The document announces an upcoming AI and OpenPOWER meetup on March 25th, 2018 in San Ramon, California from 4-7:30pm where attendees can learn about the latest advances in artificial intelligence and deep learning tools from industry leaders and pioneers and discuss how these technologies are impacting their industries. Prominent speakers will discuss topics ranging from machine learning performance and best practices to AI research at NASA and scalable machine learning with Apache SystemML on Power systems. The meetup aims to gather cutting-edge insights on AI from innovators across different sectors.
GET A SNEAK-PEEK INTO THE FUTURE OF ARTIFICIAL INTELLIGENCE!
Deepsphere.AI is conducting a two-day ‘Applied Artificial Intelligence Workshop’ for middle and high school students on July 24 and July 25, 2021.
It is a virtual instructor-led workshop that focuses on creating awareness about the real-world application of artificial intelligence and provides hands-on training to the students by implementing industry use cases on an enterprise-grade lab infrastructure.
In this two-day program, the students will gain knowledge on Data Science, Intelligence Automation, and Digital Transformation. They will also learn how to develop machine learning models on an advanced computing lab infrastructure such as google cloud, google collab, and open source technologies.
Know your Instructor:
Jothi Periasamy is the subject matter expert who knows his ins and outs in the field of Data Science and Artificial Intelligence. He is currently the Board Member at the University of California, Chief Data Scientist, Applied Machine Learning & Data Engineer. He has more than 17 years of experience in management consulting and end-to-end AI (ML, DL, RPA, NLP) experience with Deloitte, E&Y, and KPMG.
Debugging AI is (almost) nothing like verifying and debugging traditionally engineered software systems, and it’s still in its infancy. In his talk, Dr Christian Betz will present a brief overview of the field of AI, explaining the properties of different approaches to AI, and in which contexts AI systems shine, explaining the current hype. From there, Christian will discuss prominent failures of AI systems and show ways to hack AI systems. He’ll explain why verifying and testing AI is hard, and will guide us through different approaches to tackle this problem.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence and Cognitive ComputingFlorian Georg
Keynote talk with some high level introduction on A.I., Cognitive systems, Machine Learning and IBM Watson & Cloud Platform @ Datadirect IT Security Forum 2017
6G WILL UNLOCK THE POWER OF AI-FOR-EVERYTHINGMahbubul Alam
We envision 6G to offer revolutionary transformation which will usher in an era of connected built-in intelligent applications, services, and networks that will auto-provision end-to-end systems to guaranteed quality of services for an agreed service level agreement, ultra-high-speed data rate, surpassing that of last-mile wired connectivity, perceived zero-latency & deterministic jitter for human safety and mission-critical applications, extremely high reliability for essential services, high spectrum-bands for haptic, holographic, extensive multimedia streaming and more, connected artificial intelligence for autonomous functions and future unknown use cases, etc.
6G will be a key enabler for equitable wealth distribution and a major driver for the green economy. It will unleash the full potential for Industrial Revolution IE 5.0 which will focus on the co-operation between human and machine, as human intelligence works in harmony with cognitive computing and machines performs mundane, repetitive, error-prone tasks. By putting humans back into industrial production with 6G enabled collaborative robots a.k.a Cobots, workers will be upskilled to provide value-added tasks in production such as setting the strategy, provide oversight and add creative input, leading to massive customization & personalization for customers. In this talk, we will examine the state of AI and its potential role in 6G.
GET A SNEAK-PEEK INTO THE FUTURE OF ARTIFICIAL INTELLIGENCE!
Deepsphere.AI is conducting a two-day ‘Applied Artificial Intelligence Workshop’ for middle and high school students on July 24 and July 25, 2021.
It is a virtual instructor-led workshop that focuses on creating awareness about the real-world application of artificial intelligence and provides hands-on training to the students by implementing industry use cases on an enterprise-grade lab infrastructure.
