Artificial Intelligence (AI) is in everything we do today. It has several levels from shallow to deep learning. It can be used in a lot of businesses and activities. I have written a lot of posts on my blog about Artificial Intelligence .
For example, you can find texts about the impact of AI on recruiting, on marketing, on dating, on gamification, on technology, or on e-health. However, with all your feedback, it seems to me that I should have started with the basics: what is Artificial Intelligence? Let’s have a look. I hope this makes things a bit clearer for all.
What is artificial intelligence?
Artificial Intelligence refers to any type of computer or machine (robots, PCs, phones…) ability to do tasks that up until recently would have been done by a human being. In general, we would have expected humans to perform such tasks using their own power of reasoning. Basically, machines simulate human intelligence and gestures thanks to a structured set of commands, algorithms, and data.
You hear very regularly about strong or weak AI, but this very simply means how strongly machines are programmed to deliver results. Does the AI program possess a narrow range of abilities (weak AI) such as Google assistant, Google translate, SIRI, Cortana or Alexa or does it possess a large range of abilities (strong AI)?
How is Artificial Intelligence transforming business?
You can find Artificial Intelligence scenarios in many applications such as, but not limited to:
- Humanoids or animals’ activities and simulation
- Chatbots and voice recognition
- Image recognition
- Facial recognition
- Computer vision
- Mechatronic (the movement activity – running, walking, jumping…)
- Natural Language Processing
- Sentiment analysis
- And so on . . .
To make it very simple for all, any application you use is made of lines of programming codes; AI is the same. Codes written for an AI application follow Neural Networks (like for humans) that mimic the structure of the human brain to summarize very complex information into concrete and tangible results. Codes need to be trained with a very significant amount of data.
Data is at the center of all AI activity. This is very important for anyone who would like to start an AI program or application. Start with its data organization. Collect data, organize data, analyze data, and make it intelligent are the four steps to ensure success in any AI activity.
What is Machine Learning?
Then you very often hear of terms like Machine Learning. Machine Learning is, as its name states, a way to provide examples to machines so it learns. Examples show what the expected output of the program should be for any given input. In clear, you show machines what you expect and then the system explores all possible computer programs to find the one that most closely generates the expected result.
One element that you need to know is the fact that machines often adopt the same unconscious biases as humans who build them. This means that when a programmer assumes an output, it is very likely possible that machines have the same assumption; hence the importance to create guidelines to prevent biases.
How does a machine learn?
In Machine Learning you also find four different types of learning:
- Supervised: where the machines observe a set of scenarios, for example wind is strong today without any rain. The outcome of this is that sail boats go out in the bay. Machines learn rules with the goal to predict the outcomes of unobserved cases. So, if in the past, sail boats go out when the wind is strong, in the future machines predict this scenario. Algorithms can then link to weather sites and learn how many days per month the wind was strong and predict how many days boats have gone out.
- Unsupervised: this is just for machines to observe a set of scenarios without observing outcomes. Machines then learn patterns that enable them to classify scenarios with similar characteristics. Strong wind can be linked to a category where weather is poor, for example.
- Semi Supervised: a mix of both supervised and unsupervised learning. Machines observe a set of scenarios, the outcome of these scenarios, learn patterns, and learn rules.
- Reinforcement: machines take action to learn a policy. The machines require feedback on the actions. Humans give feedback yes/no/maybe and machines make a decision.
In conclusion, do not be frightened by terms or by the AI concept. AI and Machine Learning are all about feeding data to an algorithm and getting an output that makes sense.
Humans need to have the upper hand as they are the programmers. They need to ensure they have a fail safe switch in case machines become out of control. AI should help humans to be augmented. The intention of AI is to manage an extremely large amount of data.
Mundane activities will become things of the past for humans. This allows humans to focus on what they do best: think! Ethics in AI is essential to ensure the “human augmented” approach is enforced without any drift into non-ethical activities.
Want to keep up with current trends in emerging technologies? You can find more at my blog.