An overview of the most important AI capabilities in marketing, advertising and content creation. I made this presentation to inform, educate and inspire people in the creative industries to familiarise themselves with the incredible toolsets that are already here and in development. I also explain how generative Ai works explore some possible new roles and business models for agencies. Hope you enjoy it!
This document discusses generative AI and its potential transformations and use cases. It outlines how generative AI could enable more low-cost experimentation, blur division boundaries, and allow "talking to data" for innovation and operational excellence. The document also references responsible AI frameworks and a pattern catalogue for developing foundation model-based systems. Potential use cases discussed include automated reporting, digital twins, data integration, operation planning, communication, and innovation applications like surrogate models and cross-discipline synthesis.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Generative AI art has a lot of issues:
Lack of Control: Generative AI art eliminates digital artists' control over their work. The results are unpredictable and often unsatisfactory, leaving artists feeling frustrated.
No Unique Signature: Generative AI art lacks a unique signature or style, making it difficult for digital artists to stand out.
Quality Control Issues: Generative AI art can be of poor quality and unsuitable for professional use. Digital artists who rely on their work to make a living may find that AI-generated work is not up to their standards.
Decreased Job Opportunities: As generative AI art becomes more popular, the demand for human digital artists may decrease, leading to fewer job opportunities.
No Emotional Connection: Generative AI art lacks the emotional connection artists can create through their work. This can make it difficult for digital artists to connect with their audience and make a lasting impact.
Limited Creative Potential: Generative AI art has limited creative potential based on algorithms and pre-defined parameters. Digital artists who seek to express their creativity and individuality may find it limiting.
Intellectual Property Concerns: Generative AI art can infringe on the intellectual property of others, leading to legal issues for the artist.
Lack of Personal Touch: Generative AI art lacks the personal touch that digital artists can bring to their work. This can result in a lack of emotion, connection, and engagement with the audience.
Decreased Income: Generative AI art is often available for free or at a low cost, making it difficult for digital artists to make a living through their work.
Loss of Craftsmanship: Generative AI art relies on technology, taking away the element of craftsmanship and hand-drawn skills that digital artists have honed over time.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
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.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
This document discusses generative AI and its potential transformations and use cases. It outlines how generative AI could enable more low-cost experimentation, blur division boundaries, and allow "talking to data" for innovation and operational excellence. The document also references responsible AI frameworks and a pattern catalogue for developing foundation model-based systems. Potential use cases discussed include automated reporting, digital twins, data integration, operation planning, communication, and innovation applications like surrogate models and cross-discipline synthesis.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Generative AI art has a lot of issues:
Lack of Control: Generative AI art eliminates digital artists' control over their work. The results are unpredictable and often unsatisfactory, leaving artists feeling frustrated.
No Unique Signature: Generative AI art lacks a unique signature or style, making it difficult for digital artists to stand out.
Quality Control Issues: Generative AI art can be of poor quality and unsuitable for professional use. Digital artists who rely on their work to make a living may find that AI-generated work is not up to their standards.
Decreased Job Opportunities: As generative AI art becomes more popular, the demand for human digital artists may decrease, leading to fewer job opportunities.
No Emotional Connection: Generative AI art lacks the emotional connection artists can create through their work. This can make it difficult for digital artists to connect with their audience and make a lasting impact.
Limited Creative Potential: Generative AI art has limited creative potential based on algorithms and pre-defined parameters. Digital artists who seek to express their creativity and individuality may find it limiting.
Intellectual Property Concerns: Generative AI art can infringe on the intellectual property of others, leading to legal issues for the artist.
Lack of Personal Touch: Generative AI art lacks the personal touch that digital artists can bring to their work. This can result in a lack of emotion, connection, and engagement with the audience.
Decreased Income: Generative AI art is often available for free or at a low cost, making it difficult for digital artists to make a living through their work.
Loss of Craftsmanship: Generative AI art relies on technology, taking away the element of craftsmanship and hand-drawn skills that digital artists have honed over time.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
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.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
- ChatGPT was launched in November 2022 and gained over 1 million users in its first 5 days, making it one of the fastest adopted digital products.
- ChatGPT is based on GPT-3, a large language model developed by OpenAI over many years using trillions of words from the internet to power conversational abilities.
