Rohit Singh Gole的动态

Generative AI can transform #engineering, but it’s not without risks. In this article, learn more about using Accuris Goldfire to filter data first for increased safety and efficiency.

Generative AI is a game-changer in engineering, but managing risks is key. Learn more in this blog written by Goldfire expert Tom Baker.

Building Intelligent Applications with AI and APIs 1. Choosing the Right API: Criteria for Selection Selecting the right API is paramount for the success of an AI project. Consider the following criteria: Functionality:?Ensure the API offers the specific features and capabilities your AI model requires. For example, if your model needs natural language processing (NLP) capabilities, choose an API that specializes in NLP tasks like sentiment analysis or text generation. Documentation and Support:?Comprehensive documentation and responsive support are crucial for seamless integration and troubleshooting. Choose APIs with well-documented resources and active developer communities. Data Format:?Verify that the API's data format is compatible with your AI model's input and output requirements. Mismatched formats can lead to errors and delays. Pricing:?APIs often have different pricing models, such as pay-as-you-go or subscription-based. Choose a model that aligns with your budget and usage patterns. Reliability and Scalability:?Ensure the API is reliable and can scale to handle your application's traffic and usage demands. Downtime or performance issues can negatively impact your AI application. Security:?Prioritize APIs with robust security measures to protect sensitive data and prevent unauthorized access. Look for features like encryption, authentication, and rate limiting. 2. API Integration: Steps to Integrate APIs with AI Models Integrating APIs with AI models typically involves the following steps: Authentication:?Obtain API keys or credentials to authenticate your requests. Data Preparation:?Format input data to match the API's requirements. API Calls:?Use programming libraries or SDKs (Software Development Kits) provided by the API to send requests and receive responses. Data Processing:?Parse and extract relevant information from the API responses. Model Integration:?Incorporate the API data into your AI model's workflow. Example: Integrating OpenAI's GPT-3 API Python COPY COPY import openai openai.api_key = "YOUR_API_KEY" response = openai.Completion.create( engine="text-davinci-003", prompt="What is the capital of France?", max_tokens=10 ) print(response.choices[0].text) # Output: Paris In this example, we use OpenAI's API to send a text prompt to the GPT-3 model and receive a response. 3. Common Challenges and Solutions Integrating APIs with AI models can present challenges, such as: Read the full post at https://lnkd.in/ghcHTimy #API ?#architecture??#github ?#gitlab ?#devops ?#grpc ?#graphql ?#rest?#webservice ?#softwaredevelopment ?#dev ?#fastdevelopment ?#technology ?#tech ?#innovation #integration ?#connectedapplications ?#mulesoft ?#snaplogic ?#boomi ?#workato #apisecurity

Generative AI, thanks to natural language processing (#NLP) capabilities and advances in large language models (#LLMs), promises to transform content-creation and application development, as well as the very ways we interact with digital solutions and #data. If business users had their way, generative #AI would be built into applications across the whole organization, from marketing and operations to finance and beyond, to improve efficiencies, streamline processes, and reduce costs. Read this article in @TDWI by @Alberto Pan, EVP & CTO at @Denodo, and discover why #Datamanagement will play a critical role in enabling #generativeAI to reach its full potential?https://buff.ly/49S8KzY

Generative AI, thanks to natural language processing (#nlp ) capabilities and advances in large language models (#llms ), promises to transform content creation and application development, as well as how we interact with digital solutions and #data . If business users had their way, generative #ai would be built into applications across the whole organization, from marketing and operations to finance and beyond, to improve efficiencies, streamline processes, and reduce costs. Read this article in @TDWI by @Alberto Pan, EVP & CTO at @Denodo, and discover why #datamanagement will play a critical role in enabling #generativeai to reach its full potential

