The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs
Speakers:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Ingrid Burton (H2O.ai - CMO)
Generative AI Use cases for Enterprise - Second SessionGene Leybzon
This document provides an overview of generative AI use cases for enterprises. It begins with addressing concerns that generative AI will replace jobs. The presentation then defines generative AI as AI that generates new content like text, images or code based on patterns learned from training data.
Several examples of generative AI outputs are shown including code, text, images and advice. Potential use cases for enterprises are then outlined, including synthetic data generation, code generation, code quality checks, customer service, and data analysis. The presentation concludes by emphasizing that people will be "replaced by someone who knows how to use AI", not AI itself.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
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
The document discusses using generative AI to improve learning products by making them better, stronger, and faster. It provides examples of using generative models for game creation, runtime design, and postmortem data analysis. It also addresses ethics and copyright challenges and considers generative AI as both a tool and potential friend. The document explores what models are, how they work, examples of applications, and resources for staying up to date on generative AI advances.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
Global Governance of Generative AI: The Right Way ForwardLilian Edwards
AI regulation has been a hot topic since the rise of machine learning (ML) in the “big data” era, but generative AI or “foundation models” tools like ChatGPT, DALL-E 2(now 3) and CoPilot, ike ML before them, may create serious societal risks, including embedding and outputting bias; generating fake news, illegal or harmful content and inadvertent “hallucinations”; infringing existing laws relating eg to copyright and privacy; as well as environmental, competition and workplace concerns.
Many nations are now considering regulation to address these worries, and can draw on a number of basic and hybrid models of governance. This paper canvasses models of mandatory comprehensive legislation (where the EU AI Act hopes to place itself as a gold standard model); vertical mandatory legislation (where China has quietly taken a lead); adapting existing law (see the many copyright lawsuits underway); and voluntary “soft law” such as codes of ethics, “blueprints”, or industry guidelines. Both the domestic and international regulatory scenes for AI are also increasingly politicised as the rise of "AI safety" hype shows. Against this backdrop what choices should smaller countries such as the UK and Australia make? will international harmonisation lead to a race to the top as with the GDPR, or the bottom - rule by tech for tech?
A brief overview of artificial intelligence (AI), followed by a few examples of practical use within small businesses, large enterprises, and nonprofits.
Spark 2019: Equifax's SVP Data & Analytics, Peter Maynard, discusses the notion (and importance) of explainable AI in the financial services sector. He looks at the work Equifax have done to crack open the black box by creating patented AI technology that helps companies make smarter, explainable decisions using AI.
generative-ai-fundamentals and Large language modelsAdventureWorld5
Thank you for the detailed review of the protein bars. I'm glad to hear you and your family are enjoying them as a healthy snack and meal replacement option. A couple suggestions based on your feedback:
- For future orders, you may want to check the expiration dates to help avoid any dried out bars towards the end of the box. Freshness is key to maintaining the moist texture.
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Artificial intelligence (AI) is driven by advances in computing power, data storage, and algorithms. It will be more disruptive than previous technological shifts. AI techniques like machine learning, deep learning, and natural language processing are making platforms and systems smart enough to learn from data and interactions to anticipate needs and automate tasks. Consumers are already using AI without realizing it through apps from Google, Facebook, Amazon, and self-driving cars. This raises expectations for smart, seamless customer experiences from businesses. The e-book will explore how companies can take advantage of AI for sales, customer service, marketing, and other business functions.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
The document provides an overview of research conducted by the London School of Economics on behalf of EY to investigate the use of artificial intelligence and machine learning in the financial services sector. It examines one use case for insurance, banking/capital markets, and wealth/asset management. The key findings are:
- Applied AI, mainly machine learning, is currently used across industries to solve isolated problems. Partnerships between large firms and startups are common.
- Prominent use cases illustrated trends in each sector, such as fraud detection in banking, predictive analytics in wealth management, and Internet of Things/home security applications in insurance.
- Both short and long term impacts are expected as machine learning capabilities advance, including changes
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
Artificial Intelligence Introduction & Business usecasesVikas Jain
This document discusses artificial intelligence and the fourth industrial revolution. It provides background on AI, including its history and increasing importance due to lower hardware costs, availability of data, and improved algorithms. It describes different types of AI and discusses how AI is being applied in various industries like customer service, retail, e-commerce, warehousing, healthcare, agriculture, and finance. It also addresses some of the threats, ethics, and vocabulary related to AI.
