The interdisciplinary M.Sc. Program in Cognitive Systems combines courses from neural/connectionist and symbolic Artificial Intelligence, Machine Learning, and Cognitive Psychology, to explore the fundamentals of perception, attention, learning, mental representation, and reasoning, in humans and machines. The M.Sc. Program is offered jointly by two public universities in Cyprus (the Open University of Cyprus and the University of Cyprus) and has been accredited by the national Quality Assurance Agency. The program is directed by academics from the participating universities, and courses are offered in English via distance learning by an international team of instructors.

The Department of Engineering Mathematics at the University of Bristol is seeking an outstanding candidate to fill the role of Professor in Artificial Intelligence. You will have the opportunity to provide visionary leadership to the department and its staff, students, & partners, helping to strengthen and further develop our already impressive research and teaching programs in AI. Our Intelligent Systems Group supports the Faculty of Engineering's AI/Data Science Theme, fostering an inclusive environment for all.

The Department of Informatics at the University of Oslo, Norway is looking for candidates to fill two permanent positions as Associate Professors in Machine Learning. The positions can be affiliated to or interact with the Robotics and Intelligent Systems (ROBIN) group at the University. Candidates with a background in artificial intelligence/machine learning related to robotics or embedded systems are encouraged to apply. The candidates will be evaluated with respect to two different profiles: 1. Associate Professor in Ethical Considerations in Machine Learning: For this position, we are looking for a candidate with a research background in machine learning including applications and a track record in analysing aspects of machine learning methodology related to ethical considerations. 2. Associate Professor in Machine Learning: This position is expected to be offered to a candidate with a strong research background in machine learning including applications. Please note that this position is announced in Norwegian and with a requirement for candidates to have fluent oral and written communication skills in both English and a Scandinavian language.

Research fellowship opportunity in 'Methods and models for artificial intelligence and statistical and automatic learning for the innovation of business processes and decisions' (Metodi e modelli per l'intelligenza artificiale e l'apprendimento statistico e automatico per l'innovazione dei processi e delle decisioni aziendali). The fellowship is for 1 year, with the possibility of extension. The research grant is issued at the Department of Economics, Business, Mathematics and Statistics (DEAMS) of the University of Trieste (Italy), with the scientific coordinator being Prof. Nicola Torelli.

The Faculty of Computer Science of HSE University invites applications for full-time, tenure-track positions of Assistant Professor in all areas of computer science including but not limited to artificial intelligence, machine learning, computer vision, programming language theory, software engineering, system programming, algorithms, computation complexity, distributed and parallel computation, bioinformatics, human-computer interaction, and robotics. The successful candidate is expected to conduct high-quality research publishable in reputable peer-reviewed journals with research support provided by the University.

The PhD student will be part of the ANR project 'REFINED' involving the Laboratory of Embedded Artificial Intelligence in CEA List in Paris, the Multispeech research team In LORIA, Nancy, and the Hearing Institute in Paris. The project aims at studying new Deep Learning based methods to improve hearing acuity of ANSD patients. A cohort of ANSD volunteers will be tested to identify spectro-temporal auditory and extra-auditory cues correlated with the speech perception. Additionally, the benefits of neural networks will be studied. However, current artificial intelligence methods are too complex to be applied to processors with low computing and memory capacities: compression and optimization methods are needed.

The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.

The Technische Universität Braunschweig is offering a W3 Full Professorship for Artificial Intelligence in interactive Systems. The position holder is expected to have a strong background in Computer Science with a focus on Artificial Intelligence/​Machine Learning, specifically in the areas of Dependable AI and Explainable AI. The researcher is expected to combine the theoretical-methodological investigation and development of learning methods with applications in interactive intelligent systems. In teaching, topic-related courses in the areas of Artificial Intelligence and Machine Learning shall complement the Bachelor's and Master's degree programs of the Department of Computer Science. Participation in the academic self-administration of the university is expected as well as the willingness to actively shape computer science at the TU Braunschweig.

