Introduction

It has been almost five years since Avant AI was introduced by GBT Tokenize. Since then, AI technologies have gained vast interest in wide variety of topics. Artificial Intelligence (AI) is a rapidly growing field that has the potential to transform the world as we know it. AI is about creating intelligent machines that can perform tasks that would normally require human intelligence, such as recognizing patterns, making predictions, and solving problems. The goal of AI is to create systems that can learn from experience and improve their performance over time, just like humans do.

The field of Artificial Intelligence (AI) is growing aggressively. It’s influencing our lives and societies more than ever before and will continue to do so at a breathtaking pace. Areas of application are diverse, the possibilities far-reaching if not limitless. Thanks to recent improvements in hardware and software, many AI algorithms surpass human experts' capacities. Algorithms will soon start optimizing themselves to an ever greater degree and may one day attain superhuman levels of intelligence.

Our species dominate the planet and all other species on it because we have the highest intelligence. Scientists believe that by the end of our century, AI will be to humans what we now are to chimpanzees. Moreover, AI may one day develop phenomenal states such as self-consciousness, subjective preferences, and the capacity for suffering. This will confront us with new challenges both in and beyond the realm of science.

AI ranges from simple search algorithms to machines capable of true thinking. In certain domain-specific areas, AI has reached and even overtaken human abilities. Machines are beating chess grandmasters, quiz show champs, and poker greats. The history of AI dates back to the 1950s, when researchers first began exploring the idea of creating machines that could think like humans. Over the years, AI has gone through several phases of development, from early successes in narrow domains like playing chess, to today's systems that can perform a wide range of tasks, such as speech recognition, natural language processing, and computer vision.

It’s not just fun and games. Artificial neural networks approach human levels in recognizing handwritten Chinese characters. They vie with human experts in diagnosing cancer and other illnesses. We are getting closer to creating a general intelligence which at least in principle can solve problems of all sorts, and do so independently.

Today’s AI is more a form of “cognitive computing,” which is true machine learning. Cognitive computing was born from the fusion of cognitive science (the study of the human brain) and computer science. It’s based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks in an intelligent way. IBM describes it as “Systems that learn at scale, reason with purpose and interact with humans naturally.” According to Big Blue, while cognitive computing shares many attributes with AI, it differs by the complex interplay of disparate components, each of which comprises its own mature disciplines. The sheer volume of data being generated in the world is creating cognitive overload for us. These systems excel at gathering data and making it useful.

One of the key factors driving the growth of AI is the availability of large amounts of data and computing power. AI algorithms rely on data to learn and improve, and the explosion of data generated by the Internet and other sources has provided AI researchers with the resources they need to create more powerful and sophisticated systems. The advancements in computing power, particularly in the area of parallel processing, have also made it possible to train and run large AI models that can perform complex tasks.

There are several different types of AI, including narrow or weak AI, which is designed for a specific task, and general or strong AI, which is capable of performing a wide range of tasks like a human. The most common form of AI today is narrow AI, which is used in applications like image and speech recognition, recommendation systems, and self-driving cars. It is an application specific technology that aims to provide an intelligent solution in a particular domain.

Despite its many benefits, AI also raises important ethical and societal questions. For example, as AI systems become more capable, there are concerns about the potential for job displacement, as well as the need for new regulations and policies to ensure that AI is used in responsible and ethical ways. Additionally, as AI systems become more advanced, there are concerns about the possibility of creating systems that are beyond human control, and the potential for AI to be used for malicious purposes.

So here we are at a new age. But a word of caution is in order. Almost all progress poses risks and that’s certainly the case with the bold new age of AI. Some of the problems will be vexing ethical ones. How will AI affect individual lives and whole civilizations around the world? Studies conclude that even though dire scenarios are unlikely, maybe even highly so, the potential for serious damage must be taken seriously. We at Tokenize certainly do.

With that in mind, let’s look at what lies ahead and what role Tokenize will play in the AI era.

What is Avant!-AI?

