Category Archives for "Technology"

Data Mining For Business Intelligence

Data Mining For Business Intelligence

Humans have been collecting data since the dawn of time. More and more has been collected over the thousands of years we have been on this planet. However, since the technology boom, this has increased exponentially.

Businesses are facing challenges to sieve through the useless data to discover patterns and understand the useful information.

Enter big data and data mining.

Big Data

Big data is the computerised processing of large amounts of information; data (structured, unstructured and semi structured) that exceeds the capacity of conventional software to be captured, managed and processed within a reasonable time.

Big data tends to refer to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.

Because there is such a large volume of data within these files, the right information must be obtained quickly.

Data sets are growing rapidly. In fact, the IDC predicts that by the year 2025, there will be 163 zettabytes of data. To put that into perspective, the current world’s largest SSD drive can hold up to 100 terabytes.

Data mining

Taking the necessary data stored, then it is important to consider different techniques of data analysis, such as the association, clustering, text analytics or data mining.

Data mining is one of the most important, because it is the process of extracting data, analysing it from various perspectives to find patterns in the database. This information is then presented a useful way to the end user by data visualisation techniques.

There are two types of data mining:

  • Descriptive: gives information about existing data;
  • Predictive: makes forecasts based on the data

The data mining process is as follows:

  1. Detecting anomalies – identifies unusual and uncommon data that are unexpected and do not follow the common pattern of other results.  Any results that have found to be anomalies could be incorrect and will require investigating into.

  1. Association rule learning – uses strict rules to identify relationships between the parameters used to obtain the data.  Similar to machine learning, the machine uses algorithms to find the solutions, however, where it is differs is machine learning determines the algorithms itself and does not require the strict rules to be set.

  1. Clustering – the machine groups together pieces of data that have similar properties to each other while leaving out data without those properties at the same time.

  1. Classification – the system learns a function that finds data that hasn’t yet been defined and categorising it into a pre-defined class.  The user will define a structure and the machine will categorise the data based on the rules of that defined structure.

  1. Regression – analyses which function estimates the least incorrect results.  It does this by understanding how the dependent variable reacts when independent variables are changed.

  1. Summarisation – presents the data in a way that is more understandable to a user.

Data mining is now so important to businesses because it saves them a lot of time and money on researching new business opportunities and enable them to make more key strategic decisions.

So how does mining aid with Business Intelligence to provide insights?

Data Mining For Business Intelligence

Data mining and business intelligence are powerful tools to capture and use knowledge. Being able to use the information gathered is at least as important as gathering it. So, it is therefore important to have business intelligence (BI).

BI is the process of transforming data into useful data, and turning that useful data into business knowledge. Without BI, organisations will not be prepared in making strategic manoeuvres.

Business Intelligence combines data analysis applications, including ad hoc analysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real-time BI, operational BI, cloud and software as a service BI, open source BI, collaborative BI and location intelligence. BI technology also includes data visualisation, tools for building BI dashboards and KPIs.

The benefit of BI to businesses is that they are bale to gain competitive edges over their rivals and improves internal operations. Everything becomes more efficient and streamlined.

Other uses of BI include financial control, production planning, company profitability and many, many more.

It’s not just the business that benefits from BI and data mining; customers also see improvements in the relationship with the organisation. Mining identifies customer habits and patterns. The business is then in a far better position to recognise what customers are looking for, improving satisfaction and loyalty to the brand.

Data Mining And BI For Business Growth

Business intelligence acts as important voice in determining where a business should be going. The results obtained could indicate where things need improving internally in order for the business to scale quicker and optimise growth.

If there are any problems identified, the solutions are quicker to obtain and can be implemented quickly and efficiently.

It also helps with proposing to enter new markets based on evidence. The knowledge obtained from data mining and business intelligence can figure out where the company will succeed by predicting outcomes before even entering the market.

Conclusion

Data mining is used to generate business intelligence. It is an increasingly popular term representing the tools and systems that enable organisations and corporations to turn business knowledge into a profit.

Data mining and business intelligence have made it so much easier for businesses to access key information quickly and efficiently from data modelling. This enables them to make far better decisions. In addition, data mining technologies have bright future in business applications, making possible new opportunities by automated prediction of trends and behaviours in these businesses.

BI is no longer a futuristic idea or concept; it’s happening right now and will only improve as technology advances over the coming years.

Regression Algorithms Used In Data Mining

Regression Algorithms Used In Data Mining

Regression algorithms are a subset of machine learning, used to model dependencies and relationships between inputted data and their expected outcomes to anticipate the results of the new data.

Regression algorithms predict the output values based on input features from the data fed in the system. The algorithms build models on the features of training data and using the model to predict value for new data.

They have many uses, most notably in the financial industry, where they are applied to discover new trends and looking at future forecasts.

Here are five of the most used regression algorithms and models used in data mining.

Linear Regression Model

Simple linear regression lets data scientists analyse two separate pieces of data and the relationships between them.

The model assumes a linear relationship exists between input variables and the singular output. This can be calculated from a linear combination of both the input variables.

Examples of linear regression models include predicting the value of houses in the real estate market and analysing road patterns to predict where the highest volume of traffic is.

Simple variable regression implies that there is only a single input variable. Where there are more than one variables, the method is known as multiple linear regression.

Unlike linear regression technique, multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.

