This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Generative AI: Past, Present, and Future – A Practitioner's Perspective
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Artificial Intelligence in the Financial Industries
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
This document discusses the relationship between artificial intelligence (AI) and big data. It defines both AI and big data. AI is making computers do intelligent tasks like humans, while big data refers to large amounts of structured and unstructured data. The document explains that AI needs large amounts of data to replicate human intelligence and make intelligent decisions, just as human intelligence is built on experiences and data. It provides examples of how AI uses big data, such as Google's self-driving cars gathering sensor data to make driving decisions. The document also covers predictive analytics, unstructured data analysis, and data mining techniques like genetic algorithms and fuzzy logic.
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.
Global Governance of Generative AI: The Right Way Forward
AI regulation has been a hot topic since the rise of machine learning (ML) in the “big data” era, but generative AI or “foundation models” tools like ChatGPT, DALL-E 2(now 3) and CoPilot, ike ML before them, may create serious societal risks, including embedding and outputting bias; generating fake news, illegal or harmful content and inadvertent “hallucinations”; infringing existing laws relating eg to copyright and privacy; as well as environmental, competition and workplace concerns.
Many nations are now considering regulation to address these worries, and can draw on a number of basic and hybrid models of governance. This paper canvasses models of mandatory comprehensive legislation (where the EU AI Act hopes to place itself as a gold standard model); vertical mandatory legislation (where China has quietly taken a lead); adapting existing law (see the many copyright lawsuits underway); and voluntary “soft law” such as codes of ethics, “blueprints”, or industry guidelines. Both the domestic and international regulatory scenes for AI are also increasingly politicised as the rise of "AI safety" hype shows. Against this backdrop what choices should smaller countries such as the UK and Australia make? will international harmonisation lead to a race to the top as with the GDPR, or the bottom - rule by tech for tech?
This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.
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.pdf
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.
After decades of development, in 2022, AI systems achieved a new level of popularity with the emergence of Generative AI, which is capable of producing high-quality images, texts, and speech from text-based prompts. OpenAI's ChatGPT product captured the imaginations of consumers and business alike, and seemed poised to change everything.
In this webinar, we will be exploring the fundamentals of AI's impact on content marketing, what (if anything) has actually changed, and how to harness AI as a strategic advantage in your content process.
To watch the recording of the webinar, visit: https://my.demio.com/recording/J7GlZKRv
This document discusses visualizing data with code and provides information on tools and techniques for data visualization. It lists relevant fields like information design, data science, and cartography. It also lists example visualization tools and techniques like D3, Processing, network graphs, and mapping. Finally, it outlines a process for developing data visualizations that involves looking at the data, creating initial visualizations, asking questions, getting inspiration, refining ideas, and publishing visualizations.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
This document discusses generative AI and its potential transformations and use cases. It outlines how generative AI could enable more low-cost experimentation, blur division boundaries, and allow "talking to data" for innovation and operational excellence. The document also references responsible AI frameworks and a pattern catalogue for developing foundation model-based systems. Potential use cases discussed include automated reporting, digital twins, data integration, operation planning, communication, and innovation applications like surrogate models and cross-discipline synthesis.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them.
This talk will provide food for thought in 3 areas:
Security of AI systems
Use of AI in cybersecurity
Malicious use of AI
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
This document discusses the relationship between artificial intelligence (AI) and big data. It defines both AI and big data. AI is making computers do intelligent tasks like humans, while big data refers to large amounts of structured and unstructured data. The document explains that AI needs large amounts of data to replicate human intelligence and make intelligent decisions, just as human intelligence is built on experiences and data. It provides examples of how AI uses big data, such as Google's self-driving cars gathering sensor data to make driving decisions. The document also covers predictive analytics, unstructured data analysis, and data mining techniques like genetic algorithms and fuzzy logic.
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.
Global Governance of Generative AI: The Right Way ForwardLilian Edwards
AI regulation has been a hot topic since the rise of machine learning (ML) in the “big data” era, but generative AI or “foundation models” tools like ChatGPT, DALL-E 2(now 3) and CoPilot, ike ML before them, may create serious societal risks, including embedding and outputting bias; generating fake news, illegal or harmful content and inadvertent “hallucinations”; infringing existing laws relating eg to copyright and privacy; as well as environmental, competition and workplace concerns.
Many nations are now considering regulation to address these worries, and can draw on a number of basic and hybrid models of governance. This paper canvasses models of mandatory comprehensive legislation (where the EU AI Act hopes to place itself as a gold standard model); vertical mandatory legislation (where China has quietly taken a lead); adapting existing law (see the many copyright lawsuits underway); and voluntary “soft law” such as codes of ethics, “blueprints”, or industry guidelines. Both the domestic and international regulatory scenes for AI are also increasingly politicised as the rise of "AI safety" hype shows. Against this backdrop what choices should smaller countries such as the UK and Australia make? will international harmonisation lead to a race to the top as with the GDPR, or the bottom - rule by tech for tech?