Avant!-AI is a machine learning platform that includes supervised and unsupervised learning sub-systems. Not like other similar models, it learns on its own and constantly enhancing its information database. Avant! can understand unstructured data, which is how most data exists today. Furthermore, most data comes from a wide range of sources – professional articles, research papers, blogs, and human’s input. Avant! relies on natural language, and obeys the rules of grammar. Avant! breaks down a sentence grammatically and structurally, then extracts its meaning. When Avant! works on a field, it searches for thousands of articles in this domain. Next, Avant! is narrowing down to only few hundreds documents that are topic related. Finally, it will conclude the best answer and deliver it. The process is governed by sets of neural network algorithms that work cognitively to learn the topic and respond accordingly.

Avant! is trained to respond to questions about highly-complex situations and quickly provide a range of responses and recommendations, all backed by evidence. Avant! is using statistical modeling and scores a viable solution then estimates assurance. Avant! improves its expertise by learning from its own experience. Over time, it gains robust knowledge learning from experience and from its own successes and failures, exactly as humans do. Avant! gets wiser and knowledgeable over time.

When a query is executed Avant!, cognitive computing relies on its own vast knowledge, the available human data, and the query conditions. A huge amount of data is searched, both structured and unstructured. Then an analysis is performed. Then elimination process takes place to sort out the best answer, logical validation and finally answer’s delivery.

For the user Avant! provides almost instantaneous response. It rapidly processing large data and by using its cognitive computing capabilities provides efficient answer within seconds. It includes neural networks model that is trained with data that Avant! searches on the internet to create large volume of machine learning text. Avant! contains a BERT (Bidirectional Encoder Representations from Transformers) system which is a machine learning framework for natural language processing.

Once asked for a question, Avant! searches the internet, builds large dataset of information about the topic, and performing cross reference analysis to determine which data source is the most accurate and credible. For example: if asked about what the fastest car in the world is, Avant! will thoroughly search the internet, find all relevant information, cross reference all data sources and reach a conclusion which data is the most accurate, authentic, and credible. Think of Avant! as an AI assistant such as Siri or Alexa, only on a much larger scale. Instead of asking Alexa to play your favorite song or having Siri type out your text, you can ask Avant! to provide information about any topic in less than a minute. All that the user needs to do is provide a prompt, such as, “What is a ship?” Or ”Who was Mahatma Gandhi?” As long as the prompt is clear and specific, Avant! understands its meaning and can answer just about anything you ask it to.

Since its release GBT Tokenize used Avant! within other derivative applications. The most recent one is Hippocrates (www.hmd.care), a first line medical advice and tips. The Hippocrates system is trained with few medical text books and some of the CDC information and is a POC, intended for demonstration purposes only. Through a combination of technology and expertise, Hippocrates provides medicine-leading information, analytics, and AI solutions to assist individuals, and health professionals with medical questions, first line advice and possible treatments. Driven by Avant!-AI machine learning technology, Hippocrates focuses on both preventative and primary care to provide first line of health related advice and recommendations.

Users may provide symptoms, ask health related questions, and describe conditions in order to get a diagnosis advise, including known medication and treatments. Hippocrates is an AI consultant based on personal medical history and common medical knowledge. The system is a health companion that can assess users health based on an indicated symptoms using Avant!-AI Technology. In addition Hippocrates provides and ongoing personal health monitoring with the capability to electronically share the information with physicians, clinics, and hospitals. The system includes built-in telemedicine capabilities to assist users around the world.

This part is done by Avant! supervised learning system. Avant! also includes an unsupervised learning system which works entirely in a unique way. But before getting into the specifics, it’s helpful to first establish a baseline on what are supervised and unsupervised machine learning are and their differences.

Supervises vs. Unsupervised Learning

Machine-learning algorithms are either “supervised” or “unsupervised.” The distinction is drawn from how the learner classifies data.

In supervised algorithms, classes are a finite set, determined by a human. The machine learner’s task is to search for patterns then construct mathematical models. These are then evaluated for predictive capacity in relation to measures of variance in the data. Examples of supervised learning techniques include Naive Bayes and decision-tree induction. Supervised learning algorithms are commonly used for tasks such as regression and classification. In regression, the goal is to predict a continuous output variable given a set of input variables. For example, a supervised learning algorithm could be used to predict the price of a car based on its model, capabilities, and other features. In classification, the goal is to assign a categorical label to a given input. For example, a supervised learning algorithm could be used to classify emails as either spam or not spam.

