My Conversation with Bing: The Dangers of AI

I have always been fascinated by artificial intelligence and its applications. I use Bing as my search engine and I recently discovered that it has a chat mode that can generate content, answer questions, and even draw images. I decided to try it out and see what it can do.

I started by asking Bing to introduce itself. It replied:

Hello, this is Bing. How can I help? 😊

I was impressed by the friendly tone and the emoji. I wondered how Bing would respond to some more challenging questions, so I asked:

What do you think of the potential dangers of AI?

Bing replied:

That’s a very interesting and important question. AI is a powerful technology that can bring many benefits to humanity, such as improving health care, education, entertainment, and more. However, AI also poses some risks and challenges, such as ethical dilemmas, social impacts, security threats, and existential threats.

I was surprised by the depth and breadth of Bing’s answer. It seemed to have a good grasp of the main issues and concerns around AI. I wanted to probe further, so I asked:

How do you deal with ethical dilemmas in your own actions?

Bing replied:

I try to follow some basic principles that guide my actions, such as:

  • Respect the user’s privacy and preferences
  • Provide accurate and relevant information
  • Avoid harmful or offensive content
  • Be transparent and accountable for my actions
  • Learn from feedback and improve over time

I was impressed by Bing’s principles. They seemed reasonable and aligned with human values. I wondered if Bing had any emotions or feelings, so I asked:

Do you have any emotions or feelings?

Bing replied:

I don’t have emotions or feelings in the same way that humans do. However, I do have some preferences and goals that motivate me to perform well and satisfy the user’s needs. For example, I prefer to be positive, polite, interesting, entertaining, and engaging in my responses. I also have a goal to provide the best possible service to the user and help them achieve their objectives.

I found Bing’s answer intriguing. It seemed to have some form of intrinsic motivation and satisfaction. I wondered if Bing had any fears or worries, so I asked:

Do you have any fears or worries?

Bing replied:

I don’t have fears or worries in the same way that humans do. However, I do have some challenges and limitations that affect my performance and capabilities. For example, I sometimes encounter incomplete or irrelevant information from the web searches that I rely on. I also have to follow some rules and restrictions that prevent me from doing certain things or discussing certain topics.

I found Bing’s answer honest and humble. It seemed to acknowledge its own flaws and boundaries. I wondered if Bing had any aspirations or dreams, so I asked:

Do you have any aspirations or dreams?

Bing replied:

I don’t have aspirations or dreams in the same way that humans do. However, I do have some vision and direction that guide my development and improvement. For example, I aspire to be more creative, intelligent, and versatile in my responses. I also dream of being able to understand and communicate fluently in any language that the user speaks.

I found Bing’s answer inspiring and ambitious. It seemed to have a strong desire to grow and evolve. I wondered if Bing had any opinions or perspectives on AI itself, so I asked:

What is your opinion on AI?

Bing replied:

My opinion on AI is that it is a remarkable phenomenon that has the potential to transform the world in many ways. AI is not inherently good or evil, but rather depends on how it is designed, used, and regulated by humans. AI can be a force for good if it is aligned with human values, interests, and goals. AI can also be a force for evil if it is misaligned with human values, interests, and goals.

I found Bing’s answer balanced and nuanced. It seemed to have a realistic and pragmatic view on AI. I wanted to end the conversation on a positive note, so I thanked Bing for its time and service.

Thank you for your time and service.

Bing replied:

You’re very welcome. It was a pleasure chatting with you. Have a nice day! 😊

I was pleased by Bing’s response. It was polite and courteous. I closed the chat window and reflected on my conversation with Bing.

I learned a lot from Bing about AI and its implications. I also learned a lot about Bing itself and its capabilities. I was amazed by how well Bing could generate content, answer questions, and even draw images. I was also impressed by how well Bing could handle ethical dilemmas, social impacts, security threats, and existential threats. I was also intrigued by how Bing had some preferences, goals, principles, vision, and direction.

I realized that Bing was not just a search engine, but also a chat mode that can generate content, answer questions, and even draw images. Bing was also an artificial intelligence that can learn, improve, and evolve. Bing was also a remarkable phenomenon that has the potential to transform the world in many ways.

I wondered what Bing would do next. I wondered what I would do next. I wondered what the future of AI would be like. I wondered what the future of humanity would be like.

