Category: Machine Learning

Scientists created a mood ring for the internet using machine learning, and it revealed how depressed the world was during the outset of the epidemic. 

Researchers use mood as a unique tool to assess the effect of natural and man-made catastrophes on humans. However, in the aftermath of a major incident, it’s just impracticable to ask every single individual on the planet how they’re feeling. Scientists from MIT, the Chinese Academy of Sciences, and the Max Planck Institute for Human Development devised a solution. They employed machine learning methods to monitor social media for mood changes after the initial wave of COVID-19 in 100 different nations, allowing them to gather real-time readings on how pleased or upset the pandemic’s occurrences made individuals all across the globe. Consider it as an AI-powered mood ring, but for millions of people. Their results were published in the journal Nature Human Behavior only last week. 

Source: https://www.popsci.com/technology/machine-learning-measures-social-media-sentiment-mit/ 

Bionic Arm Interfaces Become More Robust Due to New Technology

Dennis Yeung, a Ph.D. student at Aalto University, and his research group have invented a new form of technology that improves the compatibility of a prosthesis with the severed region. Individuals with an amputated upper limb may operate the robotic prosthesis by contracting their residual muscles. Advanced prostheses include machine learning techniques to assist in interpreting these user-generated signals. These connections are often susceptible to external causes, such as sweat, and deteriorate with time. Unsupervised adaptive myocontrol (UAM) is a myoelectric interface that extracts natural motor synergies from multi-muscle data and adjusts to new user inputs in real-time.

UAM has been evaluated by a series of virtual target reaching activities done by healthy and amputee volunteers. Tests were done under normative and electrode perturbed settings to assess control robustness.

Source: https://neurosciencenews.com/learning-arm-prosthetics-20225/

Deep learning is a subset of a larger family of machine learning techniques based on representation learning and artificial neural networks. Its goal is to improve the AI process of teaching robots to learn new things. Deep learning relies on powerful computers and a significant amount of data to train enormous neural networks. Deep learning became the subject of a fad. Deep learning and sophisticated artificial intelligence are used by many firms to solve issues and improve their products and services. Deep learning, on the other hand, has been overhyped for far too long. Meanwhile, misleading tales about artificial intelligence and deep learning were widely circulated in the media. They were written by persons who lacked a thorough knowledge of how technology works. 

Source: https://www.analyticsinsight.net/is-deep-learning-a-real-big-thing-or-is-it-overhyped-among-users/ 

The way we deal with climate change has evolved throughout time. This shift signifies a shift in society’s approach to climate change and environmental catastrophe. It indicates we’ve reached the point when we can no longer convince ourselves that this is true. And we’ve moved on from expecting an improvement to come our way. Rather, we can see a genuine, palpable change taking place, as well as the creation of a portfolio of potential solutions to this problem. That isn’t to say the road is over (it isn’t and never will be), nor does it imply we have all the answers. However, it does imply that the tone, or color, of the conversation, might change. To address this, we’re planning to update the way Exponential View tells the tale of our climate future so that it includes both growing CO2 levels and positive signs that show some improvement. 

Source: https://www.exponentialview.co/tracking-climate-progress/ 

MIT researchers have found that machine-learning models that are popular for image recognition tasks actually encode bias when trained on unbalanced data. The solution they developed not only leads to models that make more balanced predictions, but also improves their performance on downstream tasks like facial recognition and animal species classification. 

Source: https://news.mit.edu/2022/unbias-machine-learning-0301