- ChatGPT can answer questions, write stories, programs, music and more based on its vast knowledge, but cannot provide fully trustworthy information, create harmful content, or replace all human jobs.
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
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI offers both opportunities and risks for enterprises. While it could drive significant ROI through personalized experiences, thought leadership, and faster processes, there are also concerns about job losses, overreliance on automation without oversight, and inaccurate information. Effective adoption of generative AI requires experience management strategies like understanding emotional and logical customer triggers, aligning products and services to experience channels, and building a business model around a compelling brand story. A people-first approach is important to maximize benefits and mitigate risks.
The Five Levels of Generative AI for GamesJon Radoff
The document discusses 5 levels of generative AI that could be applied to games and virtual worlds, inspired by levels of autonomous vehicles. It outlines the levels for 4 types of creators: game studios, modders, players, and the game itself. The levels range from no automation to direct creativity from imagination. For each creator type, level 5 represents a state where generative AI is seamlessly integrated to directly spawn creations from ideas or prompts. The document aims to help identify opportunities for generative AI and mark progress in virtual world innovations.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
The document discusses the opportunities and risks of generative AI (GenAI) for leaders. It notes that GenAI could enable unprecedented positive impact but also dangers if not addressed. It provides 5 steps leaders should take: 1) learn GenAI fundamentals, 2) explore available GenAI services, 3) get inspired by opportunities, 4) understand hazards, and 5) take safe initial steps to unlock potential while avoiding harms. Leaders are encouraged to ground their purpose and integrity amid the possibilities and existential risks of GenAI.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Generative artificial intelligence (AI) models are reinventing communication, content creation, and information access. In this roadmap, presented at Bessemer's annual Seed Summit, Partner Talia Goldberg explores the technological advancements driving AI solutions and how these changes are opening up new promising area of investment.
Learn more about Generative AI:
https://www.bvp.com/atlas/is-ai-gener...
https://www.bvp.com/atlas/roadmap-the...
https://www.bvp.com/atlas/entering-th...
Subscribe for venture insights: https://bessemervp.team/subscribe
About Bessemer Venture Partners —
We help entrepreneurs lay strong foundations to build and forge long-standing companies. With more than 135 IPOs and 200 portfolio companies in the enterprise, consumer and healthcare spaces, Bessemer supports founders and CEOs from their early days through every stage of growth. Our global portfolio includes Pinterest, Shopify, Twilio, Yelp, LinkedIn, PagerDuty, DocuSign, Wix, Fiverr and Toast, and has more than $20 billion of assets under management.
Connect with us —
Subscribe to our channel: https://bit.ly/3oVeW4k
Visit our website bvp.com: https://bit.ly/3bzFXaE
Sign up for our newsletter: https://bit.ly/3SoVY3D
Find Bessemer on LinkedIn: https://bit.ly/3zZpGoS
Find Bessemer on Twitter: https://bit.ly/3JsVJAF
Find Bessemer on Instagram: https://bit.ly/3BH8but
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
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
The world of content marketing is no stranger to evolution. From the early days of static web pages to the dynamic, data-driven landscapes of today, the industry has constantly adapted to embrace new technologies and trends.
Role, application and use cases of ai-ml in next-gen social networks (1)prachi gupta
This document discusses artificial intelligence and machine learning applications for social media marketing and the next generation. It defines AI and machine learning, and describes how they are currently used for social media marketing, including advertising, content curation, image recognition, and sentiment analysis. It also discusses chatbots and the limits of current AI, which lack personalization, adaptability, and self-learning abilities needed for next generation applications.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
- ChatGPT was launched in November 2022 and gained over 1 million users in its first 5 days, making it one of the fastest adopted digital products.
- ChatGPT is based on GPT-3, a large language model developed by OpenAI over many years using trillions of words from the internet to power conversational abilities.
- ChatGPT can answer questions, write stories, programs, music and more based on its vast knowledge, but cannot provide fully trustworthy information, create harmful content, or replace all human jobs.
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
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI offers both opportunities and risks for enterprises. While it could drive significant ROI through personalized experiences, thought leadership, and faster processes, there are also concerns about job losses, overreliance on automation without oversight, and inaccurate information. Effective adoption of generative AI requires experience management strategies like understanding emotional and logical customer triggers, aligning products and services to experience channels, and building a business model around a compelling brand story. A people-first approach is important to maximize benefits and mitigate risks.