This presentation was made on May 13, 2020 and the video recording of it can be viewed here: https://youtu.be/QAgYASr1SHA
Description:
Are AI and AutoML overhyped or the answer to our problems?
Beyond the hyperbole, what are AutoML and AI?
How are they helpful, and when are they not?
Why are they more relevant and valuable than ever?
Our world is changing rapidly, and that implies many organizations will need to adapt quickly. AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business. AI empowers data teams to scale and deliver trusted, production-ready models in an easier, faster, more cost-effective way than traditional machine learning approaches.
AI and AutoML are not magic but it can be transformative, find out how at this virtual meetup. Get practical tips and see AutoML in action with a real-world example. We’ll demonstrate how AutoML can augment your Data Scientists, supercharging your team and giving your organization the AI edge in record time.
Speakers' Bio:
James Orton: He has over a decade of experience in analytics and data science across a number of industries. He has managed data science teams and large scale projects, before more recently launching his own startup. His vision for AI and that of H2O.ai were so closely aligned, it was a fortuitous opportunity for James to join H2O.ai in the Australia and New Zealand region.
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
This document provides an overview of artificial intelligence trends presented by Aarthi Srinivasan, Director of Product Management. It discusses growing investments in AI startups and by large corporations, with focus on automotive, healthcare, finance and education. Examples of applications include disease diagnosis, drug discovery, autonomous vehicles, facial and voice recognition. The presentation also provides guidance on structuring an AI product team and creating a machine learning-backed product vision.
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
This presentation was made on June 16, 2020.
A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA
AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.
H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.
Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start.
About Parul Pandey:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Once you’ve made the decision to leverage AI and/or machine learning, now you need to figure out how you will source the training data that is necessary for a fully functioning algorithm. Depending on your use case, you might need a significant amount of training data, and you’ll want to consider how that is labeled and annotated too.
View Applause's webinar with Cognilytica principal analysts Ronald Schmelzer and Kathleen Walch, alongside Kristin Simonini, Applause’s Vice President of Product, as they tackle the modern challenges that today’s companies face with sourcing training data.
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Diego Oppenheimer discusses the rise of algorithm marketplaces and the new "algorithm economy". Key points include:
- Advances in machine learning, computer vision, speech recognition and natural language processing are enabling algorithms to interpret unstructured data at scale.
- Algorithm marketplaces allow algorithms to be hosted, discovered, monetized and composed modularly to address a wide range of use cases across many industries.
- The algorithm economy will lower barriers to applying machine intelligence and foster innovation as algorithms become reusable assets that creators and users can both benefit from.
Disrupting with Data: Lessons from Silicon ValleyAnand Rajaraman
This document summarizes Anand Rajaraman's presentation on data-driven disruption. It discusses 5 generations of data-driven applications that have followed available data sources:
1) Leveraging private, structured data for competitive advantage.
2) Harnessing public data.
3) Leveraging "semi-public" social and mobile data shared with user consent.
4) Combining public, semi-public, and private data.
5) Adding artificial intelligence to leverage massive amounts of proprietary training data.
The presentation covers lessons around startup opportunities in infrastructure, analytics, and intelligent applications, and discusses the difference between data-driven disruption versus optimization. It also discusses challenges around human-machine
This document provides an agenda for a presentation on AI and machine learning for financial professionals. The presentation will be given by Sri Krishnamurthy, founder and CEO of QuantUniversity. The agenda includes introductions of the speaker and an overview of QuantUniversity. It then covers key trends in AI/ML, the basics of machine learning in 30 minutes, building a machine learning application in 10 steps, and case studies of how AI/ML are used in finance from companies like Bank of America, Ravenpack, and Northfield.
Bridging the Sales/Marketing Microcontent Gap with AIJoseph Fung
Presented at the Sales Enablement Society 2018 Annual Conference, this session dug into the strategies and tactics involved in leveraging Microcontent. If you're a Sales Enablement professional, this presentation will provide you with the tools necessary to get started with Microcontent today, regardless of the AI platform you use.
In today’s dynamic business landscape, AI has emerged as a game-changing force, offering marketers unparalleled opportunities to enhance customer engagement, optimize campaigns, and drive meaningful growth. Join us as Isabelle uncovers the true value of AI in marketing and the key factors for successful AI adoption. Move beyond the buzz to discover smarter content creation, advanced segmentation, and automation.
Key Takeaways:
1. Learn about micro segmentation. AI can help and be a multiplier effect in world where micro-segmentation and hyper-personalization will become the norm.2. How to use Generative AI: Crafting tailor-made content for each user segment, ensuring the message feels authentic and natural.3. Understanding the customer journey, analyzing touch points to predict behavior in a multidimensional world.
