Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
Strata San Jose 2016: Deep Learning is eating your lunch -- and mineSri Ambati
In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge.
Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment—every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Liu Ren at AI Frontiers: Sensor-aware Augmented RealityAI Frontiers
Successful Human Machine Interaction (HMI) solutions need to feature three 'I's (Intuitive, Interactive, and Intelligent) in their applications as they are key success factors to ensure superior user experience for our future products. Augmented Reality (AR) as a core HMI topic is on its way to become more practical. In this talk, Liu discusses the real-world HMI challenges for industrial AR applications and present our recent advances at Bosch to address the needs of these three 'I's. Bosch sees that many of these HMI challenges (i.e. dynamic occlusion handling, robust tracking, and easy content generation) are closely related to typical AI tasks such as scene perception and understanding. Sensor-aware approaches that leverage sensor knowledge and machine learning methods are effective to address these challenges.
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science 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.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger 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.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
Arno candel h2o_a_platform_for_big_math_hadoop_summit_june2016Sri Ambati
H2O: A Platform for Big Math
From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O’s Deep Learning. Before joining H2O.ai, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named "2014 Big Data All-Star" by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
Strata San Jose 2016: Deep Learning is eating your lunch -- and mineSri Ambati
In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge.
Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment—every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Liu Ren at AI Frontiers: Sensor-aware Augmented RealityAI Frontiers
Successful Human Machine Interaction (HMI) solutions need to feature three 'I's (Intuitive, Interactive, and Intelligent) in their applications as they are key success factors to ensure superior user experience for our future products. Augmented Reality (AR) as a core HMI topic is on its way to become more practical. In this talk, Liu discusses the real-world HMI challenges for industrial AR applications and present our recent advances at Bosch to address the needs of these three 'I's. Bosch sees that many of these HMI challenges (i.e. dynamic occlusion handling, robust tracking, and easy content generation) are closely related to typical AI tasks such as scene perception and understanding. Sensor-aware approaches that leverage sensor knowledge and machine learning methods are effective to address these challenges.
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
Artificial intelligence (AI) is a multidisciplinary field of science 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.
In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger 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.
Talent: Supply, demand and concentration of talent working in the field.
Industry: Large platforms, financings and areas of application for AI-driven innovation today and tomorrow.
Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.
Collaboratively produced in East London, UK by:
- Nathan Benaich, Founder of Air Street Capital (www.airstreet.com) and RAAIS (www.raais.co).
- Ian Hogarth, Visiting Professor at UCL's IIPP (https://www.twitter.com/IIPP_UCL) and angel investor.
Arno candel h2o_a_platform_for_big_math_hadoop_summit_june2016Sri Ambati
H2O: A Platform for Big Math
From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O’s Deep Learning. Before joining H2O.ai, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named "2014 Big Data All-Star" by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Invited talk at Deep Learning Tokyo organized by Yahoo Japan!
Applications of deep learning technologies in automobile, robitics, and bio science + future directions
Nvidia Corporation, more commonly referred to as Nvidia, is an American technology company incorporated in Delaware and based in Santa Clara, California. It designs graphics processing units for the gaming and professional markets, as well as system on a chip units for the mobile computing and automotive market.
Silicom Ventures Talk Aug 2013 - GPUs and Parallel Programming create new opp...Shanker Trivedi
GPU are delivering exponential improvements in computing performance and scalability. And new parallel programming architectures such as CUDA are allowing smart technologists to harness the power of GPUs to address hitherto insoluble problems. This talk will illustrate the emerging opportunities and solutions that GPUs and parallel programming can offer in medical instruments and imaging, defense and surveillance, autonomous vehicles, the internet of things and sensory computing, manufacturing design and simulation, and seismic geology. The talk will be relevant to entrepreneurs who are thinking about the "next big thing" and to investors who may be thinking of the future mega trends.
Mainframe Customer Education Webcast: New Ironstream Facilities for Enhanced ...Precisely
Check out our latest Mainframe Customer Education Webcast, featuring new Ironstream facilities for enhanced z/OS Analytics. Product Management Directors Ed Wrazen and Ed Hallock spoke about what’s new in the Ironstream z/OS data forwarder, as well as new features and facilities including:
• Data Loss Protection
• Advanced Filtering for SMF data
• Splunk Applications for Ironstream
You'll also learn about integration with Splunk’s IT Service Intelligence for monitoring the availability of critical business services running on z/OS platforms.
