Chat Translator Keyboard and Dictionary is a translation tool that helps you chat with people of different languages in their by translating your native language to their own. English Dictionary and Translator app assists in you whatever business you run that uses English or other languages like Arabic, German, French or Spanish as the primary source of communication. You need this at business places, firms, startups, organizations, and other trade centers where you think English Translator and Keyboard might of great help to you. People around the world uses this Language Translator for translating their chat. This Chat Translator helps you cope with the problems that you face in communication with people speaking different languages.

A chatbot is a marketing tool that imitates human communication to help brands optimize their marketing efforts and engage with customers throughout the day. They leverage messaging mediums like SMS, social media sites, and website pop-ups to receive and respond to messages. Brands can program their chatbots marketing tool to respond to certain messages the same way or use machine learning to give appropriate responses to specific questions. They allow brands to respond to pressing customer issues on time, whether users want to book a flight or return an item. Customers expect quick responses from the brands they are buying from, and chatbots for marketing allow them to reply instantly.

We've reached a point "where AI and machine learning are converging" and it's helping drive operational efficiencies and customer experience (CX) innovation for a wide range of businesses. That's how author and veteran IT expert Tom Davenport began a wide-ranging discussion about the state of AI and automation in the financial services sector at a recent virtual salon with senior industry executives. Another speaker, Rob Krugman, Chief Digital Officer at financial services firm Broadridge, said his firm is "increasingly using AI models to define and select the attributes and information that is important to their customers". The session was hosted by Bill Wright, Head of AI Machine Learning and Edge Innovation for Red Hat. It was moderated by CDX.

NASA's Perseverance rover has tried out a nifty new feature for the first time, which let it'spit out' a piece of Mars rock that had been clogging its sampling tube. The trick means that Perseverance can now continue taking samples of rock from the Red Planet to search for possible signs of ancient life. The SUV-sized vehicle has been on the Red Planet since February 2021, and is slowly trundling through the Jezero Crater taking rock samples for later retrieval. On December 29, while retrieving a sample from a rock, its sixth so far, NASA engineers found they couldn't get the rock to go into the storage area. This was due to a pebble-sized piece of debris obstructing the robotic arm, blocking the entrance to the tube docking area - nearly a month later, this has been solved. NASA used an untested'un-choking procedure', that involved pointing the drill containing a clogged test tube towards the ground and rotating it at high speed.

The amount of time we spend doing repetitive work is mind-boggling, with manual computer tasks and data entry taking up a good portion of an office worker's day. Whether it's data collection, approvals, or updates, many tasks don't require creativity or intuition, essential attributes that serve to increase job satisfaction. Organizations are turning to technology, particularly robotic process automation (RPA), to offload repetitious tasks, freeing workers to perform richer, more valuable work. It handles unmodeled, "naturally grown" processes with UI-based automation – this is particularly important when it's necessary to integrate legacy systems where APIs don't exist and direct access to the data is not available. With such amazing scalability (an RPA software bot can work 24 7, 365 days a year), service levels remain constant, even during times of exceptional demand and peak volumes.

Did you miss a session from the Future of Work Summit? This article was contributed by Frederik Bussler, consultant and analyst. Around one in four American adults are underbanked, meaning they are underserved by traditional finance, and rely on high-fee alternative financial systems. For underbanked Americans, getting a loan or a credit card can range between being either difficult or next to impossible. For those who do have a credit score, it's often not a very high one.

Artificial intelligence (AI) is being used to change our lives everyday. When it comes to building AI programs, there are two approaches programmers tend to choose: supervised or unsupervised machine learning. The simple distinction between these is supervised machine learning utilizes labeled data to predict outcomes, while unsupervised machine learning does not. There are, however, some differences between the two techniques, as well as critical areas where one surpasses the other. In this article, we will break down some of these differences with examples of both supervised and unsupervised learning.

Sentence Correction using RNN is simple problem in which we provide text data in corrupted form(gramatical mistake,short forms of some words like'ppl' for'people')to the input and output we get is the correct uncorrupted form of that text data.It can be used as a preprocessing step in a language transaltion model where the input language(in corrupted form) can be converted to uncorrupted form and then pass to a model to output the translated text and thus can help in increasing the efficiency of the language translation model.This case study will be useful for increasing the efficiency as many NLP tasks,since any model will learn from uncorrupted correct text and will be able to predict correctly the target task.Moreover it would be useful in text messaging apps where we could enter a corrupted text and it would suggest us the correct uncorrupted text before sending the text to anyone. Since the task at hand comprises of textual data,in which one form of corrupted textual data is to be converted into uncorrupted form while preserving the semantic meaning of the text.The task is similar to a language translation.The task can be converted to DL problem using LSTM's,GRU's and RNN.Since these archtectures help us to take in account the semantic meaning of text and we can use encoder decoder model to encode corrupted text and then decode it to uncorrupted form. We used to different datasets for our task.one Latency: As far as the latecy is concerned.Our model should output quickly(can take seconds) if it is used as preprocessing step for any other NLP tasks.But if it is used in a text messaging apps the output should be quick within milliseconds. As mention in the research paper, we will be using categorical cross entropy.

It could hardly be more complicated: tiny particles whir around wildly with extremely high energy, countless interactions occur in the tangled mess of quantum particles, and this results in a state of matter known as "quark-gluon plasma". Immediately after the Big Bang, the entire universe was in this state; today it is produced by high-energy atomic nucleus collisions, for example at CERN. Such processes can only be studied using high-performance computers and highly complex computer simulations whose results are difficult to evaluate. Therefore, using artificial intelligence or machine learning for this purpose seems like an obvious idea. Ordinary machine-learning algorithms, however, are not suitable for this task.

Meta has unveiled the AI Research SuperCluster (RSC), a new supercomputer that's among the fastest in the world. And it'll only get faster – by the end of the year it should rank number one, with computing power on the exascale. The company formerly known as Facebook has had its fingers in the AI pie for a few years now, and it's not hard to see why. Through Facebook, Instagram and Whatsapp et al, the conglomerate generates far more data than any mere mortal could possibly process – and there's obscene amounts of money to be made in sifting through it all. Meta's RSC will be up to the task, using this data and immense computing power to train AI algorithms to better recognize objects in images and spoken words in audio, quickly translate between languages, and identify harmful content and misinformation that shouldn't be on social media.