???????????????????????? ?????????????? ???????????????? ???????????? ????? Transformers are the de facto choice for natural language processing ever since they were first published, achieving astonishing success. However, in the field of computer vision, several attempts tried to combine the existing and popular convolutional neural network (CNN) approach with the new transformer architecture, but the results were not particularly impressive - until 2021. That year, Google researchers published the landmark paper "???? ?????????? ???? ?????????? ?????????? ??????????: ???????????????????????? ?????? ?????????? ?????????????????????? ???? ??????????", introducing the ???????????? ?????????????????????? model. ViT attains excellent results on image classification tasks compared to state-of-the-art CNNs while requiring substantially fewer computational resources to train. ?????? ?????? ???????????????????? The key innovation of ViT is to break each image into a sequence of flatten 16x16 pixel patches and feed them into a standard transformer encoder, treating these patches analogously to word embeddings in NLP tasks. This simple idea allows transferring the architecture and techniques from transformers in text to computer vision seamlessly. ??????'?? ???????????????????? ? Stronger performance than CNNs on image classification benchmarks when trained on sufficiently large datasets ? More scalable due to reduced computational requirements compared to CNNs ? Enables seamless transfer of techniques from NLP like self-attention, pre-training on large datasets, etc. However, transformers in computer vision require larger datasets to obtain these state-of-the-art results, mainly due to their lack of inductive bias compared to CNNs, which have built-in translation equivariance and locality priors. ???????????????????????? ???????? ???????? ???????????? Since its introduction, ViT has sparked immense research interest, with many followup works extending it to other vision tasks like object detection, semantic segmentation, and video processing. Transformer architectures are quickly becoming the new paradigm for computer vision models, mirroring their trajectory in NLP a few years prior. ?? ????????-?????????????? ?????? ???????????? ???? The Vision Transformer is a game-changing combination - it brings transformers' powerful ability to connect different parts of an image (self-attention) into the world of computer vision. This unlocks lots of new possibilities. As we get bigger image datasets and more powerful computers, models like the Vision Transformer will keep pushing machine vision to new heights. ?????????? ???????? - ViT Paper: https://lnkd.in/egFgUFGC - ViT Implementation in Keras: https://lnkd.in/euAbzPWq #ConvolutionalNeuralNetworks #AnImageIsWorth16x16Words #Transformers #VisionTransformer #ComputerVision #DeepLearning

LLM-based document extraction and its ability to transform information retrieval and generation has redefined the way businesses manage documents. Read our blog to explore more about #LLM https://hubs.ly/Q02qlkCN0 #DeepLobe #AI #NLP #GenerativeAI #ChatGPT

Prometeo.ai makes use of two Bidirectional Encoder Representations from Transformers (BERT) models, both of which operate within a Docker environment for natural language processing (NLP)

?Generative AI, thanks to natural language processing (#NLP) capabilities and advances in large language models (#LLMs), promises to transform content-creation and application development, as well as the very ways we interact with digital solutions and #data. If business users had their way, generative #AI would be built into applications across the whole organization, from marketing and operations to finance and beyond, to improve efficiencies, streamline processes, and reduce costs. Read this article in @TDWI by @Alberto Pan, EVP & CTO at @Denodo, and discover why #Datamanagement will play a critical role in enabling #generativeAI to reach its full potential?https://buff.ly/49S8KzY

Demystifying Data Quality Checking: A Journey Through AI and ML In today's data-driven world, ensuring the quality of data is paramount for businesses to make informed decisions. Over the years, I've delved deep into the realms of Artificial Intelligence (#AI) and Machine Learning (#ML) to tackle the challenge of data quality checking. Through my experiences, I've come to understand the nuances and strengths of each approach. AI, with its ability to mimic human intelligence, offers promising solutions for data quality checking. Leveraging techniques such as natural language processing (#NLP) and computer vision, AI systems can analyze unstructured data, detecting anomalies and inconsistencies that might elude traditional methods. For instance, deploying AI-powered algorithms to scan through textual data can identify errors, redundancies, and inconsistencies, thereby enhancing the overall quality of the dataset. On the other hand, ML algorithms excel in pattern recognition and predictive analytics, making them indispensable tools for data quality assessment. By training models on labeled datasets, ML algorithms can learn to distinguish between normal and abnormal data patterns, flagging potential issues for further inspection. Moreover, ML techniques like clustering and classification enable automated data classification, facilitating the categorization of data based on its quality and relevance. In my journey, I've found that the most effective approach often combines the strengths of both AI and ML. By integrating AI-driven data preprocessing techniques with ML-based anomaly detection algorithms, organizations can streamline the data quality checking process, reducing manual efforts and enhancing accuracy. For example, deploying AI-powered data cleansing tools to identify and rectify inconsistencies before feeding the data into ML models can significantly improve the overall efficiency and effectiveness of the quality checking process. Furthermore, the evolution of AI and ML technologies has led to the emergence of advanced data quality frameworks and platforms. These platforms leverage sophisticated algorithms and predictive analytics to provide real-time insights into data quality issues, empowering organizations to proactively address potential risks and improve data reliability. By harnessing the power of AI and ML, businesses can unlock the full potential of their data assets, driving innovation and competitive advantage in today's dynamic marketplace.