Artificial intelligence (AI) and machine learning (ML) are transforming an array of industries. They’re helping businesses manage large volumes of data, mine it for insights and make better decisions.
From Cincinnati’s 80,000 public requests to a virtual financial assistant that creates credit report updates, AI is improving service and enhancing efficiency across multiple sectors.
1. Deep Learning
Deep Learning is a subset of machine learning (ML) that uses artificial neural networks, algorithms modeled to mimic the way the human brain works. It is an essential part of machine learning because it makes data analysis much faster and easier than traditional techniques.
Unlike machine learning, which requires a data scientist to select features and classifiers, Deep Learning relies on a computer to make the decisions on its own. The algorithm structures these algorithms into layers, similar to a network of neurons in the brain, and determines whether a prediction is accurate or not on its own.
One major advantage of Deep Learning is that it can scale better with more data than a traditional model can. This is because Deep Learning models tend to increase their accuracy as the amount of training data increases, while SVM and Naive Bayes classifiers stop improving after a certain saturation point.
Another key difference between deep learning and standard machine learning is that with deep learning, you don’t need a data scientist to choose features or adjust the algorithm if the predictions aren’t as accurate as you hoped for. Rather, you just provide the AI with enough data to train itself on, and let it do the rest.
There are many ways to apply deep learning in a variety of fields. For example, driverless cars can use deep learning to detect stop signs and pedestrians, enabling them to safely navigate their way to their destination. And voice control devices like Amazon Alexa rely on deep learning to recognize and respond to commands from users.
Other applications include fraud detection and financial crime prevention. Banks can use deep learning to analyze customer behavior and identify patterns that may indicate suspicious activity. They can also use deep learning to improve risk management by interrogating multiple sources to set lending limits without compromising fairness.
2. Machine Learning
Machine learning is a subset of artificial intelligence that enables computers to learn and adapt. It is used in a wide variety of applications, from facial recognition on smartphones to virtual personal assistants to self-driving cars.
Machine learning models are trained on data, including numbers, photos, text, and time series information from sensors, sales reports, or repair records. They then learn to perform specific tasks, like recognizing bank transactions or images of people.
AI & ML advancements are being implemented across nearly every industry, from healthcare to retail and financial services. These advances are enabling companies to work more efficiently and uncover valuable insights in their data.
Despite the benefits of AI & ML, they are not without their challenges. For instance, many ML initiatives fail to deliver on their promise because of data quality, data labeling and model confidence issues.
These problems can make the process of training models unfeasible. And, as AI & ML models become more complex, the amount of data that is required to train them grows exponentially.
The problem is that the sheer volume of data requires significant investment in infrastructure, and this can be a major barrier to advancing AI & ML initiatives. It also takes time and resources to collect, pre-tag and categorize a huge number of data sets.
In addition, some ML algorithms are prone to making errors when they are being trained. For example, if an algorithm is being taught to translate text from one language into another, it may miss words or grammar rules.
Some ML algorithms are also prone to bias, which can lead them to replicate and perpetuate certain forms of discrimination. For example, chatbots that are being trained to understand conversations on Twitter can pick up offensive and racist language.
3. Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of artificial intelligence that uses machine learning and deep learning to analyze human speech and text. Today, it is used in a wide range of applications, including chatbots, search engines, cybersecurity and customer service.
NLP is a key aspect of AI & ML advancements because it can handle unstructured data, which is hard for computer algorithms to process. It also gives computers the ability to understand a user’s words in context, helping them provide better answers and more personalized experiences.
Early NLP systems relied on hard coded rules, dictionary lookups and statistical methods, often supporting basic decision trees. Eventually, these methods were replaced by machine learning, which enables machines to learn from data without having to be programmed by a human.
Modern NLP frameworks use far more advanced–and precise–language modeling techniques, which rely on neural networks and other machine learning technologies to study the way language works. These models can be trained on a wide range of data sources, including social media and search engine results.
Some of the most common NLP techniques include named entity recognition, sentiment analysis, text summarization, topic modeling and aspect mining. These tools can be used to automatically identify specific words, phrases and themes in large amounts of text and determine trends or patterns in the data.
However, NLP has its limitations. For example, it is difficult for machine learning algorithms to accurately recognize sarcasm and other abstract topics. In addition, it may miss subtle tone changes in a person’s voice when analyzing speech. This is why virtual smart assistants like Siri and Alexa rely on NLP to understand a user’s inflection and tone to deliver accurate responses.
4. Machine Vision
Machine vision is the ability for computers to identify and process images on an automatic basis. It can be used in a variety of applications including automated inspection and process control, security monitoring, and vehicle guidance.
This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods, and expertise. It is a technical capability that is integrated with existing technologies in new ways and applies it with the aim to solve real-world problems.
A machine vision system uses a combination of sensors, cameras and computer software to detect objects and extract information. This information can be simple good/bad signals or more complex set of data such as the identity, position and orientation of each object in an image.
These types of technologies are often integrated into industrial robots to increase efficiency and accuracy in a variety of manufacturing and distribution applications. This includes reading barcodes and labels, as well as tracking products throughout a supply chain.
It also helps to improve safety, as a machine vision system can identify hazardous materials on a manufacturing site or inspect food items. It can track ingredients and product serial numbers and expiration dates in a pharmaceutical plant, for example, which makes quality assurance and inventory management more efficient.
AI augments these classic computer vision algorithms with models called neural networks. When a computer receives an image or video stream of images, it compares the image data to the neural network model, which identifies meaningful patterns that a human might not be able to see.
The result is a faster, more accurate detection of defects and issues at a fraction of the time it would take for humans to do it. In turn, it allows for a greater degree of automation and can help to speed up decision-making processes.
5. Robotic Process Automation (RPA)
RPA is a technology that employs computer bots to automate high-volume, repetitive, time-consuming business processes. Its popularity is primarily driven by the cost savings that it offers compared to traditional automation.
Robotic process automation (RPA) does not involve physical robots or even robotics software; it simply records manual tasks performed by employees, and then replicates them with computer robots that perform these tasks without human intervention.
This technology is a great way to reduce errors in office work, and it can be implemented without replacing existing IT infrastructure. It also allows employees to focus on higher-value activities, and it increases employee engagement and retention rates.
However, RPA is not without its challenges. It requires a strong IT foundation and an IT team that understands the technology. Likewise, C-level executives must be involved in the project to ensure business outcomes are met and new governance policies are adhered to.
One of the biggest hurdles to overcome is ensuring that RPA is implemented properly. This requires businesses to have an end-to-end view of the workflows that need to be automated. This allows them to identify which automation opportunities are best suited for the enterprise and which ones should be prioritized.
Once a company has identified the right RPA solution, it can start designing and testing new automations in a matter of hours. It can also quickly optimize the automations and improve their accuracy.
As more businesses embrace AI & ML advancements, RPA will continue to grow and evolve. With these advancements, RPA can become a powerful tool for digital transformation and transforming business processes.
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