The Five Levels of Generative AI for GamesJon Radoff
The document discusses 5 levels of generative AI that could be applied to games and virtual worlds, inspired by levels of autonomous vehicles. It outlines the levels for 4 types of creators: game studios, modders, players, and the game itself. The levels range from no automation to direct creativity from imagination. For each creator type, level 5 represents a state where generative AI is seamlessly integrated to directly spawn creations from ideas or prompts. The document aims to help identify opportunities for generative AI and mark progress in virtual world innovations.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
The document discusses the opportunities and risks of generative AI (GenAI) for leaders. It notes that GenAI could enable unprecedented positive impact but also dangers if not addressed. It provides 5 steps leaders should take: 1) learn GenAI fundamentals, 2) explore available GenAI services, 3) get inspired by opportunities, 4) understand hazards, and 5) take safe initial steps to unlock potential while avoiding harms. Leaders are encouraged to ground their purpose and integrity amid the possibilities and existential risks of GenAI.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Generative artificial intelligence (AI) models are reinventing communication, content creation, and information access. In this roadmap, presented at Bessemer's annual Seed Summit, Partner Talia Goldberg explores the technological advancements driving AI solutions and how these changes are opening up new promising area of investment.
Learn more about Generative AI:
https://www.bvp.com/atlas/is-ai-gener...
https://www.bvp.com/atlas/roadmap-the...
https://www.bvp.com/atlas/entering-th...
Subscribe for venture insights: https://bessemervp.team/subscribe
About Bessemer Venture Partners —
We help entrepreneurs lay strong foundations to build and forge long-standing companies. With more than 135 IPOs and 200 portfolio companies in the enterprise, consumer and healthcare spaces, Bessemer supports founders and CEOs from their early days through every stage of growth. Our global portfolio includes Pinterest, Shopify, Twilio, Yelp, LinkedIn, PagerDuty, DocuSign, Wix, Fiverr and Toast, and has more than $20 billion of assets under management.
Connect with us —
Subscribe to our channel: https://bit.ly/3oVeW4k
Visit our website bvp.com: https://bit.ly/3bzFXaE
Sign up for our newsletter: https://bit.ly/3SoVY3D
Find Bessemer on LinkedIn: https://bit.ly/3zZpGoS
Find Bessemer on Twitter: https://bit.ly/3JsVJAF
Find Bessemer on Instagram: https://bit.ly/3BH8but
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
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
The world of content marketing is no stranger to evolution. From the early days of static web pages to the dynamic, data-driven landscapes of today, the industry has constantly adapted to embrace new technologies and trends.
Role, application and use cases of ai-ml in next-gen social networks (1)prachi gupta
This document discusses artificial intelligence and machine learning applications for social media marketing and the next generation. It defines AI and machine learning, and describes how they are currently used for social media marketing, including advertising, content curation, image recognition, and sentiment analysis. It also discusses chatbots and the limits of current AI, which lack personalization, adaptability, and self-learning abilities needed for next generation applications.
Article-An essential guide to unleash the power of Generative AI.pdfBluebash
Generative AI is a powerful branch of artificial Intelligence that allows computers to learn patterns from existing data and then employ that knowledge to create new data
Generative AI refers to a class of machine learning algorithms that are designed to generate new data samples that are similar to those in the training data. Unlike traditional AI models that are trained to recognize patterns and make predictions, generative AI models have the ability to create entirely new data based on the patterns they have learned. This is achieved through techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer architectures, among others.
A Balancing Act: Knowing When to Use AI for Content Creation and When to Avoi...Nirvana Canada
Just a few short years ago, generative AI was something reserved for the plot line of some futuristic action flick. Today, however, AI-powered tools are everywhere churning out audio, images, text, and video content faster than the average person could type out this paragraph.
This document discusses the rise of conversational AI and how digital agents can represent brands. It notes that generative AI enables new types of interactions that are more helpful than traditional chatbots. Digital agents can automate work by having natural conversations to complete tasks on behalf of users. The document provides examples of how a sales digital agent could assist a user before, during, and after a client meeting. It outlines six key ingredients for building effective digital agents, including prompting, context, proprietary knowledge, voice, reasoning, and code generation. The challenge for brands is to design unique digital agents that embody their values and approach in order to benefit from the changes brought by conversational AI.