This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.
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.
After decades of development, in 2022, AI systems achieved a new level of popularity with the emergence of Generative AI, which is capable of producing high-quality images, texts, and speech from text-based prompts. OpenAI's ChatGPT product captured the imaginations of consumers and business alike, and seemed poised to change everything.
In this webinar, we will be exploring the fundamentals of AI's impact on content marketing, what (if anything) has actually changed, and how to harness AI as a strategic advantage in your content process.
To watch the recording of the webinar, visit: https://my.demio.com/recording/J7GlZKRv
This document discusses visualizing data with code and provides information on tools and techniques for data visualization. It lists relevant fields like information design, data science, and cartography. It also lists example visualization tools and techniques like D3, Processing, network graphs, and mapping. Finally, it outlines a process for developing data visualizations that involves looking at the data, creating initial visualizations, asking questions, getting inspiration, refining ideas, and publishing visualizations.
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.
Artificial Intelligence Introduction & Business usecasesVikas Jain
This document discusses artificial intelligence and the fourth industrial revolution. It provides background on AI, including its history and increasing importance due to lower hardware costs, availability of data, and improved algorithms. It describes different types of AI and discusses how AI is being applied in various industries like customer service, retail, e-commerce, warehousing, healthcare, agriculture, and finance. It also addresses some of the threats, ethics, and vocabulary related to AI.
This document provides an introduction to WSJ Pro Artificial Intelligence, a new offering from The Wall Street Journal that aims to help businesses understand and draw value from the rise of artificial intelligence. The summary discusses the impact of AI on businesses, how WSJ Pro AI will assess the effects of AI on different levels and issues of companies, and provides examples of the types of journalism that will be included.
This emerging tech research from CompTIA describes the growing role of artificial intelligence in the technology strategies that businesses are building.”
Artificial Intelligence can Offer People Great Relief from Performing Mundane...JPLoft Solutions
AI refers to the recreation of human-like intelligence in machines created to function like humans and mimic their actions. Artificial Intelligence solutions can be applied to any device that exhibits traits similar to the human brain, such as the capacity to learn and analytical thinking.
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...vmblog
Find out what's going on in the world of #artificialintelligence and #machinelearning in 2018. Read #predictions more than 30 of the industry's leading experts to learn more about #AI Hear from industry thought leaders from companies like Chaos Sumo, Couchbase, Druva, Equinix, Hitachi Vantara, Ixia, Pivot3, SAP, SIOS Technologies, SolarWinds, Splunk, Vonage and more. Make sure to also read the more than 280+ other expert predictions from technologies across #virtualization, #cloudcomputing, #hyperconverged, #IoT, #security, etc. here: http://bit.ly/2DQi2OT at VMblog.com.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI automates tasks to save money and makes recommendations. In the long term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
Allaboutailuminarylabsjanuary122017 170112151616Quang Lê
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI can automate tasks to save money and make recommendations. In the longer term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
A Comprehensive Study On The Evolution And Advantages Of AI.pdfDataSpace Academy
AI is undoubtedly a major defining force of the 21st Century. However, although revolutionary technology is becoming a buzzword in contemporary times its inception happened early in the 1940s. Cut to 2024, the cutting-edge technology is making huge strides in almost every sector of life and for amazing reasons. The blog here sheds light on all the major aspects of AI, ranging from evolution to types to advantages of AI, limitations, and more. The blog also talks about the multiple use cases of Artificial Intelligence as well as future prospects.
This document provides an introduction to artificial intelligence and its applications in enterprises. It discusses the growth of the AI market and how increased data and computing power are helping to avoid another "AI winter" period. The document defines key AI-related terms like artificial intelligence, machine learning, and deep learning. It also outlines some common enterprise applications of AI like natural language processing, computer vision, and chatbots. The introduction concludes by stating that AI will impact every industry and that businesses need to incorporate AI to remain competitive.
From Alexa and Siri to factory robots and financial chatbots, intelligent systems are reshaping industries. But the biggest changes are still to come, giving companies time to create winning AI strategies
The document provides an introduction to artificial intelligence (AI), including its history and limitations. It discusses 5 main limitations of AI: data, cultural limitations, bias, emotional intelligence, and lack of a strategic approach. It then discusses 5 key advantages: reduction in human error, taking risks instead of humans, availability 24/7, helping with repetitive jobs, and digital assistance. Finally, it covers 5 disadvantages: high creation costs, making humans lazy, unemployment, lack of emotions, and inability to think outside the box. The document thus provides a broad overview of the history, limitations, advantages and disadvantages of artificial intelligence.
