One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
On Friday, Elon Musk announced he was pausing his $45bn purchase of Twitter because he had only just discovered some of the accounts on the site were fake. But that's not the strangest thing that has happened to the beleaguered social media platform this week. Because on Tuesday the current top brass, perhaps trying to demonstrate their vision for the site, released a Super Nintendo-style browser game that recaps Twitter's private policy. The platform unveiled Twitter Data Dash, which plays like a vintage side-scrolling platformer that's been draped with a healthy dose of disinformation anxiety. You take control of a blue-hued puppy named Data and are tasked with retrieving five bones hidden in each of the game's day-glo urban environments.
The retail business is getting back on track and has been witnessing steady growth after the dismal impact of the third wave. There has been buoyancy in the market with the removal of lockdown restrictions. After a long time of distress and uncertainty, things are getting back to normalcy as businesses have started taking pertinent steps to resume operations and focus on sales, marketing, and inventory management. The realization of digital transformation coupled with the indispensable role of artificial intelligence (AI) has been one of the major outcomes of Covid-19 implications on the retail sector and the vast possibilities and opportunities it can create with such transformations. With the emergence of e-commerce, buyers experienced the first crucial shift that successfully made it possible for them to buy things from anywhere at any time.
The Transformer soon became the most popular model in NLP after its debut in the famous article Attention Is All You Need in 2017. The capacity to analyze text in a non-sequential manner (as opposed to RNNs) enabled large models to be trained. The introduction of an attention mechanism proved tremendously valuable in generalizing text. Before the advent of Deep Learning, previous approaches to NLP were more rule-based, with simpler (pure statistical) machine learning algorithms being taught the words and phrases to look for in the text, and particular replies being created when these phrases were discovered. Following the publication of the study, numerous popular transformers emerged, the most well-known of which is GPT (Generative Pre-trained Transformer).
During the pandemic especially, it's become overwhelming for small- and medium-sized businesses (SMBs) to answer all of their customer service requests. A Freshworks survey found that companies experienced a 71% increase in overall contact volume between February 2020 and January 2021, and expect it to increase further. At the same time, customers -- while empathetic -- have become more demanding. The same poll shows that 68% of customer service managers have seen an increase in customer expectations. What's a company to do? Automation is one route to more manageable customer experience workloads, potentially.
Artificial Intelligence (AI) has been around since the early 1900s, but it's recently been applied to business in ways that many people never imagined. Artificial Intelligence (AI) is one of the popular innovations that businesses can utilize to improve operations, save money, and expand their reach. From customer service chatbots to warehouse robots and self-driving vehicles, AI promises to make tasks faster, easier, and more efficient than ever before. Automation makes repetitive tasks easier, saving time and money. For example, artificial intelligence can be used to schedule meetings and alert employees of potential conflicts.
Do you remember the first time you started to build some SQL queries to analyse your data? I'm sure most of the time you just wanted to see the "Top selling products" or "Count of product visits by weekly". Why write SQL queries instead of just asking what you have in your mind in natural language? This is now possible thanks to the recent advancements in NLP. You can now not just use the LLM (Large Language Model) but also teach them new skills.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. There is an implicit assumption in most analytics solutions: The data analyzed and the insights derived, are almost exclusively quantitative. That is, they refer to numerical data, such as number of customers, sales and so on. But when it comes to customer feedback, perhaps the most important data is qualitative: text contained in sources such as feedback forms and surveys, tickets, chat and email messages. The problem with that data is that, while valuable, they require domain experts and a lot of time to read through and classify.
Call Center sentiment analysis is the processing of data by identifying the natural nuance of customer context and analyzing data to make customer service more empathetic. If you are employed in Call Center, the following scenario might be familiar: You get a call from a client and hear their words with stress. The cause for such a cataclysmic reaction: They got a bad rating for their products or business. Some of those reviews might be negative, formal, and neutral. Knowing what someone meant can be tricky unless you understand their emotional quotient.
Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.