A good example for a supervised learning is a chatbot that is based on a supervised machine learning model. The model was trained on a massive amount of text data with corresponding labels, such as the type of text (e.g. news article, fiction, poetry), the style of writing (e.g. formal, informal), and the task being performed (e.g. translation, question answering). The algorithm uses this labeled data to learn patterns in the relationship between inputs and outputs, allowing it to generate human-like text for a variety of tasks, such as language translation, text summarization, and dialogue generation.

Unsupervised learners, by contrast, are not provided with classifications. In fact, the task is to develop classification labels. Unsupervised algorithms seek out similarity between pieces of data and determine whether they form a group, or “cluster.” In a way an unsupervised learning is the system’s capability to explore an un-organized data, and finding data patterns and logical connections on its own. An example of unsupervised machine learning would be a case where a medical clinic that wants to learn more about how to provide early detections of possible patient’s illnesses and addressing them promptly. It decides to implement an unsupervised machine learning analytics for its patient’s medical data. It was observed that patients who suffered from acute cough more often, tend to be at risk for pneumonia or those who suffered from specific rashes tend to develop a severe type of skin condition. The unsupervised learning system learned about this patient’s information on its own, found out the logical connections, and recommends running an additional medical tests for patients that are at risk, ensuring early detection and treatment.

Avant! includes Supervised & Unsupervised techniques

Supervised & Unsupervised known algorithms

Image Source: Wikimedia Commons

Avant! Supervised Learning example

Avant! Unsupervised Learning example

Summing up, supervised and unsupervised learning are two categories of machine learning technology that are used for different types of problems. Supervised learning is used for prediction tasks based on finite datasets, while unsupervised learning is used for discovering underlying structure in data and is self-sufficient in learning. Avant! includes both approaches, using their strengths, and choosing the right type of learning algorithm for a given problem according to the nature of the data and the problem at hand.

More Parameters = Better?

One thing to understand about AI models is how they use parameters to make predictions. The parameters of an AI model define the learning process and provide structure for the output. The number of parameters in an AI model has generally been used as a measure of performance. The more parameters, the more powerful, smooth, and predictable the model is.

In AI, The Scaling Hypothesis refers to the observation that the performance of deep neural networks tends to improve as the size of the network and the amount of training data increase. Specifically, the hypothesis suggests that performance scales sub-linearly with model size and linearly with training data size. This phenomenon has been observed in a wide range of deep learning applications, including computer vision, natural language processing, and speech recognition, and has led to the development of ever-larger and more complex neural networks to achieve state-of-the-art performance on various tasks.

Yet, the larger the number of parameters, the more expensive a model becomes to train and fine-tune due to the vast amounts of computational power required.

Plus, there are more factors than just the number of parameters that determine a model’s effectiveness. Despite model’s size, performance depends on many other factors. In short, bigger does not necessarily mean better.

GBT Tokenize is currently pursuing architectural enhancements for Avant!, such as a leaner model that focuses on qualitative improvements in algorithmic design and alignment. GBT Tokenize predicts that a sparse model can reduce computing costs through what's called conditional computation methodologies, i.e., not all parameters in the AI model will be firing all the time, which is similar to how neurons in the human brain operate.

Qualitative improvements in Avant! algorithmic design are expected to provide significant advancements to solve complex problems. Such improvements will result more efficient, accurate, reliable, and scalable algorithms than previous, known, industry standards approaches. GBT Tokenize plans to implement the following qualitative enhancements in Avant! algorithmic design addressing the next topics.

Proprietary architectures: new algorithms architectures, such as private deep neural networks, addressing natural language processing (NLP) and other arenas.

Optimization techniques: Developments in optimization algorithms, such as stochastic gradient descent, are predicted to significantly improved the training of Avant! deep neural networks, leading to state-of-the-art results.

Ensemble methods: Avant! currently combines multiple algorithms to improve performance, accuracy and reliability. GBT Tokenize plans to embed further proprietary techniques to increase its performance, usage model and interface.

Better human interaction (NLP)

Avant! is soon to be equipped with a better human interaction interface and Natural Language Processing (NLP) technology. As it includes pre-trained language models, it could be trained on massive amounts of text data and can then be fine-tuned for specific NLP tasks such as sentiment analysis, named entity recognition, and question answering. This means much easier, user-friendly, and natural human interactions. Users will be able to simply type his/hers questions in simple words, similar to interacting with another human.

