algorithm


Machine translation, no match for humans: machines translate words, humans the underlying message University of Helsinki

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Many of us are familiar with Google Translate, translation applications for travellers' smartphones and the instruction manuals of various devices and products. Professional translators also make use of machines. Training a computer to translate between two specific languages takes millions of sentences or billions of words worth of text. Maarit Koponen, a postdoctoral researcher at the University of Helsinki, is investigating which errors made by machines lead to misunderstandings and how those mistakes could be identified. The learning algorithms behind machine translation are called artificial intelligence, but machines are not intelligent in the way humans or the super AIs of science-fiction films are.


Why Don't We Trust Machines when We Obviously Should?

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This is what makes them dangerous.' Previously, I talked about how AI could change the human-machine relationship. Who should be making the decisions in an autonomous car? Should humans always be able to overrule robot decisions? What if you only have a split second to react?


Using Computers To View The Unseen

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Images in this blind light transport factorization example are projected onto a wall behind the camera. A group of scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a method that can reconstruct, using no special equipment, hidden video from the subtle shadows and reflections on an observed pile of clutter. With a video camera turned on in a room, the scientists can reconstruct a video of an unseen corner of the room, even if it falls outside the camera's field of view. By observing the interplay of shadow and geometry in video, the team's algorithm predicts the way that light travels in a scene, which is known as "light transport." The system then uses that to estimate the hidden video from the observed shadows -- and it can even construct the silhouette of a live-action performance.


Do We Need A Theory of AI?

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What would a theory of artificial intelligence look like, and how might it be achieved? When designing a new engine or airplane wing, engineers can apply theories that have withstood years of scientific scrutiny, such as the Laws of Thermodynamics or Newton's Laws of Motion. To what theories --if any --can artificial intelligence (AI) researchers and technology pioneers turn when designing neural networks or algorithms? We asked experts from the fields of computer science, theoretical physics, and philosophy for their insights. The Encyclopedia Britannia defines a scientific theory as a "systematic ideational structure of broad scope, conceived by the human imagination, that encompasses a family of empirical (experiential) laws regarding regularities existing in objects and events, both observed and posited."


Artificial intelligence set to enhance the construction sector

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Construction is one of the most challenging sectors for artificial intelligence and robotics. When it comes to manual skills and holistic thinking, no advanced technology can hold a candle to human labor. So far, we have seen a robot putting up a wall, the various uses of augmented and virtual reality (AR/VR) and AI systems performing repetitive tasks in the construction industry that people no longer want to do. While the concepts of work and tasks for people have changed with Industry 4.0, by 2050, it is estimated that two-thirds of the world's population will live in cities, which means new cities, more housing and more roads. To meet this demand, the construction sector needs to build around 13,000 homes every day worldwide.


Building a home for AI

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Data center designers and builders have to stay on top of the latest developments in server hardware: the environments they create require a massive upfront investment and are expected to last at least 20 years, so they have to be ready for housing the IT equipment of the future. The latest trend in IT workloads that's set to impact the way data centers are constructed is machine learning. The ideas fueling the boom in artificial intelligence are not new - many of them were proposed in the 1950s - and the power of AI is undoubtedly being over-hyped, but there are plenty of use cases where AI tech in its current state is already bringing tangible benefits. For example, algorithms are much better than people at securing corporate networks, able to pick up on anomalies that humans and their rules-based tools might miss. Algorithms are also great at analyzing large chunks of boring text: lawyers use AI-based software to scan through case files and contracts, while universities use something similar to establish whether a paper was written by the student who submitted it, or a freelancer hired online.


r/deeplearning - Composing Bach Chorales Using Deep Learning

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This is a 30 minute talk from GOTO Copenhagen 2019 by Feynman Liang - Creator of BachBot. I've dropped the full talk abstract below for a read before diving into the talk: Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date.


This object-recognition dataset stumped the world's best computer vision models

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Computer vision models have learned to identify objects in photos so accurately that some can outperform humans on some datasets. But when those same object detectors are turned loose in the real world, their performance noticeably drops, creating reliability concerns for self-driving cars and other safety-critical systems that use machine vision. In an effort to close this performance gap, a team of MIT and IBM researchers set out to create a very different kind of object-recognition dataset. It's called ObjectNet, a play on ImageNet, the crowdsourced database of photos responsible for launching much of the modern boom in artificial intelligence. Unlike ImageNet, which features photos taken from Flickr and other social media sites, ObjectNet features photos taken by paid freelancers.


Public opinion lessons for AI regulation

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An overwhelming majority of the American public believes that artificial intelligence (AI) should be carefully managed. Nevertheless, as the three case studies in this brief show, the public does not agree on the proper regulation of AI applications. Indeed, population-level support of an AI application may belie opposition by some subpopulations. Many AI applications, such as facial recognition technology, could cause disparate harm to already vulnerable subgroups, particularly ethnic minorities and low-income individuals. In addition, partisan divisions are likely to prevent government regulation of AI applications that could be used to influence electoral politics.


Not Yet Skynet – The Past, Present, and Future of Artificial Intelligence

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Artificial intelligence is here, and it's being put to work across all manner of industries and applications. While the technology still has someway to go before we see machines capable of true independent thought and decision-making, what we do have is sending waves through the business world. While only 23 percent of businesses have incorporated the technology into the products and services they offer right now, a massive 83 percent have named AI as a strategic priority for the near future. Global spending on cognitive and AI systems is estimated to reach $57.6 billion in 2021 and the AI market is set to grow to become a $190 billion industry by 2025. What are the benefits of AI to businesses according to industry experts?