Humans have always been fascinated by the thought of man-made machines advancing to the point of self-awareness. Several movies like Terminator, I Robot have been made where robots become so intelligent that they start to take over the human race. This is where the basic concept of AI and Machine Learning comes in. 

What is exactly Machine Learning, you ask? Machine learning, as the name speaks for itself, is when machines are fed with enough data that they learn to imitate the human brain. The world is filled with data, a lot of data – pictures, music, words, spreadsheets, videos, and whatnot, and it doesn’t seem like it’s going to stop anytime soon. In fact, the data generated every day keeps increasing. Machines Learning is the art of driving meaning from all of this data. You can use tools and techniques to answer questions with your data. 

Now you must be thinking “But then, how is that different from AI?,” “Or are they both the same thing?” Well, any device that perceives its environment and takes actions in order to maximize its possibilities of success can be said to have some kind of artificial intelligence. We can say that AI exists when a machine has cognitive capabilities such as problem-solving and learning. 

AI has three levels. Narrow AI is when a machine can perform a specific task much better than a human, such as our smartphones. General AI is when a machine can perform any intellectual task as a human would perform. Strong AI is when a machine can beat humans at a lot of tasks. Machine learning can be considered as a subset of AI. For machine learning, algorithms are trained and past examples are used in a model that maps features to the corresponding outcome variable. Most of the business applications rely on this. 

This brings us to the discussion of practical applications of AI and Machine Learning and their impact on the future. There is a lot of data in the world today, generated not only by people, but also by computers, phones, and other devices, and it will only continue to increase in the following years. As the volume of data surpasses the ability for humans to make sense of it and manually write those rules, we are turning to automated systems that can learn those rules from the data and make changes in data to adapt to a shifting landscape. The very easy and basic example would be Facebook recommending a place or person to be tagged in a photo. That is clearly machine learning at play.  

The biggest example is Google itself. It is a system that utilizes machine learning at its core. From understanding the text of your query to adjusting the results according to your personal interest, and deciding which results to show you on top, Google is all about AI and machine learning. 

The immediate application of AI and machine learning include image recognition, fraud detections. text to speech and recommendation systems. Not just that, machine learning has impotent medical and business applications as well. It can be used in diabetic retinopathy and skin cancer detection to retail and transportations in terms of smart cars. In the finance and banking sector, Nomura Securities from Japan is using AI to predict patterns and analyze the insights of experienced stock traders with help of computers and find out when it’s is best to invest.

It was not that long ago when a company or product had machine learning in its offerings, it was considered novel at that time. Now every company has amalgamated machine learning in its products in some way. With time, it is becoming an expected feature. Just as we expect companies to have a website that works on your mobile app, the day is not far when it will be expected that our technology will be personalized, insightful and self-correcting. As we use machine learning to make human tasks better, faster, easier than before, we can also look further into the future when machine learning can help us do tasks that we never could have achieved on our own. 

It’s really not hard to take advantage of machine learning today; all you need is data, developers and willingness to take the plunge. Neural nets are being used in a vast number of fields and have been practically implemented in different aspects. 

Let’s find out where! 

  1. Recently neural networks have been used to restore colors to black and white photos and videos. 
  2. Image enhancement with very low-resolution images is now possible. This can be useful for crime control departments to find out culprits.
  3. Lip net is a neuro network developed by Oxford University and Google deep-mind scientist. This network can watch a silent video of a person talking, closely register lip movements and convert it into text.

More examples of how AI is used in our daily life is the voice commands we give to our smart devices that in-turn enable it to perform tasks. Amazon Echo is the biggest example of that. 

Astronomers are using AI to help them in their space expeditions as well. They are using AI to sift through years of data obtained by the Kepler telescope to identify a distant eight planet solar system. Moreover, the next Mars mission, the Mars Rover Mission, is vastly aided by AI. This is how far AI has come.

AI’s biggest accomplishment is perhaps in the gaming sector, Deepmine’s AI-based, AlpaGo software is the very first AI program that was able to beat a professional human player Fan Hui in 2015. It doesn’t stop here; another better version of AlpaGo named, AlpaGO Zero was made and then both software were used against each other for an AI vs AI faceoff. 

Summing up

In conclusion, AI and machine learning are part of one another, in fact, machine learning is a subset of artificial intelligence. Regardless of their definition, both can do wonders for us. However, we can only take full advantage of AI, if we have a high-speed internet connection with it. If you are living in Fort Worth try Spectrum Fort Worth Internet today and avail various internet packages and fast speed so all your smart devices can work seamlessly. 

Author Bio:
Baldwin Jackson is a content marketing expert who loves to write about the latest technology innovations and trends. When he is not working you can find him gaming or reading ancient cultivation history and culture.