The presentation explores the current state of investing in AI, including its industrial split, and provides a detailed outlook on AI applications in Healthcare, Transportation and Industrial sectors.
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
We have spent the last three months speaking with over 40 leading Artificial Intelligence (AI) and Machine Learning (ML) companies in Europe and Israel to gain detailed insight and understanding of the development of the market and the future direction
We are pleased to share our work and findings with you and the broader community.
You can read our report ‘International AI and ML Landscape’ here.
Investor's view on machine intelligence startups, 2.0, Jan 2017Victor Osyka
Updated deeper overview of investor's look at machine learning / deep learning startups, with slight Russian accent. =)
Some slides are courtesy of Russia.ai and personally great friend @Petr Zhegin:
#23, #28 are from http://www.russia.ai/single-post/2016/09/21/Ten-Russian-speaking-venture-capital-funds-one-may-consider-to-back-an-AI-startup
#30 insights are from http://www.slideshare.net/RussiaAI/artificial-intelligence-investment-trends-and-applications-h1-2016
Victor Osyka of Almaz Capital, http://fb.com/victor.osika, http://medium.com/@victorosyka
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
We have spent the last three months speaking with over 40 leading Artificial Intelligence (AI) and Machine Learning (ML) companies in Europe and Israel to gain detailed insight and understanding of the development of the market and the future direction
We are pleased to share our work and findings with you and the broader community.
You can read our report ‘International AI and ML Landscape’ here.
Investor's view on machine intelligence startups, 2.0, Jan 2017Victor Osyka
Updated deeper overview of investor's look at machine learning / deep learning startups, with slight Russian accent. =)
Some slides are courtesy of Russia.ai and personally great friend @Petr Zhegin:
#23, #28 are from http://www.russia.ai/single-post/2016/09/21/Ten-Russian-speaking-venture-capital-funds-one-may-consider-to-back-an-AI-startup
#30 insights are from http://www.slideshare.net/RussiaAI/artificial-intelligence-investment-trends-and-applications-h1-2016
Victor Osyka of Almaz Capital, http://fb.com/victor.osika, http://medium.com/@victorosyka
How should startups embrace the trend of IoT and Big DataRuvento Ventures
This presentation prepared by Ruvento Ventures gives comprehensive coverage of the state of IoT, Big Data and AI industries. It covers the latest trends and most successful investments in Consumer Hardware. Moreover, we tried to give pieces of advice to startups working in the intersection of IoT, Big Data and AI.
5 Important Artificial Intelligence Predictions (For 2019) Everyone Should ReadBernard Marr
Artificial intelligence (AI), machine learning and deep learning have made huge strides in 2018. In this post we look at some of the key AI predictions for 2019, where is will be used, how it will make the biggest impact, as well as the key challenges we have to address.
AI Happy Hour - Dr. Kai-Fu Lee - The Golden age of Artificial IntelligenceRicky Wong
New breakthroughs in machine learning has created two set of opportunities in artificial intelligence.
On one hand, we're seeing a tidal wave of startups in fields such as robotics, autonomous vehicles, image recognition, and speech / NLP. Separately, we're also finding AI startups leveraging big-data and solving problems in traditional, but data rich, industries (e.g. finance, retail, medical, education, etc..) and in variety of use-cases (e.g. sales, marketing, and productivity).
At Sinovation Ventures, we see China as a great platform for Artificial Intelligence to take off. China has the unique set of conditions such as large amount of untapped data and deep talent pool of engineers and scientists. As a global investment firm, we see many opportunities for US and China to partner together in the golden age of artificial intelligence.
China's Digital landscape and rising disruptors: VR and Augmented realitySoile Ollila
China is fertile ground for a thriving VR and AR industry. However, China’s VR industry will develop differently compared to the rest of the world due to its unique consumer and business landscape. The key driving factors for mixed reality industry are massive internet user and PC/mobile gamer populations, openness to new technologies and growing demand for better entertainment experience. Influx of VC investments is expected to recover and participation of major tech firms play a key role of setting the industry in a growth path. This Future Watch Study outlines the current status of mixed reality industry; China’s disruptors in this industry and future opportunities for Finnish companies. Available at www.marketopportunities.fi
Executive Summary Part 3 -- Who's Winning the Artificial Intelligence Race be...Paul Schulte
In the spirit of collaboration, I am releasing part 3 of the executive summary (of our 560 page report comparing the big 5 in China and the big 5 in the US).
