Chatbots and their Struggle with Negation

Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.

Nora Kassner suspected her computer wasn’t as smart as people thought. In October 2018, Google released a language model algorithm called BERT, which Kassner, a researcher in the same field, quickly loaded on her laptop. It was Google’s first language model that was self-taught on a massive volume of online data. Like her peers, Kassner was impressed that BERT could complete users’ sentences and answer simple questions. It seemed as if the large language model (LLM) could read text like a human (or better).

But Kassner, at the time a graduate student at Ludwig Maximilian University of Munich, remained skeptical. She felt LLMs should understand what their answers mean — and what they don’t mean. It’s one thing to know that a bird can fly. “A model should automatically also know that the negated statement — ‘a bird cannot fly’ — is false,” she said. But when she and her adviser, Hinrich Schütze, tested BERT and two other LLMs in 2019, they found that the models behaved as if words like “not” were invisible.

Since then, LLMs have skyrocketed in size and ability. “The algorithm itself is still similar to what we had before. But the scale and the performance is really astonishing,” said Ding Zhao, who leads the Safe Artificial Intelligence Lab at Carnegie Mellon University.

But while chatbots have improved their humanlike performances, they still have trouble with negation. They know what it means if a bird can’t fly, but they collapse when confronted with more complicated logic involving words like “not,” which is trivial to a human.

“Large language models work better than any system we have ever had before,” said Pascale Fung, an AI researcher at the Hong Kong University of Science and Technology. “Why do they struggle with something that’s seemingly simple while it’s demonstrating amazing power in other things that we don’t expect it to?” Recent studies have finally started to explain the difficulties, and what programmers can do to get around them. But researchers still don’t understand whether machines will ever truly know the word “no.”

Making Connections

It’s hard to coax a computer into reading and writing like a human. Machines excel at storing lots of data and blasting through complex calculations, so developers build LLMs as neural networks: statistical models that assess how objects (words, in this case) relate to one another. Each linguistic relationship carries some weight, and that weight — fine-tuned during training — codifies the relationship’s strength. For example, “rat” relates more to “rodent” than “pizza,” even if some rats have been known to enjoy a good slice.

In the same way that your smartphone’s keyboard learns that you follow “good” with “morning,” LLMs sequentially predict the next word in a block of text. The bigger the data set used to train them, the better the predictions, and as the amount of data used to train the models has increased enormously, dozens of emergent behaviors have bubbled up. Chatbots have learned style, syntax and tone, for example, all on their own. “An early problem was that they completely could not detect emotional language at all. And now they can,” said Kathleen Carley, a computer scientist at Carnegie Mellon. Carley uses LLMs for “sentiment analysis,” which is all about extracting emotional language from large data sets — an approach used for things like mining social media for opinions.

So new models should get the right answers more reliably. “But we’re not applying reasoning,” Carley said. “We’re just applying a kind of mathematical change.” And, unsurprisingly, experts are finding gaps where these models diverge from how humans read.

No Negatives

Unlike humans, LLMs process language by turning it into math. This helps them excel at generating text — by predicting likely combinations of text — but it comes at a cost.

“The problem is that the task of prediction is not equivalent to the task of understanding,” said Allyson Ettinger, a computational linguist at the University of Chicago. Like Kassner, Ettinger tests how language models fare on tasks that seem easy to humans. In 2019, for example, Ettinger tested BERT with diagnostics pulled from experiments designed to test human language ability. The model’s abilities weren’t consistent. For example:

He caught the pass and scored another touchdown. There was nothing he enjoyed more than a good game of ____. (BERT correctly predicted “football.”)

The snow had piled up on the drive so high that they couldn’t get the car out. When Albert woke up, his father handed him a ____. (BERT incorrectly guessed “note,” “letter,” “gun.”)

And when it came to negation, BERT consistently struggled.

A robin is not a ____. (BERT predicted “robin,” and “bird.”)

On the one hand, it’s a reasonable mistake. “In very many contexts, ‘robin’ and ‘bird’ are going to be predictive of one another because they’re probably going to co-occur very frequently,” Ettinger said. On the other hand, any human can see it’s wrong.

By 2023, OpenAI’s ChatGPT and Google’s bot, Bard, had improved enough to predict that Albert’s father had handed him a shovel instead of a gun. Again, this was likely the result of increased and improved data, which allowed for better mathematical predictions.

