Powering AI with Neural Networks
The neural network algorithm implements backpropagation which trains the neural network, developed by Geoffrey Hinton in the 1960s. The concept of deep neural networks is motivated by the working of the human brain. The basic of this network is neuron which is fed with multiple inputs which are basically in the form of linear combination and move on via activation function. The output generated by the activation function is the output of the neuron.
Artificial intelligence is providing software solutions which work fine in human domain expert. Considering the Turing test, the world is giving more acknowledgment to AI solutions which are implemented using deep learning and machine learning algorithms. For instance, deep learning is making it possible to analyze MRI scans on acute levels and self-driving vehicles on roads without the help of a human.
Since technology balloon is continuously filling with new innovations, the development of new AI systems that can pass the Turing test requires new machine learning data sets and constant improvement in algorithms. Deep learning in neural networks is getting on top as there are large training datasets which eventually help in low-cost computations and processing.
How neural networks are improving Machine Learning (ML)?
- AI: Artificial Intelligence
- ML: Machine Learning
- DL: Deep Learning
- RL: Reinforcement Learning
- Generative Adversarial Network
- CNN: Convolutional Neural Network
- RNN: Recurrent Neural Network
- NLP: Natural Language Processing
Today ML is not just confined to training large datasets; it is constantly evolving into more complex structures such as GAN (Generative Adversarial Network), RL (Reinforcement Learning) and DL (deep learning) which are based on neural networks. As defined earlier, neural networks work on algorithms that are trained with labeled data and differentiate and make decisions based on pattern or image recognition. Neural networks are powering up Machine learning in technological trends because of great ease in data collection, training, and processing.
- GAN is the category of machine learning comprising two neural networks working in parallel in the zero-sum game structure. Working of GAN is typically unsupervised; therefore it is helpful in reducing dependency between DL and training datasets.
- DL (deep learning) proposes two artificial neural networks which imitate the working of neurons and brain nerves of human. These two networks are Convolutional Neural Network and Recurrent Neural Network. CNN is commonly used in image recognition applications such as robotics, image searching, autonomous, etc. RNN is empowering natural-language processing based applications such as virtual homes, assistants, interpreters, chatbots, etc.
- NLP (Natural Language Processing) is one of the emerging trends in ML growth. Just like neural networks, NLP algorithms are trained on vocal and word-based data sets and serve as an assistant in home and offices.
Main deliberation in AI system development
The technology world has acquired access to cloud services and limitless computing power which in parallel, is improvising data collection and processing. The key consideration in the development of the AI system is a substantial data science toolbox and solid data pipeline. Tools such as Spark, Kubernetes and Docker aid in the conversion of AI solutions to production and in the collection of large data datasets and data pipelines. Open-source tools such as TensorFlow, Keras, and Mllib are considerably reducing effort and resources for the development of ML and DL applications. Some other successes defining factors of AI systems are:
- How difficult is it to integrate human intelligence and knowledge in creating machine learning algorithms?
- How difficult is building human trust in step-by-step automation?
- How to enable data sharing between individuals without compromising private information?
Real-life ML and neural network implications
Virtual assistants are taking the place of human assistants as they are considered to be more reliable and trustworthy than human resources in this race. Virtual assistants are not only becoming common in smart homes but also are in use by large enterprises and businesses which provide predictive insight in business operations by implementing neural networks and machine learning algorithms. For example, Marvis, a virtual assistant developed by Dartmouth College, which provides deep insights into LAN and WAN working in an organization and suggestions for Wi-Fi troubleshooting.
Marvis core development algorithm is NLP which provides answers to the network administrator for questions like “How are the Wi-Fi access points in Baker-Berry Library performing?” As more and more questions are being asked, this assistant becomes more confident by grasping its neural network and improvising the ML data set. Dartmouth is also planning to support the RF planning system which is AI-driven and automated to perform analysis of Wi-Fi systems and power settings with the help of reinforcement learning to improve user experience. The propensity of AI to change human’s life is constantly expanding. The combination of various technologies For example collection of large data sets, storage, and computation; they are authorizing AI which in return creating interference in human society including voice, images, healthcare and automobiles with real-world development. With the progress of this adoption, AI will make its headway towards more innovations which will eventually cause more positive interference in our society.