You may not have realized it. Almost every single technology that you use daily, starting from your email to your favorite social media app comes infused with Artificial intelligence. From human-centric factory floors to self-driving cars, AI seems to be getting into places where machines were expected to be off-limits.

AI  is the biggest muscle that is powering the Fourth Industrial Revolution. 

ChatGPT, which is the most promising AI tool today, has become the latest internet sensation and has also bagged the position of one of the most valued companies in the world with a whopping $29 billion valuation. 

While this massive penetration of AI comes with the promise of solving some of the most pressing challenges of our times, it is also accompanied by inscrutable “black box” algorithms, possible job displacements, and even unethical data usage. 

It is safe to say that Artificial Intelligence and its subsets consisting of Machine Learning, Natural Language Processing, Speech Recognition, Image Recognition, etc. have pros and cons in equal measures. However, mankind has always got the best out of technology while finding workarounds to suppress its harmful side effects. AI is not an exception to that. A distant look into the future based on the current developments indicates that AI’s future will be robust, filled with promises, and a better world for humans. 

There are specific trends that indicate this possibility. The purpose of this blog is to unravel those trends and showcase a holistic approach to AI without succumbing to the paranoia surrounding it! 

Advancements in Natural Language Processing (NLP)

Natural Language Processing enables AI to understand human language by translating written or voice signals into computer language that allows AI to follow commands or perform tasks. A classic example of NLP in daily life is Amazon Alexa. According to Adi Agashe, Program Manager at Microsoft, “Alexa is built based on Natural Language Processing (NLP), a procedure of converting speech into words, sounds, and ideas.” (Towards Data Science) 

Recent advancements in natural language processing (NLP) include:

Pre-training of NLP models

Pre-training NLP models on large amounts of text data has become popular as it significantly improves the accuracy of AI predictions and results. 

The most notable example is BERT (Bidirectional Encoder Representations from Transformers), which has been pre-trained on a large corpus of text and has set new state-of-the-art results on a wide range of NLP tasks.

Multi-lingual models

Latest NLP models are capable of understanding and translating multiple global languages, such as XLM-R, which is trained on a diverse set of languages. It makes AI capable of understanding 100+ languages and thus improving task performance. 

Transfer learning

In simple terms, transfer learning means banking on the training dataset of a previous NLP project as the starting point for training or fine-tuning a new NLP project. 

What impact does NLP create on customer service and customer engagement?

Natural language processing (NLP) can have a significant impact on customer service and customer engagement in several ways:

• Chatbots

NLP can be used to create chatbots that can understand and respond to customer queries in natural language. These chatbots can be integrated into websites, mobile apps, and messaging platforms, making it easy for customers to get help or information at any time.

• Automated customer service

NLP can be used to automate customer service tasks, such as answering frequently asked questions or routing customer queries to the appropriate department. This can improve the efficiency of customer service and reduce wait times for customers.

• Sentiment analysis

NLP can be used to analyze customer feedback, such as social media comments or survey responses, to understand the sentiment behind it. This can provide valuable insights into customer satisfaction and help companies identify areas for improvement.

• Personalization

NLP can be used to personalize customer interactions by analyzing customer data and using it to make recommendations or offer personalized deals.

• Voice assistants

NLP can be used to create voice assistants that can understand and respond to voice commands, which can improve the customer experience and make it more convenient for customers to interact with a business.

The mushrooming growth of explainable AI

Explainable AI, also known as “XAI” refers to the ability of an AI system to explain its decision-making process and reasoning. In other words, XAI aims to make transparent and understandable the thought process of the AI system to AI humans. 

For example, imagine a bank using an AI system to appraise loan applications before accepting or rejecting them.  With XAI, the bank would be in a better position to understand why the AI system approved or denied a particular loan application.

 Further, the bank could get into granular details like whether the loan was denied because the applicant had a low credit score, or that it approved the loan because the applicant had a high income and a long history of paying off debt. This transparency allows the bank to make sure that the AI system is making fair and unbiased decisions.

All said, Explainable AI (XAI) reinforces trust in AI systems that can help businesses make better decisions and build trust with customers and other stakeholders.

Why is XAI important and why is it gaining popularity?

Since its inception, how AI thinks and processes information to arrive at outcomes has been a heated topic of debate in the tech community. According to NextMSC, the global explainable AI market size is predicted to reach 21 billion USD by 2030 with a CAGR of 18.4% from 2022-2030.

The upward growth in XAI would be fueled by the organizational need to achieve improved decision-making, improve trust and transparency in AI, and a need for reducing the risk of bias.

