Apple’s launch of its iconic iPhone X has made a breakthrough in the world of mobile industry and a big leap in its technological advancements. Apple has established itself as the global leader for Smart Phones.

iPhone X’s much-lauded feature called the ‘Face ID’ (Facial Recognition system) using the cutting edge technology called the “Artificial Neural Networks (ANNs) to unlock the phone without a home button has run into issues with a gap in the technology.

The children under the age of 13 years and siblings with twins will have difficulty in recognising them apart by the Face ID due to their distinct facial features may not have fully developed, and in case of the twins the Face ID is unable to distinguish the two separate individual faces due to the gap in the current ANNs technology.

Let me explain this further, ANNs is a form of connectionism or computing systems based on the function and structure of the biological neural networks. ANNs is the building block of the Internet of Things (IoT).

ANNs is based on a collection of interconnected nodes similar to a vast network of neurons in a human brain. The information that flows through the network affects the structure of the ANNs due to a neural network changes or learns, in a sense based on the input and output.

It is considered just the way a human brain recognises an object. There are a brain and a neural network or neurons in our body which senses an object. There are a memory and data which is the input stored in our system which recognises the object through the Human Neural Networks (HNNs) the moment it sees it.

For example, If you hold a comb horizontally in front of a camera without showing me its side views, the comb will appear to me like a stick. Since the ANNs is not programmed for a 3D mapping of an image or the object. Although men have tried to emulate the Neural Network, unfortunately, the ANNs does not recognise the three-dimensional image mapping, as the algorithms are not defined yet.

However, even if the ANNs recognises the three-dimensional image mapping, the neural network has its limitations in the present scenario. In case of the identical twins the iPhone X, the Face ID still unlocks the phone which shouldn’t be the case. This is because the phone has captured the biometric features of the face of one twin, whereas the second twin looks at the camera the phone unlocks.

This only means that we are still at a stage where the ANNs doesn’t have the intelligence developed in it to recognise that there is a twin even if there is a 3D approach the differentiation of the object is challenging as of now. However, new technologies should be developed which recognises the twins as separate objects.

The technology that is available today has already been made use for developing the Autonomous Vehicles (AV). Autonomous vehicles should recognise the signals, cars in front, behind, side, and other movements of objects/people on the road. But, if we place an image of a human being in front of the road how the autonomous vehicle will recognise that the image is not a human being but a picture of a human?

Even though the Artificial Neural networks are working inside the car, it is still not developed entirely due to the gaps and limitations in the technology. However, the facial recognition and any other kind of biometric identification have to be built even for Home Automation or for Industrial IoT.

The Neural network forms the basis of the foundation or the block or the framework on which the entire IoT is built or structured, and the advancement in the ANNs development has to continue in parallel. In another scenario, where the sensors for a running should be able to sense any obstruction or a break-downs in the system but if in case there is a glitch in that particular sensor and fails to detect the obstacle then the whole purpose of developing the cutting-edge & game-changing IoT technology itself fails.

Hence, the ANNs must be developed for recognition of the object with its patterns, characteristics, features, behaviours, gestures, movements, etc. and defining the kind of action that needs to be performed requires the use of Artificial Intelligence (AI). AI forms the second major block for IoT which will create this entire IoT Superstructure. However, currently, there are no solutions to address the gap in the ANNs it only has basic and rudimentary solutions available for now.

The solution which I envision to address this problem is to develop not only ANNs but has to be a combination of Human and ANNs. The will require a cell to be developed in the lab which will then combine with the AI outside. The original cell of a human being and the Artificial Intelligence from outside combined will give a reliable and robust technology for future Computing and advancements for IoT verticals.

To summarise, there is a Neuron or a Neural network which is real and not an artificial one which will send information that will be tracked by the ANNs which converts it into its algorithms and provides a proper output or a command. The Industry Stakeholders should focus on continuing to advance their R&D in this space. The Future of IoT Framework will be on “Human Neural Networks (HNNs) collaborating with Artificial Neural Networks (ANNs).”