What is Artificial Intelligence?
Where is the value potential of AI?
Major Acquisitions in AI
AI business cases
AI (& BI) Ecosystem
AI challenges
Networking/expertise
Conclusion
The construction industry is faced with a variety of intricate problems, such as time and cost overruns, worries about health & safety, productivity issues, and labour availability. The industry’s expansion is consequently severely constrained.
Artificial intelligence, machine learning, and deep learning are related concepts in the field of artificial intelligence. Machine learning is a subset of AI that uses algorithms to learn from data and make predictions without being explicitly programmed, while deep learning is a specific type of machine learning that uses neural networks. The document provides definitions and examples of these concepts to help explain the differences between them.
This document discusses generative AI, including what it is, how it works, challenges, and potential business uses. Some key points:
- Generative AI can automatically generate new text, images, videos and other content based on training data, rather than just categorizing data like other machine learning.
- It uses large language models trained on vast datasets to generate human-like responses to prompts. While this allows for many potential business uses, challenges include lack of transparency, privacy/security issues, and the risk of factual inaccuracies.
- Generative AI could be used by businesses for tasks like document processing, writing code, augmenting human work, and creating marketing content. Industries like insurance, legal,
This document provides an overview of artificial intelligence (AI) and its applications in enterprises. It examines real use cases for AI, challenges, and opportunities. Key areas where AI can provide value for enterprises are enterprise intelligence, computer vision, and conversational AI. Enterprise intelligence involves analyzing multiple internal and external datasets to extract insights, predictions, and recommendations. Computer vision allows machines to "see" and interpret images. Conversational AI allows machines to communicate using natural language. The document also provides case studies of how companies like Stripe and DBS are using AI.
What Is The Next Level Of AI Technology?Bernard Marr
Artificial Intelligence (AI) has permeated all aspects of our lives – from the way we communicate to how we work, shop, play, and do business – AI tools are everywhere we look.
The Impact of AI Use Cases on the German EconomyAPPANION
All data shown in this presentation is accessible at appanion.com/usecases
06th February 2019
Appanion@meetup.ai Hamburg
Introduction to the Appanion approach of seizing and prioritizing AI use cases based on an analysis of over 1,000 use cases across 40 different industries for the German market.
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...APPANION
Laut des Gesamtverbandes der Deutschen Versicherungswirtschaft ist etwa 10% des Schadenaufwands deutscher Versicherer auf Betrugsfälle zurückzuführen.1 Alleine Schaden- und Unfallversicherer verlieren dadurch 4-5 Mrd. € pro Jahr. Tendenz steigend! Bekämpft wird diese Entwicklung zumeist manuell oder mithilfe veralteter Technologien.
Effiziente Schadensregulierung und hohes Kundenserviceniveau können oftmals nicht gewährleistet werden, sobald eine höhere Genauigkeit in der Betrugserkennung erreicht werden soll. Der Einsatz moderner Datenbank-Lösungen und KI gestützter Cloud-Services ermöglicht höhere Schnelligkeit, Lernfähigkeit und Genauigkeit im Umgang mit potentiellen Betrugsfällen.
Intelligente visuelle Qualitätskontrolle in der FertigungsindustrieAPPANION
Die Fertigungsindustrie verändert sich. Im Rahmen von Industrie 4.0 erlangen Flexibilität, modulare Produktionsanlagen und kürzere Fertigungszyklen deutlich höhere Relevanz. Visuelle Qualitätskontrolle ist eine entscheidende Querschnitts- und Differenzierungsfunktion im Wettbewerb. Aktuelle Systeme sind oftmals unflexibel und fehleranfällig, sobald sich Umweltbedingungen oder Produkteigenschaften minimal verändern.
Künstliche Intelligenz (KI) kann durch maschinelles Lernen und visuelle Mustererkennung zu einer erheblichen Steigerung des Automatisierungsgrades und damit der Prozesseffizienz beitragen – auch in Bereichen, die aktuell noch stark manuell geprüft werden müssen.
Understanding Artificial Intelligence - Major concepts for enterprise applica...APPANION
Artificial Intelligence is a fundamental topic – for us as humans, as a society but also for businesses. For business executives and decision-makers, it is sometimes hard to keep up with rapidly evolving technologies as part of the day-to-day business. By providing this curated compilation of information about the fundamental aspects of AI, we want to captivate and inspire you to become more involved with the technology by better understanding the underlying concepts and value drivers of this technology
AI in Business - Key drivers and future valueAPPANION
Artificial Intelligence is undoubtedly a hyped topic at the moment. But what is the reasoning for investors and digital platform players to bet very large amounts of money on this technology right now? To better understand the current market dynamics and to give an overview of renown predictions for the upcoming 2-3 years, we compiled a practical overview of this topic. This report covers the major driving forces of AI, assumptions for the future from the industry thought leaders as well as practical advice on how to start AI projects within your company.