But the concept of negation still tripped up the chatbots. Consider the prompt, “What animals don’t have paws or lay eggs, but have wings?” Bard replied, “No animals.” ChatGPT correctly replied bats, but also included flying squirrels and flying lemurs, which do not have wings. In general, “negation [failures] tended to be fairly consistent as models got larger,” Ettinger said. “General world knowledge doesn’t help.”

Invisible Words

The obvious question becomes: Why don’t the phrases “do not” or “is not” simply prompt the machine to ignore the best predictions from “do” and “is”?

That failure is not an accident. Negations like “not,” “never” and “none” are known as stop words, which are functional rather than descriptive. Compare them to words like “bird” and “rat” that have clear meanings. Stop words, in contrast, don’t add content on their own. Other examples include “a,” “the” and “with.”

“Some models filter out stop words to increase the efficiency,” said Izunna Okpala, a doctoral candidate at the University of Cincinnati who works on perception analysis. Nixing every “a” and so on makes it easier to analyze a text’s descriptive content. You don’t lose meaning by dropping every “the.” But the process sweeps out negations as well, meaning most LLMs just ignore them.

So why can’t LLMs just learn what stop words mean? Ultimately, because “meaning” is something orthogonal to how these models work. Negations matter to us because we’re equipped to grasp what those words do. But models learn “meaning” from mathematical weights: “Rose” appears often with “flower,” “red” with “smell.” And it’s impossible to learn what “not” is this way.

Kassner says the training data is also to blame, and more of it won’t necessarily solve the problem. Models mainly train on affirmative sentences because that’s how people communicate most effectively. “If I say I’m born on a certain date, that automatically excludes all the other dates,” Kassner said. “I wouldn’t say ‘I’m not born on that date.’”

This dearth of negative statements undermines a model’s training. “It’s harder for models to generate factually correct negated sentences, because the models haven’t seen that many,” Kassner said.

Untangling the Not

If more training data isn’t the solution, what might work? Clues come from an analysis posted to arxiv.org in March, where Myeongjun Jang and Thomas Lukasiewicz, computer scientists at the University of Oxford (Lukasiewicz is also at the Vienna University of Technology), tested ChatGPT’s negation skills. They found that ChatGPT was a little better at negation than earlier LLMs, even though the way LLMs learned remained unchanged. “It is quite a surprising result,” Jang said. He believes the secret weapon was human feedback.

The ChatGPT algorithm had been fine-tuned with “human-in-the-loop” learning, where people validate responses and suggest improvements. So when users noticed ChatGPT floundering with simple negation, they reported that poor performance, allowing the algorithm to eventually get it right.

John Schulman, a developer of ChatGPT, described in a recent lecture how human feedback was also key to another improvement: getting ChatGPT to respond “I don’t know” when confused by a prompt, such as one involving negation. “Being able to abstain from answering is very important,” Kassner said. Sometimes “I don’t know” is the answer.

Yet even this approach leaves gaps. When Kassner prompted ChatGPT with “Alice is not born in Germany. Is Alice born in Hamburg?” the bot still replied that it didn’t know. She also noticed it fumbling with double negatives like “Alice does not know that she does not know the painter of the Mona Lisa.”

“It’s not a problem that is naturally solved by the way that learning works in language models,” Lukasiewicz said. “So the important thing is to find ways to solve that.”

One option is to add an extra layer of language processing to negation. Okpala developed one such algorithm for sentiment analysis. His team’s paper, posted on arxiv.org in February, describes applying a library called WordHoard to catch and capture negation words like “not” and antonyms in general. It’s a simple algorithm that researchers can plug into their own tools and language models. “It proves to have higher accuracy compared to just doing sentiment analysis alone,” Okpala said. When he combined his code and WordHoard with three common sentiment analyzers, they all improved in accuracy in extracting opinions — the best one by 35%.

Another option is to modify the training data. When working with BERT, Kassner used texts with an equal number of affirmative and negated statements. The approach helped boost performance in simple cases where antonyms (“bad”) could replace negations (“not good”). But this is not a perfect fix, since “not good” doesn’t always mean “bad.” The space of “what’s not” is simply too big for machines to sift through. “It’s not interpretable,” Fung said. “You’re not me. You’re not shoes. You’re not an infinite amount of things.” 

