AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
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.
* "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
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
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
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.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
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.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
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.
* "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
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
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
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.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
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.
The coming generative AI trends of 2024.pdfSoluLab1231
Generative AI, short for Generative Artificial Intelligence, is a subfield of Artificial Intelligence that focuses on developing algorithms and models capable of generating new, original content. Unlike traditional AI systems that are rule-based and task-specific, generative AI possesses the ability to autonomously produce content, ranging from text and images to audio and video.
At the heart of generative AI are advanced machine learning techniques, particularly deep learning. Generative models, a category of models within the realm of generative AI, are designed to understand and replicate patterns in data, allowing them to create output that closely resembles human-generated content.
Generative AI systems learn from vast datasets to understand the underlying structures and features present in the data. Once trained, these systems can generate new content by extrapolating from the patterns they’ve learned. This capability is particularly powerful in tasks such as image synthesis, text generation, and even the creation of multimedia content.
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.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
As generative AI adoption grows at record-setting speeds and computing demands increase, hybrid processing is more important than ever. But just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices, AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential. In this talk you’ll learn:
• Why on-device AI is key
• Which generative AI models can run on device
• Why the future of AI is hybrid
• Qualcomm Technologies’ role in making hybrid AI a reality
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.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Generative AI: Redefining Creativity and Transforming Corporate LandscapeOsaka University
The advent of Generative AI is redefining the boundaries of creativity and markedly transforming the corporate landscape. One of the pioneering technologies in this domain is the Reinforcement Learning from Human Feedback (RLHF). Combined with advancements in LLM (Language Model) has emerged as a notable player. LLM offers two primary interpretations: firstly, as a machine capable of generating highly plausible texts in response to specific directives, and secondly, as a multi-lingual knowledge repository that responds to diverse inquiries.
The ramifications of these technologies are widespread, with profound impacts on various industries. They are catalyzing digital transformation within enterprises, driving significant advancements in research and development, especially within the realms of drug discovery and healthcare. In countries like Japan, Generative AI is heralded for its potential to bolster creativity. The value generated by such AI-driven innovations is estimated to be several trillion dollars annually. Intriguingly, about 75% of this value, steered by creative AI applications, is predominantly concentrated within customer operations, marketing and sales, software engineering, and R&D. These applications are pivotal in enhancing customer interactions, generating innovative content for marketing campaigns, and even crafting computer code from natural language prompts. The ripple effect of these innovations is palpable in sectors like banking, high-tech, and life sciences.
However, as with every innovation, there are certain setbacks. For instance, the traditional business model of individualized instruction, as seen in the context of professors teaching basic actions, is on the brink of obsolescence.
Looking ahead, the next five years pose pertinent questions about humanity's role amidst this technological evolution. A salient skillset will encompass the adept utilization of generative AI, paired with the discernment to accept or critique AI-generated outputs. Education, as we know it, will be reimagined. The evaluative focus will transition from verifying a student's independent work to gauging their ability to produce content surpassing their AI tools. Generative AI's disruptive nature will compel us to re-evaluate human value, reshaping the paradigms of corporate management and educational methodologies
My presentation entitled 'AI, Creativity and Generative Art', presented at the annual symposium for AI students (CKI) at Utrecht University, Fri. June 16th, 2017
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.
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.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
Responsible AI & Cybersecurity: A tale of two technology risksLiming Zhu
With the broader adoption of digital technologies and AI, organisations face the emerging risks of AI, the unfamiliar, and the intensified risk of cybersecurity, the familiar. AI and cybersecurity are intertwined, but risk silos are often created when they are dealt with at the technology and governance levels. This talk will explore the interactions between responsible AI and cybersecurity risks via industry case studies. It will show how we can break down the risk silos and use emerging trust-enhancing technologies, architecture and end-to-end software engineering/DevOps practices to connect the two worlds and uplift the risk management posture for both.
The Five Levels of Generative AI for GamesJon Radoff
A framework for understanding how generative AI and the technologies leading up to it impact the course of games and virtual worlds--for game studios, game players, modders and within the core game loop.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Get the latest announcements on Microsoft and NVIDIA's HGX-1 platform for artificial intelligence cloud computing, Facebook's new AI server, and the launch of Jetson TX2 for AI computing in cameras, sensors, and more.
The coming generative AI trends of 2024.pdfSoluLab1231
Generative AI, short for Generative Artificial Intelligence, is a subfield of Artificial Intelligence that focuses on developing algorithms and models capable of generating new, original content. Unlike traditional AI systems that are rule-based and task-specific, generative AI possesses the ability to autonomously produce content, ranging from text and images to audio and video.
At the heart of generative AI are advanced machine learning techniques, particularly deep learning. Generative models, a category of models within the realm of generative AI, are designed to understand and replicate patterns in data, allowing them to create output that closely resembles human-generated content.
