The evolution of neural networks



Artificial neural networks and machine learning are an integral part of our personal and professional lives.

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, who helped researchers discover that triclosan can be used as an antimalarial drug. Additionally, 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. Specimens as advanced as Eve, advanced chatbots, and self-driving cars suggest that the vision for artificial neural networks is taking shape!

Now, artificial intelligence is finding applications in various fields such as virtual assistants, medical research, self-driving cars, and online retail stores. But, advances in artificial intelligence and machine learning began with a mathematical model, which laid the foundation for building the future of artificial neural networks. In addition, the mathematical model was created for the sole purpose 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 dreamed of machines being our perfect companions. With artificial neural networks, the path to realizing this dream has become clearer.

The history of artificial neural networks


In 1943, Warren McCulloch and Walter Pitts laid the foundation stone for a future advanced 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. Subsequently, 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 discovered multiple problems with the Perceptron model, which have then solved by Paul Werbos in 1975. using backpropagation. Between 2009 and 2012, Recurrent Neural Networks and Deep Feedback 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 day

Artificial neural networks are growing exponentially. And the future of artificial neural networks has only brightened up with augmented reality, machine learning, artificial intelligence and big data. The combination of artificial neural networks with other technologies has made the networks more useful for a variety of different applications. Chatbots are one of the applications of artificial neural networks.

Chatbots are widely used today for customer support in all large organizations. Before the invention of chatbots, organizations hired a room full of people whose only job was to support customers and answer their questions. But with chatbots, the whole process is automated, providing total customer satisfaction with little to no human intervention. Chatbots are used by all the major brands on their websites and social media pages to engage with customers and provide a user-friendly experience. Then there are the virtual assistants. Virtual assistants like Siri, Google Assistant, and Cortana that can mimic a human conversation and perform simple tasks like booking a cab, setting reminders, providing weather information, booking a movie ticket, and playing music to do nothing. name a few.

Online retailers use machine learning neural networks to predict inventory demand based on past and current customer purchases. Navigation services, such as Google Maps, use neural networks as well as GPS technology to provide efficient and safe route suggestions. Neural networks and deep learning collect information about the busiest roads and the traffic situation on each road to suggest the most convenient and less traveled routes. The future of artificial neural networks portends the creation of self-driving cars. Data collected by machine learning and neural networks is used to test self-driving cars. Additionally, tech giants like Facebook, Google, and Apple make extensive use of neural networks for facial recognition. Using neural networks, social media platforms such as Facebook identify people using only their faces.

The healthcare sector will greatly benefit from future developments in artificial neural networks. Research suggests that artificial neural networks can be used with artificial intelligence to diagnose life-threatening diseases such as cancer and suggest effective treatment for them. In addition, neural networks and artificial intelligence will have the potential to discover new drugs to treat life-threatening diseases. Some of the new applications of neural networks include predicting earthquakes based on existing seismograms and creating works of art based on existing iconic paintings by Van Gogh, Picasso and many others.

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 further facilitate research and development. Most researchers are explicitly working 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 sets of data, which is beneficial in creating a machine that can think and work like humans. When artificial neural networks are combined with artificial intelligence, machine learning, IoT and big data, multiple possibilities can be explored in various sectors.

Artificial neural networks as well as machine learning and artificial intelligence can predict serious disease flawlessly. For example, the wave output of an ECG can be analyzed to understand a patient’s heart and predict heart attacks in time. Likewise, with an adequate amount of data, dementia can be identified at an early stage by understanding and analyzing EEG patterns. Along with diagnosis, artificial neural networks and machine learning can work together to discover drugs for the treatment of several serious diseases. In addition, the introduction of self-driving cars has the potential to reduce traffic jams and accidents.

Neural networks can be widely used to predict natural disasters such as earthquakes, floods, and volcanic eruptions. Data such as seismographs and atmospheric pressure can be collected daily to analyze and predict the occurrence of natural disasters. In addition, neural networks can effectively predict weather and climate changes. The future of artificial neural networks suggests that chatbots are having a huge impact on the retail industry. The need for human intervention will gradually be reduced and all jobs requiring human interaction will be replaced by profitable chatbots.

The invention of neural networks has revolutionized all sectors of the market. Researchers have harnessed the full potential of neural networks by using networks in conjunction with other advanced technologies such as artificial intelligence, machine learning and big data. No wonder all large organizations are betting on the future of artificial neural networks. One example of the many possibilities offered by neural networks is the prediction of stock prices. Therefore, organizations need to understand the potential of neural networks and deploy new technologies such as machine learning and artificial intelligence to harness the network at the right time and for the right business purpose.

The future of artificial neural networks will open up multiple possibilities in various industries. Therefore, organizations need to know how the adoption of neural networks will benefit the brand and create effective strategies accordingly. Additionally, hiring a team with specialized skills would make the technology adoption and implementation process easier. In addition, it is essential that every employee is well informed about the technologies deployed and how the implementation will improve the organization.


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