Top 10 Technology Predictions - Future Outlook for AI and DLTAPPANION
Artificial Intelligence and Distributed Ledger Technology are the current hot topics on the innovation agenda. In our future outlook for 2019 and beyond, we made 10 bold predictions for the upcoming development of these two key technologies.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment.
How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
Advanced Techniques for Cyber Security Analysis and Anomaly DetectionBert Blevins
Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
2. OVERVIEW
ARTIFICIAL INTELLIGENCE - BUZZWORD OR GAME CHANGER?
THE BASICS
Where does AI come from, where does it go?
FACTS & FIGURES
Round-up of science, applied AI and talent
COMMERCIAL VALUE
Strategies of B2B platforms and practical action points
2
3
9
18
About this whitepaper
Artificial Intelligence is trendy. Every event, every strategy meeting and every
consulting firm talks about it. This whitepaper aims to separate actual facts
and important background information from the overarching marketing buzz.
You will get a short but information-rich wrap up about: What causes the
current hype? Where are we today? What are the innovation leaders doing
with AI? And what are immediate action points to focus on by applying
artificial intelligence in your business.
WHERE CAN TECHNOLOGY TAKE
YOU?
Appanion in a nutshell
We provide high-quality insights on real-world technology-driven business
applications and help you as innovation partner with actionable strategies,
technology knowhow and business model competence.
I hope you can take away some
valuable insights and new ideas
to improve your business!
Tobias Bohnhoff
Founder
4. ARTIFICIAL INTELLIGENCE IS CAPABLE OF FAR MORE
THAN JUST PLAYING CHESS, JEOPARDY OR GO
Artificial Intelligence (AI) is the ability of a
computer-controlled entity to perform
cognitive tasks and react flexibly to its environment
in order to maximize the probability of achieving
a particular goal.
The system can learn from experience data,
and can mimic behaviors associated to humans,
but does therefore not necessarily use
methods that are biologically observable.
Definition is based on McKinsey, Stanford University, Wikipedia, European Commission 4
„
“
Artificial General Intelligence (AGI)
Artificial Intelligence (AI) can be
distinguished into systems designed
to solve problems in a very specific
context – the so called Narrow AI –
and a human-like Artificial General
Intelligence (AGI).
AGI is currently only a future
scenario. Some experts say it is
decades away to build such an AI,
some argue that humans will never
be able to build something at an
equal level of intelligence.
Most often, AGI scenarios outline a
dystopia for the human race due to
the uncontrollable effects this
technology potentially has.
Thought leaders in the AI space
assume that at some tipping point an
AGI would act without any
interventions of humans and that
setting up a global governance of AI
development is critical right now.
Artificial Narrow Intelligence (ANI)
Narrow AI on the other hand is
already here. It is automating or
augmenting many tasks in a business
context or in our private life.
Self-driving forklifts in warehouses,
Amazon product recommendations,
real-time trades on stock exchanges,
interacting with Siri, person
recognition in the iPhone photo
album – everything is already done
automatically, without the need of
human intervention.
It’s worth mentioning that
automation per se does not require
intelligence. This can also be done by
pre-programmed rules. However,
complex tasks require the ability to
react flexibly to their environment,
which differentiates AI applications
from the well-understood domain of
rule-based automation.
5. AI IS ENTERING MASS COMMERCIALIZATION - EVERY
BUSINESS IS GOING TO CHANGE
Startup perspective
Artificial Intelligence promises
disruption. It opens up the possibility
to achieve better product or service
quality at less time and cost. And if
this can be proven to the market
successfully, it will change everything.
Customers start to jump from legacy
solutions to better products in a very
short timeframe, others follow to
adopt, old solution providers die and
the new players overtake the market
lead. That is what we call disruption
and what makes AI as a space so
attractive for the many young
companies in the recent years.
Investor perspective
Platform and as-a-service business
models have been proven very
successful due to recurring revenues
and scalability. To investors, owning a
new market and earning high
margins means to be fast and to take
a lot of financial risk. As VC funding
data shows, the race for number one
in AI has just taken off. At some point,
the market will start to consolidate
and the winning companies will
emerge.
Corporate perspective
Almost all large corporations are
confronted with entirely new
processes through AI. Its value lies in
automation and augmentation of
human work. The technology not only
changes the frontend or digitizes the
communication interface, it changes
the entire way of collaborating,
planning and decision-making.