Generative AI systems learn from vast datasets to understand the underlying structures and features present in the data. Once trained, these systems can generate new content by extrapolating from the patterns they’ve learned. This capability is particularly powerful in tasks such as image synthesis, text generation, and even the creation of multimedia content.
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.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
As generative AI adoption grows at record-setting speeds and computing demands increase, hybrid processing is more important than ever. But just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices, AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential. In this talk you’ll learn:
• Why on-device AI is key
• Which generative AI models can run on device
• Why the future of AI is hybrid
• Qualcomm Technologies’ role in making hybrid AI a reality
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.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Generative AI: Redefining Creativity and Transforming Corporate LandscapeOsaka University
The advent of Generative AI is redefining the boundaries of creativity and markedly transforming the corporate landscape. One of the pioneering technologies in this domain is the Reinforcement Learning from Human Feedback (RLHF). Combined with advancements in LLM (Language Model) has emerged as a notable player. LLM offers two primary interpretations: firstly, as a machine capable of generating highly plausible texts in response to specific directives, and secondly, as a multi-lingual knowledge repository that responds to diverse inquiries.
The ramifications of these technologies are widespread, with profound impacts on various industries. They are catalyzing digital transformation within enterprises, driving significant advancements in research and development, especially within the realms of drug discovery and healthcare. In countries like Japan, Generative AI is heralded for its potential to bolster creativity. The value generated by such AI-driven innovations is estimated to be several trillion dollars annually. Intriguingly, about 75% of this value, steered by creative AI applications, is predominantly concentrated within customer operations, marketing and sales, software engineering, and R&D. These applications are pivotal in enhancing customer interactions, generating innovative content for marketing campaigns, and even crafting computer code from natural language prompts. The ripple effect of these innovations is palpable in sectors like banking, high-tech, and life sciences.
However, as with every innovation, there are certain setbacks. For instance, the traditional business model of individualized instruction, as seen in the context of professors teaching basic actions, is on the brink of obsolescence.
Looking ahead, the next five years pose pertinent questions about humanity's role amidst this technological evolution. A salient skillset will encompass the adept utilization of generative AI, paired with the discernment to accept or critique AI-generated outputs. Education, as we know it, will be reimagined. The evaluative focus will transition from verifying a student's independent work to gauging their ability to produce content surpassing their AI tools. Generative AI's disruptive nature will compel us to re-evaluate human value, reshaping the paradigms of corporate management and educational methodologies
My presentation entitled 'AI, Creativity and Generative Art', presented at the annual symposium for AI students (CKI) at Utrecht University, Fri. June 16th, 2017
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.
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.
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
Responsible AI & Cybersecurity: A tale of two technology risksLiming Zhu
With the broader adoption of digital technologies and AI, organisations face the emerging risks of AI, the unfamiliar, and the intensified risk of cybersecurity, the familiar. AI and cybersecurity are intertwined, but risk silos are often created when they are dealt with at the technology and governance levels. This talk will explore the interactions between responsible AI and cybersecurity risks via industry case studies. It will show how we can break down the risk silos and use emerging trust-enhancing technologies, architecture and end-to-end software engineering/DevOps practices to connect the two worlds and uplift the risk management posture for both.
The Five Levels of Generative AI for GamesJon Radoff
A framework for understanding how generative AI and the technologies leading up to it impact the course of games and virtual worlds--for game studios, game players, modders and within the core game loop.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Get the latest announcements on Microsoft and NVIDIA's HGX-1 platform for artificial intelligence cloud computing, Facebook's new AI server, and the launch of Jetson TX2 for AI computing in cameras, sensors, and more.
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
The Edge AI market is all about bringing the power of artificial intelligence to the very edge of networks and devices. It enables real-time data processing and decision-making, making devices and systems smarter and more responsive than ever.
Our passion is to inspire and enable the da Vincis and Einsteins of our time, so they can see and create the future. We pioneered graphics, accelerated computing, and AI to tackle challenges ordinary computers cannot solve. See how we're continuously inventing the future--from our early days as a chip maker to transformers of the Metaverse.
NVIDIA Is Revolutionizing Computing - June 2017 NVIDIA
Here's our latest story as well as recent major announcements, featuring the epicenter of GPU computing, the era of AI, the world's largest gaming platform, and more.
NVIDIA is the world leader in visual computing. The GPU, our invention, serves as the visual cortex
of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions like self-learning machines and self-driving cars.
Nvidia Corporation, more commonly referred to as Nvidia, is an American technology company incorporated in Delaware and based in Santa Clara, California. It designs graphics processing units for the gaming and professional markets, as well as system on a chip units for the mobile computing and automotive market.
