Computer Literacy & Computer Science


Teaching computers to plan for the future

#artificialintelligence

As humans, we've gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of "time" – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question. Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent.


A computer program that learns to "imagine" the world shows how AI can think more like us

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Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world.


A computer program that learns to "imagine" the world shows how AI can think more like us

#artificialintelligence

Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world.


Artificial Intelligence and the Economy Tackling hearing loss

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These models are computer algorithms, or smart apps, that seek to give computers the ability to learn like children for a variety of tasks. Here, we highlight how an author's work may solve a particular set of real-world tasks or problems. By doing this, we aim to foster more and more machine, learning works, to be done by more and more Jamaican people. Today, we'll highlight the machine-learning work, a paper/algorithm called'Modelling Sensorineural Hearing-impaired Listeners' Perception of Speaker Intelligibility in Noise", by UWI lecturers Dr Lindon W. Falconer, Dr AndrÈ Coy, and their overseas colleague, Professor Jon Barker. Jordan: How would you describe your work?


Deep learning methods guide computers to insect identification The Western Producer

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Three-year olds are known for a long list of bad habits: biting other kids, throwing toys at their mom and answering every question with "no." Despite those irrational behaviours, they are also smart. Show a three-year-old girl a van, a truck and a car, and she will quickly learn to identify the three types of vehicles. Digvir Jayas, vice-president of research at the University of Manitoba and grain storage expert, said computers aren't as smart as three- year olds, at least when it comes to computer vision and identifying objects. But scientists are now teaching computers to think like a three-year old, so the machines can see the differences between one object and another.


Toil and trouble: How 'Macbeth' could teach computers to think - The Boston Globe

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Patrick Winston's computer is learning about revenge, ambition, and murder. It knows that victory can make you happy. But it also knows you can't be happy if you're dead. The computer had to learn these things in order to read "Macbeth" -- or, rather, an extremely truncated version of Shakespeare's blood-soaked Scottish tragedy. At just 37 sentences, the rough summary reduces the Bard's immortal poetics to such clunkers as, "Witches had visions and danced" and "Lady Macbeth has bad dreams."



Computational Neuroscience Coursera

@machinelearnbot

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.


What's New in Deep Learning Research: Teaching Computers How to Code

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Writing programs that can create programs have been an elusive goal of artificial intelligence(AI) research for many years. As a matter of fact, the idea that AI agents can create their own programs if often seem as one of the differentiators of general AI vs. narrow AI. So important is this goal, that AI researchers have created a specific area of research known as Program Synthesis that focuses on addressing those challenges. The idea behind program synthesis is to create AI agents that can generate programs that match a given specification. We often use primitive versions of this technique when we take advantage of, for instance, the Flash Fill feature in Microsoft Excel.


Newly Developed Machine Learning Approach Could Accelerate Bioengineering

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Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. The new approach is much faster than the current way to predict the behaviour of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.