by Nexlogica Team | Jan 25, 2023 | Artificial Intelligence - Machine Learning
DALL-E (stylized as DALL·E) and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions.
Like GPT-3, DALL·E is a transformer language model. It receives both the text and the image as a single stream of data containing up to 1280 tokens, and is trained using maximum likelihood to generate all of the tokens, one after another.This training procedure allows DALL·E to not only generate an image from scratch, but also to regenerate any rectangular region of an existing image that extends to the bottom-right corner, in a way that is consistent with the text prompt.
DALL-E can generate imagery in multiple styles, including photorealistic imagery, paintings, and emoji. It can “manipulate and rearrange” objects in its images, and can correctly place design elements in novel compositions without explicit instruction. DALL·E excels at following natural language descriptions so users can plainly describe what they want to see.
Developers can now integrate DALL·E directly into their apps and products through Open AI API. More than 3 million people are already using DALL·E to extend their creativity and speed up their workflows, generating over 4 million images a day. Developers can start building with this same technology in a matter of minutes.
Microsoft is bringing DALL·E to a new graphic design app called Designer, which helps users create professional quality social media posts, invitations, digital postcards, graphics, and more.
Company is also integrating DALL·E in Bing and Microsoft Edge with Image Creator, allowing users to create images if web results don’t return what they’re looking for.
You can read more about DALL-E here.
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by Nexlogica Team | Jan 17, 2023 | Artificial Intelligence - Machine Learning
The Turing Test is a test of a machine’s ability to exhibit intelligent behavior that is indistinguishable from that of a human. It was proposed by Alan Turing in 1950 as a way to determine whether a machine can “think.” The test involves a human evaluator who engages in natural language conversations with both a human and a machine, and must determine which is which based solely on their responses. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the Turing Test.
The Turing Test has several potential benefits as a measure of machine intelligence. Some of the main advantages include:
- The test is relatively simple and easy to understand, making it accessible to a wide range of people.
- The test is based on the idea of human-like intelligence, which is a widely accepted and familiar concept.
- The test provides a clear and objective criteria for determining whether a machine is “intelligent” or not, which can help guide research and development in the field of artificial intelligence.
- The test can be used to compare the performance of different AI systems and track their progress over time.
- The test is a good way to evaluate the performance of AI in a way that is easy to replicate, compare and evaluate over time.
Additionally, the test provides a clear and objective criteria for determining whether a machine is “intelligent” or not which can help guide research and development in the field of artificial intelligence. It can be also a way to evaluate AI performance in an easy way to replicate, compare and evaluate over time.
On the other hand, there are several criticisms and limitations of the Turing Test as a measure of machine intelligence. Some of the main issues include:
- The test focuses solely on the ability to mimic human-like behavior and communication, rather than on the underlying intelligence or consciousness of the machine.
- The test is heavily dependent on the human evaluator’s subjective judgment, and may be influenced by factors such as the machine’s appearance or the human’s own biases.
- The test does not take into account the possibility that a machine could be intelligent in ways that are fundamentally different from human intelligence.
- The test does not consider the possibility of a machine deceiving the human evaluator, by providing pre-programmed or rehearsed responses rather than truly understanding the meaning of the questions.
Additionally, some argue that the test does not necessarily demonstrate true intelligence but rather the ability to simulate human-like intelligence and that it’s not a true measure of machine’s ability to think or being conscious.
The test is still widely discussed in the field of artificial intelligence. Overall, it can be said that the Turing test is a useful tool to measure certain aspects of AI but it should not be considered as the only tool or the ultimate measure of machine intelligence.
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by Nexlogica Team | Jan 16, 2023 | Artificial Intelligence - Machine Learning
OpenAI is a research company that aims to promote and develop friendly Artificial Intelligence (AI) in a way that benefits humanity as a whole. It was founded in December 2015 by Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, Wojciech Zaremba, and John Schulman. The company conducts research in various areas of AI, including deep learning, computer vision, and natural language processing (NLP). Lately OpenAI introduced Revolutionary Language Model – ChatGPT.
ChatGPT (also known as the “Generative Pre-trained Transformer”) is a language model developed by OpenAI. It is based on the transformer architecture, which was introduced in a 2017 paper by Google. The model is pre-trained on a large dataset of internet text, and can be fine-tuned for specific tasks using a smaller dataset. Because of its large size and high quality pre-training, ChatGPT can generate human-like text and perform a wide range of natural language processing tasks with high accuracy.
ChatGPT can perform a variety of natural language processing tasks such as:
- Text generation, where it can generate new text based on a given prompt or seed text.
- Text completion, where it can complete a partially written sentence or paragraph.
- Text summarization, where it can generate a summary of a given text.
- Text classification, where it can classify text into different categories based on its content.
- Text generation, where it can generate new text based on a given prompt or seed text.
- Language translation, where it can translate text from one language to another.
- Text answering, where it can answer questions by providing relevant information from a given text.
- Dialogue generation, where it can generate responses in a conversation.
- Sentiment analysis, where it can determine the sentiment (positive, negative, neutral) of a given text. and many more However, it’s important to note that the quality of the results may vary depending on the task and the quality of the input provided to the model.
Why ChatGPT is worth to use:
- Generate more human-like text and understand natural language input better than models that have not been pre-trained.
