Artificial Intelligence (AI) is growing by leaps and bounds, with estimated market size of 7.35 billion US dollars. Machine learning (ML) is a field of AI that improves our daily living in various ways. ML involves a group of algorithms that allow software systems to become more accurate and precise in predicting outcomes.
Machine learning has been at the forefront of recent years due to impressive advances in computer science, statistics, the development of neural networks, and the improved quality and quantity of datasets. Here we take a deep dive into machine learning examples to give you a better perspective. In particular, we will look into the machine learning examples in real life that impact and aim to make the world a better place.
What is machine learning?
Arthur Samuel, the artificial intelligence pioneer in the 1950s, coined the term “machine learning.” Machine learning is a branch of computer science and artificial intelligence (AI). Here the focus is on using data and algorithms to imitate the way humans learn and gradually improve their accuracy.
Today we can see many machine learning real-world examples. We may or may not be aware that machine learning is used in various applications like – voice search technology, image recognition, automated translation, self-driven cars, etc.
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What are the four types of machine learning algorithms?
As we mentioned earlier, machine learning algorithms enable machines to identify data patterns and, in turn, learn from training data. Before getting into machine learning examples in python or our highlighted real-life examples of machine learning, let’s look at the four key machine learning types with examples.
Supervised learning
In supervised learning, we feed the algorithm’s output into the system so that the machine knows the patterns before working on them. In other words, the algorithm gets trained on input data that has been labeled for a particular output. The model undergoes training until it can detect the underlying patterns and relationships between the input data and the output labels, enabling it to yield accurate labeling results when presented with never-before-seen data.
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Semi-Supervised learning
Semi-supervised learning uses a combination of a small amount of labeled data and a large amount of unlabeled data to train models. Here the approach involves supervised machine learning using labeled training data and unsupervised learning, which uses unlabeled training data.
Unsupervised learning
The unsupervised learning approach is fantastic for uncovering relationships and insights in unlabeled datasets. Models feed input data with unknown desirable outcomes. So, inferences are made based on circumstantial evidence without training or guidance. Machine learning clustering examples fall under this learning algorithm.
See more: Supervised and Unsupervised Machine Learning – Explained Through Real-World Examples
Reinforcement learning
The reinforcement learning approach in machine learning determines the best path or option to select in situations to maximize the reward. Key machine learning examples in daily life like video games, utilize this approach. Apart from video games, robotics also uses reinforcement models and algorithms. Here is another example where we at Omdena built a Content Communication Prediction Environment for Marketing purposes.
Read more: Top 10 Machine Learning Algorithms with Real-World Case Studies
How does machine learning help us in daily life?
Here are a few quick machine learning domains with examples of utility in daily life:
Social networking
Use of the appropriate emoticons, suggestions about friend tags on Facebook, filtered on Instagram, content recommendations and suggested followers on social media platforms, etc., are examples of how machine learning helps us in social networking.
Personal finance and banking solutions
Whether it’s fraud prevention, credit decisions, or checking deposits on our smartphones machine learning does it all.
Commute estimation
Identification of the route to our selected destination, estimation of the time required to reach that destination using different transportation modes, calculating traffic time, and so on are all made by machine learning.
Top 10 examples of machine learning in real life (which make the world a better place)
Machine learning impacts across industries today amidst an expansive list of applications. There are so many different applications of machine learning in our day-to-day lives. Here is a glimpse of ones that create an impact in our lives.
1. Healthcare and medical diagnosis
Source Omdena
Machine learning deals with prognostic and diagnostic issues in medicine and healthcare. Disease breakthroughs, patient monitoring and management, medical data analysis, and management of inappropriate medical data are just some of many machine learning examples in healthcare.
Omdena has utilized recurrent neural networks (RNNs) to combine sequential and static feature modeling to predict cardiac arrest.
RNNs are proven to work exceptionally well with time-series-based data. Often in actual life data, supplementary static features may be available, which cannot get directly incorporated into RNNs because of their non-sequential nature. The method described involves adding static features to RNNs to influence the learning process. A previous approach to the problem was implementing several models for each modality and combining them at the prediction level. Combining these two methods into the same model architecture allows the model to learn simultaneously from the static and temporal features.
We conclude that the addition of the static features improves the performance of the RNN than would otherwise by using the sequential and static features alone.
Image Source: Omdena
In the challenge of predicting biological age through AI, Humanity and the Omdena team compressed high throughput markers such as activity and other lifestyle action data from the user (e.g. diet, weight, socio-economic status) to develop weighted algorithms predictive of the biological age outcome.
