The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In particular it is shown that the combination with large commensense knowledge within a single reasoning system demands for an efficient and powerful association technique. This approach is also used for modelling mind-wandering and the Remote Associates Test (RAT) for testing creativity. In a general discussion we show implications of the model for a broad variety of cognitive phenomena including consciousness.

The study of generalisation in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We provide a unifying formalism and terminology for discussing different generalisation problems, building upon previous works. We go on to categorise existing benchmarks for generalisation, as well as current methods for tackling the generalisation problem. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in generalisation, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for generalisation, and we recommend building benchmarks in underexplored problem settings such as offline RL generalisation and reward-function variation.

The artificial intelligence (AI) that is already all around us cannot safely drive a car by himself, nor can they write compelling scripts. Still, every day the research to make them more capable gets new results, and in some cases, anyone with a computer and an Internet connection can help teach them something. Both Google and Microsoft have put online some experiments (interpretable as "games") with which to try to understand how to teach a computer to learn, or simply to get an idea of how smart some AI systems that already exist are. Google's can be found on the A.I. Experiments platform, while Microsoft's were mostly created by the Microsoft Garage research community. They are, of course, not the first such experiments.

As a university student, Abigail Annkah fell in love with mathematics, which soon led to her interest in artificial intelligence. After graduating from the African Institute for Mathematical Sciences, Abigail made it through the competitive process to become an AI resident at Google Research, Accra. After her residency, Google offered her a job and she’s now a research software engineer working on several high-profile projects.As Google grows its presence in Accra, we spoke to Abigail about the mapping project that motivates her, starting a new job while becoming a mother and the importance of inspiring young girls to enter STEM careers.How did your science background lead you to Google?I did my undergraduate degree in Bachelor of Science Statistics at the University of Ghana, finishing with a combined major in Mathematics and Statistics. During the second year of study, I stumbled upon Computational Maths, leading to my first taste of coding. I started taking extra credit courses, which really kickstarted my entry into AI. Then I joined the first cohort of the African Masters of Machine Intelligence program at African Institute for Mathematical Sciences with the support of Google and Facebook. The program intends to bridge the AI education gap in Africa and strengthen the growing data science ecosystem in the region — this was my first exposure to the world of Machine Learning.

Prior to this pandemic year of 2021, the term "supply chain" didn't raise many red flags for most consumers, frankly because they didn't have to think about it. Buyers were so accustomed to getting things on schedule that it rarely became a regular topic of conversation. That all changed in the second half of 2021. With the pandemic slowing down production lines and transportation in faraway places, the term "supply chain" is now regularly in headlines. This has been the greatest shock to global supply chains in modern history.

All human actions are based on anticipated futures. We cannot know the future because it does not exist yet, but we can use our current knowledge to imagine the future and make them happen. The better we understand the present and the history that has created it, the better we can understand the possibilities of the future. To appreciate the opportunities and challenges that artificial intelligence (AI) creates, we need both a good understanding of what AI is today and what the future may bring when AI is widely used in society. AI can enable new ways of learning, teaching, and education, and it may also change society in ways that pose new challenges for educational institutions.

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise. We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing QML and quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates. Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real QC. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD. We also open-source TorchQuantum (https://github.com/mit-han-lab/torchquantum) for fast training of parameterized quantum circuits to facilitate future research.

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Currently, most social robots interact with their surroundings and humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human-robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general-purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots.

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.