The UAE AI Strategy: Building Intelligent EnterprisesSaeed Al Dhaheri
1) The UAE has launched an ambitious AI strategy to boost GDP by 30% and government performance by adopting AI technologies.
2) The strategy aims to save 50% on annual government costs and ensure 90% resistance to financial crises by 2031 through widespread AI use.
3) The UAE sees great potential in AI to automate services, enhance data analytics, and transform government work - but also faces challenges in developing expertise and managing new technical requirements.
Hear guest speaker, Forrester Wave author Mike Gualtieri, discuss the importance of cognitive search and provide insights behind the newly published report, The Forrester Wave™: Cognitive Search, Q2 2019, in which Lucidworks is named a Leader.
Artificial Intelligence (AI) & The Future of Employee ServiceITSM Academy, Inc.
Dan Turchin, Astound
he bots are coming… but not to take your job. Learn how and why AI and machine learning are making humans better and how organizations like McDonald’s and adidas are delivering better service today.
Astound co-founder Dan Turchin will discuss the future of AI in IT and provide actionable tips that will guarantee your AI initiatives succeed.
Key takeaways:
How artificial intelligence is impacting IT
Why machine learning accelerates shift left strategies
How AI and natural language processing (NLP) are used to improve KPIs like MTTR, FCR, cost per ticket, and customer satisfaction
How AI-driven automation benefits the entire service lifecycle from provisioning and monitoring to incident, problem, and change management
DevDigital Presentation: How Much Are Your Digital Assets Worth?maggiedevdig
Every business is now a digital business and those where the full C-Suite understand the benefits of this are those that will be most successful.Executive leadership teams must recognize that the separation between technology and business has vanished, and that understanding and using technologies is now the key to any long lasting success.
Generative AI - The New Reality: How Key Players Are Progressing Vishal Sharma
The document discusses key players in generative AI and their progress. It provides an overview of generative AI including its evolution since 1950, where the spending is focused, how the technology works, and deployment models. It then profiles several major companies leading advancements in generative AI, including their strategies, growth areas, and risks. These companies are TSMC, Nvidia, Microsoft, Google, Amazon, Tesla, Oracle, Salesforce, SAP, and Palo Alto Networks.
This document provides an overview of seamless chatbot integration and deploying chatbots in organizations. It discusses Cocolevio, a technology solutions firm, and their focus areas including consulting, enterprise architecture, and software development. It then covers chatbot basics, market insights on chatbot growth, examples of chatbots in use, and the evolution of chatbots. Finally, it discusses deploying chatbots in organizations including using machine learning, natural language processing, and integrating chatbots across platforms in a platform agnostic way. The key points are that chatbots are growing exponentially, examples show benefits and challenges, and deploying effective chatbots requires machine learning, natural language processing, and platform agnostic integration.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Fintech workshop Part I - Law Society of Hong Kong - XccelerateHenrique Centieiro
What is fintech? What are the technologies leveraging Fintech? How AI, Blockchain, Cloud and Data Analytics are changing the financial world?
Henrique works as Innovation Project Manager implementing Fintech and Blockchain Projects for the Financial Industry
Find me here: linkedin.com/in/henriquecentieiro
Similar to AI Foundations Course Module 1 - An AI Transformation Journey (20)
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
This document provides an overview of H2O.ai, an AI company that offers products and services to democratize AI. It mentions that H2O products are backed by 10% of the world's top data scientists from Kaggle and that H2O has customers in 7 of the top 10 banks, 4 of the top 10 insurance companies, and top manufacturing companies. It also provides details on H2O's founders, funding, customers, products, and vision to make AI accessible to more organizations.
Generative AI Masterclass - Model Risk Management.pptxSri Ambati
Here are some key points about benchmarking and evaluating generative AI models like large language models:
- Foundation models require large, diverse datasets to be trained on in order to learn broad language skills and knowledge. Fine-tuning can then improve performance on specific tasks.
- Popular benchmarks evaluate models on tasks involving things like commonsense reasoning, mathematics, science questions, generating truthful vs false responses, and more. This helps identify model capabilities and limitations.
- Custom benchmarks can also be designed using tools like Eval Studio to systematically test models on specific applications or scenarios. Both automated and human evaluations are important.
- Leaderboards like HELM aggregate benchmark results to compare how different models perform across a wide range of tests and metrics.