Invited talk at Deep Learning Tokyo organized by Yahoo Japan!
Applications of deep learning technologies in automobile, robitics, and bio science + future directions
Nvidia Corporation, more commonly referred to as Nvidia, is an American technology company incorporated in Delaware and based in Santa Clara, California. It designs graphics processing units for the gaming and professional markets, as well as system on a chip units for the mobile computing and automotive market.
Silicom Ventures Talk Aug 2013 - GPUs and Parallel Programming create new opp...Shanker Trivedi
GPU are delivering exponential improvements in computing performance and scalability. And new parallel programming architectures such as CUDA are allowing smart technologists to harness the power of GPUs to address hitherto insoluble problems. This talk will illustrate the emerging opportunities and solutions that GPUs and parallel programming can offer in medical instruments and imaging, defense and surveillance, autonomous vehicles, the internet of things and sensory computing, manufacturing design and simulation, and seismic geology. The talk will be relevant to entrepreneurs who are thinking about the "next big thing" and to investors who may be thinking of the future mega trends.
Mainframe Customer Education Webcast: New Ironstream Facilities for Enhanced ...Precisely
Check out our latest Mainframe Customer Education Webcast, featuring new Ironstream facilities for enhanced z/OS Analytics. Product Management Directors Ed Wrazen and Ed Hallock spoke about what’s new in the Ironstream z/OS data forwarder, as well as new features and facilities including:
• Data Loss Protection
• Advanced Filtering for SMF data
• Splunk Applications for Ironstream
You'll also learn about integration with Splunk’s IT Service Intelligence for monitoring the availability of critical business services running on z/OS platforms.
NUS-ISS Learning Day 2016 - Big Data AnalyticsNUS-ISS
A real-time descriptive data analytics of your data seating inside of your NoSQL database. A time series data will be index to the lucene-based search server called ElasticSearch. This indexed data will then be visualised through the visualisation tool called Kibana. This tool can show charts, trends, maps and graphs based on your data. You can customise the filters to really get what you want from your data. Learn how you can quickly understand and get insights from their data.
Profile Summary
14 years of Total Experience in Python Development
10 Years in Leading Teams, Scrum Master and Management
8 Years of experience as Solution Architect in multiple projects.
Open source Contributor in Python Software Foundation
Research & Development, Proof of Concepts, SDLC process
Gathering information from Clients directly and Reporting
Agile Methodology and Cloud Technology SME
Corporate Trainer for Python, Flask and Agile
Conducting Interviews for Python, Linux, C++
Domain Exposure: Banking, Finance, Digital, Network Security, Energy, CFD,
HPSA, Server Automation
Microsoft is working hard to make Artificial Intelligence available to everyone. We not only infuse AI in our products but also give you the platform to build your very own solution, that you are a developer, a citizen data scientist or a hard core data scientist.
Introducing the Vitis Unified Software Platform for Programming FPGAsinside-BigData.com
Since their beginnings, FPGA's have been notorious for being hard to program. That could be changing with the new Vitis Unified Software Platform from Xilinx. Five years in the making, the Vitis unified software platform is designed to allow a whole new user base of software engineers and AI scientists to take advantage of the power of hardware adaptability.
"The Vitis unified software platform automatically tailors the Xilinx hardware architecture to the software or algorithmic code without the need for hardware expertise. Rather than imposing a proprietary development environment, the Vitis platform plugs into common software developer tools and utilizes a rich set of optimized open source libraries, enabling developers to focus on their algorithms. Vitis is separate to the Vivado Design Suite, which will still be supported for those who want to program using hardware code, but Vitis can also boost the productivity of hardware developers by packaging hardware modules as software-callable functions.
With exponentially increasing compute needs, engineers and scientists are often limited by the fixed nature of silicon,” said Victor Peng, president and chief executive officer, Xilinx. “Xilinx has created a singular environment that enables programmers and engineers from all disciplines to co-develop and optimize both their hardware and software, using the tools and frameworks they already know and understand. This means that they can adapt their hardware architecture to their application without the need for new silicon.”
Learn more: https://www.xilinx.com/products/design-tools/vitis.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Top 10 Most Demand IT Certifications Course in 2020 - MildainTrainingsMildain Solutions
The professionals in the field of Information Technology understands the importance of certification to their career and growth.