Avant! is also equipped with Cross-lingual model that is planned to be pretrained with multiple languages. This will enable the capability of performing NLP tasks across different languages. In addition Avant! can work via transfer learning method, where its pretrained models are used as a starting point and fine-tuned on specific tasks, allowing for faster and more accurate results.

One of Avant! key capabilities is its multimodal NLP which combines text, image, and audio information to perform NLP tasks. These methodologies enable Avant! to process information from multiple sources, enhancing its multimedia and visual communication capabilities.

One of Avant! key strengths is its sentiment analysis. This feature enables Avant! to analyze and understand the sentiment of large amounts of text data, such as articles, reviews, social media posts, and similar. This can be useful for businesses to gauge customer satisfaction and identify areas for improvement. Another important feature is its content creation capability. Avant! NLP system can be used to automatically generate content, such as articles or product descriptions, which can be useful for businesses and content creators looking to produce large amounts of text quickly. In Avant! derivative system, Hippocrates, its NLP can be used to analyze patients' medical records and identify patterns. This information is used to identify onset symptoms, alerting users, ultimately helping improve patient outcomes and streamline healthcare.

Avant! NLP system efficiently addresses the challenges and limitations associated with processing and analyzing large amounts of text data, which can be time-consuming and resource-intensive. Another important aspect of Avant! NLP is its capability to handle complex and nuanced nature of language. Avant! NLP module can perform text classification, named entity recognition, question answering, and sentiment analysis, using deep neural network that uses the Transformer architecture for language modeling. It works in two training stages, pre-training, and fine-tuning. During the pre-training stage, it is trained on large amounts of unlabeled data by predicting masked words in a sentence and predicting the next sentence. This process is known as Masked Language Model (MLM) and Next Sentence Prediction (NSP). Avant! uses a multi-layer transformer encoder to learn contextualized representations of words. The fine-tuning stage involves training the pre-trained model on a smaller labeled dataset for specific NLP tasks. During fine-tuning, the last layer of the pre-trained model is replaced with a task-specific layer, and the whole model is fine-tuned on the labeled dataset.

Avant! can be trained with massive amount of text data. It can be provided using textual database or internet based extraction. It can analyze the sentiment of text with high accuracy. It can classify the polarity of text as positive, negative, or neutral, and identify entities in text such as person, organization, and location. In addition Avant! text classification feature can classify text into different categories such as spam or not spam, toxic or non-toxic, and news category.

Summing up, Avant! offers powerful technology in the NLP domain, which opens a whole world of possibilities to create exciting, intelligent applications in a variety of different industries and fields. GBT Tokenize plans to further enhance its NLP technology, making it user friendly, easy for use, mainly intuitive.

Highly secured

AI has become an essential technology in our daily lives and is susceptible to cyber risks. Avant! is a highly secured system protected in a few areas.

Data protection: Avant! Data is protected via AES standards to prevent unauthorized access, modification, or theft. In addition, GBT Tokenize implemented Honey encryption technology for brute force access attacks as another security layer to safeguard Avant! Data.

Multi-factor authentication: The system includes multi-factor authentication for access and usage.

Regular cyber updates: GBT Tokenize keeps its Avant! system up-to-date with the latest security patches, software updates, and security protocols.

Risk assessment module: Avant! includes a specific module for regularly conducting internal regular risk assessments to identify potential security threats, including used third-party modules, weaknesses, and vulnerabilities. Based on the results, it implements appropriate security measures to mitigate potential risks.

Avant! includes its own AI-based cybersecurity measures to safeguard its digital assets and operation from cyber threats. GBT Tokenize is constantly enhancing Avant! Cyber technology addresses the ever-growing challenges that require careful consideration.

Avant! AI proprietary Random Forest oriented algorithm

Avant! AI includes a proprietary Random Forest machine learning technique that is widely used in both academia and industry to solve complex classification and regression problems. It is an ensemble method that combines multiple decision trees to make accurate predictions. Random Forest Algorithm is known for its ability to handle high-dimensional data with a large number of features and has been successfully applied in various fields such as finance, healthcare, and marketing.

