An unexpected look at radiology and how it compares to art, featuring Dr. Ronit Agid and her journey to choosing interventional neuroradiology. Dr. Ronit Agi…
Category: robotics/AI – Page 474
Today, we are excited to take the next significant step forward and introduce the Copilot key to Windows 11 PCs. In this new year, we will be ushering in a significant shift toward a more personal and intelligent computing future where AI will be seamlessly woven into Windows from the system, to the silicon, to the hardware. This will not only simplify people’s computing experience but also amplify it, making 2024 the year of the AI PC.
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A research team from UNIST has made a discovery that might revolutionize cancer treatment as we know it-new cell-engaging nano-drones that were designed to target and eliminate cancer cells selectively.
These tiny bots are called NK cell-engaging nano-drones (NKeNDs), and their success lies in their ability to engage natural killer (NK) cells, the body’s frontline defenders against cancer. Using NK cells in cancer treatment is not new, but what sets these nanodrones apart is their precision. They are engineered to zero in on cancer cells almost like guided missiles.
NASA space debris expert Don Kessler observed that, once past a certain critical mass, the total amount of space debris will keep on increasing: collisions give rise to more debris and lead to more collisions, in a chain reaction. So Clean Space is seeking not just to cut debris production from future ESA missions but to reduce the total mass of current debris, such as the robotic salvage of derelict satellites. The task is an urgent one: debris levels have increased 50% in the last five years in low orbit.
He further mentioned that the AI tool functioned like an “extra pair of eyes,” identifying potential tumors within the video footage.
In short, the AI tool assists junior doctors during colonoscopies by analyzing video footage from the endoscope and identifying potential tumors. It aids in detecting adenomas, particularly those smaller than five millimeters (mm) in diameter.
Scientists have fused brain-like tissue with electronics to make an ‘organoid neural network’ that can recognise voices and solve a complex mathematical problem. Their invention extends neuromorphic computing – the practice of modelling computers after the human brain – to a new level by directly including brain tissue in a computer.
The system was developed by a team of researchers from Indiana University, Bloomington; the University of Cincinnati and Cincinnati Children’s Hospital Medical Centre, Cincinnati; and the University of Florida, Gainesville. Their findings were published on December 11.
Artificial Intelligence.
AI and echoes of the enlightenment.
Personal Perspective: How today’s Cognitive Age is a second Enlightenment.
Exploring pre-trained models for research often poses a challenge in Machine Learning (ML) and Deep Learning (DL). Visualizing the architecture of these models usually demands setting up the specific framework they were trained on, which can be quite laborious. Without this framework, comprehending the model’s structure becomes cumbersome for AI researchers.
Some solutions enable model visualization but involve setting up the entire framework for training the model. This process can be time-consuming and intricate, deterring quick access to model architectures.
One solution to simplify the visualization of ML/DL models is the open-source tool called Netron. This tool functions as a viewer specifically designed for neural networks, supporting frameworks like TensorFlow Lite, ONNX, Caffe, Keras, etc. Netron bypasses the need to set up individual frameworks by directly presenting the model architecture, making it accessible and convenient for researchers.
How the brain adjusts connections between #neurons during learning: this new insight may guide further research on learning in brain networks and may inspire faster and more robust learning #algorithms in #artificialintelligence.
Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning. This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.
The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.
However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them. Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.