AI is conquering the world – and with it the buzzwords “machine learning” and “deep learning”. But what exactly is behind these terms? In this blog post, we look at the differences and use cases.
Machine Learning vs Deep Learning
In the field of artificial intelligence (AI), the terms “machine learning” and “deep learning” are often used interchangeably. Both terms refer to the use of artificial intelligence to train systems and make decisions. Strictly speaking, deep learning is a sub-area of machine learning. The basic idea is therefore the same. However, the main difference lies in the complexity, methodology and objective. Knowing the difference enables a targeted decision to be made in favor of a specific model – to suit the respective starting position and objective. This allows companies to save valuable time, ensure the success of the project and significantly increase the likelihood of the planned AI solution being used.
Machine Learning: Making predictions from data
Machine learning refers to the mathematical process of learning from data and deriving decisions from it. The basic principle is that a computer program is given labeled sample data, through which certain rules and dependencies are communicated to the program. The program “internalizes” these rules and can use the examples to identify new similarities and derive them from external data. In order to train a model, it requires large amounts of training data, which must be available in a suitable, structured form. The model learns a specific skill and its execution – and only this skill. As a result, this learning mechanism is very complex and time-consuming. Machine learning is used in a variety of areas, such as credit risk assessment, image classification and personalized product recommendations in the e-commerce sector.
Features and benefits of machine learning:
- Ability of computers to learn from data and recognize patterns à indispensable tool for analyzing complex data sets and automating decision-making processes
- Data that does not have clear structures or data points must be pre-processed and structured
- Based on statistical methods and relies on data to identify patterns
- The more data available and the more powerful the algorithm, the more accurate and robust the result
- Requires less demanding resources for training
- Builds on well-established and widely available frameworks
Our experience shows that the classic machine learning approach is particularly convincing in customer scenarios where the problem solution can be outlined mathematically. These include, for example, the identification of customer segments, sales and consumption forecasts, the integration of large weather models and portfolio structure analyses.
It is important that a comprehensive, labeled data set is available to enable intensive training of the model. We are currently experiencing this as the biggest obstacle to progress in AI projects.
Deep Learning: Predictions from unstructured data using neural networks
Deep learning is a discipline of machine learning. The basic principle behind it is that an enormous amount of data is presented to a computer program. The program’s task is to independently identify dependencies and “rules” within this data. This is made possible by neural networks that process and analyze data from complex structures in several stages using interconnected algorithms. A deep learning network consists of many interconnected layers. The “depth” of the system results from the number of different layers and the number of connected neurons.
Features and advantages of deep learning
- Training the models takes an enormous amount of time and requires an extraordinarily large amount of data à can only be realized by providing extensive infrastructure (and the associated high costs)
- Deep learning is often used in complex systems that require a high degree of customization and flexibility.
- Proven use cases for the deep learning approach are, for example: Face recognition, speech recognition, image and video generators
In recent years, numerous companies, including those in the insurance industry, have already started attempts to develop their own deep learning models. The projects involved a high investment of time and money. Unfortunately, mostly without the hoped-for success. However, this situation changed with the emergence of foundation models and large language models (LLM). The models are technically very mature and therefore form the perfect basis for building company-specific scenarios and workloads. This makes it possible to access models that have already been trained and skip the time-consuming process of training.
“Please also read our article on “Foundation models – just hype or the future of AI?”
Both machine learning and deep learning use statistical methods and algorithms to learn from data and make predictions. The main difference lies in the complexity of the training. ML models learn from historical data, while deep learning automates the extraction of features.
To identify the optimal solution for a specific problem in your company, an iterative approach is advisable. Different approaches can be tested, compared and optimized in order to arrive at the best solution for the respective scenario.
Conclusion: The added value lies in the combination of machine learning and the use of foundation models
Machine learning as a generic term for trained systems will continue to play an important role in the integration of AI solutions in companies in the future. However, the development of company-specific solutions based on existing foundation models will become more of a focus. This is why companies should not launch their own deep learning projects – as these are not very promising and involve a high investment of time and money. In our customer projects, we recognize that the boundaries between machine learning, deep learning, foundation models and LLMs are becoming increasingly blurred. And for good reason: hybrid use has great potential for future developments, as the strengths of the different approaches are utilized and the disadvantages offset each other.
One thing is already certain: through the intelligent use of technology, we can learn, make decisions and solve problems more effectively. Companies need to start today by identifying suitable use cases for their own business, actively making decisions on the integration of AI technologies and ultimately anchoring them in their corporate strategy. It is advisable not to struggle through the AI jungle alone, but to seek support from specialized service providers.
Fascinated by the possibilities of generative AI? Find out more about the use of this revolutionary technology as well as the X1F benefits and services related to GenAI or get in touch with us.