LLMOps: Match report from the top of the 5thSri Ambati
The document discusses LLMOps (Large Language Model Operations) compared to traditional MLOps. Some key points:
- LLMOps and MLOps face similar challenges across the development lifecycle, but LLMOps requires more GPU resources and integration is faster due to more models in each application. Evaluation is also less clear.
- The LLMOps field is around the 5th generation of models, with debates around proprietary vs open source models, and balancing privacy, cost and control.
- LLMOps platforms are emerging to provide solutions for tasks like prompting, embedding databases, evaluation, and governance, similar to how MLOps platforms have evolved.
Building, Evaluating, and Optimizing your RAG App for ProductionSri Ambati
The document discusses optimizing question answering systems called RAG (Retrieve-and-Generate) stacks. It outlines challenges with naive RAG approaches and proposes solutions like improved data representations, advanced retrieval techniques, and fine-tuning large language models. Table stakes optimizations include tuning chunk sizes, prompt engineering, and customizing LLMs. More advanced techniques involve small-to-big retrieval, multi-document agents, embedding fine-tuning, and LLM fine-tuning.
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...Sri Ambati
Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai
H2O Open Source GenAI World SF 2023
In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.
Patrick Hall, Professor, AI Risk Management, The George Washington University
H2O Open Source GenAI World SF 2023
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!
Dr. Alexy Khrabrov, Open Source Science Community Director, IBM
H2O Open Source GenAI World SF 2023
In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
The document announces the launch of the H2O GenAI App Store, which provides a collection of applications that make it easier for average users to leverage large language models through custom interfaces for specific tasks like getting gardening advice or feedback on code. The app store is designed to accelerate the development of these GenAI apps using the H2O Wave platform and provides access to H2OGPTE for retrieval augmented generation and language model calls. Developers can also contribute their own apps through the GitHub repository listed.
Applied Gen AI for the Finance Vertical Sri Ambati
Megan Kurka, Vice President, Customer Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.
This document discusses techniques for improving language models (LLMs) discussed in recent papers. It describes building blocks of LLMs like fine-tuning, foundation training, memory, and databases. Specific techniques covered include LIMA which uses 1,000 carefully curated examples, instruction backtranslation to generate question-answer pairs, fine-tuning models on API examples like Gorilla, and reducing false answers through techniques like not agreeing with incorrect user opinions. The goal is to discuss cutting edge tricks to build better LLMs.
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...Sri Ambati
Pascal Pfeiffer, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.
KGM Mastering Classification and Regression with LLMs: Insights from Kaggle C...Sri Ambati
This document discusses using large language models (LLMs) for text classification tasks. It begins by describing how LLMs are commonly used for text generation and question answering. For classification, models are usually trained supervised on labeled data. The document then explores using LLMs for zero-shot classification without training, and techniques like fine-tuning LLMs on tasks to improve performance. It provides an example of fine-tuning an LLM on a financial sentiment dataset. The document concludes by describing H2O.ai's LLM Studio tool for fine-tuning and a few Kaggle competitions where LLMs achieved success in text classification.
1) Generative AI (GenAI) enables the creation of novel content by learning patterns in unstructured data rather than labeling outputs like traditional AI.
2) Both traditional and generative AI models lack transparency and may contain biases, but generative models can additionally hallucinate or leak private information.
3) To interpret generative models, researchers evaluate accuracy globally by checking for hallucinations or undesirable content, and locally by confirming the quality of individual responses.
Introducción al Aprendizaje Automatico con H2O-3 (1)Sri Ambati
En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...Sri Ambati
Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto.
In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.
ML Model Deployment and Scoring on the Edge with Automatic ML & DFSri Ambati
Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow
YouTube Video URL: https://youtu.be/gB0bTH-L6DE
Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
Visit- https://onliveserver.com/linux-web-hosting/
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Best Programming Language for Civil EngineersAwais Yaseen
The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era.
Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.
AI Foundations Course Module 1 - An AI Transformation Journey
1. AI Foundations
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
2. 2
What you
can expect
in this
session
01 Introduction
02 What is AI and Why Is It Important Now?
03 The AI Journey & The Keys to Unlock AI
04 AI in Action: Real World Use Cases
05 Summary & What’s Next
3. Why We Are Here
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Part: 1
4. 4
AI & ML Foundations
AI Foundations
● Intro to Key AI Concepts
● No prior AI knowledge or
background necessary
● No technical or coding
experience necessary
● Exercises: Non-Technical and
introductory
ML Foundations
● Applied AI Concepts
● Some experience with Python
or R would be helpful to
success
● Exercises: Technical and
deeper
In both courses you get access to H2O.ai experts and community makers!
