4. PetaBencana brings
mobile mapping and
flood information; 28
million citizens share
information via Twitter,
augmented by water-
level-sensing devices.
7. • LeveragingAWS IoT for connecting
millions of smart meters and
exposing their data to several
applications running inAWS
• Includes C3 IoT for grid monitoring,
fraud detection, and energy
maintenance
Next Generation Smart Meters
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scanning the QR Code printed on your badge or
through the link below.
https://amzn.to/BahrainSessions
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Editor's Notes
Connecting critical health infrastructure to AWS IoT enables Philips to better monitor and maintain that equipment and continue to provide critical care. Moving data to AWS also enables Philips to perform large scale data analysis.
Look at what a conversational bot actually has to ”think” about:
Intents What you are trying to achieve Once we know this, we know what data to gather to fulfill your request.
Utterances How you may trigger the intent, Book a hotel, get me a hotel room, I need accommodation in XX
Slots The data / variables I need to complete your request
Fulfillment This is the magic, the interface can be whatever you need it to be to fit with your existing legacy systems.
You DO NOT have to re-procure ticketing / other systems to give them a voice.
Recognition is about making images and videos make sense to systems.
Object and scene detection looks at a scene and extract elements from it.
A variety of facial recognition tools enable you to look at peoples faces, understand their emotion and / age and sex whilst facial recognition tools enable you to compare or match faces to known collections.
Facial reference
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First we need a good business problem that we want to look at, for example let’s say:
Providing heating and hot water to tenants is costly and in some cases unreliable.
In this case it would be great if we can gather data that would help drive better predictive maintenance when really needed avoiding the cost wherever possible and ideally we would like to intervene before catastrophic failure of heating units.
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To start tacking this we need to gather data, - Use what you have at first
Data is likely to be in a variety of formats and not well related to each other, so we need to munge it into shape. Sagemaker provides a notebook environment to help you do this for your data sets and document it in the process.
Once you have a clean dataset, you likely want to do some visualization, feature engineering (converting dates to days of the week for example) or creating a new field for days in the field vs. using the installation date
Now we have data and we want to train our models, this can be many iterations
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Now we’ll evaluate the ability for the model to impact our business problem, if so great, let’s deploy it.
Quite likely though, we’ll be re-fitting and tweaking any parameters to help improve the effectiveness of the model.
Sagemaker can help here as we have a feature called Hyper-Parameter optimization where we use Machine Learning to tune the parameters for an ML model.
------+++++++
Once deployed, you need to run this model at scale and you want to gather data from your predictions to retrain the model and improve it over time.
Ready to use models are great, but many will have new and interesting use cases and data sets that would mean that building your own AI models really is the best option.
Doing so can be complex, let’s look at the process and why SageMaker really does make this A LOT more accessible.
Let’s have a look at an example ,
In this video you can see a few things happening - firstly we have and IoT enabled device that is feeding live telemetry to a tablet. The tabled is capturing that and is also able to control the device.
The AR bit is the image on the tablet where you can see the treadmill represented virtually on the screen, the beauty of it is that the engineer for example can quickly see how the device is opened and be given live instructions on how to diagnose a particular problem.
This could be a great tool to enable wardens or some residents to self-serve and investigate some problems before calling on a more expensive qualified engineer for example.
Sumerrian as a UI is easy to use and does not require you to have deep 3d modeling knowledge to get going, we also have a vast library of existing objects.
Think about your use cases –
Could you be using Sumerian to model out changes to some existing houses or even test opinion on a number of new designs, using VR to make that experience as immersive as possible.
For a long time, most organizations have had to make a choice between moving fast or maintaining a high degree of security. It’s a difficult choice, and inevitably security trumps all.
Talk about disruption here, taking simple ”issues” and attacking them.
Finally I’d like to talk about an example of how we’ve used a lot of the technology I’ve spoken about today to tackle some everyday problems.
When Amazon looked at the traditional retail experience, one thing that was very obvious when coming from the online retail world was the need for people to checkout and in some cases queue up to check out. As a process this is quite inefficient, having to tag object as you place them in your trolley or even worse after you have collected all the things you want, unpack them all so someone else can scan them one-by-one and then pay for your good.
We decided that we wanted to eliminate the need to checkout in the physical world. That was an easy to define problem, but a very complex one to solve.
The solution relies on a number of the technologies I spoke about today, IoT, AI/ML, Image recognition, person and object tracking as well, Amazon Go is an example of how we are looking to challenge long standing constraints and obsess about making our customers’ lives easier.
What are your long-standing constraints / problems that we can help you with? If you have ideas, please do come and talk to us we love diving into a problem.
