Human vs machine
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The document discusses the timeline of machine development from mechanical to artificial intelligence. It outlines 4 stages: 1) mechanical machines powered by humans, 2) automated machines powered by electricity, 3) machines with narrow AI expanding into more jobs, and 4) potential future machines with full AI. The document also compares human and machine abilities, finding machines superior in speed, analysis, accuracy and power use while humans retain advantages in adaptation, multitasking, social interaction and objectives. It predicts 25% of jobs may be replaced by robots by 2025, such as drivers, farmers and military soldiers.
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BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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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.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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UiPath Test Automation using UiPath Test Suite series, part 6
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
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Human vs machine
- 1. Robotics Day Human vs. Machine Presented by Alia Hamwi
- 3. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 4. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 5. Stage 1 - Mechanical
- 6. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 7. Stage 2 - Automated
- 8. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 9. Stage 3 - Machine With Narrow AI Now
- 10. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 11. Stage 4 - Machine With Full AI
- 12. Comparison between Human and Machine Abilities
- 14. 2 – Analysis And Decision Making Machine
- 16. 4 – Power Use Machine
- 17. 5 – Ability to Adapt Human
- 19. 7 – Social Interaction Human
- 20. 8 – Main Function (Objective) Human Machine
- 21. Human Vs. Machine in Job Domain
- 22. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 23. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 24. Stage 1 - Mechanical - Machine work by Human power. - Less effort. - Human still the dominant in the job work.
- 25. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 26. Stage 2 - Automated - Machine work by electricity and engines. - Machine starts to replace human in manual repeated work. - Less time. - Increase productivity.
- 27. Timeline of Machine Stage 1 Mechanical Machine Stage 2 Automated Machine Stage 3 Machine with Narrow AI Stage 4 Machine with Full AI
- 28. Stage 3 - Machine With Narrow AI - Machine expand to more jobs and domains. Let’s see:
- 29. 25% OF Jobs By 2025 will be replaced by a robot
- 30. 1- Drivers Tesla’s new driverless vehicles announce that will lunch 1 million car in 2020. This could cost millions of jobs worldwide as taxi drivers are rendered obsolete.
- 31. 2- Farmers
- 33. 4- Firefighter
- 34. 5- Astronauts
- 35. Stage 3 - Machine With Narrow AI - Good Side of Robots replacing Human: - They replace danger and hard job. - They push human to learn more high skills and innovate new job. - Bad Side of Robots replacing human: - Human need more time to adapt.
- 36. Human Vs. Machine What’s Next ?
- 39. HumanVs.Machine
- 42. Thank You