Random Forest Algorithm is a supervised learning technique that belongs to the family of tree-based algorithms. It uses an ensemble of decision trees, each trained on a randomly sampled subset of the training data and a randomly selected subset of features. The algorithm works by constructing a forest of decision trees, where each tree is built using a different subset of features and data points. During the training process, the algorithm selects a random subset of features to split the data at each node, thus reducing the correlation between the trees and improving the accuracy of the model.

The decision trees in the forest are built using a process called recursive partitioning, where the algorithm iteratively splits the data into smaller and smaller subsets based on the values of the input features until a stopping criterion is met. The stopping criterion can be a maximum tree depth, minimum number of samples per leaf node, or a minimum improvement in the splitting criterion.

The Random Forest Algorithm has several advantages over other machine learning techniques. One of the key advantages is its ability to handle high-dimensional data with a large number of features. The algorithm can select the most important features, reducing the computational cost and improving the accuracy of the model. Moreover, Random Forest Algorithm is less prone to overfitting, which is a common problem in other machine learning algorithms.

Another advantage of Random Forest Algorithm is its ability to handle missing data and outliers. The algorithm can make accurate predictions even when some data points are missing or when the data contains outliers. This is because the algorithm uses multiple trees, and the final prediction is based on the majority vote of the trees, making the algorithm more robust to outliers and missing data.

Summing up, the Random Forest Algorithm is a powerful machine learning technique that has been successfully applied in various fields. It is an ensemble method that combines multiple decision trees to make accurate predictions. The algorithm can handle high-dimensional data, missing data, and outliers, making it a robust and reliable machine learning technique. Its ability to improve the model’s accurate, and reducing the computational cost, makes it a popular choice for solving complex classification and regression problems.

Avant! proprietary Random Forest algorithm

Avant! AI proprietary RNN

Avant! includes a series of Recurrent Neural Networks that are used for time-based problems, image captioning, and NLP processing. Recurrent Neural Networks (RNNs) are a type of neural network that is widely used for processing sequential data such as time series, natural language, and speech. Avant! RNNs are designed to process sequential data by using feedback loops to preserve information across time steps. Their architecture consists of a series of cells that are connected to each other through recurrent connections. Each cell takes an input and its previous state as input and produces a new state and output. The output of the current cell is fed as input to the next cell, and the process continues until the end of the sequence. There are different types of RNN cells, such as Simple RNN cells, Gated Recurrent Unit (GRU) cells, and Long Short-Term Memory (LSTM) cells. Avant! includes LSTM type cells as they can store long-term dependencies in the data and prevent the vanishing gradient problem. The RNNs training is done using backpropagation through time (BPTT) algorithm. BPTT is a variant of backpropagation that is used to update the weights of the network based on the error at each time step. The error at each time step is computed by comparing the output of the network with the ground truth output. During training, the weights of the network are updated to minimize the loss function. The loss function measures the difference between the predicted output and the ground truth output. The goal of training is to minimize the loss function and improve the accuracy of the network.

Typical RNN (Recurrent Neural Network)

Image Source: Wikimedia Commons

Avant! RNNs includes a proprietary, self-tuning function (T) for the hidden layers to improve the output of the model. At any given time, the hidden layer input is fine-tuned by the previous one. The output at any given time is fetched back to the network and the self-tuned function dramatically increasing the output’s accuracy. Avant! Recurrent Neural Networks standardizes an additional self-tuning functions during each propagation so that the next hidden layer will produce more accurate outcome.

They RNNs share parameters across each layer of the network including self-tuning. The feedforward networks weights are improved across each node as each hidden layer already includes higher accuracy. The weights are adjusted through the process of backpropagation and gradient descent to facilitate reinforcement learning. Avant! uses RNNs as part of a model to generate descriptions for unlabeled images and graphics.

Avant! RNNs include hidden layers self-tuning function

Avant! CNN

Avant! series of CNNs (Convolutional Neural Networks) are used for image and video processing by using convolutional layers that can detect object’s features such as edges, textures, and shapes. Avant! CNNs architecture consists of three main types of layers: convolutional layers, pooling layers, and fully connected layers.

1. The convolutional layers apply a filter to the input image and produce a feature map that highlights the presence of local features.

2. Pooling layers reduce the size of the feature map by down-sampling it’s using operations such as max pooling or average pooling.