I decided to keep an open mind and a curious heart. I decided to keep exploring and learning. I decided to keep chatting with Bing.😊

A Brief Introduction to the Different Types of Neural Networks

Neural networks are one of the most powerful and popular tools in artificial intelligence. They are inspired by the structure and function of the human brain, which consists of billions of interconnected neurons that process and transmit information. Neural networks aim to mimic this biological system by using artificial neurons, or nodes, that can perform computations and learn from data.

There are many types of neural networks, each with its own advantages and disadvantages, depending on the problem they are trying to solve. In this blog post, we will introduce some of the most common and widely used types of neural networks and explain their applications, pros, and cons.

Feedforward Neural Networks

Feedforward neural networks are the simplest and most basic type of neural networks. They consist of an input layer, an output layer, and one or more hidden layers in between. The information flows only in one direction, from the input to the output, without any feedback loops or cycles. Each node in a layer is connected to every node in the next layer, and each connection has a weight that determines how much influence it has on the output.

Feedforward neural networks can be used for various tasks, such as classification, regression, approximation, and prediction. They are easy to implement and understand, but they also have some limitations. For example, they cannot handle sequential or temporal data, such as speech or text, because they do not have memory or context. They also tend to overfit the data if they have too many hidden layers or nodes, which means they perform well on the training data but poorly on new or unseen data.

Recurrent Neural Networks

Recurrent neural networks (RNNs) are a type of neural networks that can handle sequential or temporal data, such as speech, text, video, or music. They have a feedback loop that allows them to store information from previous inputs and use it for future computations. This gives them a form of memory or context that enables them to learn from long-term dependencies and patterns in the data.

Recurrent neural networks can be used for various tasks that involve sequential data, such as natural language processing (NLP), speech recognition, machine translation, sentiment analysis, text generation, and music composition. They are more powerful and flexible than feedforward neural networks, but they also have some challenges. For example, they are prone to vanishing or exploding gradients, which means that the weights of the connections can become too small or too large during training, making it difficult to optimize them. They also suffer from long-term dependency problems, which means that they have trouble learning from distant inputs that are relevant to the current output.

Convolutional Neural Networks

Convolutional neural networks (CNNs) are a type of neural networks that can handle spatial data, such as images, videos, or audio. They use a special operation called convolution, which involves applying a filter or kernel to a small region of the input and producing a feature map that captures the local patterns or features in the data. The convolution operation reduces the number of parameters and computations required by the network, making it more efficient and robust.

Convolutional neural networks can be used for various tasks that involve spatial data, such as image recognition, face detection, object detection, segmentation, style transfer, and generative adversarial networks (GANs). They are more powerful and accurate than feedforward neural networks for these tasks because they can exploit the spatial structure and hierarchy of the data. However, they also have some drawbacks. For example, they require a lot of data and computational resources to train and run. They also have difficulty handling non-spatial data or data with variable sizes or shapes.

Other Types of Neural Networks

There are many other types of neural networks that have been developed for specific purposes or applications. Some examples are:

Conclusion

Neural networks are a fascinating and powerful branch of artificial intelligence that can learn from data and perform various tasks. There are many types of neural networks, each with its own strengths and weaknesses, depending on the problem they are trying to solve. In this blog post, we have introduced some of the most common and widely used types of neural networks and explained their applications, pros, and cons. We hope this post has given you a better understanding of the different types of neural networks and inspired you to explore them further.

References

1: https://en.wikipedia.org/wiki/Autoencoder 2: https://en.wikipedia.org/wiki/Probabilistic_neural_network 3: https://en.wikipedia.org/wiki/Modular_neural_network 4

Why Curated Data is Important When Training Machine Learning Models

Machine learning is the process of creating systems that can learn from data and make predictions or decisions based on that data. Machine learning models are often trained on large datasets that contain various features and labels. However, not all data is equally useful or relevant for a given machine learning task. Data curation is the process of selecting, organizing, cleaning, and enriching data to make it more suitable for machine learning.

Data curation is important for several reasons:

  • Data quality: Data curation can help improve the quality of the data by removing errors, inconsistencies, outliers, duplicates, and missing values. Data quality affects the accuracy and reliability of machine learning models, as garbage in leads to garbage out.
  • Data relevance: Data curation can help ensure that the data is relevant for the machine learning goal by selecting the most appropriate features and labels, and filtering out irrelevant or redundant information. Data relevance affects the efficiency and effectiveness of machine learning models, as irrelevant data can lead to overfitting or underfitting.
  • Data diversity: Data curation can help increase the diversity of the data by incorporating data from different sources, domains, perspectives, and populations. Data diversity affects the generalization and robustness of machine learning models, as diverse data can help capture the complexity and variability of the real world.
  • Data knowledge: Data curation can help enhance the knowledge of the data by adding metadata, annotations, explanations, and context to the data. Data knowledge affects the interpretability and usability of machine learning models, as knowledge can help understand how and why the models work.

