Heuristic automation and machine learning can be found in many applications ranging from self-driving cars to facial recognition and speech recognition. Machine learning algorithms improve over time as more data is added, enabling programs to teach themselves. Examples of applications include self-driving cars that learn driving rules from experience, speech recognition software that detects patterns in sounds to understand words, and facial recognition systems that find face patterns to identify individuals.

1. Applications of Heuristic Automation
Nowadays, Heuristic automation can be found in many applications, ranging from self -
driving cars, to effective web search, facial recognition and speech recognition. For Machine
learning more data does mean better results, because new data will enable the computer
program to teach and improve itself. There is a link with data mining as both big data
techniques sift through data to look for patterns. The major difference is that data mining
finds patterns for humans to understand and use and machine learning uses those patterns
to improve its own program and understanding. There are quite some examples available of
well-known applications of Machine learning and they appear more often every day. Perhaps
the most well-known is that of the self-driving car by Google. But also the speech
recognition by Apple’s Siri or Facebook’s controversial facial recognition technology are
great examples of the power of machine learning. All of these tools have in common that
they improve over time the more data is added and is being used by the algorithms.
1. Self-Driving cars: Self-driving cars learn from the effect of a decision made and
use the new information to improve itself. It learns from its environment and is
capable to learn implicit rules of behavior.
2. Speech recognition: Software on the other hand learns from detecting patterns
(words) out of vibration in the air in combination with natural language processing to
understand the meaning of those words.
3. Facial recognition: Facial recognition finally works by finding patterns in images
that match those of faces in order to detect faces. It consequently matches those
recognized faces with faces in the database to put a name on it. Although this could
be very useful, it is also highly controversial as it directly impacts the privacy of
consumers.
4. Search engine optimization: Machine learning can help optimize Search Engine
Optimization (SEO). For those organizations that offer websites with continuously
changing content, Machine learning can be used to effectively monitor search engine
algorithms’ actions in order to understand the impact of changing content. Secondly,
Machine-learning algorithms are often used in recommendation engines. They help to
improve the recommending of the right products to the right customer at the right
moment.
5. Medical industry: It is also used in the medical industry where health organizations
use machine learning to help identify the symptoms a patient has in order to help

2. the doctor correctly diagnose a patient and provide the correct medicines. Healthtech
companies and healthcare organizations are using a technique called Discrete Event
Simulation to predict wait times for patients in emergency department waiting
rooms. The models use factors such as staffing levels, patient data, emergency
department charts, and even the layout of the emergency room itself to predict wait
times.
6. Protecting animals: Cornell University is working on an algorithm to identify
whales in the ocean based on audio recordings so that ships can avoid hitting them.
Also, Oregon State University is working on software that will determine which bird
species is/are on a given audio recording collected in field conditions.
7. Identifying heart failure, strokes and seizures: Researchers have found a way
to extract heart failure diagnosis criteria from free-text physician notes. They
developed a machine learning algorithm that combs through physicians free-form
text notes (in the electronic health records) and synthesize the text using a
technique called “Natural Language Processing” (NLP). Similar to the way a
cardiologist can read through another physician’s notes and figure out whether a
patient has heart failure, computers can now do the same. Singapore-based startup
Healing launched an app called JustShakeIt that enables a user t o send an
emergency alert to emergency contacts and/or caregivers simply by shaking the
phone with one hand. The program uses a machine learning algorithm to distinguish
between actual emergency shakes and everyday jostling. In addition to the
JustShakeIt app, Healing is working on a model that analyzes patients’ cell phone
accelerometer data to help identify warning signs for chronic neurological conditions.
8. Mobile Phone: There are various application on mobile phones these days which
include the word prediction capability for text messaging and search functions. Based
on pattern recognition of a person’s style of texting, the cell phone prompts the user
with words which makes the texting much simpler. This is a type of machine learning
in a simplified version.