In this two-day program, the students will gain knowledge on Data Science, Intelligence Automation, and Digital Transformation. They will also learn how to develop machine learning models on an advanced computing lab infrastructure such as google cloud, google collab, and open source technologies.
Know your Instructor:
Jothi Periasamy is the subject matter expert who knows his ins and outs in the field of Data Science and Artificial Intelligence. He is currently the Board Member at the University of California, Chief Data Scientist, Applied Machine Learning & Data Engineer. He has more than 17 years of experience in management consulting and end-to-end AI (ML, DL, RPA, NLP) experience with Deloitte, E&Y, and KPMG.
Debugging AI is (almost) nothing like verifying and debugging traditionally engineered software systems, and it’s still in its infancy. In his talk, Dr Christian Betz will present a brief overview of the field of AI, explaining the properties of different approaches to AI, and in which contexts AI systems shine, explaining the current hype. From there, Christian will discuss prominent failures of AI systems and show ways to hack AI systems. He’ll explain why verifying and testing AI is hard, and will guide us through different approaches to tackle this problem.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence and Cognitive ComputingFlorian Georg
Keynote talk with some high level introduction on A.I., Cognitive systems, Machine Learning and IBM Watson & Cloud Platform @ Datadirect IT Security Forum 2017
6G WILL UNLOCK THE POWER OF AI-FOR-EVERYTHINGMahbubul Alam
We envision 6G to offer revolutionary transformation which will usher in an era of connected built-in intelligent applications, services, and networks that will auto-provision end-to-end systems to guaranteed quality of services for an agreed service level agreement, ultra-high-speed data rate, surpassing that of last-mile wired connectivity, perceived zero-latency & deterministic jitter for human safety and mission-critical applications, extremely high reliability for essential services, high spectrum-bands for haptic, holographic, extensive multimedia streaming and more, connected artificial intelligence for autonomous functions and future unknown use cases, etc.
6G will be a key enabler for equitable wealth distribution and a major driver for the green economy. It will unleash the full potential for Industrial Revolution IE 5.0 which will focus on the co-operation between human and machine, as human intelligence works in harmony with cognitive computing and machines performs mundane, repetitive, error-prone tasks. By putting humans back into industrial production with 6G enabled collaborative robots a.k.a Cobots, workers will be upskilled to provide value-added tasks in production such as setting the strategy, provide oversight and add creative input, leading to massive customization & personalization for customers. In this talk, we will examine the state of AI and its potential role in 6G.
London Futurists - The Future of AI & SustainabilityAlex Housley
Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment?
In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place.
The event was chaired by David Wood. The camera was operated by Kiran Manam.
For more details about this event, see https://www.meetup.com/London-Futuris....
For more information about Seldon, see https://www.seldon.io/.
To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
14 Startups Leading the Artificial Intelligence (AI) RevolutionNVIDIA
Learn how these top 14 startups around the globe are using artificial intelligence (AI) and Deep Learning to impact key industries and humanity-at-large.
Conversational Architecture, CAVE Language, Data StewardshipLoren Davie
These are the slides from the presentation I gave at the Semiotics Web meetup group on Nov 1st 2014. In this talk I discussed the emergency of the ubiquitous Internet, how to discuss the design of contextual apps, and presented an approach to privacy concerns that are inherently connected.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
Learn the workings of using intelligent machines for your processes using content-ready Artificial Intelligence PowerPoint Presentation Slides. Processes like learning, reasoning, self-correction, etc. are executed by artificial intelligent machines. Incorporate ready-made artificial intelligence PPT presentation templates and maximize the chance of achieving the organizational goals. This deck comprises of templates such as artificial intelligence objectives, artificial intelligence components, artificial intelligence statistics, artificial intelligence & investment by sector, artificial intelligence in various sectors, core areas of artificial intelligence, artificial intelligence value chain elements, artificial intelligence development phases, artificial intelligence approaches, machine learning (pattern based), machine learning description, machine learning process, machine learning use cases, and more. These templates are customizable. Edit color, text, icon and font size as per your need. Grab easy-to-understand artificial intelligence PowerPoint presentation slideshow and perform tasks associated with intelligent beings. Find solutions to the business problems without human intervention. Provide better products and services with the help of AI PPT templates. Click the download button to perform difficult tasks with ease using ready-made artificial intelligence PowerPoint presentation slides. Our Artificial Intelligence Powerpoint Presentation Slides team will alert you about changing demands. Their eyes and ears are always open.
