Artificial Intelligence
Introduction
If you think back 30 years, humans could never have dreamed that artificial intelligence would take such a big step forward and have such a positive impact on our lives. Artificial Intelligence has accelerated life’s pace. Artificial intelligence (AI) has given rise to applications that are now having a significant impact on our lives.
The term AI was initially coined in 1956 at a Dartmouth meeting. Artificial intelligence (AI) is the ability of a computer or a computer-controlled robot to accomplish tasks that would normally be performed by intelligent beings. In today’s world, Artificial Intelligence has become highly popular. It is the simulation of human intelligence in computers that have been programmed to learn and mimic human actions. These machines can learn from their mistakes and execute activities that are similar to those performed by humans.
AI make Business Faster Smarter More Easier More Earner
Building an AI system is a painstaking process of reversing our features and talents in a machine and then leveraging its computing strength to outperform our abilities. To comprehend how Artificial Intelligence works, one must go deeply into the many sub-domains of AI and comprehend how those domains can be applied to various industries of the industry. Machine learning, deep learning, neural networks, computer vision, and natural language processing are examples of these fields.
Artificial Intelligence entities are constructed for a variety of goals, which is why they differ. The following are the several types of artificial intelligence:
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
Artificial Intelligence’s goal is to augment human capabilities and assist us in making complex decisions with far-reaching repercussions. AI performs regular, high-volume, automated tasks rather than automating manual ones. And it does so consistently and without tiring. Humans still need to set up the system and ask the correct questions, of course.
AI adapts by allowing data to program itself using progressive learning algorithms. In order for algorithms to learn, AI looks for structure and regularities in data. An algorithm can train itself to play chess, just as it can educate itself to recommend a product. Deep neural networks are used by AI to attain remarkable precision. Your interactions with Alexa and Google, for example, are all based on deep learning. And the more you use these things, the more accurate they become. Deep learning and object identification AI techniques can now be utilized in the medical profession to spot cancer on medical photos with greater accuracy.
Teachable Machine
If you are an AI enthusiast, you’ve probably heard about Google’s Teachable Machine. Teachable Machine is a web-based tool that was created to make machine learning accessible to everyone.
If you visit Google’s Teachable Machine site, they allow you to upload pictures of different classes and then train a client-side machine learning model on these pictures.
An example of how Teachable Machine works:
There are two classes of images you need to upload. First, you upload around 100 pictures of yourself and label them as Class 1. Then, you upload another 100 photos of your cat and label it as Class 2.
Then, you click on the “Train Model” button, and a client-side machine learning model will learn to distinguish between pictures of you and your cat.
You can then use this model to make new predictions on images.
Google released Teachable Machine some time back, so people who aren’t well versed with AI can visit the site and train their models. It allows non-technical people to get acquainted with machine learning.
You can build your version of Google’s Teachable Machine.
The steps you need to take are as follows:
Create a client-side application that allows users to upload images of multiple classes.
Collect the images, transform them, and train them on top of a pre-trained model. You can do this on the client-side using a language like JavaScript. Pre-trained machine learning models can be accessed in JavaScript through languages like ml5.js and tensorflow.js.
After the model is trained, send a notification on the screen , so the user knows it’s done. Then, get the user to upload pictures of each class to make predictions on new images.
Autocorrect Tool
Autocorrect is an application of AI that we use every day. It makes our lives easier by taking care of spelling mistakes and grammatical errors.
To build an autocorrect tool in Python, you can use the TextBlob library in Python. This library has a function called ‘correct().’ If you call this function on a piece of text, it will identify incorrect words and replace them with the closest word to the one typed.
It is a relatively simple task, but it’s essential to keep in mind that the TextBlob library isn’t perfect. The underlying algorithm cannot detect certain mispelt words and makes corrections when the initial word was correct, like replacing ‘is’ with ‘as.’
This tool isn’t able to grasp the context between thee two words and doesn’t do any kind of mapping to identify words that are commonly used together. For example, if I were to write ‘I like your short’ instead of ‘I like your shirt,’ the algorithm wouldn’t correct me. These words are spelt correctly but don’t fit in the context of the sentence.
You can enhance the limitations of this model by building your own — you can use a pre-trained NLP model like BERT that has been trained to predict words that fit into a specific context.
Fake Product Review Identification
This AI project is similar to the Instagram spam detection project listed above. There are many business owners out there who fabricate reviews for their products to get more sales misleading individuals who are looking to purchase high-quality products.
You can build a fake review identification system to solve this problem. Kaggle has a dataset called Deceptive Opinion Spam Corpus that you can use for this project. This dataset contains 1600 hotel reviews - 800 of them are positive, and another 800 are negative.
These reviews are already labelled, so you just need to do some data pre-processing and tokenisation on all this data before training your model. You can use transfer learning for this purpose with pre-trained models like BERT, RoBERTa and XLNet.