Artificial Intelligence for All - intersectionality

Editor's Notes

• #6: CS started in early 60’s. the subfields are inter-related and discoveries and innovations in one field can bring challenges, discoveries and innovations in other field. My own field software engineering is very inter related with AI as we use AI tools to develop software AND we use software engineering techniques and processes to develop AI systems. HCI can also be seen as a subfield of CS.
• #8: https://medium.com/@rana.adnanali/top-10-mobile-apps-that-make-life-easier-for-people-with-disabilities-681a5fedf3ef 1. Be My Eyes Connecting visually impaired users with volunteers through video calls, this app provides real-time assistance for tasks that require sight, such as identifying objects or reading labels. 2. VoiceOver Apple’s built-in screen reader enhances device accessibility for blind and visually impaired users by narrating what’s on the screen, enabling independent device usage. 3. Wheelmap Promoting inclusivity, this app crowd-sources information about wheelchair-accessible places, allowing users to navigate urban environments confidently. 4. Avaz Pro Catering to non-verbal individuals, this communication app employs customizable picture-based communication to foster expression and connection. 5. SoundAlert Enhancing safety for users with hearing impairments, this app alerts them to important sounds in their environment, providing awareness and security. 6. Aira By combining wearable technology and human agents, Aira assists blind and low-vision users with navigation, reading, and other daily tasks. 7. Proloquo2Go This symbol-based communication app aids those with speech and language difficulties, enabling effective expression and communication. 8. TapToTalk Focusing on language development and communication for users with autism, this app employs picture-based communication to foster interaction and learning. 9. Medisafe Managing medication schedules is made easier with this app, particularly for users with cognitive impairments, promoting adherence and well-being. 10. Dragon Anywhere Providing a hands-free control option for those with mobility impairments, this dictation app enables users to navigate mobile devices without physical input.
• #9: https://www.unwomen.org/sites/default/files/2022-01/Intersectionality-resource-guide-and-toolkit-en.pdf Intersectionality Resource Guide and Tool Kit https://youtu.be/akOe5-UsQ2o?si=GSxI73Yif1dw8y7c TEK talk 2 millions play 2016 Intersectionality is an analytical framework used to understand how different social identity categories, such as gender, race, class, sexuality, disability, and more, interact in complex ways and create unique experiences of discrimination or privilege for individuals. The term was first introduced by American lawyer and academic Kimberlé Crenshaw in the late 1980s. She wanted to explain how black women experienced discrimination in a way that was different from both white women and black men, because they faced both gender and racial discrimination at the same time. Intersectionality therefore looks at how different forms of power and oppression overlap and influence each other. For example, a black, disabled, lesbian woman may experience discrimination based on all of these identity aspects at the same time, giving her a different experience than someone who only experiences discrimination based on one of these identity categories. Intersectionality is important for understanding that social problems and injustice cannot be solved by looking at each identity category in isolation. It requires a holistic approach that takes into account the different layers of identity and how they influence each other.
• #10: Kvinner er et eksempel The Top 10 women in the world of AI in 2023 https://aimagazine.com/top10/the-top-10-women-in-the-world-of-ai-in-2023 Paper https://dl.acm.org/doi/abs/10.1145/3311927.3325322
• #11: Kvinner er et eksempel The Top 10 women in the world of AI in 2023 https://aimagazine.com/top10/the-top-10-women-in-the-world-of-ai-in-2023 Paper https://dl.acm.org/doi/abs/10.1145/3311927.3325322
• #12: Gemini 1.5 Pro Online
• #13: AI has become a catchall term for applications that perform complex tasks that once required human input, such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning (ML) and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning. To get the full value from AI, many companies are making significant investments in data science teams. Data science combines statistics, computer science, and business knowledge to extract value from various data sources.
• #14: The 1956 Dartmouth workshop was the moment that AI gained its name URL The history of AI 1950s: The first AI programs were written to run on the Ferranti Mark 1 machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz. 1960s: The Dartmouth Conference was organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon to discuss the possibility of thinking machines and artificial intelligence. 1970s: The first expert systems were developed. 1980s: The first neural networks were developed. 1990s: The first autonomous robots were developed. 2000s: The first self-driving cars were developed.google translator came in 2006 based on statistical methods. 2010s: The first AI-powered virtual assistants were developed. Deep learning revolution, AlexNet 2012 2020s: AI continues to advance and is being used in a wide range of applications, from healthcare to finance to transportation 1.
• #16: It is a big misconception that you need to be very intelligent and preferably a man to understand computers, algorithms and AI. I have been working with data since the early 80s, it was kind of by chance. And I think it is as difficult to figure out a knitting pattern as it is to understand algorithms. We need the past to have a language to talk about the present and the past K = 1000 Giga = 1000000000 104 = 10000 1017 = 100,000,000,000,000,000 Memory 36 Kbyte Speed ​​104 flops Floating point operation per second – now 8 Giga – now 1017
• #17: Margaret Hamilton (who I met once!), the first person to coin the term software engineering and who wrote the software for the Apollo expedition in the 60s is a woman (Apollo first man on the moon) Margaret Hamilton (who I met once!), the first person to coin the term software engineering and who wrote the software for the Apollo expedition in the 60s is a woman Apollo 145,000 lines of code Now millions of lines of code
• #18: According to https://www.youtube.com/watch?v=_6R7Ym6Vy_I&t=2220s All human written text = 1013 CHAT GPT 4 = 300 x 1012
• #19: NodeType#CPUsProcessor#CoresRAM[GB]#GPUsGPU type How can we compare to the system on which Open AI trains its models to give an idea https://lambdalabs.com/blog/demystifying-gpt-3 CHAT GPT4 – it costed 1 M Dollars to pre train According to https://www.youtube.com/watch?v=_6R7Ym6Vy_I&t=2220s https://www.hpc.ntnu.no/idun/
• #20: Some numbers about ICT specialists in Europe and World and who are the ICT specialists In 2022, 9.4 million people in the EU worked as ICT specialists In the world 55.3 million in 2020 3 out of 4 companies have problems finding specialists with the right skills And how many ICT specialists are women? 2% in 10 years How many years will it take if we continue like this? eurostat https://ec.europa.eu/eurostat/statistics-explained/index.php?title=ICT_specialists_in_employment In 2023, 80.6 % of men were employed as ICT specialists in the EU against 19.4 % of women.
• #24: Risiko nivå Vi har allerede regler som regulerer diskriminering i Norge og i Europa
• #27: But why? What are the challenges? And in our projects we are trying to combat these challenges, Examples from EUGAIN Calling the applicants, Welcome day, March 8 - Women's Day, Network lunches, Programming courses, social activities such as Mountain hiking, PhD party, Invite high school girls from all over the country, Presentations and workshops, Personal meeting with role models, Break down stereotypes and bias, hire women role models This is answer to question about what works Explain briefly what these projects have done and achieved since 1997 Measures such as Information Mentoring Network Anti bias training