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The Creative Ai storm
1. The AI Creative Storm
An overview of the most important AI capabilities in marketing,
advertising and content creation.
By Leandro Righini
2. Intro
AI is like a superpowered version of the cognitive skills that
humans have. It can interact with us in a way that feels
natural and can understand, learn, interpret, and reason
about complex concepts and information. And the best or
worst part depending on your own perspective is that AI can
do all these things faster and on a bigger scale than humans
can. I am personally optimistic that AI will provide tools that
increases our productivity, creativity and capabilities.
Is AI the next buzz word and is it already losing meaning?
I think its fair to say that artificial intelligence will be built
into most products, services and tools and much like the
term digital it will soon be obsolete to raise it up but for now
it's important to get a grasp of what it can offer us and what
are the opportunities.
This presentation is my attempt to provide an overview on
how generative AI works and what it offers us as well as
touching on the ethics involved in using AI.
3. Who am I?
My name is Leandro. Based in Helsinki, I'm a creative director
and filmmaker with a passion for technology.
As a curious and innovative thinker, I'm constantly exploring new
tools and ways to create compelling content with the help of
technology.
In the past year, I have been particularly interested in the
potential of AI and have been deep diving into its capabilities. I'm
excited to share some of what I've learned with you, as I believe
it has the power to inspire and transform the creative industries.
https://linktr.ee/leandrorighini
https://www.linkedin.com/in/leandro-righini/
4. How Models Work
It allows computers to create content in a way that mimics
human creativity.
• Simply put it works by using complex machine learning
models to predict the next word in a sequence or the next
image based on a prompt.
• These generative models have largely been confined to major
tech companies because training them requires massive
amounts of data and computing power. But once the core
generative model is trained and opened up to the public, it
can be “fine-tuned” for a particular purpose or niche with
much less data.
• The ease of training these models has led to people and
companies creating services using specialized models that
can be used for a wide variety of specific purposes.
• To use generative AI effectively, you still need human
involvement at both the beginning and the end of the
process.
Generative AI is already a force to be reckoned with.
5. Capabilities of AI
• Text generation: Large language models can generate text at
scale. This could be articles, product descriptions, blog post,
social media posts, press releases, instructions and fine tune
reports.
• Images, video and animation: AI is getting incredibly good at
making images and its rapidity improving at making videos.
• Avatars, human/face generation, Deepfakes and Clones:
You can now make or order your own virtual avatars to
represent your brand or present your product. What’s more, you
can use a voice clone filter to impersonate actors, celebrities or
distinctive voices.
6. Capabilities of AI
• Coding: AI's can code based on natural
language prompts (NLP). They can already
check and write code and give good
instructions on how to use it. They can also
translate code from one language to another.
• SEO: AI's can automate your SEO helping
with content, optimization and linking.
• Music: Make unique tracks based on the
tempo and tone of your video or on a prompt.
You can also generate voice artist soundalikes
that autotune to the music.
7. 24 June 2022 5 Dec 2022
Prompt: Mandela as a superhero Prompt: Mandela as a superhero
It’s happening so fast.
10. ChatGPT allows users to have natural and engaging conversations with the chatbot. Some
potential uses for ChatGPT include customer service chatbots, virtual assistants, and language
translation.
Some Go-To AI Tools for Content
Creation
Jasper & Copy AI are standout writing apps. The heart of good content is in the writing and
these tools + your own expertise are the start of your content workflow.
Midjourney is an incredible tool for creating images. It’s a general model so I use it for
quick image creation, ideation, as well as for creating seed images for use in Stable Diffusion.
RunwayML is an online editing suite that is daily adding to its arsenal of Magic AI tool.
Its useful for everything from rotoscoping, motion tracking, image creation, inpainting,
out-painting, audio cleaning, color correcting. I love this Runway and use it everyday.
11. Synthesia: Create videos from plain text in minutes starring an talking AI avatar.
More Go-To AI Tools for Content
Creation
Stable Diffusion is an open-source image diffuser that acts as the base for a lot of
experimental services. With stable diffusion you can train models, make images, restyle video,
do video animation, in-painting and out-painting and a lot of niche services – downsize it’s a
bit technical and you have to deal with some coding.