The successful candidate will work on learning algorithms for spiking neural networks in the international consortium of the international project 'Scalable Learning Neuromorphics'. We will develop in this project learning algorithms for spiking neural networks for memristive hardware implementations. This project aims to develop scalable Spiking Neural Networks (SNNs) by leveraging the integration of 3D memristors, thereby overcoming limitations of conventional Artificial Neural Networks (ANNs). Positioned at the intersection of artificial intelligence and brain-inspired computing, the initiative focuses on innovative SNN training methods, optimizing recurrent connections, and designing dedicated hardware accelerators. These advancements will uniquely contribute to scalability and energy efficiency. The endeavor addresses key challenges in event-based processing and temporal coding, aiming for substantial performance gains in both software and hardware implementations of artificial intelligence systems. Expected research outputs include novel algorithms, optimization methods, and memristor-based hardware architectures, with broad applications and potential for technology transfer.

The Center for Theoretical and Computational Neuroscience (CTCN) at Washington University in St Louis is recruiting a cohort of outstanding Postdoctoral Fellows to work at the interface between theoretical and experimental labs and help forge new collaborations. The Fellows will be part of a joint initiative between the Schools of Medicine, Engineering, and Arts and Sciences, and will collaborate with neuroscientists, mathematicians, physicists and engineers to find creative solutions to some of the most difficult problems currently facing neuroscience and artificial intelligence.

We are seeking highly qualified and motivated individuals for the positions of Assistant and Associate Professors in Artificial Intelligence (AI) and Machine Learning (ML). The successful candidate will join our esteemed faculty in the Institute for Advancing Intelligence (IAI), TCG Centre for Research and Education in Science and Technology (CREST), Kolkata, India, and contribute to our commitment to excellence in research, teaching, and academic services. TCG CREST has set up the campus in Sector V, Salt Lake City, Kolkata, India. State-of-the-art laboratories and research facilities for the individual Institutes, spacious classrooms and technology interventions for executing both off-line and on-line academic classes and programs, conference rooms, and other infrastructures provide the students and the faculty an ideal environment for creative exchanges and high-end research collaborations.

The project aims to implement neuromorphic multi-layer networks of leaky integrate-and-fire (LIF) neurons in cascade to a motorized event-based camera (DAVIS, DVS, https://inivation.com/technology/), to obtain artificial replicas of the early stages of an active vision system. Testing the models will involve the assessment of multiple and varying parameters captured under real-life and adaptive conditions. At functional level, the system will (1) consider the neural resources required to account for a range of linear/nonlinear early visual processes, and (2) provide the inference engines for relating the resulting visual representations to performance on psychophysical tasks. The visual performance of the resulting silicon model will be comparatively assessed with that of a typical human observer. The objective is twofold: on the one hand, we contribute a deeper understanding of visual processes, especially about predicting how early computation may reverberate through the sensory pathways eventually contributing to functional vision. On the other hand, we contribute to the definition of a new generation of perceptual machines to be used in robotics and in general in newly developed Artificial Intelligence systems.

The Faculty of Computer Science at UNamur has an open academic position in distributed systems. The Faculty is more than 50 years old and is located in Namur, an historical city at the center of Wallonia. We are looking for candidates that will create and contribute to collaborations in line with the Faculty's areas of interest (software engineering, data engineering, data science, artificial intelligence, security, formal methods, modeling...).

The position holder will be a member of the Hessian Center for Artificial Intelligence - hessian.AI and provides research at the Center and will also be a member of the Centre for Cognitive Science. The scientific focus of the position is on the computational and algorithmic modeling of behavioral data to understand the human mind. Exemplary research topics include computational level models of perception, cognition, decision making, action, and learning as well as extended behavior and social interactions in humans, algorithmic models that are able to simulate, predict, and explain human behavior, model-driven behavioral research on human cognition. The professorship is expected to strengthen the Hessian Center for Artificial Intelligence and TU Darmstadt’s Human Science department’s research focus on Cognitive Science. Depending on the candidate’s profile there is the opportunity to participate in joint research projects currently running at TU Darmstadt. This in particular includes the state funded cluster projects “The Adaptive Mind (TAM)” and “The Third Wave of Artificial Intelligence (3AI)”. In addition to excellent scientific credentials, we seek a strong commitment to teaching in the department’s Bachelor and Masters programs in Cognitive Science. Experience in attracting third-party funding as well as participation in academic governance is expected.

The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.

