A new wave in our world: Artificial Intelligence (AI) is the way forward!

You would have noticed a certain spike in interest in most of the leading company’s world over for Artificial Intelligence. Biggies like Microsoft and Google promptly see at as a facet that will change the future. It is intriguing therefore to know what is AI? Or what is making it so significant at this time? Undoubtedly, while most of us lay people only make an acquaintance of AI by the articles we read or snippets of someone quoting its prominence, only the brainstems behind AI can actually fathom its reach. That is about to change a little – since this article is going to give you an insight into some of the nuances of Artificial Intelligence.

As all things need to pan out at the very word go you will get to know the meaning of AI and it various nuances like” machine learning”. Explore ‘deep learning’ which one of the most prolific areas in AI. Understand how AI can constructive solve problem and how this carries a value. Furthermore, to go a little beyond and discover what took AI created in the 50’s so long to herald a change in our thinking.

The focus of most emerging trends is to ensure or give a massive significance to companies or consumers alike and this is something all venture capitalists seek for today. This is where AI is recognised as a developmental gain more prominent than that of say the, use of cloud computing or a mobile for that matter. To further what is already been written thus far, Jeff Bezos, CEO Amazon wrote, “It’s hard to overstate, how big of an impact AI is going to have on society over the next 20 years.”  Well, whether you believe it or not, emerging trends are about to change the way we perceive the world and you will understand why as you read along.

  1. Meaning of Artificial Intelligence

The art of programming intelligent systems: Artificial intelligence

Artificial Intelligence was a generic term introduced by John McCarthy, the Assistant Professor of Dartmouth University in 1956. What is meant by this is that AI is any elements of intelligent patterns displayed by either software or hardware created. “The science and engineering of making intelligent machines, especially intelligent computer programs”, as is the words of Professor McCarthy.

What is apparent is that AI has been around for eons now, albeit in a basic manner, where simple programs based on rules showed signs of excellent comprehension in very finite measures. However, the success of AI was marginal because it would be extremely challenging for people to create algorithms that could handle everyday situations.

In an instance where ascertaining when a machine would fail, or measuring market values of credible assets, getting a medical synopsis of someone’s health, engage an array of data sets and non-linear associations with these variables. Owing to this fact it would be extremely hard to optimise the forecast because the data cannot be used to its potential. Whereas, if you look at say, translating languages, or identifying certain elements in an image, to determine these specific rules that explain a characteristic is not possible. To break this down, it would be extremely hard to create rules that work in these various aspects, even when it comes to getting around the specs of a rabbit.

In stating the above, what if data optimisation and feature specification and the intricate forecast around it where reassigned from the programmer to the program? This is the eventuality of Artificial Intelligence and how it changes thinking.

Nuances of Machine Learning:

As shown in the figure above one can clearly see that every aspect of AI is machine learning, but not every aspect of machine learning can be deemed as Machine learning (ML). Machine learning is definitely a division of AI, and the any interest into the boarder scheme of AI truly owes its credit to ML, for in this area progress in largely palpable and happens sooner.

What ML does is it allows room for creating algorithms that can issues that are multifaceted for humans to resolve. Paraphrasing one of the AI leading men Arthur Samuel, who had mentioned that machine learning, allows computers the aptitude to grasp concepts with no overt programming that need to be done, way back in 1959.

In any specific use case, the use of ML is to create a specific forecast. Therefore all the information specific to the area will be fed into the algorithm (for instance how many times a person has seen a particular serial) and to also ascertain information around it to make a decisive forecast or prediction (like which episode of the serial was watch repeatedly). What needs s to be understood is the fact that when computers are given the aptitude to learn, we further the ability of optimisation in the algorithm. This is that we gain an insight into the value of each variable from the existing data and see decisive forecasts about things to come.  It allows us to push ahead and design the program to pick up exact characteristics at the word go.

All the algorithms in machine learning only get better with schooling. While, in the beginning an algorithm will have instances where the outputs are recognised, the algorithm will ascertain the variations between the forecast made and the right outputs, what it does is that it values each probable aspect and input until it is able to produce very accurate forecasts- almost like fine tuning a guitar for the right tone. Therefore what defines an algorithm in ML, is the value of each forecast get significantly better with practice. It always better to feed the algorithm with more data (certainly, to a point –of course!), this will obviously result with a much more accurate forecasting engine. (From image 1 and 2 below: where the size of the data sets needed is extremely context needy, therefore, it is tough to make a general take on it.)

