Since the advent of Transformers in 2017, Large Language Models (LLMs) have completely changed the process of training ML models for language tasks. Earlier, for a given task and a given dataset, we used to play around with various models like RNNs, LSTMs, Decision Trees, etc by training each of them on a subset of the data and testing on the rest. And whichever model gave the best accuracy was chosen as the winner. Of course, a lot of model hyper-parameters also needed to be tuned and experimented with. And for many problems, feature engineering was also necessary.

The much-awaited comparison is finally here: machine learning vs data science. The terms "data science" and "machine learning" are among the most popular terms in the industry in the twenty-first century. These two methods are being used by everyone, from first-year computer science students to large organizations like Netflix and Amazon. The fields of data science and machine learning are related to the use of data to improve the development of new products, services, infrastructure systems, and other things. Both correspond to highly sought-after and lucrative job options.

Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule is presented that performs online clustering with an upper limit on the number of clusters to be found rather than a fixed cluster count. Instead of using orthogonal weight or output activation constraints, activation sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy the same location in the input domain. An update method is also presented for adjusting the widths of the Gaussian neurons in cases where the data samples can be represented by means and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets to create filters capturing the input patterns of pixel patches of various sizes. The experimental results demonstrate stability in the learned parameters across a large number of training samples.

Online Gaussian processes (GPs), typically used for learning models from time-series data, are more flexible and robust than offline GPs. Both local and sparse approximations of GPs can efficiently learn complex models online. Yet, these approaches assume that all signals are relatively accurate and that all data are available for learning without misleading data. Besides, the online learning capacity of GPs is limited for high-dimension problems and long-term tasks in practice. This paper proposes a sparse online GP (SOGP) with a forgetting mechanism to forget distant model information at a specific rate. The proposed approach combines two general data deletion schemes for the basis vector set of SOGP: The position information-based scheme and the oldest points-based scheme. We apply our approach to learn the inverse dynamics of a collaborative robot with 7 degrees of freedom under a two-segment trajectory tracking problem with task switching. Both simulations and experiments have shown that the proposed approach achieves better tracking accuracy and predictive smoothness compared with the two general data deletion schemes.

Current deep learning solutions are well known for not informing whether they can reliably classify an example during inference. One of the most effective ways to build more reliable deep learning solutions is to improve their performance in the so-called out-of-distribution detection task, which essentially consists of "know that you do not know" or "know the unknown". In other words, out-of-distribution detection capable systems may reject performing a nonsense classification when submitted to instances of classes on which the neural network was not trained. This thesis tackles the defiant out-of-distribution detection task by proposing novel loss functions and detection scores. Uncertainty estimation is also a crucial auxiliary task in building more robust deep learning systems. Therefore, we also deal with this robustness-related task, which evaluates how realistic the probabilities presented by the deep neural network are. To demonstrate the effectiveness of our approach, in addition to a substantial set of experiments, which includes state-of-the-art results, we use arguments based on the principle of maximum entropy to establish the theoretical foundation of the proposed approaches. Unlike most current methods, our losses and scores are seamless and principled solutions that produce accurate predictions in addition to fast and efficient inference. Moreover, our approaches can be incorporated into current and future projects simply by replacing the loss used to train the deep neural network and computing a rapid score for detection.

This work connects models for virus spread on networks with their equivalent neural network representations. Based on this connection, we propose a new neural network architecture, called Transmission Neural Networks (TransNNs) where activation functions are primarily associated with links and are allowed to have different activation levels. Furthermore, this connection leads to the discovery and the derivation of three new activation functions with tunable or trainable parameters. Moreover, we prove that TransNNs with a single hidden layer and a fixed non-zero bias term are universal function approximators. Finally, we present new fundamental derivations of continuous time epidemic network models based on TransNNs.

In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment, and you will build projects in NumPy and PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nanodegree program is an ideal point-of-entry. In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence.

Cloverleaf Analytics, an insurance intelligence solutions company, recently announced partnerships with auto insurance carriers United Automobile Insurance Company and Citizens United Reciprocal Exchange (CURE). The deals aim to leverage Cloverleaf's proprietary policy management capabilities and Guidewire's digital claims experience to "enable advanced insights regarding quote, policy and claims enterprise data in real-time," according to a company press release. The partnerships come off the heels of Clovereaf's investment into its suite of new AI-driven advanced analytics and data security technologies. Robert Clark, president and CEO of Cloverleaf Analytics, told Digital Insurance that the company is consolidating and aggregating data in real-time, into one place. "In our data lake we're pulling all of their premiums, their losses, reinsurance billing, their treaties, cash calls, everything," says Clark. "Once we pull it in, we aggregate that up in our data warehouse where we calculate over 900 different KPIs. On top of that, we've got over 28 different machine learning libraries and AI capabilities, including full integration of Python and R. We have actuaries creating scripts and learning models that will actually learn from their data and make recommendations."

Ali Hirsa, a professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, has always been interested in the possibility of merging art and technology. This curiosity led him to collaborate with New York-based artist Marco Gallotta on projects in which they could apply AI algorithms to works of art created by Gallotta. Hirsa recently discussed this partnership with Columbia News, along with his career path from Wall Street to university professor, and advice for those contemplating a similar journey. I have known Marco for eight years. I became familiar with his art during an auction at the primary school that our kids were attending.

What does it mean when we say that a model "learns from experience"? The system architect doesn't "teach" the model anything formal. Rather, the model learns from -- and subsequently the model's entire world is defined by -- the data used to train it. The model has access to past experience and history to be used as training data, and long established learning techniques are used to understand and discern any underlying patterns. The understanding and recognition of these patterns are codified as intelligence so that the model knows what to make of the data when it is encountered in a "post-training" environment.