Data Science & AI Trends 2023

from | 23 February 2023 | Basics

The year 2023 holds new challenges, trends, but also opportunities for companies: next-generation chatbots, Europe-wide AI regulation and the latest AI hardware developments - the AI ecosystem is currently changing very quickly. And the next few years will see New business models based on AI show that the active use of data in combination with ML (machine learning) practices and AI are proving their worth. Those who want to profit from this in the future must now Making data a resource in the company and establish. Using current data science & AI trends, we show what is already possible today and which trends await us in 2023 and beyond.

Generative AI on the rise

With ChatGPT, StableDiffusion and Co. generative AI models made headlines last year - and will remain a central data science & AI trend in 2023: Google is planning a language model that speaks over 400 languages, OpenAI is already working on its new language model GPT-4, applications and tools based on large AI models are almost sprouting from the ground. Generative AI models are able to create texts or images, write summaries, analyse or generate programming code with the help of a prompt (an instruction). Models like ChatGPT show how conversational AI can help answer specific questions more specifically and quickly.

Train large AI models, Requires a lot of computing power and large amounts of data. Therefore, the creation of such models is very complex. But ChatGPT, StableDiffusion and co. show that generative AI models are already capable today, real added values create. Generative AI models can be used to create digital images and illustrations, answer search engine queries faster and generate or summarise complex texts.

AI hardware to the edge

As the democratisation and proliferation of AI continues, several factors are leading to the shift of AI systems to the edge. Data centres, with fast and massive hardware, offer the ability to train and use AI models faster, but have critical disadvantages in several areas due to their fixed location and limited connectivity. The availability of smaller models and edge AI hardware make it possible to overcome bottlenecks such as bandwidth or data storage regulations, allowing new applications "on the edge" through concepts such as Federated Learning.

The rise of the Edge computing is creating a market for smart and responsive devices in sectors such as healthcare, finance and manufacturing. The new devices, which are more efficient due to the lower Falling costs promote the establishment of Edge AI for example, in smart warehouses, manufacturing or utilities. Positive side effects of this data trend, such as Energy savings and reduction of the CO₂ footprint lead to more sustainability in the use of AI.

Challenge 23 Whitepaper Cover

No complex AI forecasting models are needed to determine: The year 2023 will be a big challenge for companies. The use of data analytics & artificial intelligence holds great potential for overcoming future and acute challenges. Learn which solutions have proven themselves in practice with the help of 17 tried and tested use cases. 

Regulation of AI systems

Despite the enormous potential of AI trends, there are still complex legal and ethical issues to be resolved. A lawsuit in the US against Microsoft's AI programming assistant "GitHub Copilot" is an exemplary example of many more cases to come. GitHub introduced an AI-powered programming assistant last year that was trained on large amounts of open source code - including those with licences that require attribution.

In Europe, the increasing use of AI applications is leading to reactions and consequences on the part of the European Union: Within the framework of the European AI Act, the EU is striving for Requirements for the development and use of AI applications in compliance with European standards and values. Companies should therefore already familiarise themselves with the requirements and framework conditions of the EU AI Act to be able to continue to use AI productively in the future.

Ethical and Explainable AI

Especially in safety-critical applications for AI, it is enormously important for legal and ethical reasons alone to be able to understand algorithmic decisions. One only has to think of autonomous driving, credit scoring or medical applications: which factors lead to which result - and is that the desired result? Even in the case of less critical decisions, the Interpretability of the model Whether it is to create confidence in the model's predictions or simply to find out the reasons for the value of a forecast.

Methods and tools such as LIME, SHAP, CXPlain or Global Surrogate Models make it possible to make ML models more explainable. This data science trend will play a decisive role in the years to come and will replace the current Significantly change the way we deal with AI. In many areas plays XAI already play an important role today - for example, in AI-based claims processing for insurance companies, in the autonomous drivingThe use of AI in health care applications or simply to build trust in a certain AI model.

Adaptive AI systems

Adaptive AI systems are - unlike conventional systems - able to adapt to changes in the real world. By adapting the code, certain model parameters or building blocks within an MLOps pipeline, these AI models are particularly flexible to use. AutoMLmodel retraining or other mechanisms within the runtime and development environments. Adaptive AI systems more adaptive and resilient to change. The combination of different methods such as agent-based learning and techniques such as Reinforcement Learning enable AI systems to adapt their behaviour to changing real-world circumstances.

By learning behavioural patterns from previous human and machine experiences and within runtime environments, adaptive AI delivers faster and better results. A simple example of adaptive AI is a learning system that customises the subject matter and learning speed to the learner for more effective and efficient support. Companies that invest in the development of adaptive AI systems can benefit in the long term from ML models with consistent performance and achieve a shorter product lifecycle for ML models through MLOps practices.

We hope that this article on the latest Data Science and AI trends has given you informative insights. If you are interested in implementing Data Science and AI solutions for your company or have any further questions, please do not hesitate to contact us. Contact us at any time for a no-obligation consultation.


Luke Lux

Lukas Lux is a working student in the Customer & Strategy department at Alexander Thamm GmbH. In addition to his studies in Sales Engineering & Product Management with a focus on IT Engineering, he is concerned with the latest trends and technologies in the field of Data & AI and compiles them for you in cooperation with our [at]experts.

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