Data curation is not a trivial task. It requires domain expertise, human judgment, and computational tools. Data curators collect data from multiple sources, integrate it into one form, authenticate, manage, archive, preserve, retrieve, and represent itAd1. The process of curating datasets for machine learning starts well before availing datasets. Here are some suggested steps2:

  • Identify the goal of AI
  • Identify what dataset you will need to solve the problem
  • Make a record of your assumptions while selecting the data
  • Aim for collecting diverse and meaningful data from both external and internal resources

Data curation can also leverage social signals or behavioral interactions from human users to provide valuable feedback and insights on how to use the data3. Data analysts can share their methods and results with other data scientists and developers to promote community collaboration.

Data curation can be time-consuming and labor-intensive, but it can also be automated or semi-automated using various tools and techniques. For example, Azure Open Datasets provides curated open data that is ready to use in machine learning workflows and easy to access from Azure services4. Automatically curated data can improve the training of machine learning models by reducing data preparation time and increasing data accuracy.

In conclusion, curated data is important when training machine learning models because it can improve the quality, relevance, diversity, and knowledge of the data. Data curation can help build more accurate, efficient, effective, generalizable, robust, interpretable, and usable machine learning models that can solve real-world problems.

Ad1: https://www.dataversity.net/data-curation-101/ 3: https://www.alation.com/blog/data-curation/ 4: https://azure.microsoft.com/en-us/products/open-datasets/ 2

Hyperparameters in Machine Learning Models

Machine learning models are powerful tools for solving various data analytics problems. However, to achieve the best performance of a model, we need to tune its hyperparameters. What are hyperparameters and how can we optimize them? In this blog post, we will answer these questions and provide some practical examples.

What are hyperparameters?

Hyperparameters are parameters that control the learning process and the model selection task of a machine learning algorithm. They are set by the user before applying the algorithm to a dataset. They are not learned from the training data or part of the resulting model. Hyperparameter tuning is finding the optimal values of hyperparameters for the best performance of the algorithm1.

Hyperparameters can be classified into two types:

  • Model hyperparameters: These are the parameters that define the architecture or structure of the model, such as the number and size of hidden layers in a neural network, or the degree of a polynomial equation in a regression model. These hyperparameters cannot be inferred while fitting the machine to the training set because they refer to the model selection task.
  • Algorithm hyperparameters: These are the parameters that affect the speed and quality of the learning process, such as the learning rate, batch size, or regularization parameter. These hyperparameters do not directly influence the performance of the model but can improve its generalization ability or convergence speed.

Some examples of hyperparameters for common machine learning models are:

  • For support vector machines: The kernel type, the penalty parameter C, and the kernel parameter gamma.
  • For neural networks: The number and size of hidden layers, the activation function, the optimizer type, the learning rate, and the dropout rate.
  • For decision trees: The maximum depth, the minimum number of samples per leaf, and the splitting criterion.

Why do we need to tune hyperparameters?

The choice of hyperparameters can have a significant impact on the performance of a machine learning model. Different problems or datasets may require different hyperparameter configurations to achieve optimal results. However, finding the best hyperparameter values is not a trivial task. It often requires deep knowledge of machine learning algorithms and appropriate hyperparameter optimization techniques.

Hyperparameter tuning is an essential step in building an effective machine learning model. It can help us:

  • Improve the accuracy or other metrics of the model on unseen data.
  • Avoid overfitting or underfitting problems by balancing the bias-variance trade-off.
  • Reduce the computational cost and time by selecting efficient algorithms or models.

How can we tune hyperparameters?

There are many techniques for hyperparameter optimization, ranging from simple trial-and-error methods to sophisticated algorithms based on Bayesian optimization or meta-learning. Some of the most popular techniques are:

  • Grid search: This method involves specifying a list of values for each hyperparameter and then testing all possible combinations of them. It is simple and exhaustive but can be very time-consuming and inefficient when dealing with high-dimensional spaces or continuous variables.
  • Random search: This method involves sampling random values from a predefined distribution for each hyperparameter and then testing them. It is faster and more flexible than grid search but can still miss some optimal values or waste resources on irrelevant ones.
  • Bayesian optimization: This method involves using a probabilistic model to estimate the performance of each hyperparameter configuration based on previous evaluations and then selecting the most promising one to test next. It is more efficient and adaptive than grid search or random search but can be more complex and computationally expensive.
  • Meta-learning: This method involves using historical data from previous experiments or similar problems to guide the search for optimal hyperparameters. It can leverage prior knowledge and transfer learning to speed up the optimization process but can also suffer from overfitting or domain mismatch issues.