Play.ht offers high quality AI voices for your videos, podcasts and presentations. Some of them are great,
some are ok, but overall, a really useful service with an easy-to-use UI.
Voice AI is an app with a active community training voice clones. I’m not sure how the legality of it works
but its really interesting and fun to play with and it allows you to filter your voice in real time which is
great for doing demo voice overs in different voice styles. Need a PC though.
13. • Personalization: Generative AI can be used to create personalized
advertisements or marketing materials that are tailored to individual customers
or target audiences. For example, a generative AI system could create a
personalized email marketing campaign that is based on a customer's past
purchase history or interests.
• Content creation: Generative AI can be used to create original content for
marketing and advertising campaigns. This can include things like product
photography, brand storytelling, video content, social media posts and content,
product descriptions, or even entire websites.
• Targeting: Generative AI can be used to analyze data about consumers and
create targeted advertising campaigns based on that analysis. This can allow
marketers to reach the right audience with the right message at the right time.
• Efficiency: Generative AI can also be used to streamline and automate certain
aspects of the marketing and advertising process, allowing companies to be
more efficient and effective in their campaigns.
Transformation of Marketing
and Advertising
14. The role of Creative Agents
A creative agent will be highly skilled in the use of natural language
processing (NLP) will be trained in various specialized artificial
intelligence (AI) tools.
They may also have access to an AGI (Artificial General Intelligence)
assistant, which is a type of AI that is designed to be capable of
learning and adapting to a wide range of tasks and contexts, in order
to help with ideation and planning.
With the aid of AI, this creative agent is able to produce a range of
content, including strategy, copy (written content), images, design,
and other types of content.
This means that they are able to use their expertise in NLP and their
familiarity with AI tools to create high-quality, engaging, and effective
content that is tailored to their audience.
The use of an AGI assistant can also help them to come up with new
ideas and approaches, allowing them to stay ahead of the curve and
remain creative and innovative in their work.
AI + Creative
15. • The agency works with the client to understand their business goals, target
audience, and helps define the creative direction.
• The creative director at the agency then uses this information to guide the
development of a specialized AI model that is trained on the client's data. This
model is designed to produce various types of advertising assets, such as copy,
images, videos, and more.
• The agency employs creative agents, who are responsible for managing the AI
model and ensuring that it’s producing high-quality assets that align with the
client's vision/goals.
• The creative agents work closely with the creative director to review and refine
the output of the AI model, as needed.
• The agency then uses the assets produced by the AI model to execute
marketing campaigns for the client, using various channels such as social
media and paid advertising.
• The agency tracks the performance of these campaigns and provides regular
reporting to the client, highlighting key metrics such as reach, engagement, and
conversion rates.
Agency + AI Hybrids
16. AI is a tool, not a
threat: use it to your
advantage
• Stay up-to-date with the latest AI technologies and how they are being
used in your industry
• Develop a diverse skill set: in addition to your core skills, it can be
helpful to develop technical skills related to AI and data analysis. This
will allow you to use AI-powered tools and platforms and understand
how to interpret and use the data they produce.
• Focus on creative problem-solving: While AI can handle many routine
tasks, it is still not as good as humans at creative problem-solving. By
focusing on this aspect of your work, you can differentiate yourself and
add value to your team or organization.
• Embrace the benefits of AI: Instead of seeing AI as a threat, try to see it
as an opportunity. AI can help you streamline your work and allow you
to focus on the most creative and strategic aspects of your job.
• Collaborate with AI: By working with AI, you can leverage its strengths
and enhance your own skills and expertise.
17. Ethical considerations of AI
Some of the ethical considerations that should be taken into account
include:
Transparency: It is important to be transparent about the use of AI in
content creation and to clearly label AI-generated content as such.
This helps to ensure that readers or viewers are aware of the source
of the content and can make informed decisions about its reliability
and credibility.
Accountability: It is important to hold those who use generative AI
accountable for the content they create. This includes ensuring that
AI-generated content is accurate and does not mislead or deceive
people and holding those who use AI to produce fake or misleading
content accountable for their actions.