In ML you will experience over 15 different ways to move towards using it, every method applies a varied algorithmic arrangement that strives for accurate forecasts based on the inputs from the data. The method of deep learning is gaining momentum for its ability to yield amazing results in upcoming areas, you will see how this aspect unfold as you read on. Most of the other approaches are as significant even though it is not garnering much recognition, because these can be applied to a wider spectrum of practice cases. These algorithms include:

  • Random forests’ are known to bring out most effective forecasts by producing a myriad of decision trees.
  • Bayesian networks’ examines the correlation among each variable and identifying each variable by implementing the method of probability.
  • Support vector machines develop representations that allocate new contributions to a specific category among all the categories it has been loaded with.

The ‘ensemble’ method is by clubbing many approaches to receive a desired outcome; of course as is with most creations every method has its fair share of rewards and challenges. The kind of data set is largely relevant to determining which method of algorithm is to be utilised to bring out the desired reults. In other words it’s great to play around as a developer to optimise these methods into yielding better forecasts.

Desire and varied thoughts can alternate the applied cases of machine learning. The probabilities of designing algorithms for varied reasons can be obtained only with accurate data being applied. For instance prompting a person with choices that may appeal to them based on earlier transactions made; or determining when a message was read; even being able to forecast when an engine on an assembly line is faulty; ascertaining if a bank transaction was from the original source and so much more.

The design of offloading in Deep Learning:

As relevant as many of these methods of algorithms in machine learning are aspects like grasping speech or identifying specific elements housed in an image can prove to be difficult at best to design programs that carry it out. This is largely due to the fact that it is not likely to state characteristics that are dependable and sensible. A simple instance where a computer program is designed to recognise images of bikes, you cannot state the characteristics of the bike for this will enable an algorithm to take a course of action that yields accurate recognition in all situations. Of course, bikes come in various patterns, dimensions and hues and with this it is evident that their situation, point of reference and cause can vary. So many variables such as lighting or the background can influence the look of the product. All this means that a vast set of changeable facts will make it harder to create rules around it. And if it has been created the outcome will have no productive solution. Furthermore, a program would have to be designed for every variable in the product that needs to be recognised.

Artificial Intelligence has been transformed by the introduction of deep learning (DL). An ancillary of machine learning, deep learning is one of the 15 methods utilised by it. Again here through the image below you will see how all ML not deep learning, however, all deep is learning is machine learning.

The programmer avoids the job of describing characteristics based on the inputs received from the data aka feature specification, or it avoids the job of valuing the data inputs to give precise forecasts aka optimisation, by using Deep Learning, the algorithm designed can do both; this is one facet that’s very valuable.

This can be done because of the advance made in deep learning to sculpt the brain and the world around it remains the same. A function not far from your own body, you will notice how the brain processes challenging situations, some may include speech and identifying several things – this is not obtained by any rules you give it but by various stimuli and through constant rehearsal. A picture of a truck to a child is an experience made by a forecast of what it may be (child says truck) and the stimulating affirmations around (parent says correct) will make the child understand the world better. In very simple terms we constantly imbibe concepts by practicing and not by compartmentalising it with heavy rules.

How deep learning functions is very simple because it applies the same method. The workings of neurons in the brain are linked with synthetic, software- like processor that estimates the function. A neural network is created by this method that gets its data (an image of a truck) and assesses it; further to make a forecast about it and is given feedback to ascertain if the details are accurate. In a case where the outcome is negative, the links among each neuron are tuned with the algorithm and this will alter the outcome for forecasts made ahead. In most cases you will find the system go off centre on many occasions. This is sort itself out with the inputs of innumerable instances, because the link among the neurons will be altered to aid the neural system make accurate predictions in most situations.

What can be achieved via this method of growing efficiency is that now we are able to:

  • Identify objects with an image;
  • to be able to interpret languages on the go;
  • speech patterns can be optimised to manage applications (Amazon Alexa, Microsoft Cortana, Apple’s Siri are great examples of this);
  • forecast how a DNA profile can be altered by a genetic disparity;
  • to determine outlook given by a customer through feedback in a review;
  • Ability to identify tumours in a medical description or imagery; and more.

However, DL does not work in the best way for every situation. It needs a vast amount of data inputs and examples for it to function optimally. DL requires the use of a wider processing control to coach and work the neural system. With this method it is very tough to identify how a neural system has made forecasts – leading to an issue where there is a lack of ability to explain. Nevertheless, DL helps developers simplify their tasks by eliminating the difficulty of feature specifications and it makes possible for accurate forecasts in a wider spectrum of issues.  DL is a formidable tool owned by all AI programmers.

