In the past, man-made intelligence was only achievable after feeding the computer huge amounts of data, setting it up and making it ready for any possible occurrence. An artificial memory needed to be in place, and running. Deep Blue, a popular chess-playing computer created by IBM, is one such example that was famous in the mid-20th century.
To make it a worthy competitor against chess gurus, Deep Blue was pre-equipped with all possible moves that any smart player would think of. With capable processors and with rapid access to important pre-fed information, Deep Blue was able to 'predict' the possible formulas the opponent would consider employing. As such, Deep Blue would beat the opponent, or end in stalemate at the least.
Emergence of the Neural Network
It is said that necessity leads to invention. Deep Blue had some limitations that became evident over time. To start with, engineers had to spend a good number of years designing a program that had all the possible strategies in Deep Blue’s database. After years of designing and programming, Deep Blue had to be able to refer to this ‘store’ more swiftly than any human being. It had to be a fast thinker. The results were worth it but by definition, Deep Blue remained a machine player acting on pre-set rules and guidelines, below the natural intelligence of a human being. It was no match to a human being’s wide and wild imagination, let alone their flexibility. There was the need for more research.
Luckily, information technology researchers and developers never give up. They keep developing new applications and putting them up for trial. There are many researchers who have a keen interest in artificial intelligence, and have invested their time and resources to revise the old concept of neural networks. This aims at enabling machines to establish links between things more or less like a human brain does. The research is proving fruitful, as machines can now learn how to recognize pictures and images identified by a human being. They can recognize what a cat looks like, and tell it apart from a dog. These networks enable a machine to recognize the main features of an item, such as the color, shape, or texture. Neural networks rely on link building.
Using Neural Networks for Cat Identification
The more this machine gets to see different pictures of cats and dogs, the more neural networks it established. The new networks work more like an update does. Specialists in mobile app development will tell you about the need to keep updating a systems database. These updates allow a machine to identify finer features of different animals and objects. For instance, the neural networks are able to identify the difference between the noses and hair of a cat from that of a dog, as a human being would. Unlike Deep Blue, which solely relied on pre-fed information, a machine that uses neural network regularly analyses information, updates itself and then proceeds to use it. This is what enables it to recognize and differentiate things with no human input. Following this, the machine is able to categorize cats, dogs, and unknown species.
Google's Quick, Draw! - a product that has been available since 2016 - is one of the largest investments that have been helpful to researchers in neural networks. The company’s neural network known as Sketch-RNN (Recurrent Neural Network) is on record for demonstrating that machines can master how to draw animals, such as cats. This became achievable following the interpretation of about 5,000,000 drawings created by Quick, Draw! users.
You may be wondering whether this makes Sketch-RNN unbeatable in as far as drawings are concerned. This is not the case. There are those who equate its drawings to that of a toddler. Nonetheless, this is a tool that keeps evolving. Over time, the machine has grasped how to create its own product, as opposed to just reproducing a drawing by a human being. To prove this, Sketch-RNN has images proving the machine did not fall for a trick that a cat has three eyes. The machine follows what it has been learning over time.
In the end, Sketch-RNN has been able to create drawings on its own, with zero human interference. One of the most exciting is the final ability of Sketch-RNN to successfully finish a drawing that has been half-done by a human. The fact that the machine had the ability to reach some important decisions was quite fascinating. For instance, it would create bigger eyes for owls, fit mosquitoes with wings, and give an oval shape to the human face among others. The images on Sketch-RNN show the machine was able to automatically complete segments of a drawing left out by a human being.
Top Machine Learning Applications around the World
Besides drawing cats and dogs, are there other applications that use machine learning in the real world? The reality is that this technology is currently employed in many applications.
Other top uses include:
Recognizing images. The technique differentiates categories and characters as discussed above.
Providing medical diagnosis. This technology has been helpful in solving a number of medical problems by analyzing a different diagnosis.
Recognizing speech. This includes speech translation or putting speech into text. Different softwares are able to tell the words that are pronounced.
In classification. The technique makes it easy to identify different variables which makes classification achievable.
Applied in regression.
In extraction. Machine learning helps in separating structured information from that which is not. Extracting information is necessary for different articles and business reports that need to have summaries in MS-Excel or other formats.
Machine learning is used by people in computer games and sports outcome predictions. These machines can predict some possible outcomes without any human assistance.
In A Nutshell
As seen above, the machine could go beyond recognizing a cat from mere sketches. It can grasp the finer details of its appearance. It is able to tell this animal has two eyes and some whiskers. That a machine can complete a drawing left half-complete by a human being is very inspiring. Creativity at its best! As to whether a learning machine can fully replace the need for human resources remains unproven.
Having a field that can imitate the human’s way of learning is a great milestone in artificial intelligence. Neural networks have set the idea that machines can 'acquire' knowledge just as we do. That if provided with the opportunity to learn, machines can complete different tasks with minimum or no human supervision. In any case, we understand cats better by seeing and living with them in our lives. By learning how cats behave and appear, machines too can learn.
Have you had any experience with a learning machine? How did you find its intelligence capacity? Kindly share your experience by commenting in the section below. If you are in search of qualified PHP developers, browse through the qualified ones here.