Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge encoded in causal models, but their application to real-world problems remains limited. This article explores the application of IIT in predicting blood glucose levels in Type 1 Diabetes Mellitus (T1DM) patients. The study utilizes an acyclic version of the simglucose simulator approved by the FDA to train a Multi-Layer Perceptron (MLP) model, employing IIT to impose causal relationships. Results show that the model trained with IIT effectively abstracted the causal structure and outperformed the standard one in terms of predictive performance across different prediction horizons (PHs) post-meal. Furthermore, the breakdown of the counterfactual loss can be leveraged to explain which part of the causal mechanisms are more or less effectively captured by the model. These preliminary results suggest the potential of IIT in enhancing predictive models in healthcare by effectively complying with expert knowledge.

The alignment between human objectives and machine learning models built on these objectives is a crucial yet challenging problem for achieving Trustworthy AI, particularly when preparing for superintelligence (SI). First, given that SI does not exist today, empirical analysis for direct evidence is difficult. Second, SI is assumed to be more intelligent than humans, capable of deceiving us into underestimating its intelligence, making output-based analysis unreliable. Lastly, what kind of unexpected property SI might have is still unclear. To address these challenges, we propose the Superficial Consciousness Hypothesis under Information Integration Theory (IIT), suggesting that SI could exhibit a complex information-theoretic state like a conscious agent while unconscious. To validate this, we use a hypothetical scenario where SI can update its parameters "at will" to achieve its own objective (mesa-objective) under the constraint of the human objective (base objective). We show that a practical estimate of IIT's consciousness metric is relevant to the widely used perplexity metric, and train GPT-2 with those two objectives. Our preliminary result suggests that this SI-simulating GPT-2 could simultaneously follow the two objectives, supporting the feasibility of the Superficial Consciousness Hypothesis.

"How many tennis balls can fit in a passenger plane?" Neeraj, a young economics graduate from the premier Indian Institute of Technology (IIT), was given 15 minutes to solve this question during his interview rounds at Nation With Namo (NwN), one of the in-house political consultancies of India's governing Bharatiya Janata Party (BJP). He got the calculation right and joined a small team of graduates from India's top universities who were dispatched to the eastern state of Tripura to conduct surveys, collect and analyse voter data for elections that were due in February last year. Their job was to identify who was not voting for the BJP, separate them into demographic cohorts – age, gender, caste, tribe, religion – find a common concern, issue or fear and strategise how to exploit that in the BJP's favour. And they were to do all this while staying under the radar.

Language tasks involving character-level manipulations (e.g., spelling corrections, arithmetic operations, word games) are challenging for models operating on subword units. To address this, we develop a causal intervention framework to learn robust and interpretable character representations inside subword-based language models. Our method treats each character as a typed variable in a causal model and learns such causal structures by adapting the interchange intervention training method of Geiger et al. (2021). We additionally introduce a suite of character-level tasks that systematically vary in their dependence on meaning and sequence-level context. While character-level models still perform best on purely form-based tasks like string reversal, our method outperforms character-level models on more complex tasks that blend form, meaning, and context, such as spelling correction in context and word search games. Compared with standard subword-based models, our approach also significantly improves robustness on unseen token sequences and leads to human-interpretable internal representations of characters.

For years now, Hakwan Lau has suffered from an inner torment. Lau is a neuroscientist who studies the sense of awareness that all of us experience during our every waking moment. How this awareness arises from ordinary matter is an ancient mystery. Several scientific theories purport to explain it, and Lau feels that one of them, called integrated information theory (IIT), has received a disproportionate amount of media attention. He's annoyed that its proponents tout it as the dominant theory in the press.