  1. To understand how DL functions:

Now that you know the important aspects of this very useful sub-set of machine learning, it is also necessary that you understand DL’s functionality. As mentioned before deep learning uses a synthetic neural network, a linking of various neurons that perform like calculators that run on software.

There are one or more key in factors when it come to a synthetic neuron. Once it has been fed with the variable the artificial neuron will be able to conduct very precise mathematical computations and arrive at a prediction. The value of each feedback received and the organization of the examples fed in and the results obtained thereafter, in a neuron. This is called the input – output function which can differ. An artificial neuron can be made up of:

  • linear unit – where the forecast is related to the value of the input or feedback given;
  • threshold unit – when the cumulative input has a particular significance, the output will be placed in one or two points; or
  • sigmoid unit – where with the changes in inputs fed in the output differs constantly, however, not in the same sequential pattern as the inputs.

When there are synapses between neurons and they are linked to one another it forms a neural network; in this instance the output of one neuron becomes an input for the other neuron. (As seen in the image below)

Numerous levels of neurons are arranged to make up a neural network, this is why it is called deep learning. The network will assess data got from the ‘input layer’, for instance many images; whereas, the outcome will come from the output layer. What lies between these two layers are hidden layers where significant functions happen. On one stage of the neural network the outputs received from each neuron will add up to becoming inputs for the neurons on the next level of the network. (The image below is a clear example of this).

If you look at the image identifying algorithm, in the instance where it identifies faces in the image. So the first level in the neural network does the job of identifying examples that have local differences, or what we call low level features, like edges. Higher level features are obtained as the image moves along the neural network; this will determine specifics that improve from edges to a specific part of the lips and from then on to the face. (An example of this is in the image below)

When the neural network has had practice the output layer will convey the likelihood of the image being a particular kind. (For example: it will say human face 97%, a ball 2%, or a twig 1%)

What is done is to expose the neural network to a vast amount of tagged instances. If there are any mistakes it is picked up and the value of the links between each neuron by adjust the algorithm to yield better results. Thereafter the network repeats the optimisation process many times, it is only then that the arrangement is set up and undefined images are measured.

What is mentioned above is an uncomplicated neural network however structures can differ and some can be more multifaceted than others. Some of the changes or variables include links between neurons on the same layer; at every level; and the relation of the output of the neuron that is hooked on to the prior layers of the neural network, called recursive neural networks.

You have to own special skills to create and advance a neural network. These aspects can involve arranging the network to suit a specific application, to feeding in appropriate data sets, regulating the arrangement of the neural network in tandem with the progress, and merging several methods.

  1. The significance of AI Artificial Intelligence bears a huge significance in today’s world as it deals with issues that are intensely hard, and the remedies that are got from these issues can be used in areas significant to human comforts – which can touch upon health, economy, education, or even areas such as entertainment, transport and commodities. Since its founding days in the 1950’s AI has taken interest these five areas of probe:
  2. Reasoning:  This constructively solves issues through coherent assumptions.
  3. Knowledge: the aptitude to stand for information that revolves about the world – deals with the knowledge of certain factors, actions and circumstances that exist in the world, that these factors have characteristics and these can be indentified and placed into groups.
  4. Planning: when you place and attain goals – with this one can arrive at a particular state in the future world that is attractive, and the series of conduct can be taken on that will result in development of it.
  5. Communication: Where written and spoken speech patterns are understood and assimilated.
  6. Perception:  where one can infer the world from various pictures, sounds and many aspects related to the feedbacks got from sensory stimuli.

The development of AI in various aspects can be revolutionary capacities rather than an evolutionary one. Here are some uses of AI that define this fact:

  1. Analysing: Aids in assessing financial issues; in playing games; in financial request that need to be dispensed; and even with an independent weapons network.
  2. Data: A knowledge of how drug are made; in-depth medical identification; advice from the media; forecast on purchases made; curbing fraudulent activities; and even trading in the financial markets.
  3. Organising: Many things can be planned with AI, for instance managing inventories; navigating; scheduling; predicting trends in the demand chain; and much more.
  4. Medium of interacting: As we stand it is imperative that the mode of communication is optimised in any given scenario, from daily assistants and customer support; voice control; interpretation of various dialect in real-time; to intellectual agents.
  5. Insight: Perceptions of aspects like security and surveillance; of a medical analysis; or independent vehicles.

