Artificial neural networks and machine learning are a big part of our personal and work life.
But where did it all start and, what predictions can be made about the future of artificial neural networks?
A team led by Ross King at the Manchester Institute of Biotechnology created an artificial intelligence scientist named Eve, which helped researchers discover that Triclosan can be used as an anti-malaria drug. Additionally, the research published by the team found that Triclosan could be used for certain strains that have developed resistance to other common drug therapies for malaria. Such advanced specimens like Eve, advanced chatbots, and autonomous cars, suggest that the vision for artificial neural networks is actually shaping up!
Now, artificial intelligence finds application in various different areas such as virtual assistants, medical research, self-driving cars, and online retail stores. But, the advancements in artificial intelligence and machine learning started with a mathematical model, which laid the groundwork to build the future of artificial neural networks. Additionally, the mathematical model was created with the sole motive of building a machine that possesses the ability to think like humans. The idea of teaching AI to navigate like our brains is as old as the invention of computers. We have always dreamt of machines being our perfect companions. With artificial neural networks, the path to achieving that dream has become clearer.
The History of Artificial Neural Networks
In 1943, Warren McCulloch and Walter Pitts laid the first brick in the foundation of an advanced future of artificial neural networks. Warren McCulloch and Walter Pitts developed a mathematical model of an artificial neural network using threshold logic to mimic how a neuron works in a human brain. Thereafter, Frank Rosenblatt created the “Perceptron” model, which was the first of its kind to perform pattern recognition, in 1958. But, Marvin Minsky and Seymour Papert found multiple problems with the Perceptron model, which were later solved by Paul Werbos in 1975 using Back Propagation. Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks were created by Jürgen Schmidhuber’s research group, which won eight international competitions in pattern recognition and machine learning.
Artificial Neural Networks in the Present
Artificial neural networks are advancing exponentially. And the future of artificial neural networks has only become brighter with augmented reality, machine learning, artificial intelligence, and big data. Combining artificial neural networks with other technologies have made the networks more useful for various different applications. One of the applications of artificial neural networks is chatbots.
Chatbots are extensively used for customer support in all major organizations, today. Before the invention of chatbots, organizations hired a room full of people whose only job was to provide customers with support and answer their queries. But with chatbots the entire process is automated, providing complete customer satisfaction with little to no human intervention. Chatbots are used by all major brands on their websites and social media pages to interact with the customers and to provide a user-friendly experience. Then there are the virtual assistants. Virtual assistants like Siri, Google Assistant, and Cortana that can mimic human conversation and perform simple tasks such as booking a cab, setting reminders, providing weather information, booking a movie ticket, and playing music to name a few.
Online retailers use neural networks with machine learning to predict demand for inventory based on past and current purchases of customers. Navigation services, such as Google Maps, use neural networks along with the GPS technology to provide time effective and safe route suggestions. Neural networks and deep learning collect information about which roads are the most frequently traveled and the traffic situation on each road to suggest routes that are most convenient and have the least possible traffic. The future of artificial neural networks hints towards the creation of self-driving cars. The data collected by machine learning and neural networks is used to test self-driving cars. Additionally, technology giants like Facebook, Google, and Apple, extensively use neural networks for facial recognition. With the help of neural networks, social media platforms such as Facebook are identifying people using their faces alone.
The healthcare sector is going to greatly benefit from the developments in the future of artificial neural networks. Research suggests that artificial neural networks can be used with artificial intelligence to diagnose fatal diseases such as cancer and suggest effective treatment for the same. Moreover, neural networks and artificial intelligence will have the potential to discover new drugs for treating life-threatening diseases. Some of the novel applications of neural networks include earthquake prediction based on the existing seismograms and creating artworks based on existing iconic paintings by Van Gogh, Picasso, and many more.
The Future of Artificial Neural Networks
Governments and private organizations have realized the true potential of the future of artificial neural networks. All major organizations have increased their funding in artificial intelligence, neural networks, and machine learning to facilitate more research and development. Most researchers are working explicitly to create more advanced artificial intelligence systems that can adapt to new data like the human brain does. Neural networks and machine learning possess the ability to learn from large data sets, which are beneficial to create a machine that can think and work like humans. When artificial neural networks are clubbed with artificial intelligence, machine learning, IoT, and big data, multiple possibilities can be explored in various sectors.
Artificial neural networks along with machine learning and artificial intelligence can flawlessly predict severe illnesses. For example, the output of waves of an ECG can be analyzed to understand a patient’s heart and predict heart attacks well in time. Similarly, with an adequate amount of data, dementia can be identified in the early stages by understanding and analyzing EEG patterns. Along with diagnosis, artificial neural networks and machine learning can work together for discovering drugs for the treatment of multiple serious illnesses. Furthermore, the introduction of autonomous cars has the potential of reducing traffic jams and accidents.
Neural networks can be extensively used for predicting natural calamities like earthquakes, floods, and volcanic eruptions. Data like seismographs and atmospheric pressure can be collected on a daily basis to analyze and predict the occurrence of natural calamities. Additionally, neural networks can effectively predict changes in the weather and the climate. The future of artificial neural networks hints that chatbots are impacting the retail industry tremendously. The need for human intervention will gradually be reduced and all the jobs that require human interaction will be replaced with cost-effective chatbots.
The invention of neural networks has revolutionized every sector in the market. Researchers have unlocked the full potential of neural networks by using networks in association with other advanced technologies such as artificial intelligence, machine learning, and big data. No wonder, every major organization is betting on the future of artificial neural networks. An example of the many possibilities with neural networks is the prediction of share prices in the stock market. Hence, organizations need to understand the potential of neural networks and deploy new technologies like machine learning and artificial intelligence to leverage the network at the right time and for the right business objective.
The future of artificial neural networks is going to unlock multiple possibilities in various business sectors. Hence, Organizations must know how the adoption of neural networks is going to benefit the brand and create effective strategies accordingly. Additionally, hiring a team having niche skills would make the process of adoption and implementation of the technology way smoother. And, it is essential that every employee is well-informed about the technologies being deployed, and how the implementation will make the organization better.