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-                <h1 class="fw-bolder" style="text-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)">Knowledge-Guided Machine learning</h1>
-                <p class="lead" style="text-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.40)">1st European Knowledge-Guided Machine learning workshop - September 22 2023, Turin, Italy</p>
+                <h1 class="fw-bolder" style="text-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)">Tiny Machine Learning</h1>
+                <p class="lead" style="text-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.40)">Special Session on Tiny Machine Learning - June 30 2024, Yokohama, Japan</p>
                 <!-- <a class="btn btn-lg btn-light disabled" href="https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Track/37/Submission/Create">Submit a paper!</a> -->
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@@ -46,34 +46,23 @@ <h1 class="fw-bolder" style="text-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)">Kno
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                         <h2 class="mb-4">About this Workshop</h2>
                         <p class="lead">
-                            We are glad to announce the <strong>1st european Knowledge-Guided Machine learning workshop</strong>, to be held in Turin, Italy in ECML-PKDD 2023. 
+                            We are glad to announce the <strong>Special Session on Tiny Machine Learning</strong>, to be held in Yokohama, Japan in WCCI 2024. 
                             <!-- Both the workshop and the main conference will run in hybrid mode, with the possibility to attend in person or remotely. -->
                             <br>
-                            All the information about the conference can be found at the <a href="https://2023.ecmlpkdd.org/">official website</a>.
+                            All the information about the conference can be found at the <a href="https://2024.ieeewcci.org/">official website</a>.
                         </p>
                         <p>
-                            Even though machine learning (ML) and deep learning (DL) algorithms have achieved amazing results in
-                            many commercial and business applications, data-driven models have so far met with limited success in
-                            many scientific domains. Limitations of data-driven models arise due to their intrinsic black-box nature,
-                            their large data requirements, their inability to produce physically consistent results, and the lack of
-                            generalizability to out-of-sample scenarios. More generally, the popularity and success of ML-based systems
-                            has put the spotlight on issues such as explainability, bias, fairness, and sustainability. There is thus a
-                            growing interest in developing <strong>Knowledge Guided Machine Learning (KGML)</strong> approaches that can leverage
-                            decades (sometimes centuries) of accumulated scientific knowledge and combine them with ML techniques
-                            to reap the benefits of both approaches. Addressing these issues naturally leads to systems in which
-                            representation of prior and domain knowledge in various forms, from physical and simulation models to
-                            symbolic and logical representations, plays a central role and must be integrated into ML models and
-                            pipelines. On the other hand, ML also offers solutions for long-standing challenges in the field of Knowledge
-                            Representation (KR), for instance related to efficient, neurally-guided, noise-tolerant and ampliative
-                            inference, knowledge acquisition, efficient reasoning, and limitations of existing physical and symbolic
-                            models. The synergy between ML and KR has the potential to lead to new advancements in fundamental AI
-                            challenges including, but not limited to, learning symbolic generalizations from raw (multi-modal) data,
-                            using knowledge to facilitate data-efficient learning, speeding up inference, supporting interpretability of
-                            learned outcomes and integration of symbolic planning and reinforcement learning.
+                            The computing everywhere paradigm is paving the way for the pervasive diffusion of tiny devices 
+                            (such as Internet-of-things or edge computing devices) endowed with intelligent abilities. 
+                            Achieving this goal requires machine and deep learning solutions to be completely redesigned to 
+                            fit the severe technological constraints on computation, memory, and power consumption typically 
+                            characterizing these tiny devices. This is exactly where Tiny Machine Learning (TinyML) comes 
+                            into play. TinyML is a new and promising area of Machine Learning aimed at designing and developing
+                            Machine and Deep Learning solutions that can be executed on tiny devices.
+
                             <br><br>
-                            The workshop aims to provide researchers and practitioners with a dedicated forum for discussing new
-                            ideas and research results at the intersection of machine learning and knowledge representation, both
-                            from a theoretical and application standpoint.
+                            This special session aims at exploring the latest advancements, models, algorithms, methodologies, and applications in TinyML. 
+
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                         <!-- <p>
                             Even though machine learning (ML) algorithms are reaching surprising results in various research fields, they still have limitations when facing specific problems, especially when they are black boxes. Notably, data-driven models have met with limited success in many scientific domains. This is due to their large data requirements, inability to produce physically consistent results, and lack of generalizability to out-of-sample scenarios. Instead of a purely data-driven approach that ignores decades (sometimes centuries) of accumulated knowledge in the science domains, there is an increasing interest in Knowledge Guided Machine Learning (KGML) approaches to integrate such knowledge into the ML models. This goal can also be addressed by leveraging insights and methods from the Knowledge Representation and Reasoning (KR) field.
@@ -82,8 +71,8 @@ <h2 class="mb-4">About this Workshop</h2>
                             <br><br>
                             The workshop aims to provide researchers and practitioners with a dedicated forum for discussing new ideas and research results at the intersection of machine learning and knowledge representation.
                         </p> -->
-                        <a href="https://2023.ecmlpkdd.org/">
-                            <img src="https://2023.ecmlpkdd.org/wp-content/uploads/2022/11/cropped-thumbnail_logo-1.png" alt="ECML-PKDD Logo">
+                        <a href="https://2024.ieeewcci.org/">
+                            <img src="https://confcats-siteplex.s3.us-east-1.amazonaws.com/wcci24/large_wcci24_logo_alt_01_5228b6c13f.png" alt="WCCI Logo">
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