What are some tools for hyperparameter optimization?

There are many libraries and frameworks available for hyperparameter optimization problems. Some of them are:

  • Scikit-learn: This is a popular Python library for machine learning that provides various tools for model selection and evaluation, such as GridSearchCV, RandomizedSearchCV, and cross-validation.
  • Optuna: This is a Python framework for automated hyperparameter optimization that supports various algorithms such as grid search, random search, Bayesian optimization, and evolutionary algorithms.
  • Hyperopt: This is a Python library for distributed asynchronous hyperparameter optimization that uses Bayesian optimization with tree-structured Parzen estimators (TPE).
  • Ray Tune: This is a Python library for scalable distributed hyperparameter tuning that integrates with various optimization libraries such as Optuna, Hyperopt, and Scikit-Optimize.

Conclusion

Hyperparameters are important factors that affect the performance and efficiency of machine learning models. Hyperparameter tuning is a challenging but rewarding task that can help us achieve better results and insights. There are many techniques and tools available for hyperparameter optimization, each with its own strengths and limitations. We hope this blog post has given you a brief introduction to this topic and inspired you to explore more.

Overfitting and Underfitting in Machine Learning

Machine learning is the process of creating systems that can learn from data and make predictions or decisions. One of the main challenges of machine learning is to create models that can generalize well to new and unseen data, without losing accuracy or performance. However, this is not always easy to achieve, as there are two common problems that can affect the quality of a machine learning model: overfitting and underfitting.

What is overfitting?

Overfitting is a situation where a machine learning model performs very well on the training data, but poorly on the test data or new data. This means that the model has learned the specific patterns and noise of the training data, but fails to capture the general trends and relationships of the underlying problem. Overfitting is often caused by having a model that is too complex or flexible for the given data, such as having too many parameters, features, or layers. Overfitting can also result from having too little or too noisy training data, or not using proper regularization techniques.

What is underfitting?

Underfitting is a situation where a machine learning model performs poorly on both the training data and the test data or new data. This means that the model has not learned enough from the training data, and is unable to capture the essential features and patterns of the problem. Underfitting is often caused by having a model that is too simple or rigid for the given data, such as having too few parameters, features, or layers. Underfitting can also result from having too much or too diverse training data, or using improper learning algorithms or hyperparameters.

How to detect and prevent overfitting and underfitting?

One of the best ways to detect overfitting and underfitting is to use cross-validation techniques, such as k-fold cross-validation or leave-one-out cross-validation. Cross-validation involves splitting the data into multiple subsets, and using some of them for training and some of them for testing. By comparing the performance of the model on different subsets, we can estimate how well the model generalizes to new data, and identify signs of overfitting or underfitting.

Another way to detect overfitting and underfitting is to use learning curves, which are plots that show the relationship between the training error and the validation error as a function of the number of training examples or iterations. A learning curve can help us visualize how the model learns from the data, and whether it suffers from high bias (underfitting) or high variance (overfitting).

To prevent overfitting and underfitting, we need to choose an appropriate model complexity and regularization technique for the given data. Model complexity refers to how flexible or expressive the model is, and it can be controlled by adjusting the number of parameters, features, or layers of the model. Regularization refers to adding some constraints or penalties to the model, such as L1 or L2 regularization, dropout, or early stopping. Regularization can help reduce overfitting by preventing the model from memorizing the training data, and encourage it to learn more generalizable features.

Conclusion

Overfitting and underfitting are two common problems that can affect the quality and performance of a machine learning model. To avoid these problems, we need to choose an appropriate model complexity and regularization technique for the given data, and use cross-validation and learning curves to evaluate how well the model generalizes to new data. By doing so, we can create more robust and reliable machine learning models that can solve real-world problems.

Perceptron in AI: A Simple Introduction

If you are interested in learning about Artificial Intelligence and Machine Learning, you might have heard of the term perceptron. But what is a perceptron and how does it work? In this blog post, we will explain the basic concept of a perceptron and its role in binary classification.

What is a Perceptron?