A quick guide to artificial intelligence working - TechaheadJatin Sapra
It is already on its way to achieving so as it has empowered the mobile app development agencies to build what was once assumed impossible. Despite this, much of this field remains undiscovered.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
#OSSPARIS19 - Overcoming open source challenges in reinforcement learning - W...Paris Open Source Summit
#IA Track - Practical applications
Reinforcement learning is a rapidly growing branch of artificial intelligence that has achieved super-human performance in board games such as Go and chess and video games such as Starcraft. Research papers and open code in this field are widely available.
However, unlike other fields of machine learning, open code and research has so far largely failed to translate into real world applications.
In this talk, we leverage the indust.ai team's experience in developing their own reinforcement learning activity to discuss the challenges involved. These include poor reproducibility, varying code quality, prohibitive computation and data requirements, the difference in mindset between traditional machine learning and reinforcement learning, and the difficulty of finding the skills required to transfer academic research to the real world. We will also present some of our approaches for overcoming these issues.
London Futurists - The Future of AI & SustainabilityAlex Housley
Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment?
In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place.
The event was chaired by David Wood. The camera was operated by Kiran Manam.
For more details about this event, see https://www.meetup.com/London-Futuris....
For more information about Seldon, see https://www.seldon.io/.
To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
14 Startups Leading the Artificial Intelligence (AI) RevolutionNVIDIA
Learn how these top 14 startups around the globe are using artificial intelligence (AI) and Deep Learning to impact key industries and humanity-at-large.
Conversational Architecture, CAVE Language, Data StewardshipLoren Davie
These are the slides from the presentation I gave at the Semiotics Web meetup group on Nov 1st 2014. In this talk I discussed the emergency of the ubiquitous Internet, how to discuss the design of contextual apps, and presented an approach to privacy concerns that are inherently connected.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
Learn the workings of using intelligent machines for your processes using content-ready Artificial Intelligence PowerPoint Presentation Slides. Processes like learning, reasoning, self-correction, etc. are executed by artificial intelligent machines. Incorporate ready-made artificial intelligence PPT presentation templates and maximize the chance of achieving the organizational goals. This deck comprises of templates such as artificial intelligence objectives, artificial intelligence components, artificial intelligence statistics, artificial intelligence & investment by sector, artificial intelligence in various sectors, core areas of artificial intelligence, artificial intelligence value chain elements, artificial intelligence development phases, artificial intelligence approaches, machine learning (pattern based), machine learning description, machine learning process, machine learning use cases, and more. These templates are customizable. Edit color, text, icon and font size as per your need. Grab easy-to-understand artificial intelligence PowerPoint presentation slideshow and perform tasks associated with intelligent beings. Find solutions to the business problems without human intervention. Provide better products and services with the help of AI PPT templates. Click the download button to perform difficult tasks with ease using ready-made artificial intelligence PowerPoint presentation slides. Our Artificial Intelligence Powerpoint Presentation Slides team will alert you about changing demands. Their eyes and ears are always open.
A quick guide to artificial intelligence working - TechaheadJatin Sapra
It is already on its way to achieving so as it has empowered the mobile app development agencies to build what was once assumed impossible. Despite this, much of this field remains undiscovered.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
#OSSPARIS19 - Overcoming open source challenges in reinforcement learning - W...Paris Open Source Summit
#IA Track - Practical applications
Reinforcement learning is a rapidly growing branch of artificial intelligence that has achieved super-human performance in board games such as Go and chess and video games such as Starcraft. Research papers and open code in this field are widely available.
