As we dive into the world of technology and digitalization, it is essential to understand the benefits of what lies ahead. Artificial intelligence and machine learning are the basis of a radically new approach to business. We now have the tools to overcome digital barriers, from fighting cyber threats to improving our customer marketing.
According to a study by McKinsey & Company, 50% of companies have implemented AI in at least one business function. AI and machine learning may save a lot of time and money if you know how to use them.
What should we know about artificial intelligence?
AI is a field of computer science that lets machines or computer programs learn and perform intelligent tasks that humans typically perform.
Simply put, people think of artificial intelligence as robots doing our jobs, but they don’t realize AI is part of our daily lives; for example, AI has made travel more accessible. People used to turn to printed maps, but with maps and navigation, you can get an idea of optimal routes, alternative routes, traffic jams, roadblocks, etc.
Artificial intelligence is unlimited to machine learning and deep learning. It also consists of other fields such as object detection, robotics, natural language processing, etc.
What is machine learning?
Machine learning is the study of algorithms, and statistical models machines use to perform a specific task without explicit instructions. Such machines rely on pattern analysis and predict outcomes. AI machine learning is a subset of artificial intelligence that includes the following steps:
- data collection,
- information preparation,
- model definition,
- model training,
- model evaluation,
- making forecasts.
Most industries have understood the importance of machine learning by seeing excellent results in their products and services. Such sectors include financial services, transportation services, government services, medical services, etc.
The connection between ML and various subfields of AI
We understand AI and machine learning are inextricably linked, but how? Artificial intelligence refers to the science of teaching computers to perform human tasks. Machine learning refers to a specific subset of AI that teaches a machine how to learn. Machine learning models may artificially establish a point of view by looking for patterns and drawing conclusions from the data. So instead of creating code that tells the machine exactly how to think, we can now ask the correct questions and let the computer figure it out.
Natural language processing
Natural language processing is an area of machine learning in which machines learn to understand natural language as humans speak and write instead of the data and numbers commonly used to program computers. It allows machines to recognize, understand, and respond to speech, create new text and translate it into different languages. Natural language processing allows using standard technologies, including chatbots and digital assistants like Siri or Alexa.
Neural networks are a well-known class of machine learning algorithms. Artificial neural networks based on the human brain, in which thousands or millions of processing nodes are interconnected, forming layers.
Deep learning networks are multilayer neural networks. They may process large amounts of data and define the «weight» of every link in the network. For instance, some neural network layers can detect individual facial features, like eyes, cheeks, or lips in a picture recognition system. In contrast, another layer can say whether these features appear in such a way as to indicate the face.
Like neural networks, deep learning models are used in many areas of machine learning, including autonomous machines, chatbots, and medical diagnostics.
With the advent of digital cameras and imaging, the development of this sub-field of AI has become inevitable. Computer vision is the ability to identify and process objects in the visual world accurately. A computer can acquire an image in several ways — through pictures or live video, which is most commonly used in facial recognition software. The computer then uses deep learning models to process image properties based on an extensive collection of prelabeled images in its memory. From there, computer vision can quickly identify the object.
One way to teach a computer to mimic human reasoning is to use a neural network, a set of algorithms modeled on the human brain. A neural network helps a computer system achieve AI through deep learning. This close connection explains why the idea of comparing AI and machine learning is actually about how artificial intelligence and machine learning deal together.
Differences between artificial intelligence and machine learning
We use AI in any industry to solve problems that require human intelligence. Conversely, ML is a subset of AI that solves specific issues by learning from data and making predictions. That’s why it can be said that all machine learning is AI, but not all AI is machine learning.
|Artificial intelligence||Machine learning|
|Artificial intelligence is a technology that allows a machine to mimic human behavior.||Machine learning is a subset of AI that lets a machine learn from past data without being explicitly programmed automatically.|
|The main goal of AI is to make an intelligent computer system look like a person to solve complicated problems||Machine learning lets machines learn from data to produce the most accurate results possible.|
|In AI, specialists create intelligent systems capable of performing tasks like humans.||In ML, we teach machines with data to perform a specific task and provide an accurate result.|
|Machine learning and deep learning are the two fundamental subsets of AI.||Deep learning is the essential subset of machine learning.|
|AI has a wide range of possibilities.||Machine learning has a limited scope.|
|AI is working to form an intelligent system capable of performing various complex tasks.||Machine learning works to create systems that can only perform the specific tasks for which they were trained.|
|The AI system maximizes the chances of a successful outcome.||Machine learning is mainly about accuracy and patterns.|
|The most popular AI applications are Siri, expert systems, online games, intelligent robots, etc.||The best-known machine learning applications are the online recommender system, Google search algorithms, tag suggestions for automatic Facebook friends, etc.|
|AI can be divided into three types depending on the capabilities: weak, general, and strong.||AI machine learning comes in three types: supervised, unsupervised, and reinforcement.|
|It consists of learning, reasoning, and self-correction.||It provides for learning and self-correction when new information is introduced.|
|AI uses structured, semi-structured, and unstructured databases to work.||Machine learning works with structured and semi-structured data.|
Artificial intelligence and machine learning are widely used in different ways. There are many real-world examples of both technologies. We don’t even realize it, and our work is done thanks to AI and ML. In summary, AI is responsible for solving problems that require human intelligence, while machine learning is responsible for solving problems after learning data and providing predictions.