You can earn a badge for AI & ML Foundations by successfully completing the assessments at the
end of each module (not required).
Session: X
5. 5
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Session 2: Shifting to the Next Step in the AI Transformation Journey
Study Group
Session 3: AI Transformation and Covid-19
Module 2: Demystifying AI
Module 3: Machine Learning Foundations
AI Foundations Overview
You Are Here
Interested in knowing the full
schedule for the AI Foundations
course? View the schedule on
the community learning site
8. 8
AI Projects & Challenges
Not Currently
Working on an AI
Project
59%
9. What is Artificial Intelligence?
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Part: 2
10. 10
Artificial intelligence (AI)
• A field of computer science that provides the ability for a computer to
learn and reason like humans using several available techniques.
• It is an important field for those who want to extract meaningful insights
from massive amounts of data in a timely and systematic manner
AI & The Role of Machine Learning
AI today is largely powered by Machine Learning (ML)
• ML happens when a computer can take lots of data (examples) and
learn patterns from it to make predictions on new data based on those
learned patterns.
11. 11
AI ML
NLP
Expert Systems
RL
DL
AI is more than just Machine Learning
Math
Optimizations
Grammars
Knowledge and
Graph Systems
Computer
Vision
Robotics
H2O.ai’s AI Glossary
Module: 3
12. 1212
AI Transformation
Why Now?
1950s 1980s
2000s 20202010s
Digital
transformation
AI
transformation
• Math
• Statistics
• Algorithms
• Expensive computing
• Early AI in research
• Expert Systems
• Rules Engines
• CPU and storage
enterprise wide
• WWW
• Search
• IoT begins
• Big Data (Hadoop)
• Rise of GPUs for AI
• Efficient storage
• Faster compute
• IoT miniaturization
• Networks everywhere
• Data science skills
• Public cloud emerges
Businesses are ready for an AI transformation
Perfect storm
✔ Open source algorithms
& frameworks
✔ High performance and
cost-effective compute
& storage
✔ Advanced data science
skills available
✔ More data than ever before
✔ AI can solve complex
business problems
✔ Fast on ramp & cloud economics
Algorithms, Data and Compute become Commodities
13. 13
AI Spans Industries and Use Cases
Wholesale / Commercial
Banking
• Know Your Customers (KYC)
• Anti-Money Laundering (AML)
Card / Payments Business
• Transaction frauds
• Collusion fraud
• Real-time targeting
• Credit risk scoring
• In-context promotion
Retail Banking
• Deposit fraud
• Customer churn prediction
• Auto-loan
Financial Services
• Early cancer detection
• Product recommendations
• Personalized prescription
matching
• Medical claim fraud detection
• Flu season prediction
• Drug discovery
• ER and hospital management
• Remote patient monitoring
• Medical test predictions
Healthcare and
Life Science
• Predictive maintenance
• Avoidable truck-rolls
• Customer churn prediction
• Improved customer viewing
experience
• Master data management
• In-context promotions
• Intelligent ad placements
• Personalized program
recommendations
Telecom
• Funnel predictions
• Personalized ads
• Fraud detection
• Next best offer
• Next best action
• Customer segmentation
• Customer churn
• Customer recommendations
• Ad predictions and fraud
Marketing and RetailMarketing and Retail
14. Confidential14
Examples of the impact of AI Transformations
…real-time individualized experience
…dynamic yield optimizationBreak then fix
…personalized quality of serviceCustomer service silos
…personalized healthcareMass treatment
…real-time trade surveillanceDaily risk analysis
Mass branding
WITH AIPRE-AI
AI allows
organizations to
shift interactions
from…
Reactive
Post Transaction
Proactive
Pre Decision
15. The AI Journey & The Keys to
Unlock AI
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Part: 3
16. Confidential16
AI Business Value: A Journey in Four Phases
1 2 3 4Potential Operational Strategic Data-Driven
Enterprise AI Journey
Awareness & Interest
Evaluate Business Value
Technical Evaluation
Point
Deployment
Point
Production
Enterprise Deployment
Enterprise Production
Modern Data Architecture
Industry Leadership
Session: 2
17. 17
Who is on the team?
Business leader, data
scientists, IT professional
Determine the problems you
want to solve with metrics (time,
money, # of customers, etc)
Determine where you have
data, need data, and can
use technology to find
answers and predictions.