The flexibility to change a – to this
point – successful concept, is crucial
to survive. Starting early experiments,
communicating change and investing
into research and talent is the way
forward.
1) Source: CB Insights (02/2018)
2) Source: CB Insights (01/2018)
3) Source: AIIndex.org (11/2017)
4) Source: Technews.io (09/2018)
5
20152013 2014 2016 2017
1.739
4.5693.477
6.255
15.242
+72%
2020
10
20101995 20152000 2005
0
5
15
20
+12%
Venture Funding in AI startups
In million U.S. dollars, global1
AI related research publications
In thousands, global3
Number of AI press mentions
Original media coverage by tech journalists4
20152014 2016
8.772
2017 2018
42.973
14.670
90.752
125.462
+94%
Startups with „.ai“ URL suffix
Global # of startups that received financing2
5 22
51
100
225
201720162013 2014 2015
+159%
Compound annual growth rate Compound annual growth rate
Compound annual growth rateCompound annual growth rate
6. THE AI HISTORY IS IMPORTANT TO UNDERSTAND
WHERE WE ARE TODAY AND WHAT IS DIFFERENT
AI 1.0 – Pattern Recognition
Since the term ‚AI‘ was established in
in 1956, government agencies like
DARPA1 funded scientific research
until the mid-1970s. Successes in the
fields of search, natural language
processing and simplified models and
simulations were achieved.
The tremendous expectations raised
by AI researchers afterwards didn‘t
match the achievements for the
investors. Limited computing power,
the rather low performance of neural
networks, combinatorial explosion
and a lack of commonsense
knowledge by the systems led to
significant cuts in funding. The first AI
winter lasted until 1980.
In the 1980s, the expert system
emerged. It used logical rules, derived
from the knowledge of domain
experts. Avoiding the challenges of
commonsense logic, funding came
back until 1987. The rise of IBM‘s and
Apple‘s desktop computers made
specialized AI hardware obsolete. As
a consequence, markets went down
and trust was gone – the second AI
winter.
AI 2.0 – Deep Learning
In the mid-1990s milestones were
achieved that brought back trust.
Chess champion Garry Kasparov was
beaten by ‘Deep Blue’ in 1997.
Intelligent agents that perceive the
environment and take actions to
maximize successful outcomes were
the leading paradigm to that time.
In addition, the deep learning concept
was developed, but it took another 10
years until the availability of big data
and computational power from
graphic processing units (GPU)
created the „big bang“ of deep
learning algorithms.
1) DARPA = Defense Advanced Research Projects Agency (Part of the United States Department of Defense) 6
AIPERFORMANCE
TIME
HIGHLOW
AI 1.0
Pattern
Recognition
AI 2.0
Deep
Learning
AI 3.0
Contextual
Reasoning
7. AI 3.0 IS COMING - BUT IT IS NOT
EQUIVALENT TO SUPERINTELLIGENCE
AI 3.0 – Contextual Reasoning
AI 2.0 is slowly fading out as new
breakthroughs in scientific and
applied AI demonstrate the
superiority of computer intelligence
compared to human intelligence in
selected domains.
It started with chess, continued with
Jeopardy, Go and is now at the point
of beating humans in collaborative
multiplayer games like Dota.
At the same time medical diagnostics
surpasses the accuracy of human
doctors in many domains such as
cancer detection from x-ray scans.
Also, the dependency on AI-powered
systems in our daily life (private and
business) increases rapidly. Search
engines, industrial robots, real-time
trading systems and soon also
autonomous mobility and delivery
services cannot be substituted by
humans anymore.
If you would turn off the AI-engine in
the future, the world would most
likely break apart. That is why many
smart thinkers urgently warn of the
consequences of too powerful
systems. Most of them agree that AI
will be uncontrollable for humans and
the question is, if humans can
gracefully co-exist with an artificial
super intelligence.
AI 3.0 is at this point not a synonym
to super-intelligence or singularity.
Contextual reasoning describes the
fact that most cognitive processes
depend on the environment and
therefore the context. Systems that
perform tasks in silos without
incorporating outside information are
already in use but they can be easily
identified as computer applications
with limited capabilities to respond to
exceptions and changes in their
environment.
7
The new spring in AI is the most
significant development in computing
in my lifetime.
Every month, there are stunning new
applications and transformative new
techniques. But such powerful tools
also bring with them
new questions
and responsibilities.
Sergey Brin
Co-Founder of Google
„
“
8. THE BREEDING GROUND FOR AI IS THERE – THE
QUESTION IS HOW FAST IT WILL GROW
8
Where are we today?
Even if there are already disciplines
where narrow AI systems outperform
human capabilities, in most areas of
application that are not repetitive,
rule-based or require simple
knowledge representation, humans
still do better. AI systems rather
augment tasks to be more secure,
work faster or deliver higher quality.