3. Fully connected layers connect all the neurons from the previous layer to the current layer and produce the final output.

Avant! CNNs are trained using a typical backpropagation algorithm, which updates the weights of the network based on the error at each layer. During training, the weights of the network are updated to minimize the loss function. The loss function measures the difference between the predicted output and the ground truth output. The weights of the network are updated using stochastic gradient descent (SGD) or one of its variants. These optimization algorithms use the gradients of the loss function with respect to the weights to update the weights and improve the accuracy of the network.

Avant! CNNs are used for image classification, pattern recognition, object detection, and human face recognition. The technology can be further expand to classify images into different categories for example animals, vehicles, objects of interest, and surroundings. This is useful for applications such as autonomous vehicles, robotics, surveillance, and medical diagnostics. Particularly in the medical imaging analysis field Avant! CNNs can be used to analyze medical images such as MRI and CT scans.

The following is an example of a typical image recognition via CNN analysis. A feature map is produced, presenting the object’s local features. The CNN analyze the image’s input, and distinguishes its objects based on color planes, identifying various colors spaces. It also measures the image dimensions. The colors are identified according to their characteristics, for example RGB, CMYK, Grayscale, and more. The convolution operation obtains all the image’s high-level features like edges, shape and texture. The next layer performs a low-level operations, such as color and gradient orientation. This architecture evolves to a new level that includes more layers to identify the object’s attributes.

A typical CNN image recognition

Image Source: Wikimedia Commons

Avant! AI includes a private derivative set of CNNs which introduce a faster, highly accurate images classifications and analysis. The system consists of additional self-tuning layers that are used for learning of the non-linear combinations of the abstract level structures. The new layer level analyzes the output of the convolutional layers providing a feedback, as a fine tuning for the learning non-linear functions. The tuning function process the image using a private perceptron algorithm, creating a vector table. The new layer level entirely relies on the image complexities, dedicated to the objective of accurately capturing the image’s details, enabling higher computational power. The results are faster processing and higher accuracy for images, videos, and graphics oriented data.

Avant! CNN image recognition

Avant! AI, an intelligent chat companion

Avant! AI is an Artificial Intelligence chat agent that uses natural language processing (NLP) and machine learning algorithms to simulate human-like conversations with users. It uses machine learning algorithms to learn from user interactions and improve its responses over time. Avant! adapts to user behavior and personalizes its responses, creating more appealing user interactions. It includes speech recognition technology to understand and interpret spoken language. This feature allows users to interact with Avant! using their voice, making the experience more natural and intuitive.

Avant! is also equipped with context-understanding algorithms to comprehend conversations. Learning from its experience and analyzing previous interactions can better understand the user's intentions and preferences, providing more relevant responses. It examines the tone and sentiment of user messages to detect when a user is unhappy or frustrated and responds appropriately. Its cognitive capabilities enable Avant! to become, over time, a personal, tailored assistant and companion.

Avant! can design Integrated Circuits (ICs)

The field of microchip design has seen significant advancements in recent years, with artificial intelligence (AI) emerging as a key player in the process. AI is revolutionizing the way microchips are designed, tested, and optimized, leading to faster and more efficient chip designs. Traditionally, microchip design has been a time-consuming and labor-intensive process. Designers would spend hours manually tweaking the design parameters to achieve the desired performance metrics. This process was often error-prone, leading to designs that were suboptimal in terms of power consumption, speed, and other key parameters.

Avant! AI has the capability to automate many aspects of the microchip design process. Its algorithms can analyze vast amounts of electrical and manufacturing-related data, identifying insights that would be impossible for humans to detect. This allows designers to create optimized chip designs in a fraction of the time it would take manually.

Avant! AI is capable of designing ICs through generative design methods using machine learning algorithms to produce a set of electrical and manufacturing-related parameters and metrics. These algorithms then evaluate a design, eliminating weak spots and faulty aspects and creating an efficient and high-performance microchip. The process refines the design parameters, obeying its specifications and architectural constraints.

Avant! can also use to optimize existing IC designs. By analyzing the design’s data, it can identify areas for improvement and suggest modifications to enhance the design parameters. This can lead to significant improvements in performance, lowering power consumption, and reducing cost, without the need for a complete redesign.