Find answers efficiently.
Learn from others in the
data science community
Ask the Right Questions
Data & Technology
Community
Create a Data Culture
Understand and explain the
models. Use leading edge
technologies to guard for
bias, explain a model, and
present this to regulators
Trust in AI
2
1
3
4
5
5 Keys
to unlock AI
18. Confidential18
Machine learning is as much
a cultural transformation as a
business transformation.
1Who’s On
Your Team?
IT Leader
Business Leader
Data Scientist
Find the right talent within.
Data. Data. Data.
20. 20
• Basing decisions on data rather than intuition
• Data-driven decisions + big data technologies = Improved business
performance
Why Data-Driven Decision Making Is Vital
Discover new
meaning in data
Predictive &
actionable insights
Build confidence in
decision-making
Communicate data
stories for impact
Create valuable
Data Products
21. Confidential21
AI Enables Data Products To Be Created That…
Provide insight
Increase revenue
Open Markets
Improve OperationsCreate new features
Data
22. Confidential22
Saving Lives
Supply Chain
Optimization
Digital Marketing
Insurance
Underwriting
Fraud Detection
Customer ChurnModel BuildingDebt Scoring
Propensity to
Lease
Bad credit
detection
+
700%
Marketing
Campaign
Effectiveness
$
20M/year
Savings
+
2X
Effectiveness in
Identification
25%
Time Reduction in
Planning
10%
Increase in Sepsis
Detection
$
10M/month
Debit Reduction
$
1.5M/month
Call Center Savings
50%
Time Reduction in
Model Building
25%
Increase
Customer Churn
Prediction
8%
More Accurate
Predictions of Bad
Credit
Winning
with AI
25. Confidential25
Executive Sponsorship Needed for AI to Succeed
Centralized Organization – Hub & Spoke model where the data science
team supplies analytics to multiple business units
AI Teams
27. Confidential27
Executive Sponsorship Needed for AI to Succeed
Hybrid Organization – Contains both a centralized AI team, but each
business unit has its own AI capabilities
AI Team
28. Confidential28
Determine the problems you
want to solve with metrics.
2Ask the Right
Questions
Want to save time?
Want to save money?
Increase customer base?
29. 29
Turning Business Questions to ML Problems
How Much
• How much will each
customer invest?
• How much will each
customer invest each
month?
• What will the cost of
stock X be?
• How will the exchange
rate change next week?
Which One
• Who will default on a
loan?
• Which customer will
churn?
• Which customers can I
upsell?
• Who will pre-pay their
mortgage?
• Which product is a
customer likely to buy?
• How does a customer
feel about a product or
company?
Grouping
• How should we
segment customers?
• What topics are in our
customer feedback?
• Based on similar
customers, what is the
next best offer?
Module: 3
31. Confidential31
Build or Buy?Open Source?
Cloud on on-prem? Data.
4Technology
Considerations
Determine where you have data, need
data, and can use technology to find
answers and predictions. Find answers
efficiently.
33. Confidential33
Rich AI Ecosystem - Too Many Choices?
Databases Big Data/Distributed
Computing
Cloud Computing
Programming Languages Business Intelligence Data Science/Analytics/AI
• Typical frontline store
of data (relational,
graph, etc)
• May be hosted in
cloud if volume of data
warrants it
• If data is too big to be
useful for accessing it
you can use big data
platforms for
distributed, parallel,
high-performance
computing
• In terms of accessing,
isolating, cleaning,
transforming data,
these are the big 3.
• Python + R are
consistently used for
DS & modeling
• Most common
resources for
descriptive statistics
and dashboarding
(specialize in
descriptive stats)
• For predictive & advanced analytic
insights use Data Science/AI
platforms (and py+R) to apply the
highest quality methods.
• Cloud computing may be needed
to run heavy math for these
models.
Module: 5
ML
Foundations
ML
Foundations
35. Confidential35
How Can You Build Trust in AI?
Data Scientist
Why did they do that?
Why not something else?
When will customer churn?
When will customer not churn?
When can I trust you?
What if an attribute changed?
How do I correct an error?
Training Data
Learning Function
Training Model
Output / Scores
Customer Churn
Customer Activity
Learning
Process
Module: 6
36. Confidential36
Why Does Machine Learning Explanation Matter?
I understand why
I understand why not
I know when customer will churn
I know when customer will not churn
I know when to trust ML model
When can I trust you?