Informed importance tempering (IIT) is an easy-to-implement MCMC algorithm that can be seen as an extension of the familiar Metropolis-Hastings algorithm with the special feature that informed proposals are always accepted, and which was shown in Zhou and Smith (2022) to converge much more quickly in some common circumstances. This work develops a new, comprehensive guide to the use of IIT in many situations. First, we propose two IIT schemes that run faster than existing informed MCMC methods on discrete spaces by not requiring the posterior evaluation of all neighboring states. Second, we integrate IIT with other MCMC techniques, including simulated tempering, pseudo-marginal and multiple-try methods (on general state spaces), which have been conventionally implemented as Metropolis-Hastings schemes and can suffer from low acceptance rates. The use of IIT allows us to always accept proposals and brings about new opportunities for optimizing the sampler which are not possible under the Metropolis-Hastings framework. Numerical examples illustrating our findings are provided for each proposed algorithm, and a general theory on the complexity of IIT methods is developed.

The problem of underrepresentation of women studying STEM subjects is well known and is being faced by several nations across the world. The field of computer science is no exception to this deteriorating gender ratio, nor is the Indian case. The male:female population ratio in India is 1.06, but the ratio of females making it to engineering institutions is lower, at 1.79.1 In absolute numbers, India produces around 1.5 million engineers from its 6,000 engineering institutions across the country.2 When it comes to employ-ability, 4.03% of male engineering students are employable by IT product firms, while only 2.54% of females are employable by these firms, and 16.67% of males as against 15.49% of females are employable by IT Services organizations. If we shift our focus to the employability of the graduates of top engineering institutions in the country--Indian Institutes of Technology (IITs), National Institutes of Technology (NITs), and other leading engineering educational institutions including International Institutes of Information Technology (IIITs)--employability among fresh graduates in IT product roles increases to 22.67%, and in IT Services roles, it is 36.29%.1

In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashion. To achieve this, we present the new method of interchange intervention training (IIT). In IIT, we (1) align variables in a causal model (e.g., a deterministic program or Bayesian network) with representations in a neural model and (2) train the neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in both models are set to be the value they would be for a source input. IIT is fully differentiable, flexibly combines with other objectives, and guarantees that the target causal model is a causal abstraction of the neural model when its loss is zero. We evaluate IIT on a structural vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural language inference task (MQNLI). We compare IIT against multi-task training objectives and data augmentation. In all our experiments, IIT achieves the best results and produces neural models that are more interpretable in the sense that they more successfully realize the target causal model.

IIT specifies a unique number, a system's integrated information, labeled by the Greek letter φ (pronounced phi). If φ is zero, the system does not feel like anything; indeed, the system does not exist as a whole, as it is fully reducible to its constituent components. The larger φ, the more conscious a system is, and the more irreducible. Given an accurate and complete description of a system, IIT predicts both the quantity and the quality of its experience (if any). IIT predicts that because of the structure of the human brain, people have high values of φ, while animals have smaller (but positive) values and classical digital computers have almost none.

