PODCAST SATELLITE10th of Elul, 5782 Prince HandleyPresident / RegentUniversity of Excellence NEW AI & THE 4TH INDUSTRIAL REVOLUTION FUTURE OF ARTIFICIAL INTELLIGENCE האינטליגנציה המלאכותית החדשה Listen HERE >>> Prince Handley 24/7 Commentary (FREE) > Email this message to a friend and help them! ______________________ DESCRIPTION WARNING: What you are about to learn will challenge your intellect. It will also enlighten you to “behind the scenes” activity that is happening today … and affecting your FUTURE. We will discuss the 4th Industrial Revolution (IR-4) and WHY―unlike the previous three Industrial Revolutions―it will be dangerous. People can lose their rights, their jobs … their lives as a result of traveling “uncharted” waters. Even more dangerous will be the result of our developing Artificial Intelligence (AI) that lives in Cyber Space that we do NOT really understand. _______________________ NEW AI & THE 4TH INDUSTRIAL REVOLUTIONFUTURE OF ARTIFICIAL INTELLIGENCE The 4th Industrial Revolution will be more of a radical change than the first three … even though they were”shockers” in their inception. Civilization has journeyed the route and use of fire, agriculture, the wheel, electricity, mass production, synthetic chemicals, the internet, block chain, self-driving cars, AI growing people in laboratories, and downloading our brains into computers. Let's examine the first three Industrial Revolutions and see from whence we have journeyed. FIRST INDUSTRIAL REVOLUTION ~ IR-1 The was marked by a transition from hand production methods to machines through the use of steam power and water power. The implementation of new technologies took a long time, so the period which this refers to was between 1760 and 1820, or 1840 in Europe and the United States. SECOND INDUSTRIAL REVOLUTION ~ IR-2 The , also known as the Technological Revolution, is the period between 1871 and 1914 that resulted from installations of extensive railroad and telegraph networks, which allowed for faster transfer of people and ideas, as well as electricity. Increasing electrification allowed for factories to develop the modern production line. THIRD INDUSTRIAL REVOLUTION ~ IR-3 The Third Industrial Revolution, also known as the Digital Revolution, occurred in the late 20th century. The production of the Z1 computer, which used binary and Boolean logic, was the beginning of more advanced digital developments. The next significant development in communication technologies was the supercomputer. FOURTH INDUSTRIAL REVOLUTION ~ IR-4 The Fourth Industrial Revolution is the trend towards automation and data exchange in manufacturing technologies and processes which include cyber-physical systems (CPS), IoT, industrial Internet of Things, cloud computing, cognitive computing, and artificial intelligence. The combination of machine learning and computational power allows machines to carry out highly complicated tasks. Also, in cooperation with Smart Factories. NOTE: Computerization and digitalization were building blocks leading us to IR 4.0 The Smart Factory is no longer a vision. While different model factories represent the feasible, many enterprises already clarify with examples practically, how the Smart Factory functions. The technical foundations on which the Smart Factory―the intelligent factory―is based are cyber-physical systems that communicate with each other using the Internet of Things and Services. An important part of this process is the exchange of data between the product and the production line. This enables a much more efficient connection of the Supply Chain and better organization within any production environment. Within modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. SO WHAT DOES THIS MEAN TO US Artificial Intelligence has brought us a long way. However, AI may take us too far. The “danger zone” is when it will be able to think on the same level as a human. To develop a construct upon which to investigate, let's examine the three different TYPES of AI. AI ~ ARTIFICIAL INTELLIGENCE OR WEAK AI / ANI ~ NARROW INTELLIGENCE Artificial intelligence is a computer system that can perform complex tasks that would otherwise require human minds—such as visual perception, speech recognition, decision-making, and translation between languages. The majority of these machines rely on deep learning and programming, which helps “teach” them to process vast amounts of data to recognize patterns and carry out actions. It is essentially recreating the human mind in machine form, similar to what is being carried out in Smart Factories today (as well as other areas of processing and bio-development). Artificial Intelligence works on a supervised learning system, where various sets of data are provided to the machines, to learn from examples. This helps AI to classify objects or predict the results. AI performs intelligent tasks, but its reach is very narrow and limited as it can only provide an outcome that is already programmed. It cannot make unpredictable decisions on its own, like a human brain can. AI is also referred to as Narrow AI [ANI] or Weak AI. This type of artificial intelligence is one that focuses primarily on one single narrow task, with a limited range of abilities. If you think of an example of AI that exists in our lives right now, it is ANI. AGI - ARTIFICIAL GENERAL INTELLIGENCE OR TRUE (REAL) INTELLIGENCE AGI technology would be on the level of a human mind. Due to this fact, it will probably be some time before we truly grasp AGI, as we still don’t know all there is to know about the human brain itself. However, in concept at least, AGI would be able to think on the same level as a human, much like Sonny the robot in I-Robot featuring Will Smith. Artificial General Intelligence, on the contrary, is the intelligence of a machine that could perform all the intellectual tasks performed by human beings. It possesses the ability to analyze a situation on its own and take a calculative decision, like humans can, without having to be programmed in advance. We are actually nearing that in some of our Smart Factories. As I noted previously, within modular structured Smart Factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. ASI - ARTIFICIAL SUPER INTELLIGENCE This is where it gets a little theoretical and a touch scary. ASI refers to AI technology that will match and then surpass the human mind. To be classed as an ASI, the technology would have to be more capable than a human in every single way possible. Not only could these AI things carry out tasks, but they would even be capable of having emotions and relationships. NOTE: The evolution from AGI to ASI would in theory be much faster than it is taking us to get from ANI to AGI right now, since AGI would allow computers to “think” and exponentially improve themselves once they are able to really learn from experience and by trial and error. If a transition to ASI ever happens, the exponential growth that is in theory expected to occur at this point is often called an Intelligence Explosion … SINGULARITY! NOTE: We should ensure a safe and ethical functioning of AI in all fields and make it a priority in further development. However, once systems start “thinking” on their own―with NO knowledge of God―what are the limits?! WHAT ABOUT NEW GLOBAL GOVERNANCE The future Global Leader [Antimashiach / FALSE messiah] … along with his False Prophet … will demand the populace to take a digital “mark” on their right hands or forehead that will “connect” them with a Smart System: without which they can neither BUY nor SELL. ARE YOU READY FOR THIS ► Brain modification allowing receptors to gain access to—or receive messages from—paranormal and Satanic occult sources. ► Downloading—via the transfer of artificial intelligence (AI) information—through brain-machine interfacing, a desire for the “Mark of the Beast.” ► Corrupted spermatozoa which could fertilize an ovum producing a hybrid being: a non—other than normal—human life form. [Think: Nephilim] ► Receiving fallen—demonically anointed—influence via psycho-neural pathways. SUMMARY I have alerted you to what the New Global Governance Leader―Antimashiach―FALSE messiah will use in the End Times. Teach AND prepare your children and grandchildren about what is and will be happening. Make sure that YOU and your progeny are prepared for Heaven. Here is HOW you can be sure >>> Baruch haba b'Shem ADONAI Your friend, Prince Handley ______________________ [Scroll down past English, Spanish and French] ______________________

Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have been widely explored to tackle label ambiguity issues in PLL, which regard the true label as a latent variable to be identified. However, identifying the true labels accurately and completely remains challenging, causing noise in pseudo labels during model training. In this paper, we propose a new method called CroSel, which leverages historical prediction information from models to identify true labels for most training examples. First, we introduce a cross selection strategy, which enables two deep models to select true labels of partially labeled data for each other. Besides, we propose a novel consistent regularization term called co-mix to avoid sample waste and tiny noise caused by false selection. In this way, CroSel can pick out the true labels of most examples with high precision. Extensive experiments demonstrate the superiority of CroSel, which consistently outperforms previous state-of-the-art methods on benchmark datasets. Additionally, our method achieves over 90\% accuracy and quantity for selecting true labels on CIFAR-type datasets under various settings.

Most approaches for self-supervised learning (SSL) are optimised on curated balanced datasets, e.g. ImageNet, despite the fact that natural data usually exhibits long-tail distributions. In particular, we investigate the role of the temperature parameter τ in the contrastive loss, by analysing the loss through the lens of average distance maximisation, and find that a large τ emphasises group-wise discrimination, whereas a small τ leads to a higher degree of instance discrimination. While τ has thus far been treated exclusively as a constant hyperparameter, in this work, we propose to employ a dynamic τ and show that a simple cosine schedule can yield significant improvements in the learnt representations. Such a schedule results in a constant'task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details. Since frequent classes benefit from the former, while infrequent classes require the latter, we find this method to consistently improve separation between the classes in long-tail data without any additional computational cost. Deep Neural Networks have shown remarkable capabilities at learning representations of their inputs that are useful for a variety of tasks. Especially since the advent of recent self-supervised learning (SSL) techniques, rapid progress towards learning universally useful representations has been made. Currently, however, SSL on images is mainly carried out on benchmark datasets that have been constructed and curated for supervised learning (e.g. Although the labels of curated datasets are not explicitly used in SSL, the structure of the data still follows the predefined set of classes.

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (such as: under, in front of, and facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: the human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs' by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.

Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downstream few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. In addition, conventional data augmentation strategies such as synonym substitution, though widely adopted in low-resource scenarios, only bring marginal improvements for prompt-based few-shot learning. Thus, an important research question arises: how to design effective data augmentation methods for prompt-based few-shot tuning? To this end, considering the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation framework PromptDA, which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks demonstrate the superior performance of the proposed framework by effectively leveraging label semantics and data augmentation for natural language understanding. Our code is available at https://github.com/canyuchen/PromptDA.

Salient Span Masking (SSM) has shown itself to be an effective strategy to improve closed-book question answering performance. SSM extends general masked language model pretraining by creating additional unsupervised training sentences that mask a single entity or date span, thus oversampling factual information. Despite the success of this paradigm, the span types and sampling strategies are relatively arbitrary and not widely studied for other tasks. Thus, we investigate SSM from the perspective of temporal tasks, where learning a good representation of various temporal expressions is important. To that end, we introduce Temporal Span Masking (TSM) intermediate training. First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg. +5.8 points. Further, we are able to achieve additional improvements (avg. +0.29 points) by adding the TSM task. These comprise the new best reported results on the targeted tasks. Our analysis suggests that the effectiveness of SSM stems from the sentences chosen in the training data rather than the mask choice: sentences with entities frequently also contain temporal expressions. Nonetheless, the additional targeted spans of TSM can still improve performance, especially in a zero-shot context.

Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage. Learning an industrial insertion policy in real is challenging as the collision between the parts and the environment can cause slippage or breakage of the part. In this paper, we present a safe self-supervised method to learn a visuo-tactile insertion policy that is robust to grasp pose variations. The method reduces human input and collisions between the part and the receptacle. The method divides the insertion task into two phases. In the first align phase, a tactile-based grasp pose estimation model is learned to align the insertion part with the receptacle. In the second insert phase, a vision-based policy is learned to guide the part into the receptacle. The robot uses force-torque sensing to achieve a safe self-supervised data collection pipeline. Physical experiments on the USB insertion task from the NIST Assembly Taskboard suggest that the resulting policies can achieve 45/45 insertion successes on 45 different initial grasp poses, improving on two baselines: (1) a behavior cloning agent trained on 50 human insertion demonstrations (1/45) and (2) an online RL policy (TD3) trained in real (0/45).

Recent advances in self-supervised learning have integrated Masked Image Modeling (MIM) and Siamese Networks into a unified framework that leverages the benefits of both techniques. However, several issues remain unaddressed when applying conventional erase-based masking with Siamese ConvNets. These include (I) the inability to drop uninformative masked regions in ConvNets as they process data continuously, resulting in low training efficiency compared to ViT models; and (II) the mismatch between erase-based masking and the contrastive-based objective in Siamese ConvNets, which differs from the MIM approach. In this paper, we propose a filling-based masking strategy called MixMask to prevent information incompleteness caused by the randomly erased regions in an image in the vanilla masking method. Furthermore, we introduce a flexible loss function design that considers the semantic distance change between two different mixed views to adapt the integrated architecture and prevent mismatches between the transformed input and objective in Masked Siamese ConvNets (MSCN). We conducted extensive experiments on various datasets, including CIFAR-100, Tiny-ImageNet, and ImageNet-1K. The results demonstrate that our proposed framework achieves superior accuracy on linear probing, semi-supervised, and supervised finetuning, outperforming the state-of-the-art MSCN by a significant margin. Additionally, we demonstrate the superiority of our approach in object detection and segmentation tasks. Our source code is available at https://github.com/LightnessOfBeing/MixMask.

In the domain of question-answering in NLP, the retrieval of Frequently Asked Questions (FAQ) is an important sub-area which is well researched and has been worked upon for many languages. Here, in response to a user query, a retrieval system typically returns the relevant FAQs from a knowledge-base. The efficacy of such a system depends on its ability to establish semantic match between the query and the FAQs in real-time. The task becomes challenging due to the inherent lexical gap between queries and FAQs, lack of sufficient context in FAQ titles, scarcity of labeled data and high retrieval latency. In this work, we propose a bi-encoder-based query-FAQ matching model that leverages multiple combinations of FAQ fields (like, question, answer, and category) both during model training and inference. Our proposed Multi-Field Bi-Encoder (MFBE) model benefits from the additional context resulting from multiple FAQ fields and performs well even with minimal labeled data. We empirically support this claim through experiments on proprietary as well as open-source public datasets in both unsupervised and supervised settings. Our model achieves around 27% and 23% better top-1 accuracy for the FAQ retrieval task on internal and open datasets, respectively over the best performing baseline.

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum SVM. Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This work proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single Quadratic Unconstrained Binary Optimization (QUBO) problem solved with quantum annealing. The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. The results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve accuracy that is comparable to standard SVM methods and, more importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time. This work shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.