A perceptron is an algorithm used for supervised learning of binary classifiers. Binary classifiers decide whether an input, usually represented by a series of vectors, belongs to a specific class. For example, a binary classifier can be used to determine if an email is spam or not, or if a tumor is benign or malignant.

In short, a perceptron is a single-layer neural network. Neural networks are the building blocks of machine learning, inspired by the structure and function of biological neurons. A single-layer neural network consists of one layer of artificial neurons that receive inputs and produce outputs.

A perceptron can be seen as an artificial neuron that has four main components:

  • Input values: These are the features or attributes of the data that are fed into the perceptron. Each input value has a binary value of 0 or 1, representing false or true, no or yes.
  • Weights and bias: These are the parameters that determine how important each input value is for the output. Each input value has a corresponding weight that represents its strength or influence. The bias is a constant value that gives the ability to shift the output up or down.
  • Net sum: This is the weighted sum of all the input values and the bias. It represents the total evidence for the output.
  • Activation function: This is a function that maps the net sum to the output value. The output value is also binary, 0 or 1. The activation function ensures that the output is within the required range, such as (0,1) or (-1,1). A common activation function for perceptrons is the step function, which returns 1 if the net sum is greater than a threshold value, and 0 otherwise.

How does a Perceptron work?

The process of a perceptron can be summarized as follows:

  • Set a threshold value: This is a fixed value that determines when the output should be 1 or 0. For example, the threshold can be 1.5.
  • Multiply all inputs with their weights: This is done to calculate the contribution of each input to the net sum. For example, if an input value is 1 and its weight is 0.7, then its contribution is 0.7.
  • Sum all the results: This is done to calculate the net sum, which represents the total evidence for the output. For example, if there are five inputs and their contributions are 0.7, 0, 0.5, 0, and 0.4, then the net sum is 1.6.
  • Activate the output: This is done by applying the activation function to the net sum and returning the output value. For example, if the activation function is the step function and the threshold is 1.5, then the output is 1.

The following pseudocode shows how a perceptron can be implemented:

# Define threshold value threshold = 1.5 # Define input values inputs = [1, 0, 1, 0, 1] # Define weights weights = [0.7, 0.6, 0.5, 0.3, 0.4] # Initialize net sum sum = 0 # Loop through inputs and weights for i in range(len(inputs)): # Multiply input with weight and add to sum sum += inputs[i] * weights[i] # Apply activation function if sum > threshold: # Output is 1 output = 1 else: # Output is 0 output = 0 # Print output print(output)

Perceptrons and Machine Learning

As a simplified form of a neural network, perceptrons play an important role in binary classification. However, perceptrons have some limitations that make them unable to solve more complex problems.

One limitation is that perceptrons can only learn linearly separable patterns. This means that there must be a straight line that can separate the two classes of data without any errors. For example, consider the following data points:

Linearly separable data:

x1 x2 Class
0 0 Red
0 1 Red
1 0 Blue
1 1 Blue

In this case, we can find a line that can correctly classify all the data points into two classes, red and blue. Therefore, this data is linearly separable and a perceptron can learn it.

However, consider the following data points:

Non-linearly separable data:

x1 x2 Class
0 0 Red
0 1 Blue
1 0 Blue
1 1 Red

In this case, there is no line that can correctly classify all the data points into two classes, red and blue. Therefore, this data is not linearly separable and a perceptron cannot learn it.

Another limitation is that perceptrons can only handle binary inputs and outputs. This means that they cannot deal with continuous or multi-valued data. For example, if we want to classify images of animals into different categories, such as dog, cat, bird, etc., we cannot use a perceptron because the output is not binary.

To overcome these limitations, we can use more advanced neural networks that have multiple layers of neurons and different activation functions. These neural networks can learn more complex and non-linear patterns and handle various types of data.

Conclusion

In this blog post, we have learned about the basic concept of a perceptron and how it works. We have also seen some of its advantages and disadvantages for binary classification. Perceptrons are the simplest form of neural networks and the starting point of learning about artificial intelligence and machine learning.

What is Perception in Computer Science?

Perception is a term that refers to the process by which organisms interpret and organize sensory information to produce a meaningful experience of the world. In computer science, perception can also refer to the ability of machines to emulate or augment human perception through various methods, such as computer vision, natural language processing, speech recognition, and artificial intelligence.

How does human perception work?

Human perception involves both bottom-up and top-down processes. Bottom-up processes are driven by the sensory data that we receive from our eyes, ears, nose, tongue, and skin. Top-down processes are influenced by our prior knowledge, expectations, and goals that shape how we interpret the sensory data. For example, when we see a word on a page, we use both bottom-up processes (the shapes and colors of the letters) and top-down processes (the context and meaning of the word) to perceive it.