One business application of the multiple regression algorithm is its use in the insurance industry to decide whether or not a claim is valid and needs to be paid out. This example has many different variables to consider, so a linear regression algorithm wouldn’t be appropriate,

Multivariate Regression Algorithm

This technique is used when there is more than one predictor variable in a multivariate regression model and the model is called a multivariate multiple regression. It’s one of the simplest regression models used by data scientists.

Multivariate regression algorithms are used to predict the response variable for a set of explanatory variables. This regression technique can be implemented efficiently with the help of matrix operations.

These algorithms are used as part of the AI revolution of the medical industry. Doctors require a lot of data collected from their patients, including heart rates and cholesterol levels to external factors such as how much they exercise.

They may want to investigate the relationships between their patient’s activity compared to how much cholesterol they have in the body.

Logistic Regression

This next data mining regression algorithm is another popular method used in the financial industry, particularly in the credit checking business. This is because logistic regression requires a binary response.

In regression analysis, logistic regression estimates the parameters of a logistic model. More formally, a logistic model is one where the log-odds of the probability of an event is a linear combination of independent or predictor variables.

There are two possible dependent variable values: “0” and “1”. These are used to represent outcomes such as pass/fail or win/lose.

One of the major upsides is of this popular algorithm is that one can include more than one dependent variable which can be continuous or dichotomous. The other major advantage of this supervised machine learning algorithm is that it provides a quantified value to measure the strength of association according to the rest of the variables.

Going back to the credit scoring industry, it can be easy to see how it is used; companies apply logistic regression to see if a customer meets the necessary criteria to be eligible for a loan/credit card/etc.

Lasso Regression

Lasso (ie Least Absolute Selection Shrinkage Operator) regression algorithms are used to obtain the subset of predictors that minimize prediction error for a quantitative response variable. The algorithm operates by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward a zero.

If the algorithm assigns a coefficient to zero, the variable is no longer used as part of the model. Those that have a non-zero coefficient are then used as part of the response.

Explanatory variables can be either quantitative, categorical or both. Lasso regression analysis is basically a shrinkage and variable selection method to determine which of the predictors are most important.

Lasso regression algorithms have been widely used in financial networks and economics. In finance, the models have used stock market forecasting, such as how it will react to economic updates and predicting where to invest or the stocks and shares to stay away from.

Support Vector Machines

The final regression data mining algorithm is the support vector machine (SVM). This machine learning regression model is a supervised learning model with associated learning algorithms to analyse data used for classification.

Support vector machine algorithms build models that assign new examples to one category or the other, making it a non-probabilistic binary linear classifier.

The model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

Support vector machine regression algorithms have found several applications, including recognition systems that can detect whether an image contains a face. If the SVM identifies a face, it produces a box around it. Data visualisation techniques are included within the SVM algorithm.

Top Machine Learning Applications

Top Machine Learning Applications

Machine learning is a hot topic at the moment. It has got people coming up with ideas about how a robot can teach itself to solve all their problems.

It has already had a big impact on modern society, from its use in recommendation engines to Apple’s Siri virtual assistant.

Here are some of the best applications of machine learning being used today:

Virtual Personal Assistants

Speech Icon, Voice, Talking, AudioWe have just mentioned Siri, but Amazon’s Alexa and Google’s own version are other examples of virtual personal assistants being used every day. They all have a similar purpose: to find information and assist in answering queries.

These applications use machine learning techniques to collect information based on how they have been previously used in the past. They may also reach out to other phone/tablet/etc applications to find the answers.

The results are then saved for future references so if they are asked to set an alarm the following morning, they will have a good idea of the time.

Social Media Applications

Companies like Facebook, Instagram and Twitter using machine learning for a whole number of reasons, ranging from personalised ads to tailoring a news feed.

Further examples include:

Face recognition

When a picture is uploaded to Facebook or Instagram, their algorithms will be able identify who is in the image. They scan the picture for similar features to previous photos and match them to people from the friends list.

Smartphone Screen Social Media Snapchat Fa

Friend suggestions

Machine learning processes are also used when suggesting to add a friend or someone to follow. They see a list of mutual friends and followers and come up with suggestions based on similar connections. This extends to suggestions for liking a certain group or following a hashtag.

These social media sites will also monitor the pages visited and profiles/chats visited frequently and come up with suggestions based on the activity.

Language Translation

 

Machine learning plays a large role in translating one language to another. The best varieties understand the context of what is being said and adapt.

The technology behind the translation tool is called ‘machine translation’. It has enabled the world to interact with people from all corners of the world, without it life would not be as easy as it is now.

It has provided a sort of confidence to travellers and business associates to safely venture into foreign lands with the conviction that language will no longer be a barrier.

Applications may combine language translation with a voice recognition system to save time on typing.

Spam Mail

Email Marketing, Online Marketing

Email clients detect which emails are considered spam and those that are not by machine learning processes. Filters are continually updated to ensure the right messages are coming through.

The program identifies the frequency of emails sent from a provider and bases whether they are considered spam on previous interactions with the email address or the company it is being sent from.

A lot of spam mail contains malware and viruses. However, the majority of malware coding are related to previously filtered versions.

Machine learning processes enable security systems to detect similar coding patterns and identify the malware.

Healthcare Industry

Artificial intelligence in healthcare is helping to save lives every day. Machine learning is being used to reduce waiting times for patients, so they can get the help they need.

Some of the factors that are involved in producing the algorithms include patient records, notes and doctor and nurse availability. The systems scan through this information can come up with the best treatment options available.

