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Category: robotics/AI – Page 1,426

What is concept drift?

Concept drift occurs when there are changes in the distribution of the training set examples.

At the most basic level, concept drift causes data points that were once considered an example of one concept to be seen as another concept entirely over time.

For instance, fraud detection models are at risk for concept drift when the concept of fraud is constantly changing.

This can cause model performance to degrade, especially over extended periods where concept drift continues to occur without being detected by your monitoring systems.

A critical problem for companies when integrating machine learning in their business processes is not knowing why they don’t perform well after a while. The reason is called concept drift. Here’s an informational guide to understanding the concept well.

Continue reading “Why Do Machine Learning Models Die In Silence?” | >

To make fusion energy a viable resource for the world’s energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT’s Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces — factors that impact fusion reactor designs.

To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma’s behavior. However, “first principles” simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop “reduced” computer models that run much faster, but with quantified levels of accuracy.

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DeepScribe, an AI-powered medical transcription platform, has raised $30 million in Series A funding led by Nina Achadjian at Index Ventures, with participation from Scale.ai CEO Alex Wang, Figma CEO Dylan Field and existing investors Bee Partners, Stage 2 Capital and 1984 Ventures. The company’s latest round of funding follows its $5.2 million seed round announced in May 2021. DeepScribe was founded in 2017 by Akilesh Bapu, Matthew Ko and Kairui Zeng with the aim of unburdening doctors from tedious data entry and allowing them to focus on their patients.

In 2019, DeepScribe launched its ambient voice AI technology that summarizes natural patient-physician conversations. The idea for DeepScribe was prompted by Bapu and Ko’s own experiences. Bapu’s father was an oncologist and he saw the toll that documentation had on his father’s work/life balance. On the other hand, Ko saw how the burden of clinical documentation was impacting patients’ perception of care when he was the care coordinator for his mother when she was diagnosed with breast cancer.

After being frustrated with the care his mother was receiving, Ko turned to Bapu and his father for help. The pair then began to understand the importance of clinical documentation and realized that recent breakthroughs in artificial intelligence and natural language processing were not being used to remedy the situation. They then decided to create a platform that would address the problem.

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University of Utah engineers have built a robotic exoskeleton that gives people with prosthetic legs a power boost that makes walking less difficult.

“It’s equivalent to taking off a 26-pound backpack [while walking],” lead researcher Tommaso Lenzi said in a press release. “That is a really big improvement.”

The challenge: About 220,000 people in the U.S. have had above-knee amputations, meaning their leg was amputated somewhere between the knee and hip.

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However, as Malcolm Murdock, machine-learning engineer and author of the 2019 novel The Quantum Price, puts it, “AI doesn’t have to be sentient to kill us all. There are plenty of other scenarios that will wipe us out before sentient AI becomes a problem.”

“We are entering dangerous and uncharted territory with the rise of surveillance and tracking through data, and we have almost no understanding of the potential implications.” —Andrew Lohn, Georgetown University.

In interviews with AI experts, IEEE Spectrum has uncovered six real-world AI worst-case scenarios that are far more mundane than those depicted in the movies. But they’re no less dystopian. And most don’t require a malevolent dictator to bring them to full fruition. Rather, they could simply happen by default, unfolding organically—that is, if nothing is done to stop them. To prevent these worst-case scenarios, we must abandon our pop-culture notions of AI and get serious about its unintended consequences.

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