However, unlike other fields of machine learning, open code and research has so far largely failed to translate into real world applications.
In this talk, we leverage the indust.ai team's experience in developing their own reinforcement learning activity to discuss the challenges involved. These include poor reproducibility, varying code quality, prohibitive computation and data requirements, the difference in mindset between traditional machine learning and reinforcement learning, and the difficulty of finding the skills required to transfer academic research to the real world. We will also present some of our approaches for overcoming these issues.
State of AI Report 2023 - ONLINE presentationssuser2750ef
State of AI Report 2023 - ONLINE.pptx
When conducting a PEST analysis for the Syrian conflict, it's important to consider the political, economic, socio-cultural, and technological factors that have influenced and continue to impact the situation in Syria. Here's a high-level overview of a PEST analysis for the Syrian conflict:
1. Political Factors:
- Government Instability: Ongoing civil war and conflict have led to political instability and a complex power struggle between various factions and international players.
- Foreign Intervention: Involvement of external powers and regional actors has exacerbated the conflict and added geopolitical complexities to the situation.
- International Relations: Relations with global powers like the United States, Russia, and regional players like Iran and Turkey significantly impact the conflict dynamics.
2. Economic Factors:
- Humanitarian Crisis: The conflict has resulted in a severe humanitarian crisis, causing widespread displacement, destruction of infrastructure, and economic decline.
- Sanctions and Trade Barriers: International sanctions and disrupted trade have further worsened the economic situation in Syria, affecting the livelihoods of the population.
- Resource Depletion: Conflict-driven resource depletion, including loss of agricultural lands and disruption of industries, has weakened the economy.
3. Socio-cultural Factors:
- Civilian Suffering: The conflict has led to a significant loss of life, displacement of populations, and severe trauma among civilians, impacting social cohesion and community structures.
- Ethnic and Religious Divisions: Deep-seated ethnic and religious divisions have fueled the conflict, leading to sectarian tensions and societal fragmentation.
- Refugee Crisis: The conflict has triggered a massive refugee crisis, with millions of Syrians seeking asylum in neighboring countries and beyond, straining regional stability.
4. Technological Factors:
- Communication and Propaganda: Technology, including social media, has been used for communication, mobilization, and spreading propaganda by various actors in the conflict.
- Warfare Technology: Advancements in warfare technology and the use of drones, cyber warfare, and other advanced weaponry have transformed the nature of conflict in Syria.
- Cybersecurity Concerns: The conflict has also raised concerns about cybersecurity threats, misinformation campaigns, and digital vulnerabilities in the region.
This analysis provides a broad understanding of the multifaceted nature of the Syrian conflict, highlighting the diverse factors at play and the complex challenges facing Syria and the international community.
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
leewayhertz.com-How to build a generative AI solution From prototyping to pro...KristiLBurns
Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.
Benefiting from Semantic AI along the data life cycleMartin Kaltenböck
Slides of 1 hour session of Martin Kaltenböck (CFO and Managing Partner of Semantic Web Company / PoolParty Software Ltd) on 19 March 2019 in Boston, US at the Enterprise Data World 2019, with its title: Benefiting from Semantic AI along the data life cycle.
Generative AI models are transforming various fields by creating realistic images, text, music, and videos. This guide will take you through the essential steps and considerations for building a generative AI model, providing a comprehensive understanding of the process.
Building a generative AI solution involves defining the problem, collecting and processing data, selecting suitable models, training and fine-tuning them, and deploying the system effectively. It’s essential to gather high-quality data, choose appropriate algorithms, ensure security, and stay updated with advancements.
Generative AI: A Comprehensive Tech Stack BreakdownBenjaminlapid1
Build a reliable and effective generative AI system with the right generative AI tech stack that helps create smarter solutions and drive growth.
