The document summarizes the evolution of artificial intelligence (AI) from the 1950s to the present. It discusses three waves of AI development: handcrafted knowledge in the early period, statistical learning from the 1960s to 1980s, and contextual adaptation from the 1990s onward. Recent advances are driven by increased computing power, data availability, and new algorithms. Deep learning is increasingly important and applications include voice control, natural language processing, and computer vision. While AI has great potential, a lack of talent and data is creating a bifurcated ecosystem with large tech firms at the top.
Tata Consultancy Services Ltd is a leading global IT services, consulting and business solutions organization offering transformational as well as outsourcing services to global enterprises. It has a global presence, deep domain expertise in multiple industry verticals
Artificial intelligence is impacting both the economy and daily lives of Chinese people and is widely used across many industries. A comprehensive report on the AI technologies in China is offered by Daxue consulting
UbiBot’s specializes in providing access to your IoT data wherever and whenever you need. We offer products @ IoT big data platform as well as wireless smart sensors . We have WiFi and GPRS enabled sensors that meant for data monitoring and helpful in bringing high quality IoT gateways.
Tata Consultancy Services Ltd is a leading global IT services, consulting and business solutions organization offering transformational as well as outsourcing services to global enterprises. It has a global presence, deep domain expertise in multiple industry verticals
Artificial intelligence is impacting both the economy and daily lives of Chinese people and is widely used across many industries. A comprehensive report on the AI technologies in China is offered by Daxue consulting
UbiBot’s specializes in providing access to your IoT data wherever and whenever you need. We offer products @ IoT big data platform as well as wireless smart sensors . We have WiFi and GPRS enabled sensors that meant for data monitoring and helpful in bringing high quality IoT gateways.
Artificial Intelligence (AI) is one of the fastest growing fields of technology thanks to its strong and increasingly diversified commercial revenue stream. The anticipated benefits of the next wave of AI encouraged politicians, economists and policy makers to pay more attention to AI. The next wave of strong/general AI and superintelligence will open the doors to create machines able to behave cognitively like a super human at both individual level and group level in unstructured, dynamic and partially observable environments. This may represent a significant existential risk to humanity if not regulated and smartly directed toward the benefit of humanity. Aligned with 17 Sustainable Development Goals (SDGs) adopted by UN Member States, next wave of AI can play instrumental roles in achieving these goals. This talk highlights the role of AI as an enabler for achieving the SDGs.
What would professional sports look like with AI referees and other smart tec...Entefy
Does the idea of watching sporting events with AI referees sound futuristic? It certainly might. But when you take a look around the world of professional sports—football, soccer, fencing, basketball—advanced technologies are already having an impact on the roles of referees, coaches, players, and fans.
In fact, “precursor” technologies that provide the sensory input data for yet-to-be-invented AI algorithms are already in use. In some sports, athletes’ uniforms feature wearable devices and refs are using smart technologies to call plays. Technology looks likely to have a serious impact on how games are played and watched.
This presentation highlights key points from our article about how AI and other smart technologies might impact the future of professional sports. These slides provide an overview of the systems in use today, the rapid implementation of new smart technologies, and what fully automated refereeing might look like.
For additional analysis and links to our background sources, read “What would the Super Bowl look like with AI referees and other smart technologies?" on our blog at https://blog.entefy.com/view/304/What-would-the-Super-Bowl-look-like-with-AI-referees-and-other-smart-technologies.
How Ecosystem Economics™ Predicts the Winners in the Digital AgeJulie Meyer
Julie Meyer presents Ecosystem Economics™ - the Ariadne Capital Investment Methodology and EntrepreneurCountry Global Operating System - which she has presented at TED, corporate Board rooms, throughout the business media, and in EC Global workshops
The presentation gives an overview of Infinite Uptime. We are a global industrial IoT solution provider to improve productivity, monitoring & predictive maintenance in engineering & processing industries.