One study used computer assisted diagnosis (CAD)when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed.

Transportation

Car, Transportation System, VehicleGeo-locations use computer vision methods to deliver warnings to drivers such as traffic.

Maps are evolving to show the best route to get to a destination. Depending on the time of day and the likelihood of running into a rush hour jam, systems learn how to use this data to come up the best way to travel.

GPS navigation applications use current locations and velocities which are then saved and stored at a central server for managing traffic. This data is then used to build a map of current traffic.

While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS. Machine learning in such scenarios helps to estimate the regions where congestion can be found on daily experiences.

Online Searching

Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time a search is made, Google monitors how the user reacts to the results.

Clicking on the first result indicates that the search was a success. On the other hand, clicking on to the second page or entering a new search into the bar indicates that the results didn’t satisfy the query.

The machine learning program can pick up on this and will try to provide better results next time.

Recommendation Engines

Many retailers use recommenders to analyse activity on an online store to suggest items similar to those already viewed. The activity is compared to all the other users to determine what the customer is likely to buy next.

The more products viewed, the more data these programs capture and are able to provide more accurate and better suggestions. They are also intelligent enough to realise if someone is purchasing particular products at certain times of the year of if they are being bought as gifts.

Recommendation engines are now also used as part of streaming services, like Netflix and Spotify to bring music and TV suggestions.

What Philosophical And Ethical Questions Are Raised By Artificial Intelligence?

What Philosophical And Ethical Questions Are Raised By Artificial Intelligence?

There are many benefits of implementing machines capable of AI, including increased efficiency, reliability and costs. The possibilities seem to be endless.

However, it is for this reason that leading people and businesses across the globe have their concerns, including Elon Musk and the late Stephen Hawking.

Here are some of the main AI ethical issues that we are facing.

What If AI Systems Become Conscious?

Machines will become more and more automated as technology advances, leaving them capable of making decisions. This leads to more control and responsibility being left with the machines. Ultimately, these decisions could lead to an AI system developing consciousness.

There are some suggestions that DNA holds the key to machines developing consciousness. But with this potential comes a lot of uncertainty.

How can a machine decide if something is the right thing to do?

The case that comes to mind here is when a self-driving car faces choice between hitting pedestrians or crashing. The machine must act in some way based on its own thinking and reasoning.

Once we consider machines as entities that can perceive, feel and act, it’s not a huge leap to ponder their legal status. Should they be treated like animals of comparable intelligence? Will we consider the suffering of “feeling” machines?

This then leads to questions like if they act like a human, think and feel like a human, are they human? Do they get human rights?

What if robots and intelligent systems become incomparable to humans because they are so alike? How is it possible to identify them as a robot in the first place? How can you be sure you’re not a robot if there are no distinguishable differences between the two?

All these questions pose a significant role in how AI will play a role in our future society.

How Do We Protect It From Being Used For The Wong Reasons?

Cyber-security

As technology advances, it’s just as likely that it may be used for good or malicious reasons. For example, robots may be used in the future to replace human soldiers on the battlefield.

However, this point also applies to the AI systems themselves, particularly for cyber-warfare.

This means that cyber security and online protection will be needed more than ever before. The measures taken to improve security will improve drastically. If a machine can out-think a defence system, there is potential for significant damage.

But it’s not just humans that we need to be wary of.

One of the biggest artificial intelligence ethical issues surrounds what happens if AI systems turn against us?

Stuart Russell from The Center for Human-Compatible Artificial Intelligence says that this is not actually the biggest risk; the real ethical issue is that we will end up programming a machine to carry out a task and by doing so will cause us harm.

For example, if we wanted AI to stop the deforestation problem, a machine may find that the cause if because of human activity. The solution: remove all humans.

If this is the case, it is likely machines will be able to perform what we ask but simply have a misunderstanding of the consequences.

With the correct teaching, systems will learn to predict outcomes, carry out the task the most efficient way possible without the risk of harming human lives.

How Do Machines Affect The Way We Interact?

Artificially intelligent bots are becoming better and better at modelling human conversation and relationships. This is best proven in 2014 by a robot named Eugene Goostman passing the Turing Challenge.

Eugene Goostman spoke with a panel of 30 judges. Each judge partook in a textual conversation with the robot and a human at the same time. It managed to convince 10 out of 30 judges that they were talking to a human as opposed to a robot.

This was a huge achievement and signified the start of an age where we will talk and interact with a machine as if it were human. While humans are limited in the attention and kindness that they can expend on another person, artificial bots can channel virtually unlimited resources into building relationships.

Machines are already doing this on a daily basis, especially in the sales industry. A/B testing ensures things like product pages and headlines are optimised to grab our attention. The more noticeable, the more likely we are to purchase what they have to offer.

These are basic examples and over time, opportunities will arise to lend a hand in improving social behaviour.

Will AI Systems Replace Jobs?

The hierarchy of labour is concerned primarily with business process automation.

As the human race has evolved over time, we have always been looking for ways to make life easier. This leaves us with more time to spend on more complex and demanding areas. The industrial revolution could not be a better example of this.

The AI era will mean the same thing. Jobs and tasks that can be automated by a capable machine likely will be. The biggest sector to be hit is likely jobs that require manual labour. If it means that quality of life becomes better because of the change, this will be the ethical choice.

The issue lies in how most people use their time. Many labourers rely on giving up most of their week to put food on the table and look after themselves and their families.