Part 3 looks first at the internal direction of R&D for each company. US firms are focusing on language, data anlytics and robotics. In a dramatic contrast, Chinese firms are focusing on financial applications, blockchain and computing. Then it looks at the patterns of external M&A. US firms are focusing on consolidating monopoly positions as well as physical assets such as Motorola, Nokia. Why? Chinese firms are acquiring lifestyle companies. Third, we look at the excess cash flow for M&A for tech companies (and market cap to use stock) and the paucity of cash flow for banks. Are banks simply running out of cash flow to buy or build a viable future? Lastly, we do a detailed credit analysis of all companies. Interesting results. Baidu’s credit data are deteriorating at the fastest rate.
To receive the full 560 report, send your email to paul@schulte-research.com. I personally hired several exceptionally talented students from UST and USC. Please support our students -- and pay them a superior wage. They are our future. I am available to go through the entire report in a 90 minute consultation.
Artificial Intelligence (AI) will create $13 trillion in value by 2030, according to McKinsey. That's a pretty good reason to take a closer look at the AI market and see what's under the hood. And that's exactly what I did in the Enterprise VC: 2019 AI Market Review deck.
This PPT is about AI 100 Startups all over the world based on "The AI 100 -CB insights".
In this research paper, you can find each capital, scale, general info(ref: CB Insights), and features.
As Artificial Intelligence (AI) proliferates, a divide is emerging. Between nations and within industries, winners and losers are emerging in the race for adoption, the war for talent and the competition for value creation.
Artificial Intelligence in China - A Snapshot from the Chinese WebJanna Lipenkova
China plans to become a leading player in AI in the next few decades - this ambition, together with numerous social and ethical challenges, turns Chinese AI into a highly vibrant, but also controversial topic. This report analyzes Chinese online texts pertaining to AI in the period of December 10, 2018 to January 5, 2019.
Future Watch: China's Digital Landscape and Rising Disruptors - Module 2.6 Ar...Team Finland Future Watch
AI is transforming industries in China just as we expect it to revolutionize our industries and organizations. As witnessed in many existing practical AI applications already available today, AI is not a futuristic concept anymore. The question today is, who will lead the next AI revolution? In many terms China can be the driving force of this development.
Kai-Fu Lee at AI Frontiers : The Era of Artificial IntelligenceAI Frontiers
In this talk, I will talk about the four waves of Artificial Intelligence (AI) , and how AI will permeate every part of our lives in the next decade. I will also talk about how this will be different from previous technology revolutions -- it will be faster and be driven by not one superpower, but two (US and China). AI will add $16 trillion to our global GDP, but also cause many challenges that will be hard to solve. I will talk in particular about AI replacing routine jobs -- the consequences, the proposed solutions that don't work (such as UBI), and end with a blueprint of co-existence between humans and AI.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here.
We consider the following key dimensions in our report:
- Research: Technology breakthroughs and their capabilities.
- Talent: Supply, demand and concentration of talent working in the field.
- Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
- China: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
- Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
Using neural networks methods in reinforcement learning tasksRussia.AI
The presentation covers the core issues associated with reinforcement learning and explores architectures of deep neural networks used by Google DeepMind playing Atari games and Go.
Deep learning: technology overview and trendsRussia.AI
The presentation explains the core concepts behind deep learning as well as provides an overview of the main architectures of neural networks. A deeper view on employing neural networks for computer vision is provided.
How should startups embrace the trend of IoT and Big DataRuvento Ventures
This presentation prepared by Ruvento Ventures gives comprehensive coverage of the state of IoT, Big Data and AI industries. It covers the latest trends and most successful investments in Consumer Hardware. Moreover, we tried to give pieces of advice to startups working in the intersection of IoT, Big Data and AI.
5 Important Artificial Intelligence Predictions (For 2019) Everyone Should ReadBernard Marr
Artificial intelligence (AI), machine learning and deep learning have made huge strides in 2018. In this post we look at some of the key AI predictions for 2019, where is will be used, how it will make the biggest impact, as well as the key challenges we have to address.
AI Happy Hour - Dr. Kai-Fu Lee - The Golden age of Artificial IntelligenceRicky Wong
New breakthroughs in machine learning has created two set of opportunities in artificial intelligence.
On one hand, we're seeing a tidal wave of startups in fields such as robotics, autonomous vehicles, image recognition, and speech / NLP. Separately, we're also finding AI startups leveraging big-data and solving problems in traditional, but data rich, industries (e.g. finance, retail, medical, education, etc..) and in variety of use-cases (e.g. sales, marketing, and productivity).
