Overview of causal inference in machine learning

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In a major operator's network control center complaints are flooding in. The network is down across a large US city; calls are getting dropped and critical infrastructure is slow to respond. Pulling up the system's event history, the manager sees that new 5G towers were installed in the affected area today. Did installing those towers cause the outage, or was it merely a coincidence? In circumstances such as these, being able to answer this question accurately is crucial for Ericsson.


Robots aren't taking our jobs -- they're becoming our bosses

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On conference stages and at campaign rallies, tech executives and politicians warn of a looming automation crisis -- one where workers are gradually, then all at once, replaced by intelligent machines. But their warnings mask the fact that an automation crisis has already arrived. The robots are here, they're working in management, and they're grinding workers into the ground. The robots are watching over hotel housekeepers, telling them which room to clean and tracking how quickly they do it. They're managing software developers, monitoring their clicks and scrolls and docking their pay if they work too slowly. They're listening to call center workers, telling them what to say, how to say it, and keeping them constantly, maximally busy. While we've been watching the horizon for the self-driving trucks, perpetually five years away, the robots arrived in the form of the supervisor, the foreman, the middle manager. These automated systems can detect inefficiencies that a human manager never would -- a moment's downtime between calls, a habit of lingering at the coffee machine after finishing a task, a new route that, if all goes perfectly, could get a few more packages delivered in a day. But for workers, what look like inefficiencies to an algorithm were their last reserves of respite and autonomy, and as these little breaks and minor freedoms get optimized out, their jobs are becoming more intense, stressful, and dangerous. Over the last several months, I've spoken with more than 20 workers in six countries. For many of them, their greatest fear isn't that robots might come for their jobs: it's that robots have already become their boss. In few sectors are the perils of automated management more apparent than at Amazon.


Can't Define AI? Try Defining Intelligence

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Dr. Alex Wissner-Gross wears many hats: he runs a company called Gemedy in Boston focused on artificial general intelligence (AGI), he has a number of academic appointments at Harvard and MIT, and he advises a number of governmental agencies. His goal is to ensure that the benefits of artificial intelligence (AI) are redistributed through the economy. You would think that someone with those titles and roles would have the definition of AI nailed. Turns out that the answer to the question of AI's definition is complicated by one fact: we don't have a precise definition of intelligence itself. On a recent AI Today podcast, Professor Alex Wissner-Gross shares his insights into AI and intelligence more broadly.


AI and Machine Learning for Financial Institutions: Breaking down the terminology - Experian Insights

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While many companies are interested in implementing technology with advanced analytic capabilities, the concepts behind the technology can often be hard to understand. Demystifying the terminology around artificial intelligence and machine learning is one of the first steps for successful implementation.


Algorithmic consent: Why informed consent matters in an age of Artificial Intelligence

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Some time back, I had a doctor's appointment which I booked via an app. On reaching the center, which was run by a well-known fitness platform, I was told that they would collect my vitals (pulse, temperate, heart rate, and blood pressure, among others) before I could meet the doctor. I was also asked to sign a printed copy of terms and conditions, which read that they could share any and all information collected with any third parties. Additionally, when asked about the privacy policy, I was told that it was not available. This experience points to few of the many problems that exist within the idea of informed user/consumer consent, as shown above.


Appen High-Quality Training Data for Machine Learning

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Our skilled project managers use multiple quality control methods and mechanisms to meet and exceed quality standards for training data. Quality assurance is built into both the platform and processes at Appen. With a crowd of over 1 million skilled contractors operating in 130 countries and 180 languages and dialects, Appen can collect and label high volumes of image, text, speech, audio, and video data used to build and improve artificial intelligence systems. Our platform and solutions are purpose-built to handle large-scale data collection and annotation projects, on demand. With deep expertise planning and recruiting to meet a variety of uses cases for our clients, we can quickly ramp up new projects in new markets.


How To Implement Artificial Intelligence In Mobile App Development

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Artificial Intelligence (AI) is one of the few emerging technologies that promise to bring about some striking transformations in the blooming world of Android app development. When it comes to improving business relations, growth, and expectations, this technology has got the highest spotlight that cannot be overlooked by anyone looking to make a meaningful impact in the business world through technology. It is interesting to see how AI is growing rapidly to become the next big thing the world has ever known. Today, many app development companies around the world are not only interested in adopting AI but are also focused on putting the technology into the hands of people. Basically, they are looking to introduce it through apps in their mobile devices.


Are Tech Leaders and Governments Prepared for Rapidly Advancing Artificial Intelligence? - IEEE Innovation at Work

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As AI evolves, it's expected to give a boost to some highly anticipated technologies, including gene editing, which Gates says has the potential to squelch diseases like malaria. However, it also poses complex challenges. While the public worries that AI can replace human labor in the workforce, many experts are instead concerned about the dangers it poses to privacy and safety. Will AI-backed facial recognition applications create a surveillance nightmare for the public? Another looming question: As AI rapidly develops, will regulations be able to keep up?


Machine learning makes a better Luke Skywalker hand

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A 3D-printed prosthetic hand controlled using a new AI-based approach could significantly lower the cost of bionic limbs for amputees. Real need: There are approximately 540,000 upper-limb amputees in the United States, but sophisticated "myoelectric" prosthetics, controlled by muscle contractions, are still very expensive. Such devices cost between $25,000 and $75,000 (not including maintenance and repair), and they can be difficult to use because it is hard for software to distinguish between different muscle flexes. Handy invention: Researchers in Japan came up with a cheaper, smarter myoelectric device. Their five-fingered, 3D-printed hand is controlled using a neural network trained to recognize combined signals--or, as they call them, "muscle synergies."


Deep Learning in the Cosmos: Ranking 3 Machine Learning (ML) Applications

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Deep learning has helped advance the state-of-the-art in multiple fields over the last decade, with scientific research as no exception. We've previously discussed Deepmind's impressive debut in protein folding prediction, as well as a project by Stanford students studying protein complex binding operations, which are both examples of using deep learning to study very small things. Deep learning has likewise found applications in scientific research at the opposite end of the scale spectrum. In this post we'll discuss some recent applications of deep learning used to study cosmology, aka the study of the universe. As you might imagine, this topic encompasses a wide variety of sub-categories.