The information provided in this guide is backed by real data. Let us look at the top IT certifications that will remain to be a trend in 2020.
Mildaintrainings https://mildaintrainings.com/ offers Several trainings all over the world.
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Start Getting Your Feet Wet in Open Source Machine and Deep Learning Ian Gomez
At H2O.ai we see a world where all software will incorporate AI, and we’re focused on bringing AI to business through software. H2O.ai is the maker behind H2O, the leading open source machine and deep learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
In this webinar, you will learn about the scalable H2O core platform and the distributed algorithms it supports. H2O integrates seamlessly with the R and the Python environments. We will show you how to leverage the power of H2O algorithms in R, Python and H2O Flow interface. Come with an open mind and some high level knowledge of machine learning, and you will take away a stream of knowledge for your next ML/DL project.
Amy Wang is a math hacker at H2O, as well as the Sales Engineering Lead. She graduated from Hunter College in NYC with a Masters in Applied Mathematics and Statistics with a heavy concentration on numerical analysis and financial mathematics.
Her interest in applicable math eventually lead her to big data and finding the appropriate mediums for data analysis.
Desmond is a Senior Director of Marketing at H2O.ai. In his 15+ years of career in Enterprise Software, Desmond worked in Distributed Systems, Storage, Virtualization, MPP databases, Streaming Analytics Platform, and most recently Machine Learning. He obtained his Master’s degree in Computer Science from Stanford University and MBA degree from UC Berkeley, Haas School of Business.
Lesser Known Opportunities in TechnologyCalen Legaspi
A lot of technopreneurs are building me-too businesses, while many opportunities remain underserved. This presentation discusses these underserved opportunities.
Calen Legaspi, CEO of Orange and Bronze Software Labs, discusses the challenges in outsourcing and how to overcome these by taking advantage of readily-available tools found online. He encourages aspiring technopreneurs to consider "The Internet of Things" as their next venture.
As an aspiring software developer or IT professional, what technology trends should you know about to build a flourishing career in IT? Orange and Bronze CEO, Calen Legaspi, discusses which technologies are hot and which are in danger of becoming obsolete.
www.orangeandbronze.com
A late upload. This slide was presented on Aug 31, 2019, when I delivered a talk for AIoT seminar in University of Lambung Mangkurat, Banjarbaru. It's part of Republic of IoT 2019 event.
SQL Server 2017 Deep Dive - @Ignite 2017Travis Wright
This was a presentation given at Ignite 2017 on SQL Server 2017. It covers the main new capabilities of SQL Server 2017. The video recording of the session is available here: https://myignite.microsoft.com/sessions/54946?source=sessions
Similar to Technology and AI sharing - From 2016 to Y2017 and Beyond (20)
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.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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.
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
6. Before We Start…
• AL, machine learning, and deep learning are different, but in
the sharing we may not discuss about it.
• Abbreviation:
– AI: Artificial Intelligence
– ML: Machine Learning
– DL: Deep Learning
• A lot of reference URL in the slides. Enjoy!
– Articles / media reports / posts
– Video clips
7. AI > Machine Learning > Deep Learning
Source: http://bit.ly/2h4AfLl
8. Best Short Definition of AI
Source: http://bit.ly/2h4z52B
AI = Training Data + Machine
Learning + Human-in-the-loop
12. Top 10 Strategic Tech Trends - Intelligent
AI & Advanced Machine Learning
• AI, machine learning, deep learning, neural networks, natural language processing (NLP)
• Parallel processing power + advanced algorithms + massive datasets
• Real-time analytics
Intelligent Apps
• Virtual personal assistants (VPAs)
• Existing application with AI capabilities enabled.
• 3 focus areas: advanced analytics, AI-powered and increasingly autonomous business
processes and AI-powered immersive, conversational and continuous interfaces.
Intelligent Things
• Robots, drones, and autonomous vehicles.
13. Top 10 Strategic Tech Trends - Digital
Virtual & Augmented Reality
• Training scenarios and remote experiences.
• Enterprises should look for targeted applications of VR and AR through 2020.
Digital Twin
• Dynamic software model + sensors
• Users collaborate with data scientists and IT/BA professionals.
Blockchain
• Bitcoin
• FinTech
14. Top 10 Strategic Tech Trends - Mesh
Conversational Systems
• Communicate across the digital device mesh (e.g., sensors, appliances, IoT systems) using text / voice / sight / sound /
tactile.
