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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

Elon Musk’s Neuralink Confronts First Human Trial Malfunction

Aayush Tyagi
Aayush Tyagi Last Updated : 10 May, 2024
2 min read

Neuralink’s Malfunction!!!

In recent news, Elon Musk’s neural technology company, Neuralink, conducted its first human trial of a brain chip on a 29-year-old man named Noland Arbaugh. However, an unexpected malfunction occurred, prompting the company to rectify the issue and share the experience.

Neuralink’s brain-computer interface was intended to capture neural signals through available 1,024 electrodes on 64 fibers, thinner than a strand of human hair. However, many of these fibers pull away from the brain tissue while neuralink human trials, causing it to fail. The organization addressed the situation promptly, and within weeks or months, the usage of a system modification resulted in a steady and quick recovery.

Lessons Learnt

The device’s failure raised several worries within the brain-implant community. It reports that the unique design of the Neuralink does not allow for the movement the brain tissue needs. Regular BAIs are soft enough to stay on top of the brain tissue. The functionality of the Neuralink requires additional clarification. Therefore, the question of the malfunction is related to a unique design, but, in general, the company must tweak its product’s features. Nonetheless, this malfunction may impact broader human trials regarding the possibility of getting FDA approval.

Also read: Introduction to Neural Network in Machine Learning

The Future of Brain-Computer Interfaces

Nonetheless, Elon Musk Neuralink continues to push the boundaries of technology, seeking to scale its current research into brain-computer interfaces for more applications. The business aims to improve text entry and cursor control by designing systems to control physical devices, robotic arms, and movement in wheelchairs. The possibilities presented by the company’s technology are limitless and could significantly affect the movement and functionality of persons with paralysis and other motor problems.

Also read: Building a Brain Tumor Classifier Using Deep Learning

Neuralink

Our Say

This week’s Neuralink malfunction is a timely reminder of the many pitfalls of creating a new technology for people. As Neuralink pushes the boundaries of the brain-computer interface, it is important to be aware of the pitfalls that could determine such a future. As more and more researchers and innovators strive to make it better and more effective, we will see significant achievements in this field.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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