Category Archives for "Business"

Case Study – The UK Fire Safety Scheme


Having worked internationally for over 10 years in a fire engineering and in a fire, risk assessing capacity I recognized the importance for informing the public for just how fire safe a building is. Upon my return to the UK back in 2014, I started work on what would become The Fire Safety Scheme ® , which aspires to be the UK’s standard for fire risk assessment undertaking, addressing the challenge for knowing just how fire safe a building is prior to using its facilities.
Historically assessors have used different tools, different methodologies, and when assessors have been benchmarked, were found to possess a vast range of experiences, knowledge and skills. This often led to inconsistent findings, which are not commensurate to the degree of fire risk. It was therefore important for me to develop an intelligent application tool to assist the fire risk assessor with their assessment undertaking and for that application to be intelligent linking
consumer needs for audited suppliers for fire safety products and or service providers.

Initial approach

Initially I produced a scope of works, an excel version of the tool to aid understanding and I then contacted this work with a Manchester based software development business. Their advice was to approach the project using a web-based design, such as framework 7. This approach, I was informed is typical because it is cost effective and for the most part application users wouldn’t know the difference between a web or application-based platform. A 16-week project commenced, however my lessons in business have been seldom learnt without a cost of some kind, as in this case, time commitment cost, financial and many lost customer opportunities. I experience all these and more, even to the point where I produced a bibliography of excuses for their procrastination. The project sadly run into two and a half years, while alarm bells were ringing after the initial 16-week program, and much to my own annoyance, I allowed this work to continue after 16 weeks is another foolish lesson in business experienced the hard way. The contractor in question eventually closed down and staff were let go for other opportunities. This left my project incomplete, out of pocket with a product unfit for customer consumption, therefore a very disappointing experience indeed.

Revised approach

Most businesses would have given in at this juncture, however as a subject matter expert believing in fire safety and wanting to make a real difference, I searched for a credible business development partner. Artimus (Artificial Intelligence Multiuse Solutions) are a software specialist based in the heart of Wales. The primary benefits for using their services were; having had an initial consultation I gained confidence in their abilities, they understood the application creation process necessary for my ideas, and their team integrated into my business seamlessly to fully appreciate the application and my customer user experience to be created. Following our consultation, we mapped out the scope of work taking cognisance of the work previously produced to avoid previous pitfalls. We agreed a program of works with various gateways aimed to check progress against scope continuously. The focus during our collaboration resulted on a high-quality product, with a platform that can be built upon for future improvements. I am pleased to say that Artimus are our software partner of choice for all of our development needs. Artimus are subject matter experts who are approachable, easy to do business with, having delivery on time and within my budget. It has been a pleasure to use their services.

Jason Hill CEO, The Fire Safety Scheme ®

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.


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.

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.


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.

It’s our Birthday!

It’s our Birthday!

It’s been exactly one year since we have launched and what a year it has been!

As a present to ourselves, we have completely redesigned our website.  Take a look for yourselves and let us know what you think!

It has been a very busy and exciting 12 months and we have developed so much in such a short period of time.  Below is a summary of all the things we have managed to achieve:


Eagle Lab Cardiff

First off, there has been a lot of changes to the business in terms of logistics and team growth.  We have now moved our offices to the Barclays Eagle Labs in Cardiff city centre. This has enabled us to expand our working facilities and we plan to use the new space very soon.

I mentioned that we have grown as a team; we have been delighted to recruit some highly skilled new team members this year.  Having the new office space has allowed us to accommodate the bigger team, empowering us to lead the AI revolution.

As of 1 July 2018, we are also excited to take on a student from Cardiff University for a year in industry.  We believe it is important to get undergraduates the necessary experience they need to achieve their future aspirations.

Active involvement in the economic future 

During the year we entered a competition for the chance to speak at the AI Wales digital festival.  We were very proud to be selected winners of the contest.

Digital Festival Wales

As such, we set up a stand at the festival where we seized the opportunity to increase our exposure and our presence in the Welsh business space.  We showcased our work to other participators of the festival. Our services impressed a variety of businesses such that we took on a number of clients.

Not only that, we have been invited to do a talk at the latest AI Wales talk event, details of which you can find here in order to sign up and attend.  As it so happens, this falls directly on our birthday so come and say hello and join in with the birthday celebration.