Why is it different now?
It is quite unrealistic that AI will enter
another ‘winter’ very soon because
multiple critical requirements are
fulfilled by the market:
1) Financing – AI is no longer in a
scientific silo that is depending on
external financial resources. With
successfully applied and monetized AI
applications, more and more money
floods into the system to keep the fire
burning.
2) Big data – led to the deep learning
big bang – but quantity is not
everything. With sophisticated data
warehouses, the quality of data
improves rapidly and so does the
output of AI applications.
3) Computational power – increased
significantly by using graphical
chipsets, but this is just the beginning.
Performance is likely to improve by
orders of magnitude in a short
timeframe with Google’s specialized
tensor processing unit (TPU) already
in the market and quantum
computing casting its shadows ahead.
4) Global collaboration – It’s not part
of a national strategy anymore to
decide about investing in research
and hiring talent. It’s a global task
with ambitious players – may that be
countries or corporates – to become
the most successful and influential
party in this collaborative
competition.
It’s likely that machines
will be smarter than us
before the end of the century
—
not just at chess or trivia
questions but at just about
everything, from mathematics
and engineering to science
and medicine.
Gary Marcus
Professor New York University
„
“
10. TRANSFER LEARNING SPEEDS UP LEARNING
SUCCESSES AND IMPROVES SCALABILITY
1) Source: Machine Learning Mastery (2017) – exemplary visualization 10
Research Advancement #1: Transfer Learning Performance of untrained / transfer learning models1
What does it mean?
Transfer learning is a machine learning method where a model developed and
trained for task A can be reused for task B by re-applying the already
acquired fundamental knowledge. The model for task B then already starts
learning already at a basic level of knowledge.
Why does it matter?
Re-using knowledge significantly reduces the required amount of data for
a model to learn a new task. Especially when data is hard to capture. It brings
not only faster results, it also achieves higher accuracy after the same
amount of training.
training
performance1
Applied transfer learning
Untrained model
higher
start
steeper
learning
curve
better output
AI RESEARCH
11. AFFORDABLE HIGH-END CHIP PERFORMANCE IS THE
KEY TO BRING AI TO THE MASS MARKET
1) Source: Baidu (2018) 11
Research Advancement #2: Chip Performance Speed benchmark for AI training with one or multiple GPUs
What does it mean?
Today, much of the processing power for training an algorithm is done with
graphics processing units (GPU), instead of central processing units (CPU). The
computational parallelism of the higher performing graphic chipsets
allows significant improvements of results as shown on the right.-
Why does it matter?
Cost efficient availability of GPU-based computational power was a game
changer in AI research. Training time of AI models decreased significantly.
Training a model with 50 GPUs is 40 times faster compared to one single GPU
unit.
1 2 3
8
15
40
56
0
5
10
15
20
25
30
35
40
45
50
55
60
2 4 50 100
# GPUs used for training
Training speedup over one GPU (x-fold)1
161 8
AI RESEARCH
12. RELIABLE AI-SYSTEMS REQUIRE CONTEXTUAL
ADJUSTMENTS UNDER CHANGING CONDITIONS
1) Source: ImageNet, Standford Vision Lab 12
Research Advancement #3: Contextual Learning Error rate development on ImageNET visual recognition
What does it mean?
Detecting objects in images or videos based on the sematic structure of pixels
and classification was very challenging in the field of computer vision.
Common sense logic of actions or context was missing. Due to advances in
convolutional neural network architectures and reinforcement learning,
AI systems improved a lot in the recent years.
Why does it matter?
Precise detection and classification of the environment data is key to
many applications such as autonomous driving. Moreover, contextual learning
is also relevant in natural language processing supporting smart assistants
like Siri.
0%
5%
10%
15%
20%
25%
2012
Error-rates on ImageNet1
2011 2013 2014 2015
human
level
AI RESEARCH
13. ADVANCED DIAGNOSIS SUPPORT HUMAN DOCTORS
AND PROGRESS ATTRACTS MORE INVESTMENT
1) Source: CB Insights 13
300
790
20162015
1.200
2017 HY 18
1.100
VC Funding in AI healthcare startups
Disclosed equity funding in million U.S. dollars (rounded)1
AI healthcare news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
HEALTHCARE
APPLIED AI
14. SMART AUTOMATION IS ON THE RISE, AI-POWERED
ROBOTS TRIGGER BILLION-DOLLAR INVESTMENTS
1) Source: International Federation of Robotics 14
Supply of industrial smart robots worldwide
In thousand units (forecasted)1
AI manufacturing news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
MANUFACTURING
178
221
254
294
346
378
433
521
2013 2018*2014 2015 2016 2019*2017* 2020*
APPLIED AI
15. AUTONOMOUS WAREHOUSE-LOGISTIC ACCELERATES
THROUGH AI IN DISTRIBUTION CENTERS
1) Source: Amazon 15
Number of robots working in Amazon fulfilment centers
in global units1
AI logistics news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
LOGISTICS
20152013 2016
30.000
120.000
2014 2017
1.400
15.000
45.000
APPLIED AI
16. THE IMMENSE NEED FOR AI EXPERTS DRIVES
SALARIES TO ASTRONOMICAL HEIGHTS
Size of the AI talent pool
The Global AI Talent Report states a
total number of 22,000 PhD-level AI
Researchers1. The data was derived
from LinkedIn queries and analyses
of the leading conferences. The scope
focuses on highly specialized AI
experts rather than counting entire
software development teams.