The history of science includes numerous challenging problems, including the “hard problem” ([ 1 ][1]) of consciousness: Why does an assembly of neurons—no matter how complex, such as the human brain—give rise to perceptions and feelings that are consciously experienced, such as the sweetness of chocolate or the tenderness of a loving caress on one's cheek? Beyond satisfying this millennia-old existential curiosity, understanding consciousness bears substantial medical and ethical implications, from evaluating whether someone is conscious after brain injury to determining whether nonhuman animals, fetuses, cell organoids, or even advanced machines ([ 2 ][2]) are conscious. A comprehensive and agreed-upon theory of consciousness is necessary to answer the question of which systems—biologically evolved or artificially designed—experience anything and to define the ethical boundaries of our actions toward them. The research projects described here will hopefully point the way and indicate whether some of today's major theories hold water or not. After prosperous decades of focused scientific investigation zeroing in on the neural correlates of consciousness ([ 3 ][3]), a number of candidate theories of consciousness have emerged. These have independently gained substantial empirical support ([ 4 ][4]–[ 7 ][5]), led to empirically testable predictions, and resulted in major improvements in the evaluation of consciousness at the bedside ([ 8 ][6], [ 9 ][7]). Notwithstanding this progress, the conjectures being put forward by the different theories make diverging claims and predictions that cannot all be simultaneously true. Moreover, the theories evolve and continue to adapt as further data accumulates, with hardly any cross-talk between them. How can we then narrow down on which theory better explains conscious experience? The road to a possible solution may be paved by means of a new form of cooperation among scientific adversaries. Championed by Daniel Kahneman in the field of behavioral economics ([ 10 ][8]) and predated by Arthur Eddington's observational study to test Einstein's theory of general relativity against Newton's theory of gravitation ([ 11 ][9]), adversarial collaboration rests on identifying the most diagnostic points of divergence between competing theories, reaching agreement on precisely what they predict, and then designing experiments that directly test those diverging predictions. During the past 2 years, several groups have adopted this approach, following an initiative that aims to accelerate research in consciousness. So far, several theories of consciousness are being evaluated in this manner to test competing explanations for where and when neural activity gives rise to subjective experience. The global neuronal workspace theory (GNWT) ([ 4 ][4]) claims that consciousness is instantiated by the global broadcasting and amplification of information across an interconnected network of prefrontal-parietal areas and many high-level sensory cortical areas. The sensory areas carry out different functions that range from feature processing to object or word recognition. Information in those sensory areas is processed in encapsulated modules, remaining unconscious. The frontal-parietal networks support integrative and executive functions, including selective attention and working memory. According to the GNWT, a stimulus must be attended to trigger activity that helps distribute this sensory information to many parts of the brain for further processing and report. It is this global broadcasting across many modules of specialized subsystems that constitutes consciousness. Conversely, the integrated information theory (IIT) (5) holds that consciousness should be understood in terms of cause-effect “power” that reflects the amount of maximally irreducible integrated information generated by certain neuronal architectures. On the basis of mathematical and neuroanatomical considerations, the IIT holds that the posterior cortex is ideally situated for generating a maximum of integrated information. In this theory, consciousness is not input-output information processing but the intrinsic ability or power of a neuronal network to influence itself. That is, the neuronal substrate of consciousness perpetuates itself for as long as the experience exists. The more cause-effect power a system has, the more conscious it is. For the IIT, the content of an experience is a structure of causes and effects (integrated information), whereas for the GNWT, it is a message that is broadcast globally. ![Figure][10] Testing hypotheses by adversarial collaboration The neural correlates of consciousness for the global neuronal workspace theory (GNWT) and for the integrated information theory (IIT) occupy distinct and overlapping regions in the brain. Each theory predicts synchronization of activity between or within these regions. GRAPHIC: N. CARY/ SCIENCE Another controversy occurs between first-order ([ 12 ][11], [ 13 ][12]) and higher-order ([ 6 ][13], [ 14 ][14]) theories of consciousness. The former claims that reverberating activity in sensory areas suffices for consciousness, whereas the latter claims that a second, higher-order brain state must represent or “point at” these first-order sensory activations for them to be consciously experienced. Both controversies are the types of theoretical