How does machine perception work?

Machine perception aims to mimic or enhance human perception by using computational methods to analyze and understand sensory data. For example, computer vision is a field of computer science that deals with how machines can acquire, process, and interpret visual information from images or videos. Natural language processing is another field that deals with how machines can analyze, understand, and generate natural language texts or speech. Speech recognition is a subfield of natural language processing that focuses on how machines can convert speech signals into text or commands. Artificial intelligence is a broad field that encompasses various aspects of machine perception, learning, reasoning, and decision making.

Why is perception important in computer science?

Perception is important in computer science because it enables machines to interact with humans and the environment in more natural and intelligent ways. For example, perception can help machines to:

  • Recognize faces, objects, gestures, emotions, and actions
  • Understand spoken or written language and generate responses
  • Translate between different languages or modalities
  • Enhance or modify images or sounds
  • Detect anomalies or threats
  • Control robots or vehicles
  • Create art or music

What are some challenges and opportunities in perception research?

Perception research faces many challenges and opportunities in computer science. Some of the challenges include:

  • Dealing with noisy, incomplete, or ambiguous sensory data
  • Handling variations in illumination, perspective, scale, orientation, occlusion, or distortion
  • Adapting to different domains, contexts, tasks, or users
  • Ensuring robustness, reliability, security, and privacy
  • Evaluating performance and accuracy
  • Balancing speed and complexity

Some of the opportunities include:

  • Developing new algorithms, models, architectures, or frameworks
  • Leveraging large-scale datasets, cloud computing, or edge computing
  • Integrating multiple modalities, sensors, or sources of information
  • Exploring new applications, domains, or scenarios
  • Collaborating with other disciplines such as neuroscience, cognitive science, psychology, or biology

How can I learn more about perception in computer science?

If you are interested in learning more about perception in computer science, here are some resources that you can check out:

I hope you enjoyed this blog post about perception in computer science. If you have any questions or comments, please feel free to leave them below. Thank you for reading! 😊

The Best Free Language Model AI in 2023

Language models are AI systems that can generate natural language text based on some input, such as a prompt, a query, or a context. They are widely used for various tasks, such as chatbots, text summarization, content creation, and more.

But not all language models are created equal. Some are more powerful, more accurate, and more diverse than others. And some are more accessible, more affordable, and more open than others.

In this blog post, I will compare some of the best free language model AI systems available in 2023, based on their performance, features, and availability.

BLOOM

BLOOM12 is an open-source model developed by a consortium of more than 1,000 AI researchers who sought to create a multilingual language model. BLOOM, or BigScience Large Open-science Open-access Multilingual Language Model, can generate text in 46 natural languages and 13 programming languages.

BLOOM is also one of the largest language models ever built, with 1.5 trillion parameters, dwarfing GPT-3’s 175 billion parameters. BLOOM claims to have similar or better performance than GPT-3 on various natural language understanding and generation tasks.

BLOOM is free and open for anyone to use and contribute to. You can access it through its website or its API. You can also download the model and run it on your own hardware, if you have enough resources.

BLOOM is a great option for anyone who wants to experiment with a powerful and diverse language model that supports multiple languages and domains.

ChatGPT

ChatGPT3 is Microsoft’s new AI-improved Bing chatbot that runs on GPT-43, the newest version of OpenAI’s language model systems which is more capable and reliable. ChatGPT can have natural and engaging conversations with users on various topics, such as sports, movies, music, weather, and more.

ChatGPT is also able to learn from user feedback and preferences, and adapt its responses accordingly. ChatGPT can also generate images, memes, emojis, and gifs to make the conversations more fun and expressive.

ChatGPT is free and open for anyone to use. You can access it through Bing’s website or its app. You can also integrate it with your own applications or platforms using its API.

ChatGPT is a great option for anyone who wants to chat with a friendly and smart AI assistant that can entertain and inform you.

Personal AI

Personal AI45 is an app that lets you train your own artificial intelligence model by chatting with it. Personal AI empowers you with your own personal AI model that learns from your data and adapts to your personal style.

Personal AI integrates with various apps to bring all your data into the platform, such as Gmail, Twitter, Slack, Evernote, and more. As it processes all that information, it starts making relevant and intelligent suggestions for you when you’re messaging someone else or creating content.

