Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognise AI-generated images through computer vision. Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs. The model is capable of generating complex visual attributes, such as photorealistic reflections in water. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study then proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake. Following hyperparameter tuning and the training of 36 individual network topologies, the optimal approach could correctly classify the images with 92.98% accuracy. Finally, this study implements explainable AI via Gradient Class Activation Mapping to explore which features within the images are useful for classification. Interpretation reveals interesting concepts within the image, in particular, noting that the actual entity itself does not hold useful information for classification; instead, the model focuses on small visual imperfections in the background of the images. The complete dataset engineered for this study, referred to as the CIFAKE dataset, is made publicly available to the research community for future work.

The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for their situation, are more often associated with criminal offenses than the wealthy strata of society and even incur criminal offenses simply as a result of being poor. While no evidence has been found in the literature that correlates poverty and overall criminality rates, this paper offers evidence of a collective belief that associates both concepts. This brief report measures the societal bias that correlates criminality with the poor, as compared to the rich, by using Natural Language Processing (NLP) techniques in Twitter. The paper quantifies the level of crime-poverty bias in a panel of eight different English-speaking countries. The regional differences in the association between crime and poverty cannot be justified based on different levels of inequality or unemployment, which the literature correlates to property crimes. The variation in the observed rates of crime-poverty bias for different geographic locations could be influenced by cultural factors and the tendency to overestimate the equality of opportunities and social mobility in specific countries. These results have consequences for policy-making and open a new path of research for poverty mitigation with the focus not only on the poor but on society as a whole. Acting on the collective bias against the poor would facilitate the approval of poverty reduction policies, as well as the restoration of the dignity of the persons affected.

Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, used extensively in NLP, encode some generic knowledge but rarely enumerate such exceptions and knowing when a generic statement holds or does not hold true is crucial for developing a comprehensive understanding of generics. We present a novel framework informed by linguistic theory to generate exemplars -- specific cases when a generic holds true or false. We generate ~19k exemplars for ~650 generics and show that our framework outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplars, and the challenges exemplars pose for the task of natural language inference.

Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a certain prediction? We seek to answer this question by providing a language for describing how training data influences predictions, through a causal framework. Importantly, our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone. Addressing the problem of extracting factual knowledge from pretrained language models (PLMs), we focus on simple data statistics such as co-occurrence counts and show that these statistics do influence the predictions of PLMs, suggesting that such models rely on shallow heuristics. Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.

The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with high probability when its randomness is fixed and it is run on a new i.i.d. sample drawn from the same distribution. Using replicable algorithms for data analysis can facilitate the verification of published results by ensuring that the results of an analysis will be the same with high probability, even when that analysis is performed on a new data set. In this work, we establish new connections and separations between replicability and standard notions of algorithmic stability. In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems. Conversely, we show any such equivalence must break down computationally: there exist statistical problems that are easy under differential privacy, but that cannot be solved replicably without breaking public-key cryptography. Furthermore, these results are tight: our reductions are statistically optimal, and we show that any computational separation between DP and replicability must imply the existence of one-way functions. Our statistical reductions give a new algorithmic framework for translating between notions of stability, which we instantiate to answer several open questions in replicability and privacy. This includes giving sample-efficient replicable algorithms for various PAC learning, distribution estimation, and distribution testing problems, algorithmic amplification of $\delta$ in approximate DP, conversions from item-level to user-level privacy, and the existence of private agnostic-to-realizable learning reductions under structured distributions.

Several methods for triclustering three-dimensional data require as hyperparameters the cluster size set or the number of clusters in each dimension. These methods raise an issue since, for real datasets, those inputs cannot be known without extreme cost. Recently introduced, the Multi-Slice Clustering (MSC) tackles this issue by using a threshold parameter to perform the data clustering. The MSC finds signal slices that lie in a lower dimensional subspace of 3rd-order rank-1 tensor datasets. The present work addresses an extension of this algorithm, namely the MSC-DBSCAN, that extracts several slice clusters that lie in different subspaces, when the 3rd-order dataset is a sum of r 1 rank-1 tensors. Our algorithm uses the same input as the MSC algorithm and reduces to the same cluster solution for rank-1 tensor dataset.

The deceleration of global poverty reduction in the last decades suggests that traditional redistribution policies are losing their effectiveness. Alternative ways to work towards the #1 United Nations Sustainable Development Goal (poverty eradication) are required. NGOs have insistingly denounced the criminalization of poverty, and the social science literature suggests that discrimination against the poor (a phenomenon known as aporophobia) could constitute a brake to the fight against poverty. This paper describes a proposal for an agent-based model to examine the impact that aporophobia at the institutional level has on poverty levels. This aporophobia agent-based model (AABM) will first be applied to a case study in the city of Barcelona. The regulatory environment is central to the model, since aporophobia has been identified in the legal framework. The AABM presented in this paper constitutes a cornerstone to obtain empirical evidence, in a non-invasive way, on the causal relationship between aporophobia and poverty levels. The simulations that will be generated based on the AABM have the potential to inform a new generation of poverty reduction policies, which act not only on the redistribution of wealth but also on the discrimination of the poor.

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability.

Bill Gates shared his support for the progress of artificial intelligence, proclaiming the ChatGPT-like technology to be'as revolutionary as mobile phones and the internet.' Gates, 67, believes'the rise of AI' is poised to improve humanity, increase productivity and reduce worldwide inequalities, along with accelerating the develop new vaccines. The technology is capable of discovering new pathways to design drugs accordingly and spot errors that are otherwise missed by human eyes. The Microsoft founder has spent billions of dollars to bring treatments to the developing world and believes AI tools is our biggest weapon against deadly diseases and viruses. ''One of the Gates Foundation's priorities in AI is to make sure these tools are used for the health problems that affect the poorest people in the world, including AIDS, TB, and malaria,' Gates shared in a recent blog post.

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] ______________________