Changing expectation of AI and voice interaction

January 26th, 2018 No comments
Reading Time: 6 minutes

Artificial Intelligence, Machine Learning, Cognitive Computing, Deep Learning, etc. all seem to point to essentially the same thing: Computers being able to do perform tasks in a way that we would consider them smart.

Watching my children interact with Google Home got me thinking about how the technology is progressing and the future of work.

  1. How our expectations of technology grow quickly; and
  2. the legal and ethical issues that can come.

 

As background, recently I was in the US and was given a Google Home Mini by a colleague with the challenge of building something cool with it. Watching the very quick evolution of the way that they interacted with it was a bit of an eye opener.

As you’d imagine, the initial steps were pretty timid…

  • “Hey Google, play music.”;
  • “hey Google, what’s the temperature”; and
  • “Hey Google, tell me a joke”.

These were basic requests and asking for information about the now. Then they started to test it…

  • “Hey Google, what the weather for tomorrow” and
  • “Hey Google, play Fine Music FM”.

The latter getting my attention as they were able to connect to an internet radio service and start to stream music; I thought I’d locked it down to just Spotify and my selection appropriate music.

What really got me thinking was how they then started to mine the internet for information. This year they are working through some projects and so my 9yo and 7yo started asking  “OK Google, what is Malaria”.. “OK google, tell me more about the symptoms”… and finally when my 7yo says “Hey Google, you’re awesome” to which it replied “you’re pretty good yourself.”.

My 7yo turns to my wife and I and says, “I think she likes me!”.

Up until this point I was pretty certain that they knew it was a computer. They’ve interacted with Apple’s Siri a lot in the past, knowing it was a robot and even complained about the pronunciations and semi-robotic responses. The fact that the voice is fairly realistic, has great intonation and was responsive in a very human way truly had them baffled; signs of a well designed conversation. Even when we asked the same question via Google search on our phones and showed that it was just reading the first response, it was pretty hard to convince my 7yo that it was a robot.

Growth in Expectation

Seeing the expectation of the technology rapidly rise, going from a toy to a useable tool is dramatic. I’ve seen this in the past where a new piece of technology was deployed, when it was done well, the expectation of what is possible and what it should do can quickly outstrip the initial capability of the system.

The use of AI and Machine Learning, and having the experience learn from the interactions, is how we are able to take the initial experience and have it grow with our expectations. Sometimes this is training the system using captured information of interactions and correct responses to build the basis of the rules that the machine needs to create and the logic it needs to follow. Training AIs can be as simple as a dozen samples, like Google’s Dialogflow, or tens of thousands of samples. It tends to come down to the algorithm, the complexity of the task and the accuracy you need.

Garbage in and Garbage out: One of the biggest issues with learning algorithms is, garbage in and garbage out. The repositories that they use, be it the data sources or the training sources will ultimately affect the outcomes. Back when I was at IBM, building the training question and answer sets for a WATSON engine, required a lot of time and effort to ensure that each was validated and tested. Through triggering Applications in Google Home, I’m finding that some of the responses can be bizarre, especially if I was the one that tried to create the dialogue; nothing like my 3yo telling me that he want’s the story app I created to stop telling him his favourite story.

 

Legal and Ethical issues

AI has the huge potential to remove human error, introduce new levels of efficiencies, and by taking out the people, bring costs of delivering services down significantly. However, through learning algorithms, there is the risk in what is captured and how it’s used. Watching learning algorithms add to their repertoire is pretty amazing, but very quickly biases can creep in.

Learned Biases and the actions of intelligent systems isn’t a new problem, though the example I easily recall is Microsoft’s Tay becoming racist in under 24 hours and having to be taken offline. Putting guide rails on the algorithms and black-listing content and behaviours is some of how the behaviours are curbed, however, people always find a way to use things in unpredictable ways.

As with the example of how my 7yo interacted with the the AI assistant, very quickly AI will become indistinguishable from human interaction and people will make decisions and take action based on the recommendations or logic provided by AI. What should worry everyone is how misinformation, fake news and peoples agendas can shape the way people interact with and use the advice of these systems.

Today there are laws and guidelines being put in place in certain geographies to prepare for the ethical and liability issues that will eventually ensue. I tend to agree with Elon Musk’s view that there needs to be a lot more effort put into how we control and govern AI because it will quickly become a lot more complex and ingrained into society, moving past the novel toy of today.

My final initial thought is on Data Mining. How is what is going in being used? What’s being recorded? How much is being stored? who has access to it? what does this mean.

Each platform is different in how they capture, store and potentially re-use recordings of what you say or do. I love the idea of conveniently asking questions in natural language and being able to data mine the full depth of the Internet, but at what cost. I suppose we won’t really know until something bad happens.

For now it will stay a toy that is switched on when I want it and be where I can keep an eye on what my kids ask it.

 

 

 

The Procrastination Beast…

January 10th, 2018 No comments
Reading Time: 2 minutes

Procrastinate

verb (used with object), procrastinated, procrastinating.
1. to put off till another day or time; defer; delay.

