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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.

Mental Health Week – AI’s Developing Relationship with Mental Health Solutions

AI’s Developing Relationship with Mental Health Solutions

 

Mental health is something which society has come to recognise as an important issue, with this year’s recent Mental Health Awareness Week just one of many vital initiatives. One of the less common words mentioned in the broader conversation is the role of technology and more specifically, AI. Yet, take a closer look and you will find that the relationship between mental health and AI is only growing.

 

Just one area that this is most apparent within, is that of early detection. We know that early detection is of crucial importance to the prompt and successful treatment of patients. Detect markers that indicate a high probability of cancer at very early stages is already an easy task for AI. With voice technology becoming a household reality, it now has the unique capacity to analyse complex data and establish patterns in our speech, where even highly skilled doctors cannot. This presents real potential for AI to provide tell-tale signs of early-stage developmental disorders, mental illness and degenerative neurological diseases and consequently, help doctors and patients better predict, monitor and track these and other related conditions.

 

This does not mean that diagnosing patients has to be left solely to AI. Rather, the technology can become an important tool alongside health professionals and most importantly, by aiding early diagnosis, it can save lives.

 

With 1 in 4 people experiencing mental health problems, mental health has become the single biggest cause of disability in the UK. This has consequences for individuals and business; 12.5 million work days are lost every year because of stress, depression or anxiety. In 2017, NHS England spent £9.7 billion on Mental Healthcare. According to IBM Research, the global cost of mental health conditions is set to surge to a phenomenal 6.0 trillion US$ by 2030. Another valuable aspect of AI, therefore, is its potential to remove the fear of judgement and reduce the perception of social stigma. These are still common barriers for people seeking professional help.

 

Integrating AI into mental health solutions has become both an exciting and important field within health, with developments in technology across the world. In Australia, Simon D’Alfonso of The National Center of Excellence in Youth Mental has developed the Health Moderate Online Social Therapy (MOST) project.

 

A multidisciplinary group of scientists from Australia and China are also leading a virtual counsellor program. Here, the virtual counsellor takes the place of a psychologist and offers both advice and support alongside stress management. Meanwhile, UK Researchers from Harvard University and their colleagues at the University of Vermont have successfully combined machine learning tools and Instagram to improve depression screening. Using indicators such as colour analysis, metadata, and algorithmic face detection, they have achieved 70 percent accuracy in detecting signs of depression. Previous studies solely involving GPs have resulted in 42 percent accuracy.

 

AI tools are also creating new treatment protocols. One of these is Woebot. Woebot is an AI-based chatbot app designed by Alison Darcy, a clinical psychologist at Stanford. It offers users cognitive-behavioural therapy (CBT). Unlike traditional clinical therapy, it is available whenever someone needs it. The benefit of Weobet is that it can, therefore, widen access to such therapy and in doing so, remove some of the barriers to seeking help while building conversational (and emotional) intelligence as they learn from their patients.

 

With these developments, AI is set to become an integral part of mental health solutions.

Codez Academy’s participants complete their Get Into Digital Campaign during their placement week at ARTIMUS – Week Overview

Progress Review: Codez Academy’s participants complete their Get Into Digital Campaign during their placement week at ARTIMUS

After their successful placement in one of Cardiff’s fastest-growing AI start-ups, Harry and Steven have excelled at the targets set out by ARTIMUS. They have been provided with a review which assesses their technical abilities and skillsets which aims to identify their strengths and weaknesses. Harry and Steven were also given an insight into the uprising world of artificial intelligence, industry-approved web design frameworks and client relations and negotiations. Due to their proficiency and hard-working ethic, ARTIMUS decided to help these participants with their first web design client as a gesture of good will and in an attempt to build a platform for two very bright futures.

 

To keep our followers updated on our participant’s progress as promised, we caught up with Harry and Steven after their successful placement. Harry told us that, “coding a digital CV from scratch has helped me showcase both my abilities and learn from Toby and the team’s coding capabilities, which in no doubt will prove to be invaluable for me in the future”. Mr White’s passion and ambition towards the AI industry has opened my eyes and his intuition has driven me to build on my talents and future”. Steven was also very pleased with his progress throughout his last week. This is what he had to say; “I did not expect such a fast-paced environment when I began this week’s placement at ARTIMUS. Their team were very welcoming and helpful with any assistance we needed. I have undoubtedly built on my skills and gained confidence in my abilities. Mr White has provided us with a platform to build on and I am extremely grateful for their time and belief in us.”

To view Steven’s online CV head to www.stevebensonwebdesign.co.uk

To view Harry’s online CV head to www.harrypikestudios.co.uk

ARTIMUS’ founder, Toby White, said:

“It is always a pleasure to teach, mentor and provide both students and the community with the guidance and knowledge needed for them to excel. Harry and Steven left at the end of this week knowing that they have made themselves far more employable and have been significantly provided with a deeper understanding into the world of coding and web design. They have pleased everyone with their will to learn and our door is always open for them and anyone else for that matter which is ready to build on their understanding of coding and artificial intelligence.”

What Is The Get Into Digital Campaign?

The Prince’s Trust Get into programme covers 16 to 30-year-olds and provides the experience and training needed to move into employment. If you are unemployed or training and live in the UK, the Prince’s Trust gives you the chance to gain work experience and receive training in a specific sector through their “Get Into” programme whilst focusing on employability skills to help you move into another job after the course. Their participants select the area in which they wish to specialise in with programmes covering sectors on retail, construction, logistics, tech and hospitality. The Get Into programme is completely free and will not affect your jobseeker’s allowance. The Prince’s Trust will cover your travel costs and may be able to support with lunch and childcare costs during their course.

 

What Is Codez Academy? 

Codez Academy gives people from all ages the opportunity to learn new skills, develop a digital know-how, and build on the foundations of an online presence through one-to-one sessions and advanced evening courses. The Codez Academy believes that “code is the foundations of all things digital.” They argue that knowing how to code will provide you with the fundamental knowledge to build anything from websites to apps to games. Their range of courses focuses on hands-on learning in an interactive and immersive environment. Codez Academy’s designated tutors work closely with each of their students to ensure that they get the very most out of their learning experience to come away with practical and relevant skills and with an opportunity to enter into a modern and rewarding career.

 

About ARTIMUS

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 Info@artimus-uk.com