Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for an interpretation of neural networks as special cases of kernel regression. However, does this theorem of equivalence hold in practice? In this paper, we revisit the derivation of the NTK rigorously and conduct numerical experiments to evaluate this equivalence theorem. We observe that adding a layer to a neural network and the corresponding updated NTK do not yield matching changes in the predictor error. Furthermore, we observe that kernel regression with a Gaussian process kernel in the literature that does not account for neural network training produces prediction errors very close to that of kernel regression with NTKs. These observations suggest the equivalence theorem does not hold well in practice and puts into question whether neural tangent kernels adequately address the training process of neural networks.

This article presents the implementation and evaluation of a behavior cloning approach for route following with autonomous cars. Behavior cloning is a machine-learning technique in which a neural network is trained to mimic the driving behavior of a human operator. Using camera data that captures the environment and the vehicle's movement, the neural network learns to predict the control actions necessary to follow a predetermined route. Mini-autonomous cars, which provide a good benchmark for use, are employed as a testing platform. This approach simplifies the control system by directly mapping the driver's movements to the control outputs, avoiding the need for complex algorithms. We performed an evaluation in a 13-meter sizer route, where our vehicle was evaluated. The results show that behavior cloning allows for a smooth and precise route, allowing it to be a full-sized vehicle and enabling an effective transition from small-scale experiments to real-world implementations.

Machine Learning's Large Language Models (LLMs) -- like ChatGPT, GPT3 and BERT -- have recently captured the attention of the world. Put simply, LLMs are artificial intelligence (AI) tools that read, summarize, translate and generate text. They're able to predict which words would come next in a sentence with high confidence, which allows them to generate language similar to how humans speak and write. These models are so advanced, in fact, that some have even questioned their ability to achieve sentience. But, while it's no secret that LLMs have become an important foundation for conversational AI systems, many people incorrectly assume that LLMs will eventually be the silver bullet that will solve all conversational AI problems -- and that's just not the case.

We propose Few-Example Clustering (FEC), a In this paper, we propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning novel clustering algorithm based on the hypothesis that the to cluster few examples. Our method is composed contrastive learner with the ground-truth cluster assignment of the following three steps: (1) generation of candidate is trained faster than the others. This hypothesis is built on cluster assignments, (2) contrastive learning the phenomenon that deep neural networks initially learn for each cluster assignment, and (3) selection patterns from the training examples. FEC is composed of of the best candidate. Based on the hypothesis the following three steps (see Figure 1): (1) generation of that the contrastive learner with the ground-truth candidate cluster assignments, (2) contrastive learning for cluster assignment is trained faster than the others, each cluster assignment, and (3) selection of the best candidate.

The reason that we must keep the labels consistent is because SAP requires a very specific way of labelling data in order to form an order. However, the labelling from OCR engines usually just find the relationship between the boxes and title as figures below. As you can see, similar data in different forms are labelled in very different ways. However, in order to allow SAP to directly create an order using the OCR data, we will need to make the label consistent. Label "customer_name" will not be "customer name" or "CustomerName"). It is impossible for the OCR engine to achieve this consistency.

Plus, the market size is predicted to reach USD 30.5 billion by 2025. A recent Synack Report claims that combining cybersecurity talent and AI-enabled technology results in 20x more effective attack surface coverage than traditional methods. But it's difficult to truly understand the implication of these numbers. Most content on the topic leaves the reader to do all the math, connect the dots, and try to understand the real problem behind the numbers, all by themselves – an overwhelming task. Two of the most interesting topics on the internet today are Artificial Intelligence and Cybersecurity.

Recent advancements in Artificial Intelligence namely in Deep Learning has heightened its adoption in many applications. Some are playing important roles to the extent that we are heavily dependent on them for our livelihood. However, as with all technologies, there are vulnerabilities that malicious actors could exploit. A form of exploitation is to turn these technologies, intended for good, to become dual-purposed instruments to support deviant acts like malicious software trojans. As part of proactive defense, researchers are proactively identifying such vulnerabilities so that protective measures could be developed subsequently. This research explores a novel blackbox trojanising approach using a simple network structure modification to any deep learning image classification model that would transform a benign model into a deviant one with a simple manipulation of the weights to induce specific types of errors. Propositions to protect the occurrence of such simple exploits are discussed in this research. This research highlights the importance of providing sufficient safeguards to these models so that the intended good of AI innovation and adoption may be protected.

The code that accompanies this article can be downloaded here. Couple of months back we investigated parts of TensorFlow's ecosystem beyond standard library. To be more precise, we investigated TensorFlow.js and how you can build and train models in the browser and/or in the Node.js. However, we didn't manage one important topic – integration. What we want to cover in this article is what happens when you have a neural network built and trained using TensorFlow and Python, and you have to integrate it in one Angular application.

Last week we launched our'Layered' report in partnership with Zappar - a groundbreaking study of the consumer, neurological and brand impact of augmented reality. For the first time ever, augmented reality has been put under the spotlight and dissected to gain valuable insights into AR's core benefits and use cases. In this post, I wanted to share some key findings and emerging trends - including AR's applications and impact from a marketing, brand and consumer perspective now, as well as its implications for the future. While both AR and VR can be considered part of the fourth wave of immersive computing (after The PC, Internet & Mobile waves), AR holds more sway right now because of its immediate impact on brands. Infrastructure development - the growth of a developer ecosystem that includes Apple's ARKit and Google's ARCore (and of course ZapWorks) will act as a catalyst for creators and innovators to unlock the accessibility and ease of use to create valuable AR experiences for the masses.

Until now, our evolution was biological. We've developed additional layers of the brain, upright posture, and other physical attributes to adapt to our ever-changing lifestyle. However, we've reached a point in time where our biological evolution can't keep up with the pace of change. Look how far humanity has come. What an elaborate and complex world we have built.