Bard, which competes with OpenAI's ChatGPT and Microsoft's Bing, will now be able to look through and summarize emails from Gmail, search through Google Docs and check flight prices with Google Flights, without users needing to leave the AI tool's main screen. Bard appears as a box where a user enters a question. Until now, responses have been limited to simple text replies or photos from Google Images, but the latest updates mean YouTube videos, links to Google Doc files, and summaries of Gmail emails can appear embedded within Bard's responses.

How is AI taking over rule-based chatbots? Is AI the future of chatbots? We will answer these questions in this article. The chatbot industry is growing really fast year by year as many companies try to use chatbots to reduce customer service costs. With rapid advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), more and more chatbots are made and released each day, and they serve a different purpose.

How is AI taking over rule-based chatbots? Is AI the future of chatbots? We will answer these questions in this article. The chatbot industry is growing really fast year by year as many companies try to use chatbots to reduce customer service costs. With rapid advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), more and more chatbots are made and released each day, and they serve a different purpose.

There's a race on to get into your ears. And Audible is trying to win it. The Amazon-owned audiobook company is just the latest to launch a new range of audio content, in the form of podcasts. It comes as Google attempts to take its spot by offering audiobooks, and other companies like Spotify launch their own podcasts. Audible is referring to the new podcasts as "Audio Shows". But they work in the same way as podcast – they are cut down into a range of shorter episodes that are intended to be listened to in a series, and fans can subscribe and then receive new episodes when they come out.

When big tech firms use machine learning to improve their software, the process is usually a very centralized one. Companies like Google and Apple gather information about how you use their apps; collect it in one place; and then train new algorithms using this aggregated data. The end result for users could be anything from sharper photos on your phone's camera, to better a search function in your email app. This method is effective, but the back-and-forth of updating apps and gathering feedback is time-consuming. And it's not great for user privacy, as companies have to store data on how you use your apps on their servers.