Deep learning

deep learning
Deep learning is a type of that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations

What it is and why it matters

Deep learning is a type of that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

The evolution of deep learning

Deep learning is one of the foundations of artificial intelligence (AI), and the current interest in deep learning is due in part to the buzz surrounding AI. Deep learning techniques have improved the ability to classify, recognize, detect and describe – in one word, understand.

For example, deep learning is used to classify images, recognize speech, detect objects and describe content. Systems such as Siri and Cortana are powered, in part, by deep learning.

Several developments are now advancing deep learning:

  • Algorithmic improvements have boosted the performance of deep learning methods.
  • New machine learning approaches have improved accuracy of models.
  • New classes of neural networks have been developed that fit well for applications like text translation and image classification.
  • We have a lot more data available to build neural networks with many deep layers, including streaming data from the Internet of Things, textual data from social media, physicians notes and investigative transcripts.
  • Computational advances of distributed cloud computing and graphics processing units have put incredible computing power at our disposal. This level of computing power is necessary to train deep algorithms.

At the same time, human-to-machine interfaces have evolved greatly as well. The mouse and the keyboard are being replaced with gesture, swipe, touch and natural language, ushering in a renewed interest in AI and deep learning.

The evolution of deep learning

Deep learning is one of the foundations of artificial intelligence (AI), and the current interest in deep learning is due in part to the buzz surrounding AI. Deep learning techniques have improved the ability to classify, recognize, detect and describe – in one word, understand.

For example, deep learning is used to classify images, recognize speech, detect objects and describe content. Systems such as Siri and Cortana are powered, in part, by deep learning.

Several developments are now advancing deep learning:

  • Algorithmic improvements have boosted the performance of deep learning methods.
  • New machine learning approaches have improved accuracy of models.
  • New classes of neural networks have been developed that fit well for applications like text translation and image classification.
  • We have a lot more data available to build neural networks with many deep layers, including streaming data from the Internet of Things, textual data from social media, physicians notes and investigative transcripts.
  • Computational advances of distributed cloud computing and graphics processing units have put incredible computing power at our disposal. This level of computing power is necessary to train deep algorithms.

At the same time, human-to-machine interfaces have evolved greatly as well. The mouse and the keyboard are being replaced with gesture, swipe, touch and natural language, ushering in a renewed interest in AI and deep learning.

How is deep learning being used?
To the outside eye, deep learning may appear to be in a research phase as computer science researchers and data scientists continue to test its capabilities. However, deep learning has many practical applications that businesses are using today, and many more that will be used as research continues. Popular uses today include:

Speech recognition
Both the business and academic worlds have embraced deep learning for speech recognition. Xbox, Skype, Google Now and Apple’s Siri®, to name a few, are already employing deep learning technologies in their systems to recognize human speech and voice patterns.
Image recognition
One practical application of image recognition is automatic image captioning and scene description. This could be crucial in law enforcement investigations for identifying criminal activity in thousands of photos submitted by bystanders in a crowded area where a crime has occurred. Self-driving cars will also benefit from image recognition through the use of 360-degree camera technology.

Natural language processing
Neural networks, a central component of deep learning, have been used to process and analyze written text for many years. A specialization of text mining, this technique can be used to discover patterns in customer complaints, physician notes or news reports, to name a few.
Recommendation systems
Amazon and Netflix have popularized the notion of a recommendation system with a good chance of knowing what you might be interested in next, based on past behavior. Deep learning can be used to enhance recommendations in complex environments such as music interests or clothing preferences across multiple platforms.

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The artificial intelligence uses frame by frame comparison to detect an object that was not previously there or the disappearance of an object from the field of view.  It learns what ‘normal’ looks like and spots differences.  In ‘Supervised’ mode, alerts to objects such as pool toys or outdoor furniture being moved will be suppressed to avoid false alarms.  When you leave the pool area a new ‘normal’ is established.

By using a combination of two cameras, one to identify individuals as they enter the designated area and the other to monitor the whole area, the artificial intelligence can keep track of an identified individual for as long as they remain within the field of view.  Both cameras are connected to the same processor so the first can pass the identity to the second, allowing the second to continue showing the identity of the individual even when their face is not visible to the camera.

In case you are concerned about privacy, be assured that nobody sees the feed from your camera unless an emergency is detected and not acknowledged locally.  Instead, the artificial intelligence identifies key points on the human body such as shoulders, elbows, wrists, hips, knees and ankles.  It uses the relative position of these key points to determine the pose of the body and has been trained to recognise poses that indicate danger. 

‘Supervised’ mode is designed for use when swimming is planned, with a responsible adult present.  It won’t bother you with constant alerts as people enter the area but the system will still raise the alarm if someone disappears underwater for longer than you have deemed acceptable. 

We strongly encourage use of a Pool Angel lanyard during such sessions so that there is no doubt over who has assumed responsibility for keeping watch over children in the pool.  Child drownings can happen even with multiple adults present if they all assume that someone else is paying attention.  Pool Angel offers you an added layer of protection; by comparing the number of people detected frame by frame, the artificial intelligence can spot when someone is missing and raise the alarm.    

Because the artificial intelligence can learn from experience it can learn to tell the difference between your pet and local wildlife that might encroach on your pool area.  This means that you can keep your pets safe without being disturbed by false alarms during the night when animals may encroach on your pool area; although you might be intrigued to view clips of your nocturnal visitors in the morning.  A short video clip is stored each time something is detected.

If an adult is detected in the pool area the system will alert you and prompt you to switch to ‘Supervised’ mode if you haven’t already done so.  This mode is designed for planned use of the pool and will suppress alerts to entry and exit from the pool area.  When the last adult leaves the area the system detects that too and prompts you to switch back to keeping watch over the empty pool.  An emergency alarm is raised if the departure of that adult leaves an unsupervised child in the pool area.

Although we refer to the boundary around a swimming pool, the camera can be used to keep watch over any boundary you designate. It can keep watch over a trampoline, climbing frame, the tool shed, any area that could present a danger to unsupervised children. By comparing what was present in a previous frame with what is currently in frame, the artificial intelligence can detect the arrival of something or someone new in the designated area.