It is a known fact that machine learning is fast gaining momentum where it is being adopted by considerable amount divisions and over vast range of processes. If you take a specific area in the corporate environment, such as human resource functionality in any firm that draws will apply ML to a myriad of the processes followed:

  • By sifting through job profiles and identifying specific matches to a job description it will enhance the performance of recruitment;
  • By gauging nuance of the retrenchments and attritions; or analysing employee necessities it aids in higher levels of workforce management;
  • By deeming what content is best suited for the employee it yields in better workforce learning; and
  • Furthermore it can be used to forecast the potential of losing a valuable employee, thus resulting in lesser employee churn.

The process of adopting ML will become normalised over time. It will be seen as an important asset to every developer, who will tend to enhance the process that is available and further go on to change it again.

What ML brings with the next set of consequences will have an enormous effect on current impact. What deep learning has enabled is the sense of vision to the computers, which are now able to analyse independent vehicles, further making them accessible. And if you are wondering what impact they will have, it is significant because as we stand today, 90% of people and 80% of cargo are ferried by road in the UK alone. This means that independent vehicles will have an impact, here are some aspects:

  • It is known that almost every accident on the road is caused by some amount of negligence, therefore safety can be addressed
  • Being such a large industry the process of employment can be intrinsically bettered
  • A research has shown that there could be a fall of about 63% in car insurance premiums in the UK over time, therefore insurance can be addressed and eliminated by ML.
  • The use of services like on-demand vehicles or cabs versus using own vehicles is on the rise, this sector economics can be optimised in future;
  • Apart from this – regulations, better city planning, and much more can be achieved.

 

  • Understanding why AI is coming to fruition now:

What started way back in 1950 and with many fallbacks, why is AI on the rise now? This is because in the recent years fresh algorithms that were created have yielded better abilities for practice and the data sets fed into the algorithms are vast; there has also been a significant availability of better hardware and services like clouds that can help programmers in adopting these methods.

  1. Algorithms that have been enhanced

Yes, deep learning has been around for some time now, as you will see in the article that was published in 1965, that talked about the arrangements for the first successful, many layered neural network. Of course, there has been a significant growth in the sphere in the past decade that has changed and enhanced results vastly.

CNN or convolutional neural networks has rapidly altered our ability to perceive and recognise objects in an image. The design element for CNN was got from animal visual cortex’s, where every layer filters and processes the existence of a definite pattern. Microsoft has introduced a CNN based vision computing system to recognise object within an image more efficiently that humans. They had also stated that this was the first outcome of surpassing human abilities (With a significant increase of 0.2%). Wider uses of CNN include identifying speech patterns and videos.

With the development of recurrent neural networks (RNNs) there is a significant spike in identifying speech and handwriting patterns (seen in the image below). Not like the traditional neural network where it is fed with data first, the RNN’s methodology has feedback links that allow information to go into a loop. The LSTM or long term-short term memory design is the new more radical type of RNN that is found today. RNN has various additional memory cells and links, its ability to register data that was fed many steps before recognise it and put into use with predictions of what will come is fascinating. An example of this can be seen when you are prompted with words in a message based on what you have already typed. Google started adopting LSTM’s in their Android to command the speech recognition network. In the recent past, engineers from Microsoft stated that aa word error rate of 5.9% was achieved by their systems – another significant milestone when comparing it to human abilities.

  1. Well engineered Hardware

As we all know now that neural networks only get better with practice in deep learning, this is where Graphical Processing Units (GPUs) steps in; they are focused electronic circuits that aid in cutting the time used by the neural networks to train.

An interesting fact is that new age GPUs came into existence in the late 90’s to speed up 3D gaming and the many 3D growth applications. Matrix calculations are the constant use of mathematical processes that were engaged in zooming cameras in a 3D world. Even today when microprocessors with its series of architectural designs, that comprise of the CPUs which control the computers are still outdated to aid this job. The contrasting architectural elements of the GPUs are made to further enhance the matrix computations effectively.

The widespread application of matrix computation can happen with practice in a neural network. It turns out that GPUs made for 3D gaming work perfectly well in deep learning as well. GPUs have been found to contribute a 5x enhancement in practice time for a neural network, and it can yield up to 10x or higher probabilities on bigger issues, this is a significant bonus to ML. In deep learning patterns GPUs can be integrated with software creation packs that can be set to improve practice speed even more.

