What is AI? A Brief Explanation for layman

In the past two years, Artificial Intelligence has become one of the hottest topics. Thanks to AlphaGo, the general public believes A.I. is a monster that had beaten human and sooner it is going to take their jobs away. As a machine learning engineer, I see the necessity to explain what is A.I. to the layman which could clear the illustration forged by the media. Here we go.

“AI is all about figuring what to do when you don’t know what to do.”, Peter Norvig.

Artificial Intelligence is not a magical program that can think like a human. The mechanism behind it is to utilise different mathematic algorithms to learn from a tremendous amount of structured data to solve dedicated cognitive problems that originally can be only solved by human intelligence. And the robust part of A.I. is it can acquire a pattern from the provided data to solve a problem in high accuracy. Meanwhile, we don’t even know what is actually happening inside the program but the algorithm would take care of it. In a simple word, A.I. learns from the data feed by human to solve dedicated problem. Let’s take a look to these six major subfields in A.I.

1. Computer Vision:
e.g. Recognizing object from real world images or video, just like the iPhone X’s Face ID.

2. Planning:
e.g. Searching for the shortest path between your home and the nearest shopping mall.

3. Natural Language Processing:
e.g. Interpreting what are you saying, writing, etc.

4. Knowledge Representation and Reasoning:
e.g. Chatbot that answer customer questions.

5. Robotics:
e.g. The self driving car that take care of your travel.

6. Machine Learning:
e.g Predicting the housing price by learning from the historical data.

How does Artificial Intelligence Agent solve problems?

So, how does A.I. learn? Typically, there are three learning methods, Supervised Learning, Unsupervised Learning and Reinforcement Learning. In supervised learning, we would label the correct answer inside the data and the agent would learn the pattern by comparing the result with the target label in order to achieve accurate prediction.; In unsupervised learning, we would not tell what we are looking for but let the learning algorithm to help us divide the data into category as many as we desired.; Lastly, reinforcement learning is more like learning by doing. At the beginning, we do not have any labeled data, however, we would set up different policies with corresponding reward or penalty. The A.I. would learn the optimal by trying different actions in each condition to achieve the highest reward under the specified environment and policies.

Can Artificial Intelligence Agent replace human?

To develop an A.I. agent to solve a task, there are four basic criteria.

• 1. We need to clearly define the problem domain and the learning objective.
• 2. We have to provide structured data for the agent or we are able to design a good environment, policies and reward system for reinforcement learning.
• 3. The data provided must contain useful information for the agent to learn from it. If the data fed in have no value, the agent would never be able to solve the task.
• 4. Decide an evaluation method to measure the performance of the agent. Otherwise, we won’t know the model is accurate or not.

Rule no.1: Garbage In, Garbage Out.

From my experience, the hardest part of building a prediction model is to decide what sample data should I use and how to gather those data. Luckily, there are many open-sourced data and algorithm library that could let us to train our model much easier than few year before. However, you could also gather the desired data by yourselves.

Conclusion ~ Will it take our job?

Undoubtedly, A.I. would disrupt our world in the soon future, however, we should firstly understand it and think of the opportunities created by A.I. instead of blindly worrying the fourth industrial revolution would replace all of the jobs. A.I. technology required a large amount of training data to look for a specific pattern in order to resolve the problem. And it has its own bottleneck such as: Do we have the data? Does the specific problem has a pattern? Can we structure the information into computer interpretable data? Is the computer powerful enough to handle a huge amount of data. Moreover, A.I. would soon replaces jobs that are highly repeatable, however, it would also create many job posts, for instance: data scientist, data security engineer, etc. On top of it, applying the new technology to the real world needs to solve many political, legislative and humane problems which might take years. Let’s say the self driving car application, lots of researches and experiments proved that self driving car is safer than human driving. However, once it killed people in an accident (which happened lately in U.S.), who is going to take the responsibility? In addition, do we need a driver sitting in the driver seat in case of any malfunction, so he can switch it to manual mode to save lives? If a driver is compulsory for self driving car after legislation, it ain’t gonna bring a big impact on the driver job.

It is very likely a large amount of traditional labor force, especially the grassroots class would be replaced by Robots and AI, however, the replacing process might take years and the society would have time to digest it gradually. The most important thing is we should learn about what is AI and think about how is it going to change our job instead of blindly panic. Hopefully this read could give you a brief glance of the A.I. world and motivated you to learn more about A.I.