We will be presenting about the ethics of self driving cars and it will be led by our founder Toby White.  This will be another great opportunity for us to connect with you and explain why AI is already becoming a vital part in business progression and growth.

Finally, as you will have noted from our homepage, we had the brilliant opportunity to have conversations with the Welsh government about the future of AI.  This included how many AI technologies can be be implemented now to improve business performance and also how it will grow in the future.

The talk was led by founder Toby White and he had further conversations with the authorities about the upcoming AI sector deal.  Artimus is going to be heavily involved with the new deal in place so you can expect to see us intertwined with the advancements in future technology.

But we are not stopping here.

The next 12 months are going to be just as exciting as the previous. 

We are delighted to be part of IBM’s new PowerAI power platform that is now in development.  We that this is an exciting new chapter in Artimus’ reach in that such a giant in the technology industry in IBM shares the same vision for the future of AI as Artimus.

We cannot wait to get started on this and share what we have to offer to the world.

As an internal goal, we are aiming to launch a product available for purchase by the end of September.  This AI technology will be available to all businesses to help them achieve their goals in whatever sector they service.

Finally, we will be helping our friends over at Codez Academy in supporting the next generation of people develop their coding skills specifically towards AI.  More details on this will be coming soon.

The year ahead promises to show that Artimus is leading the innovation in AI.  We hope that you will share the journey with us.

Artificial Intelligence and Smart Production in Factories

Artificial Intelligence and Smart Production in Factories

We are on the brink of a fourth industrial revolution which will change the way we use and consume everything from cars to shoes.

(4-minute Read)


Modern consumers are pickier than ever. We all want customised, personalised and unique products and prefer local, smaller producers over large-scale manufacturers as well as requiring them to be cheaper than their mass-produced counterparts. Therefore, factories power plants and manufacturing centres across the globe must turn to Process Automation, Machine Learning, Computer Vision and other fields of AI to meet rising consumer demands, ultimately transforming the way in which we make, move and market anything.

The first three industrial revolutions made mechanisation, mass production and automation what is now considered as mainstream amongst today’s production methods. Now, more than half a century after the first robots worked on production methods still being used today, Artificial Intelligence and Machine Learning are shaking things up again.


So, what is Artificial Intelligence currently capable of?

Artificial intelligence is just now finding its niche in manufacturing with production line managers discovering that applications consisting of AI algorithms are able to make complex decisions on an instant real-time basis – something no human employee could possibly be capable of. With AI’s future becoming ubiquitous, the future of Artificial Intelligence in manufacturing is already becoming a staple within emerging markets through their unrivalled sensory abilities, capabilities; not just on the factory floor, but also predicting what will be needed and when.

AI is essential for survival. Innovative manufacturers already use various types of artificial intelligence to tackle many of their challenges. The next few points will assess the ways in which the ‘industrial revolution 4.0’ is shaping trends in smart factories such as data exchange, predictive maintenance and adaptive manufacturing. So what really are the key benefits AI offers to manufacturing organisations?

Informed Decisions

With the right foundations in place, manufacturers will be able to monetise AI, making informed decisions at each stage in the production process in real-time.

Demand-Driven Production

Lost revenues (either in the form of stagnant inventory or lost sales) can derive from overestimating or underestimating consumer demand. Real-time demand visibility can be achieved by connecting consumer apps and IoT with industrial IoT – developing a proactive rather than reactive environment. With the rise in the popularity of products such as Google Home, Amazon Echo and the Apple HomePod, consumer trends and behavioural data can inform downstream supply chain and manufacturing activities


Sensors are able to spot defects amongst products on the production line as well as throughout the production process. This data is then uploaded simultaneously to the cloud so that it can be verified, immediately removing the defective part and order a replacement. These faster feedback loops allow manufacturers to overcome obstacles such as low yield.


As AI’s use cases are constantly evolving and expanding, Artificial Intelligence will become pivotal in all areas of an organisation from fraud prevention to predictive ordering and opportunity assessment; all of which bring time productivity and cost benefits which can be passed onto the customer.

Powerful Insights & Cost Saving

With the ability to have real-time problem-solving, manufacturers could potentially solve millions of pounds in recalls, repairs and lost business. These intelligent insights have the potential to produce powerful information and have the ability to turn them into tangible outcomes.