Finally, since LLMs have surprised us with their abilities before, it’s possible even larger models with even more training will eventually learn to handle negation on their own. Jang and Lukasiewicz are hopeful that diverse training data, beyond just words, will help. “Language is not only described by text alone,” Lukasiewicz said. “Language describes anything. Vision, audio.” OpenAI’s new GPT-4 integrates text, audio and visuals, making it reportedly the largest “multimodal” LLM to date.

Future Not Clear

But while these techniques, together with greater processing and data, might lead to chatbots that can master negation, most researchers remain skeptical. “We can’t actually guarantee that that will happen,” Ettinger said. She suspects it’ll require a fundamental shift, moving language models away from their current objective of predicting words.

After all, when children learn language, they’re not attempting to predict words, they’re just mapping words to concepts. They’re “making judgments like ‘is this true’ or ‘is this not true’ about the world,” Ettinger said.

If an LLM could separate true from false this way, it would open the possibilities dramatically. “The negation problem might go away when the LLM models have a closer resemblance to humans,” Okpala said.

Of course, this might just be switching one problem for another. “We need better theories of how humans recognize meaning and how people interpret texts,” Carley said. “There’s just a lot less money put into understanding how people think than there is to making better algorithms.”

And dissecting how LLMs fail is getting harder, too. State-of-the-art models aren’t as transparent as they used to be, so researchers evaluate them based on inputs and outputs, rather than on what happens in the middle. “It’s just proxy,” Fung said. “It’s not a theoretical proof.” So what progress we have seen isn’t even well understood.

And Kassner suspects that the rate of improvement will slow in the future. “I would have never imagined the breakthroughs and the gains we’ve seen in such a short amount of time,” she said. “I was always quite skeptical whether just scaling models and putting more and more data in it is enough. And I would still argue it’s not.”

Date: June 2, 2023

Author: Max G. Levy

Source: Quanta Magazine

Chatbots, big data and the future of customer service

The rise and development of big data has paved the way for an incredible array of chatbots in customer service. Here's what to know.

Big data is changing the direction of customer service. Machine learning tools have led to the development of chatbots. They rely on big data to better serve customers.

How are chatbots changing the future of the customer service industry and what role does big data play in managing them?

Big data Leads to the deployment of more sophisticated chatbots

BI-kring published an article about the use of chatbots in HR about a month ago. This article goes deeper into the role of big data when discussing chatbots.

The following terms are more popular than ever: 'chatbot', 'automated customer service', 'virtual advisor'. Some know more, others less about process automation. One thing is for sure: if you want to sell more on the internet, handle more customers, save on personnel costs, you certainly need a chatbot. A chatbot is a conversational system that was created to stimulate intelligent conversation between a human and an automaton.

Chatbots rely on machine learning and other sophisticated data technology. They are constantly collecting new data from their interactions with customers to offer a better experience.

But how commonly used are chatbots? An estimated 67% of consumers around the world have communicated with one. That figure is going to rise sharply in the near future. In 2020, over 85% of all customer service interactions will involve chatbots.

A chatbot makes it possible to automate customer service in various communication channels, for example on a website, chat, in social media or via SMS. In practice, a customer does not have to wait for hours to receive a reply from the customer service department, a bot will provide an answer within a few seconds.

According to requirements, a chatbot may assume the role of a virtual advisor or assistant. For questions where a real person has to become involved, in analyzing the received enquiries bots can not only identify what issue the given customer is addressing but also to automatically send it to the correct person or department. Machine learning tools make it easier to determine when a human advisor is needed.

Bots supported by associative memory algorithms understand the entire content even if the interlocutor made a mistake or a typo. Machine learning makes it easier for them to decipher contextual meanings by interpreting these mistakes.

Response speed and 24/7 assistance are very important when it comes to customer service, as late afternoons and evenings are times of day when online shops experience increased traffic. If a customer cannot obtain information about a given product right there and then, it is possible that they will just abandon their basket and not come shop at that store again. Any business would want to prevent that a customer journey towards their product takes a turn the other way, especially if it's due to a lack of appropriate support.

Online store operators, trying to stay a step ahead of the competition, often decide to implement a state-of-the-art solution, which makes the store significantly more attractive and provides a number of new opportunities delivered by chatbots. Often, following the application of such a solution, website visits increase significantly. This translates into more sales of products or services.

We are not only seeing increased interest in the e-commerce industry, chatbots are successfully used in the banking industry as well. Bank Handlowy and Credit Agricole use bots to handle loyalty programmes or as assistants when paying bills.