However, there will become plenty of opportunities for them to learn new skills that they will still be able to contribute to society.

It is entirely possible that someday, these same people will look back and think they can’t believe they did these tasks for a living.

Conclusion

AI systems are capable of doing amazing things. While there may be some risks, it’s imperative to remind ourselves AI has so much potential to help and improve daily life.

It’s up to us to manage how it is implemented into society.

Best Books On Artificial Intelligence And Machine Learning

Best Books On Artificial Intelligence And Machine Learning

Here is a list of some of our favourite and best artificial intelligence books.

No matter how much you understand the concept, each of these books will help further your knowledge.

1.  Artificial Intelligence: Guide for Absolute Beginner

Image result for Artificial Intelligence: Guide for Absolute Beginner

This AI book is a must for anyone looking to learning the basics.

The overall aim is to explore and examine key concepts, methods and techniques used in Artificial Intelligence. It provides readers with the information and tools necessary to start understanding smart machines, deep learning, machine learning, big data, speech recognition, cognitive computing and weak and strong artificial intelligence.

The book presents the following points:

  • An Introduction To Descriptive Statistics
  • An Introduction To Artificial Intelligence
  • The Artificial Intelligence Ecosystem
  • Big Data And Artificial Intelligence
  • Embracing Emerging Technology
  • Exploring Data Types
  • Associated Techniques
  • Data Mining

2.  Mining of Massive Datasets

Mining of Massive Datasets

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets.

It begins with a discussion of the map-reduce framework, an important tool for parallelising algorithms automatically.

Some of the preceding chapters include:

  • The tricks of locality-sensitive hashing
  • Stream processing algorithms for mining data that arrives too fast for exhaustive processing
  • The PageRank idea and related tricks for organising the Web
  • The problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

3.  Deep Learning

Deep Learning

This artificial intelligence book gives an introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Deep Learning is perfect for university students, people looking for a career in AI in either industry or research or engineers developing a new product or platform.

This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

It describes deep learning techniques used by practitioners in industry, such as:

  • Deep feedforward networks
  • Regularisation
  • Optimisation algorithms
  • Convolutional networks
  • Sequence modeling
  • Practical methodology

Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

4.  Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this artificial intelligence textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks.

These include:

  • Discussing the computational complexity of learning and the concepts of convexity and stability
  • Important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning
  • Emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

5.  Python Machine Learning By Example

Python Machine Learning

This AI book is for anyone interested in entering the data science stream with machine learning. This book starts with an introduction to machine learning and Python and shows you how to complete the setup.

Moving ahead, you will learn all the important concepts such as:

  • Exploratory data analysis
  • Data preprocessing
  • Feature extraction
  • Data visualisation and clustering
  • Classification
  • Regression and model performance evaluation

An interesting feature of this book is that it gives you a step by step process to build your own models from scratch. Towards the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.

6.  Probabilistic Programming and Bayesian Methods for Hackers

Bayesian Methods for Hackers

This book illustrates the Bayesian inference through probabilistic programming with PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib.

It starts by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, it introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback.

You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing.

Some of the topics this book covers include:

  • Learning the Bayesian “state of mind” and its practical implications
  • Understanding how computers perform Bayesian inference
  • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
  • Using Bayesian inference to improve A/B testing

7.  Think Stats: Probability and Statistics for Programmers

Think Stats

The final book on this list covers how to perform  statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses.

You’ll explore distributions, rules of probability, visualisation, and many other tools and concepts.

By the end of the book you will be able to:

  • Develop an understanding of probability and statistics by writing and testing code
  • Run experiments to test statistical behavior, such as generating samples from several distributions
  • Use simulations to understand concepts that are hard to grasp mathematically
  • Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
  • Use statistical inference to answer questions about real-world data

Best Artificial Intelligence Tools To Use

Artificial intelligence is fast becoming a vital component of the way that businesses operate and play a major role in key strategic decision making.

Intelligent business applications are now using data science and machine learning techniques for greater impact on speed of decision making, what useful data is and how to find and incorporate the new information.

The whole point of artificial intelligence in computer science is to make business operations easier and faster.

Here are some of the best tools that you should be looking into adopting into your enterprise.

Tensorflow

Tensorflow is used for dataflow programming and machine learning applications for artificial neural networks (ANNs).  It is a decentralised software development programme meaning that it is open to public participation between peers and is prominent in writing such as coding.

Tensorflow was developed by Google and it can run on a variety of different CPUs and GPUs.  It is a mathematical library of computations and algorithms used for machine learning and these are expressed as dataflow graphs.

Tensorflow programmes are stateful ie the computations are designed to remember preceding events that a user has inputted to the system.

It is used as part of Google’s DeepDream program, which uses a convolutional neural network to find and enhance patterns in images.

In essence, it is a data visualisation technique.  It is available to use in multiple languages such as Python API, C API, C++ and Java.

The benefits of using Tensorflow as an AI service are as follows:

It allows automatic function differentiation

Tensorflow is able to use differentiation techniques to analyse and calculate the derivative of an inputted function.  It has the capability to differentiate automatically and present the data with different visualisation methods.

Examples include dataflow graphs to make the data easier to understand and analyse.

Tensorflow also allows a user to define the underlying architecture of an algorithm.

It runs with optimal performance, regardless of your supporting hardware

Tensorflow allows asynchronous operations meaning that when it runs a series of programs, it does not have to wait for results in order to process other events outside of the defined originals.

