Today doctors have a limited view of what goes on in our body.
They can talk to us, take scans, run tests, and examine genetics, which all play as powerful tools for diagnosing an illness, but currently, there is only so much you can do in terms of early diagnoses.
However, that could all change because the future of brain-computer interfaces are on the tips of our fingers.
You might think I’m joking, but check this out:
This little thing on the finger above, is a Neural Dust sensor, about the size of a grain of sand.
One day, we will be able to have these tiny implants inside our bodies. They’ll float next organs, beside nerves, inside blood vessels, and inhabit our brains. …
I don’t have many clear memories from my early childhood.
In fact, most of my hazy memories consist of me playing log tag outside with my friends, sharing snacks at lunch, throwing crabapples at moving cars (yeah… my friends and I probably shouldn’t have done this), and trading Pokemon cards at recess.
Although, there is one memory that I clearly remember from my early childhood, and it’s one I’ll never forget.
It happened in the first grade, during storytime. I was bored, and instead of paying attention to my teacher, naturally, I let my mind wander around its own thoughts.
While looking around to find a clock that could tell me when school was over, I noticed at the corner of my eye that one of my classmates was having a seizure. …
To some people, the idea of a brain-computer interface may seem alarming, if not scary.
There are a lot of dystopian ideas associated with these devices, such as that people will be able to control your mind, telepathy, A.I. will take over your brains, you’ll become a cyborg.
Although brain-comper interfaces don’t currently serve these purposes, maybe one day they will be able to do such futuristic things.
In this article, I’ll give you a brief insight into the purposes of brain-computer interfaces, and how they are being implemented today to change our future.
Before we dive into brain-computer interfaces, we need to briefly understand how the brain communicates. …
The Canadian Cancer Society presents the following statistics related to brain tumours in 2020:
The Brain Tumour Charity also states that brain tumours are the biggest cancer killer of children and adults under the age of 40 worldwide. …
If you don’t already know, I belong to an amazing STEM program called The Knowledge Society. During the month of November, innovators from TKS Toronto had the opportunity in teams to come up with a solution to improve customer service at Instacart. My group decided to target the produce issue at Instacart since it has been known that some consumers have received rotten produce from shoppers.
It’s insane how you can detect pneumonia in images using deep learning models, saving an individual’s life. According to the World Health Organization, pneumonia kills around 2 million children under the age of 5 years and is repeatedly estimated as the single leading cause of child mortality. This means that pneumonia kills more children than HIV, malaria, and measles combined.
Additionally, the World Health Organization states that 95 % of childhood pneumonia occurs in developing countries, more specifically in the regions of Southeast Asia and Africa. What causes pneumonia? Bacterial and viral pathogens are the leading causes.
Bacterial and viral pneumonia are treated in different manners. In terms of bacterial pneumonia, urgent care is necessary along with antibiotic treatment, in contrast to viral pneumonia which is treated through supportive care. …
K-Nearest Neighbour, or otherwise known as KNN is a machine learning algorithm that has a list of available cases and classifies data based on a similarity measure system. This model partakes in supervised learning and is most commonly used as a regression algorithm.
What does it mean for a model to be supervised?
I bet many of you have had to deal with little children, whether it’d be younger siblings, cousins, neighbours, and the list goes on. Young children are curious about their surroundings and curious to learn about the things around them in general.
We can think of our computer as a young child. It does not know everything but is curious to learn. …
Just a heads up, I programmed this neural network in Python using PyTorch. I also wrote my model in Pycharm but I would advise that if you choose to write this code (or really any deep learning model), use Google Colaboratory or Jupyter Notebooks (unless you can train models on your GPU). I recommend this because it will take a bit of time for your model to perform (took 5 minutes each time for this particular project).
Additionally, this article is not meant for someone who is new to programming. In fact, if you are new to programming, I strongly suggest you hold off on machine learning and learn the fundamentals of programming first before exploring this field. …
Recurrent Neural Networks are neural networks that are specialized in modeling sequence data. Essentially RNNs are designed to capture information from time sequences and time-series data.
But what exactly does this all mean?
Let's say we have a still picture of a frisbee gliding in the air.
Now, what if we wanted to predict the direction of the frisbee?
Well, we could go ahead and take a guess, although the guess would probably not be accurate since we are guessing at random. …