large amounts of medical data and providing
recommendations for diagnosis and treatment.
Marketing
7. 7/17
Adaptive AI helps companies personalize their marketing efforts and target their ads
more effectively in the following ways:
Customer segmentation: Using adaptive AI algorithms, marketers can analyze
customer data, such as demographics, purchasing behavior, and online activity, to
divide customers into segments allowing marketers to target specific groups with
tailored messages and offers.
Predictive analytics and personalized marketing: Adaptive AI algorithms
use customer data to predict customers’ purchasing behavior by identifying the
segment of customers and their likelihood of the particular products.
Multi-channel integration: You can integrate adaptive AI algorithms with
multiple marketing channels, such as email, social media, and websites, to provide
customers with a seamless experience across all touchpoints.
Real-time optimization: Adaptive AI algorithms can monitor and analyze
customer interactions in real-time to optimize marketing campaigns based on
previous emails’ open rates and click-through rates.
Finance
There is a surge of adaptive AI implementation in the financial sector due to increased
risks of fraudulent activities. However, adaptive AI is not limited to fraud detection only;
there is wide use in the following financial activities to improve overall financial
decisions:
Credit scoring: Adaptive AI algorithms analyze large amounts of data, such as
credit history, employment information, and payment behavior of customers,
predicting the likelihood of a borrower repaying a loan. This lets lenders make more
informed decisions about loan approvals and interest rates of the same.
Fraud detection: Adaptive AI algorithms help identify patterns and anomalies in
financial transactions, allowing them to detect and prevent fraudulent activities. For
example, it can detect unusual transactions, such as large withdrawals from a
customer’s account, and alert financial institutions about potential fraud.
Risk management: Adaptive AI algorithms analyze market trends and historical
data and, by comparing both, can predict market behavior and help financial
institutions manage risk. For example, it can analyze stock prices and market
indicators to predict market trends while allowing financial institutions to make
more informed investment decisions.
Customer segmentation: Adaptive AI algorithms analyze customer data based
on common characteristics, such as their financial behavior and risk profiles for
customer segmentation. Financial institutions can use the same method to target
specific groups with tailored products and services.
Portfolio optimization: By analyzing market trends, historical data, and
financial projections, adaptive AI algorithms can optimize investment portfolios to
predict future performance.
Education
8. 8/17
Adaptive AI plays a crucial role in personalizing learning and improving student
engagement. For example, it can provide customized recommendations for educational
content based on a student’s learning style and progress and adjust its approach as the
student’s needs change.
Here is an overview of how adaptive AI can enhance the education industry:
Personalized learning: Adaptive AI algorithms can analyze student data, such as
performance history and learning style, to personalize learning content and
activities allowing students to receive tailored instruction based on their individual
needs and preferences.
Skill assessment: Adaptive AI algorithms can assess student proficiency in
specific skills, such as reading comprehension, math, or science. This allows
educators to monitor student progress and adjust instruction accordingly.
Adaptive testing: Adaptive AI algorithms can be used to create adaptive tests,
which adjust the difficulty of questions based on a student’s responses. This allows
for a more accurate assessment of a student’s abilities and helps to identify areas of
strengths and weaknesses.
Student engagement: Adaptive AI algorithms can monitor student engagement
and identify students at risk of falling behind. For example, it can analyze student
interactions with educational content to identify students needing additional
support or interventions.
Content recommendation: Adaptive AI algorithms can recommend educational
content and activities to students based on their interests and performance history.
This can help students find content that is more engaging and relevant to them.
Supply chain management
Adaptive AI helps companies optimize supply chain processes in various aspects, as
described below:
Demand forecasting: Adaptive AI algorithms analyze sales data and market
trends to predict future product demand allowing supply chain managers to make
informed production, inventory and distribution decisions.
Inventory optimization: Adaptive AI algorithms optimize inventory levels based
on demand forecasts and supplier lead times, helping supply chain managers to
avoid overstocking or stock shortages.
Route optimization: Adaptive AI algorithms can be used to optimize delivery
routes based on factors such as traffic, delivery time windows, and driver schedules.
This helps to reduce delivery times and increase efficiency.
Supply chain risk management: Adaptive AI algorithms analyze data on
supplier performance, delivery times, and economic indicators to identify potential
risks in the supply chain, allowing supply chain managers to proactively manage risk
and reduce disruptions’ impact.