Mesh App and Service Architecture (MASA)
• Flexible enough to allow rapid evolution of user needs and how they interact with technology.
• Apps connect and communicate and with other apps using agile architecture with, for example, HTTP/REST JSON.
Digital Technology Platforms
• Information systems, customer experience, analytics and intelligence, IoT and business ecosystems.
• New platforms and services for IoT, AI and conversational systems will be a key focus through 2020.
Adaptive Security Architecture
• Multilayered security and use of user and entity behavior analytics will become a requirement for virtually every
enterprise.
• Security in the IoT environment
15. With data, advanced AI, and computing
power, everything will be “more”
intelligent.
16. Programming Language and Tool
Ranking
FOCUSING ON DATA SCIENCE AND AI / MACHINE LEARNING / DEEP LEARNING
20. Top 20 Python ML Open Source Project
Top projects are ML, DL
Projects on GitHub. A lot
of them are new in top 20
in Y2016.
Source: link
21. DL Software w/ Default Support for AWS and Python
Software Platform Interface GPU
Support
Recurrent
nets
Convolution
al nets
RBM/DBNs
Parallel
execution
Caffe
Linux, Mac OS X, AWS,
Windows support by
Microsoft Research
C++, command
line, Python, MATLAB
Yes Yes Yes No Yes
Deeplearning4j
Linux, Mac OS
X, Windows, Android (Cross-
platform)
Java, Scala, Clojure Yes Yes Yes Yes Yes
Keras
Linux, Mac OS X, Windows
Python
Yes Yes Yes Yes Yes
Microsoft Cognitive
Toolkit - CNTK
Windows, Linux (OSX via
Docker on roadmap)
Python, C++, Command line,
BrainScript (.NET on roadmap)
Yes Yes Yes No Yes
MXNet
Linux, Mac OS X, Windows,
AWS, Android,
iOS, JavaScript
C++, Python, Julia, Matlab, JavaSc
ript, Go, R, Scala
Yes Yes Yes Yes Yes
PaddlePaddle Linux, Mac OS X Python, C++ Yes Yes Yes ? Yes
TensorFlow
Linux, Mac OS X, Windows
Python, (C/C++ public API only for
executing graphs)
Yes Yes Yes Yes Yes
Theano Cross-platform Python Yes Yes Yes Yes Yes
Torch
Linux, Mac OS X, Windows,
Android, iOS
Lua, LuaJIT, C, utility library
for C++/OpenCL
Yes Yes Yes Yes Yes
Source: link
22. Evaluate
Which is the best programming language to data / AI / ML /
DL?
How to select deep learning software?
On-premise or cloud / API platform?
23. Use Case:
Eva can get current product customer
account on Facebook Messenger chatbot
using natural language query and voice
command.
29. AI Talent Wars / Acquisition
• Giant corporations are soaking up AI talent.
• Top AI researchers -> industry with humongous data.
• “The cost of acquiring a top AI researcher is comparable to
the cost of acquiring an NFL quarterback.”
• AI talent shortage.
Source: link, link
32. For AI talent, hire from outside, or train
and transit our developers for AI-powered
projects?
33. Gap for the Transition
• Academic background
• Differences between computer program and brain (AI tries
to simulate brain)
– Computer program: define the general to store specifics
– Brain: store the specific to identify the general
Source: link
35. AI > Machine Learning > Deep Learning
Source: http://bit.ly/2h4AfLl
36. One of the Biggest Crowdsourcing Project
– Started in Y2007
– On Amazon Mechanical Turk Marketplace
• 48,940 workers
• 167 countries
– Total number of images: 14,197,122 (as of 2010/4/30)
42. Published AI Documents by Country
(Y2015, Top 10)
* Taiwan ranked #11.Source: link, link
43. Main Developments in 2016
(From Top AI Researchers)
Reinforcement
Learning
Inhuman
Encryption
GAN NLP
Machine
Translation
Lip Reading
Speech
Recognition
WaveNet
Computer
Vision
Hype
Source: link
47. Rule of Thumb (Mostly from Andrew Ng)
Why
• Add value to our business.
When
• “If a typical person can do a mental task with less than one second of thought, we can probably automate it
using AI either now or in the near future.”
What
• A large amount of data.
How
• Choose tool(s) and “customize to our business context and data.”
Evaluation
• If AI error rate surpasses human-level performance.