Know us more: https://www.infinite-uptime.com/
Social Links:
- https://www.facebook.com/infiniteuptime/
- https://www.linkedin.com/company/infinite-uptime/
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
Artificial Intelligence (AI) is one of the fastest growing fields of technology thanks to its strong and increasingly diversified commercial revenue stream. The anticipated benefits of the next wave of AI encouraged politicians, economists and policy makers to pay more attention to AI. The next wave of strong/general AI and superintelligence will open the doors to create machines able to behave cognitively like a super human at both individual level and group level in unstructured, dynamic and partially observable environments. This may represent a significant existential risk to humanity if not regulated and smartly directed toward the benefit of humanity. Aligned with 17 Sustainable Development Goals (SDGs) adopted by UN Member States, next wave of AI can play instrumental roles in achieving these goals. This talk highlights the role of AI as an enabler for achieving the SDGs.
What would professional sports look like with AI referees and other smart tec...Entefy
Does the idea of watching sporting events with AI referees sound futuristic? It certainly might. But when you take a look around the world of professional sports—football, soccer, fencing, basketball—advanced technologies are already having an impact on the roles of referees, coaches, players, and fans.
In fact, “precursor” technologies that provide the sensory input data for yet-to-be-invented AI algorithms are already in use. In some sports, athletes’ uniforms feature wearable devices and refs are using smart technologies to call plays. Technology looks likely to have a serious impact on how games are played and watched.
This presentation highlights key points from our article about how AI and other smart technologies might impact the future of professional sports. These slides provide an overview of the systems in use today, the rapid implementation of new smart technologies, and what fully automated refereeing might look like.
For additional analysis and links to our background sources, read “What would the Super Bowl look like with AI referees and other smart technologies?" on our blog at https://blog.entefy.com/view/304/What-would-the-Super-Bowl-look-like-with-AI-referees-and-other-smart-technologies.
How Ecosystem Economics™ Predicts the Winners in the Digital AgeJulie Meyer
Julie Meyer presents Ecosystem Economics™ - the Ariadne Capital Investment Methodology and EntrepreneurCountry Global Operating System - which she has presented at TED, corporate Board rooms, throughout the business media, and in EC Global workshops
The presentation gives an overview of Infinite Uptime. We are a global industrial IoT solution provider to improve productivity, monitoring & predictive maintenance in engineering & processing industries.
Know us more: https://www.infinite-uptime.com/
Social Links:
- https://www.facebook.com/infiniteuptime/
- https://www.linkedin.com/company/infinite-uptime/
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate
activities that presently require human intelligence. Recent successes in A.I. include
computerized medical diagnosticians and systems that automatically customize
hardware to particular user requirements. The major problem areas addressed in A.I. can
be summarized as Perception, Manipulation, Reasoning, Communication, and Learning.
Perception is concerned with building models of the physical world from sensory input
(visual, audio, etc.). Manipulation is concerned with articulating appendages (e.g.,
mechanical arms, locomotion devices) in order to effect a desired state in the physical
world. Reasoning is concerned with higher level cognitive functions such as planning,
drawing inferential conclusions from a world model, diagnosing, designing, etc.
Communication treats the problem understanding and conveying information through
the use of language. Finally, Learning treats the problem of automatically improving
system performance over time based on the system's experience. Many important
technical concepts have arisen from A.I. that unify these diverse problem areas and that
form the foundation of the scientific discipline. Generally, A.I. systems function based
on a Knowledge Base of facts and rules that characterize the system's domain of
proficiency. The elements of a Knowledge Base consist of independently valid (or at
least plausible) chunks of information. The system must automatically organize and
utilize this information to solve the specific problems that it encounters. This
organization process can be generally characterized as a Search directed toward specific
goals. The search is made complex because of the need to determine the relevance of
information and because of the frequent occurrence of uncertain and ambiguous data.
Heuristics provide the A.I. system with a mechanism for focusing its attention and
controlling its searching processes. The necessarily adaptive organization of A.I.
systems yields the requirement for A.I. computational Architectures. All knowledge
utilized by the system must be represented within such an architecture. The acquisition
and encoding of real-world knowledge into A.I. architecture comprises the subfield of
Knowledge Engineering.
KEYWORDS – Artificial Intelligence, Machine Learning, Deep Learning, Encoding,
Subfield, Perception, Manipulation, Reasoning, Communication, and Learning.
