Digitally Speaking

Justin Price
February 7, 2019

It’s an undeniable fact that Artificial Intelligence (AI) is changing the way we live. From facial recognition and voice technology assistants to self-driving cars, AI has crept into our lives and as consumers we use it without a second thought.

But its impact across a wide range of business sectors is perhaps the hottest topic in tech right now. AI has developed and matured to the stage where, for some functions and operations, the levels of accuracy have overtaken human skills.

Yet with stories of mind-boggling complexity, escalating project timescales and spiralling costs, the much-hyped technology is still regarded by many business owners with confusion and as a risk. Justin Price, Data Scientist and AI lead here at Logicalis recently led a webinar about the business reality of AI. One of his key points was that knowing what you want to achieve and setting realistic expectations are the best guarantees of a successful first AI adoption.

Choose the right AI tool for your business need

During my meetings with clients, and from talking to CIOs, it has become apparent that confusion reigns over the terminology used to describe Artificial Intelligence.

There are three key terms at play. All three fall under the umbrella term of AI, and are often used interchangeably but each has a different meaning. AI is a technology that retrains or ‘learns’ patterns and other specified behaviours to achieve a set goal. Crucially, it is about producing something which didn’t exist before.

Other types of AI behaviour include:

  • Robotic Process Automation or RPA – a software designed to reduce the burden of simple but repetitive tasks done by humans
  • Machine Learning – essentially probability mathematics used to spot patterns in very large samples of data
  • Deep Learning – a Neural Network which mimics the way the human brain works to examine large data sets, and unstructured data formats such as HD images and video

Being aware of the subtle differences and uses of these terms allows a greater understanding of which tool will best-support your business’ data insight needs.

Make sure your data is up to the job

When delivering an AI project, around 80% of the total effort and time will go into making sure your data is correct. Underestimating the importance of top-quality data is a common pitfall for organisations because, just like any other IT tool, AI will perform poorly if you have low quality data.

Data must be well structured, and it must be in format that’s consistent and compatible with the AI model. Don’t forget that AI is a process which must be regularly re-trained to ensure accuracy, so ongoing maintenance is essential.

Brace yourself for complexity

Never underestimate the breadth and complexity of what is involved in building and delivering an AI project. For many CIOs this will be their first experience of AI and, even with the right data, there are many variables at play that can add to both the costs and timescale of implementation.

Working with the right partner is essential to guide you through the first project. We recommend undertaking an initial project with a fixed fee whereby you can deliver a functioning result while you establish trust and credibility with your solution provider.

As well as the importance of good data, another critical factor in delivering a successful AI project is finding a solution that is scalable. It’s one thing to write an AI model on a laptop, but it’s a completely different thing to write a model in a way that will survive deployment across a business. Getting expert advice will help you decide on the right foundation to support your project. This is where Logicalis can add real value.  We have the skills and know how to advise whether your AI initiatives can use existing infrastructure, or if the AI applications would require new servers with new performance capabilities.  And if new infrastructure is needed, we can guide your organisation toward on-premise or a hybrid-cloud expansion.

Know when AI is (and isn’t) the right tool for the job

With a plethora of impressive use cases available to businesses spanning most sectors, it’s no wonder AI is the tech tool of the moment.

But AI is just another technology and won’t be the right choice for every business looking to gain insight from their data. The importance of prioritising people, process and culture in any AI project has already been discussed by my colleague Fanni Vig in a previous blog, and is absolutely essential to ensure your business isn’t trying to use AI where a different tool could deliver the desired results.

At the highest level, AI allows you to work through far larger data sets than previously possible. It can be used to help automate your data workflows, redirecting low-difficulty but high-repetition task to bots, which allows people previously engaged in these tasks to work more efficiently.

This process creates a new way of working that may have greater implications across the business as roles change and skills need to be channelled in different directions.

Introducing AI is a truly cross-business decision. And let’s not forget that, at the most fundamental level, using AI to harness your data is an investment that must show a return.

AI adoption steps

Finally, get advice from the experts

While the impetus to adopt AI may come from the IT department, the results generated can help drive cross-company productivity; help differentiate businesses from their competitors; and delight your customers through a more tailored service. The impact cannot be underestimated. But neither can the complexity.

If you’re considering whether AI could help you get more from your data, let Logicalis guide you through your first successful deployment. We will collaborate with you every step of the way to:

  • Help you decide which area(s) of your business will benefit most from AI
  • Help you identify where the relevant data resides and help you access and structure it
  • Deliver a business-ready solution which is scalable to meet your needs
  • Advise on infrastructure requirements
  • Analyse the data to provide rich insights in to your business

 

Category: Analytics

Pepper, the humanoid robot talks AI

Justin Price
December 10, 2018

Logicalis recently attended IBM’s Think London event where Pepper the Robot was our honorary guest. With people lining up to talk to Pepper, we battled through the crowds to ask a few questions about the hot topics of the day, such as implementing AI. 

Q. Hi Pepper! Can you tell us about Think London?

IBM Think London was an incredible event where technology met humanity. There were a range of industry leaders and experts there to advise people on topics such as choosing the best fit cloud model for your business, leveraging data better, and innovating faster.