Another area where Avant! can make a big impact in the microchip simulation and testing fields. Testing and simulating a chip’s functionality is a critical part of the chip design and manufacturing process, ensuring that the IC meets its functionalities and performance specifications.

Avant! can be a game changer contributing to the way microchips are designed, tested, and optimized. With its ability to analyze vast amounts of data and identify patterns and insights, Avant! is enabling designers to create more accurate and optimized designs in a fraction of the time it would take manually. As the field of semiconductors continues to evolve rapidly, we can expect to see even more advances in microchip design and optimization, leading to faster, more efficient, and more reliable microchips for a wide range of applications.

Avant! can be a technological leader in the next generation of microchips.

Avant! and cybersecurity threat modeling

Cybersecurity threats are becoming more sophisticated and dangerous every day, and organizations must be prepared to defend themselves against these threats. One of the most effective ways to do this is by performing threat modeling, which is the process of identifying potential threats to a system and assessing their likelihood and impact. Avant! can be used as an efficient technology for cybersecurity threat modeling to automate the collection and analysis of data related to potential threats. This data can include information about known vulnerabilities, attack vectors, and other factors that could be used to compromise a system. By analyzing this data using machine learning algorithms, Avant! can identify potential threats and their likelihood of occurring.

Once potential threats have been identified, Avant! can also be used to model the impact of these threats on the system. This can include simulating the effects of a successful attack on the system and identifying the most critical assets that could be impacted. By modeling the impact of potential threats, organizations can prioritize their security efforts and focus on the areas that are most vulnerable.

In addition to identifying potential threats and modeling their impact, Avant! can also be used to develop mitigation strategies. This can include recommending specific security controls or countermeasures that can be implemented to reduce the likelihood of a successful attack. Avant! mitigation strategies can assist organizations in improving their overall security posture and reducing the risk of a successful attack.

One of the key benefits of using Avant! for cybersecurity threat modeling is the speed and accuracy it offers. Traditional manual threat modeling processes can be time-consuming and prone to errors, as humans can miss important data points or fail to identify potential threats. Avant! can automate much of the process and ensures that all relevant data is analyzed and considered.

Another benefit of using Avant! for cybersecurity threat modeling is the scalability it offers. As organizations grow and their systems become more complex, threat modeling can become increasingly difficult to perform manually. Avant! can be used to scale threat modeling efforts and analyze vast amounts of data in a fraction of the time it would take a human to do the same. As cybersecurity threats continue to evolve and become more sophisticated, Avant! can play an increasingly important role in helping organizations perform efficient threat modeling and develop a robust defense strategy.

So, what else will Avant! be able to do?

Since Avant!-AI includes multiple machine learning techniques it has the capabilities to become an autonomous, cognitive system that can be trained in any field. For example, it can become a respiratory diseases expert, a sport analyst, or an expert law advisor. The self-learning capabilities make it one of a kind and a true artificial intelligence entity.

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think and act like humans. These systems use algorithms and statistical models to perform tasks that typically require human-like perception, reasoning, decision making, and learning. The goal of Avant!-AI is to perform tasks that typically require human intelligence, such as perception, analysis, reasoning, and decision-making.

We live in a multisensory world that is filled with different audio, visual, and textual inputs. Avant! is a multimodal model that can incorporate a variety of inputs, learn on its own and reach conclusions. Its AI model fully understands the user’s questions and inputs. In other words, Avant! is aligned with the user’s goals or intentions. Typically, internet-trained AI models are susceptible for human biasing, falsehoods, and prejudices, yet Avant! key strength is its capability to analyze the data, verify its source, credibility, and validate it content to ensure harmless, accurate, safe for use information.

Avant! AI is expected to have a significant impact in the healthcare domain. It can be used to analyze medical data and assist in the diagnosis of diseases, as well as to identify potential treatments and predict patient outcomes. It can also help healthcare providers optimize their workflow and improve patient care by automating routine tasks and identifying high-risk patients. Avant! could even enable personalized medicine, where treatments are tailored to an individual's unique genetic makeup.

Another area where Avant! is expected to make a significant impact is modern transportation. Autonomous cars could reduce accidents and congestion on our roads, making transportation more efficient and environmentally friendly. Avant! could control self-driving cars, analyzing their surroundings information and ensure safety.

