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Another Microsoft course completed: Introduction to Artificial Intelligence

I am pleased to report that I have just passed another Microsoft course, this time from the Microsoft Professional Program for Artificial Intelligence:

Introduction to Artificial Intelligence, with a final mark of 100%.

This was a fascinating course, providing a very good introduction to machine learning, text analysis, computer vision (including face recognition and video analysis) and conversation as a platform (chatbots and Natural Language Processing [NLP]).

PJLeeMicrosoftDAT263xIntroductionToArtificialIntelligenceFinalMark(100PC)Oct2018.jpg

Let your users ask “What’s my next step?” – a very useful AI addition to your apps

One example of how #AI can make it easier for your staff, customers or suppliers to interact with your software tools is to add a combined”Next Step / Tell me what you want to do” facility.

This uses natural language processing (NLP) combined with knowledge of who the user is (and what their role is, e.g. whether they are a member of staff, a customer, or a supplier, or a user with admin rights for example) and the context (which page or part of the app they are on, and what data they have stored in the system), to add two powerful new ways for the user to interact (with minimal training) with the app:

What’s my next step?

On any page, simply clicking the Go button asks the system “What’s my next step?”. The system then look intelligently at the user’s identity, role, data and location within the app and makes one or more suggestions as to what the user could usefully do next to make the most of the app.

Here are a couple of examples, taken from InQA’s WebPocketMoney application (referred to in this previous post).

AI built in to the heart of user interfaces

Within a few short years, some companies and organisations will have adopted Artificial Intelligence (AI) in at least one part of their work: interfacing with their customers. (I’m using customers in the widest sense of the word: it could be students in education, or patients in healthcare for example).

Imagine the following:

Instead of having to log in to a website or an application, the application simply recognises the user’s face or voice
Instead of having to click on a menu to navigate the app, the user can just talk to it, either by speaking or using a chatbot type interface.
Instead of calling customer service (and being told “you are currently number two in a queue” or “Our business hours are 0900 to 1700 Monday to Friday, please call back during those times” ), they can get an immediate response (24 hours a day, 365 days a year) from a chatbot.

If customers have a choice between interacting with one organisation in that way, or another in the more traditional way, I think they will vote with their feet.

It’s a straightforward matter of economics

President’s Award for input on the topic of data science

I am delighted to have received a President’s Award for input on data science from outgoing Institute and Faculty of Actuaries President Marjorie Ngwenya, FIA at yesterday’s AGM at Staple Inn in London.

The IFoA is a tremendously vibrant organisation and I believe IFoA and other actuaries have an important role to play in helping businesses and organisations make the most from the torrents of data becoming available, whilst also helping protect consumers from unethical use of such data. In particular, I am very pleased that the IFoA is collaborating with the Royal Statistical Society in the vital area of the ethical use of data in data science. (For example a joint event was held earlier this month on the Industrialisation and Professionalisation of Data Science)

Azure Machine Learning Studio: the best place for initial data analysis?

While looking at a (relatively small, 1.7 million records) big data example of New York Yellow Cab taxi trips, I am coming to the conclusion that the best place (if as we do you are using Microsoft tools) for initial analysis, including the all important first step of finding outliers/errors, is Azure Machine Learning Studio (Azure ML, as opposed to Excel, Power BI or bespoke analysis using e.g. Kendo UI).

Why Azure ML for initial analysis?

It loads data quite quickly (e.g. just over a minute to import almost 2 million records from an Azure SQL database). This is currently much quicker than Power BI.
It automatically produces histograms and box plots of numeric fields (see the images below, and above, where the field FareAmount has been selected). We can tell immediately from the box plot that there are several outliers (and in fact probable errors that will need to be either corrected or removed, in that FareAmount should not have negative values!).

Uploading Big Data: it’s very different from normal data

The above screenshot shows an initial analysis (in Microsoft Power BI) of 1,723,099 records of New York taxi trip records uploaded to the cloud. The top chart shows a scatter plot of Trip Distance in miles against the Total Fare Amount (in US $). This useful chart shows straightaway that there are some outliers in the data (e.g. some trips cost over $1,000 despite being only for short distances). These records are almost certainly errors (where e.g. the fare was entered with the decimal point in the wrong place, e.g. $1000.00 instead of $10.00) and should be corrected or removed. Similar errors in the Trip Distance fields had already been removed in that 2 records had implausible distance values (e.g. 300,833 miles for a total fare of $14.16, and 1,666 miles for a total fare of $10.30).