At Sinovation Ventures, we see China as a great platform for Artificial Intelligence to take off. China has the unique set of conditions such as large amount of untapped data and deep talent pool of engineers and scientists. As a global investment firm, we see many opportunities for US and China to partner together in the golden age of artificial intelligence.
China's Digital landscape and rising disruptors: VR and Augmented realitySoile Ollila
China is fertile ground for a thriving VR and AR industry. However, China’s VR industry will develop differently compared to the rest of the world due to its unique consumer and business landscape. The key driving factors for mixed reality industry are massive internet user and PC/mobile gamer populations, openness to new technologies and growing demand for better entertainment experience. Influx of VC investments is expected to recover and participation of major tech firms play a key role of setting the industry in a growth path. This Future Watch Study outlines the current status of mixed reality industry; China’s disruptors in this industry and future opportunities for Finnish companies. Available at www.marketopportunities.fi
Executive Summary Part 3 -- Who's Winning the Artificial Intelligence Race be...Paul Schulte
In the spirit of collaboration, I am releasing part 3 of the executive summary (of our 560 page report comparing the big 5 in China and the big 5 in the US).
Part 3 looks first at the internal direction of R&D for each company. US firms are focusing on language, data anlytics and robotics. In a dramatic contrast, Chinese firms are focusing on financial applications, blockchain and computing. Then it looks at the patterns of external M&A. US firms are focusing on consolidating monopoly positions as well as physical assets such as Motorola, Nokia. Why? Chinese firms are acquiring lifestyle companies. Third, we look at the excess cash flow for M&A for tech companies (and market cap to use stock) and the paucity of cash flow for banks. Are banks simply running out of cash flow to buy or build a viable future? Lastly, we do a detailed credit analysis of all companies. Interesting results. Baidu’s credit data are deteriorating at the fastest rate.
To receive the full 560 report, send your email to paul@schulte-research.com. I personally hired several exceptionally talented students from UST and USC. Please support our students -- and pay them a superior wage. They are our future. I am available to go through the entire report in a 90 minute consultation.
Artificial Intelligence (AI) will create $13 trillion in value by 2030, according to McKinsey. That's a pretty good reason to take a closer look at the AI market and see what's under the hood. And that's exactly what I did in the Enterprise VC: 2019 AI Market Review deck.
This PPT is about AI 100 Startups all over the world based on "The AI 100 -CB insights".
In this research paper, you can find each capital, scale, general info(ref: CB Insights), and features.
As Artificial Intelligence (AI) proliferates, a divide is emerging. Between nations and within industries, winners and losers are emerging in the race for adoption, the war for talent and the competition for value creation.
Artificial Intelligence in China - A Snapshot from the Chinese WebJanna Lipenkova
China plans to become a leading player in AI in the next few decades - this ambition, together with numerous social and ethical challenges, turns Chinese AI into a highly vibrant, but also controversial topic. This report analyzes Chinese online texts pertaining to AI in the period of December 10, 2018 to January 5, 2019.
Future Watch: China's Digital Landscape and Rising Disruptors - Module 2.6 Ar...Team Finland Future Watch
AI is transforming industries in China just as we expect it to revolutionize our industries and organizations. As witnessed in many existing practical AI applications already available today, AI is not a futuristic concept anymore. The question today is, who will lead the next AI revolution? In many terms China can be the driving force of this development.
Kai-Fu Lee at AI Frontiers : The Era of Artificial IntelligenceAI Frontiers
In this talk, I will talk about the four waves of Artificial Intelligence (AI) , and how AI will permeate every part of our lives in the next decade. I will also talk about how this will be different from previous technology revolutions -- it will be faster and be driven by not one superpower, but two (US and China). AI will add $16 trillion to our global GDP, but also cause many challenges that will be hard to solve. I will talk in particular about AI replacing routine jobs -- the consequences, the proposed solutions that don't work (such as UBI), and end with a blueprint of co-existence between humans and AI.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here.
We consider the following key dimensions in our report:
- Research: Technology breakthroughs and their capabilities.
- Talent: Supply, demand and concentration of talent working in the field.
- Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
- China: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
- Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
Using neural networks methods in reinforcement learning tasksRussia.AI
The presentation covers the core issues associated with reinforcement learning and explores architectures of deep neural networks used by Google DeepMind playing Atari games and Go.
