Brain cells, clusters of learning elements and artificial intelligence.

The learning elements that constitute the building block of Neural Networks are similar to the brain cells in humans. The similarity, coming from how the brain works as far as learning, memory, recollection, understanding and making associations are concerned. The brain’s primary equipment, is the enormity of cells – brain cells (100 billion cells) – that it has at its disposal. To be precise, the more the amount of cells in an individual’s brain, the more learning they would be able to accomplish, with deeper associations and understanding. The reason for this being that, brain cells get used, when learning takes place and when memory is formed. Such that if the brain, runs out of these cellular structures, assimilation becomes difficult. Under such circumstances, recreation and rejuvenation becomes necessary, if brain work is to continue.

To add to this, thoughts are created when sensory information from the body, arrives at the brain and creates cellular clusters. Which are unique patterns, that become the building block of memory, formed as a cerebral version of the impulse received. After much of this, has been stored, a collection of these become memory, and can be replayed or recalled. This is made possible, based on the nature of used brain cells, which form geometrical arrangements. These when energised by the central nervous system, begin to play back any captured sensory input. They take the form of an emotion or recollective imagination.

This is why addictive substances, such as alcohol, which remove brain cells, are really not good. As they equally remove brain cells, that have become part of a memory, an emotion or a though. Such people complain of memory loss, loss of cognitive ability and detachment from past experiences.

The same science is used in building machine learning algorithms, which employ neural networks, to emulate the human brain and its learning ability. Neural networks, would be able to gather on speed when compared to the brain in performance. The human brain, at the highest level of focus and concentration, operates at 30 Hertz. Where as modern day computer CPUs, which are the housing for neural network algorithms, operate at 2 Giga Herz (2,000,000,000 Hertz) speed. Making the learning of more intricate data possible, using algorithms.

Neural networks, learn by substituting brain cells with numbers in a N by N matrix formation. They recognise patterns in a problem, which is captured as data, and converted to a numerical vector. This data could be in the form of text, sound, an image or time series. Such that, for each feature in the data being learned, a neural network matrix assumes a cluster formation. Which can result in several layers of clustering, for the learning and recognition of more complex and intricate patterns. An approach known as deep learning. This mathematical analogue, that works similar to the human brain, is known as the algorithm.

Algorithms with sufficient learning elements, such as the human brain, can be used to automate anything from labelling email as spam or not spam to the self driving of vehicles. If they are trained with a large enough training set, having a good set of features to learn from. With this approach, algorithms come up classifications such as the RGB values, of pixels, of images and the ID numbers of persons. The predictive analysis of the pattern in a timely series of recurring event, such as customer churn, employee resignations, health issues, machine break downs and dates when they are most likely to re occur. The clustering of products for anomaly detection or people for behavioural traits that could lead to fraud.

Beyond the iPhone:

Ever since Apple launched the iPhone in 2007, the ground shaking effects it had on the smart phone industry, caused tech enthusiasts to wonder what the next possibly game changer could be. Considering the massive turnover in sales the iPhone has had in the brief period of time it’s around. With its developer APIs, application centric user interfaces and the elegant touch screen design. One would wonder what new features, on top of these, for a smart phone product, could change the face of the market once again.

Newer iPhone versions, such as the the iPhone X, seem to be the only new comers that rock the boat. With Apple expected to increase its iPhone X production by 45%, to meet consumer demand. It seems the company’s internal research and development, for mobile devices, is more innovative than that of its competitors.

However a lot of work has been currently going on in other frontiers. Google ended its google glasses project to begin, a glass 2 version called, Project Aura. Microsoft has been developing the Holo lens under project Baraboo using the windows, mixed reality platform and Facebook has been doing more with Virtual Reality headset, Oculus rift.

Someone would guess that the next thing, beyond the iPhone, could be a device that unlocks applications from their 2D existence and liberates them into the 3D environment. This would be a good place to start, as the chief selling point of the current generation of smart devices, has been their seeming three dimensional look and feel. Along with the fact that a user interacts with the applications, using a more intelligent approach(touch screen), as you would things in the real world.

A mishmash of technologies such as virtual reality, augmented reality, computer vision with biometrics would make this possible..The application of headset technologies would make this possible. With the use of hand gestures for invoking, using and closing applications and the optimisation of walls and flat surfaces as Virtual Reality (VR) screens; when running multiple application instances. Applications would practically leap out of the box and begin an unencumbered existence, side by side objects in the real world.

This is expected from the new wearable products being developed by Google, Microsoft and Facebook. Which would run not only games and video applications but others such as email, social networking etc. The same kind of allure, that accompanied iPhone’s first versions, would make this new device to gain popularity. And If end users prefer its wearable nature to previous hand held devices, it could just be the iPhone killer.