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Artificial intelligence development is starting to slow down, Facebook head of AI says

In the case of AI development, according to a recent study published by OpenAI, the amount of computing power used in AI training has doubled every 3.4 months, a massive acceleration to the standard progression we are used to.

Because of that speed of advancement, OpenAI believes that AI has required a 300,000-times increase in computing power since 2012, as opposed to the seven-times increase that it would typically get under Moore's law.

Limitations to the development of processing power is already starting to slow the progress of AI and machine learning, especially since research shows that the one thing that leads to predictably better performance from AI systems is access to more computing power.

developing a quantum processor capable of solving computations that would take a standard computer more than 10,000 years to complete — though the claim has been called into question.

These could represent points of potential progress for AI, which requires increasing access to computing power to continue the rapid progress it has made in a short period of time.

Earlier this year, it was reported that school districts across the country are using AI systems that are unfairly punishing people of color and disadvantaged students for mistakes that humans could more accurately interpret and process.

In 2015, Pro Publica showed instances of automated sentencing systems displaying racial bias by falsely suggesting a higher rate of recidivism for black defendants while predicting a far lower rate of recidivism for white defendants.

Similarly, a study found that predictive crime tools have a tendency to disproportionately push police into minority neighborhoods even when crime statistics in the area don't reflect the need for more policing.

Until these data sets and the collection process is improved and stripped of human biases, AI will likely continue to perpetuate and even exacerbate these flaws, limiting the capability to learn and improve.

While it still has room to grow in some areas, the science fiction future in which machines learn and improve on the fly, performing human-like thinking and processing tasks, is probably not in our near-term future.

Helping machines perceive some laws of physics

The model could be used to help build smarter artificial intelligence and, in turn, provide information to help scientists understand infant cognition.

While tracking the objects, the model outputs a signal at each video frame that correlates to a level of “surprise” — the bigger the signal, the greater the surprise.

If an object ever dramatically mismatches the model’s predictions — by, say, vanishing or teleporting across a scene — its surprise levels will spike.

In response to videos showing objects moving in physically plausible and implausible ways, the model registered levels of surprise that matched levels reported by humans who had watched the same videos.

Mismatched realities ADEPT relies on two modules: an “inverse graphics” module that captures object representations from raw images, and a “physics engine” that predicts the objects’ future representations from a distribution of possibilities.

“Similarly, young infants also don’t seem to care much about some properties like shape when making physical predictions.” These coarse object descriptions are fed into a physics engine — software that simulates behavior of physical systems, such as rigid or fluidic bodies, and is commonly used for films, video games, and computer graphics.

The only explanation is that it disappeared, so that’s surprising.’” Violation of expectations In development psychology, researchers run “violation of expectations” tests in which infants are shown pairs of videos.

Specifically, the scenarios examined the model’s ability to capture notions of permanence (objects do not appear or disappear for no reason), continuity (objects move along connected trajectories), and solidity (objects cannot move through one another).

For example, in a video where an object moving at a certain speed disappears behind a wall and immediately comes out the other side, the object might have sped up dramatically when it went behind the wall or it might have teleported to the other side.

The researchers also found traditional neural networks that learn physics from observations — but don’t explicitly represent objects — are far less accurate at differentiating surprising from unsurprising scenes, and their picks for surprising scenes don’t often align with humans.

Studies, for example, show that infants up until a certain age actually aren’t very surprised when objects completely change in some ways — such as if a truck disappears behind a wall, but reemerges as a duck.

Artificial Intelligence-Based Algorithm for Intensive Care of Traumatic Brain Injury

Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care.

At its best, such an algorithm could predict the outcome of the individual patient and give objective data regarding the condition and prognosis of the patient and how it changes during treatment.

Although this is a proof-of-concept and it will still take some time before we can implement algorithms like this into daily clinical practice, our study reflects how and into what direction modern intensive care is evolving', says Rahul Raj, Adjunct Professor of Experimental Neurosurgery from HUS and one of the authors of the paper.

Still, the accuracy of both algorithms is surprisingly good, considering that the simpler model is based upon only three main variables and the more complex upon five main variables', tells Eetu Pursiainen, Data Scientist from the Analytics and AI Development Department at HUS, one of the authors and main coders of the algorithms.

An artificial intelligence algorithm can learn the laws of quantum mechanics

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