The Center for Brain Science (CBS) and Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University seek a tenure-track faculty member to lead an innovative research program working across the fields of Computational Neuroscience and Machine Learning to discover how brain computation can benefit artificial systems and how principles of computation and learning in artificial systems can be used to understand the brain. Current faculty use a variety of approaches to learn how brains compute and govern cognition and behavior. The successful candidate will be appointed an Institute Investigator within the Kempner Institute and will hold an academic appointment in an appropriate department in the life or physical sciences in the Faculty of Arts and Sciences at Harvard University.

The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The group is actively seeking a talented graduate student to join the team, who will expand the computational modeling framework Cobel-RL (https://doi.org/10.3389/fninf.2023.1134405) and use it to study how episodic memory might be used to learn to navigate.

SPKI is expanding, and is in search of a highly motivated data scientist / ML engineer who wants to contribute to the development and implementation of new artificial intelligence (AI) tools for health. The work will be done in a highly interdisciplinary environment, and you will collaborate with a team consisting of clinicians, scientists from the university and technologists, legal experts, industry partners, as well as personnel responsible for ICT, data security, privacy concerns and more. This environment also includes researchers at The Machine Learning Group and Visual Intelligence.

The Cooperative Graduate School Accessibility through AI-based Assistive Technology (KATE - www.kate.kit.edu) is a new cooperative and interdisciplinary graduate school between the Karlsruhe Institute of Technology (KIT) and the Hochschule Karlsruhe (HKA). The program revolves around investigating state-of-the-art artificial intelligence methods in order to improve the autonomy and participation of persons with special needs. Different dissertation topics ranging from AI-based methods for text, audio, and multimedia document processing, AI methods for interactive training and assistance systems, to investigating the consequences and ethical, legal, social, and societal implications of AI systems for people with disabilities will be offered. The sponsored persons will work on a selected topic scientifically in depth within the framework of their doctorate and will receive an overall view of all relevant topics - including medical causes as well as their effects, the needs of the target groups, AI-based approaches, ethics, technology assessment, and societal aspects - through the exchange within the doctoral college for this purpose.

Koç University College of Engineering invites applications for Full-time Faculty positions in Computer Science and Engineering starting in Fall 2024. The ideal candidates are expected to have a visionary research agenda with an exceptional track record in research and publication, demonstrating a deep commitment to academic excellence and innovation; together with a keen commitment to teaching and learning. Faculty members are expected to teach undergraduate and graduate courses in addition to maintaining a vigorous research program, collaborating across multiple disciplines, and leveraging the research infrastructure of Koç University such as Koç University Is Bank Artificial Intelligence Research Center (KUIS AI) and Koç University Translational Medicine Research Center (KUTTAM).

Applications are invited for an academic position in machine learning in the School of Informatics at the University of Edinburgh, as part of a continuing expansion in Machine Learning and Artificial Intelligence. The appointment will be full-time and open-ended. The successful candidate will have (or be near to completing) a PhD, an established research agenda and the enthusiasm and ability to undertake original research, and to lead a research group. They will show excellent teaching capability and engagement with academic supervision. We are seeking current and future leaders in the field. We seek candidates with research interests in the development of cutting-edge machine learning methods. Candidates will have a research interests in principled approaches to machine learning, machine learning for novel or critical applications, and/or the development of novel methods of wide applicability and with state-of-the-art capability.

POSITION 1, "Graph Representation Learning with Biomedical Applications": The use of Artificial Intelligence (AI) methodology is currently accelerating progress in the area of drug discovery at an impressive speed. Recent successes include the discovery of antibiotics using AI pipelines (Stokes et al., 2020; Liu et al. 2023) as well as the release of the already very impactful AlphaFold model which predicts the three dimensional structure of proteins (Jumper et al., 2021). This rapid scientific progress is also triggering increased industrial interest with Google’s Deepmind announcing the foundation of a new Alphabet subsidiary called Isomorphic Labs with the goal of industrialising AI-driven drug discovery. We are looking for a candidate willing to work in this exciting and dynamic space of scientific progress. Specifically, we would aim to involve the candidate in several projects in which we explore the potential of Graph Representation Learning methodology in the context of Biomedical applications. POSITION 2, "Multimodal Graph Generative Models": Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. The research to be conducted during this project will capitalize on the potential of graph generative models and recent relevant efforts in the Biomedical domain. We will investigate the challenges of multi modality in the context of defining architectures for graph generation under the proper prompt. We expect our designed architectures to be useful in different areas including power grid/telecom/social networks design.