The team has built a system that takes in the user attributes and lifestyle actions that are being monitored on one side (activity rates, sleep, meditation, diet, etc.) and uses the ongoing increases or decreases in the user’s Biological Age measure to decide which actions were most effective and in what combinations and when. The system then also matched across users with similar attributes to use the insights and weightings set for one user to affect the weightings given to actions and the combination of actions to another user.
Read full case study here: How can AI help people slow their aging down using causal inference
2. Face detection in images
Machine learning finds its application in face detection amidst non-face objects such as buildings, landscapes, or other human body parts, such as legs or hands. It plays a crucial role in fortifying surveillance techniques by tracking down terrorists and criminals, making the world a safer place.
Image Source: Demo of Child Growth Monitor application
Child Growth Monitor (CGM) is a game-changer application in this space as it replaces traditional methods of anthropometric measurement which are complex, slow, and expensive, frequently resulting in poor data and wrong assessments of the situation. In the challenge Identifying Malnutrshed Children through Computer Vision, Omdena and CGM predict the measurement of height, weight, and mid-upper arm circumference (MUAC) of children under age 5 using its open-sourced state-of-the-art neural network algorithms to determine if a child is malnourished or not. The goal of this challenge was to increase the accuracy of CGM’s neural networks’ prediction, so that 90% of children get a height measurement with less than 1cm error.
3. Commute predictions
Machine Learning in platforms that use maps and routing ensures punctuality through ML algorithms to calculate the quickest route having less traffic, arrival time, the pick-up location, and the best optimal route to a destination. Machine learning techniques have incorporated a deep learning model to explore transportation traffic, intricate roadway interactions, and environmental elements. It has helped address many traffic bottlenecks, thereby enhancing a nation’s safety, economy, and quality of life. Emergency vehicles like ambulances can find the shortest and quickest way to reach a hospital, saving lives. Besides, people can save time rather than getting stuck in traffic and have a more productive day.
4. Public safety
Sexual Abuse Hotspots, Source: Omdena
Machine learning can improve community safety by preventing, reducing, and responding to crimes. 30 data scientists and machine learning engineers collaborated with an award-winning NGO, Safecity, to predict sexual harassment hotspots through machine learning-driven heatmaps.
5. Agriculture
Crop Yield Predication, Source: Omdena
Machine learning in agriculture enables precise and efficient farming with less manpower for high-quality production. Machine learning also provides invaluable insights and recommendations about crops so that farmers can minimize their losses.
Using satellite imagery for Google Earth Engine (GEE) images and Jupyter, Omdena built an app for crop yield prediction in Senegal, Africa, that helps to improve food security.
Image Source: Omdena
Another use case in Agriculture is the Omdena challenge with OKO using satellite imagery to detect and assess the damage of armyworms in farming, where the team developed an AI pipeline for generating, preprocessing, and training classification algorithms; with a developed web application connected to the deployed model solving the problem of damage assessment of armyworms attack on plants.
Using satellite images, the team was able to detect and identify the damage assessment of either or all together (depending on data availability):
- Fall Armyworm
- Africa Armyworm
- Locust Desert, with surge/outbreak in Mali (or Ivory Coast or Ouganda)
Learn more about this challenge in our article supervised machine learning for damage assessment in agriculture
6. Smart assistants
Siri, Alexa, and Google Assistants are just some of the smart assistants used in everyday life to carry out activities like setting reminders, alarms, checking the weather, etc. Voice-based smart assistants have numerous societal benefits as they bring people together, make visually or physically challenged people independent, and help them become more independent. Smart assistants also render a sense of companionship to people who live alone.
7. Government industry and policymaking
The use of machine learning helps authorities track and manage the huge amount of data generated by public surveillance devices. Data analysis in real-time for anomalies and threats by law enforcement agencies helps track criminals and missing children. Thus, internet service providers are more successful in identifying instances of suspicious online activity pointing to child exploitation.
Another example is where a team of data scientists and ML engineers at, Omdena successfully applied machine learning to enhance public sector transparency by enabling increased access to government contract opportunities.
8. Workplace safety
Machine learning applications enhance workplace safety by reducing workplace accidents, helping companies detect potentially ill employees as they arrive on-site, and aiding organizations in managing natural disasters.
9. Safeguarding the environment
Machine learning algorithms can help in boosting environmental sustainability. A good example is IBM’s Green Horizon Project, wherein environmental statistics from varied assets and sensors are leveraged to produce pollution forecasts. The aim is to bring down the environmental impact.
10. Cyber security
Applications like PayPal and GPay use machine learning for tracking transactions and differentiating between illegitimate and legitimate transactions. In this way, machine learning maximizes cyber security by preventing online monetary fraud.
Besides the above-listed applications, substantial other sectors and areas implement ML technologies.
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