Future Trends and Developments

With AI gradually being implemented into more and more organisations and steadily being considered as ‘mainstream,’ a sense of trust and dependability will surface, bringing a level of intelligence that becomes mission critical to industries.

Manufacturers are building their own technology foundations through the use of this technology due to its proven cost-effective implementation and guaranteed efficiency. The advancement to de-centralised Artificial Intelligence – where AI solutions can ‘speak’ at every level of the supply chain from customers through to vendors, data sharing and decision-making. This in effect will be performed immediately and instantaneously whereas it would have taken teams of people days and weeks to replicate.

With emerging technologies (Blockchain, Augmented Reality and AI) coming to the fore, inevitably, the convergence of these trends will happen promptly, meaning that manufacturers should look to embrace the direction of the manufacturing process and invest in these smart technologies in order to gain a competitive advantage whilst taking a cost-effective approach – disruptors stand to gain the most from these new and exciting technologies.


Key Takeaway for Manufacturers in any field

With data being the core ingredient for AI, big data, machine learning and computer vision working in tandem with complementary technologies such as 3D printing and IoT, will enable the modular manufacturing required to meet rising consumer needs.

Order-driven production not only enables dynamic supply chains, but it also reduces any manufacturing waste. Manufacturers are able to leverage real-time digital platforms connecting consumer and industrial data to configure the assembly of products on an ad hoc basis. With the ability and flexibility of having production lines quickly reconfigured, they will be cost-effectively and be able to create new, unique products, in which both the consumers and the manufacturers benefit.


For more information on how ARTIMUS can help implement Artificial Intelligence solutions into your manufacturing plant or production line, please contact us on 02920 099 610 or via email at


ARTIMUS is a Bespoke Solutions company based in the South West region of the UK. They Specialise in Artificial intelligence solutions and pride themselves on being able to provide the best, highest quality development and project management services that you can find on the market. Their mission statement is to “Focus on innovation: we want to create unique, outstanding technologies that will help us get one step closer to the simplest future possible. We are fully committed to bringing in artificial intelligence and multi-use solutions that work for companies all over the globe.”

How is Artificial Intelligence revolutionising the Medical Landscape?

How is Artificial Intelligence revolutionising the Medical Landscape?

AI is improving our healthcare and quality of life by giving us the opportunity to create highly-specialised personalised medicine like never before and aid in the research and prevention of diseases.

(4-Minute Read)


Imagine contracting an illness and yet being able to receive an accurate diagnosis alongside a recommended treatment plan in just 10 minutes with minimal consultation times between patients. Sounds like something that can be done in the future, right? Well, this is actually happening right now with the help of Artificial Intelligence (AI).

AI has the ability to learn from every single bit of data it is exposed to and rapidly re-evaluate its analysis as more and more data is compiled. This grants doctors and researchers with the ability and possibility to accurately identifier misnomers, subsequently, providing us with better solutions to our health problems.


So, how exactly is AI contributing to the prevention of Illness?

Artificial Intelligence is not just limited to analysing traditional data from spreadsheets – as is commonly perceived. AI has the function of interpreting and aggregating imaging, text, handwritten notes, speech, test results, sensory data and even cross-reference and analyse large amounts of demographic and geo-spatial data. Through this, AI is able to identify commonalities and produce insights which were previously impossible with data silos or the extent of time needed for humans to individually examine large datasets.

With the ability to simultaneously cross-examine and analyse, AI is able to consider what could have been described as seemingly unrelated external factors which doctors and researchers may not consider as relevant at the time. For example, environmental factors such as humidity, pollution, elevation, agriculture and proximity to certain dense mineral deposits (such as: AI in the medical research sector has identified that continuous exposure to erionite found in gravel in some parts of the world increases the risk of contracting mesothelioma). This ability to rapidly analyse data and potential correlations have allowed us to create a data-driven comprehensive and holistic insight into a person’s health along with all their factors considered.


How is Artificial Intelligence being used today in the medical industry to anticipate and contribute to future prevention?