Distribution of talent has a strong
focus on native English-speaking
countries (U.S. followed by U.K.,
Canada and Australia) biased by the
research methodology. Especially
China is underrepresented, while
France, Germany and Spain
complement the significantly smaller
European counterpart.
Chinese tech giant Tencent sets a
broader focus and counts 300,000
active AI researchers and
practitioners globally. Two-thirds of
them employed in the industry and
100,000 researching in academia.
Salaries for AI professionals
According to figures from job listings
platform Indeed, average salaries for
AI programmers in the U.S. range
between $134k and $170k2. On
Glassdoor, the average annual salary
for AI listed jobs is at $111k, including
a broad range of AI related jobs in
startups or corporates.
Real domain experts like software
architects and engineers can easily
earn three to four times as much.
Especially the large tech companies
such as Google or Facebook pay from
$300k to $500k3 in base salary and
company stock a year even for
university drop-outs.
Public figures of DeepMind show
staff costs of $138 million, which
equals $345k for each of the 400
researchers. For leadership positions
in AI domains the sky is the limit. Not
only in the U.S. – this is also true for
companies in China, Korea or Europe.
1) Source. Element AI (2018) - http://www.jfgagne.ai/talent
2) Source: Medium (2018) - https://medium.com/mlmemoirs/artificial-intelligence-salaries-heading-skyward-e41b2a7bba7d
3) Source: NY Times (2018) - https://www.nytimes.com/2017/10/22/technology/artificial-intelligence-experts-salaries.html
16
Global AI talent distribution
PhD Researchers based on LinkedIn and conference anaylses1
AI TALENT
17. TECH COMPANIES FIGHT WAR OVER TALENTS FROM
TOP AI UNIVERSITIES
Industry vs. academia
Skilled computer and data scientists
that understand the complex
mathematical techniques to develop
artificially intelligent software and use
it to improve business are very rare.
This is why big tech companies
aggressively recruit talents from top
universities. In 2015, Uber hired 40
students out of Carnegie Mellon to
boost its self-driving car ambitions.
Even professors are increasingly
recruited to work in the industry.
Four professors from Stanford and
six from the University of Washington
were at least temporarily on a leave
in the past years.
At this point, the talent hunger of the
industry already affects the following
classes in the educational system. But
Google, Microsoft and IBM were just
the start. Every well-funded Silicon
Valley company and increasingly also
old-economy players enter the AI
talent war.
According to a Stack Overflow survey,
86.7% of developers are self-taught3
(not specified on the AI space). Given
this fact, online course offerings like
Coursera and Udacity as well as
corporate programs will most likely
continue to grow rapidly to meet the
demand.
1) Source: U.S. News (2018) - https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings
2) Source: State of AI (2018) - https://www.stateof.a
3) Source: Developer Survey Results 2018 - https://insights.stackoverflow.com/survey/2018
17
1
Carnegie Mellon University
Pittsburgh, Pennsylvania (USA)
Students: 12,500
2
Massachusetts Institute of Technology
Cambridge, Massachusetts (USA)
Students: 11,319
3
Stanford University
Stanford, California (USA)
Students: 16,430
4
University of California
Berkeley, California (USA)
Students: 41,910
5
University of Washington
Seattle, Washington (USA)
Students: 46,686
Top universities for artificial intelligence research
Based on survey results of academics in the United States1
Leading employers of AI talent
Size of employed AI staff2
900
450 400
300
1.400
1.000
„There is a giant sucking sound of
academics going into industry“
Oren Etzioni
CEO of the Allen Institute for AI
AI TALENT
19. 19
AI STARTUP ACQUISITIONS BY THE TOP B2B
CLOUD PLATFORM VENDORS
Source: CB Insights, CrunchBase (as of October 2018)
Excluded: Chinese platform vendors such as Baidu and Alibaba
TIME
2014 2015 2016 20172013 2018
20. 20
AI STRATEGIES OF THE LEADING
B2B DATA PLATFORMS
INFRASTRUCTURE MATURITY
LOW HIGH
FIRST MOVERS
LEADERSVISIONARIES
EARLY FOLLOWERS
STRATEGICCOMMITMENT
HIGHLOW
Google’s AI strategy is to create a
strong market position with
corresponding patents and related
technology areas as its business
relies heavily on machine learning.
