Digitally Speaking
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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