Organisations such as the Institute of Cancer Research in London and The National Cancer Institute in the USA are developing Artificial Intelligence frameworks which aim to enhance and contribute to exponential growth within cancer research. With the help of these intelligent frameworks implemented in their research, AI leverages, studies and extracts millions of patient records autonomously and simultaneously on a constant basis with the goal of understanding how cancer spreads and reoccurs – a prime example of AI being used to analyse large amounts of data within the medical sector which allows doctors to draw quick conclusions.


A study found in the Neurobiology of Ageing concluded that with the help of AI, intelligent software could be able to detect signs of Alzheimer’s in patient’s brain scans before doctors. Currently, AI frameworks are being used to analyse scans of healthy brains alongside brains diagnosed with Alzheimer’s so that intelligent systems are able to continuously learn and identify the telling systems of the disease.



AI will ‘design’ specialised medication in an instant

Increasing advancements in the development of Narrow Artificial Intelligence has allowed these intelligent technologies to revolutionise industries. The impact this has had has been significant throughout the medical industry as explained above, particularly on medical imaging, radiology and scanning, but the technology has far greater potential to achieve a much more comprehensive transformation in healthcare.

Let us look at pharmaceutical supply chains for example. AI solutions could potentially alter the traditional process of designing drugs. Intelligent systems are able to reduce the drug production circle, therefore, helping pharmaceutical companies discover new and more effective drugs without the burdening trials as well as achieving a significant reduction in accumulating costs.

The ‘traditional’ and current drug discovery and production method consists of a ‘trial and error’ process which can take up to 12 years and nearly £1.15 billion to bring a new effective drug to the market. It should also be taken into account that many of these drugs fail the rigorous testing methods which means that there are only very few experimental drugs that ever see the medicine cabinet – a huge inefficiency for the medical industry on precious time and resources which could be used elsewhere for other research areas.

Thanks to Artificial Intelligence, smart algorithms use the power of intelligent systems to teach themselves (click to see Machine Learning for more info) complex biochemical principals and the factors that are ultimately the most predictive in relation to the effectiveness of a drug. With this technology present, it could transform the way we analyse hundreds of millions of molecules and their interactions, whether it be linear or non-linear, on a simultaneous basis. Companies in the medical sector are continuously implementing Artificial Intelligence solutions to design personalised drugs and treatments faster than any form of traditional healthcare, with the goal of improving the current treatment process and overall quality of life through the discovery of the best possible medication.


For more information on how ARTIMUS can help implement Artificial Intelligence solutions into your business strategy, please contact us on 02920 099 610 or via email at


ARTIMUS is a Bespoke Solutions company based in the South West region of the UK. They Specialise in Artificial intelligence solutions and pride themselves on being able to provide the best, highest quality development and project management services that you can find on the market. Their mission statement is to “Focus on innovation: we want to create unique, outstanding technologies that will help us get one step closer to the simplest future possible. We are fully committed to bringing in artificial intelligence and multi-use solutions that work for companies all over the globe.”

AI & Humans Are Collaborating To Redefine The Way We Work



AI is undoubtedly shaping our future within the world of work. There is a new realm of possibilities which can be achieved with this match made in heaven.

4-Minute Read

Ever since the industrial revolution, businesses are bringing forward ideas which leverage the newest technology to augment human workers in order to streamline efficiency and productivity whilst cutting unnecessary costs. You may be thinking that Artificial Intelligence may indeed make us redundant, but the reality is, that this is most probably not the case. Although, thanks to the recent advancements in AI, we can now retrospect a different working environment to one that we have become accustomed to. So, what does Business Process Automation (BPA), Machine Learning (ML) and Artificial Intelligence (AI) propose and mean for tomorrow’s workforce? The reality is that the future workplace will likely involve hybrids which involve both human and machine intelligence working in harmony towards achieving common business goals.

Shaping The Way We Work

Automation isn’t a relatively new ground-breaking concept – it’s been around for over a hundred years. There are many areas in the workplace where monotonous tasks can be permanently shifted from human employees to intelligent learning software so that work can be completed faster and eliminating the risks of any human error. Subsequently, this improves overall efficiency levels, leading to a better bottom line for the organisation as a whole. Due to the increases in the output of the quality of work, service levels are also seen to take a positive hit – so in theory, everybody wins. However, automation powered by Artificial Intelligence has taken this relatively basic concept to an entirely new level. This form of intelligence doesn’t just involve programming a machine simple tasks as is often the assumption. Rather, it is about relying on technology which is ‘smart’ enough to learn and adapt over time without the need of any human input. Let’s take chatbots as an example; a technology which is capable of using continuous data gatherings from customer inquiries to develop a robust catalogue of answers – the more it is used, the smarter it becomes.