Click here for more information: https://www.leewayhertz.com/generative-ai-tech-stack/
Artificial intelligence (Ai) is back, and the tech industry’s interest is stronger than ever. Ai will have an important impact on the design and creation of software. Application development and delivery (AD&D) professionals need to understand the potential benefits Ai will bring, not only to how they build software but also to the nature of the applications themselves. in parallel, AD&D pros should not ignore the challenges and risks that come with Ai. this report is the first of a series that will examine the impact of Ai on software development and separate myth from reality
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.
Intelligence artificielle. Pourquoi et comment. Web à Québec 2017.Sylvain Carle
Pourquoi il y a tant de “buzz” autour de l’intelligence artificielle maintenant? Un peu de recul pour comprendre ce qui s’est passé dans les dernières années, l’état de la situation actuelle et un peu de perspective sur ce qui s’en vient (si mes intuitions sont bonnes). https://webaquebec.org/programmation/opportunites-et-defis-de-lintelligence-artificielle-pour-les-developpeurs
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Ai open powermeetupmarch25th
1. AI and OpenPOWER Meetup
Spend a half day learning about Artificial Intelligence's latest real-world impact
and gather the latest cutting-edge insights from the pioneers in your industry.
Discover advances in deep learning tools and techniques from the world’s
leading innovators across industry, research and financial sector
We’re excited to announce that the prominent speakers will be joining us at the
AI and OpenPOWER ADG Meet up,
on 25th March,2018
At 4.00 pm to 7.30 pm
2603 Camino Ramon #200, San Ramon,
CA 94583, USA
2. Title : Learning at Leadership Scale: Performance, Deployment Experiences, and Best
Practices
Abstract: HPC centers have been traditionally configured for simulation workloads, but
data analytics (including deep learning) methods and frameworks have been increasingly
applied alongside simulation on scientific datasets. These frameworks do not always fit well
with job schedulers, large parallel file systems, and MPI backends, and also vary in
performance based on whether the underlying architecture is a CPU or an accelerator like
a GPU or a combination. We discuss examples of how machine learning and deep learning
workflows are being deployed on next- generation systems at the Oak Ridge Leadership
Computing Facility including the lead up to the Power and Volta-based Summit system from
experiences on OLCF’s Titan and SummitDev. We will share benchmarks between native
compiled versus containers-based systems as well as best practices for deploying learning
and models on leadership resources supporting scientific workflows.
Jack Wells is the Director of Science for the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of
Science national user facility, and home to the Titan and upcoming Summit supercomputers, located at Oak Ridge
National Laboratory (ORNL). Wells is responsible for the scientific outcomes of the OLCF’s user programs. Wells
has a Ph.D. in physics from Vanderbilt University, and has authored or co-authored over 100 scientific papers and
edited 1 book, spanning nanoscience, materials science and engineering, nuclear and atomic physics computational
science, applied mathematics, and novel analytics measuring the impact of scientific publications.
3. Key Note speaker : Graham Mackintosh
Title : AI Research and OpenPOWER at the NASA Frontier Development Lab
Abstract: The NASA Frontier Development Lab (FDL) is an AI research accelerator
established to apply emerging AI technologies to space science challenges which
are central to NASA's mission priorities. The program is managed by the SETI
Institute, and brings together the AI capabilities of NASA, academic institutions, and
commercial partners to tackle complex space science problems, such as predicting
extreme solar events that can damage satellites and endanger the lives of
astronauts. In this session, we will present a summary of the NASA FDL research
results to date, and outline how the SETI Institute, as a member of the OpenPower
Foundation, plans to leverage the Power platform to expand its FDL 2018 research
program.
Graham Mackintosh is a pioneer in the field of advanced analytics, and has applied his thought leadership
into multiple new domains for big data analysis, high performance cloud computing, AI and Deep Learning.
He currently works as an AI consultant for NASA and the SETI Institute to provide technical and program
management support across a range of space science domains, including the application of deep learning
technologies to space weather, planetary defense and astrobiology. Prior to his current role as an AI
consultant, Graham worked in the Emerging Technology division of IBM Corporation, and led successful AI
initiatives at CERN, the SETI Institute, NASA Frontier Development Lab, and with the US Federal
Government.