In order to analyse big data, it often needs to be moved from its original sources (e.g. separate csv or txt files, or a stream) to somewhere where it can be collated and processed (e.g. an online database, or Microsoft PowerBI, or an xdf, extensible data format, file that can be analysed by Microsoft R Server).

Why adding chatbots makes financial sense for your organisation

Adding a chatbot to your organisation’s website can provide a more interactive experience for your users while at the same time reducing demands on your staff’s time. Chatbots can help to:

free your team to deal with more complex enquiries or tasks
speed up employee training by providing a very accessible and intuitive source for staff to obtain information internally
automate complex workflows (such as providing quotes or booking services)
provide availability 24/7, 365 days a year
provide an alternative user interface for your apps than the traditional point and click menu/button system

Why do data scientists use R and Python, as opposed to other languages like C#?

As a “proper” programmer, used to programming in heavy duty, compiled languages like C# (and before that C++ and C), my reaction on discovering during my Data Science journey that R and Python are heavily used by data scientists was: why??

Why would anyone use an interpreted language, which is therefore bound to be slower, and why would anyone go to the trouble of using yet another language when there are perfectly good compiled languages around like C#, F# and VB.net?

The answer seems to be partly that R and Python are free (open source), and also because R and Python have excellent visualisation tools, which the other languages currently lack.

WebPocketMoney 1.0 released!

(Posted by Patrick Lee on 6 May 2017 at a different location, but migrated here on 05 Feb 2018).

We are delighted to announce the release earlier this week of WebPocketMoney.com, our cloud based app that enables parents and their children to track and manage pocket money online.

A new, sophisticated online tool for parents and children to track and manage pocket money balances

This web app has been designed to help parents and children manage pocket money in a user-friendly way that encourages saving. It uses notional accounts rather than physical accounts (which are often difficult to create for children) and this has the advantage that you can offer your children much higher interest rates (e.g. 5% or 10% per month) than are commonly available in the real world, in order to encourage them to save.

Want to go digital/move your business to the web/cloud or add chatbots to your site?Contact us

Products

WebPocketMoney

Parents: do you wish there was an easy to use online tool to help you manage your children’s pocket money and encourage them to spend and save wisely?

WebPocketMoney is designed for you!

Secure online system for parents and children to manage pocket money
Children can read but not amend their balances
Helps teach children to save!

CompanyAdmin

Do you need an easy to use online tool to help you and your staff to record and keep track of expenses?

CompanyAdmin is designed for you!

Secure online tool for small to medium companies to enable employees to log expenses, holidays and time records

IFA Tools

Are you finding it difficult to navigate the maze of pensions freedom in the UK?

IFA Tools is designed for you!

For UK IFAs and other financially aware individuals
Secure easy to use and secure wealth planners
Powerful dashboard to see the effects of what ifs
Powerful reporting facility for IFAs to produce automated reports to give to clients

Pensions Concerto

Are you finding key information about defined benefit pension plans expensive and slow to come by?

Pensions Concerto is designed for you!

For advisers, trustees and plan sponsors
Secure online valuation and cashflow projection tool
Powerful dashboards to see the effect of what ifs

Recent News

Another Microsoft course completed: Introduction to Artificial Intelligence

by pjlee01October 12, 2018October 12, 2018

I am pleased to report that I have just passed another Microsoft course, this time from the Microsoft Professional Program for Artificial Intelligence: Introduction to Artificial Intelligence, with a final mark of 100%. This was a fascinating course, providing a…

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InQA Limited

Another Microsoft course completed: Introduction to Artificial Intelligence

Let your users ask “What’s my next step?” – a very useful AI addition to your apps

What’s my next step?

Some reasons why your company/organisation should start using AI now!

AI built in to the heart of user interfaces

It’s a straightforward matter of economics

President’s Award for input on the topic of data science

Azure Machine Learning Studio: the best place for initial data analysis?

Why Azure ML for initial analysis?

Uploading Big Data: it’s very different from normal data

Why adding chatbots makes financial sense for your organisation