9. 9/17
Predictive maintenance: Adaptive AI algorithms can predict when equipment in
the supply chain will likely fail, allowing maintenance to be scheduled proactively
while minimizing downtime and reducing the impact of equipment failures.
Retail
Adaptive AI can improve the customer experience in retail by providing personalized
product recommendations, optimizing prices, and managing inventory.
Here are some of the areas of retail that adaptive AI can enhance:
Customer segmentation: Adaptive AI algorithms analyze customer data, such as
purchase history and behavior, to segment customers into groups with similar
characteristics allowing retailers to tailor their marketing and customer service to
meet each group’s specific needs and preferences.
Personalized product recommendations: Adaptive AI algorithms analyze
customer data for personalized product recommendations. For example, they can
recommend products based on a customer’s purchase history, browsing behavior, or
interests.
Enhanced customer service: Adaptive AI algorithms provide customer service
through chatbots or virtual assistants who can respond to customer queries and
provide information about products, prices, and shipping effectively and in real
time.
Inventory management: Adaptive AI algorithms optimize inventory levels based
on demand forecasts and sales data, helping retailers to avoid overstocking or stock
shortages.
Predictive maintenance: Adaptive AI algorithms predict when equipment, such
as cash registers or self-service kiosks, will likely fail, allowing maintenance to be
scheduled proactively. This helps to minimize downtime and reduce the impact of
equipment failures.
Energy
Adaptive AI can be used to optimize energy consumption and improve energy efficiency,
as well as to monitor and predict energy demand.
Here are some of the ways how adaptive AI can be applied in the energy sector:
Predictive maintenance: Adaptive AI algorithms predict when energy
equipment, such as turbines or generators, is likely to fail, allowing maintenance to
be scheduled proactively. This helps to minimize downtime reducing the impact of
equipment failures.
Demand forecasting: Adaptive AI algorithms help analyze energy consumption
data and market trends to predict future demand for energy allowing energy
providers to make informed production, distribution, and pricing decisions.
10. 10/17
Portfolio optimization: Adaptive AI algorithms optimize energy portfolios by
balancing the risk and returns of different energy sources. This helps energy
providers to make informed investment decisions and manage risk.
Grid management: Adaptive AI algorithms help manage the energy grid by
optimizing energy production and distribution, helping to minimize energy waste
and improve the overall efficiency of the grid.
Risk management: Adaptive AI algorithms analyze data on market trends,
production capacity, and weather patterns to identify potential risks in the energy
sector. This allows energy providers to manage risk and reduce disruptions’ impact
proactively.
Transportation
Adaptive AI help optimize routes, reduce fuel consumption, predict maintenance needs,
and improve safety in transportation systems.
Here are some of the details of how adaptive AI can be applied in transportation:
Predictive maintenance: Adaptive AI algorithms predict when vehicles like cars,
buses, or trains will likely need maintenance, allowing maintenance to be scheduled
proactively. This helps to minimize downtime and reduce the impact of equipment
failures.
Route optimization: Adaptive AI algorithms help optimize transportation routes
based on real-time traffic data and demand patterns, which helps to minimize travel
time, reduce fuel consumption, and improve overall efficiency.
Safety monitoring: Adaptive AI algorithms help monitor vehicle performance,
traffic patterns, and weather conditions to identify potential safety risks allowing
transportation providers to proactively manage safety and reduce the impact of
accidents or incidents.
Demand forecasting: Adaptive AI algorithms help analyze transportation
demand patterns and predict future demand for transportation services. This allows
transportation providers to make informed decisions about vehicle deployment and
pricing.
Fraud detection: Adaptive AI algorithms can identify fraudulent activity in
transportation systems, such as ticket fraud or unauthorized vehicle use, helping
transportation providers reduce losses and improve overall security.
Adaptive AI system needs to be built and implemented in a way so that they can modify
their behavior based on changes in the environment and context. Here are the detailed
steps for implementing adaptive AI.
11. 11/17
How to implement Adaptive AI for business
LeewayHertz
1
Define the system’s
objectives
2
Data collection
7
Continuous monitoring
and improvement
3
Model training
6
Deploy the model
4
Contextual analysis
5
Evaluate and fine-tune
the model
Step 1: Define the system’s objectives
Defining the system’s objectives is a critical step while implementing adaptive AI, which
should provide a clear direction for the system’s development and help determine the
desired outcome. When defining the system’s objectives, the following key aspects need to
be considered:
Identifying the desired outcome: The first step is to determine what the system is
supposed to achieve, which could include classifying images, predicting user
behavior, or optimizing resource allocation.