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
Are you ready to take your smart solutions to the next level? During this session, you will learn how to integrate AI and ML platforms for analysis and processing of digital twin data in systems based on FIWARE. In this session, we'll cover all aspects of ML Ops, including how to deploy ML in edge systems, and show you how to use AI and ML to turn data into actionable insights and business value.
You'll discover how to leverage the power of AI and ML to optimize your smart solutions and gain a competitive edge. You'll learn how to implement ML Ops in solutions powered by FIWARE, enabling you to easily deploy machine learning models and update them in real-time. We'll also discuss how to deploy ML in edge systems, allowing you to process data locally and avoid the latency of sending it to a central server.
This session will invite participants to discover how to put data to work and turn it into wisdom that drives business value. Moreover, different experiences on how to automatize ML, especially regarding training and deploying ML models into solutions powered by FIWARE in different real scenarios.
If you are interested in learning how to turn machine learning models towards perfection and deliver ML solutions more easily as part of solutions powered by FIWARE, to extract the most out of data, this session is for you.
This is the slideshow for a presentation I gave as part of my graduate coursework at the Institute for Innovation and Public Purpose at University College London (UCL IIPP). Drawing on the work of IIPP professors including Carlota Perez (techno-economic paradigms), Mariana Mazzucato (“The Entrepreneurial State”), and Tim O’Reilly, I evaluate the innovation trajectory of Deep Neural Networks as a method of machine learning. I trace the history of machine learning to its present-day and conclude that while Deep Neural Networks have not yet reached technological maturity, they are already starting to encounter barriers to exponential growth and innovation. These slides were designed to be read independently from the spoken portion. If you found this useful or interesting, please message me on LinkedIn! - Justin Beirold
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. The evolution of Artificial Intelligence (AI) since the 1950s went
through several phases…….
Source: Artificial Intelligence by Giorgio Fumera from University of Cagliari | AI: 15 Key Moments in the Story of Artificial Intelligence by BBC | Future Progress in Artificial Intelligence: A
Survey of Expert Opinion
Further Advancement
(Mid 1980s–1990s)
Great Expansion
(Mid 1960s–early 1980s)
Recent Developments
(Mid 1990s – )
Starting from solving problems
with toys and real-world situations
including game playing, proving
theorems, natural language
processing (NLP), recognizing
objects in images
Research funding from DAPRA’s
strategic computing program in US,
Fifth Generation Computer System in
Japan and ESPRIT in Europe
Technical and theoretical advances
including the rise of machine learning
Many successful commercial
applications including: games, robots,
driverless vehicles, homes,
recommender systems, automated
trading, translating systems, aircraft
autopilots, fraud detectors, search
engines, etc.
Early Explorations
(1950s–1960s)
Alan Turing
introduced
the Turing
test
“Artificial
Intelligence”
was created at
Dartmouth
conference
1956
1950 1966
1969
Shakey
mobile
robot
MIT’s
“summer
vision
project”
1973
AI winter
1981
AI’s
commercial
value started
to realize
1990
Rodney Brooks was
inspired by
advances in
neuroscience,
which had started
to explain the
mysteries of
human cognition
1997
IBM’s Deep Blue
defeated the
world chess
champion
2002
iRobot -
first home
robot
2011
IBM’s Watson
won
"Jeopardy!“
2012
Google Brain
recognised a
cat from
millions of
unlabeled
videos
2016
Google's
AlphaGo
defeated the Go
world champion
The Future
AI experts estimate a 90%
chance of machines
achieving human-level
intelligence by 2075 and
superintelligence within 30
years of attaining human-
level intelligence
3. That can be clustered into three waves of AI development that may
be described as - handcrafted knowledge, statistical learning and
contextual adaptation
Source: A DARPA Perspective on AI
Abstracting
Perceiving
Learning
Reasoning
2 Statistical learning
Systems based on statistical models developed
to address specific challenges and trained using
big data
• Examples: voice recognition, face
recognition
• Features: Nuanced classification and
prediction capabilities. No contextual
capability and minimal reasoning ability
• Challenges: statistically impressive but
individually unreliable, inherent flaws can be
exploited, skewed training data creates
maladaptation, “blackbox”
AI Wave
1 Handcrafted Knowledge
Systems that have established sets of rules
to represent knowledge in well-defined
domains
• Examples: logistics program
scheduling, game-playing programs
• Features: Enables reasoning over
narrowly defined problems. No learning
capability and poor handling of
uncertainty
• Challenges: The structure of the
knowledge is defined by humans. The
specifics are explored by the machine.
