It was previously thought that children younger than 7 couldn't find efficient solutions to complex problems, but new research suggests that much earlier, children can happen upon known sorting algorithms used by computer scientists Complex problem-solving may arise earlier in a child's development than previously thought Children as young as 4 years old are capable of finding efficient solutions to complex problems, such as independently inventing sorting algorithms developed by computer scientists. The scientists behind the finding say these skills emerge far earlier than previously thought, and should force a rethink of developmental psychology. Take control of your brain's master switch to optimise how you think Experiments carried out by Swiss psychologist Jean Piaget and widely popularised in the 1960s asked children to physically sort a collection of sticks into length order, a task Piaget called seriation. His tests revealed until around age 7, children applied no structured strategies; they approached the problem in messy ways through trial and error. But new research by Huiwen Alex Yang and his colleagues at University of California, Berkeley, shows a minority of even 4-year-old children can develop algorithmic solutions to the same task, and by 5 years old more than a quarter are capable of the same thing.

The solution of a linear system appears in the vast majority of Linear Algebra operations [1], as well as related numerical methods, in statistical modelling, machine learning algorithms, numerical solution of differential equations, etc. These algorithms are essential for applications in almost any discipline involving computations, such as Engineering, Physics, Data Science, Finance, etc., among others [2]. The history of attempts to solve a Linear System is long, comprising the well-known Gaussian Elimination Algorithm for square systems [3,4]. A variety of types occur when formulating a linear system, such as systems with equal number of equations and unknowns (formulated with square input matrices), or systems with a few coefficients compared to the number of Equations (so called tall or underdetermined systems [5-7]), where an exact solution does not occur and we try to identify the best possible solution in terms of residual errors, as well as wide (or overdetermined) systems [8-10], with more coefficients than equations, which have infinite solutions, and we try to identify one. These both are non-square systems. Accordingly, the input matrix can be dense [11,12], with all the elements non zeros, or sparse [13, 14], with a few non zeros elements.

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr's three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the Moral Tractability Thesis (MTT).

A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event.

A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a problem class, no matter its representation or complexity -- like algorithms in computer science. Neural networks are powerful models for processing sensory data, discovering hidden patterns, and learning complex functions, but they struggle to learn such iterative, sequential or hierarchical algorithmic strategies. Extending neural networks with external memories has increased their capacities in learning such strategies, but they are still prone to data variations, struggle to learn scalable and transferable solutions, and require massive training data. We present the Neural Harvard Computer (NHC), a memory-augmented network based architecture, that employs abstraction by decoupling algorithmic operations from data manipulations, realized by splitting the information flow and separated modules. This abstraction mechanism and evolutionary training enable the learning of robust and scalable algorithmic solutions. On a diverse set of 11 algorithms with varying complexities, we show that the NHC reliably learns algorithmic solutions with strong generalization and abstraction: perfect generalization and scaling to arbitrary task configurations and complexities far beyond seen during training, and being independent of the data representation and the task domain.

Advances in computer hardware and software and engineering methodologies in the 1960s and 1970s led to an increased use of computers by engineers. In design, this use has been limited almost exclusively to algorithmic solutions such as finite-element methods and circuit simulators. However, a number of problems encountered in design are not amenable to purely algorithmic solutions. These problems are often ill structured (the term ill-structured problems is used here to denote problems that do not have a clearly defined algorithmic solution), and an experienced engineer deals with them using judgment and experience. AI techniques, in particular the knowledge-based system (KBS) technology, offer a methodology to solve these ill-structured design problems. In this article, we describe several research projects that utilize KBS techniques for design automation.

A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-augmented networks, that is inspired by the von Neumann and Harvard architectures of modern computers. This architecture enables the learning of abstract algorithmic solutions via Evolution Strategies in a reinforcement learning setting. Applied to Sokoban, sliding block puzzle and robotic manipulation tasks, we show that the architecture can learn algorithmic solutions with strong generalization and abstraction: scaling to arbitrary task configurations and complexities, and being independent of both the data representation and the task domain.

It's impossible to read about the future of healthcare without encountering two pixelated vowels that, together, represent the hopes and fears of an industry seeking more intelligent solutions. Though the field of artificial intelligence (AI) has been around since 1956, it has made precious few contributions to medical practice. Only recently has the hype of machine-based learning begun to merge with reality. Confusion surrounding AI--its applications in healthcare and even its definition--remains widespread in popular media. Today, AI is shorthand for any task a computer can perform just as well as, if not better than, humans.

Artificial intelligence and machine learning systems are gaining a lot of ground in healthcare, with everyone from tech giants like Google and Amazon to startup companies building systems for healthcare provider organizations. Claims about algorithms beating physicians at their job are made every month. But are all algorithms made equal? Will invest in machine learning result in meaningful gains for an organization? The Algorithm Science team at Partners Connected Health invests a great deal of time thinking about the right questions, working out potential pitfalls and developing best practices in evaluating machine learning based solutions.

It's impossible to read about the future of healthcare without encountering two pixilated vowels that, together, represent the hopes and fears of an industry seeking more intelligent solutions. Though the field of artificial intelligence (AI) has been around since 1956, it has made precious few contributions to medical practice. Only recently has the hype of machine-based learning begun to merge with reality. What Is Artificial Intelligence, Really? Confusion surrounding AI – its applications in healthcare and even its definition – remains widespread in popular media.