Setting performance metrics: To measure the system’s success, performance metrics
should be aligned with the desired outcome providing a clear way to evaluate the
system’s performance. Some of the well-known performance metrics of a machine
learning model are accuracy, recall, precision, F1-score, and AUC, which evaluates
the system performance.
Target audience: Understanding the target audience is crucial for defining any
system’s objectives. The data type used for system modeling and decision models
depends highly on the target audience. For example, an adaptive AI system for
financial advisors will have a different target audience and desired outcome than an
adaptive AI system for health care providers.
Step 2: Data collection
Data works as the foundation for training machine learning models and making informed
decisions in building any AI model. Here are the key aspects to consider when collecting
data for adaptive AI:
12. 12/17
Relevance: The data should be relevant to the system’s objectives, which means it
should be directly related to the targetted problem and the desired outcome.
Diversity: It’s important to collect diverse data representing the target audience
which will help the system to generalize and make informed decisions in different
situations widening the scope of intense testing.
Timeliness: The data should be up-to-date and reflective of the current context
while being updated continuously so that the system’s behavior should be adapted
to the changing context.
Storage: The collected data should be stored in a centralized database for easy
access and analysis. The database should be secure and scalable to accommodate the
growth of the data over time.
Privacy: It’s important to consider privacy when collecting data. Personal
information should be protected, and appropriate measures should be taken to
ensure that the data is not used for any malicious purposes.
Step 3: Model training
Model training is the process of training a machine learning model on a dataset to make
predictions. This critical step of adaptive AI implementation provides the foundation for
decision-making. Here are the key aspects to consider when training a model for adaptive
AI:
Algorithm selection: A machine learning algorithm’s choice depends on the
problem’s nature, the type of data, and the desired outcome. Popular algorithms
used to train an adaptive AI model are supervised, unsupervised, and reinforcement
learning.
Data preparation: The data used to train the model undergo intense preprocessing
and transformation steps to convert it into a format the machine learning algorithm
can use. This process includes cleaning, normalizing, and splitting the data into
training and validation sets.
Hyperparameter tuning: Hyperparameters can significantly impact the model’s
performance which is why the optimal value of it should be identified and set before
training the model using the tuning process.
Model evaluation: The trained model should be evaluated on a validation set to
measure its performance. You must align the performance metrics with the system’s
objectives, reflecting the desired outcome.
Model improvement: Based on the model evaluation results, there should be scope
for continuous improvement by retraining the model on a larger dataset, changing
the algorithm, or adjusting the hyperparameters.
Step 4: Contextual analysis
Contextual analysis is the process of analyzing the current context and making informed
decisions based on it, allowing the system to respond in real-time. Here are the key
aspects to consider when performing contextual analysis for an adaptive AI system:
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Data sources: The system should collect data from relevant sources, including
sensors, logs, and other sources that provide contextual information about the
environment and target audience, helping the system to understand the current
context comprehensively.
Data processing: The collected data should be processed and transformed into a
machine learning model format by cleaning, normalizing, and transforming the data
for making predictions.
Model prediction: The trained machine learning models should be able to predict
based on the current context, where predictions should align with the system’s
objectives, providing a basis for making informed decisions.
Feedback loop: The decisions made by the system should be monitored and
evaluated to create a feedback loop allowing the system to adapt continuously to
environmental changes and improve its performance over time.
Step 5: Evaluate and fine-tune the model
Fine-tuning an AI model involves parameters or architecture adjusting for performance
improvement depending on the model type and its targetted problem.
Some common techniques include:
Hyperparameter tuning: This is done to adjust parameters not learned during
training, such as the learning rate, number of hidden units in a neural network, or
regularization strength. Hyperparameter tuning can be done either manually by
trying different values or automatically using grid or random search techniques.
Feature engineering: This technique involves adding or removing features from the
model training data to improve the model’s overall performance.
Model architecture: This involves changing the model structure itself, such as
adding or removing layers or changing the type of activation function used. You can
adjust the model architecture to improve the model’s ability to capture complex
relationships in the data.