At the 2018 GPU Technology Conference in Silicon Valley, NVIDIA CEO Jensen Huang announced the new "double-sized" 32GB Volta GPU; unveiled the NVIDIA DGX-2, the power of 300 servers in a box; showed an expanded inference platform with TensorRT 4 and Kubernetes on NVIDIA GPU; and revealed the NVIDIA GPU Cloud registry with 30 GPU-optimized containers and made it available from more cloud service providers. GTC attendees also got a sneak peek of the latest NVIDIA DRIVE software stack and the next DRIVE AI car computer, "Orin," along with developments in the NVIDIA Isaac platform for robotics and Project Clara, NVIDIA's medical imaging supercomputer.
A Year of Innovation Using the DGX-1 AI SupercomputerNVIDIA
As one of TechCrunch's top AI stories, the NVIDIA DGX-1 has pioneered advancements in healthcare, data analytics, and robotic solutions with leading researchers and enterprises around the world.
We pioneered accelerated computing to tackle challenges no one else can solve. Now, the AI moment has arrived. Discover how our work in AI and the metaverse is profoundly impacting society and transforming the world’s largest industries.
Promising to transform trillion-dollar industries and address the “grand challenges” of our time, NVIDIA founder and CEO Jensen Huang shared a vision of an era where intelligence is created on an industrial scale and woven into real and virtual worlds at GTC 2022.
Outlining a sweeping vision for the “age of AI,” NVIDIA CEO Jensen Huang Monday kicked off the GPU Technology Conference.
Huang made major announcements in data centers, edge AI, collaboration tools and healthcare in a talk simultaneously released in nine episodes, each under 10 minutes.
“AI requires a whole reinvention of computing – full-stack rethinking – from chips, to systems, algorithms, tools, the ecosystem,” Huang said, standing in front of the stove of his Silicon Valley home.
Behind a series of announcements touching on everything from healthcare to robotics to videoconferencing, Huang’s underlying story was simple: AI is changing everything, which has put NVIDIA at the intersection of changes that touch every facet of modern life.
More and more of those changes can be seen, first, in Huang’s kitchen, with its playful bouquet of colorful spatulas, that has served as the increasingly familiar backdrop for announcements throughout the COVID-19 pandemic.
“NVIDIA is a full stack computing company – we love working on extremely hard computing problems that have great impact on the world – this is right in our wheelhouse,” Huang said. “We are all-in, to advance and democratize this new form of computing – for the age of AI.”
This GTC is one of the biggest yet. It features more than 1,000 sessions—400 more than the last GTC—in 40 topic areas. And it’s the first to run across the world’s time zones, with sessions in English, Chinese, Korean, Japanese, and Hebrew.
The Best of AI and HPC in Healthcare and Life SciencesNVIDIA
Trends. Success stories. Training. Networking.
The GPU Technology Conference brings this all to one place. Meet the people pioneering the future of healthcare and life sciences and learn how to apply the latest AI and HPC tools to your research.
NVIDIA CEO Jensen Huang Presentation at Supercomputing 2019NVIDIA
Broadening support for GPU-accelerated supercomputing to a fast-growing new platform, NVIDIA founder and CEO Jensen Huang introduced a reference design for building GPU-accelerated Arm servers, with wide industry backing.
NVIDIA BioBert, an optimized version of BioBert was created specifically for biomedical and clinical domains, providing this community easy access to state-of-the-art NLP models.
Top 5 Deep Learning and AI Stories - August 30, 2019NVIDIA
Read the top five news stories in artificial intelligence and learn how innovations in AI are transforming business across industries like healthcare and finance and how your business can derive tangible benefits by implementing AI the right way.
Seven Ways to Boost Artificial Intelligence ResearchNVIDIA
Higher education institutions have long been the backbone of scientific breakthroughs, view this slideshare to learn seven easy ways to help elevate your research.
Learn about the benefits of joining the NVIDIA Developer Program and the resources available to you as a registered developer. This slideshare also provides the steps of getting started in the program as well as an overview of the developer engagement platforms at your disposal. developer.nvidia.com/join
If you were unable to attend GTC 2019 or couldn't make it to all of the sessions you had on your list, check out the top four DGX POD sessions from the conference on-demand.
In this special edition of "This week in Data Science," we focus on the top 5 sessions for data scientists from GTC 2019, with links to the free sessions available on demand.
This Week in Data Science - Top 5 News - April 26, 2019NVIDIA
What's new in data science? Flip through this week's Top 5 to read a report on the most coveted skills for data scientists, top universities building AI labs, data science workstations for AI deployment, and more.
NVIDIA CEO Jensen Huang's keynote address at the GPU Technology Conference 2019 (#GTC19) in Silicon Valley, where he introduced breakthroughs in pro graphics with NVIDIA Omniverse; in data science with NVIDIA-powered Data Science Workstations; in inference and enterprise computing with NVIDIA T4 GPU-powered servers; in autonomous machines with NVIDIA Jetson Nano and the NVIDIA Isaac SDK; in autonomous vehicles with NVIDIA Safety Force Field and DRIVE Constellation; and much more.