Recent advances in artificial intelligence have led to new forms of misinformation, including highly realistic “deepfake” synthetic media. We conduct three experiments to investigate how and why retail investors react to deepfake financial news. Results from the first two experiments provide evidence that investors use a “realism heuristic,” responding more intensely to audio and video deepfakes as their perceptual realism increases. In the third experiment, we introduce an intervention to prompt analytical thinking, varying whether participants make analytical judgments about credibility or intuitive investment judgments. When making intuitive investment judgments, investors are strongly influenced by both more and less realistic deepfakes. When making analytical credibility judgments, investors are able to discern the non-credibility of less realistic deepfakes but struggle with more realistic deepfakes. Thus, while analytical thinking can reduce the impact of less realistic deepfakes, highly realistic deepfakes are able to overcome this analytical scrutiny. Our results suggest that deepfake financial news poses novel threats to investors.

Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Recently, artificial intelligence (AI) has become a global priority. Rapid and ongoing technological advancements in AI have prompted European legislative initiatives to regulate its use. In April 2021, the European Commission submitted a proposal for a Regulation that would harmonize artificial intelligence rules across the EU, including the law enforcement sector. Consequently, law enforcement officials await the outcome of the ongoing inter-institutional negotiations (trilogue) with great anticipation, as it will define how to capitalize on the opportunities presented by AI and how to prevent criminals from abusing this emergent technology.

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.

Epilepsy surgery is a safe but underutilised treatment for drug-resistant focal epilepsy. One challenge in the presurgical evaluation of patients with drug-resistant epilepsy are patients considered “MRI negative”, i.e. where a structural brain abnormality has not been identified on MRI. A major pathology in “MRI negative” patients is focal cortical dysplasia (FCD), where lesions are often small or subtle and easily missed by visual inspection. In recent years, there has been an explosion in artificial intelligence (AI) research in the field of healthcare. Automated FCD detection is an area where the application of AI may translate into significant improvements in the presurgical evaluation of patients with focal epilepsy. I will provide an overview of our automated FCD detection work, the Multicentre Epilepsy Lesion Detection (MELD) project and how AI algorithms are beginning to be integrated into epilepsy presurgical planning at Great Ormond Street Hospital and elsewhere around the world. Finally, I will discuss the challenges and future work required to bring AI to the forefront of care for patients with epilepsy.

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this talk, I will examine the validity and potential application of this seemingly intuitive link between consciousness and intelligence. I will do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST), and demonstrating that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we will turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Given this apparent trend, I will use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a unified model. I believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function.

AI embedded in real systems, such as in satellites, robots and other autonomous devices, must make fast, safe decisions even when the environment changes, or under limitations on the available power; to do so, such systems must be adaptive in real time. To date, edge computing has no real adaptivity – rather the AI must be trained in advance, typically on a large dataset with much computational power needed; once fielded, the AI is frozen: It is unable to use its experience to operate if environment proves outside its training or to improve its expertise; and worse, since datasets cannot cover all possible real-world situations, systems with such frozen intelligent control are likely to fail. Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature, and in particular neuroscience, for its underlying principles and mechanisms and then translates them to this new technology. Our presentation will introduce a number of state-of-the-art approaches to achieve AI adaptive learning, including from the DARPA’s L2M program and subsequent developments. Many environments are affected by temporal changes, such as the time of day, week, season, etc. A way to create adaptive systems which are both small and robust is by making them aware of time and able to comprehend temporal patterns in the environment. We will describe our current research in temporal AI, while also considering power constraints.

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture (VSA) scheme. We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

With the increasing development of automatic systems and artificial intelligence, face recognition is becoming increasingly important in forensic and civil contexts. However, face recognition has yet to be thoroughly empirically studied to provide an adequate scientific and legal framework for investigative and court purposes. This observation sets the foundation for the research. We focus on issues related to face images and the use of automatic systems. Our objective is to validate a likelihood ratio computation methodology for interpreting comparison scores from automatic face recognition systems (score-based likelihood ratio, SLR). We collected three types of traces: portraits (ID), video surveillance footage recorded by ATM and by a wide-angle camera (CCTV). The performance of two automatic face recognition systems is compared: the commercial IDEMIA Morphoface (MFE) system and the open source FaceNet algorithm.