Ensemble methods: Through these methods, you can combine multiple models to
create a more robust and accurate adaptive AI model, which can improve the
model’s performance by combining the strengths or output of multiple models to
make a final decision.
Transfer learning: This learning process uses a pre-trained model as a starting point
and fine-tunes it for a specific task. For example, you can use transfer learning to
improve the model’s performance by leveraging knowledge learned from related
tasks.
Step 6: Deploy the model
Deploying a model in an adaptive AI implementation means making the model available
for use in a real-world setting or production environment, which typically involves the
following steps:
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Model preparation: This involves making the model production ready, such as
converting it into a TensorFlow SavedModel or a PyTorch script.
Infrastructure setup: Next, the necessary infrastructure must be set up to support
the deployment of the model, which may involve setting up a server or cloud
environment to host the model or deploying the model to a mobile device or edge
device.
Deployment: Once the infrastructure is set up, the model can be deployed, which
may involve uploading the model to a server or cloud environment or installing the
model on a mobile device or edge device.
Model management: After the model has been deployed, it is important to manage it
effectively, which may involve monitoring its performance, updating the model as
necessary, and ensuring that it is available and accessible to users.
Integration: The final step is integrating the deployed model into the overall system,
which may involve integrating the model with other system components, such as a
user interface, a database, or other models.
Step 7: Continuous monitoring and improvement
Monitoring and maintaining an adaptive AI system after implementation ensures that the
system continues to function correctly and effectively over time. It involves the following
steps:
Performance monitoring: This step is crucial to ensure the system functions
correctly and effectively. It may involve monitoring the accuracy and reliability of
the model and its overall performance.
Data collection and analysis: Continuous data feed to the system can provide
valuable information about the system, whether it is functioning properly and what
changes may be necessary to improve its performance enhancement. This may
involve collecting data about the model’s predictions, the input data, and the overall
system performance.
Model retraining: Over time, the system may need to be retrained to account for
data changes or improve its performance. This may involve updating the training
data, fine-tuning the model, or using transfer learning to adapt the model to new
tasks.
System updates: The system may need to be updated to address any issues or
incorporate new features or functionality. This may involve updating the software,
hardware, or other system components.
User feedback: Gathering user feedback can provide valuable information about the
system’s use and what changes may be necessary to improve its performance. This
may involve conducting surveys or interviews with users or collecting feedback
through user testing or other means.
Best practices for implementing adaptive AI
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A clear understanding of the problem allows you to identify the relevant
information and training data to train your adaptive AI system. It also helps you to
determine the appropriate algorithmic approach for your system and to establish
the performance metrics that will be used to evaluate its effectiveness. Defining the
goals for your adaptive AI system provides a clear target for the team to work
towards. It helps to focus the project while ensuring the effective allocation of
resources. Having SMART (specific, measurable, achievable, relevant, and time-
bound) goals will help you evaluate your adaptive AI system progress and make
necessary adjustments.
Acquiring high-quality data free from errors, inconsistencies, and duplicates is
essential to establish a robust adaptive AI system that will learn from this data and
make predictions based on it. If the training data is of poor quality, the system
cannot model the problem, leading to suboptimal performance. Besides, diversity in
the training data is also important to ensure the system can learn from a wide range
of examples while remaining generalized to new cases. This is particularly important
in adaptive AI systems because they are designed to adapt to changes in the problem
space in a real-time manner. Also, ensuring diverse training data enables the system
to handle new and unexpected situations effectively.
Selecting the right algorithms is essential for achieving effective results with
adaptive AI. Several algorithms are particularly well-suited to adaptive systems,
including reinforcement learning and online learning algorithms. However, the
selection should be problem specific and depends on the data type used for training.
For example, for streaming data, online learning algorithms are suitable, whereas
reinforcement learning algorithms are often used in decision-making problems
where the system must make a series of decisions over time.
Regular performance monitoring and using metrics relevant to your goals is
essential for evaluating the effectiveness of an adaptive AI system. This is
particularly important in adaptive AI systems, as they are supposed to perform in
real time. Through monitoring, you can track the progress of your system, ensuring
the desired outcomes. Besides, it helps identify any issues or problems with your
system early on and make necessary adjustments to improve its performance.