Many industries such as finance, healthcare, and defence are now looking at XAI as a means to increase the transparency, accountability and trust of their AI systems. The growth in the explainable AI market driven by the increasing demand for transparent and trustworthy AI is a trend that will shape Artificial intelligence in 2023 and beyond.

The rise of generative models for text and images

Generative models, such as GPT-3, is a type of artificial intelligence that can generate new text, images, or other types of media based on prompts/instructions provided by the user. ChatGPT-3, which became extremely popular in recent months, is a language generation model developed by OpenAI. It has been trained on a massive amount of text data collected till 2021 and has been proven capable of generating high-quality text that mimics the writing style and tone of humans.

Content creation is perhaps the biggest and most popular use case of GPT-3. For example, GPT-3 can be used to generate news articles, blog posts, product descriptions, and other types of written content based on prompts. Prompts are short text instructions that the user provides the AI model to act on. The prompt asks the AI system to take on a specific role, for eg: a marketer, a project manager, a programmer, etc., and perform tasks like write code, product descriptions, etc. The model can also be fine-tuned to write in a specific style or tone, making it a useful tool for digital marketing.

(FYI, this blog is written by a human because there are specific instructions from top search engines from Google about how they flag AI-created content!) 

Following content creation, personalization is another use case that GPT-3 can support. By analyzing customer data taken from emails, chatbot conversations, surveys, etc. GPT-3 can generate tailored content that is specific to an individual customer’s interests and preferences.

Caveat aboutChatGPT-3

In its current state, ChatGPT-3 is not entirely foolproof. It has several limitations beginning with the limited data upon which it is trained. Further, there is a risk that information provided by GPT-3 could be erroneous, often taking the form of misinformation. Perhaps, one development that the IT space can look forward to is regulating the use of such tools and putting in place controls to avoid misuse. 

Further, OpenAI itself has written on its blog that “ChatGPT is not connected to the internet, and it can occasionally produce incorrect answers. It has limited knowledge of the world and events after 2021 and may also occasionally produce harmful instructions or biased content.” All these factors make AI-produced content risky to use. 

Cybersecurity becoming sharper with Artificial Intelligence

Cyber threats show no sign of decline. Cybercriminals are getting more inventive with every passing day. The excessive reliance on smartphones and the pandemic-led growth of the digital economy have also resulted in a sudden spurt of cyber security threats. 

Fortunately, AI is evolving fast enough to lend a helping hand to humans to thwart cyber securities. According to Prudence Research, the global Ai in cybersecurity market size is expected to touch $102.78 billion by 2032.

Source: https://www.globenewswire.com/news-release/2023/01/23/2593136/0/en/Artificial-Intelligence-AI-In-Cybersecurity-Market-Size-USD-102-78-BN-by-2032.html

AI can be used in several ways to enhance cybersecurity, including

Threat detection 

AI-based systems can be used to detect and analyze patterns of behavior that indicate a cyber attack. For example, an AI system can monitor network traffic for unusual patterns that may indicate a phishing attempt or a malware infection. This is typically useful in thwarting botnet attacks that use brute force to take down a website or a user’s profile. 

Incident response 

AI can be used to automate incident response procedures, such as isolating infected systems and containing the spread of malware. AI-based systems can also help security teams to triage and prioritize security alerts, enabling them to respond to the most critical threats more quickly. This prioritization of threats and their special treatments helps in thwarting the further spread of the attack, causing a widespread downtime that ransomware usually does. 

Vulnerability management 

AI can be used to identify vulnerabilities in systems and applications, such as unpatched software, and recommend or perform remediation actions. They can be used in conjunction with quality assurance tests that are used to spot vulnerabilities in the system. 

There are still more AI use cases for cyber security, however, they would be limited to automating repetitive and time-consuming tasks that otherwise human cyber security experts used to do earlier. 

In conclusion: Inching towards an AI-dominant future

In conclusion, the advancements in Artificial Intelligence are rapidly changing the way we interact with technology. It is also having a residual effect on several major industries and how they operate in their respective domains. 

The advancements in Natural Language Processing (NLP) are making it possible for machines to understand and generate results accurately and with the same context and brilliance that humans are renowned for. The use of chatbots for customer service and the subsequent increase in customer satisfaction is a worthwhile metric that proves this fact. 

Explainable AI (XAI) is getting better at removing the stigma and trust issues that the world has with AI. Very soon, XAI will make it easy for organizations to trust AI more leading to better decision-making and improved service to their customers. 

Lastly, the use of AI in cybersecurity is also on the rise, helping organizations to detect and respond to cyber threats more effectively and proactively than their human counterparts. 

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