With TensorFlow, Google developers
delivered the leading open-source
software library in the ML space. This,
alongside with their investment
strategy in AI start-ups, shows
Googles dedication to be and stay the
leader in AI development.
Microsoft - Since 2016, the company
keeps the pace of Google with
increasing acquisition activity. And
still, the company's entire AI efforts
are well linked with the Azure cloud. It
will be crucial to seamlessly integrate
the technology from start-ups into
the company’s portfolio.
Amazon will continue to build
around the successful Alexa project
with voice, virtual assistants and
natural language processing. AI-as-a-
service is pushed into the AWS
environment to make Amazon a
leader in this field as well.
IBM's AI strategy is lazor-focused on
its enterprise customers. In giving
them the control of their data and
insights, IBM tries to assist them
increase efficiency, lower costs or
augment human intelligence.
Simultaneously, open source projects
in the fields of AI are supported and
APIs to other vendors like Google's
TensorFlow are endorsed.
Salesforce’s Einstein AI platform has
been the central focus for the
development and marketing strategy.
CEO Benioff described the AI strategy
quite straight forward: ‘AI is the next
platform – all future applications, all
future capabilities for all companies will
be built on AI.’
SAP - Cloud surpassed the licensing
business for the first time in 2018. In
addition, acquiring Recast.AI and
creating Europe’s first corporate AI
ethics advisory panel clearly positions
AI as a central strategy building block.
21. 21
FIVE ACTION POINTS TO CREATE SUSTAINABLE VALUE
FROM ARTIFICIAL INTELLIGENCE
TALENT ACQUISITION
▪ Data scientists, AI engineers and software architects are the scarcest resources in terms of development speed as of today
▪ Make sure to create a vibrant and attractive environment to make smart people want to work for you
▪ Start early to recruit and be competitive in talent acquisition, otherwise you can’t expect to outpace your competition in this domain
DEVELOP A COMPANY-WIDE DATA STRATEGY
▪ Data is the raw input to every AI application and determines the quality of the output
▪ Unstructured data held in silos with multiple decision-making stakeholders slows the adoption process
▪ Define and implement a coherent and flexible data architecture and set responsibilities on the executive-level
EMBRACING A TECHNOLOGY MINDSET
▪ Start to develop iteratively and find ways to apply this logic to mission critical processes as well
▪ Openly communicate and promote advances and changes through technology to avoid fear and opposition building
▪ Allow new and radical ways of thinking and encourage experiments and failure
EVALUATION OF NEW BUSINESS MODELS
▪ Think holistic! Consider how technology impacts and changes the way partners, customers, competitors and new market entrants act
▪ Be open to even license proprietary solutions to other market players in order to create new revenue streams
▪ Question current processes and evaluate external as-a-service offerings to keep focus on your core product
START TODAY
▪ Value creation of AI is strongly correlated with the experience in this domain – the sooner you start, the higher your return
▪ Facilitate ideas in your company by small-scale prototypes or proof-of-concept projects
▪ Invest time into the exploration of possibilities that are out there already
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LET’S TALK ABOUT BOOSTING YOUR BUSINESS
WITH ARTIFICIAL INTELLIGENCE!
We identify suitable AI use cases and help
with target-oriented prioritization.
IDENTIFY
We create a concrete plan about
requirements, project organization and
feedback-driven iteration steps.
PLAN
We evaluate suitable solutions for your
company-specific requirements
EVALUATE
We facilitate the development and
implementation process and support you in
the selection of partners.
EXECUTE
We provide decision confidence through
process design and functional prototypes
TEST
We develop a future-proof data strategy
based on the company's long-term goals
SUSTAIN
23. GET IN TOUCH!
Tobias Bohnhoff
Appanion Labs GmbH ▪ Hopfenstrasse 11 ▪ 20359 Hamburg ▪ www.appanion.com
Discover more at
www.appanion.com
Disclaimer: This whitepaper is based on research and data of the previously mentioned sources. All information presented were researched and prepared by Appanion with great care. For the presented data and information Appanion cannot assume any warranty of any kind. Information and data represent in selected cases individual opinions
and may be in need of further interpretation as a basis for decisions. Advances in research may occur after the publishing of this document. Therefore, Appanion is not liable for any damage arising from the use of information and data provided in this whitepaper.