Artificial intelligence (AI), which is the simulation of human intelligence, has become very prominent in the news, discussions and applications over the past five years. But the truth is that AI has been around for much longer, as I started developing AI applications back in 1987.
Today, we are surrounded in our daily lives with AI – in social media, online shopping, Google search engines, ridesharing, commercial flights (autopilots), banking and many more. Since it is difficult for most us to identify these implementations, and since we can’t distinguish between AI and non-AI systems, there are four basic forms of AI and I will explain them as follows:
1. Reactive machines – good examples are IBM’s Deep Blue, which beat the international chess grandmaster Garry Kasparov in the late 1990s.
2. Limited memory – machines that look into the past, such as parts of self-driving car software.
3. Theory of mind – these machines are more advanced and not only form representations of the world, but also entities in the world. It is this replication of how humans and creatures in the world have thoughts and emotions which affect their own behaviour.
4. Self awareness – this is the most advanced form of AI where these machines/systems develop a consciousness. Conscious beings are aware of themselves, know about their internal states and are able to predict feelings of others. The movies iRobot and Terminator are very good examples of this self awareness.
Why is there such excitement surrounding the first few levels of implementation of AI today? It is because we can use these AI systems/algorithms to handle massive amounts of data and learning, then developing patterns in this data, which we humans can’t do as efficiently. They operate without breaks, and they continue to learn at a speed of the computers that they operate on, 24/7. Good examples can be found in the medical field. In radiology, AI has achieved the ability to detect cancer in mammograms at a 96.6 per cent specificity level, which is much greater than the average radiologist today. It continues to learn at breakneck speeds, continually getting better with its discovery of cancer.
Now, how do we translate these systems and examples to our world of yachting?
The examples that I am about to give may frighten, or even anger, some people who might feel that their own professions are at risk. But in reality I believe that this assistance will allow us to be freed up to perform tasks that we are much better at.
This is an easy one as we move into self-learning autopilots, that are connected online with volumes of information about sea conditions, weather patterns, direct reporting from other ships and yachts, along with on-board navigation instrumentation. They can make much better decisions and never become distracted. This is happening today, in the shipping industry, in a limited fashion.
The only way to combat dynamically changing data attacks is to deploy sound and sophisticated AI appliances that morph their shields incautiously to protect the integrity of the yacht’s systems, and the owner’s data.
We are using AI routines in our security systems to understand the patterns of the yacht’s operation, identifying intrusions more quickly and immediately identifying the correct access to all areas of the yacht through facial recognition.
Alarm, monitoring and control (AMS) Another area that benefits from the use of AI algorithms. These algorithms move the AMS from a reactionary system to a truly forward-watching and preventive system.
By implementing AI to learn the patterns of the owner and guests, these systems can then anticipate their needs. Simply knowing their system vitals such as blood pressure, heart rate, temperature would allow an AI-driven guest services system to react precisely and immediately. I envision that robotics will be deployed in the future to interact directly with the owner and guests. Consistency in this service would be a major benefit, along with the ability to be available at all hours of the day and night to the varying schedules of each individual.
These systems, in general, have the label of AV (audio visual). I have a design for a ‘living yacht’, where the yacht reacts dynamically to the individual and/or individuals in an area of the yacht. Using AI learning we can change the environment in the area of the yacht to provide the entertainment experience that is matched directly to the audience, including their current temperament and past histories of likes, which will provide the ultimate user experience.
Approaching AI with the right attitude is very important, and it might be best to consider it as a tool with which to make our lives better and easier. As individuals, we will not stop the implementation of AI in our daily lives. If we embrace it, we can help steer the direction that it is headed so that it benefits us.
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