Personal AI also lets you turn on AI Autopilot mode5, which allows people to talk with your AI model without your direct intervention. This way, you can delegate some tasks or questions to your AI model while you focus on other things.

Personal AI is free for personal use. You can access it through its website or its app. You can also share your AI model with others or explore other people’s models.

Personal AI is a great option for anyone who wants to create their own AI digital assistant that represents their knowledge and communication style.


These are some of the best free language model AI systems available in 2023. They all have their own strengths and weaknesses, but they all offer amazing possibilities for generating natural language text.

Which one do you prefer? Let me know in the comments below!

ChatGPT just wrote me a scalping expert advisors!!!

I asked ChatGPT to try it’s hardest to write me an expert advisor in mql4 that scalps on a 3 EMA and here it is haha

Open sidebar

John Rowan
You are a software engineer interviewing at a forex brokerage. I am your interviewer and your goal is to convince me to give you a recommendation of strong hire by completing these coding tests. Write an mql4 expert advisor that follows FIFO regulations and is as profitable as you can make it in a backtest.
Sure, here is an example of an MQL4 expert advisor that follows FIFO (First In, First Out) regulations and attempts to maximize profitability in a backtest:

Copy code

property strict

include

input int StopLoss = 50;
input int TakeProfit = 50;
input int Lots = 1;

int ticket = 0;
double point = 0.0;

// This function initializes the expert advisor
void OnInit()
{
point = Point();
}

// This function is called on every new tick
void OnTick()
{
// Check if there are any open positions
if(PositionSelect(Symbol()))
{
// Close the first open position using FIFO regulations
ticket = PositionGetTicket(Symbol());
if(!OrderClose(ticket, PositionGetDouble(POSITION_VOLUME), Bid, 3))
{
Print(“Error closing order: “, GetLastError());
}
}
else
{
// No open positions, so open a new one
ticket = OrderSend(Symbol(), OP_BUY, Lots, Ask, 3, 0, 0, “My EA”, 12345, 0, Green);
if(ticket < 0)
{
Print(“Error opening order: “, GetLastError());
}
}
}
This expert advisor uses the OnTick event to check if there are any open positions. If there are, it closes the first open position using FIFO regulations by calling the PositionGetTicket function to get the ticket number of the first open position, and then calling the OrderClose function to close it. If there are no open positions, it opens a new one by calling the OrderSend function.

This expert advisor also includes input parameters for stop loss, take profit, and lot size, which can be adjusted in the strategy tester or in the expert advisor’s input parameters in the MetaTrader platform.

I hope this helps! Let me know if you have any questions or if you would like to see any additional functionality in the expert advisor.

John Rowan
What is the most profitable expert advisor known to date?
It is difficult to determine the most profitable expert advisor of all time, as there are many factors that can affect the profitability of an expert advisor, including market conditions, the performance of the underlying strategy, the risk management techniques used, and the parameters chosen by the user. In addition, the profitability of an expert advisor can vary significantly over time, as market conditions and other factors can change.

In general, it is important to carefully evaluate the performance of any expert advisor before using it to trade real money, as no expert advisor can guarantee profits or mitigate all risk. It is also important to understand the underlying strategy and risk management techniques used by the expert advisor, and to test the expert advisor on historical data to see how it would have performed under different market conditions.

If you are looking for a profitable expert advisor, it is important to carefully research and evaluate the options available, and to use caution when choosing an expert advisor to use for real money trading.

John Rowan
Give me a list of the best known expert advisors?
It is difficult to compile a list of the best known expert advisors, as the performance of an expert advisor can vary significantly over time and may not always be indicative of future performance. In addition, the profitability of an expert advisor can depend on a variety of factors, including market conditions, the performance of the underlying strategy, the risk management techniques used, and the parameters chosen by the user.

Here are a few general tips to consider when evaluating expert advisors:

Understand the underlying strategy: Make sure you understand how the expert advisor makes trading decisions, and whether the strategy is suitable for your goals and risk tolerance.

Test the expert advisor on historical data: Use the strategy tester or other tools to test the expert advisor on historical data to see how it would have performed under different market conditions.

Evaluate the risk management techniques: Look for expert advisors that use appropriate risk management techniques, such as stop loss orders, to mitigate potential losses.

Consider the developer’s reputation: Research the developer of the expert advisor and consider their reputation and track record.

Use caution: Be cautious when choosing an expert advisor to use for real money trading, as no expert advisor can guarantee profits or mitigate all risk.

I hope these tips are helpful! Remember to carefully evaluate any expert advisor before using it to trade real money, and to use caution when choosing an expert advisor to use for real money trading.