It is able to be programmed in a variety of different languages such as Python and C++, meaning that you can deploy a model to run a computation in the most common styles.

It has a flexible architecture

Tensorflow provides the user with the ability to draw up a variety of different versions of the same model and run the algorithms at the same time.

Further to this, Tensorflow has been designed so that internal API is consistent, meaning that any migration to a previous version is possible but the API will not break when doing so.

It has great portability

As previously mentioned, Tensorflow can run on a number of different hardware systems.  You can use on desktops, laptops, GPUs, CPUs and even on sufficient mobile platforms.

As part of its portability feature, you can even deploy a live model directly to your system.  You don’t need a series of other supporting hardware to use Tensorflow when on the go.

Keras

Similar to Tensorflow, Keras is another open source neural network library but specifically written in Python.  It is able to run on top of Tensorflow and designed to operate deep learning method.

The main purpose of Keras is to provide fast experimentation with deep neural networks (DNNs).

Like Tensorflow, Keras is very portable and can used on a variety of platforms including GPUs, on smartphones running on iOS and Android and also the Raspberry Pi.

Keras

There are plenty of advantages to using Keras:

It is easy to use

Keras is designed to provide consistent and simple APIs that are easy to follow.

It reduces the number of actions needed to complete a process but if there are any errors in an algorithm, Keras gives clear and precise feedback in how to solve and overcome the problems.

Keras enables you to use your time more efficiently and provide solutions to problems quickly using its DNNs.

It can integrate lower-level deep learning languages

Even though Keras is an easy AI tool to use, it remains very flexible in that it is simple to translate algorithms or computations built in one language into a system that is built in another.

Since Keras runs on top of Tensorflow, the Keras API can comfortably accommodate Tensorflow’s dataflow programming.

It supports multiple backend engines

When you develop computational models using Keras, there are many different data access layers that can be accessed, such as the Tensorflow backend.

Models that you develop can be learned on many different hardware platforms that go beyond the CPU level.  Keras has built in support for multi-GPU data parallelism, meaning that it can focus in distributing data across different nodes which operate on the data in parallel.

RPA programmes

Another AI tool to use in your business is robotic process automation (RPA).  This is an emerging form of AI.  Where traditional programs require human interaction in order to produce a set of instructions to carry out a task, RPA expands on the user inputs and then as part of the automation, repeats the actions straight into the graphical user interface (GUI).UiPath

RPA has similar processes to tools that specialise in testing a product’s GUI to ensure that it meets a defined set of specifications, but differs in that RPA tools can handle multiple sets of data to be actioned across multiple platforms simultaneously.

Once RPA systems are programmed to understand a process, it can communicate with associated systems accordingly.

To name a couple, UiPath and Blue Prism are some of the leading firms in the field of RPA.

The advantages of incorporating RPA programmes into your business are as follows:

Save on valuable resources

Historically, the cost of moving jobs from one location to another has been an effective method of saving on the cost of employment.

This has typically meant that business operations are taken to an offshore region.  More often than not, it is more cost efficient to run certain aspects abroad rather than in your local domain.

The use of RPA is the next chapter; where previously you would need to hire someone to perform tasks, RPA allows a cheaper alternative in that a robot can perform these tasks for you.  This will save you not only money but also valuable time.

RPA is easier to scale

Following on from the saving on resources, RPA has the ability to scale a lot quicker than by hiring now employees are moving operations to another location.

A new employee can take a lot of time bedding in to learning process systems and applications whereas an RPA can be deployed straight away.

Once that specific RPA is set, you can expect to see results quicker meaning that you will be able to grow your business more efficiently than before; your business will not need to be held back by human resources.

Process consistency

RPA programmes will always be able to operate in the same way in order to complete the task.

Human input will often result in different methods in order to achieve a task, especially if more than a single person will be working on that job, meaning that end results can become inconsistent.

RPA eliminates this as once they are programmed to operate in a certain way, they will not change and will have precise and accurate results.

Question answering systems

The final AI system that you should you to be using into your business is a question answering program such as Watson.  Developed by IBM, Watson is a computer system that is able to answer questions that are composed by natural language processing.

The computer system was initially developed to answer questions on the quiz show Jeopardy! and, in 2011, the Watson computer system competed on the show against champions Brad Rutter and Ken Jennings, ultimately winning the first place prize of $1 million.

IBM Watson

Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases.

It’s main innovation was not that is is able to create a brand new algorithm to answer the question, but rather that is able to execute many tested and proven language analysis algorithms at the same time.

Watson is more likely to provide the correct answer to a question based on the more independent algorithms that find the same answer.

Once Watson has collected a small number of solutions that could potentially solve the problem, it checks the answers against its database to ascertain whether the solution makes sense or not.

Watson has been implemented into many fields already meaning that there an abundance of advantages of using.

The finance sector

In the financial sector, Watson can use its questioning and answering capabilities to provide financial advice and management.  It is able to advise on potential risk of lending to a customer.

Watson is also being used in customer service applications in order to give them their most preferable method of contact.  It decides whether it should be via phone, online web chat or even in person.  IBM say that USAA was one of the initial firms looking for technology.

Watson is also provide assistance in wealth management in order to provide sound advice by identifying trends in markets and relaying it to customers.

The health sector

Watson is able to use inputted data about a customer and provide solutions to their needs.  This is an advantage that can be applied to any business or organisation.