Adapted Job Roles

The truth about technology being implemented in our workplace is that Business Process Automation does in fact adapt and create new roles and opportunities for us human workers to pursue. The reality is that there are certain areas throughout the business where technology simply cannot replicate ‘human touch’ – no matter how far the intelligence of the automation, it is something which cannot be replaced. For instance, a chatbot can be programmed to perform basic customer support, although it is not ‘intelligent’ enough to manage complex situations. To break this down even further, let us look at employers. Intelligent Automation could be very effective and efficient in identifying the ideal candidates for a job opening, but the actual hiring process is still and will be a human-centric function.

Humans & Technology in Harmony

Rather than technology and humans working independently, the two should be working together towards achieving the greater good for the organisation. Ideally, the best way to approach this AI overhaul in businesses is to view it from a more holistic perspective. For instance, Process Automation can be leveraged to handle the majority of mundane and repetitive tasks whilst seamlessly transferring more complex issues to human workers – this enables an ideal standard of work. Executives then use this information to make strategic data-driven decisions, project and plan for the future whilst the machines handle the data mining process, identifying and extracting the most relevant data available. Ultimately, this facilitates businesses achieve greater innovation, hence, those who adopt an AI approach will lead the charge in their respective fields.

Redefining Work

Artificial Intelligence is continuously evolving, growing and being increasingly implanted into our day-to-day lives. The main aim of organisations which apply these technologies is to improve their workforce’s efficiency, make tasks more enjoyable and make the lives of their employees much easier. With intelligent technologies becoming smarter, more and more tasks will be carried out through AI-related technologies. Studies indicate that by 2022, many tasks of businesses involving lawyers, doctors, medicine and IT will be handled by intelligent technology. With our experiences working with many service professionals, we can confirm that this is the direction that will improve everyone’s quality of life and satisfaction within their respective fields as well as by improving their quality of service like never before. This doesn’t necessarily mean that there will be certain unemployment for those working on the front lines. 79% of executives were surveyed saying that they expect an increase in new jobs with the adoption of business automation. Furthermore, 94% of executives said that there will be a significant increase in demand for roles which demand soft skills such as communication, creative problem solving and collaboration. Ultimately, the way we work is changing and this reality is what allows us to adapt and evolve as people. For those who welcome artificial intelligence and all of its intelligent subfields and opportunities it presents, the future certainly looks exciting and filled with innovation in a continuously fiercely competitive and updated business landscape.


For more information on how ARTIMUS can help implement Process Automation into your business strategy, please contact us on 02920 099 610 or via email at



ARTIMUS is a Bespoke Solutions company based in the South West region of the UK. They Specialise in Artificial intelligence solutions and pride themselves on being able to provide the best, highest quality development and project management services that you can find on the market. Their mission statement is to “Focus on innovation: we want to create unique, outstanding technologies that will help us get one step closer to the simplest future possible. We are fully committed to bringing in artificial intelligence and multi-use solutions that work for companies all over the globe.”

You interact with artificial intelligence on a daily basis and you don’t even know it!

You interact with artificial intelligence on a daily basis and you don’t even know it!


AI is everywhere! If someone came up to you and said that you’ve spent all day interacting with artificial intelligence, you’d probably try and recall any potential instances of accidentally running into a robot.

5-Minute read

AI comes in many forms, shapes and sizes – some of which may surprise you. Not only has artificial intelligence been made mainstream within a number of industries, but it is also allowing us to streamline our businesses and optimise our lives. From social media to public services, we are all coming across some form of AI every single day and it may surprise you on how broadly it is being incorporated without us even taking notice.


According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times are steadily climbing year-on-year, resulting in over 42 hours of rush hour traffic delay per commuter in 2014 – more than a full work week per year carrying an estimated $150 billion loss in productivity. This presents a valuable opportunity for AI technologies to be implemented in order to create a tangible, visible impact in everyday life.

