judgment


AI And Emotions: How Far Can We Take This Connection?

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A spy movie with its paraphernalia of cool gadgets and technologies has always enticed audiences. In these movies, we have seen the use of a polygraph to detect if somebody is being truthful or not. Needless to say, polygraph is a multi-billion dollar industry and plays a crucial role in crime adjudication. Polygraphs do not have any "intelligence" built into them. They are simple machines that do what they were designed to do: measure vital statistics like blood pressure and pulse to reach a conclusion.


Machine Learning Saves 'Avengers' VFX Artists Time

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For visual-effects artists, time is always a struggle. When the call comes in to create something spectacular, artists and supervisors have to calculate how much run- way they have to get from the point of the idea for the vfx to the deadline. On "Avengers: Infinity War," the vfx crew found that a new innovation -- machine learning -- made it possible to create the character Thanos in a way that would have simply been impossible without it. The filmmakers envisioned a version of Thanos -- played by Josh Brolin -- that would be CG, but also incorporate all the subtle facial expressions and delicate hallmarks of a physical performance that could only been done by an actor. They knew that the facial tracking tech was there but asking vfx artists to manually adjust every inch of the CG version of the face of Thanos once they had all the tracking and scanning information would have been a disaster.


Will A.I. Put Lawyers Out Of Business?

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What is the law but a series of algorithms? Codified instructions proscribing dos and don'ts--ifs and thens. Sounds a lot like computer programming, right? The legal system, on the other hand, is not as straightforward as coding. Just consider the complicated state of justice today, whether it be problems stemming from backlogged courts, overburdened public defenders, and swathes of defendants disproportionately accused of crimes.


Bias in the ER - Issue 45: Power - Nautilus

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They must be doing something." Amos and Danny didn't have much doubt that a lot of people would get the questions they had dreamed up wrong--because Danny and Amos had gotten them, or versions of them, wrong. If they both committed the same mental errors, or were tempted to commit them, they assumed--rightly, as it turned out--that most other people would commit them, too. The questions they had spent the year cooking up were not so much experiments as they were little dramas: Here, look, this is what the uncertain human mind actually does. Their first paper had shown that people faced with a problem that had a statistically correct answer did not think like statisticians.


Not everything in the supply chain should be automated

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With new advances in robotics and artificial intelligence, virtually every player in the supply chain is looking to some form of automation. Walmart is testing grocery-picking robots. Ryder partnered with Fetch Robotics to install drones, sensors and wearable technology in its warehouses. And in its latest earnings call, DHL said it was "seeing about 25% productivity improvement" from robotics. Automation, the companies bet, can increase efficiency, reduce costs and improve performance.


Data Scientist's Dilemma: The Cold Start Problem – Ten Machine Learning Examples

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The ancient philosopher Confucius has been credited with saying "study your past to know your future." This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data? This would be a problem.


Do People Trust Algorithms More Than Companies Realize?

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Since the 1950s, researchers have documented the many types of predictions in which algorithms outperform humans. Algorithms beat doctors and pathologists in predicting the survival of cancer patients, occurrence of heart attacks, and severity of diseases. Algorithms predict recidivism of parolees better than parole boards. And they predict whether a business will go bankrupt better than loan officers. According to anecdotes in a classic book on the accuracy of algorithms, many of these earliest findings were met with skepticism.


Leveraging Artificial Intelligence To Gain Long-Term Vision

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I see from your phone number that you have a flight later today. Would you like to ...." And so, the conversation with a chatbot goes on. This may be a useful time-saver, or more accurately, a useful money-saver, but is it really driving any economic value? The same question can be asked about the massive customer preferences artificial intelligence (AI) systems -- economic driver or the force behind materialism? Now, don't get me wrong.


Data Scientist's Dilemma: The Cold Start Problem – Ten Machine Learning Examples

#artificialintelligence

The ancient philosopher Confucius has been credited with saying "study your past to know your future." This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data? This would be a problem.


Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

Neural Information Processing Systems

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algorithm generates such a correction by solving a series of linear constraint satisfaction problems. The technique is evaluated on three neural network models: one predicting whether an applicant will pay a mortgage, one predicting whether a first-order theorem can be proved efficiently by a solver using certain heuristics, and the final one judging whether a drawing is an accurate rendition of a canonical drawing of a cat.