Specifically, Watson is having a huge impact in the health sector.  It is now being used in some of the best cancer treatment hospitals in the United States.  Hospitals such as Memorial Sloan Kettering Cancer Center and University of Texas MD Anderson Cancer Center.

In terms of cancer research itself, Watson is speeding up DNA analysis in cancer patients to help make their treatment more effective.

Watson is also able to provide doctors and physicians with providing correct and accurate patient diagnoses.  A dermatology app called schEMA allows doctors to input patient data.  Using natural-language processing (NLP), it helps identify potential symptoms and treatments.

Additionally, Watson uses vision recognition to help doctors read scans such as X-rays and MRI scans.

The retail sector

North Face, the outdoor and activewear giant, has partnered with IBM’s Watson.  Their aim is to create an app that is based around finding the right clothing specifically for the customer.

In essence the app works like an online personal shopper to create a much more personalised shopping experience.

AI is being used to solve the problem of bridging the gap between purchasing products online or instore.

They can take on board what a customer is looking for, asking questions to narrow down potential solutions.

KeyworX.org Software Case Study

KeyworX.org Software Case Study

In early 2018 we built a piece of software for HQ SEO Ltd’s Amazon marketing division.

The tool itself was called KeyworX and was designed to be an intelligent, accurate Amazon organic rank tracking system that automatically finds a specific product’s ranking positions for individual search terms. This would collect data which can then be used for modelling, prediction and more.

Organic rankings are a highly important KPI for Amazon businesses as this directly translates to organic sales and hence higher profits.

The product itself was designed to be simple, easy to use and very user-friendly allowing non-technical founders to use the product effectively.

The software was built with the application to show Amazon business owners what parts of their marketing were working and what could potentially be a low ROI strategy.

HQ SEO can now use this data for their clients and personal tests, eventually integrating machine learning capacities to help analyse this data and reverse engineer, using artificial intelligence, what marketing strategies and decisions are working well and generating a positive return on investment, and what strategies should be refined or eliminated completely.

Tom Buckland – Founder of HQ SEO Testimonial:

We started work with Artimus in early 2018. Ironing out the details was very easy and quick which was super important to us. We wanted a piece of software that was simple for customers to use and extremely accurate.

This was the main issue with the marketplace; softwares were clunky, difficult to use and the churn rate of users is very high. We wanted something very clean to simply show users what is working for them and what’s not.

Version one was completed within 2 months at a very affordable rate for the quality of work delivered, although there were a few issues and the product wasn’t ready for market. Artimus explained their approach and we implemented their recommended changes which resulted in a highly improved version two, which is now available and working with a 98% accuracy rating, one of the highest in the marketplace currently.

Moving into the future of the project, we’ll be looking to see how we can integrate some more advanced machine learning elements to distinguish, automatically, the types of marketing that are having the greatest ROI for clients, and the strategies that are most effective.

Working with Artimus was a very smooth process. No project is without issues but how these issues are resolved and more importantly, the speed at which these are resolved, was very important to us. I was impressed with their approach and we’ll be working with them again in the end of 2018 to improve KeyworX even further, add more features and apply various techniques to the data to work out ways to improve our ranking process.

What Are Recommendation Engines & How Do They Help Consumers?

What Are Recommendation Engines & How Do They Help Consumers?

You browse through your news feed or your favourite online and store.  Next, you notice that one of your friends has liked a page you’d be interested in or purchased an item you like.  Are you then suggested to like the same page or buy something similar to your friend?

But how did they know it would be suitable for you?  Because of recommendation engines.

There is so much data being collected that finding a way of scanning through it and picking out the useful data has never been more relevant.

Recommendation engines allow this data to be filtered.  The user on the other end is able to see the benefits because the only data that they see is tailored to them and their preferences.

Defining a recommendation engine

A recommendation engine is a piece of software that gives the user a list of selections based on the data it collects from their browsing preferences.

You will find a lot of recommendations when you browse online c-commerce stores.  The site will be able to see what kind of books, clothes, films etc you like and use that data to suggest to you other items that you may like.

The most advanced recommenders using machine learning techniques to predict items that the user will like and work in an active environment.

There may be changes to an item that will mean the chances of a user selecting them to increase dramatically.  This is particularly true in the retail sector when there is a sale so the recommendation engine will adapt.

A recommender system comes with the list by two methods: collaborative filtering and content-based filtering.

Collaborative filtering

This recommender system looks at the user’s previous behaviour to predict what items they may be interested in, based on other users with the same preferences.

Collaborative filtering has a key advantage in that it does not need to analyse the content of a listing or product to come up with an accurate suggestion.

However, collaborative filtering does have one major drawback.  In order

to make accurate recommendations, a lot of data is required.  If it has not

already been acquired, the predictions may be wide of the mark.

The method assumes that previous buyers, readers, etc will have the same taste as the current website user and that their older preferences will not have changed and will not change much going forward.

It creates a model using both implicit and explicit data, including

  • how many times a user views the item;
  • keeping records of what the user has purchased previously in the past;
  • presenting the user with two options and making a note of their selection;

Aside from shopping, collaborative filtering is used by other large companies in popular sectors.

For example, Facebook, the largest social media company in the world, uses collaborative filtering, notably when suggesting to make new friends.  It analyses who you have made connections with in the past, who your friends associate with and come with suggestions.

Spotify does the same with music.  It recommends new artists or tracks to you based on what you have previously browsed and played.

