Human or Machine: Reflections on Turing-Inspired Testing for the Everyday
Speaker: David Harel, Weizmann Institute of Science, IL
Abstract: In his seminal paper “Computing Machinery and Intelligence”, Alan Turing introduced the “imitation game” as part of exploring the concept of machine intelligence. The Turing Test has since been the subject of much analysis, debate, refinement and extension. Here we sidestep the question of whether a particular machine can be labeled intelligent, or can be said to match human capabilities in a given context. Instead, but inspired by Turing, we draw attention to the seemingly simpler challenge of determining whether one is interacting with a human or with a machine, in the context of everyday life.
We are interested in reflecting upon the importance of this Human-or-Machine question and the use one may make of a reliable answer thereto. Whereas Turing’s original test is widely considered to be more of a thought experiment, the Human-or-Machine question as discussed here has obvious practical significance. And while the jury is still not in regarding the possibility of machines that can mimic human behavior with high fidelity in everyday contexts, we argue that near-term exploration of the issues raised here can contribute to development methods for computerized systems, and may also improve our understanding of human behavior in general. Of course, future strict regulatory restrictions on the use of AI systems might affect some of the issues we raise here. However, we feel that the regulators themselves should be fully aware of them too.
(This is joint work with Asaf Marron)
Deep Neural Networks, Explanations, and Rationality
Speaker: Edward A. Lee, UC Berkeley, US
Abstract: “Rationality” is the principle that humans make decisions on the basis of step-by-step (algorithmic) reasoning using systematic rules of logic. An ideal “explanation” for a decision is a chronicle of the steps used to arrive at the decision. Herb Simon’s “bounded rationality” is the observation that the ability of a human brain to handle algorithmic complexity and data is limited. As a consequence, human decision-making in complex cases mixes some rationality with a great deal of intuition, relying more on Daniel Kahneman’s “System 1” than “System 2”.
A DNN-based AI, similarly, does not arrive at a decision through a rational process in this sense. An understanding of the mechanisms of the DNN yields little or no insight into any rational explanation for its decisions. The DNN is operating in a manner more like System 1 than System 2. Humans, however, are quite good at constructing post-facto rationalizations of their intuitive decisions.
If we demand rational explanations for AI decisions, engineers will inevitably develop AIs that are very effective at constructing such post-facto rationalizations. With their ability to handle vast amounts of data, the AIs will learn to build rationalizations using many more precedents than any human could, thereby constructing rationalizations for ANY decision that will become very hard to refute. The demand for explanations, therefore, could backfire, resulting in effectively ceding to the AIs much more power.
In this talk, I will discuss similarities and differences between human and AI decision making and will speculate on how, as a society, we might be able to proceed to leverage AIs in ways that benefit humans.
Technology and Democracy
Speaker: Moshe Y. Vardi, Rice University, US
Abstract: U.S. society is in the throes of deep societal polarization that not only leads to political paralysis, but also threatens the very foundations of democracy. The phrase “The Disunited States of America” is often mentioned. Other countries are displaying similar polarization. How did we get here? What went wrong?
In this talk I argue that the current state of affairs is the results of the confluence of two tsunamis that have unfolded over the past 40 years. On one hand, there was the tsunami of technology – from the introduction of the IBM PC in 1981 to the current domination of public discourse by social media. On the other hand, there was a tsunami of neoliberal economic policies. I will argue that the combination of these two tsunamis led to both economic polarization and cognitive polarization.
Graph Neural Networks: Everything is Connected
Speaker: Matthias Fey, Founding engineer at Kumo.ai
Abstract: Our world is highly rich in structure, composed of objects, their relations and hierarchies. Despite the ubiquity of graphs in our world, most modern machine learning methods fail to properly handle such rich structural representations. Recently, a universal class of neural networks emerged that can seamlessly operate on graph-structured data, summarized under the umbrella term Graph Neural Networks (GNNs).
In this task, we will introduce the concept of Graph Neural Networks and the general framework of neural message passing. We thoroughly analyze the expressive power of GNNs and show-case how they relate and generalize concepts of Convolutional Neural Networks and Transformers to arbitrarily structured data. In particular, we argue for the injection of structural and compositional inductive biases into deep learning models. Despite recent trends in neural networks regarding LLMs, such models manifest our understanding of a structured world, require less computational budget, and are easier to understand and explain.
Democracy in the Digital Era
Keynote by Moshe Y. Vardi: Technology and Democracy
Moderator: George Metakides
Abstract: For more than a decade now, studies by different organizations on the state of democracy world-wide, while using different indices and methodologies, arrive at very similar conclusions: there has been a continuous quantitative and qualitative decline of democratic practices, including participation in and integrity of elections, civil liberties and the rule of law.
Many analysts trace the origin of this decline back to the period 1990’s, following the fall of the Iron Curtain and characterized by the euphoric belief that democracy was a sort of natural state that would be inevitably not only preserved but spread broadly via capitalism and globalization.
This optimism was further reinforced in the 1990’s and 2000’s by the “blossoming” of the internet and the World Wide Web which promised to usher in a digital cultural renaissance which would reinvent and strengthen democracy.
This optimism turned out to be utopic, as democracy today is seen to be facing threats some of which are in fact magnified by the socio-political impact of digital technologies.
While economic inequalities, the effects of unrestrained globalization and constitutional fault lines are cited as the leading causes for the decline of democracy, these are more and more closely intertwined with the role played by digital technologies and the role of Big Tech and their platforms in particular.
In the current context with the potentially transformational generative AI developments, the concentration of economic and political power in the hands of a very small number of very big companies further magnifies the threats to democratic processes and institutions and the erosion and manipulation of the public sphere.
We are in fact witnessing an immense concentration of economic and political power which, those holding it, can use it to wield vast control over both our civic and individual lives. Technology, since the beginning of history, had significant and occasionally transformational socio-political impact with, inadvertently, positive and negative aspects.
The Monday afternoon session aims to examine the democracy technology interaction, identify threats and opportunities and, when possible, formulate proposals for sustaining democracy in the Digital Era.