Reducing real-time commute times for each individual person, 24 hours a day and 7 days a week is something a human being is simply not capable of. A single trip may involve multiple transportation methods (i.e. driving to the train station, taking the train and using ride-share services to get to your final destination), not to mention the expected and unexpected events which constrict traffic flow: traffic, road works, accident, diversions and weather conditions. Here is how you interact with AI:

1) Google’s AI-Powered Predictors

By using anonymised location data from smartphones, Google Maps can analyse traffic flow at any given moment in time. Not only that, but with its acquisition of crowdsourced traffic app Waze, Google Maps can easily incorporate user-reported traffic incidents. The access to these vast amounts of data being uploaded into its proprietary algorithms results in Google Maps diverting and warning you beforehand about any unexpected incidents to ensure your commute remains efficient.

Transport and AI2) Ridesharing Apps (Such as Uber and Lyft)

Ever wonder how these apps determine the price of your fare, reduce wait times and how they pair you with other passengers to minimise detours? Thank Machine Learning (ML). It is what allows us to create such efficient transportation services available at your fingertips.

Jeff Schneider (Uber’s lead engineer) discussed how the company uses ML to predict the rider demand which ensures that the “surge pricing” (short periods of sharp price increases to decrease rider demand and increase driver supply) is relative to the supply and demand of passengers and drivers. Not only is machine learning used in Uber’s taxi systems, it is also used for ETAs, determining optimum pickup locations and fraud detection within its payment services.

3) AI in commercial flights

A surprisingly early use of AI technologies which dates as far back as 1914 comes from the autopilot systems used within the aviation industry. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for take-off and landing.


Banking and Personal Finance

Machine learning is playing an integral role in the many phases of the financial ecosystem which range from loan approval, asset management and risk assessments. There are now more uses for machine learning in the banking and finance sector than ever before which is made available by the SMART technologies we create.

1) Mobile Banking

Most banks now grant you the ability to deposit and transfer cheques and funds via their smartphone and web apps – eliminating the need for us to physically go to the bank. According to the Security and Exchange Commission, the vast majority of banks now heavily rely on AI and ML technologies to decipher and convert handwriting on cheques into text via OCR in order to ensure authenticity and prevent fraudulent activities.

2) Fraud Detection

Due to the volume of financial transactions being far too high for humans to manually review each transaction (correlated to the popularity of online banking and fast payment systems), AI and neural networks are implemented to work in conjunction; learning what types of transactions are fraudulent via specific algorithms. This works by detecting factors which affect the neural network’s final output such as transaction frequency, size and parties involved.

3) Credit Decisions

Every time we apply for a loan or credit card, financial institutions quickly determine whether they accept or decline our application to be entitled to access their services. Their use of AI and ML technologies essentially determines your credit and risk scores, ultimately influencing interest rates and credit amounts for each and every individual customer. MIT researchers found that machine learning could be used to reduce a bank’s losses on delinquent customers by up to 25%.


Social Media

So how do we interact with artificial intelligence through our social media? The truth is, AI is what helps our everyday user experiences on social media platforms get better. The development of deep learning technologies to sort through large amounts of data helps adjust the platforms’ suggestions, news feeds, trending topics, hashtags and even tagging your appropriate friends in photos – all without spending too much manpower in data analysis.

1) Facebook

When you upload your photos to Facebook, the platform automatically detects faces and suggests tagging the people in it. How does Facebook do this? Well, Facebook uses AI and neural networks to recognise faces and power the facial recognition software. Their use of AI doesn’t stop there. Facebook also uses AI to personalise your newsfeed, ensuring that you see posts which match your interests and from paid advertisements. Better targeted ads mean that you’re more likely to purchase something from these advertisers because of your matched interests, avoiding ads which are not relevant to you.

2) Instagram

Instagram uses machine learning to identify the contextual meaning of emojis which have been steadily replacing slang (for example, a laughing emoji could replace the word “lol”). By algorithmically identifying the sentiments behind emojis, Instagram can create and auto-suggest emojis and emoji hashtags. Although this may seem like a trivial application of AI, Instagram has seen a substantial increase in emoji use amongst all demographics, therefore, being able to understand and analyse this emoji-to-text translation at a large scale sets the basis for further analysis on how people use Instagram.