Failure of the autonomous cars in the
DARPA Grand Challenge
3 Contextual Adaptation
• Systems that construct contextual
explanatory models for classes of real
world phenomena
• Examples: Image recognition
• Features: Ability to perceive, learn,
abstract and reason
• Models that generate explanations of
how an object might have been created
to explain and drive decisions
4. These waves of AI developments are advancing rapidly, driven by
machine and deep learning
Faster and more powerful
computation (GPUs)
Greater data availability Development of new algorithms Tech giants are opening up resources to
enable others to develop better AI (e.g.
TensorFlow, Amazon AI)
Availability of cloud-based
infrastructure
Factors driving the rapid advancement of AI
Source: The Fourth Industrial Revolution: a Primer on Artificial Intelligence by David Kelnar
Human intelligence exhibited by
machines
Focal Areas of AI
• Reasoning
• Knowledge
• Planning (including navigation)
• Natural language processing
• Perception
Statistical techniques enable predictions by
machines to improve with experience
Beyond deep learning, it includes various
approaches:
• Random forests: create multitudes of
decision trees to optimise a prediction
• Bayesian networks: use a probabilistic
approach to analyze variables and the
relationships between them
• Support vector machines: be fed
categorized examples and create models to
assign new inputs to one of the categories
• It models the brain and uses an artificial
‘neural network’ - a collection of neurons
connected together
• It is useful because the algorithm undertakes
the tasks of feature specification (defining
the features to analyze from the data) or
optimization (weighing the data to deliver an
accurate prediction)
Artificial Intelligence
Deep Learning
Machine Learning
A subset of AI
A subset of machine learning
The broadest term
1950's 1960's 1970's 1980's 1990's 2000's 2010's
5. • Deep learning uses neural
networks loosely analogous to the
observed behaviour of a biological
brain's axons
• It consists of multiple hidden layers
of networks between the input and
output layers and are trained
separately, breaking down the
characteristics of the data into
multiple parts and combining all
the layers in the end to provide the
output
Trend 1: Looking ahead, deep learning is expected to be an
important technique in AI
Input layer: data
can be fed into
the network
Hidden Layer:
information is
processed
Output Layer:
results come out
Source: Computer Science: The Learning Machines by Nature | The Fourth Industrial Revolution: a Primer on Artificial Intelligence by David Kelnar | From not Working to Neural Networking
by The Economist | Analytics Vidhya | Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica | Learning Deep Architectures for AI By Yoshua Bengio
Neural networks
Illustration of using deep learning neutral networks for facial
recognition
1. Identify pixels of light and dark 2. Learn to identify edges and
shapes
3. Learn to identify more complex
shapes and objects
4. Learn which shapes and objects
define a human face
Deep Learning Characteristics
• Deep learning helps reduce the time and effort spent on feature engineering. Deep learning is increasingly used in conjunction with machine learning,
natural language processing (NLP), computer vision or machine reasoning
• Deep learning is typically employed for feature extraction on a larger or more complex set of data while employing machine learning algorithms to perform
basic clustering or regression learning tasks when features of the data have been determined
• The performance of deep learning neural networks (DNN) has been demonstrated to increase on a linear scale with the increase in number of DNN layers,
necessitating hardware to process and train these algorithms to also grow in scale
Image source: Andrew Ng
6. Trend 2: There are different types of learning, with semi-supervised
and reinforcement learning gaining traction
Source: Three Things You Need to Know About Machine Learning by Medha Agarwal | Machine Learning by TechJini
• Given pre-determined features
and labeled data
• Direct feedback
• Predict outcome
• Example: traditional insurance
underwriting
• Challenge: need for large
amount of labeled data which
is costly and time-consuming
Supervised Semi- Supervised Unsupervised Reinforcement
LearningTypes
• Given unlabelled and
unstructured data
• No feedback
• Find hidden structure
• Example: customer
segmentation
• Challenge: tend to be less
accurate
• A blend of supervised and
unsupervised learning
• Used for situations in which
there is some labelled data but
not a lot
• Example: Gmail spam
• It is expected to see increasing
usage for large data sets,
where data labelling is an issue
• Experience driven sequential
