This paper presents a novel mathematical framework for bridging the explanatory gap (Levine, 1983) between consciousness and its physical correlates. Specifically, we propose that qualia correspond to singularities in the mathematical representations of neural network topology. Crucially, we do not claim that qualia are singularities or that singularities "explain" why qualia feel as they do. Instead, we propose that singularities serve as principled, coordinate-invariant markers of points where attempts at purely quantitative description of a system's dynamics reach an in-principle limit. By integrating these formal markers of irreducibility into models of the physical correlates of consciousness, we establish a framework that recognizes qualia as phenomena inherently beyond reduction to complexity, computation, or information. This approach draws on insights from philosophy of mind, mathematics, cognitive neuroscience, and artificial intelligence (AI). It does not solve the hard problem of consciousness (Chalmers, 1995), but it advances the discourse by integrating the irreducible nature of qualia into a rigorous, physicalist framework. While primarily theoretical, these insights also open avenues for future AI and artificial consciousness (AC) research, suggesting that recognizing and harnessing irreducible topological features may be an important unlock in moving beyond incremental, scale-based improvements and toward artificial general intelligence (AGI) and AC.

Understanding intelligence is a central pursuit in neuroscience, cognitive science, and artificial intelligence. Intelligence encompasses learning, problem-solving, creativity, and even consciousness. Recent advancements in geometric analysis have revealed new insights into high-dimensional information representation and organisation, exposing intrinsic data structures and dynamic processes within neural and artificial systems. However, a comprehensive framework that unifies the static and dynamic aspects of intelligence is still lacking. This manuscript proposes a mathematical framework based on Riemannian geometry to describe the structure and dynamics of intelligence and consciousness. Intelligence elements are conceptualised as tokens embedded in a high-dimensional space. The learned token embeddings capture the interconnections of tokens across various scenarios and tasks, forming manifolds in the intelligence space. Thought flow is depicted as the sequential activation of tokens along geodesics within these manifolds. During the navigation of geodesics, consciousness, as a self-referential process, perceives the thought flow, evaluates it against predictions, and provides feedback through prediction errors, adjusting the geodesic: non-zero prediction errors, such as learning, lead to the restructuring of the curved manifolds, thus changing the geodesic of thought flow. This dynamic interaction integrates new information, evolves the geometry and facilitates learning. The geometry of intelligence guides consciousness, and consciousness structures the geometry of intelligence. By integrating geometric concepts, this proposed theory offers a unified, mathematically framework for describing the structure and dynamics of intelligence and consciousness. Applicable to biological and artificial intelligence, this framework may pave the way for future research and empirical validation.

Time series forecasting has seen many methods attempted over the past few decades, including traditional technical analysis, algorithmic statistical models, and more recent machine learning and artificial intelligence approaches. Recently, neural networks have been incorporated into the forecasting scenario, such as the LSTM and conventional RNN approaches, which utilize short-term and long-term dependencies. This study evaluates traditional forecasting methods, such as ARIMA, SARIMA, and SARIMAX, and newer neural network approaches, such as DF-RNN, DSSM, and Deep AR, built using RNNs. The standard NIFTY-50 dataset from Kaggle is used to assess these models using metrics such as MSE, RMSE, MAPE, POCID, and Theil's U. Results show that Deep AR outperformed all other conventional deep learning and traditional approaches, with the lowest MAPE of 0.01 and RMSE of 189. Additionally, the performance of Deep AR and GRU did not degrade when the amount of training data was reduced, suggesting that these models may not require a large amount of data to achieve consistent and reliable performance. The study demonstrates that incorporating deep learning approaches in a forecasting scenario significantly outperforms conventional approaches and can handle complex datasets, with potential applications in various domains, such as weather predictions and other time series applications in a real-world scenario.

When we do a search on the Internet, the search engine very often suggests the site of the community encyclopedia Wikipedia. It contains about 6.5 million articles by volunteer contributors, but how can we know if these are reliable, even though the sources of the articles are cited? Meta relied on Meta AI's research and advances to develop SPHERE, an open source model capable of automatically analyzing hundreds of thousands of citations at a time to check whether they actually support the corresponding claims, it recently published it on the Github platform. Meta said it is not partnering with Wikimedia, the foundation that runs Wikipedia, on this project. Its goal is to create a platform to help Wikipedia editors systematically spot citation problems and quickly correct the citation or the corresponding article content.

Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. ML models can be trained to benefit businesses in numerous ways, by quickly processing huge volumes of data, identifying patterns, finding anomalies or testing correlations that would be difficult for a human to do unaided. Model training is at the heart of the data science development lifecycle where the data science team works to fit the best weights and biases to an algorithm to minimize the loss function over prediction range. Loss functions define how to optimize the ML algorithms. A data science team may use different types of loss functions depending on the project objectives, the type of data used and the type of algorithm.

Machine Learning Models play a vital part in Artificial Intelligence. In simple words, they are mathematical representations. In other words, they are the output we receive after training a process. What a machine learning model does is discovers the patterns in a training dataset. In other words, machine learning models map inputs to the outputs of the given dataset.

Expertise in AI requires integrating computational, conceptual, and mathematical knowledge and representations. We propose this trifecta as an "AI triplet," similar in spirit to the "chemistry triplet" that has influenced the past four decades of chemistry education. We describe a rationale for this triplet and how it maps onto topics commonly taught in AI courses, such as tree search and gradient descent. Also, similar to impacts of the chemistry triplet on chemistry education, we suggest an initial example of how considering the AI triplet may help pinpoint obstacles in AI education, i.e., how student learning might be scaffolded to approach expert-level flexibility in moving between the points of the triplet.

Davit Buniatyan is the Founding CEO at Activeloop, the company behind the fastest-growing dataset format specifically designed for AI. The preceding 10 years have been about improving machine learning models and making them accessible to regular data scientists and developers. Today, deep learning frameworks like PyTorch and TensorFlow have made cutting-edge advances in model architecture accessible to the tech community. The marginal returns on model optimization have reduced significantly. Our focus now must shift to the data on which we train our models on, as Andrew Ng pointed out.

When we pass any function to hidden layer, known as activation function.The task of activation function is to filter, normalize and non linearize the dataset and it also fires the input to perceptron of the next layer. In neural networks we update weights and biases in reference with error at output using Back propagation. Back Propagation would be possible only because of activation function. When we will be approaching deep learning problems most of the times we would deal with complex datasets. Non linear function gives clear understanding about complex datasets and can be differentiated multiple times, will give weights and baises for different- different layers and hence, will provide better learning opportunity for neural networks.

Machine learning is a pioneer subset of Artificial Intelligence, where Machines learn by itself using the available dataset. For the optimization of any machine learning model, an acceptable loss function must be selected. A Loss function characterizes how well the model performs over the training dataset. Loss functions express the discrepancy between the predictions of the model being trained and also the actual problem instances. If the deviation between predicted result and actual results is too much, then loss function would have a very high value.