Responsible use: It is important to use generative AI responsibly and
to consider the potential impacts of this technology on individuals,
communities, and society as a whole. This includes considering the
potential risks and benefits of using generative AI, as well as the
ethical implications of using this technology.
18. Drawbacks of using AI
One potential drawback of using generative AI for content creation is
the risk of creating misleading or fake content.
Generative AI algorithms are able to produce large amounts of
realistic-looking content quickly, but this content may not always be
accurate or reliable. For example, AI-generated news articles or social
media posts could contain false or misleading information that could
be used to manipulate public opinion or deceive people.
Another potential drawback is the potential for AI to replace human
jobs in the creative industries.
As AI algorithms become more advanced, they may be able to perform
tasks that were previously carried out by humans, such as writing
articles, creating social media posts or doing voice overs.
This could lead to job loss and disruption in the industry, as well as
potential ethical concerns about the use of AI in place of human
labour.
19. How to train a Generative AI Model
An example of how you would train a generative AI model to create
music:
1.Collect music samples: First, you will need to gather a large dataset
of music samples that you want the model to learn from. These could
be MIDI files or audio recordings of songs in a specific genre, for
example.
2.Pre-process the data: You will then need to prepare the data by
formatting it in a way that can be used to train the model, such as
converting audio files to spectrograms or quantizing MIDI files into a
series of notes and durations.
3.Choose and set up a model: Next, you will need to choose a type of
model to use and set it up with the right layers and settings.
4.Train the model: Once the model is set up, you can start training it
using the pre-processed data. This will involve feeding the data into
the model and using a special computer program to adjust the model's
settings so that it can learn to create new music.
5.Test the model: After training the model, you can test it by having it
generate new music and listening to see how well it does.
6.Make improvements: If the model doesn't create good music, you
can try adjusting its settings or adding more data to try again.
I hope this helps! Let me know if you have any questions.
Collect music
samples
Clean and explore
Data
Process the Data
Choose and Set up
Model
Train the model
Test the model
Make
Improvements
Deploy model
20. Image credit
https://dugas.ch/artificial_curiosity/img/GPT_architecture/in_out.png
How AI’s write so well
Writing
They are not actually that intelligent. They have a
huge dataset and well trained ability to predict
the next words.
Basically an AI predicts the next word in a
sequence based on the words that come before it
using a technique called "sequence generation".
The AI can then use this prediction to generate a
complete piece of text that is coherent and flows
naturally.
22. AI Poetry and Art
Dead Robot Society is a TikTok project that reimagines old Masters with new technology. All the poems, art, animation and
voices are made with AI. I originally started this project to test and understand the ability of Copy.ai. It very quickly became a
full workflow using Midjourney for images, RunwayML for animation, Descript for captions, PlayHT for AI voices, VoiceAI for
voice cloning.
My voice - style of
Morgan Freeman
My voice - style of
Oprah Winfrey
AI Voice
AI Voice
23. AI Scene Mixer – Top Gun
AI Scene Mixer is a YouTube project that remixes movie scenes and trailer in different artstyles. The goal isn’t to do it
perfectly. I’m more interested in how well you can easily remix video images into new styles – for me this is a real indicator on
how we are progressing towards remixable and personalised movies and content. Imagine just being able to drop yourself
into a movie or deciding you want to watch Lord of the Rings with a female Frodo. This is an example of a remix of Top Gun.
25. No AI’s were harmed in the making
of this presentation.
All images in this presentation are AI-generated
images that were created by the me using my
own original prompts.
I also used ChatGPT and copy.ai as writing
assistants, to help with organising my thoughts
and expressing some of my ideas.
26. Thanks for reading!
I hope you enjoyed reading through my presentation as much as I enjoyed
creating it.
I am extremely excited and inspired by the incredible tools that are now
available to us in the creative world. I have seen a huge increase in my
own creative output and productivity thanks to the use of AI, and I can't
wait to see what the future holds for this exciting field.
Overall, I believe that AI has the potential to be a powerful labor-saving
tool for the creative industries, but it’s important to remember that it is
only a tool and should be used in combination with human creativity and
intuition.
With the right balance of technology and artistry, we can achieve truly
incredible things.
If you found this useful or interesting, please consider sharing it on
LinkedIn, or if you or your team is interested in consultation or
collaboration, please feel free to get in touch.
All the Best
Leandro Righini