  1. Data that’s far-reaching

Data sets can range from inputs can vary from as little as thousand instances to millions of instances, in a neural network for deep learning. There has been a significant spike in accessibility of data and in its creation. Currently we are in the third wave of data, about 2.2 exabytes of data are produced by humans on an everyday scale; it is important to know that 90% of the worlds data has been developed in the last two years.

In 1980 we saw went through the first wave of data development, which included the developments of documents and transactional information. This was caused by the propagation of desktop PC’s that was linked to the internet. The sudden spike of amorphous media that had emails, photos, videos and music, also the web data and meta-data got from ever-present, and smartphones for the second wave of data. Today industries use machine sensors and create an extra scrutiny for analytical and meta –data in the home.

Most of the data used today can be broadcasted by way of the internet, what serves as a substitute with the spike in data production by humanity is the ballooning of traffic on the internet. What is forecasted is that by 2020 we will be moving about 61,000GB every second this is comparatively larger that the 100GB per day in 1992.

 

ML has significantly progressed which has resulted from expert data resources and this goes way beyond the accessibility of universal data. For instance, the easily accessible data of ImageNet, crossing tem million hand – defined images. This has aided the growth of object categorization in deep learning algorithms.

  1. What do Cloud services bring to AI?

Cloud-based machine learning set ups and services are widely popular with programmers who use ML which was a result of the same, got from the best cloud providers in the industry.

Surroundings for repetition and model –building, GPUs as a service which is ascendable, and many services related is comprised in a cloud-based infrastructure and many companies such as Amazon, Google, Apple and IBM offer it, to cut costs and challenges of creating machine learning possibilities.

They even cater to the constant mushrooming array of cloud-based machine learning services that involve identifying to interpreting languages which programmers can use straight away in the applications they create. With Google’s ML, it is easy to avail of service for categories like vision – which has object recognition, precise content discovery, face recognition and an analysis of image outlooks; speech – which has identifying speech patterns and conversion from speech to messages; analysing messages – which recognises entities, analyses emotions, detects languages and interprets it as well; and job browsing for employees – which aids in high-level based matching and the surfacing of opportunities. There are more than 21 services offered by Microsoft Cognitive Services and they include search, knowledge, speech, and vision.

  1. Business and Curiosity

It is formidable the way the world perceives AI today; it has grown massively in the last five years alone, and has a significant number of investments in AI companies by venture capitalists. It is clear to say that machine learning is definitely proving to be a lucrative investment for venture capitalists, entrepreneurs and for the curious, as this is currently the world virtuous realm. Awareness has further enhanced possibilities for further enhancements.

in the pipeline

If it has not been stated before – it is clear to see how relevant and important machine learning will be and how many benefits it will bring about. The many facets that will be out there may vary from independent vehicles to new ways of human computing dealings.  However, there will be plenty of variables that will be under the radar, but will helm a more capable and proficient everyday business processes and customer services.

A change in theory will have very high expectations but this will surpass its temporary potential. There will be a sense of disappointment that will come with AI, but this will come with an extended and more permanent understanding of its worth as ML is applied to enhance and redefine the active systems.

What history has indicated is that through industrial revolutions we saw a change in production and communication with a fresh source of power and transmission. In 1780, the first industrial revolution saw steam power being used for mechanise production. Electricity was used to drive mass production in the second industrial revolution in the 1870’s. The third industrial revolution in the 1970’s saw Electronics and software to compute production and communication. Currently, software is at its peak and seeping into every facets of the world, the focus now is to process the large sums of data. The current wave allows us to so more wisely; ML will be advantageous both in a historic sense and in simple ways.

 

 

About the author

Asif Amod

I am a webtrepreneur, full stack developer and a technology evangelist. I have been coding from a young age. I also speak to databases and make servers do what I say, I am passionate about continuous learning. I thrive on challenges that require lateral thinking - no matter what language or technology. I have founded and Co-founded a number of startups and have created SaaS web applications for different industries. I am currently open to discussing and explore other opportunities.

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By Asif Amod

About Me

I am a webtrepreneur, full stack developer and a technology evangelist. I have been coding from a young age. I also speak to databases and make servers do what I say, I am passionate about continuous learning. I thrive on challenges that require lateral thinking – no matter what language or technology. I have founded and Co-founded a number of startups and have created SaaS web applications for different industries. I am currently open to discussing and explore other opportunities.

Member of The Internet Defense League

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