Deep learning: technology overview and trendsRussia.AI
The presentation explains the core concepts behind deep learning as well as provides an overview of the main architectures of neural networks. A deeper view on employing neural networks for computer vision is provided.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Prisma and other stylization apps: explaining tech behindRussia.AI
A fantastic presentation explaining how texture synthesis, neural doodles, and image style transfer work. The presentation by Dmitry Ulyanov also covers issues of efficiency of algorithms.
Intelligent automation allows your business to not only do things differently, but to do different things. Discover 3 lessons learned to guide your intelligent automation path:
The Investment Digest Allsop & CBRE - H1 2016Robert Hoban
The inaugural edition of a bi-annual comprehensive study of the entire commercial investment market in Ireland. Deal sizes from €29k to €950m, auction and private treaty.
GGV Capital Jenny Lee: Next Gen Wearables, Transportation and Robotics June 2016GGV Capital
Jenny Lee, Managing Partner, GGV Capital shared her insights on next generation wearables, transportation and robotics at the WSJ Converge conference in Hong Kong. Here’s the presentation.
Review of AI Maturity Models in Automotive SME Manufacturinggerogepatton
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
REVIEW OF AI MATURITY MODELS IN AUTOMOTIVE SME MANUFACTURINGijaia
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
Website URL:https://www.airccse.org/journal/ijaia/ijaia.html Review of AI Mat...gerogepatton
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
The aims of the presentation are to:
- Present what leading analysts say on trends in 2015
- Outline what new skills will be required in 2015+
- Suggest some market niches for growth
with focus on IT market.
Flint Capital and international venture capital investor provides an investor's perspective on a state of Ai (artificial intelligence) in 2018 and the impact of various factors like algorithms development, hardware development, datasets development, opensource software development, investments and overall interest to a topic on a growth the category. Deep deal analysis and Ai market data and artificial intelligence market growth as is at the end of 2017, including Gartner, Forester, CBInsights, Pitchbook data sources as long as Flint Capital own analytics. Ai investment framework is given as an example of a potential investors approach to analyze startups and business cases. Plus World top 5 most active vc investors in Ai.
Software development is part of the DNA of Ingenia. In more than 20 years of existence, we have evolved with the technologies available, offering our clients applications, developments and products to satisfy their needs.
2. Economic Impact and Societal Considerations for Policy Decisions.Saurabh Mishra
This group reviewed resource allocation questions related to public and private investment including challenges for skill-reallocation, economic loss, including job loss, qualified labor force reduction, distributional challenges, income inequality, and opportunities for economic diversification.
Tracxn Research: Enterprise Collaboration Landscape, August 2016Tracxn
File synchronisation and sharing (Box, Dropbox), conferencing (BlueJeans Network, Fuze), and messaging and chat room (Slack, Symphony) are the top three business models, each having seen over a billion dollars in investments.
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
The enterprise software industry is being transformed by substantial investor capital, Cloud 2.0, artificial intelligence, data protection, preferred platforms, and a talent shortage, leading stakeholders of all kinds to make big changes, and big choices.
Purpose: The slides provide an overview on the Internet of Things trend
Content: Summary information about the Internet of Things marketplace, including trends drivers, spending trends, industry business cases, and adoption challenges. Also included are links to additional resources.
How To Use This Report: This report is best read/studied and used as a learning document. You may want to view the slides in slideshow mode so you can easily follow the links
Available on Slideshare: This presentation (and other HorizonWatch Trend Reports for 2016) will be available publically on Slideshare at http://www.slideshare.net/horizonwatching
Please Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.
Tracxn Research — Artificial Intelligence Startup Landscape, September 2016Tracxn
Notable investments in 2016 include antivirus and endpoint protection vendor Cylance ($100M Series D), Digital Reasoning ($40M, Series D), and Globality, ($27M, Series B).
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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
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.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
2. Table of content
Summary and overview of data collection
Presenter’s bio
Definition of AI
Overview of technology progress
Investment trends
M&A activity highlights
Precedent investments by industry
Implementation – case studies
Conclusion: how to cope with AI disruption
3. Summary and overview of data collection
Summary
Defining AI as a set of technologies allows to describe it better. Core technologies, like computer vision and
machine learning developed rapidly in the recent years.
Investmentsin AI also grew, from 0.9% of all venture capital investmentsin 2012 to 3.1% in H1 2016.
