Facebook AI [Artificial Intelligence] And Machine Learning Platform
Surely everyone knows Facebook and its social network. However, Facebook is not just a social network but is also one of the companies that do the most research in technological development and Artificial Intelligence. Facebook has quite a few AI and Machine Learning platforms. Many of these are still in development today and day by day they continue to improve to the point of seeking perfection. Unfortunately, this is still impossible, but much progress has indeed been made in recent years. As if that were not enough, Facebook also makes various AI platforms freely available to any user so that people become familiar and more people join in developing these AIs.
What Platforms Does Facebook Offer
Practically anything we need regarding Machine Learning we have at our fingertips included for free. Within their AI page, we can find various frameworks and tools. On the other hand, we can also find libraries and data sets, languages, discourses, and reasoning. Within each of these sections, there are various ML and AI platforms. Some of these platforms are even revolutionary in their sector, such as PyTorch, which is a Deep Learning framework which is a Python package designed to perform numerical calculations using tensor programming. It also allows its execution in GPU to speed up calculations.
The main advantage that PyTorch offers over other platforms is that it takes into account aspects of real life. To quickly understand this can be done with an example. If you throw two billiard balls, one against the other, the logical thing is that they collide. However, things like these are not contemplated on other platforms. They are in charge of controlling the movement of the ball, verifying its trajectory, and making sure that it remains a ball. However, they cannot take into account what would happen if it collided with something. In PyTorch, this can be done, since variables can be changed after a process has started. On the other platforms, you have to predefine all the possible variables at the beginning.
Another great platform is ONNX, which is responsible for representing Deep Learning models, which allows developers to move AI models between different platforms. ONNX supports many well-known frameworks such as PyTorch, Caffe2, Microsoft Cognitive Toolkit, and has added support for CoreML, TensorFlow, Qualcomm SNPE, Nvidia’s TensorRT, and Intel’s graph.
Now that we know different frameworks, let’s get to know other sections that are also very interesting. One of the available platforms is the translator. Every time we realize that there are more platforms of translators available. However, each one has sections that others do not have and it is always good to be able to compare different platforms and choose the one that seems best in practice. In addition to this, Facebook has a platform called ParlAI, which is compatible with PyTorch and TensorFlow. This is responsible for collecting voice-to-text data and understanding it, even being able to answer questions as chatbots currently do.
To finish, it is necessary to name a tool that will be increasingly necessary for the future. We are talking about the tool dubbed Hateful Memes. This is in charge of addressing a great challenge today, such as classifying memes according to whether they really try to cause laughter, or on the other hand, simply try to be hateful to some group of people or even directed towards a single person. The big problem with this is that memes are usually a photograph accompanied by text. It is easy for us to understand that photography and text go together to give it meaning. However, a machine would interpret them separately. That is why Hateful Memes has developed a way to classify memes into 3 different types:
1- Predominantly text
2- Predominant image and text
3- Predominant image
By cataloging them in these 3 groups it is easier to understand what the meaning of each meme is. Once the machine has been able to understand the meaning that is given to that meme, it is time to classify whether it is hateful or not, thus managing to eliminate posts that are harmful to various groups of people. To conclude, we must add that Facebook is doing a great job in developing its AI and highlighting how important it is to allow other people to try its AI for free and understand how it works. In this way more people will join the development of ML and who knows, maybe a decade from now we will have ML tools capable of performing tasks exactly as a person would.
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