Source: link
49. 5 Big Predictions for AI in 2017 (MIT Press)
Positive reinforcement
•Reinforcement Learning
•AlphaGo -> Master -> ?
Dueling neural networks
•GAN (Generative Adversarial Networks)
•Learn from unlabeled data
China’s AI boom
Language learning
•NLP
•Image caption -> description
Backlash to the hype
Source: link
50. Key Trends in 2017 (From Top AI Researchers)
NLP
Unsupervised
Learning
Deep Learning in
Healthcare
Chatbot
Self-driving Car Computer Vision
Hybrid deep
learning with other
ML/AI techniques
AutoML
Commodify Deep
Learning
Source: link
52. In the race to build the best AI, there’s already
one clear winner
中國大陸人稱
“皮衣教主”
Source: link
53. GTC 2016 (GPU Technology Conference)
AI Revolution
GPU Supercomputer & Acceleration for Data Center
Computer Vision, VR
AI City by Y2020 (1B+ Cameras)
Self-Driving Car
AI Computing Ecosystem
Source: link
58. HPC Competition (On-going)
• GPU is current leader.
• Major cloud computing platforms support both GPU and
FPGA, e.g.
59. Major DL Software Supports GPU Acceleration
Software Platform Interface GPU
Support
Recurrent
nets
Convolution
al nets
RBM/DBNs
Parallel
execution
Caffe
Linux, Mac OS X, AWS,
Windows support by
Microsoft Research
C++, command
line, Python, MATLAB
Yes Yes Yes No Yes
Deeplearning4j
Linux, Mac OS
X, Windows, Android (Cross-
platform)
Java, Scala, Clojure Yes Yes Yes Yes Yes
Keras
Linux, Mac OS X, Windows
Python
Yes Yes Yes Yes Yes
Microsoft Cognitive
Toolkit - CNTK
Windows, Linux (OSX via
Docker on roadmap)
Python, C++, Command line,
BrainScript (.NET on roadmap)
Yes Yes Yes No Yes
MXNet
Linux, Mac OS X, Windows,
AWS, Android,
iOS, JavaScript
C++, Python, Julia, Matlab, JavaSc
ript, Go, R, Scala
Yes Yes Yes Yes Yes
PaddlePaddle Linux, Mac OS X Python, C++ Yes Yes Yes ? Yes
TensorFlow
Linux, Mac OS X, Windows
Python, (C/C++ public API only for
executing graphs)
Yes Yes Yes Yes Yes
Theano Cross-platform Python Yes Yes Yes Yes Yes
Torch
Linux, Mac OS X, Windows,
Android, iOS
Lua, LuaJIT, C, utility library
for C++/OpenCL
Yes Yes Yes Yes Yes
Source: link
75. Is “Current” AI Smart?
1. Ask Allo “What should be my New Year’s
resolution be?” Ask several times to get
more resolutions.
2. See what you get!
3. Did you get the same answers in the
article?
4. Is this the AI we look forward to?
Source: link
中國人工智能大會 CCAI (China Conference on Artificial Intelligence): http://ccai.caai.cn/
百度世界大會: http://baiduworld.baidu.com/
NIPS (Conference on Neural Information Processing Systems): https://nips.cc/
GTC Taiwan (GPU Technology Conference): https://www.gputechconf.com.tw/
Bay Area Deep Learning School: http://www.bayareadlschool.org/
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
The 7 Myths of AI
http://www.datasciencecentral.com/profiles/blogs/the-7-myths-of-ai-by-robin-bordoli
http://www.gartner.com/newsroom/id/3412017
Gartner’s Top 10 Strategic Technology Trends for 2017
Artificial intelligence, machine learning, and smart things promise an intelligent future. (October 18, 2016)
http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/
Gartner:2017 年十大策略科技趨勢預測
https://buzzorange.com/techorange/2016/10/25/gartner-2017-tech/
TIOBE Index for December 2016
http://www.tiobe.com/tiobe-index/
R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html
R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html
Top 20 Python Machine Learning Open Source Projects
http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html
Comparison of deep learning software
https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
Microsoft Cognitive Services
https://www.microsoft.com/cognitive-services/en-us/
Microsoft Cognitive Services: Introducing the Seeing AI project
http://bit.ly/2i8JOgc
https://www.luis.ai/
