Artificial Intelligence and Machine Learning
Can you imagine how living used to be before the technological revolution of the 18th century? Have you ever thought about how our lives would have been without Thomas Edison’s invention of the light bulb in the 19th century, Karl Benz’s invention of the automobile or even John Atanasoff invention of the first electronic digital computer?
These are just mere examples of how our lives have changed over the last 1000 years. We invented numbers and letters, planes that made distances seem so short, medical equipment that help us in saving lives and digitalized the whole world while doing so.
This is all credited to the human brain; its capability, capacity and endless creativity. We have the intellectual power to think rationally, behave accordingly, monitor wisely and develop widely all at the same time. Until this day, we have proven that our intellectual intelligence is unmatchable and irreplaceable, or is it?
In fact, and because of our intellects, we were able to transform “thinking” from being solely confined by humans and animals to be impeded in machines and systems, and we called it “Artificial Intelligence” (AI). We even went further when we taught these machines to “learn” from their mistakes and correct them without having to program the systems that tells them what to do. This is what we call “Machine Learning” (ML).
So, throughout this blog post, we’ll be tackling the meaning of artificial intelligence and machine learning, why they’re important and where you can find data that can help you begin your journey with AI and ML.
When it comes to defining AI and ML, there is a misconception that they have the same meaning. In fact, machine learning is considered to be a subset of artificial intelligence.
In simple terms, artificial intelligence is a branch of computer science concerned with developing human-like-machines that can perform tasks which typically require humans. These tasks range from using AI to provide conversations to comfort the lonely (chat-bots) to developing self-driving cars that assure high levels of safety. Uncertainty remains one of the key challenges facing AI. Uncertainty differentiates machines with weak AI that are not made to think for themselves, but created to respond to specific situations and machines with strong AI, that are capable of thinking and acting just like humans.
Strong AI knows how to think even when situations are unprecedented and accompanied with a high level of uncertainty. Achieving a it is not an easy thing to do, the first thing uncertainty management capabilities needs to consider is combining proof about a new situation with gained knowledge about a similar situation, in order to draw interpretations, and foresee the impacts of certain activities.
Machine Learning, on the other hand, is an application of AI that provides systems the ability to automatically learn and improve from experiences without being explicitly programmed.
ML uses statistical programmed techniques, also known as algorithms, to receive data, analyze it and estimate outcomes. These algorithms are used to build intelligent computer systems that can learn from the stored and new data in order to improve operations, enhance performance and assure “intelligent” responses. ML algorithms are usually classified as supervised or unsupervised.
Supervised ML algorithms can apply what has been learned by the analysis of the stored training data set to react to new data using labeled examples and come up with sufficient outcomes. They can compare their estimated outcome with the correct one in order to modify any errors found.
However, Unsupervised ML model provides unlabelled data that the algorithms tries to make sense of, they study how systems can gather a capacity to portray a concealed structure from unlabelled data. In this case, the system doesn’t come up with an output but it can study the new data and draw out inferences on how to show the data’s hidden structures.
AI and ML are not only the involvement of the past or the work of the present but they’re also the promise to the future. According to Oliver Schabenberger, SAS Executive Vice President and Chief Technology Officer: “AI is helping to embed “greater smartness into machines” but it is not taking over the world”.
Being this said, not everyone agrees on the benefits of AI. Some consider that the more we train our machines to be “intelligent”, the more we are teaching them how to be the dominant form of intelligence on the face of this planet. In addition, it is undeniable that the more we revolutionize the society, there is an increasing risk of people losing their jobs and being replaced. For this blog, I will be focusing on the importance of AI (including ML), so here are some examples why AI and ML important:
They reduce human casualties: whether we are using artificial intelligence to program and create a robot that fights in wars or work in a dangerous workplace, AI and ML reduce the human casualties. Take Tesla for example, they incorporated artificial intelligence not only to “enhance” their cars, but also to create new future to car driving. Tesla’s autopilot is designed with unsupervised algorithms that is said to be 9X safer than the human driving. This is because the unsupervised algorithms use billions of data to train itself and come up with outcomes for unprecedented situations the car might be placed in. This is only one example of how AI reduces human casualties; reducing impacts of disasters is another example of how it works. So basically, AI and ML reduce the human danger by incorporating and learning from big data to develop outcomes.
They achieve high accuracy: Artificial intelligence accomplishes inconceivable accuracy through neural systems – which was beforehand incomprehensible. For instance, your connections with Alexa, Google Search and Google Photos are completely founded on deep learning – and they continue getting progressively exact the more we use them. In the medical field, AI procedures from profound learning, and object recognition would now be utilized to discover malignant growth on MRIs with a similar precision as exceptionally trained radiologists.
They rely on big data: the core of AI is analyzing data. Yes, it’s a proof to the power of the human brain and yes machines can reach a point where they can hold surgeries and save lives, but this is going to be left in the imagination if there is no use of data. You need millions of data to train learning models because that’s how they directly learn. So, the more data you gather and feed them, the more accurate they’ll become and the less error your outcomes will have.
Believe it or not, this is not as hard to answer as it might seem like. Nowadays, there are companies based solely to provide users with data from millions of websites. These data can be customized and extracted based on the individuals needs and to best fit their use cases; whether for basic surveying or to enhance their artificial intelligence and machine learning. A great example of data-providing company is ProxyCrawl. If you’d like to check it out click here.
ProxyCrawl provides its clients with html documents extracted from millions of websites. It has multiple products that use artificial intelligence and machine learning. The Crawler for example, is a product present at ProxyCrawl that incorporates AI and ML.
Let’s say you want to get big data from a specific website and you send a request for the Crawler to get the data, whenever the crawler is faced with a problem or wasn’t able to get the data, it automatically tries again 100 times until it successfully gets the required amount of data. Each time it fails, the mistakes “learned” by the crawler are automatically fixed and it tries again to get the request back.
This is just a small example of how you can get data to help you become the next pioneer in a strong technological of artificial intelligence and machine learning.