As questions of race and justice have risen to the fore across the sciences, the ALBA Network has invited Dr Shakir Mohamed (Senior Research Scientist at DeepMind, UK) to provide a keynote speech on Artificial Intelligence and racism, and the implications for scientific research, that will be followed by a discussion chaired by Dr Konrad Kording (Department of Neuroscience at University of Pennsylvania, US - neuromatch co-founder)

The Interdisciplinary College is an annual spring school which offers a dense state-of-the-art course program in neurobiology, neural computation, cognitive science/psychology, artificial intelligence, machine learning, robotics and philosophy. It is aimed at students, postgraduates and researchers from academia and industry. This year's focus theme "Flexibility" covers (but not be limited to) the nervous system, the mind, communication, and AI & robotics. All this will be packed into a rich, interdisciplinary program of single- and multi-lecture courses, and less traditional formats.

Ample evidence suggests that the brain generates internal simulations of the outside world to guide our thoughts and actions. These mental representations, or cognitive maps, are thought to be essential for our very comprehension of reality. I will discuss what is known about the informational structure of cognitive maps, their neural underpinnings, and how they relate to behavior, evolution, disease, and the current revolution in artificial intelligence.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. Over the past years, we used a combination we used a combination of extracellular recordings, neuronal decoding approaches, and state of the art optogenetic manipulations to identify key neuronal elements and mechanisms controlling defensive fear responses. I will present an overview of our recent work ranging from analyses of dedicated neuronal types and oscillatory and synchronization mechanisms to artificial intelligence approaches used to decode the activity or large population of neurons. Ultimately these analyses allowed the identification of high dimensional representations of defensive behavior unfolding within prefrontal networks.

Ten of thousands of open reading frames (ORFs) are hidden within transcripts. They have eluded annotations because they are either small or within unsuspected locations. These are named alternative ORFs (altORFs) or small ORFs and have recently been highlighted by innovative proteogenomic approaches, such as our OpenProt resource, revealing their existence and implications in biological functions. Due to the absence of altORFs from annotations, pathogenic mutations within these are being ignored. I will discuss our latest progress on the re-analysis of large-scale proteomics datasets to improve our knowledge of proteomic diversity, and the functional characterization of a second protein coded by the FUS gene. Finally, I will explain the need to map the coding potential of the transcriptome using artificial intelligence rather than with conventional annotations that do not capture the full translational activity of ribosomes.

To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent. In this meeting we bring together experts from Mathematics, Artificial Intelligence and Neuroscience for a three day long hybrid meeting. We will have talks on mathematical tools in particular Topology to understand high dimensional data, explainable AI, how AI can help neuroscience and to what extent the brain may be using algorithms similar to the ones used in modern machine learning. Finally we will wrap up with a discussion on some aspects of neural hardware that may not have been considered in machine learning.

In this talk, Frans Cronje will be speaking about the journey towards autonomous manufacturing. He will demonstrate how artificial intelligence (AI) can be implemented to achieve a reduction in poor quality costs in manufacturing. The talk will showcase the power of applied AI.

The seminar will first outline the architecture of the human mind, specifying general and domain-specific mental processes. The place of causal reasoning and its relations with the other processes will be specified. Experimental, psychometric, developmental, and brain-based evidence will be summarized. The main message of the talk is that causal thought involves domain-specific core processes rooted in perception and served by special brain networks which capture interactions between objects. With development, causal reasoning is increasingly associated with a general abstraction system which generates general principles underlying inductive, analogical, and deductive reasoning and also heuristics for specifying causal relations. These associations are discussed in some detail. Possible implications for artificial intelligence and educational implications are also discussed.

Artificial intelligence (AI) and machine learning (ML) can facilitate new paradigms and solutions in almost every research field. Collaboration is essential to achieve tangible and concrete progress in impactful and meaningful AI and ML research, due to its transdisciplinary nature. Come and meet University of Pretoria (UP) academics that are embracing and exploring the opportunities that AI and ML offer to transcend the conventional boundaries of their disciplines. Join the discussion to debate this new frontier of opportunities and challenges that may enable you to look beyond the obvious, and discover new directions and opportunities that we may offer for tomorrow — together!

What does an executive at a South African Bank, a machine learning lead, and a CEO of an AI company have in common? They all will be on a panel talking about careers in Data Science, Machine Learning and Artificial Intelligence