Cross-industrial business applications, as well as specific applications in Healthcare,Manufacturingand
Industrial Services and Transportation/Logistics represent 57% of $1.6B invested in AI in H1 2016.
Successful implementations of AI tech are already available in industries like Telecommunications, Banking
and Manufacturing.
Data collection
Data on investment in AI was collected and analyzed by Flint Capital from diverse sources including
Pitchbook and Crunchbase.
251 company in the US, the UK and Canada were identifiedvia key-word search and manually checked. We
included only those companies which received venture capital financing and were privately held at the time
of our research.
4. Peter Zhegin
A venture capital professionaland an AI enthusiast
Associate at Flint Capital, an internationalventure capital fund with exposure to cognitive technologies.
Investmentsinclude:
• CyberX – machine learningfor Industrial IoT,
• Findo – an NLP-powered smart search engine,
• Epistema – collaborative knowledge analytics platform,
• AudioBurst – transcribes audio and understands the meaning of spoken words in real-time,
and others…
Co-lead at Russia.AI – a non profit initiative aiming to support Russian speaking AI entrepreneurs.
Previously: Ozon.ru, ABRT Venture Fund.
MSc in Management,Leeds UniversityBusiness School,
MA in History, Moscow City Pedagogical University.
5. Artificial Intelligence (AI) may be considered as a set of several technologies
Narrative definitions are too wide and they hardly describe what AI actually is
Cognitive technologies comprising AI (2)
36%
(1) Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010).
Cited from: One Hundred Year Study on Artificial Intelligence (AI100),” Stanford University, accessed August 1, 2016.
(2) Deloitte University Pres, http://www.theatlantic.com/sponsored/deloitte-shifts/demystifying-artificial-intelligence/257/
For example: ’Artificial intelligence is that activity devoted to making machines intelligent, and intelligence
is that quality that enables an entity to function appropriately and with foresight in its environment’ (1).
AI as a set of technologies
6. 0
100
200
300
400
500
2015 Amazon’s Picking
Challenge champion
2016 Amazon’s Picking
Challenge champion
Human worker
Progress in technology is stunning
Robots are quickly mastering difficult tasks
# of items picked per hour
by a robot vs. a human worker (3)
(3) http://futurism.com/deep-learning-ai-leads-robot-to-victory-in-amazons-picking-challenge/
(4) http://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not?frsc=dg%7Cd
The gap between a human
and a robot is still wide
3x progress
Error rate on ImageNet
visual recognition challenge, % (4)
0%
5%
10%
15%
20%
25%
30%
2011 2012 2013 2014 2015
Goes beyond
human level
7. Investments in AI have been growing rapidly in the last five years
AI investments grew from 0.9% of world’s venture capital in 2012 to 3.1% in H1 2016
Venture capital investments in AI and other sectors, FY 2012 - H1 2016, Worldwide,$B and % (5)
(5) AI excludes accelerator and incubator deals https://www.cbinsights.com/research-venture-capital-Q2-2016 , https://www.cbinsights.com/blog/artificial-intelligence-funding-trends/,
https://www.cbinsights.com/blog/artificial-intelligence-funding-trends-q216/
44.8
50.3
89.1
128.5
52.2
0.4
0.8
2.2
2.4
1.69
0.9%
1.5%
2.4%
1.8%
3.1%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
FY 2012 FY 2013 FY 2014 FY 2015 H1 2016
VentureinvestmentsinAIas%oftotal
Ventureinvestments$B
Other venture investments AI venture investments AI venture investments as % of total
AI – 3%
of VC
invested
in tech =
$1.69B
Annual investment
in AI ~$2.4B
8. 24 AI tech companies were acquired or went public for the total disclosed value of $1.2B
Selected AI tech companies acquired in H1 2016
Corporations are interested in getting AI tech in H1 2016
Acquirers of AI tech
are very diverse in
terms of industries
and
business models
9. 531
263
189
170
107
102
99
61
33
24 24 16 15
Cross-industrial Consumer products/Services
Healthcare Infrastructure
Transportation/Logistics Manufacturing/Industrial services
Retail/Commerce Finance/Insurance/Legal
Agriculture Aerospace
Security/Defense Education
Construction/Maintenance/Utilities
Cross-industrial business functions like HR or Marketing attract AI investments
Healthcare, transportation,and manufacturing are approached by AI investors as well
Venture capital investments in AI by industry, H1 2016, US,UK, and Canada, $M (6)
(6) Data from Flint Capital
Applications of AI in
Marketing, HR, Business
Intelligence, Sales, and
Administrative functions
AI in manufacturing
processes and analytical
applications in
manufacturing,
warehousing
Self-driving cars for
consumer and business
purposes, route-planning
software
Medical imaging,
surgery robotics,
analytical software
Core technologies e.g.
