I: Introduction (intro)
Cloud computing is a service that allows you to rent computers and software online, instead of having to buy and maintain your own hardware. It is becoming increasingly popular for machine learning applications, because it allows researchers to scale the computational requirements of their algorithms. Today we will discuss what cloud computing means for machine learning, and why it has gained popularity in the field.
II: Body (body)
On a basic level, cloud computing refers to renting computer resources from a third party. These resources could include storage, processing power, and bandwidth. For example, Netflix uses cloud computing to stream video over the internet without requiring users to download large files. You could also think about this in terms of renting a car instead of buying one. Say you go on vacation and you want to drive to another city. You can either rent a car or bring your own car. If you choose to rent a car, you will not have to worry about purchasing it or maintaining it. You also do not need to spend time searching for a car rental agency before your trip. The same applies to the cloud. If you are working on a project that requires more processing power than you can afford on your own, you can rent the extra resources online. This frees up your time to work on other important things.
III: Conclusion (conclusion)
The beauty of cloud computing is that it allows researchers to work on more projects than they would be able to otherwise. It was not too long ago when every part of the research process needed to be done on-site at the user’s facility. For example, if you wanted to run an experiment with 20 participants, you would have to set up 20 separate computers with 20 separate monitors, mice, keyboards, etc. Now, the researcher only has to worry about setting up one computer with all the necessary software and hardware. All the other resources are provided by the cloud services company, such as Amazon Web Services (AWS). Imagine how much time is saved by not worrying about procuring resources like this.
Now let’s take a look at some options for each part of the outline:
I: Introduction (intro)
Cloud computing is becoming increasingly popular for machine learning applications, because it allows researchers to scale the computational requirements of their algorithms.
II: Body (body)
A: What is a neuron?
Neurons are cells that are found throughout the brain and spinal cord. They are responsible for receiving messages from neurons through synapses, which are tiny gaps between neurons. A synapse is analogous to a relay station on an electrical grid. Messages are transmitted across synapses through neurotransmitters. At the end of each neuron, there is a terminal button on which neurotransmitters are released. Receptors at the next neuron capture these neurotransmitters so messages can be relayed across the gap between neurons. Depending on the type of neurotransmitter released, messages may either increase or decrease the firing rate of the next neuron.
When messages are relayed through many neurons sequentially, this is known as neural computation. Neural computation is thought to be essential for solving complex tasks using cognition. Insect brains are thought to contain less neurons than mammalian brains, but are still able to solve complex tasks without suffering from catastrophic forgetting. This suggests that neural computation may not be necessary for higher cognitive functions like memory or language.
If messages are relayed through many neurons simultaneously, this is called neural coordination. Neural coordination is thought to be crucial for connecting different parts of cognition together. In humans and animals with well-developed brains, neural coordination occurs in two distinct ways: synchronous and asynchronous. Synchronous coordination involves allocating more processing power to certain parts of the brain while turning down others. This creates a global winner-takes-all system in which one neuron or object wins out over everything else. Asynchronous coordination involves shifting between different tasks rapidly and repeatedly. This has been shown in rats and mice with less-developed brains. Humans and animals with well-developed brains engage in synchronous coordination when performing motor activities that require fine motor control and precision. This occurs during activities like walking where multiple muscles must work together quickly and precisely. During cognitively challenging tasks, humans and animals with well-developed brains use asynchronous coordination. This allows them to focus attention on multiple aspects of a problem at once. This is similar to the behavior observed in rats and mice with less-developed brains who tend to alternate between tasks rapidly. The difference seems to be that well-developed brains perform these tasks much faster and more accurately. Humans and animals with well-developed brains may then be able to engage in simultaneous tasks after training because their brain has become accustomed to shifting rapidly from task-to-task.
III: Conclusion (conclusion)
So far we have discussed why cloud computing is useful for machine learning applications and what a neuron is. In conclusion, we want to summarize everything we have learned so far by explaining why these topics are related. We should return back to our discussion of neural computation vs. neural coordination before concluding our article.
Social media networks have been very successful at engaging large audiences across the globe due to asynchronous coordination between multiple social interactions. While messaging apps like WhatsApp, Facebook Messenger, Kik, Snapchat, and Instagram Stories have all been successful at signing up new users, it is difficult for developers to monetize these networks. One solution would be to introduce synchronous coordination into these networks using features like livestreaming where users watch specific content at specific times. In situations like these, the most popular content wins out over everything else because users develop a strong preference for watching content that has already been selected by other users. Live events also fall under synchronous coordination because everyone watches the same content at the same time. However, introducing synchronous coordination into social media networks may cause a decline in engagement because users lose interest in posts published after live events end. There is also some evidence that shows that asynchronous coordination is better for retention than synchronous coordination. This may be because people remember information better when they do not know when it will be presented again in future. It may also be possible that people remember information better when they can remember when they saw it last instead of focusing on how soon they will see it again. More research needs to be performed in this area before developers can make decisions about when they should introduce synchronous coordination into social media networks.