(source: dictionary.com)

I’m currently on holiday with my family and thought that whilst the kids are playing Minecraft on separate devices, dinner is in the oven, and I’ve got some time to myself I thought it would be a great opportunity to start getting into some of those things I’ve put off for months.

 

No such luck. The procrastination beast has come and settled in.

 

We all procrastinate from time to time, putting off those things we need to do; or those things we tell ourselves are important and should get to.

 

There are a number of reasons why we procrastinate:

  1. Insufficient structure
  2. The task isn’t fun
  3. Timing and the link to risk or reward
  4. Anxiety fuelled avoidance
  5. Lack of confidence in one’s ability to succeed

Ordinarily I’d call one of my many smart colleagues or friends and after a few minutes I’d find the inspiration I need to get back into it. There are many different techniques to get past the beast, however, today I’ve decided to kick-back, relax and embrace the lack of motivation.

 

After all, I’m on holiday.

Categories: Uncategorized Tags:

Strategy to Execution – Hoshin Kanri

November 11th, 2017 Comments off
Reading Time: 3 minutes

I can’t believe it’s been 7 weeks since moving to ServiceNow and the Inspire team. In this time I feel like I’ve gone from knowing everything and being absolutely confident in what I’m doing to clear signs of the Imposter Syndrome showing. As one of my old colleagues reminds me, this is normal.

In these last few weeks I’ve been learning, and re learning, a lot of the things I used to do and scratching in the deep dark recesses of my brain for those nuggets of knowledge. So rather than do this aimlessly I thought I’d start putting some of these ideas down here.

My new role has me helping clients with taking their Strategy and mapping it through to clear objectives and measurable outcomes. One of the challenges is finding different ways to help clients see this and then be able to continue to develop this on their own.

Today I was looking at Hoshin Kanri and the 7 step planning cycle.

Hoshin Kanri is a policy management process that attempts to link the corporate strategic direction with the measures, goals and actions of those doing the work.  Or more simply, get everyone pointed in the same direction.

The 7 steps are:

  1. Establish Organisational Vision
  2. Develop strategic plan (3-5 year)
  3. Develop annual objectives
  4. Deploy objectives
  5. Implement
  6. Regular reviews of progress
  7. Annual Review

These steps are good, and there are some very detailed templates and matrices out there if you need them, but I found it easier to look at it from the simplified approach of the idea of the flow from vision through to measurable actions. Mapping the Vision to Objectives (these essentially being Business Drivers) which set the Goals (being the Outcomes you wish to achieve) that drive Actions (that need to be measurable).

 

My crude attempt to illustrate this is to show that there is clear ownership of Vision by Senior executives and together they work with middle management to create the Objectives (and define the Business Drivers) and finally middle management take those Objectives and work with their teams to develop specific measurable Actions; based on the agreed Goals and Outcomes that the teams execute on.

A lot of the time, this doesn’t happen. I’ve worked in many businesses where no one knew what the corporate vision was, let alone there being clearly communicated objectives, goals and measures.

The final piece of the puzzle is measuring these Actions regularly and adjusting the objectives, goals and actions as necessarily.

So to summarise:

  1. Develop Strategic Objectives and Goals based on Vision
  2. Get consensus of the objectives, goals and actions
  3. Implement what’s been agreed
  4. Measure it constantly (and review those measurements)
  5. Adjust accordingly.

This may seem basic and straight forward, but implementing it in a meaningful way, with good thought, and actually measuring it is hard, especially if the processes are manual and are not being reported back effectively in a timely manner… But that’s a post for another time.

 

Of moving on and getting things done.

September 30th, 2017 Comments off
Reading Time: 1

It’s been some time since I’ve posted here. And I had all the intent in the world of getting back into things, but life doesn’t always let you do these things. But to bring all those spam-bots up to speed, things have changed.

To help facilitate a new start and a new perspective I’ve made the leap out of Big Blue and the world of leading “mega deals”, contract negotiations and generally herding cats, and joined ServiceNow’s Inspire team. I’ve been a fan of ServiceNow for some time now and this is a fantastic opportunity to work in a group of  ex-CxOs, senior consultants and industry experts; with the sole purpose to help ServiceNow’s lighthouse customers succeed.

Looking forwarding to getting that need to post idea’s back…. Here’s hoping.

Categories: Career Tags:

Oh No!

April 21st, 2017 Comments off
Reading Time: 1
image by Tom Woodward

image by Tom Woodward

I only recently realised that it’s been over a year since I posted anything here.

Sad I know.

The last 18 months have been a little hectic with moving from one company to another, and refocusing on being a husband and a father rather than on a career and living out of a suitcase.

That said there are some exciting things in the works, one of which is a book. Whilst there isn’t a lot to say about it yet, it will be based on a couple of things, Wardley Mapping and Outsourcing. I’ll also add that my co-writers and I will bring rather unique “insiders” perspective to the process; we might even start to release some drafts here.