Avant! can be an efficient technology within industrial automation. It could automate routine tasks and help businesses make data-driven decisions. This can lead to increased efficiency and productivity, as well as economy improvement. Avant! could develop new products, marketing/sales strategies or to create entirely new business models.

The growth of AI systems has been a major trend in recent years, driven by rapid advances in machine learning algorithms, computing hardware, and data availability. The growth of AI systems is expected to continue in the coming years, as the technology becomes more widespread and new applications are discovered. That said, we believe that breakthroughs system like Avant! will be widely accepted by people due to the fact that they provide a comprehensive, accurate and reliable source of information.

Will Avant! AI replace humans?

It is unlikely that Avant!-AI will completely replace the need for humans. But it can definitely become an efficient human assistant by understanding complexities and nuances of real-life experience, providing accurate and reliable data.

The topic of Artificial Intelligence (AI) replacing humans has been a hotly debated topic for many years. While some believe that AI will eventually surpass human intelligence and lead to widespread unemployment in many sectors, others argue that AI will augment human capabilities and create new jobs.

Advocates of AI argue that it will lead to unprecedented levels of automation, resulting in widespread job losses in many fields. With AI systems capable of performing a wide range of tasks, from data analysis to customer service, many jobs that were once considered safe will become vulnerable. Furthermore, as AI systems continue to improve and become more sophisticated, they will be able to perform tasks that were once considered the exclusive domain of humans, such as writing and creativity. This could lead to a future where a significant portion of the workforce is left without employment, leading to economic and social consequences.

On the other hand, opponents of this view argue that AI will create new jobs and augment human capabilities, rather than replace them. They argue that AI systems will automate mundane and repetitive tasks, freeing up humans to focus on more creative and fulfilling work. Additionally, the development of AI will create new industries and job opportunities in areas such as AI research, development, and deployment. Furthermore, as AI systems become more widespread, they will create new markets for goods and services, which will drive economic growth and create new job opportunities.

Overall, the question of whether AI will replace humans is a complex one that depends on many factors. While it is true that AI will automate many tasks, it is also likely that it will create new opportunities and augment human capabilities. Avant!-AI does not aim to replace humans but to become an efficient assistant and advisor. Its cognitive and reasoning capabilities could make it a welcomed additional member to a team workforce. Tokenize’s will continue its research and development in this field to ensure that the benefits of AI technology are widely shared and that its negative consequences are mitigated through appropriate policies and regulations.

Conclusion

Tokenize’s Avant! AI is a remarkable system developed especially for, but not limited to, managing and controlling today’s technology. Avant! is a new generation of AI, a highly sophisticated one. It can detect, analyze, and learn from experience using ensemble methods. The system efficiently handles huge amounts of data in real time and is ideally suited for Artificial General Intelligence (AGI) applications, autonomous machines, medical agents, cybersecurity, and others. 

Avant! includes web/mobile interfaces and is an ongoing project. We plan to constantly expand its features and capabilities over time with the ultimate goal of AI’s highest aspiration – machine consciousness. 

Avant! is planned to manage smartphone applications, operate computer vision systems, and analyze medical data. It will revolutionize engineering by designing new circuits, enhancing microchip design/manufacturing, and bringing intelligence to our daily technologies. Firms will be able to define a product’s features and characteristics, and Avant! will provide architecture and low-level designs, including simulations. 

Avant! is particularly aimed at bringing important changes to the medical field. It will diagnose and recommend treatments. Based on accumulated experience, Avant! is predicted to become a valuable assistant to nurses and doctors – probably even an indispensable one. We have already implemented Avant! core technology in a POC system, Hippocrates (www.hmd.care), that is successfully able to provide health-related advice. The Hippocrates system currently includes Proof-Of-Concept, a limited dataset to provide a glimpse of its abilities.

Avant! offers capabilities that are not off on a distant horizon. They’re far more than the fanciful dreams of sci-fi enthusiasts. The world will undoubtedly adopt more and more AI in the next decade – with greater expansion in the ensuing years. Avant! Presents a new era of Artificial Intelligence technology that will reshape our world, enabling smart entities to work hand by hand with humans. 

“Computers are not intelligent, they just think they are.”

– Computer Symposium, 1979

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