Problem Solving


Artificial Intelligence: What everyone should know!

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Artificial Intelligence is a topic that has been getting a lot of attention, mostly because of the rapid improvement that this field has undertaken. Amazing innovations today, are setting foundations for amazing achievements such as medical Research and even Flying Cars. Back in the 1950s, the fathers of the field Minsky and McCarthy described artificial intelligence to be any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. As you can see, this is a fairly broad description so, nowadays, everything associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity is described as AI. Now that we realized what AI actually means, let's find out what is it used for today!


Look out: Here comes the next wave of smarter, nimbler and more collaborative AI - Digirupt IO

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When many think about the progress of AI and its impact on work, they envision a world where the robots and software thinking machines do all of the work and there's little room left for the work humans used to do. It's certainly not the future for the AI and human workforce that the Defense Advanced Research Projects Agency (DARPA) sees. DARPA is the agency that helped usher in the Internet, as well as the original expert systems of the 1960s through 1980s, as well as the big data analysis and machine learning systems that lay the foundation for natural language processing, self-driving cars, personal assistant bots. Now DARPA is leading the efforts to make AI and humans even more collaborative co-workers. AI has proven some of its value in the form of very targeted and specialized systems.



Google open-sources PlaNet, an AI agent that learns about the world from images

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But it's not always practical; model-free approaches, which aim to get agents to directly predict actions from observations about their world, can take weeks of training. Model-based reinforcement learning is a viable alternative -- it has agents come up with a general model of their environment they can use to plan ahead. But in order to accurately forecast actions in unfamiliar surroundings, those agents have to formulate rules from experience. Toward that end, Google in collaboration with DeepMind today introduced the Deep Planning Network (PlaNet) agent, which learns a world model from image inputs and leverages it for planning. It's able to solve a variety of image-based tasks with up to 5,000 percent the data efficiency, Google says, while maintaining competitiveness with advanced model-free agents.


Enabling Endless Possibilities, AI and IoT Also Demand Focus

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The prospect of autonomous shuttles becoming mainstream in the not-too-distant future led Bosch engineers to wonder how to handle potential problems that arise in such vehicles when no person is physically present to address them. "That's where this idea around in-vehicle sensing came," Mansuetti said. "When you think about transportation as a service now, or mobility as a service, what are all those things that could happen?" A passenger could leave a smartphone or wallet behind, step into a dirty vehicle or a physical confrontation could occur between two passengers in the vehicle. "When nobody's there, then you just start thinking about these things."


Inducing Sparse Coding and And-Or Grammar from Generator Network

arXiv.org Artificial Intelligence

We introduce an explainable generative model by applying sparse operation on the feature maps of the generator network. Meaningful hierarchical representations are obtained using the proposed generative model with sparse activations. The convolutional kernels from the bottom layer to the top layer of the generator network can learn primitives such as edges and colors, object parts, and whole objects layer by layer. From the perspective of the generator network, we propose a method for inducing both sparse coding and the AND-OR grammar for images. Experiments show that our method is capable of learning meaningful and explainable hierarchical representations.


Problem-solving males become more attractive to female budgerigars

Science

Darwin proposed that mate choice might contribute to the evolution of cognitive abilities. An open question is whether observing the cognitive skills of an individual makes it more attractive as a mate. In this study, we demonstrated that initially less-preferred budgerigar males became preferred after females observed that these males, but not the initially preferred ones, were able to solve extractive foraging problems. This preference shift did not occur in control experiments in which females observed males with free access to food or in which females observed female demonstrators solving these extractive foraging problems. Our results suggest that direct observation of problem-solving skills increases male attractiveness and that this could contribute to the evolution of the cognitive abilities underlying such skills.


Alexa Can Help Kids With Homework, But Don't Forget Problem-Solving Skills

NPR

How do virtual assistants like Alexa affect children's learning experiences? Some experts say easy answers delivered by technology can hurt the development of problem-solving skills in kids. How do virtual assistants like Alexa affect children's learning experiences? Some experts say easy answers delivered by technology can hurt the development of problem-solving skills in kids.


Here's Why Machine Learning Wins Hands Down Against Conventional Programming

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Ever since its commencement, machine learning has been on a quest to transform the programming industry, in the most fundamental ways. It has been dubbed as the'part of AI that works'. According to PayScale, the average pay for a data scientist, IT with Machine Learning skills is INR 855,503 per year. James Scott, Senior Fellow, Institute for Critical Infrastructure Technology, had written, "Signature-based malware detection is dead. Machine learning based Artificial Intelligence is the most potent defense to the next gen adversary and the mutating hash."


Problem solving with "AI Challenger Global AI Contest"

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In this article I will share my experience solving a video classification problem in a Chinese machine learning competition. There are a lot of data science platforms for competitors. We used to think about Kaggle - the most popular one. Anyway, there is a number of other platforms that provide data scientists with challenging tasks, and it is a good time to explore them. This is why me and my teammate Alexey Grigorev entered Short video real-time classification competition at Chinese platform AI Challenger.