4. Dr. Berthold Reinwald is a Principal RSM at IBM Research - Almaden. He is the technical lead for
Apache SystemML. His research interests include scalable analytics platforms and database
technology
which he contributes to IBM Watson.
Title: Scalable Machine/Deep Learning with Apache SystemML on
Power
Abstract: We will present perspectives and challenges of machine/deep
learning in the enterprise. We will cover a variety of use cases from different
vertical industries, discuss the state of the art, and take a critical look at
challenges in systems development. We will draw from the experience in the
development of Apache SystemML, an open source project for declarative,
large scale machine/deep learning, and show deep learning examples running
on Power.
speaker : Berthold Reinwald (IBM Research)
5. Dr Sudha Jamthe is the CEO of IoTDisruptions.com and a globally recognized thought leader at the junction
of IoT and Autonomous Vehicles. She brings twenty years of digital transformation experience from building
organizations, shaping new technology ecosystems and mentoring leaders at eBay, PayPal, Harcourt, and
GTE. She teaches the IoT Business course and "The Business of Self-Driving Cars" Course at Stanford
Continuing Studies Program and enjoys mentor industry professionals to shape emerging technology
ecosystems. She advises corporate and city leaders on regional economic development using technology with
a focus on innovation gaps and social equality.
Title: AI Trends towards a Driverless World
Speaker is author of '2030 The Driverless World' about the junction of
Autonomous cars and Cognitive IoT. She is the author of three IoT books,
'IoT Disruptions,' 'IoT Disruptions 2020' and 'The Internet of Things Business
Primer'. She is the producer of 'The IoT Show' on YouTube. Sudha is a
champion for STEM programs and 'Girls Who Code,' and hosts mentor
programs for kids.
Key Note speaker : Sudha Jamthe
6. Jim Spohrer is IBM Director, Cognitive Opentech Group (COG) leading open source AI work at IBM. Previously, he
was Director IBM Global University Programs, co-founded IBM Almaden Service Research Group, ISSIP Service
Science community, and was founding CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a
Distinguished Engineer Scientist Technologist (DEST), he developed next generation learning platforms. Earlier
(19740-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company for
speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow
and winner of the Gummesson Service Research award as well as Vargo & Lusch Service-Dominant Logic award.
Title: The Future of AI: Measuring Progress and Preparing
Abstract: An industry perspective and forecast of where technology
is going, including the what and when for "solving" Artificial
Intelligence (AI), is presented. Next, the benefits and challenges will
be discussed, including impact on jobs, both near term via
Intelligence Augmentation (IA) and longer term via automation. The
impact on different sectors of the economy will be explored, and
how best to prepare for the changes that are anticipated (hint:
befriend someone studying GitHub open AI code + data + models +
containers).
Key Note speaker : Jim Spohrer (IBM)
7. Vinod Iyengar Vinod Iyengar heads alliances and product marketing at H2O. He is also a trained data scientist and works extensively with our
customers and partners to spread the word of how artificial intelligence can help enterprise transform their businesses.
Title: Driverless AI
Abstract: Driverless AI accelerates data science workflows by automating feature engineering, model tuning,
ensembling and model deployment. Driverless AI turns Kaggle-winning recipes into production-ready code and is
specifically designed to avoid common mistakes such as under or overfitting, data leakage or improper model
validation. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high
modeling accuracy
With Driverless AI, everyone can now train and deploy modeling pipelines with just a few clicks from the GUI.
Advanced users can use the client/server API through a variety of language such as Python, Java, C++, go, C# and
many more. To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage
of the latest computer hardware.
Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU on IBM Power platform, both in the cloud
or on-premise. There are two more product innovations in Driverless AI: statistically rigorous automatic data
visualization and interactive model interpretation with reason codes and explanations in plain English. Both help
data scientists and analysts to quickly validate the data and models.