The benefits of AI & machine learning
It’s common to think of AI and ML as complex and expensive, but there are many benefits when used correctly. Businesses can streamline operations, eliminate manual processes, and move faster. According to Forbes, in 2021, 76% of enterprises prioritize artificial intelligence and machine learning over other IT initiatives. Here is a list of the fundamental advantages:
- Improved customer experience: Customers benefit the most from AI technologies. Bridging the gap between customer wishes and business responses is made possible by automated chatbots, triggered emails, and other personalized messaging systems. Machine learning and NP make delivering a timely and customized customer experience more straightforward. It also takes the pressure off your support staff, increasing efficiency and eliminating manual workflows.
- Error Reduction: once the foundation of your artificial intelligence and automation models is in place, you will notice manual errors begin to disappear. Corrective tasks such as data processing or onboarding become background processes, not because they are no longer critical, but because they no longer need to be constantly monitored. Minor inaccuracies disappear because the machine only understands precision.
- Automation: the most common result of AI is that no business processes are negatively impacted by automation. From communications and marketing to support, a technology function can eliminate inefficiencies from every corner of your business. Additionally, removing manual workflows from your organization frees up resources for ideas and seemingly unavailable projects.
- Decision making: the aim of AI has always been to create more intelligent decisions. It’s not that we’re incapable of critical thinking as humans; it’s just that we’re limited in how fast we can process and coordinate mountains of data. AI takes over the job of delivering data, analyzing trends, and predicting outcomes while excluding human emotions. It can take raw data and transform it into an objective solution.
Incorporating machine learning into a business strategy allows you to solve more complex problems. These technologies allow not only finding solutions but also scaling them. From customer support operations issues to cyber security threats, incorporating AI into your solution gives you a fundamental approach that saves time, money, and resources.
Applications of AI and machine learning
Companies in different industries are building applications that leverage the link between artificial intelligence and machine learning. Below there are just a few of the initiatives where AI machine learning is helping companies transform their processes and products:
- Retail: retailers utilize artificial intelligence and machine learning to optimize inventory, create recommendations, and improve the customer experience through visual search.
- Banking and finance: in finance, AI and machine learning are valuable tools for various purposes, including fraud detection, risk prediction, and better financial advice.
- Cyber security: AI and machine learning are potent cybersecurity weapons that help companies protect themselves and their customers through anomaly detection.
- Transport: AI and machine learning are critical in transportation applications, where they help firms improve route efficiency and use predictive analytics to predict traffic.
- Healthcare: medical institutions often use artificial intelligence and machine learning in applications such as image processing to improve cancer detection and predictive analytics for genomics research.
- Sales and marketing management: professionals use artificial intelligence and machine learning for personalized offers, campaign optimization, sales forecasting, sentiment analysis, and customer churn prediction.
- Customer service: organizations across industries use chatbots and cognitive search to answer questions, gauge customer intent, and help online.
- Production: manufacturing companies are using artificial intelligence and machine learning for predictive maintenance and improving the efficiency of their operations.
Programmers select a machine learning model to use, provide data, and let the computer model train to look for patterns or make predictions. The function of a machine learning system can be descriptive, meaning the system utilize the data to define what happened; predictive, meaning the system uses data to predict what will happen; or prescriptive, which means the system will use the data to make suggestions about what actions to take.
What are the challenges facing machine learning and artificial intelligence?
- The impact of artificial intelligence on people’s lives and the economy has been astonishing. Artificial intelligence could add about $15.7 trillion to the global economy by 2030. Putting this into perspective, we are talking about the combined output of China and India today. But is everything so cloudless? Let’s talk about the challenges of using AI and ML technologies:
- Computing power: machine learning and deep learning are stepping stones to this AI and require an ever-increasing number of cores and GPUs to run efficiently, which is daunting to many developers.
- Data privacy and security: the main factor on which all deep and machine learning models are based is the availability of data and resources for their training. Yes, we have data, but since millions of users worldwide generate this data, there is a possibility that this data can be used for destructive purposes.
- The problem of bias: the excellent or harmful nature of an AI system depends on the amount of data they are trained on. Therefore, being able to get good data is the solution for good AI systems in the future. But in reality, the day-to-day data that organizations collect is sparse and of little value.
While these AI and ML challenges seem very oppressive and disruptive to humanity, through the collective efforts of humans, we can bring about these changes very effectively. According to Microsoft, the next generation of engineers must improve their skills in these cutting-edge new technologies for a chance to work with future organizations.
The future of AI & machine learning
Most companies today are looking to scale and expand their business; artificial intelligence and machine learning are powerful tools that can help them achieve this faster. Moreover, AI and machine learning are becoming essential technologies for companies looking to stay competitive. With the right tools, a company will increase customer satisfaction, reduce errors, and improve operational efficiency. And as deep learning technologies continue to evolve, the future of these tools will become increasingly confident.