teach a child by showing various pictures of it and thus enabling the machine to define
its own steps to understand the difference between an apple and orange.
2. Natural Language Processing: This method is widely stated as the automatic
manipulation of natural language. For example, speech and text could be used in
many ways to teach AI. The best example would be the email spam detection service.
Nowadays every spam and junk emails automatically get sorted to the spam by using
intelligent AIs that can understand natural language well.
3. Vision: The eyes we have enable us to better perceive and learn about the world.
Similarly, having vision enables the machines to capture and analyze the visual
information around them by using a camera. We can even enable the machine to see
through the wall which is beyond human capabilities. Thus, having vision greatly helps
the AI to become smarter. This entire process is done through machine learning. We can
say that these two fields are interconnected.
4. Robotics: Robots are usually used to perform the tasks that are too dangerous or difficult
for humans to perform effectively. Robots are used in assembly lines, hospitals, and
restaurants. These Robots open a wide range of opportunities for the AIs to explore.
5. Autonomous Vehicles: In recent years this department has gained a lot of attention.
Thanks to companies like Google and Tesla for working on self-driving vehicles. These
vehicles will revolutionize the automobile. By over serving the roads and environment,
the AI on such vehicles is getting smarter and smarter.
Biggest Challenge for AI
The real big challenge for AI is the trust factor. After all, we are the ones who putting trust into
these machines. So we play a major role in defining ethical intelligence versus unethical intelli-
gence. This is because; AI is largely dependent on deep learning algorithms to bring intelligence
to these machines. These deep learning algorithms are dependent on the data feed to learn and
build their decision-making capabilities. Here is the problem as they are as good or smart as the
data we provide them. Hence, their decision-making is also influenced by human factors, as we
are the ones who decide which data to be fed to these machines to make them smart.
Industry Adaptation of AI
Without even noticing, AI is becoming an integral part of our daily urban lives. We encounter AI
powered applications multiple times a day and our lives are largely dependent on this external intel-
ligence. For example, our latest phones won’t even open without looking at our face and authen-
ticating our identity based on our facial structure, known as face recognition. There are countless
examples like this, for example, soon your car will refuse to drive without hearing your voice, your
house door will not open until it verifies your fingerprint and retina scan (many houses are already
doing this). With these types of applications, AI is now coming out of the business world into the
consumer world with the help of connected devices that is, in fact, IoT (Internet of Things). Similarly,
most of the things that we use daily are made in factories, which are powered by AI. AI is also pen-
etrating manufacturing, distribution, logistics, and industries. All these areas are getting connected
to improve productivity of the manufacturing processes by automating most complex and chal-
lenging tasks such as improving demand and supply model, price based stock management, and so
on. Since this intelligence is based on connections between these machines, IoT plays a significant
role or you may say AI plays an important role in IoT. It depends on how you look at it.
Chapter 9 Artificial Intelligence for IoT 233
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