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It also shows you care for content in the bag.

In this age of technology, where everything connects to the cloud or needs an app, it takes a simple bit of engineering to stand out. A seatbelt for bags while you drive around like there’s no tomorrow, as reported by Gizmodo, clearly fits into this category.

The best place to put a bag of groceries or even take-out food when you are driving alone is the passenger seat, right next to you. Not only can you keep an eye on it while you drive, but it is also unlikely that you will forget it in the car and have to make a trip back to retrieve it later.

However, bags that tend to get greasy or leak out some liquid do not deserve a seat of honor and are put where they belong, on the floor. If you are with me so far, then you surely wouldn’t mind shelling out 22 dollars to get your hands on BAGO, a harness that secures the bag there.

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If you have been looking for ways to reduce your stress while driving, here is one. For everything else, wait for autonomous cars.

Although robots are more than capable today of carrying out all kinds of business tasks efficiently and accurately, the concept of building machines that can think like humans has always been a dream for tech companies and smart city developers. However, the actual way in which the human mind works and processes information is up for debate, with several parties having conflicting opinions regarding the same. Once enough data is generated, simulation models can be created to build software that can think along the same rational or emotional lines as humans. Human thinking is generally influenced by a variety of factors—cognitive, behavioral, geometric, kinematic and physical. Using cognitive modeling, such factors can be considered while attempting to create robots that think and behave like humans.

The concept of human thinking is still too vague to be accurately replicated in robots. Even then, multiple types of approaches could be taken to reach the ideal end result—enabling AI and robotic tools to think like humans.

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Teaching robots to think like humans may be the next frontier for tech developers and researchers involved in the ongoing development of technologies such as AI and robotics.

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.

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.

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 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.

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.