decision-making
• Occasional feedback in the
form of a reward
• Example: Game (AlphaGo),
robots, autonomous driving
• Challenge: require a significant
amount of data
• Algorithms with fewer layers. For
instance, logistic regression, support
vector machine
• Better for relatively less complex and
smaller datasets
• New technique that uses many layers of
neural networks
• Useful for complex target function
and large datasets
LearningTechniques
Shallow Deep
7. Source: Expect Deeper and Cheaper Machine Learning by IEEE Spectrum | How AI is Shaking up the Chip Market by Wired | A Machine Learning Landscape by Karl Freund from Moor
Insights & Strategy | Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica
Trend 3: Shift from GPUs to AI optimized hardware
• Graphics processing units (GPUs) have been the dominant hardware platform for AI applications and are expected to drive advances in performance,
especially for high-performance deep learning systems
• At the same time, the emergence of alternative hardware platforms like field-programmable gate arrays (FPGAs), application-specific integrated circuits
(ASICs), and specialized processor architectures are competing with GPUs on performance, cost, and power consumption
• As AI algorithms advance to account for applications with dynamic inputs (e.g. autonomous driving, personalized medicine), the evolving nature of algorithms
and workloads will determine suited architecture. Processors will increasingly be “right-sized” to align capabilities and cost with specific workloads.
Hardware across the Machine Learning Landscape
There are two key aspects:
Training refers to training the neural network with
massive amounts of sample data. It is typically
performed in large datacenters on GPUs, almost
exclusively provided by NVIDIA for the time being.
Inference refers to using a trained model to provide
outputs on real-world data.
• It is usually done at the application or client end
point, rather than on the server or cloud
• Inference requires fewer hardware resources, and
depending on the application, can be performed
using a central processing unit or non-specialized
hardware. This could be FPGA, ASICs, digital signal
processor, etc
• There are rising expectations that inference will
move locally to mobile devices
Image source: Moor Insights & Strategy
8. • Rapid advances in machine learning and NLP have also enabled voice
control to become more practical
• Companies like Baidu, Apple, Google, Microsoft, and Amazon are
making significant progress in voice recognition using DNNs
• Voice control may soon be sufficiently reliable for interacting with an
array of devices, robots, and home appliances coupled with open source
But some challenges remain:
• The complexity, subtlety and power of language
• An analysis of the way people use Alexa and Google’s assistant platforms shows that third-party apps have not been well used nor
particularly sticky
In the past Now & Future
Developers published over 10,000 skills on Amazon Alexa by 23 Feb 2017
Improvement of word accuracy rates by platform
950 1000 1400 2000 3000 4000 5191 6068 7053
10000
2016
May
2016
Jun
2016
Jul
2016
Aug
2016
Sep
2016
Oct
2016
Nov
2016
Dec
2017
Jan
2017
Feb
Trend 4: Machine learning and natural language processing give
rise to voice as the next conversational interface
Source: Amazon Alexa Now has 10k Skills, Including Europe by Voicebot.ai | MIT Technology Review
Image source: Internet Trends 2016 by KPCB
9. Generalized Intelligence: broad mental capacity
that influences performance on cognitive ability
measures (Examples: emotional intelligence,
creativity, intuition)
Reasoning: solving problems through logical
deduction (Examples: legal assessment, financial
asset management, games)
Knowledge: representing knowledge about the
world. (Examples: medical diagnosis, drug creation,
fraud prevention)
Planning: establishing and achieving goals
(Examples: logistics, physical and digital network
optimization, predictive maintenance)
Communication: understanding written and
spoken language (Examples: voice control, real-time
translation and transcription)
Perception: deducing things about the world from
visual images, sounds, and other sensory inputs
(Examples: autonomous vehicles, medical diagnosis,
surveillance)
1
2
3
4
5
6
Goals of AI
Reasoning
Knowledge
PlanningPerception
Generalized
Intelligence
Communication
1
2
3
4
5
6
AI Challenge Resolution Capabilities
Trend 5: AI is especially important given its ability to solve
challenging problems and potentially impact almost every industry
Source: The Fourth Industrial Revolution: a primer on AI by David Kelna | AI, Deep Learning, and Machine Learning: A Primer by Frank Chen
Horizon Robotics uses large-scale cloud-based
deep neural network algorithms on high-