computer vision
or NLP
Consumer applications:
toys & games, photo
editing, household
robotics
Cross-industrial
Consumer
Healthcare
Infrastructure
Transportation
Manufacturing
Every industry uses
AI in its own way
10. Data – the key component of healthcare and AI’s target
95% of investments in AI that helps to collect, analyze and predict
AI in Healthcare, sub-segments & examples, H1 2016, US, UK, and Canada, $M (7)
97
50
33
8 1
Data digitalisation/Interpretation
Analycal processes augmentation
Data-related processes automation
Manual+Cognitive processes augmentation
Interaction/Communication automation
Exoatlet*
Visiongate
Restoration robotics
(7) Data from Flint Capital
* When here and further a company is marked by ‘*’ – the company was not included in the sample pf H1 2016 investments and is used as an example
11. 103
4
Manual processes automation Analycal processes augmentation
Transportation – self-driving cars is the key topic
>95% of investments in Transportation segment were allocated to self-driving cars
AI in Transportation, sub-segments & examples, H1 2016, US, UK, and Canada, $M (8)
NAMI-Yandex-KamAz*
Clearmetal
Zoox
(8) Data from Flint Capital
12. 57
43
3
Analycal processes augmentation
Manual processes automation
Data digitalisation/Interpretation
Manufacturing/Industrial services are balancing between analytics and robotizing
AI is applied almost equally to analytical and operational elements of production process
AI in Manufacturing/Industrial services, sub-segments & examples, H1 2016, US, UK, and Canada, $M (9)
Seegrid
(9) Data from Flint Capital
RoboCV*
Senseye
CyberX*
14. Telco's, banking, steel production, consumer services – AI seems to be industry agnostic
Case-studies of AI tech implementations
(10) http://eprints.lse.ac.uk/64516/1/OUWRPS_15_02_published.pdf ,
(11) SDBA Group presentation, 2016
(12) https://yandexdatafactory.com/case-studies/ydfs-recommender-system-to-decrease-steelmaking-costs-at-magnitogorsk-iron-and-steel-works/
(13) Findo’s presentation, 2016
Telefonica Retail bank Magnitogorsk Iron & Steel Works
By Blue Prism (UK) By SBDA Group (RU) By Yandex Data Factory (RU)
Telefónica O2 automated 15 core
processes including SIM swaps,
credit checks, and others,
representing about 35 percent of
all back office.
FTEs had been reduced on the
automated processes by a few
hundred. UK-based people were
redeployed to other service areas
and the business continued to
grow (10).
ML enabled the bank to send to cli
ents very targeted messages releva
nt to their real life events.
As a result, usage of some services
increased e.g. from 1% to 6% for
paying parking fines
online and from 41% to 74% for
mobile top-up services (11).
Yandex Data Factory created a rec
ommender system, integrated into
MMK’s software, that helps to
reduce ferroalloy use by
an average of 5%.
This equates to annual savings of
more than $4m in production costs
(12).
Several industries have already started harvesting fruit of AI implementations
Consumers
By Findo (RU/US)
Uses DL and train generative statis
tical models on texts. AI helps
with automatic tagging and
allows users to search by
description, not keywords (13).
15. Several factors contributes to the success of AI, one has to find a way to exploit it
Factors contributingto AI development and ways to exploit it
(13) Jasnam S. Sidhu, and David Moloney, Price Waterhouse Coopers Presentation at Digital Catapult’s event, London, September 2016
Factors moving AI forward How to benefit from AI (13)
Ø Progress in technology;
Ø Growing investments;
Ø Interest and resources of the leading corporations;
Ø Cross-industrial and cross-functional character.
Ø Track new companies / products in AI;
Ø Define clear priorities on what to look at;
Ø Develop a relevant strategy;
Ø Build a relevant talent pool;
Ø Experiment with AI.
AI is here to stay, paying attention to it is curtail for success of a business
16. Thankyou!
Please feel free to let me knowif youwouldlove to knowmoreaboutAI
applications,marketsandinvestmenttrends
and/or
youare developinganAIstartup
pz@flintcap.com
checkfor insightsandupdates