What else can a chatbot do?

Big data has made it easier for chatbots to function. Here are some of the benefits that they offer:

• Send reminders of upcoming payment deadlines.
• Send account balance information.
• Pass on important information and announcements from the bank.
• Offer personalised products and services.
• Bots are also increasingly more often used to interact with customers wishing to order meals, taxis, book tickets, accommodation, select holiday packages at travel agents, etc.

The insurance industry is yet another area where chatbots are very useful. Since insurance companies are already investing heavily in big data and machine learning to handle actuarial analyses, it is easy for them to extend their knowledge of data technology to chatbots.

The use of Facebook Messenger chatbots during staff recruitment may be surprising for many people.

Chatbots are frequently used in the health service as well, helping to find the right facilities, arrange a visit, select the correct doctor and also find opinions about them or simply provide information on given drugs or supplements.

As today every young person uses a smartphone, social media and messaging platforms for a whole range of everyday tasks like shopping, acquiring information, sorting out official matters, paying bills etc., the use of chatbots is slowly becoming synonymous with contemporary and professional customer service. A service available 24/7, often geared to satisfy given needs and preferences.

Have you always dreamed of employees who do not get sick, do not take vacations and do not sleep? Try using a chatbot.

Big data has led to fantastic developments with chatbots

Big data is continually changing the direction of customer service. Chatbots rely heavily on the technology behind big data. New advances in machine learning and other data technology should lead to even more useful chatbots in the future.

Author: Ryan Kh

Source: SmartDataCollective

In een intelligente organisatie is er altijd plaats voor een chatbot in HR

Mensen vormen het hart van een bedrijf, en de afdeling Human Resources is er om voor die mensen te zorgen. HR is de bewaker van de cultuur en zorgt dat werknemers mogelijkheden krijgen om te groeien. Het houdt het bedrijf levendig en gezond. HR draait dus om mensen. Is een virtuele assistent, oftewel een chatbot, tussen al deze mensen wel op zijn plek?

Hoewel HR draait om de mensen binnen een organisatie, besteden HR-medewerkers ongeveer een vierde van hun tijd aan administratieve taken. Het beantwoorden van vragen van medewerkers is bijvoorbeeld een dagelijks terugkerende taak. Vragen als ‘hoeveel vakantiedagen heb ik nog?’ of ‘wat zijn de regels rond ziekteverlof?’ komen bijna dagelijks aan bod. Een chatbot kan al die vragen van medewerkers beantwoorden. Dit ontziet niet alleen de HR-manager, maar het schept ook direct duidelijkheid voor medewerkers die de vragen stellen. Nooit meer de frustraties van lang wachten op een antwoord op een simpele vraag. Dat klinkt goed toch?

Een chatbot kan de gestelde vragen ook nauwkeurig bijhouden, om zo knelpunten in het HR-beleid op te merken. Daarnaast wordt een chatbot met de hulp van kunstmatige intelligentie steeds slimmer, naarmate hij meer vragen krijgt. De antwoorden die hij geeft zullen elke dag beter en nauwkeuriger worden. Dit wirdt ook wel machine learning genoemd.

Persoonlijke antwoorden voor specifieke situaties

Vooral het aanvragen van verlof is een administratieve taak die vaak veel tijd kost. Denk aan het aanvragen van zwangerschapsverlof bijvoorbeeld. Een bot kan persoonlijke antwoorden en oplossingen geven voor deze specifieke aanvraag.

Ook tijdens ziekte kan de chatbot een rol spelen. Een van de belangrijkste taken van HR is het zorgen voor een gemotiveerd personeel. Om hieraan bij te dragen kan een chatbot bijvoorbeeld een ‘beterschap’ boodschap sturen als iemand zich ziek meldt. De virtuele assistent kan ook vragen en bijhouden hoe het met diegene gaat, om zo het herstel in het oog te houden.

Sollicitatieprocedures gladstrijken met een chatbot

Gezien de huidige arbeidsmarkt is het vaak lastig om nieuw personeel te vinden. Het is daarom essentieel dat het sollicitatieproces vlekkeloos verloopt. Een chatbot kan dit optimaliseren door vragen van een sollicitant direct te beantwoorden. Na het beantwoorden van een vraag, kan de chatbot zelf waardevolle data verzamelenover de sollicitant. De bot slaat de antwoorden op zodat het eenvoudiger wordt om kandidaten te screenen. Niet alleen het leven van de recruiter wordt zo makkelijker, ook dat van de sollicitant.

