Posts

Your Human-AI Co-Existence (5 Minutes Survey)

/in , /by
The Future of AI starts with You
If we want to achieve a more sustainable and AI-friendly future, we need to start with your individual participation.

Your responses are anonymous and your personal data will not be recorded.

How can you participate?

By filling out this five minute-long and anonymous survey, you can help us in making AI technology more accessible and understandable:

https://forms.gle/DTHmeD9v6XbwXeFn9

Why is that important?

Establishing adaptable and interpretable AI machinery is crucial for individuals and governments to catch up with the speed of technology. Key is not promoting solely development-friendly AI and regulatory overseeing frameworks, but rather working on transparency and readability of technologies through insightful guidelines so that participation for the individual is made possible. This includes the topic of informational self-determination through open legislation frameworks, policies, and ethical guidelines. Both the collective and individual aspect are important for AI technology progression, but a future towards sustainable-friendly AI as an enabler of the 17 sustainable development goals (SDGs) and targets rather than an inhibitor starts with open participation and constant confrontation of the individual with AI technology. One global example that affects us all is ongoing climate change [2], and here we need AI – and the workhorse machine learning (ML) – to contribute to what is clearly the greatest challenge facing humanity. Each and every one of us can contribute to the global challenges of climate change, and we want to explore how AI can help us do that.

[1] Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M. & Fuso Nerini, F. 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11, (233), 1–10, https://doi.org/10.1038/s41467-019-14108-y

[2] Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N. & Waldman-Brown, A. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55, (2), 1–96, https://doi.org/10.1145/3485128

[3] This page is: https://human-centered.ai/2023/09/21/human-ai-5-minutes-survey

 

Thank you very much:

Andreas HOLZINGER, Heimo MUELLER, Jianlong ZHOU, Fang CHEN

Human Centered AI Lab Austria and Human-Centered AI Lab Australia

 

Research Seminar, Wednesday, December, 9, 2020

/in , /by

HCAI research seminar: “Towards Games in explainable AI” and “simultaneous neural nets and synthetiziced literate-logic-programs”

Read more

ICML Workshop interpretable machine learning, July, 18, 2020

/in , /by

Welcome to our XXAI ICML 2020 workshop: extending explainable ai beyond deep models and classifiers

Read more

Call for Papers “explainable AI 2020”, University College Dublin, August 24-28 (closed)

/in /by

Accepted Papers will be published in the Springer/Nature Lecture Notes in Computer Science Volume “Cross Domain Conference for Machine Learning and Knowlege Extraction” (CD-MAKE 2020)

Read more

Ten Commandments for Human-AI interaction – Which are the most important?

/in , /by

In the following we present “10 Commandments for human-AI interaction” and ask our colleagues from the international AI/ML-community to comment on these. We will collect the results and present it openly to the international research community.

Read more

AI will change Radiology – NOT replace Radiologists

/in /by

After the rather shocking statement of Geoffrey HINTON during the Machine Learning and Market for Intelligence Conference in Toronto, where he recommended that hospitals should stop training radiologists, because deep learning will replace them (watch video below), on March, 27, 2018 Thomas H. DAVENPORT and Keith J. DREYER published a really nice article on “AI will change radiology, but it won’t replace radiologists” (see [1]) – which supports our human-in-the-loop approach: for sure, AI/machine learning (difference here) will change workflows, but we envision that the expert will be augmented by new technologies, i.e. routine (boring) tasks will be replaced by automatic algorithms, but this will free up expert time to spent on challenging (cool) tasks and more research – and there are plenty of problems where we need human intelligence!

[1] https://hbr.org/2018/03/ai-will-change-radiology-but-it-wont-replace-radiologists

 

 

Human-in-the-loop AI

/in /by

Human-in-the-Loop-AI

This is really very interesting. In the recent April, 5, 2018, TWiML & AI (This Week in Machine Learning and Artificial Intelligence) podcast, Robert MUNRO (a graduate from Stanford University, who is an recognized expert in combining human and machine intelligence) reports on the newly branded Figure Eight [1] company, formerly known as CrowdFlower. Their Human-in-the-Loop AI platform supports data science & machine learning teams working on various topics, including autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Most recently on disaster response and epidemiology. This is a further proof on the enormous importance and potential usefulness of the human-in-the-loop interactive machine Leanring (iML) approach! Listen to this awesome discussion led excellently by Sam CHARRINGTON:

https://twimlai.com/twiml-talk-125-human-loop-ai-emergency-response-robert-munro/

This discussion fits well to the previous discussion with Jeff DEAN (head of the Google Brain team) – who emphasized the importance of health and the limits of automatic approaches including deep learning. Enjoy to listen directly at:

https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/

[1] https://www.figure-eight.com/resources/human-in-the-loop

 

What is the difference between AI/ML/DL?

/in /by
Read more