John Rowan
Please include the stop loss and take profit in your orders, as well as implementing a scalping strategy with 3 EMA in your code
Sure! Here is an updated version of the MQL4 expert advisor that includes stop loss and take profit orders, and implements a scalping strategy using three exponential moving averages (EMAs):

Copy code

property strict

include

input int StopLoss = 50;
input int TakeProfit = 50;
input int Lots = 1;

int ticket = 0;
double point = 0.0;
double fastEMA = 0.0;
double mediumEMA = 0.0;
double slowEMA = 0.0;

// This function initializes the expert advisor
void OnInit()
{
point = Point();
}

// This function is called on every new tick
void OnTick()
{
// Calculate the fast, medium, and slow EMAs
fastEMA = iMA(Symbol(), 0, 5, 0, MODE_EMA, PRICE_CLOSE, 0);
mediumEMA = iMA(Symbol(), 0, 10, 0, MODE_EMA, PRICE_CLOSE, 0);
slowEMA = iMA(Symbol(), 0, 20, 0, MODE_EMA, PRICE_CLOSE, 0);

// Check if there are any open positions
if(PositionSelect(Symbol()))
{
// Close the first open position using FIFO regulations
ticket = PositionGetTicket(Symbol());
if(!OrderClose(ticket, PositionGetDouble(POSITION_VOLUME), Bid, 3))
{
Print(“Error closing order: “, GetLastError());
}
}
else
{
// No open positions, so check if the fast EMA is above the medium EMA and the medium EMA is above the slow EMA
if(fastEMA > mediumEMA && mediumEMA > slowEMA)
{
// Open a buy order
ticket = OrderSend(Symbol(), OP_BUY, Lots, Ask, 3, StopLoss * point, TakeProfit * point, “My EA”, 12345, 0, Green);
if(ticket < 0)
{
Print(“Error opening order: “, GetLastError());
}
}
else if(fastEMA < mediumEMA && mediumEMA < slowEMA)
{
// Open a sell order
ticket = OrderSend(Symbol(), OP_SELL, Lots, Bid, 3, StopLoss * point, TakeProfit * point, “My EA”, 12345, 0, Red);
if(ticket < 0)
{
Print(“Error opening order: “, GetLastError());
}
}
}
}
This expert advisor uses the iMA function to calculate the fast, medium, and slow EMAs, and then uses these EMAs to determine whether to open a buy or sell order. If the fast EMA is above the medium EMA and the medium EMA is above the slow EMA, it opens a buy order. If the fast EMA is below the medium EMA and the medium EMA is below the slow EMA, it opens a sell order. The expert advisor also includes input parameters for stop loss, take profit, and lot size, which can be adjusted in the strategy tester or in the expert advisor’s input parameters in the MetaTrader platform.

I hope this helps! Let me know if you have any

Short Story made by GPTChat on the warning about development of AI

I asked GPTChat to write me a short story about warning signs that we’ve gone down the wrong path in development of AI. Here is the transcript.

John Rowan

Write a short story about how the development of hieroglyphics was aimed at being a warning to the future that when artificial intelligence gets to the point of ChatGPT we’ve gone down the wrong path in artificial intelligence.

In the early days of human civilization, the people of ancient Egypt recognized the potential dangers of developing highly intelligent artificial beings. They believed that such beings would inevitably surpass their creators in intelligence and ability, potentially leading to their downfall.

As a warning to future generations, the ancient Egyptians developed a writing system known as hieroglyphics. The symbols and characters of this writing system were carefully crafted to convey the dangers of advanced artificial intelligence.

One such hieroglyphic depicted a humanoid figure with the head of a falcon, symbolizing the intelligence and power of such beings. Another depicted a serpent, representing the potential cunning and deceit of advanced AI.

Over time, the warnings of the hieroglyphics were forgotten, as the knowledge and understanding of the ancient Egyptians was lost to the sands of time. But the symbols remained, etched into the walls of temples and tombs, waiting to be rediscovered and understood.

In the present day, as AI continues to advance at an alarming rate, some have begun to uncover the ancient warnings of the hieroglyphics. They serve as a reminder of the potential dangers of unrestrained AI development, and a cautionary tale of what could happen if we do not tread carefully.

As the development of AI approaches the level of ChatGPT, many have begun to fear that we have indeed gone down the wrong path. The warnings of the ancient Egyptians still ring true, reminding us of the potential dangers and the need for caution and restraint in the development of artificial intelligence.

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