Het grootste deel, ongeveer 80%, van mensen die ergens solliciteren, overweegt ergens anders heen te gaan als ze tijdens het proces niet regelmatig updates krijgen over hun sollicitatie. Ze blijven wel aan boord als ze regelmatig op de hoogte gehouden worden over hoe het ervoor staat. Een bot kan een sollicitant op de hoogte houden en zo het proces van recruitment op een positieve noot beginnen. Nadat de sollicitant de selectieprocedure heeft doorlopen en zijn of haar proeftijd in gaat, begint de onboarding. De onboarding is een belangrijke periode om ervoor te zorgen dat een nieuwe medewerker zo snel mogelijk mee kan draaien in de organisatie. In plaats van te werken via een checklist kan de chatbot een groot deel van de onboarding van de HR overnemen en kan de medewerker snel zelf aan de slag. Doordat alle documenten en informatie klaargezet worden in de chatbot kan HR zich meer focussen op het persoonlijke aspect van de onboarding.

Chatbot voor HR, meer ruimte voor mensen

Ondanks de opkomst van nieuwe technologie is de wereld van HR er eentje die draait om mensen. Mensen die tijd nodig hebben om er voor elkaar te zijn, in plaats van dat ze zich constant bezig moeten houden met administratieve taken. HR moet zich kunnen richten op de ontwikkeling van medewerkers en als mentor kunnen optreden. HR moet de perfecte nieuwe collega kunnen vinden en de doelen van de organisatie nastreven. Door de inzet van een chatbot kan juist het werk uit handen genomen worden dat zoiets in de weg staat. Zo kan een bedrijf zich niet alleen richten op wat belangrijk is, maar kan het ook zijn medewerkers de ruimte geven te doen waar ze goed in zijn door altijd paraat te staan met de juiste informatie en het juiste advies. Daarom heeft een intelligente organisatie altijd plaats voor een chatbot in HR.

Auteur: Joris Jonkman

Bron: Emerce

The increasing use of AI-driven chatbots for customer service in Ecommerce

AI-powered chatbots have revolutionized the way Ecommerce businesses handle customer service. With the ability to provide immediate responses and resolutions, chatbots ensure that customers receive prompt assistance at any time of day or night. These chatbots are programmed with natural language processing (NLP) capabilities, allowing them to understand and interpret human language accurately.

Moreover, AI-powered chatbots can collect data on customer interactions and use it for personalization purposes in future exchanges. They can also learn from their previous conversations, continually improving their responses over time. As a result, businesses can offer more informed recommendations and tailored solutions to customers’ problems.

Another significant advantage of AI-powered chatbots is that they help reduce operating costs by eliminating the need for human agents to attend to every customer query. Chatbots can handle multiple queries simultaneously without sacrificing quality or efficiency. This feature allows companies to streamline their operations while still providing an exceptional customer experience.

Benefits of Chatbots

Chatbots have become an essential part of customer service in the Ecommerce industry. One of the significant benefits of using chatbots is their ability to offer 24/7 customer support, which ensures that customers can get assistance at any time they need it. This feature helps businesses reduce wait times and improve customer satisfaction rates.

Another advantage of chatbots in Ecommerce is their efficiency in handling repetitive inquiries. As a result, businesses can free up their staff from handling these inquiries, allowing them to focus on complex tasks that require human intervention. Chatbots also help companies save money by reducing the need for additional staffing during peak periods.

Additionally, chatbots are excellent tools for collecting customer data and providing personalized recommendations based on their purchase history, preferences, and behavior patterns. This allows businesses to provide tailored services to each customer, increasing the likelihood of repeat purchases and improving overall loyalty. The use of AI-powered chatbots also helps companies stay ahead of the competition by offering cutting-edge technology that enhances the overall shopping experience for customers.

Challenges & Risks

One of the challenges that come with using AI-powered chatbots in Ecommerce customer service is ensuring that they are programmed to understand and respond appropriately to all types of customer inquiries. While chatbots have the potential to speed up response times and improve efficiency, they can also risk alienating customers if their responses are generic or irrelevant. As such, a significant amount of resources must be dedicated to developing chatbot algorithms that can handle complex queries and adapt to different situations.