Content-based filtering

This is another very common recommender system that uses the descriptions of an item to provide predictions, mainly using keywords.

An item is selected by the user.  The system picks up on the selection, analyses it and comes with suggestions that best fit the same description.

The more information it can gather, the better the idea it has of the user and can provide more accurate recommendations.

In order for the system to know what

the characteristics of the item are, it creates an item profile.

Each characteristic of an item is given a value.  The more a user searches for a specific keyword about an item, the more weighted that value becomes.

The recommendation engine will give suggestions more focused towards the higher weighted features.

Content-based filtering systems also base their recommendations on what the user rates highly.  It will analyse the keywords from the content the user has shown to like and produce results based on these.

For example, YouTube videos have a like rating system where a user may say whether or not they like or dislike that video.

Based on what a user likes and dislikes, it will tailor the recommended content.

However, with all this comes an issue: can the system make accurate predictions using only one source of content and then using that information to cover all other types of content?

Content-based filtering can sometimes become quite limiting.  Being able to recommend blog posts based on other blog posts makes sense, but suggesting podcasts, videos, forums would be even more useful.

Privacy concerns

Recommender systems have always been faced with problems over how they manipulate a user’s data.  The more personal the information, the greater the chance that the user’s data privacy is being compromised.

In order to get the best results, users must provide the systems with highly sensitive and personal information.

These systems are able to collect and contain a very large amount of information about a user.  If the security is not up to scratch, the information could get into the wrong hands.

Countries across the world have begun to restrict what data can be used and how it can be used.  Most notably, the GDPR has recently come into practice.  Failing to comply will result in severe punishments so it’s important that recommender engines comply.

Conclusion

Recommendation engines are used to provide assistance to a user in order to help them find other items they like.  They help customers be more efficient in making decisions because the solutions are effectively given to them.

More and more businesses are going to want to start using this form of AI to become more competitive, meaning AI and humans collaborating to improve overall performance.

Recommendation systems can present a user with items or options that a user may not have been able to find.  A normal search engine is not able to do that as they require specific inputs to give the user results.

How can AI help the problems of wealth distribution

AI in relation to wealth distribution

The gap between the rich and the poor has been steadily growing but with the advancements in AI and automation, is it likely that this gap will get even wider?

One of the main benefits of using AI is that tasks can be completed faster and more efficiently than ever before.  Businesses are lapping it up because it saves valuable time and money.

However, it is feared that what may seem a great thing may actually further disconnect the wealthy and everyone else.

Advancements in artificial intelligence mean that jobs are being replaced by intelligent systems.  This has actually been in place for a number of years now, with machines replacing workers in factories on the assembly line for example, but obviously, now things are much more advanced.

Automation has been hitting manual labour jobs hard because employers can save a lot of employment costs and ensuring the jobs are doing with minimal errors.

The difference nowadays is that the machines are getting smarter.  Smart technologies are everywhere, from TVs to security systems to kettles.  Technology is starting to eliminate even the most basic of tasks such as boiling water.

There is a history of advancements

AI will continue to develop to where it will replace jobs that do not require much training in order to function necessary tasks.

But this is not anything new.

Human civilisation has always found a way to improve efficiency.

Take the industrial revolution for example.  Machines were implemented to improve production output.

A number of inventions were made that saw the improvement in the textile industry and the rise steam powered engines.

Modern history is no different.

The invention of the internet has seen people switch from doing everyday things in person to doing them online.

One of the big benefactors of this has been online retailers.  The normal shopping experience of visiting the local supermarket or high street is being replaced with internet spending.

Why?  Because it is so much easier.

Doing things from your own home is less stressful and takes a lot less time.

AI on the workforce

The main issues that surround this is fear of the unknown.  Will AI end up costing everyone their job and only the rich can survive?

Highly unlikely.

While it may be likely that automation will end up replacing a lot of untrained and lower skill-based jobs, people will be able to learn an entirely new skill.

Automation systems that have machine learning abilities will not be able to replace everything a human can do.  It can be trained to think like a human but it won’t be on the same level.

Think about it from the perspective of Kallum Pickering, an analyst at Berenberg:

Producers will only automate if doing so is profitable. For profit to occur, producers need a market to sell to in the first place.

Keeping this in mind helps to highlight the critical flaw of the argument: if robots replaced all workers, thereby creating mass unemployment, to whom would the producers sell?

Because demand is infinite whereas supply is scarce, the displaced workers always have the opportunity to find fresh employment to produce something that satisfies demand elsewhere.

As you can see, automation will have to increase the number of jobs in order for companies that create and develop the technologies to sell their product.

The more jobs created, the fewer unemployed and so the gap between the rich and poor should close.

The flip side

What is really interesting is that in the UK, the rate of unemployment has been falling over the last number of years.

However, wages do not seem to be rising as fast as they should be.

Is this because of automation?  Perhaps.

With the rise in machines being used to replace jobs, the ex-workers have to find some other form of employment.

AI is strong in logic and complex thinking but struggles with basic tasks.  For example, a computer will be able to easily solve difficult mathematical equations but won’t fare as well as a postman without being embedded into a movement device.

It most likely pays less to deliver letters but because a computer can’t do it, the unemployed have little option but to take these jobs.

So if this were to happen, it could be said that AI will increase economic inequality because jobs will cease to pay well based on the low skills that are required to do them.