Check out these DLI training courses at GTC 2019 designed for developers, data scientists & researchers looking to solve the world’s most challenging problems with accelerated computing.
Transforming Healthcare at GTC Silicon ValleyNVIDIA
The GPU Technology Conference (GTC) brings together the leading minds in AI and healthcare that are driving advances in the industry - from top radiology departments and medical research institutions to the hottest startups from around the world. Can't miss panels and trainings at GTC Silicon Valley
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the upcoming NVIDIA GTC 2019, complete schedule of GPU hackathons and more!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
4. GPU Deep Learning is the foundation for the
fourth industrial revolution driven by AI.
5. GPU deep learning is a new computing model that spans training,
inference, and the billions of intelligent devices that will take
advantage of deep learning to perform intelligent tasks.
6. GPU Deep Learning in the cloud will accelerate enterprise AI.
Powerful partnerships are already in place.
7. “We’ve worked with Microsoft to
create a lightning-fast AI
platform that is available from
on-premises with our DGX-1
supercomputer to the Microsoft
Azure cloud.”
- Jen-Hsun Huang
CEO at NVIDIA
PRESS RELEASE
8.
9. “By working closely with NVIDIA
and harnessing the power of
GPU-accelerated systems, we’ve
made Cognitive Toolkit and
Microsoft Azure the fastest,
most versatile AI platform.”
- Harry Shum
Executive VP of AI and
Research at Microsoft
PRESS RELEASE
10.
11. “With Microsoft’s global reach,
every company around the world
can now tap the power of AI to
transform their business.”
- Jen-Hsun Huang
CEO at NVIDIA
PRESS RELEASE
12. IBM CEO Ginne Rometty saw a
two trillion dollar opportunity
in machine learning for
enterprise.
This ignited their recent drive
in advancing cloud computing
…
READ MORE
13. This joint solution, runs on
an IBM server built
specifically for AI and
utilizes NVIDIA NVLink to
optimize AI into the
enterprise.
… And developed PowerAI
with NVIDIA’s GPUDL toolkit
that accelerates machine
learning training and AI tasks.
14.
15. “PowerAI democratizes Deep
Learning and other advanced
analytics technologies by giving
enterprise data scientists and
research scientists alike an easy to
deploy platform to rapidly advance
their journey on AI.”
-Ken King
General Manager for
OpenPOWER
TechRepublic
16. “Putting NVIDIA’s technology into
the IBM system will speed up
performance for such emerging
workloads as AI, deep learning, and
data analytics.”
-eWeek
READ MORE
17. -Karl Freund
Moor Insights & Strategy
“With these two partnerships in place, NVIDIA has aligned itself
with two of the largest suppliers of enterprise IT technology and
services to accelerate the adoption of AI in the enterprise and help
take AI into the mainstream of global businesses.”
READ NOW
18. GOOGLE
On Google Cloud, GPU Deep Learning is available for Google Compute Engine and
Google Cloud Machine Learning users to create new AI services and applications.
Tesla P100 delivers a 12x increase in
neural network training performance
compared with a previous generation
offering.
READ MORE
19. Amazon has boosted their cloud-
computing performance with new
GPU-Accelerated AWS instances that
are designed for large-scale machine
learning, deep learning, molecular
modeling, and computational finance
workloads.
READ MORE
20. “These (P2) instances were designed to
chew through tough large-scale
machine learning, deep learning,
computational fluid dynamics, seismic
analysis, molecular modeling, genomics,
and computational finance workloads.”
-Jeff Barr
Chief Evangelist at AWS
ZDNet
21. SAP
Enterprise software giant SAP is
now using DGX-1 supercomputers
to build machine learning
solutions for SAP’s 320,000
customers in 190 countries. The
system is designed to help data
scientists innovate get to market
faster with AI applications.
READ MORE
22. SAP
“SAP and ourselves are working together to bring hundreds of
customers of SAP the benefit of AI computing. Now with the
partnership of SAP, we will soon have applications running on
these [DGX-1] servers to serve the world’s largest enterprises.”
-Jen-Hsun Huang
CEO at NVIDIA
ITPro UK
23. ““We expect in the real-time space,
robotics and computer vision
becoming a reality, predictive
analytics in the big-data space, even
software writing software. The goal is
always knowledge, and the
partnership with SAP is going to be
key for us to make this a reality.”
-Guy Gueritz
Global Account Manager of SAP, NVIDIA
SAP NEWS
24. These partnerships with the world’s largest
companies allow NVIDIA GPU Deep Learning platform
to be available for every company.
EXPLORE HERE
25. This movement has quickly accelerated
AI into one of today’s biggest enterprise
technologies.
READ MORE