As 5G developments get underway, the focus of wireless research is shifting towards the definition of 6G. In this process, sophisticated technologies such as Terrahurt's communications, ultra-massive MIMO, or network virtualization and disaggregation are coming into play. Our adoption will boost network performance with reduced latencies at the sub-millisecond scale, terabyte per second peak data rates, or location accuracy of centimetre resolution. This in turn will enable futuristic use cases such as holographic communications, tactile internet for remote operations, or massive connectivity. However, efficiently running 6G networks will become increasingly complex, and to manage such complexity, higher automation levels will be needed. In fulfilling these goals, artificial intelligence and machine learning techniques will play a pivotal role. Designing communication systems requires many experts from a variety of fields that interact with each other. The Ariste theirs project is focused on what we call the physical layer. Once the stream of data is converted into bits, the physical layer processes it into electromagnetic signals which travel through the air and are steered towards the receiver, where the original data is accurately reconstructed. This process involves numerous steps which have been refined over decades with sophisticated mathematics and engineering. Today, AI methods have reached maturity and can assist these processing steps to boost performance, robustness, and the reliability of the physical layer. In the Ariste theirs project, we at CTTC investigate how to further develop AI, machine and statistical learning techniques, as well as how to harness their potential for the design of beyond 5G and 6G networks. We also assess their performance in proof of concept demonstrators. Our experimental platform is composed of modern equipments such as GPU, acute march stations, upper defined radio, modules, plug distribution, and patch antenaries. This equipment allows us to simulate the broad range of settings for a 5G or 6G air interface. For example, we can experiment with neural networks to replace it traditional, well-known family of common dualizations with more efficient deep learning solutions. They do align signals in a two-dig grade but in more high-dimensional signal space. This allows for a more efficient separation of quad boards and lower bit error rates. Part of this demonstration was featured at the Mobile World Congress 2022. We also apply machine learning tools based on based and inference and factor graphs cooperatively in their positioning problems. We design robust algorithms and test them on ultra-vegan commercial devices. Their performance beats that of traditional methods. A dataset from the experiments is now openly available in the Zenubo public repository. We also investigate how to improve global navigation satellite systems in order to tackle the challenges arising by modern navigation and positioning systems and services. Our research approach is based on the systematic application of statistical inference and adaptive algorithms to the design of navigation devices. We have developed a software-defined GPS and Galileo receiver with state-of-the-art performance. Our testnet has allowed us to test and improve our implementations in a continuous and systematic fashion. This has resulted in two more than 35 publications and a freely available open source code. On the more theoretical side, as an example, we have applied modern AI techniques such as reinforcement learning to random access problems. And by doing so, we managed to outperform conventional techniques on speed and accuracy by margins of up to 30%. These and other results have in policy and prestigious journals and flagship conferences of the IEEE, with over 15 articles on 38 pages respectively. In addition, we are supervising one PhD thesis on AI and machine learning for multiple antenna systems. We have also collaborated with renowned researchers from the Imperial College London in the UK, EPFL in Switzerland, Texas AM University in the US to name a few. Leveraging on Aristidus work has allowed us to participate in projects funded by the European Commission, mostly within the 5GPP program. The European Space Agency for national programs such as UNICO, INKO, EIMASTE. And finally, we have actively collaborated in technology transfer projects and disseminated our work to companies such as Airbus, Huawei, World Sensing, Nokia or Nvidia.