Category: MachineLearning

What New Things Could Machine Learning Enable?

Benedict Evans of Andreessen Horowitz, a venture capital firm in Silicon Valley, has a thought-provoking blog post on Ways to Think About Machine Learning.

Benedict asks what new things machine learning (ML) could enable. What important problems might it actually be able to solve? There are (too) many examples of machine learning being used to analyse images, audio and text, usually using the same example data. However, the main question for organisations is how can they use ML? What should they look for in data? What can be done?

Much of the emphasis is currently on making use of existing captured data. However, such data is often trapped in siloed company departments and usually needs copious amounts of pre-processing to make it suitable for machine learning.

We believe some easier-to-exploit and more profound opportunities exist if you use new data from sensors attached to physical things to create new data. Data from physical things can provide deeper insights than existing company administrative data. The data can also be captured in more suitable formats and can be shared rather than stored by protectionist company departments.

For example, let’s take movement xyz that’s just one aspect of movement that can be detected by beacons. Machine learning allows use of accelerometer xyz motor vibration to predict the motor is about to fail. Human posture, recorded as xyz allows detection that patients are overly-wobbly and might be due for a fall. The same human posture information can be used to classify sports moves and fine tune player movement. xyz from a vehicle can be used to classify how well a person is driving and hence allow insurers provide behavioural based insurance. xyz from human movement might even allow that movement to uniquely identify a person and be used as a form of identification. The possibilities and opportunities are extensive.

As previously mentioned, the above examples are just one aspect of movement. If you also consider movement between zones, movement from stationary and fall detection itself, more usecases become evident. Sensor beacons also allow measuring of temperature, humidity, air pressure, light and magnetism (hall effect), proximity and heart rate. There are so many possibilities it can seem difficult to know where to start.

One solution is to look at your business rather than technical solutions or even machine learning. Don’t expect or look for a ready-made solution or product as the most appropriate machine learning solutions will usually need be custom and proprietary to your company. Start by looking for aspects of your business that are currently very costly or very risky. How might more ‘intelligence’ be used to cut these costs or reduce these risks?

Practical examples are How might we use less fuel? How might we use less people? How might we concentrate on the types of work that are least risky? How might be preempt costly or risky situations? How might we predict stoppages or over-runs?

Next, use your organisation domain experts to assess what data might be needed to measure data associated with these situations. Humans often have insight that patterns in particular data types will help classify and predict situations. They just can work out the patterns. That’s where machine learning excels.

Read About AI Machine Learning with Beacons

Prognostics, Predictive Maintainance Using Sensor Beacons

A growing use of sensor beacons is in prognostics. Prognostics replaces human inspection with continuously automated monitoring. This cuts costs and potentially detects when things are about to fail rather than when they have failed. This makes processes proactive rather than reactive thus providing for smoother process planning and reducing the knock-on affects of failures. It can also reduce the need for over excessive and costly component replacement that’s sometimes used to reduce in-process failure.

Prognostics is implemented by examining the time series data from sensors, such as those monitoring temperature or vibration, in order to detect anomalies and make forecasts on the remaining useful life of components. The problems with analysing such data values are that they are usually complex and noisy.

Machine learning’s capacity to analyse very large amounts of high dimensional data can take prognostics to a new level. In some circumstances, adding in additional data such as audio and image data can enhance the capabilities and provide for continuously self-learning systems.

A downside of using machine learning is that it requires lots of data. This usually requires a gateway, smartphone, tablet or IoT Edge device to collect initial data. Once the data has been obtained, it need to be categorised, filtered and converted into a form suitable for machine learning. The machine learning results in a ‘model’ that can be used in production systems to provide for classification and prediction.

Machine Learning Sensor Data

Mobile World Live, the media arm of the GSMA, has a new article titled IoT data impossible to use without AI.

The article title is over-dramatic because IoT data can be used without AI. However, as the article goes on to say, AI is …

‘vital to unlocking the “true potential” of IoT’

… that has more truth.

As usual, these things are said with no example or context. Let’s look at a simple example.

Let’s say we want to use x y z accelerometer data from one of our sensor beacons to measure a person’s movement. If we wanted to know if the person is falling we could test for limits on the x y z. This doesn’t use AI. Now consider if we want to know if person is walking, standing, running, lying down (their ‘posture’). You can look at the data forever looking for right patterns of data. Even if you found a pattern, it probably wouldn’t work with a different person. AI machine learning provides a solution. A simplistic explanation is that it can take recordings of x y z of these postures from multiple people and create a model. This model can then be used with new data to classify the posture.

AI solves problems that previously seemed too complex and impossible to solve by humans. Solving such problems often improves efficiency, saves costs, increases competitiveness and can even create new intellectual property for your organisation.

However, don’t automatically turn to AI to make sense of sensor data. Don’t over-complicate things if the data can be analysed using conventional programming.

Machine Learning with Beacons

Sensor Cognition for Making Sense of Beacon Data

As mentioned yesterday, in the Mr Beacon interview with Ajay Malik, beacon positioning and sensor data will be increasingly used as input for AI machine learning (ML). Beacons are a great way of providing the large amounts of data required of ML.

At BeaconZone, we have recently started using beacon data in machine learning. This has been a natural progression of use of our BeaconRTLS to collect large data sets. We have a new subsidiary Sensor Cognition that provides services to extract and use intelligence from sensor data. Unlike most other ML companies, we aren’t interested in computer vision, speech and language recognition, even though the first two could be inferred to be from sensor data. Instead, we specialise in extracting intelligence from industry time series position and sensor data.

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