The less money in circulation, the less wealth can be distributed.

How to manage the spread of wealth

The more that technology replaces humans in the workplace, efficiency will increase which in turn, increases wealth.

The current problem we have is that when we start to see the benefits, they should be available to everyone.

This is the major point that needs to be tackled to solve the wealth distribution problem.

The fewer people that are in work, the more the government will have to support the unemployed with extra welfare programmes.

At the same time, the jobs that are created must reflect economic sustainability in order for workers to maintain a worthwhile lifestyle.

There are schemes that are being trialled now in order to prepare for the robots age such as the universal basic income.  This has been rolled out in Finland and is being said to make people want to go out and get a job.

But this is just one potential route that can be taken and governments across the globe will have had discussions about how to keep up with advancements.

There must be a genuine attempt at sorting this out so we are not left behind.

As Larry Elliott describes:

Inequality, without a sustained attempt at the redistribution of income, wealth and opportunity, will increase.

Conclusion

If used properly, integrating AI into modern life will only help society advance to levels we have never experienced before.

However, using too much too soon could put too many people out of work and increasing the gap.  It is important that those that could be threatened by the integration are educated on how they can stay in work but not have to settle being worse off because of a robot.

What Is The Captcha Library & How Does It’s Existence Fuel The AI Revolution

What Is The Captcha Library & How Does It’s Existence Fuel The AI Revolution?

A lot of websites, whether you’re signing up or purchasing something, are now asking you to prove you are not a robot.

How many times have you seen the image on the right and being asked to answer a question to show you’re human?

It’s everywhere; CAPTCHA has taken over and is becoming a major part of the AI revolution.

But what is it?

Let’s start a the beginning.

What is CAPTCHA?

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a challenge-response test used to tell human and robotic behaviour apart.

The most common type of CAPTCHA was first invented in 1997 which required users to distinguish the type of letters in a sentence.  The test was that the images were usually distorted in some way as a computer would find it difficult to read.

The tests are carried out by computers rather than humans.  This often leads to CAPTCHA tests being referred to as reverse Turing tests.

A lot of web applications are now implementing CAPTCHA as part of their on-screen security measures.

Properties of CAPTCHA tests

CAPTCHAs are fully automated computer operations that require little to no human input and maintenance.  This means that businesses can save a lot of time and money on resources and maintain consistency.  Once programmed, CAPTCHA programmes provide accurate and reliable tests.

The algorithm used to create the CAPTCHA must be made available to the public, though it may be covered legally by a patent. This is because breaking the CAPTCHA programme requires the solution to a difficult problem in the field of artificial intelligence (AI). 

Modern text-based CAPTCHAs are designed such that they require the following abilities to be used at the same time:

Invariant recognition

Invariant recognition refers to the ability to see the numerous amounts of different possibilities to how a shape can look or presented.  A computer needs to be taught how to successfully identify these possibilities as a human has an edge; the brain.  Teaching these to a computer is actually incredibly difficult.

Segmentation

Segmentation is another power of CAPTCHA.  This enables the programme how to separate and distinguish one letter from another.   Again, this is another challenging task for CAPTCHAs the letters are usually clustered together with no white space between.

Unlike computers, are very good at distinguishing patterns.  Computers have to separate the recognition and segmentation processes whereas humans compute both at the same time.  The human brain combines both into the same process.

Context

Context is the final skill but is just as important as the previous two.  CAPTCHA must be understood to correctly identify each character in the given phrase. For example, in one segment of a CAPTCHA, a letter might look like an ‘o’.  However, after reading the word and understanding the context, it becomes clear that the letter is actually an ‘a’.

Humans are able to automatically understand what context the given text is applied.  By being able to do this, humans cannot be tricked into thinking one letter is another.

On their own, each of the three above challenges is a tough task for a computer to complete.  All three at the same is ridiculously hard.  This is what drives the consistency that CAPTCHA provides a computer system.

CAPTCHA and AI

Most CAPTCHAs are used for security reasons; as we saw at the beginning of the article, the reCAPTCHA check is being used by numerous businesses.

However, CAPTCHAs are also a standard for AI technologies.  As said by von Ahn, Blum and Langford in their article on using hard AI problems for security:

Any program that has high success over a CAPTCHA can be used to solve an unsolved Artificial Intelligence (AI) problem

A difficult example of an AI problem is that of speech recognition.   CAPTCHA programmes may use this technique as the underlying method for identifying human v robotic interaction.

von Ahn, Blum and Langford go on to say in the article that as CAPTCHA is used for security purposes, it is important that the AI problems that use it are useful.

If the AI problem is useful, there is either a way to differentiate between computers and humans, or a useful AI problem has been solved.

Each time a CAPTCHA is solved, the computer is taught how to do it again.  This is a machine learning technique; each time the computer is taught the solution, it becomes more accurate.

But where did they get these from?

Original CAPTCHA strings were actually scans of complicated words from old books that existing computers couldn’t recognise. The original developers wanted to use the CAPTCHA system to finally convert some of the oldest works in existence into digital format. In this process, they had found that traditional scanning methods such as OCR could not detect certain words. Naturally, to go through these words manually would take them an infinite amount of time due to the sheer size of the number of books they were keen to convert! Thus, using these indetectable words as their dataset on the original CAPTCHA system was not only a safe solution to the captcha problem (as they could be sure computers hadn’t recognised the text), but also they could use this to get a lot of people working on this at once to help convert the books!