performance and low-power brain processing
units for applications in smart homes and
autonomous cars
(www.horizon-robotics.com)
Dynamic Yield delivers an end-to-end platform
for personalization in eCommerce, media, travel
industry using machine learning to improve
targeting capabilities
(www.dynamicyield.com)
Xuebajun is a mobile application that helps
students solve homework questions using
Scene Text Recognition (STR) Technology and
deep learning to improve character recognition
rate
(www.xueba100.com)
LightCyber uses machine learning to map out
and monitor all users and devices on a
company network. It detects behavioural
anomalies and selects meaningful actionable
alerts for escalation
(www.lightcyber.com)
Maxent provides anti-fraud software as a
service based on machine learning techniques
(www.maxent-inc.com)
Selected Vertex Portfolio Companies
10. Firms
Talent | Data
Trend 6: At the same time, a dearth of talent and data is driving
the emergence of a bifurcated AI industry ecosystem that is top-
heavy and long-tailed
Dominant Players | Technology & Financial
Services Companies
• Have access to very large training datasets and the ability
to drive the advancement in algorithms
• Focus on highly scalable use cases like image recognition
or patient data processing, which avail the most
significant revenue opportunities or help enhance existing
services and products to gain a competitive edge
• Be expected to lead the top-heavy AI ecosystem because
of specialty in high performance computing systems that
power advanced use cases such as predictive
maintenance, algorithmic trading or static image
recognition
A dearth of talent and data in AI are critical challenges that play to the strengths of dominant players like big technology
firms and financial service companies creating a bifurcated AI industry ecosystem comprising:
Small & Medium-sized Enterprises (SMEs) |
Startups
• Possess relatively smaller data sets
• Focus on using or enhancing existing algorithms (many
are increasingly open-sourced) as well as employing high-
performance cloud services
• There are many niche applications where AI is expected to
add value and startups can compete effectively with the
bigger players
Source: Artificial Intelligence: 10 Trends to Watch in 2017 and Beyond by Tractica | Artificial Intelligence is the New Electricity by Andrew Ng | Banks and Tech Firms Battle Over
Something Akin to Gold: Your Data by the New York Times| JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours by Bloomberg | Wells Fargo Increases Emphasis on
Emerging Technologies by Wells Fargo
11. Final Comments
Vertex has invested in companies across geographies addressing different industry
applications leveraging AI to transform their service offerings. These include Xuebajun in
education, Horizon Robotics in autonomous cars and smart homes, Maxent in fraud
detection, Dynamic Yield in customer personalization and optimization, and LightCyber in
cybersecurity (recently acquired by Palo Alto Networks).
With each investment, we’re learning more about success strategies using AI to transform
industries. We’re excited to be active investors in this space and are looking forward to the
journey ahead.
12. Thanks for reading!
Disclaimer
This presentation has been compiled for informational purposes only. It does not constitute a recommendation to any party. The presentation relies on data and
insights from a wide range of sources including public and private companies, market research firms, government agencies and industry professionals. We cite
specific sources where information is public. The presentation is also informed by non-public information and insights.
Information provided by third parties may not have been independently verified. Vertex Holdings believes such information to be reliable and adequately
comprehensive but does not represent that such information is in all respects accurate or complete. Vertex Holdings shall not be held liable for any information
provided.
Any information or opinions provided in this report are as of the date of the report and Vertex Holdings is under no obligation to update the information or
communicate that any updates have been made.
About Vertex Holdings
Vertex Holdings, a member of Temasek Holdings, focuses on venture capital investment opportunities in the information
technology and healthcare markets, primarily through our global family of direct investment venture funds. Headquartered
in Singapore, we collaborate with a network of global investors who specialize in local markets. The Vertex Global Network
encompasses Silicon Valley, China, Israel, India, Taiwan and Southeast Asia.
Contact us: Brian Toh
btoh@vertexholdings.com
Tracy Jin
tjin@vertexholdings.com
James Lee
jlee@vertexholdings.com