Year One - Your first year will cover the foundational skills of software engineering and AI, including an introduction to computer graphics and practical mathematical skills. You'll develop an understanding of basic knowledge representation, problem solving techniques and architectures used to build intelligent systems. You'll extend the statistical and mathematical concepts covered in year 1, while discovering machine learning principles and exploring the vital field of human-centred design. Year Three - Expand your technical knowledge and round out your skill set with a focus on data mining, visualisation and creative enterprise. In your final semester you'll explore intuitive approaches to AI, with advanced tech-work integrated learning culminating in the production of an industry-standard capstone project.

General Definition: Artificial intelligence is a branch of computer science, which is concerned about the study of mechanisms of intelligent human behavior. It is done by simulation using artificial artefacts, usually with computer programs on a calculator (computer simulation). Realistic definition: The general meaning of AI suffers from the fact that the terms "intelligence" and "intelligent human behavior" themselves are not yet well defined and understood. On the other hand, AI is also a tool that can be used to empirically test theories of intelligence. The execution of programs on computers represents empirical experiments.

Fox News Flash top headlines for Oct. 24 are here. Check out what's clicking on Foxnews.com The owner of the Rubik's Cube has lost an appeal to regain the European Union trademark rights to the classic puzzle's iconic shape in a new twist to the ongoing legal drama. Rubik's Brand Ltd. lost the protection rights to the puzzle's shape in 2017, after the EU's top court ruled that law prevents the firm from having "a monopoly on technical solutions or functional characteristics of a product," Bloomberg reported. The EU General Court in Luxembourg upheld that decision on Thursday.

OpenAI, a non-profit co-founded by Elon Musk, recently unveiled its newest trick: A robot hand that can'solve' Rubik's Cube. Whether this is a feat of science or mere prestidigitation is a matter of some debate in the AI community right now. In case you missed it, OpenAI posted an article on its blog last week titled "Solving Rubik's Cube With a Robot Hand." Based on this title, you'd be forgiven if you thought the research discussed in said article was about solving Rubik's Cube with a robot hand. Don't get me wrong, OpenAI created a software and machine learning pipeline by which a robot hand can physically manipulate a Rubik's Cube from an'unsolved' state to a solved one. But the truly impressive bit here is that a robot hand can hold an object and move it around (to accomplish a goal) without dropping it.

Once again, a robot can do something I cannot do. Researchers at the artificial intelligence lab OpenAI just revealed that its humanoid robotic hand can solve a Rubik's cube. The researchers utilized a pair of neural networks to make it happen. The team has been working on this project, named Dactyl, since the middle of 2017, and they felt showing their robotic hand could solve a Rubik's cube would show it had adequate dexterity. It can now solve the cube about 60 percent of the time.

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties; in turn, they are fundamental operators for building deep GNNs that learn effective, hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarsened versions of a graph by leveraging on its topology only. During training, the GNN learns new representations for the vertices and fits them to a pyramid of coarsened graphs, which is computed in a pre-processing step. As theoretical contributions, we first demonstrate the equivalence between the MAXCUT partition and the node decimation procedure on which NDP is based. Then, we propose a procedure to sparsify the coarsened graphs for reducing the computational complexity in the GNN; we also demonstrate that it is possible to drop many edges without significantly altering the graph spectra of coarsened graphs. Experimental results show that NDP grants a significantly lower computational cost once compared to state-of-the-art graph pooling operators, while reaching, at the same time, competitive accuracy performance on a variety of graph classification tasks.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle.

Deductive formalisms have been strongly developed in recent years; among them, Answer Set Programming (ASP) gained some momentum, and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to AI; indeed, in several contexts, other approaches result to be more useful. Typical Bioinformatics tasks, for instance classification, are currently carried out mostly by Machine Learning (ML) based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.

OpenAI has come up with a new robot capable of solving a Rubik's Cube with a single hand. The AI-based company trained neural networks in simulation using reinforcement learning to make this achievement possible. The company has been working on this project since May 2017 and has now achieved its goal marking this as a milestone towards its progress in the field of AI. The time taken by the robotic hand varies depending on how the cube is shuffled but on average, it takes about four minutes to solve the puzzle. However, it is worth noting that this is not the first-ever robot that managed to solve the Rubik's cube.

Medical data analytics startup Healx raised $56M from Atomico and others. Standard Cognition patented an inventory management system. Here's what went down in artificial intelligence this week. Become a CB Insights customer. If you're already a customer, log in here.