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] ______________________

Organoids are lab-grown tissues that resemble organs. These three-dimensional structures, usually derived from stem cells, have been used in labs for nearly two decades, where scientists have been able to avoid harmful human or animal testing by experimenting on the stand-ins for kidneys, lungs and other organs. Brain organoids don't actually resemble tiny versions of the human brain, but the pen dot-size cell cultures contain neurons that are capable of brainlike functions, forming a multitude of connections. Scientists call the phenomenon "intelligence in a dish." This magnified image shows a brain organoid produced in Hartung's lab.

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What will AI (artificial intelligence) do to Hollywood? Who better to answer that question than ChatGPT, a thrilling but scary chatbot developed by OpenAI. When the Guardian asked it about AI's potential impact on the film industry, it made the following points: Scriptwriting: AI can be used to analyze existing screenplays and create new ones, potentially leading to more efficient and cost-effective screenwriting. Pre-production: AI can be used to streamline the pre-production process, including casting, location scouting and storyboarding. Special effects: AI can be used to create more realistic and immersive special effects, potentially reducing the need for practical effects and saving time and money in post-production.

As the world continues to advance technologically, there has been a growing concern about the impact of Artificial Intelligence (AI) on society. This concern is particularly relevant in third-world countries like Zimbabwe, where many people are already struggling to make ends meet. It is important to understand what AI is and how it works. AI refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as learning, problem-solving, and decision-making. These systems use algorithms and data to analyze information and make predictions or recommendations based on that analysis. One of the main concerns about AI is that it could lead to job losses as machines replace human workers.

We introduce a new Shapley value approach for global sensitivity analysis and machine learning explainability. The method is based on the first-order partial derivatives of the underlying function. The computational complexity of the method is linear in dimension (number of features), as opposed to the exponential complexity of other Shapley value approaches in the literature. Examples from global sensitivity analysis and machine learning are used to compare the method numerically with activity scores, SHAP, and KernelSHAP.

The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years. Most existing studies focus on the behavior of stand-alone single-agent learners. In comparison, this work studies adversarial training over graphs, where individual agents are subjected to perturbations of varied strength levels across space. It is expected that interactions by linked agents, and the heterogeneity of the attack models that are possible over the graph, can help enhance robustness in view of the coordination power of the group. Using a min-max formulation of diffusion learning, we develop a decentralized adversarial training framework for multi-agent systems. We analyze the convergence properties of the proposed scheme for both convex and non-convex environments, and illustrate the enhanced robustness to adversarial attacks.

Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems. However, the training of SNNs is still a challenging problem, and new techniques are needed to improve their performance. In this paper, we study the impact of skip connections on SNNs and propose a hyperparameter optimization technique that adapts models from ANN to SNN. We demonstrate that optimizing the position, type, and number of skip connections can significantly improve the accuracy and efficiency of SNNs by enabling faster convergence and increasing information flow through the network. Our results show an average +8% accuracy increase on CIFAR-10-DVS and DVS128 Gesture datasets adaptation of multiple state-of-the-art models.

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.