The paper describes an implementation of deep reinforcement learning through self-supervised learning within the proof assistant HOL4. A close interaction between the machine learning modules and the HOL4 library is achieved by the choice of tree neural networks (TNNs) as machine learning models and the internal use of HOL4 terms to represent tree structures of TNNs. Recursive improvement is possible when a given task is expressed as a search problem. In this case, a Monte Carlo Tree Search (MCTS) algorithm guided by a TNN can be used to explore the search space and produce better examples for training the next TNN. As an illustration, tasks over propositional and arithmetical terms, representative of fundamental theorem proving techniques, are specified and learned: truth estimation, end-to-end computation, term rewriting and term synthesis.

Internet services like social media, online discussion forums, and crowdsourcing marketplaces have transformed how people participate in the information ecology and digital economy. These services empower mostly urban, affluent, and literate people, and improve their reach to information and instrumental needs. However, these services currently exclude billions of people worldwide who are too poor to afford Internet-enabled devices, too remote to access the Internet, or too low literate to navigate the mostly text-driven Internet. In India and Pakistan alone, there are nearly 1.1 billion people offline. Although 70% of their populations have access to mobile phones, most people still use basic or feature phones, making it difficult to extend existing Internet services on these devices running custom operating systems.

Theoretical computer science has been a vibrant part of computing research in India for the past 30 years. India has always had a strong mathematical tradition. One could also argue that in the 1980s and 1990s, theory offered a unique opportunity to keep up with international research in computing despite limited access to state-of-the-art hardware. The Annual International Conference Foundations of Software Technology and Theoretical Computer Science (FSTTCS) was launched in 1981. FSTTCS2 allowed Indian researchers a natural opportunity to interact with leading academics worldwide.

In June, Amazon announced it was close to being able to offer for package deliveries by drone for its Prime Air service. That same month, Uber said it plans to test food delivery by aerial drone in crowded cities. And drone delivery company Flytrex already touts the ability to deliver drinks via unmanned vehicle on the golf course. Despite such announcements, drones are not crowding the skies over major cities and population centers just yet. But that may be about to change.

I have always been drawn to books and argumentation. In primary school, I was an avid debater, representing my school in competitions and winning laurels. In my early teens, my parents deemed it necessary to ban me from reading novels, as I would do little else. Looking back now, I guess this background foreshadowed my choice of a career as a lawyer. No one particularly influenced my decision; law seemed a natural fit and an easy choice for me.

"Call of Duty: Modern Warfare" releases October 25th and is anticipated to be the franchise's best game yet. Can a video game be too real? That's a concern being raised about "Call of Duty Modern Warfare," the latest salvo in the multibillion-dollar video game series. The new game, out Friday, has one scene set in a London townhouse known to harbor terrorists where British special operations forces are investigating. Inside, they find several people dressed as civilians.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

It is originally formulated as a hierarchical generative model: a document is generated from a mixture of topics, and a word in the document is generated by first choosing a topic from a document-specific distribution, and then choosing the word from the topic-specific distribution. The main difficulty of topic modeling is the optimization problem, which is NPhard in the worst case due to the intractability of the posterior inference. Existing methods aim to find approximate solutions to the difficult optimization problem, which falls into the framework of matrix factorization. Matrix factorization based topic modeling maps documents into a low-dimensional semantic space by decomposing the documents into a weighted combination of a set of topic distributions: D CW where D (:,d) represents the d -th document which is a column vector over a set of words with a vocabulary size of v, C (:,g) denotes the g -th topic which is a probability mass function over the vocabulary, and W ( g,d) denotes the probability of the g -th topic in the d -th document.

The famous St Petersburg Paradox shows that the theory of expected value does not capture the real-world economics of decision-making problem. Over the years, many economic theories were developed to resolve the paradox and explain the subjective utility of the expected outcomes and risk aversion. In this paper, we use the concept of the net utility to resolve the St Petersburg paradox. The reason why the principle of absolute instead of net utility does not work is because it is a first order approximation of some unknown utility function. Because the net utility concept is able to explain both behavioral economics and the St Petersburg paradox it is deemed a universal approach to handling utility. Finally, this paper explored how artificial intelligent (AI) agent will make choices and observed that if AI agent uses the nominal utility approach it will see infinite reward while if it uses the net utility approach it will see the limited reward that human beings see.

Machine Intelligence For You (MIFY) is a Benin company which is specialized in Artificial Intelligence, Internet-of-things, Embedded Systems, and their applications. It is the organizer of MIFY Artificial Intelligence Contest (MAIC). MAIC is a yearly international artificial intelligence competition in which the participants find the best algorithm to play a turn-taking strategy game with a time limit for each decision. This game is a society game from Africa and through the world. By this way, MIFY aims to promote artificial intelligence and these games. MAIC is organized in 3 main phases: Group phase, Semi-final, and Final.