Key Note speaker : Vinod Iyengar
8. Title: AI In Bank( Citi Bank )
Abstract: Improved Customer Focus: There is a growing expectation to personalize the services provided to the
customer and retain the customer.
AI has the Capability to segment the Data into various groups and can be used to segment the customers based on the
data from the conversation through telephonic & chat, visits to Branch & ATM, Usage across the various Channels (Net
banking, App) and personalize the Service.
Based on the Customer Actions, Transactions, Behaviors and Sentimental Analysis performed using AI Bank can provide
offers, products or the Next Best Action for a customer. This can lead to better Customer Loyalty and Retention.
Mundane Manual tasks by the Customers can be automated using the AI technology. Provide support in the validation of
the various KYC Documents, searching Documents for providing guidance, etc.
Suggest Investments opportunities based on data gathered. Models trained based on the Historical Data, Macro Economic
Data can support the Bank in adopting Investment Strategies.
Uppili Rajagopalan Senior Executive in Financial Industry where he has 20
years of experience . Uppili leads Global O&T Alternate Customer Contact team. He provides leadership and
expertise for ASIA & EMEA regional delivery and application development for Chat, Digital Virtual Agent, eDelivery and
Robotic process automation initiatives.
Uppili holds a Bachelors in Computer Science & Masters in Business Administration
Key Note speaker : Uppili Rajagopalan
9. Dr. Berthold Reinwald is a Principal RSM at IBM Research - Almaden. He is the technical lead for
Apache SystemML. His research interests include scalable analytics platforms and database
technology
which he contributes to IBM Watson.
Title: Scalable Machine/Deep Learning with Apache SystemML on
Power
Abstract: We will present perspectives and challenges of machine/deep
learning in the enterprise. We will cover a variety of use cases from different
vertical industries, discuss the state of the art, and take a critical look at
challenges in systems development. We will draw from the experience in the
development of Apache SystemML, an open source project for declarative,
large scale machine/deep learning, and show deep learning examples running
on Power.
speaker : Berthold Reinwald (IBM Research)
10. Leo Reiter : Chief Technology office at Nimbix and is a virtualization and cloud computing pioneer
with over 20 years of experience in software development and technology strategy. Prior to Nimbix,
Mr. Reiter was co-founder and CTO of Virtual Bridges, an early innovator in server-based computing
and private cloud platforms for Enterprise. Mr. Reiter is an entrepreneur with a strong background in
Lean Startup and Agile methodologies.
Title: PowerAI on Nimbix Cloud
Abstract: Leo will be sharing the PowerAI and HPC features running Nimbix
Cloud . He will also share the various industry based use cases and
customer experiences on the Nimbix Cloud platform .
Key Note speaker : Leo Reiter from Nimbix ( AI
Cloud )
11. Jussi kukkonun , self-directed and driven vice president with a comprehensive
background in system integration and IT solution provider business delivering
data center solutions for enterprise, high performance computing and cloud
and hosting provider customers and leading cross-functional teams to ensure
success and achieve goals. Known as an innovative thinker with strong
product marketing, business development, go-to-market strategy, partnership
and data storage acumen. Recognized for maximizing performance by
implementing appropriate strategies through analysis of details to gain
understanding of the competitive position, emerging issues, trends and
relationships.