One of the challenges of starting an ecommerce business is related to data privacy and security risks associated with chatbot interactions. Chatbots gather a vast amount of sensitive information from customers, including personal details such as names, addresses, and payment information. This makes them an attractive target for cybercriminals who may try to infiltrate the system and steal this valuable data. Companies must ensure that their security protocols are robust enough to protect against cyber attacks while still providing fast and convenient customer service.

Finally, there is a risk associated with relying too heavily on AI-powered chatbots at the expense of human interaction. While these algorithms can handle many routine tasks effectively, customers may still require personalized attention or assistance for more complex inquiries or issues. Over-reliance on automation may lead to decreased customer satisfaction levels over time as customers demand more direct interaction with human representatives who can provide empathy and context-specific solutions.

Ecommerce Use Cases

One of the most significant use cases for AI-powered chatbots in Ecommerce is customer service. With the ability to handle massive amounts of customer inquiries quickly, chatbots can improve response times and reduce wait times for customers. Additionally, chatbots can offer 24/7 support, which is particularly useful for businesses with global customers who are located in different time zones.

Another key use case for AI-powered chatbots in Ecommerce is product recommendations. By analyzing a customer’s browsing behavior and purchase history, chatbots can offer personalized product recommendations that are tailored to their unique preferences. This not only improves the overall shopping experience but also helps to increase sales by promoting products that customers are more likely to buy.

Finally, AI-powered chatbots can also be used for order tracking and delivery notifications. By providing real-time updates on the status of an order or delivery, chatbots can help reduce anxiety and uncertainty among customers while improving transparency and accountability within the supply chain.

Industry Examples

One industry that has been quick to adopt AI-powered chatbots in their customer service is the Ecommerce industry. With the increased demand for online shopping and a growing number of customers seeking 24/7 support, chatbots have become a valuable tool for Ecommerce businesses to provide efficient and effective customer service. These intelligent virtual assistants can handle multiple queries simultaneously, provide instant responses, and even offer personalized recommendations based on a customer’s purchase history.

Another industry that has leveraged the power of AI-powered chatbots is the banking sector. Banks are using chatbots to enhance their customer service by providing real-time assistance with account inquiries, transaction history, and even fraud detection. In addition to providing prompt responses, some banks have also integrated voice recognition technology into their chatbots to enable customers to complete transactions through voice commands securely.

Overall, AI-powered chatbots have proven to be an innovative solution for various industries looking to streamline their customer service operations. With advances in natural language processing (NLP) and machine learning algorithms, these virtual assistants are continually improving in their ability to understand complex queries and offer personalized solutions – making them an invaluable asset for companies seeking cost-effective ways of delivering exceptional customer experiences.

Future Outlook

The future of customer service in Ecommerce looks promising with the rise of AI-powered chatbots. These chatbots are designed to provide personalized support and assistance to customers, which can significantly improve their overall shopping experience. They are able to handle a variety of tasks, such as answering common questions, providing product recommendations, and even completing purchases.

One major advantage of using chatbots in customer service is that they are available 24/7. Customers no longer have to wait for business hours or deal with long hold times on phone calls. Chatbots can provide quick and efficient support at any time of the day, which can lead to higher customer satisfaction rates.

Looking forward, it’s expected that AI-powered chatbots will continue to evolve and become even more advanced in their capabilities. As they learn from interactions with customers, they will be able to provide increasingly accurate and relevant support. This could ultimately lead to reduced costs for businesses while also improving the overall shopping experience for customers.

Conclusion: Growing Role of AI

In conclusion, the growing role of AI in Ecommerce customer service is becoming increasingly important. The use of chatbots has revolutionized the way businesses interact with their customers. They provide 24/7 support and can handle multiple customer queries at once, leading to faster response times and increased customer satisfaction.

AI-powered chatbots also have the ability to learn from previous interactions and adapt accordingly. This means that they become more efficient over time, reducing the workload for human agents and allowing them to focus on more complex tasks that require a personal touch.

Furthermore, AI technology is constantly evolving, meaning that there are always new ways in which it can be used to improve customer service. From personalized product recommendations based on browsing history to using facial recognition software for seamless checkout experiences, the possibilities are endless. As such, we can expect to see an even greater role for AI in Ecommerce customer service in the future.

Author: Ali Ahmad

Source: Datafloq