I was delighted to join my friends at Logicalis at the show, informing visitors about how they can modernise infrastructures, transform the way IT is delivered, and unlock the value of data. As ‘architects of change’, Logicalis were able to give me lots of examples of where they have delivered solutions and services that take advantage of the benefits driven by cloud, mobility, big data analytics and AI.

Pepper the humanoid robot at the Logicalis stand at IBM Think London

Q. There was a lot of talk about Artificial Intelligence at the show. Can you tell us a bit more?

Everyone is talking about Artificial Intelligence currently and the hype around it in the media is extraordinary. Logicalis recently released its sixth annual global CIO survey  which revealed that nearly a fifth (19%) of CIOs claim their organisations are already using AI. Moreover, 66% said that AI will be in use within their organisations within three years. In reality though, only 4% of organisations have successfully deployed AI.

Exciting times, yet the report also sounded a note of caution about how to make the best use of AI. You need a partner with strong capabilities and know how to help you implement it correctly. And it’s worth noting that, outside of the IT department, the business area most likely to use AI is customer service (17% say it is in use here). Old style chat bots have got nothing on AI in its purest form – technology that is able to learn and adapt independently based on context. Like yours truly…

Q. What sectors are driving AI adoption? 

Last year financial fraud losses in the UK totalled £768 million. AI is a powerful tool that can provide financial organisations with the ability to combat this fraud challenge; from automating risk analysis, detecting and investigating fraud, assisting regulatory intelligence and automating IT functions.

AI can also provide retail organisations with a range of benefits including personalised shopping experiences, dynamic pricing or the use of real time tracking to optimise logistics.

Given the potential of AI, it’s not surprising that many businesses want to introduce it into their organisation to increase competitive advantage.

Thank you for chatting with us and giving us an insight into the event Pepper!

Want to learn more about implementing AI?

Join our live webinar on Wednesday 19th December at 11am to hear Justin Price, AI Lead and Chief Data Scientist and Scott Hodges, Solutions Architect discuss how to deliver a scalable AI strategy.

You will discover:

  • The main challenges businesses face in implementing AI 
  • How to put AI into practice inside your organisation
  • Powerful Artificial Intelligence and machine learning use cases
  • Practical advice for an AI-ready infrastructure

Register now

 

 

Justin Price
November 8, 2017

Year by year we are generating increasingly large volumes of data which require more complex and powerful tools to analyse in order to produce meaningful insights.

What is machine learning?

Anticipating the need for more efficient ways of spotting patterns in large datasets on mass, Machine Learning was developed to give computers the ability to learn without being explicitly programmed.

Today, it largely remains a human-supervised process, at least in the developmental stage. This consists of monitoring a computer’s progress as it works through a number of “observations” in a data set arranged to help train the computer into spotting patterns between attributes as quickly and efficiently as possible. Once the computer has started to build a model to represent the patterns identified, the computer then goes through a looping process, seeking to develop a better model with each iteration.

How is it useful?

The aim of this is to allow computers to learn for themselves, knowing when to anticipate fluctuation between variables which then helps us to forecast what may happen in future. With a computer model trained on a specific data problem or relationship, it then allows data professions to produce reliable decisions and results, leading to the discovering of new insights which would have remained hidden without this new analytical technique.

Real-world Examples

Think this sounds like rocket science? Every time you’ve bought something from an online shop and had recommendations based on your purchase – that’s based on machine learning. Over thousands of purchases the website has been able to aggregate the data and spot correlations based on real buying users’ buying patterns, and then present the most relevant patterns back to you based on what you did or bought. You may see these as “recommended for you” or “this was frequently bought with that”. Amazon and Ebay have been doing this for years, and more recently, Netflix.

This sounds fantastic – but where can this help us going forward?

Deep learning

This is distinguished from other data science practices by the use of deep neural networks. This means that the data models pass through networks of nodes, in a structure which mimics the human brain. Structures like this are able to adapt to the data they are processing, in order to execute in the most efficient manner.

Using these leading techniques, some of the examples now look ready to have profound impacts on how we live and interact with each other.We are currently looking at the imminent launch of commercially available real-time language translation which requires a speed of analysis and processing never available before. Similar innovations have evolved in handwriting-to-text conversion with “smartpads” such as the Bamboo Spark, which bridge the gap between technology and traditional note taking.

Other applications mimic the human components of understanding; classify, recognise, detect and describe (according to SAS.com). This has now entered main-stream use with anti-spam measures on website contact forms, where the software knows which squares contain images of cars, or street signs.

Particularly within the healthcare industry, huge leaps are made where scanned images of CT scans have been “taught” how to spot the early sign of lung cancer in Szechwan People’s Hospital, China. This has come in to meet a great need as there is a shortage of trained radiologists to examine patients.

In summary, there have been huge leaps in data analysis and science in the last couple years. The future looks bright for the wider range of real world issues to which we can apply more and more sophisticated techniques and tackle previously impossible challenges. Get in touch and let’s see what we can do for you.

Category: Analytics, Automation

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