3) Snapchat

For those not familiar, Snapchat introduced their facial filters back in 2015. These filters tracked facial movements, allowing users to add animated effects and digital masks which adjusted when their faces moved (similar to Apple’s animoji concept). So how does this work? The filters are powered via machine learning which tracks movements in real-time video which allows us to interact and personalised our “selfies” in ways never before imaginable.


To wrap it up

Data Modelling ConclusionWe have just scratched the surface of the abundance of AI, ML and neural networks encountered in our everyday lives. Many industries still form a greater habitual interaction with artificial intelligence far beyond what’s explored in this article. But AI doesn’t necessarily have to be limited to corporate uses. AI can also be applied to activities such as our own hobbies. For example, casual chess players are now using AI-powered chess engines to analyse their games and practice custom tactics. But it doesn’t stop here. Bloggers often use their ML optimised mailing lists to optimise reader engagement and open rates.

As you can see, AI is developing an important role in our current and future lifestyles. Without it, arguably, we would feel a bit lost seeing as so much of these technologies are implemented into our daily activities.




ARTIMUS is a Bespoke Solutions company based in the South West region of the UK. They Specialise in Artificial intelligence solutions and pride themselves on being able to provide the best, highest quality development and project management services that you can find on the market.

Their mission statement is to “Focus on innovation: we want to create unique, outstanding technologies that will help us get one step closer to the simplest future possible. We are fully committed to bringing in artificial intelligence and multi-use solutions that work for companies all over the globe.”

To see how ARTIMUS can assist you with any of these services please contact us on 02920 099 610 or via email at


AI Wales’ First Meetup: A Success!

AI Wales’ First Meetup: A Success!

AI Wales’ first event at Tramshed Tech saw over 70 people attend the event. Individuals from an array of employment learned how Artificial intelligence could be implemented to help start-ups, SMEs, employees and their business development.

(2 Minute Read)


AI Wales provided attendees with an in an introduction to Artificial Intelligence and its multiple segments in order to build a foundation of knowledge for the upcoming weeks – it’s never too late to come to our events as we always provided an overview of what has been talked about during the last few meetups! After all, the aim of AI Wales is to raise awareness and inform you on how to reach the next level of competence!

Introduction to mainstream AI topics such as self-driving cars, business automation, data analysis, machine learning and biometric identification was covered so that our audience could develop an insight into the direction Artificial Intelligence technologies are heading, and clear up misconceptions commonly held surrounding the topics.


We caught up with Toby White (Artificial Intelligence specialist & Founder of ARTIMUS) during his Q&A. This is what he had to say:

“We were very impressed with the engagement and enthusiasm of our audience. We covered an array of topics throughout this first meetup which aims to provide a solid foundation and direction for future areas. The networking opportunity was very enjoyable, and we are definitely looking forward to seeing new faces in the upcoming weeks (and having more pizza). I would say that this event has been a success and provided everyone with everything we’d hoped for from our first meetup.”


The aim was to involve the audience in contributing towards the shape of AI Wales’ future talks and meetups, so, we encouraged networking amongst ourselves whilst enjoying some pizza and beer on the evening of the event to complement our Q&A with our enthusiastic audience – ultimately, the purpose these talks is to be shaped by the direction of the group so that we can deliver the information our followers want to learn.

Our group’s first talk was delivered by Jaymie Thomas (CTO of Automise), David Pugh (Technology Specialist), and our very own Toby White (Founder at ARTIMUS). If you consider yourself an expert in the AI and emerging technologies field, why not contact us about delivering presentations at our meetups and share your stories and insights with the group? We are looking forward to meeting all of you and making you all aware of the vision we have in shaping Cardiff’s Artificial Intelligence landscape.



ARTIMUS is a Bespoke Solutions company based in the South West region of the UK. They Specialise in Artificial intelligence solutions and pride themselves on being able to provide the best, highest quality development and project management services that you can find on the market.

Their mission statement is to “Focus on innovation: we want to create unique, outstanding technologies that will help us get one step closer to the simplest future possible. We are fully committed to bringing in artificial intelligence and multi-use solutions that work for companies all over the globe.”

To see how ARTIMUS can assist you with any of these services please contact us on +44 7826 852 610 or via email at