I know what influences the prediction
I know why you erred
Training Data
explainable
model
Explanation
Interface
Customer Churn
Customer Activity
New
Learning
Process
Business Analyst
37. AI In Action: Real World Examples
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Part: 4
39. Confidential39
AI Journey at PwC
3. Digital Transformation
Digital Transformations enabled PWC
to generate new, larger insights with
more dynamic data
1. Talent Challenges
PWC needed a rapid & innovative
approach to attrittion and upskilling
2. Manual Limitations
PWC Auditors were stuck doing far too
many repetitive and redundant tasks, that
were prime for automation
4. The Future + AI
PWC wanted to leverage the
foundation built through digital to
identify high-value, innovative use
cases leveraging AI alongside H2O
40. Confidential40
GL.ai
2015-2017
• Co-innovation with
H2O.ai
• Saved months or
even years
pinpointing errors,
reducing risks and
finding fraud
immediately
• Enables PWC
experts to work on
high risk
situations, not
mundane tasks
A
Multi-Ye
ar AI
Journey
42. Confidential42 Confidential42
Empowering PwC to be an Award-Winning AI Company
“The reason this is such a brilliant tool is
its ability to look at different risks, in
context, at the same time. For example,
it would be uneconomical for an auditor
to look at every single user’s pattern of
activity to decide what’s unusual. With
GL.ai, the algorithms do it for us.”
“
Gary Rapsy
Global Assurance Disruption and Innovation
Leader at PwC
“Part of the reason for wanting to work
with H2O.ai is the passion and
purpose around advancing finance and
democratizing AI for Finance.”
“
Laura Needham
Partner, PwC UK
43. Confidential43
Wells Fargo has an Enterprise-wide AI Initiative Underway
100+
Data Scientists on
Driverless AI across
business units
Trust
Using MLI to explain
results to consumers
and regulators
“H2O.ai has the best and fastest GLM.
They listen to us, and are addressing our
needs. I am very impressed.”
Agus Sudjianto
EVP, Head of Corporate Model Risk
Wells Fargo
Anti-money laundering
Predictive Banking
Consumer 360
Personalized Banking
Transactions
Financial Decision Making
Consumer spending insights
Credit Card Fraud
Data Quality
Use Cases
“ Time Savings
Decreased
deployment time
44. Confidential44
A Decade of Data Science at Nationwide Insurance
45+
Centralized data
science team using
H2O.ai
Millions $
Annual savings
“H2O.ai provides us the power and
flexibility we need to solve business
problems with machine learning. We are
able to do more with less and do it faster.
Our results are proof of the power of AI in
action.”
Shannon Terry
Vice President, Predictive Analytics
Customer churn
Customer retention
Call routing
Risk segmentation
Business segmentation
Fraud
Underwriting
Customer expansion
Customer 360
Use Cases
“ 25 Billion
Scored from 500K
models instantiated
in 10 years
45. Confidential45
Capital One Transformation Yielding Results
Customer
Satisfaction
Using AI to streamline customer
calls and answer questions bette
New Business
Established a new revenue
stream with new data
products
“H2O.ai worked closely with Capital One
on not only identifying opportunities in
our business, but they were a true
partners in transforming our business
and leading us to the path of data and AI
transformation.”
Karthik Aaravabhoomi
Former Capital One Transformation Leader
Cybersecurity
Anti-money laundering
Predictive Banking
Consumer 360
Personalized Banking
Transactions
Financial Decision Making
Consumer spending insights
Credit Card Fraud
Data Quality
Use Cases
“ $ Tens of Millions
Enhanced and personalized
transactions saving millions of
dollars
46. Confidential46
Major US Telecom Creating a Model Factory
100+
Models in production
in a model factory
Trust
Using MLI to explain
results to consumers
and regulators
“Driverless AI is giving amazing results
in terms of feature and model
performance.”
Customer subscriber churn
Recommendation engines
Network anomaly detection
Fraud detection
Campaign optimization
Customer propensity
Next best offer
Tower placements (5G)
Predictive Maintenance
Use Cases
“ Time Savings
Distributed data
science team getting
results faster
47. Confidential47
Leveraging AI for Bond Pricing
5
50+
Leverage AI to buy
personal loans for funds
and separate accounts
“H2O Driverless AI speeds up machine learning
by automating our data science workflow. With
the new recipe capability, we can extend and
customize the platform to meet our needs, such
as estimating the prepayment risk of underlying
loans in fixed-income assets like
mortgage-backed securities. Driverless AI is
helping us accelerate our AI journey.”