清潔工到斯坦福,人工智能科學家李飛飛的逆襲之路
http://bit.ly/2gDyCG7
Giant Corporations Are Hoarding the World’s AI Talent (2016/11/17)
https://www.wired.com/2016/11/giant-corporations-hoarding-worlds-ai-talent/
如何評價李飛飛和李佳加盟谷歌?看看AI 達人怎麼說
http://bangqu.com/gpu/blog/5058
A.I. is too hard for programmers
http://www.computerworld.com/article/2928992/emerging-technology/a-i-is-too-hard-for-programmers.html
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
How we teach computers to understand pictures | Fei Fei Li
https://youtu.be/40riCqvRoMs
ImageNet
http://image-net.org/index
Source: https://www.52ml.net/wp-content/uploads/2016/08/imagenethistory.png
Large Scale Visual Recognition Challenge (ILSVRC)
Microsoft Researchers’ Algorithm Sets ImageNet Challenge Milestone (2015/2/10)
https://www.microsoft.com/en-us/research/blog/microsoft-researchers-algorithm-sets-imagenet-challenge-milestone/
The State of Artificial Intelligence in 15 Visuals (2016/6/16)
http://www.appcessories.co.uk/artificial-intelligence/
Machine Learning
NLP
Computer Vision
VPA
Speech Recognition
Smart Robots
Recommendation Engine
Gesture Control
Content Aware Computing
Speech to Speech Translation
Video Content Recognition
Emerging from Y2012
Hot in China
“Deep Learning” Google Trends: https://www.google.com/trends/explore?date=all&geo=US&q=deep%20learning
Scimago Journal & Country Rank
http://www.scimagojr.com/countryrank.php?category=1702
在人工智慧研究領域 美國與中國領先各國
http://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=13039
US, China most active in AI research, report finds (2016/12/9)
http://asia.nikkei.com/Tech-Science/Science/US-China-most-active-in-AI-research-report-finds
人工智慧經濟席捲全球
http://udn.com/news/story/6860/2100235
Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017
http://bit.ly/2i2hrBj
AI Has Beaten Humans at Lip-Reading
http://bit.ly/2fMLeMw
Intel Core i7 6700HQ CPU (8 cores)
NVIDIA GeForce GTX-1060 video card (6GB RAM)
CUDA 8.0
TensorFlow
FPGA: Field Programmable Gate Array
TPU: Tensor Processing Unit
Does the future lie with CPU+GPU or CPU+FPGA?
https://www.scientific-computing.com/news/analysis-opinion/does-future-lie-cpugpu-or-cpufpga
Comparison of deep learning software
https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
AI Winter Isn’t Coming (2016/12/7)
http://bit.ly/2hepl70
罗辑思维"时间的朋友2016"跨年演讲 04 智能革命
http://bit.ly/2iBtyD0
強人工智慧 (Strong AI / Artificial General Intelligence)
弱人工智慧 (Weak AI / Applied AI)
Google uses DeepMind AI to cut data center energy bills
http://www.theverge.com/2016/7/21/12246258/google-deepmind-ai-data-center-cooling
百度世界大會2016
http://baiduworld.baidu.com/
How a Japanese cucumber farmer is using deep learning and TensorFlow
http://bit.ly/2i8d06S
AI for Hobbyists: DIYers Use Deep Learning to Shoo Cats, Harass Ants
http://bit.ly/2hCM9O4
Chasing Cats
http://myplace.frontier.com/~r.bond/cats/cats.htm
Google’s AI assistant has 5 New Year’s resolutions for you
http://bit.ly/2iBzaNM
罗辑思维"时间的朋友2016"跨年演讲 04 智能革命
http://bit.ly/2iBtyD0
Where machines could replace humans—and where they can’t (yet) (2016/7)
http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet
Japanese white-collar workers are already being replaced by artificial intelligence
http://bit.ly/2hNHZ8d
Cybersecurity trends 2017: malicious machine learning, state-sponsored attacks, ransomware and malware
http://www.cso.com.au/article/612128/cybersecurity-trends-2017-malicious-machine-learning-state-sponsored-attacks-ransomware-malware/
2017 Predictions for AI, Big Data, IoT, Cybersecurity, and Jobs from Senior Tech Executives
http://blog.level3.com/transformation/2017-predictions-ai-big-data-iot-cybersecurity-jobs-senior-tech-executives/
防火墙做不到的事,人工智能可以吗?
http://app.fortunechina.com/mobile/article/276577.htm