Concept drift occurs when the data distribution changes over time, causing the
system’s performance to deteriorate. For example, concept drift could occur when
customer preferences change in a behavior prediction system. Unless the AI system
identifies these changes, its predictions become less accurate, resulting in
performance deterioration. To handle this issue, it’s important to implement
detection methods for data distribution and update the system accordingly. This
can be done using techniques such as online learning algorithms, which can
continuously learn and adapt to new data as it becomes available. Another approach
is regularly retraining the system on the latest data to ensure it is up-to-date,
which could be combined with online learning algorithms to balance real-time
adaptation while improving overall performance.
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Implementing an effective testing and validation framework is crucial for
ensuring the accuracy and reliability of an adaptive AI system. This is essential to
validate the performance of your system and identify any issues or bugs that may
affect its accuracy and reliability. Hence, when implementing a testing and
validation framework, it is important to use various testing methods, including unit,
integration, and performance testing. While unit testing allows you to test individual
system components to ensure they function correctly, integration testing validates
the interaction between different system components. On the other hand,
performance testing measures the system’s efficiency and scalability. Using diverse
test data that accurately represents the problem space is also important, including
both normal and edge cases and any unexpected scenarios. Alongside, using a
diverse range of test data allows you to validate the performance of your system
under different conditions and identify any areas that need improvement.
Monitoring the fairness and ethics of an adaptive AI system is critical to avoid
any unforeseen harm or discrimination. For example, a biased AI system might
discriminate against certain groups of people based on race, gender, or age resulting
in unfair treatment or outcomes, such as denial of access to services or employment
opportunities. To ensure the fairness and ethics of an adaptive AI system, it’s
important to monitor its decisions and outcomes to identify the signs of bias or
discrimination. This can be done using fairness metrics, which measure how much
the system treats different groups equally. In addition, it’s important to regularly
review the training data and algorithms used in the system to ensure they are free
from biases and if necessary, remove any data that may contain biases. You can
apply the algorithms designed to reduce the impact of biases in the training data.
Finally, it’s important to have clear and transparent policies aligning with how the
system is designed, along with required measures to ensure its fairness and ethics.
This allows stakeholders to understand the system’s decision-making processes and
provides accountability for any unfair or unethical outcomes.
One of the challenges of adaptive AI systems is that they are difficult to understand
and interpret, as they constantly evolve and change in response to new data, leading
to difficulties for stakeholders to understand the reasoning behind the system’s
decisions, resulting in a lack of trust and accountability. To address
this, explainable AI (XAI) techniques are used to provide a clear and concise
explanation of the system’s decisions. XAI is a subfield of AI that focuses on
developing transparent, understandable, and interpretable systems. For example,
this can include visualizations of the system’s internal workings, such as decision
trees or heatmaps, or methods for generating natural language explanations of the
system’s decisions. Using XAI techniques helps build trust in the system, as
stakeholders can see how it arrived at its decisions and understand its reasoning
while providing greater transparency and accountability, as the system’s decision-
making processes can be audited and reviewed to ensure they are fair and ethical.
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With the increasing use of AI systems, security becomes an alarming issue as they
are prone to potential threats raised by malicious actors. For example, an attacker
could try to manipulate the system’s inputs causing it to make incorrect decisions or
access sensitive information stored in the system. So, ensuring the robustness
and security of an adaptive AI system is the need of the hour. So, it’s important to
assess the system thoroughly and implement security measures by applying security
audits, penetration testing, and other security assessments.
Endnote
Adaptive AI transforms the landscape of ML with its complex system approach, allowing
the AI to seek its goals while simultaneously learning the optimum path and adapting its
learning methods to align with real-world scenarios in the most effective way. With its
flexibility and continuous learning abilities, adaptive AI surpasses traditional AI while its
usability in multiple areas fits into any business application seamlessly and transforms
how businesses operate and compete. From increased efficiency and automation to better
decision-making and personalization, the benefits of adaptive AI are far-reaching. By
adopting this innovative technology, companies can gain a competitive edge,enhance
customer satisfaction, achieve cost savings, and improve risk management and decision-
making processes all at once. To conclude, in a world that is constantly advancing
technologically, implementing adaptive AI is not just a choice but a necessity for growth
and success. Don’t fall behind, tap into the potential of adaptive AI today.
Want to transform the way you do business with cutting-edge adaptive AI
solutions? Contact LeewayHertz AI experts to integrate adaptive AI
solutions into your business!