AI Tech Week: Research
AI Tech Week: Research
It’s time for the first ever 𝗔𝗜 𝗧𝗲𝗰𝗵 𝗪𝗲𝗲𝗸! Perspectives from research, entrepreneurship, talent and applications will all be covered during four different afternoon events. Do we see you there?
Interested in state-of-the-art research in Amsterdam? Four ICAI Amsterdam Labs will talk about the research they are undertaking in collaboration with academia, industry and government. We cover it all in this afternoon event about AI research.
16:30 Welcome & Introduction
16:35 Talk #1 Algorithmic fairness in wild: a conversation
17:05 Talk #2
17:35 Coffee Break
17:40 Talk #3: Real-World Learning
18:10 Talk #4: Learning from Controlled Sources
𝗖𝗵𝗮𝗶𝗿: 𝗠𝗮𝗮𝗿𝘁𝗲𝗻 𝗱𝗲 𝗥𝗶𝗷𝗸𝗲
Professor of Artificial Intelligence and Information Retrieval
𝗧𝗮𝗹𝗸 #𝟭 𝗯𝘆 𝗛𝗶𝗻𝗱𝗮 𝗛𝗮𝗻𝗲𝗱 𝗮𝗻𝗱 𝗦𝗮𝗿𝗮 𝗔𝗹𝘁𝗮𝗺𝗶𝗿𝗮𝗻𝗼
Algorithmic fairness in wild: a conversation
This talk is a conversation between an industry practitioner (Hinda Haned) and a fair AI PhD researcher (Sara Altamirano) around the question: how can we make AI-driven systems fair in practice? As the AI community is increasingly devising new debiasing or algorithmic fixes, we revisit what it means from the data scientist’s perspective what fair AI or debiasing AI entails in the day-to-day work. Through use cases, we will illustrate the challenges of ensuring fair AI in practice and the current gap in how this issue cannot be fixed through the algorithmic lens alone.
𝗧𝗮𝗹𝗸 #𝟮 𝗯𝘆 𝗠𝗶𝗰𝗵𝗮𝗲𝗹 𝗖𝗼𝗰𝗵𝗲𝘇
More information follows.
𝗧𝗮𝗹𝗸 #𝟯 𝗯𝘆 𝗖𝗲𝗲𝘀 𝗦𝗻𝗼𝗲𝗸
Progress in artificial intelligence has been astonishing in the past decade. Cars self-driving on highways, machines beating go-masters, and cameras categorizing images in a pixel-precise fashion are now common place, thanks to data-and-label supervised deep learning. Despite the impressive advances, it is becoming increasingly clear that deep learning networks are heavily biased towards their training conditions and become brittle when deployed under real-world situations that differ from those perceived during learning in terms of data, labels and objectives. Simply scaling-up along all dimensions at training time seems a dead end, not only because of the compute, storage and ethical expenses, but especially as humans are easily able to generalize robustly in a data-efficient fashion. Several learning paradigms have been proposed to account for the limitations of deep learning with the i.i.d. assumption. Shifting data distributions are attacked by domain adaptation and domain generalization, changing label vocabularies are the topic of interest in zero-shot and open world learning, while varying objectives are covered in meta-learning and continual learning regimes. However, there is as of yet no learning methodology that can dynamically learn to generalize and adapt across domains, labels and tasks simultaneously, and do so in a data-efficient and fair fashion. This is the ambitious long-term goal of ‘real-world learning’ and I will present some initial works from my lab to achieve it.
𝗧𝗮𝗹𝗸 #𝟰 𝗯𝘆 𝗢𝗻𝗻𝗼 𝗭𝗼𝗲𝘁𝗲𝗿
Learning from Controlled Sources:
The classic supervised learning problem that is taught in machine learning courses and is the subject of many machine learning competitions, is often too narrow to reflect the problems that we face in practice. Historical datasets typically reflect a combination of a source of randomness (for example customers making browsing and buying decisions) and a controlling mechanism such as a ranker or highlighting heuristics (badges, promotions, etc.).
𝗧𝗵𝗶𝘀 𝗲𝘃𝗲𝗻𝘁 𝗶𝘀 𝗵𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗛𝗼𝗽𝗶𝗻, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗴𝗲𝘁 𝗮 𝘁𝗶𝗰𝗸𝗲𝘁 𝘁𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗲𝘃𝗲𝗻𝘁: