* László Gulyás (EIT Digital Hungary) - The (Smart) Handmaid’s Tale – A Brief History of Artificial Intelligence
The 2010s brought a new renessaince of Artificial Intelligence (AI). Thus, it is, once again, in the forefront of media and public interest. It is also a subject of much speculation, worry, and maybe expectation about the future. In reaction to this, I will give a brief (and somewhat subjective) overview of the history of AI. I will touch upon the ever-changing expectations, the difficulty of defining AI and the ensuing waves of high hopes and deep disappointment. I will also disuss how the disappointments that followed so far were independent of the actual technology and results provided by prior AI research or from its daily applications. I will point out the role of the Internet and mobile phones in the recent re-emergence of the AI hype. Finally, I will discuss what I think as the major difference between the ’current wave of AI’ and the prior periods of activity, including the role of private companies and investors.
* István Lázár (Ericsson) - Machine learning models into real products and services
As being in an early phase, most of the best practices around machine learning addresses the requirements of the academia, not the industry: i.e. the training and evaluation of the models.
The best practices around the productization of machine learning, however, are less elaborated. We miss the well-established processes, tools and frameworks for deploying, monitoring, and managing the machine learning models. Especially, when the models are supposed to be deployed in traditional on-premise environments.
Potential challenges in this domain: the concerns regarding the quality of a trained model, locality aspects of the model training, the degradation of the deployed model, the opacity of the ML models, etc. These issues may lead to misbehaving ML models which in turn can have catastrophic consequences on the product and the development process.
* András Benczúr (MTA SZTAKI) -
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