12. Vasanth Ram is a Hardware Professional and has built GPUs, Tablets, Laptops at
Imagination Technologies, AMD & Intel with an emphasis on Low Power. He also does
researchinLowPowerDatacenterschedulersuitableforIaaSCloudwithMachineLearning
based Workload Prediction and Host Provisioning. He is passionate about solving real-
world problems and has built Computer Vision-based Autonomous and semi-autonomous
Robots. He also consults in Hyper-scale & Hyper-converged Datacenters Architectures
(Compute, Storage & Network) and end-to-end ASIC paper to production for CPUs and
GPUs for startups, consulting firms and enterprises. He Has Master of Science in
Computer Engineering from University of California Santa Barbara and Bachelor of
EngineeringinElectronicsEngineeringfromCoimbatoreInstituteofTechnology,India
AGKarunakaranwasthefoundingPresidentandCEOofGDATechnologiesInc.,
aleadingIntellectualPropertylicensingandelectronicsdesignservicesCompany.GDAwas
purchasedbyL&TInfotech,IndiainMarch2007.AtGDA,hewasresponsibleforleadership
development,growthstrategy,prudentcashmanagementandworkedwithleading
Semiconductorcompaniestocommercializethesiliconintellectualpropertyblocks.
UnderhisleadershipGDAmadeittotheINC500list,Si100lists,grewto400employees,
deliveredonsignificantproductdevelopmentengagementswithleadingsystemsand
semiconductorcompaniesandbecamealeadingsupplierofHighspeedSerialI/O
Semiconductor Intellectual blocks. He has deep experience in bootstrapping companies
andinstrategicM&Atransactions,havingconsummateddealsforGDAwithL&TInfotech
andRambus.
Editor's Notes
The Future of AI: Measuring Progress and Preparing
An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated.
Speaker Bio:
Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award.
More information here:
Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14
Bio and CV: http://service-science.info/archives/2233
Optional Business, Marketing, and Technical Pre-reads:
IBM Bluemine: Industry Predictions 2018:
"2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent."
Another predication to consider:
...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings.
See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html
Also, see Tencent paper and Github code:
ArXiv: https://arxiv.org/abs/1606.01549
Github: https://github.com/bdhingra/ga-reader
IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/
And to understand why solving AI is still very, very, very hard, in spite of all the hype:
Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf
Optional Pre-read for Societal Implications:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy
The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced.
We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality.
The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years.
When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.
The Future of AI: Measuring Progress and Preparing
An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated.
Speaker Bio:
Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award.
More information here:
Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14
Bio and CV: http://service-science.info/archives/2233
Optional Business, Marketing, and Technical Pre-reads:
IBM Bluemine: Industry Predictions 2018:
"2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent."
Another predication to consider:
...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings.
See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html
Also, see Tencent paper and Github code:
ArXiv: https://arxiv.org/abs/1606.01549
Github: https://github.com/bdhingra/ga-reader
IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/
And to understand why solving AI is still very, very, very hard, in spite of all the hype:
Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf
Optional Pre-read for Societal Implications:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy
The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced.
We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality.
The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years.
When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.
The Future of AI: Measuring Progress and Preparing
An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated.
Speaker Bio:
Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award.
More information here:
Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14
Bio and CV: http://service-science.info/archives/2233
Optional Business, Marketing, and Technical Pre-reads:
IBM Bluemine: Industry Predictions 2018:
"2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent."
Another predication to consider:
...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings.
See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html
Also, see Tencent paper and Github code:
ArXiv: https://arxiv.org/abs/1606.01549
Github: https://github.com/bdhingra/ga-reader
IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/
And to understand why solving AI is still very, very, very hard, in spite of all the hype:
Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf
Optional Pre-read for Societal Implications:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy
The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced.
We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality.
The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years.
When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.
The Future of AI: Measuring Progress and Preparing
An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated.
Speaker Bio:
Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award.
More information here:
Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14
Bio and CV: http://service-science.info/archives/2233
Optional Business, Marketing, and Technical Pre-reads:
IBM Bluemine: Industry Predictions 2018:
"2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent."
Another predication to consider:
...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings.
See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html
Also, see Tencent paper and Github code:
ArXiv: https://arxiv.org/abs/1606.01549
Github: https://github.com/bdhingra/ga-reader
IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/
And to understand why solving AI is still very, very, very hard, in spite of all the hype:
Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf
Optional Pre-read for Societal Implications:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy
The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced.
We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality.
The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years.
When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.