disagreements that are currently being empirically tested by use of the adversarial collaboration approach. One of these collaborations, the COGITATE consortium (Collaboration On GNW and IIT: Testing Alternative Theories of Experience), is collecting data and has recently released a detailed preregistered report that outlines the methods, predictions, and planned analyses (). These experiments were designed by neuroscientists and philosophers who are not directly associated with the theories but are in close collaboration with advocates from each theory. The experiments are being conducted in six independent laboratories. Briefly, one of the experimental designs involves an engaging video game with seen and unseen stimuli in the background to determine whether neural correlates of the visual experience are present irrespective of the task. In another experiment, stimuli are shown for variable durations to investigate for how long the neural correlate of the visual experience exists. Neuronal activity in human subjects is measured with both invasive and noninvasive methodologies, from functional magnetic resonance imaging and simultaneous magnetoencephalography and electroencephalography to invasive electrocorticography, and is integrated across methodologies to test the theories' predictions. These focus on two key questions: Where are the anatomical footprints of consciousness in the brain: Are they located in a posterior cortical “hot zone” ([ 15 ][15]) advocated by the IIT, or is the prefrontal cortex necessary ([ 4 ][4]) as predicted by the GNWT? And, how are conscious percepts maintained over time: Is the underlying neural state maintained as long as the conscious experience lasts, in line with the IIT, or is the system initially ignited and then decays and remains silent until a new ignition marks the onset of a new percept, as the GNWT holds (see the figure)? Once the brain data are collected and analyzed, they will be made available to anyone. Relying on adversarial dialogue and collaboration, open science practices, standardized protocols, internal replication, and team science, these initiatives aim to promote empirical progress in the field of consciousness and to change the sociology of scientific practice in general. Solving big questions may require “big science” because such questions are more likely to be solved in unison rather than through isolated, parallel, small-scale attempts. The adversarial collaboration approach builds on the success of large-scale collaborative institutes (such as the Allen Institute for Brain Science) and projects such as the Human Connectome Project or the International Brain Laboratory in neuroscience, which were preceded by initiatives in physics such as the Large Hadron Collider at the European Organization for Nuclear Research (CERN) or the Laser Interferometer Gravitational-Wave Observatory (LIGO) experiment. With this series of adversarial collaborations, neuroscientists will get closer to understanding consciousness and how it fits into the physical world while improving scientific practices along the way. As for the initial theories undergoing this approach, it may be that neither the GNWT nor the IIT are quite correct. No matter the outcome, the field can use the results to make progress in framing new thinking about consciousness and testing other potential theories in the same way. The problem of consciousness will surely remain difficult, but understanding the ancient mind-body problem will become a little bit easier. 1. [↵][16]1. D. J. Chalmers , J. Conscious. Stud. 2, 200 (1995). [OpenUrl][17] 2. [↵][18]1. T. Bayne et al ., Trends Neurosci. 43, 6 (2020). [OpenUrl][19][PubMed][20] 3. [↵][21]1. F. Crick, 2. C. Koch , Nat. Neurosci. 6, 119 (2003). [OpenUrl][22][CrossRef][23][PubMed][24][Web of Science][25] 4. [↵][26]1. G. A. Mashour, 2. P. Roelfsema, 3. J. P. Changeux, 4. S. Dehaene , Neuron 105, 776 (2020). [OpenUrl][27] 5. 1. G. Tononi, 2. M. Boly, 3. M. Massimini, 4. C. Koch , Nat. Rev. Neurosci. 17, 450 (2016). [OpenUrl][28][CrossRef][29][PubMed][30] 6. [↵][31]1. R. Brown et al ., Trends Cogn. Sci. 23, 754 (2019). [OpenUrl][32][CrossRef][33][PubMed][34] 7. [↵][35]1. V. A. F. Lamme , Cogn. Neurosci. 1, 204 (2010). [OpenUrl][36][CrossRef][37][PubMed][38][Web of Science][39] 8. [↵][40]1. A. Demertzi et al ., Sci. Adv. 5, eaat7603 (2019). 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[OpenUrl][69] Acknowledgments: COGITATE is supported by a grant from the Templeton World Charity Foundation (TWCF) ([www.templetonworldcharity.org/accelerating-research-consciousness-our-structured-adversarial-collaboration-projects][70]). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of TWCF. L.M. is a Canadian Insititute for Advanced Research Tanenbaum Fellow in the Brain, Mind, and Consciousness program. C.K. thanks the Allen Institute founder, Paul G. Allen, for his vision, encouragement, and support. The authors thank D. Potgieter for championing the adversarial collaboration concept and acknowledge the COGITATE consortium: K. Bentz, H. Blumenfeld, D. Chalmers, F. de Lange, S. Dehaene, S. Devore, F. Fallon, O. Ferrante, U. Gorska, R. Hirschhorn, O. Jensen, A. Khalaf, C. Koch, C. Kozma, G. Kreiman, A. Lepauvre, L. Liu, H. Luo, L. Melloni, L. Mudrik, M. Pitts, D. Richter, G. Tononi. 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