Software Engineering Insights

Generative Artificial Intelligence (AI) has revolutionized the way businesses interact with their customers, and Salesforce is no exception. As one of the world's leading CRM platforms, Salesforce has been leveraging generative AI technology to enhance its features and capabilities.

ChatGPT, the conversational Artificial Intelligence (AI) developed by OpenAI and backed by Microsoft, has taken several industries by storm. A number of AI solutions are set to change how businesses function and ChatGPT is expected to rule the roost for the next decade or so. Whether engaging the customer in new ways, enhancing workflows, or assisting with coding, the potential applications of ChatGPT are endless.

With the advent of Artificial Intelligence (AI), businesses have dramatically changed their operations and processes. As AI applications and tools have developed, businesses have been able to make more informed decisions and automate repetitive tasks, resulting in more efficient and effective operations. One of the most advanced AI algorithms that have gained prominence across various industries is Generative AI.

Conversational Artificial Intelligence (AI) is unarguably the most talked about subdomain of AI today. While ChatGPT's introduction by Microsoft in November last year shifted the world's attention to AI more than ever before, the discussion is further being ignited with Google's answer to it, termed Bard.

Conversational Artificial Intelligence (AI) has been making waves in the technology industry, especially with the introduction of Microsoft's ChatGPT. However, this chatbot has now found stiff competition with the announcement of a new rival that has been developed by Google, known as Google Bard. The next few months will witness how Bard will fare against ChatGPT in producing optimized, relevant results to all kinds of user queries.

With the latest advancements in Natural Language Processing(NLP), generative Artificial Intelligence (AI) and other foundation models have completely changed the flow of business processes. By automating tasks that were previously done by humans, generative AI services have increased productivity and efficiency, reduced costs, and created new growth opportunities.

Technology is embedded in every aspect of our lives. We rely on it for almost everything, especially to communicate with others. Simply, take the example of Siri or Alexa that we use in our day-to-day lives. Ever wondered how these virtual assistants understand and interpret our commands? How do they pick up terms and carry out the tasks according to our instructions? For example, when we ask Alexa to set an alarm for us.

PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation.  However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.

The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) technologies has led to the growing adoption of TensorFlow, an end-to-end ML platform. TensorFlow enables AI developers to accelerate ML tasks at every step of the workflow but also has certain shortcomings and understanding both sides is very essential to fully leverage this platform. 

An essential component of textual and verbal data is context and understanding the context requires some amount of sentiment analysis. To comprehend and categorize subjective feelings from communications data, Natural Language Processing (NLP) and Machine Learning (ML) methods have been used in the past. Sentiment analysis is frequently used in professional settings to comprehend customer evaluations, identify email spam, etc.