- Flexibility: ChatGPT can be fine-tuned for a variety of specific tasks, such as text generation, text completion, text summarization, and language translation, which makes it a versatile model that can be used for different applications.
- High Quality: The pre-training of the model is done on a massive dataset and fine-tuning it with a smaller dataset results in high quality and accurate results.
- Advance features: ChatGPT has the ability to perform tasks such as text answering, dialogue generation, and sentiment analysis, which makes it a powerful tool for various NLP use cases.
- Accessibility: The pre-trained weights and the fine-tuning code is available in open-source libraries such as Hugging Face and OpenAI API, which makes it accessible to researchers and developers around the world.
Overall, ChatGPT’s pre-training, flexibility, high quality, advanced features, and accessibility make it a valuable tool for natural language processing tasks.
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by Nexlogica Team | Dec 23, 2022 | Artificial Intelligence - Machine Learning, Uncategorized
Artificial neural networks and related deep learning are conquering other areas of the industry.
It underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. The use of networks built of artificial neurons allows to create software that imitates the work of the human brain, which translates into an increase in the efficiency of business processes and companies.
The Neural Network is constructed from 3 type of layers:
- Input layer — initial data for the neural network.
- Hidden layers — intermediate layer between input and output layer and place where all the computation is done.
- Output layer — produce the result for given inputs.
The input layer is used to retrieve data and pass it on to the first hidden layer.
In hidden layers, calculations are performed, as well as the learning process itself.
The output layer calculates the output values obtained from the entire network, and then sends the obtained results to the outside.
Each node has a weight and a threshold – when the threshold value exceeds the allowable value, it activates and sends data to the next layer. Neural networks need training data from which they learn to function properly. As they receive more data, they can improve their performance.
Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks, and each has benefits for specific use cases. However, they all function in somewhat similar ways — by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.
Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It’s no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model’s first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.
Deep learning will be developed, and deep neural networks will find application in completely new areas. It is already predicted that they can be used in driving autonomous cars or in the entertainment sector to analyze the behavior of users of a streaming service, or add sound to silent movies.
You can read more about Artificial Neural Network here.
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by Nexlogica Team | Dec 22, 2022 | Artificial Intelligence - Machine Learning
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
Computer programs that use deep learning go through much the same process as the toddler learning to identify things around him. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
Unlike the toddler, who will take weeks or even months to understand the concept of eg. bed, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have beds in them within a few minutes.
To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive because most of the data humans and machines create is unstructured and is not labeled.
Deep learning examples
Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.
Use cases today for deep learning include all types of big data analytics applications, especially those focused on NLP, language translation, medical diagnosis, stock market trading signals, network security and image recognition.
Specific fields in which deep learning is currently being used include the following:
- Customer experience (CX). Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
- Text generation. Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.
- Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
Industrial automation. Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.
- Adding color. Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
- Medical research. Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.
- Computer vision. Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.
You can read more about Deep Learning here.
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by Nexlogica Team | Dec 16, 2022 | Artificial Intelligence - Machine Learning, Uncategorized
DoNotPay, the company that offers the “world’s first robot lawyer,” has announced a new AI-powered chatbot that will help you negotiate bills, unsubscribe, and more.
The latest tool from DoNotPay can have a back-and-forth conversation with a company’s customer service representative through live chat or email.
In a demo of the tool posted by DoNotPay CEO Joshua Browder, the chatbot manages to get a discount on a Comcast internet bill through Xfinity’s live chat. Once it connects with a customer service representative, the bot asks for a better rate using account details provided by the customer. The chatbot cites problems with Xfinity’s services and threatens to take legal action, to which the representative responds by offering to take $10 off the customer’s monthly internet bill.
This tool builds upon the many neat services DoNotPay already offers, which mainly allows customers can generate and submit templates to various entities, helping them to file complaints, cancel subscriptions, fight parking tickets, and much more. It even uses machine learning to highlight the most important parts of a terms of service agreement and helps customers shield their photos from facial recognition searches. But this is the first time DoNotPay’s using an AI chatbot to interact with a representative in real time.
DoNotPay’s bot issues convincingly human-like answers throughout the entire interaction with Xfinity, save for a hiccup where the tool says “[insert email address]” instead of providing the customer’s actual email. Browder tells The Verge that DoNotPay will clean up some of its responses before it goes live — and make the bot sound less polite, as it’s pretty heavy on the “thank-yous.”
DoNotPay’s bot is built on top of OpenAI’s GPT-3 API, the underlying toolset used by OpenAI’s ChatGPT chatbot that tons of people have been playing around with to generate detailed (and sometimes nonsensical) responses. DoNotPay’s tool is made for a specific purpose, though, and Browder seems to view it as an opportunity to expand the number of tasks it can tackle, like chatting with a representative to cancel a customer’s subscription or negotiating a credit report.
If the chatbot doesn’t know an answer to a particular question, Browder says it won’t start making things up. “It will just stop in its tracks and ask the user for help” when it’s unsure, Browder explains. The company’s working on ways to alert users whenever this happens so that they don’t have to sit in front of their computer and monitor the tool. Browder tells The Verge that users could eventually respond to the AI’s questions over text message so that it can continue its “conversation.”
You can read more about DoNotPay chatbox here.
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