Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike traditional feature engineering, which requires manual crafting of features, representation learning aims to learn features that are more useful and relevant for tasks such as classification, prediction, and clustering. This approach is integral in the performance of deep learning models, where layers of representation are learned hierarchically to capture increasingly abstract features of the data (Bengio et al., 2013). The importance of representation learning lies in its ability to make complex data more accessible for machine learning algorithms. By learning meaningful representations, models can improve generalization to unseen data and reduce the reliance on domain-specific knowledge, thus enabling the application of machine learning in more diverse and complex domains (LeCun et al., 2015). Techniques such as autoencoders, word embeddings, and convolutional neural networks are prime examples of how representation learning has revolutionized tasks in natural language processing, computer vision, and beyond (Goodfellow et al., 2016). As the field progresses, advancements in representation learning continue to enhance the capabilities of machine learning models, driving innovation in areas such as transfer learning, where representations learned in one context are adapted for use in another, and in unsupervised learning, where representations are learned without explicit labels (Radford et al., 2021). These developments underscore the growing significance of representation learning in shaping the future of artificial intelligence.

In the domain of autonomous vehicles (AVs), decision-making is a critical factor that significantly influences the efficacy of autonomous navigation. As the field progresses, the enhancement of decision-making capabilities in complex environments has become a central area of research within data-driven methodologies. Despite notable advances, existing learning-based decision-making strategies in autonomous vehicles continue to reveal opportunities for further refinement, particularly in the articulation of policies and the assurance of safety. In this study, the decision-making challenges associated with autonomous vehicles are conceptualized through the framework of the Constrained Markov Decision Process (CMDP) and approached as a sequence modeling problem. Utilizing the Generative Pre-trained Transformer (GPT), we introduce a novel decision-making model tailored for AVs, which incorporates entropy regularization techniques to bolster exploration and enhance safety performance. Comprehensive experiments conducted across various scenarios affirm that our approach surpasses several established baseline methods, particularly in terms of safety and overall efficacy.

This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.

Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning (ML) in this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing them according to the disaster management cycle. We compile a list of existing benchmark datasets introduced in the past five years. We propose a web platform - NADBenchmarks - where researchers can search for benchmark datasets for natural disasters, and we develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions for topics where they can contribute new benchmark datasets.

Half a year has passed since my book "Deep Reinforcement Learning Hands-On" has seen the light. It took me almost a year to write the book and after some time of rest from writing I've discovered that explaining RL methods and turning theoretical papers into working code is a lot of fun for me and I don't want to stop. Luckily, RL domain is evolving, so, there are lots of topics to write about. In mass perception, Deep Reinforcement Learning is a tool to be used mostly for game playing. This is not surprising, given the fact, that historically, the first success in the field was achieved in Atari game suite by Deep Mind in 2015. Atari benchmark suite turned out to be very successful for RL problems and, even now, lots of research papers are using it for demonstrating the efficiency of their methods. As the RL field progresses, the classical 53 Atari games continue to become less and less challenging (at the time of writing more than half of games are solved with super-human accuracy) and researches turn to more complex games, like StarCraft and Dota2. But this bias towards games creates a false impression "RL is about playing games'', which is very far from the truth. In my book, published in June 2018, I've tried to counterbalance this by accompanying Atari games with the examples from other domains, including stock trading (chapter 8), chatbots and NLP problems (chapter 12), web navigation automation (chapter 13), continuous control (chapters 14…16) and boards games (chapter 18). In fact RL having very flexible MDP model potentially could be applied to a wide variety of domains, where computer games is just one convenient and spectacular example of the complicated decision making. In this article I've tried to write a detailed description of the recent attempt to apply RL to a field of combinatorial optimisation. The paper discussed was published by the group of researchers from UCI (University of California, Irvine) and called "Solving the Rubik's Cube Without Human Knowledge''.

Looking back, 2020 and the Covid-19 pandemic has been extremely difficult and disruptive to business and our personal lives. However, 2020 was not only deleterious – at least not with regard to technology. In many technology fields progress has accelerated significantly. Two of these areas are artificial intelligence (AI) and robotics, which will play a prominent role in 2021 and following years. Over the last few years AI has grown in importance in a wide variety of fields such as healthcare, bioscience, education, transport, marketing, finance, cybersecurity and many more.

We are looking for someone inquisitive and keen to make an impact using innovative analytical methods for our client. You must have a keen interest in machine learning and be ready to expand your skills as the field progresses. Whilst you enjoy research and proposing solutions to the rest of the team, you keep an equal focus on delivering business value. Equally, you will enjoy learning about the business needs and working with a range of stakeholders.

Artificial intelligence is complicated; in this field, progress is, accordingly, difficult for the layman. The make to the Alphabet Holding company belonging to the AI Company deep mind has done in the past more frequently by in-game competitions against people the Superiority of its developments in order to demonstrate research progress. Up to this proof of the dominance in the year 2015, the traditional Board game was considered to be too complicated for machines. And the South Korean Lee Sedol as a more powerful opponent who was at least 18 Times champion of the world, and because of its unconventional and creative game in his home country a national hero. Although no one would be offended that a car can cover a distance faster than a human.

Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.

Currently, artificial intelligence (AI) technologies are able to exhibit seemingly-human traits. Some are intentionally humanoid, and others perform tasks that we normally associate strictly with humanity -- songwriting, teaching, and visual art. But as the field progresses, companies and developers are re-thinking the basis of artificial intelligence by examining our own intelligence and how we might effectively mimic it using machinery and software. IBM is one such company, as they have embarked on the ambitious quest to teach AI to act more like the human brain. Many existing machine learning systems are built around the need to draw from sets of data.