“
Chris Pham
Senior VP Data Management and Data Science
Franklin Templeton
Customer segmentation
Next Best Offer
Loan Default Prediction
Buying Pattern Prediction
Exchange Rate Prediction
Investment Prediction
Customer Sentiment
Use Cases
Business Groups
Using AI
Data Scientists
on Driverless AI
Millions $
48. Confidential48
Using AI to Deliver Fresh Fruit in the Fastest Possible Way
Speed
2
Leverage AI to find the
fastest route to reduce
spoilage
“We are getting great results with
H2O Driverless AI. What once took
us 3 to 5 months using traditional
data science methods, can now be
done in 3 to 5 weeks without
having to add any additional data
scientists to the team.”
“
Gonzalo Bustos
Head of Data Analytics
Hortifrut
Supply chain transportation
optimization
Perishable predictions
Reducing claims
Use Cases
Reduction of modeling
time 3 to 5 months to 3
to 5 weeks
Data Scientists
on Driverless AI
Millions $
PRODUCER AND DISTRIBUTOR OF 25% OF THE WORLD’S BERRIES
49. Confidential49
Protecting Your Assets with AI
“After evaluating several solutions
in our search for the ideal AI
platform, we chose H2O.ai
because it provides us with the
transparency we needed into our
machine learning processes,
much more flexibility than the
other tools we evaluated, and the
strongest machine learning
explainability capabilities on the
market. H2O.ai provides new
avenues of innovation and allows
us to build quality and insightful
ML tools for our business
stakeholders.”
“
Andrew Langsner
Underwriting
Customer experience
Claim evaluations
Inventory stocking
Loss prevention
Lifetime value of a customer
Use Cases
Engagement
Increased customer
satisfaction (both the
insured and the retailers)
Trust
Using MLI to explain
results to consumers
and regulators
$ Millions
Increased insurance
policies with both
consumers and
retailers
50. Confidential50
Improving Leads to Leases with AI
95%
85%
Monthly savings on
marketing expenses
for Real Estate clients
“Driverless AI helped us gain
an edge with our Intelligent
Marketing Cloud for our
clients. AI to do AI, truly is
improving our system on a
daily basis.”
“
Martin Stein
Chief Product Officer
G5
COVID-19 impact on senior
housing
Sentiment analysis
Customer call center
predictions
Use Cases
Level of accuracy of
leads to leases
Leasing agents now
have qualified leads
85% of the time
$1.5M/ month
REAL ESTATE MARKETING
51. Confidential51
Driving Marketing Engagement with AI
700%
11%
Model training time
savings
“Driverless AI has made it easy to
try AI solutions in our own
environment and context. It
allowed us to quickly see the
benefits in our domain. Driverless
AI has cut down the overall
model development time in
about half. ”
“
Scott Pete
Director of Analytics and Insights
Predicting churn
Fraud detection
Next best experience
Brand marketing campaigns
Use Cases
Marketing campaign
cost savings and
effectiveness
ROI on marketing and
loyalty programs
25-30%
BRAND LOYALTY AND MARKETING
52. What’s Next?
Module 1: An AI Transformation Journey
Session 1: An AI Transformation Journey Primer
Part: 5
53. Confidential53
• AI is not only ML & doesn’t exist without data
• AI Transformation requires a journey through the phases of maturity:
– Potential
– Operational
– Strategic
– Data-Driven
• Unlocking the value of AI Requires:
– Resources: Data & Talent
– Asking the right question for the problem you are solving
– Communication & Community
– Getting the right technology in place
– Building trust in the use of AI
What We’ve Covered So Far
Recording will be
posted w/in 2
days
54. Confidential54
1. The next session Shifting to the Next Step in the AI Transformation Journey will be held on
Thursday July 2, 2020 @ 7:00AM PDT
2. There’s a special session on Monday July 6, 2020 @7:00AM PDT on AI Transformation Stories
related to Covid-19.
Upcoming Sessions
55. Confidential55 Confidential55
Quizzes & Study Groups
• Each session within a module will have a small quiz to complete and all
quizzes for that module will be due before the next module starts.
• There are 2 options available for you to ask additional questions or get
assistance on AI concepts covered in the sessions:
– A Study Group for each Module will be held on Saturdays @ 7:00AM PDT
– Ask Me Anything will be held on Sundays @7:00AM PDT
• Reminder: Don’t forget to complete Quiz 1: An AI Transformation
Journey by Tuesday July 7, 2020 to earn your badge!