Imagine coming home from a rough day at work in a heated house which is also playing your favourite music, and your ‘smart kitchen’ is all set up to start cooking – it is all possible. The idea of artificial intelligence (AI) intruding into our homes is not new, but it is developing rapidly – AI can learn and predict the needs of a user. Besides, it can contribute to better control of one’s energy and water use. Here, I will outline the (dis)advantages of a smart meter and its contribution to a smart home.

Smart speaker technologies such as Amazon’s Echo (Alexa), Google Home, and Apple’s HomePod (Siri) have all made first steps through their voice interaction competencies, with Google Nest making the first steps towards its smart thermostat. These devices can all be linked to a smart meter. When this smart meter is installed in a visible location, they can serve as a reminder and motivator for homeowners to monitor their energy use.

In the end, human involvement will scarcely be needed since all smart home technologies will grow into interconnectedness and will become more intelligent. With integrating AI in these technologies, the systems will adapt intelligently to the needs of the homeowner – with an approach relating to the behaviour and historical preferences. Here, you can think about opening windows and sunshades, turning on/off lights, regulating cooling and heating, and hot water systems. For example, in times of hot weather, AI technologies could decide to open a window when the outside temperature is desirable, or they can start cycling the air conditioning. Besides, what the appropriate temperature should be can be based on the person that is home and in which room they are located.

However, what happens when your internet connection fails? You cannot take control of the smart home technology without a good, strong internet connection. Besides, it is very costly to implement all these technologies and create a smart home. Since smart home devices communicate with each other, these devices send all kinds of data over the internet. Privacy will not be that much of an issue for lights, but this is different for smart cameras or voice recordings of smart speakers.

As I said before, these technologies are developing rapidly. Smart meters can be better programmed to maximize efficiency when they become more advanced. They can do amazing things, but you should also be aware of the risks.

I am really curious to see what will happen next. What do you think? Please share your thoughts and let’s discuss!

References

Philip, R.E. (2020). How AI and IoT can help to make smart home. [Online] Available at: https://medium.com/@rohithaelsa/how-ai-and-iot-can-help-to-make-smart-home-5133d050505f [Accessed on 7 October 2020]

Saberi, O., & Menes, R. (2020). Artificial Intelligence and the Future for Smart Homes. [Online] The World Bank. Available at: https://www.ifc.org/wps/wcm/connect/6fc5b622-05cb-4ee9-b720-ab07591ac90e/EMCompass-Note-78-AI-Smart-Homes.pdf?MOD=AJPERES&CVID=n0S3dro

Smarthomeweb (n.d.). Smart home nadelen die je zelf kunt tackelen. [Online] Available at: https://www.smarthomeweb.nl/smart-home-nadelen/ [Accessed on 7 October 2020]

News articles, weather reports or even business insights, Artificial Intelligence (AI) can play a significant role in generating these kinds of texts. AI has become a big part of our lives – it is deeply rooted in many day-to-day activities. A lot of companies have implemented AI in their business to take advantage of its benefits. In some cases, AI is even capable of replacing humans in their job. While people were needed to write the simplest content in the past, computers are now capable of writing these texts. But how does this technology work?

The technology that is used to help computers understand language the same way as humans do is called Natural Language Processing (NLP). Here, you can think of understanding the feeling of a text, speech recognition, and generating reactions to questions. Deep learning – which is a subfield of AI – is used in many NLP implementations, such as chatbots that handle customer service questions, auto-spellcheck, and AI assistants as Siri on smartphones.

NLP can be divided into Natural Language Generator (NLG) and Natural Language Understanding (NLU). While NLG software can write, NLU reads human language and turns its unstructured data into structured data to make it understandable to computers.

The first step in an NLG process is defining what format of the content is desired. Each content type has a unique writing style and structure. Secondly, the end-user, NLG solution or software provider builds the narrative design. The end-goal of NLG is to generate an output that could have been written by a human.

The use of NLP has both advantages and disadvantages. Many companies have decided to implement this technology to make journalists’ jobs easier and faster – the system is not replacing them. Writers will be given more time to focus on the content and writing style of an article, instead of spending their time on fact-checking and research. A downside of AI written content is the chance of discrediting reliable media channels, by not having quality content as a priority. In short, AI can be of high value when generating written content, but one must still assure a text’s quality.

Please let me know your thoughts on this subject. Do you think you will be able to identify content that was created by AI? And do you think AI will ever be able to generate human-like responses?

References

Beysolow, T. (2018). Applied natural language processing with python: implementing machine learning and deep learning algorithms for natural language processing. Berkeley, CA: Apress. Available at: https://eur-on-worldcat-org.eur.idm.oclc.org/oclc/1052613113 (Accessed: 2020)

Kendall, S. (2020). What is Natural Language Generator (NLG)? [Online] Available at: https://narrativescience.com/resource/blog/what-is-natural-language-generation/  (Accessed: 4 October 2020)

Sciforce (2019). A Comprehensive Guide to Natural Language Generation. [Online] Available at: https://medium.com/sciforce/a-comprehensive-guide-to-natural-language-generation-dd63a4b6e548 (Accessed: 4 October 2020)

Martin, N. (2019). Did A Robot Write This? How AI Is Impacting Journalism. [Online] Available at: https://www.forbes.com/sites/nicolemartin1/2019/02/08/did-a-robot-write-this-how-ai-is-impacting-journalism/#57e329a67795 (Accessed: 4 October 2020)