Emerging Trends in Artificial Intelligence and Machine Learning in 2023 & Beyond

Introduction

You call artificial intelligence and machine learning magic. Your friend, on the contrary, deems it as just another revolution – devouring some jobs, flooding with a double of new jobs. While this debate continues in the chorus, PwC’s global AI study says that the global economy will see a boost of 14% in GDP by 2030, amounting to a potential increase of $15.7 trillion.

And why not? This technology has reshaped the market, introduced Alexa, got Netflix to give you binge-worthy recommendations, eased the effort you put into communicating with a customer service agent – and recently, once again – made headlines with ChatGPT.

There are a number of emerging trends in AI and machine learning which potentially have or will impact the way industries thrive and sustain.

If you are an aspiring tech professional, it’s both important and exciting for you to tap the possibilities these star technology terms have for you. That said, read about the hottest trends in AI and ML in 2023 and how they are fueling business growth.

What are AI and Machine Learning?

Artificial intelligence refers to computer systems or algorithms that can simulate human intelligence and mimic cognitive functions, including problem-solving. As the term suggests, “artificial intelligence” is a human-like cognitive ability. This implies that specific algorithms and systems can “learn or comprehend beyond what has been told” independently if provided with data and a set of instructions. Recommendation engines like Spotify and virtual assistants like Apple’s Siri are popular examples of this technology.

Machine learning is an area of artificial intelligence that allows a computer system to predict and decide by extracting information from structured and semi-structured data. It uses data to create models that can be used to perform certain tasks like predicting sales. Image recognition, Google translation, and auto-friend tagging suggestions on Facebook are everyday examples of machine learning.

Differences Between AI and Machine Learning

Before we explore emerging trends in AI and machine learning in 2023, let’s be clear on one fact: AI and ML are not the same— they share eminent differences.

It is simple. AI is present in a variety of applications that mimic humans, and ML enhances the reasoning power of such applications. Simply, AI is a broader concept.

Nevertheless, you will often find these two terms being used together. More often than not, you will find them working together. Take search engines as a testament to this. When you type something in the search bar, it’ll use machine learning algorithms to predict what you may want to search.

Artificial Intelligence Machine Learning
Artificial intelligence (AI) refers to the ability of computer systems to perform tasks that require human intelligence. Machine Learning (ML) refers to the use of data and algorithms to learn and adapt.
It is focused on decision-making. ML is focused on learning using machine learning algorithms.
It aims to develop computer systems that can plan, interpret, learn, and decide like humans. It aims to learn by creating its own algorithms.
It uses structured, semi-structured, and unstructured data. It uses structured and semi-structured data.
AI requires minimum human intervention. In ML, human expertise is required to train algorithms.
Siri, translation software like Google Translate, Google Assistant, and chatbots are common examples of artificial intelligence. Recommendation engines, Facebook friend suggestions, traffic alerts, etc. are everyday examples of machine learning.

Natural Language Processing (NLP)

Natural Language Processing is one of the popular trends in AI and machine learning in 2023. It is an AI technology that makes monotonous language-based processes smooth sailing. The technology eradicates the necessity of manually typing content by capturing human language using algorithms that interpret, manipulate, and output automatically.

Today, businesses take a hand from NLP applications, such as language translation, text extraction, and sentiment analysis. AI and ML experts are working on various interaction approaches that are no different from that of a human, as it may help them explore the potential of NLP. Businesses in different sectors are tapping their AI-driven prowess to enhance a number of functions.

Banking and Finance

Banking and financial institutions use NLP applications for customer management and document search. For example, HDFC and ICICI bank utilizes NLP for robust customer engagement via chatbots. This helps the banking professionals to understand the client without them being physically present.

Healthcare

The healthcare sector can save time spent on clinical documentation, speech recognition, and interpreting clinical data with the help of NLP solutions. Computer-aided coding (CAC) is another area where NLP is significantly used in the healthcare industry. It comes in handy when certain patients need personalized health solutions. IBM Watson’s NLP capabilities, IBM’s AI engine, were used for healthcare management at the Memorial Sloan-Kettering Cancer Center.

Manufacturing

The manufacturing industry is embracing this technology by providing solutions like task automation, quality control (by scanning data to identify patterns), maintenance & repair (by analyzing sensor and equipment data), and predictive maintenance. For example, the European Union (EU) plans to incorporate NLP in studying building information to enhance the efficiency and productivity of the construction industry.

Other real-world examples of NLP in action include Chatbots. The chatbot market is estimated to reach from $40.9 million in 2018 to $454.8 million by 2027. Apart from chatbots, Alexa, Google Assistant, and Siri are the iconic names in the world of NLP.

Computer Vision

Computer vision is a branch of AI that allows computer systems to derive insights using visual data and images and act accordingly based on the information. In simple words, just as AI enables computers to mimic the human brain, computer vision helps them to “see.” As a result, computer vision works quite similarly to the way the human eye does. Human vision uses information based on visually perceived data. The machine uses visual data through algorithms, videos, and images. The data is then parsed and segregated into different categories.

Transportation

The transportation industry is embracing technological advancements, with computer vision taking the forefront. Pedestrian detection, self-driving cars, and road condition monitoring are the finest examples of their implementation. Autonomous driving technology heavily relies on this technology. Companies like Tesla, Toyota, etc., actively work on robust computer vision mechanisms that enable self-driving systems to function properly.

Healthcare

Computer vision has changed the way doctors analyze cancer detection, X-Ray analysis, and CT scans. While doctors still manually check diagnostic results and read reports, computer vision does its fair share of jobs by automating various tasks like analyzing images. For example, the UK NHS specialists use the NVIDIA DGX-2 system in their radiology operations.

Construction

Construction business is one of the fastest ones to adopt computer vision – and do it fondly. Many of the crucial tasks like workplace hazard detection, asset inspection, and monitoring machines and equipment for maintenance requirements are the ways in which the industry has leveraged computer vision.

Apart from these use cases of computer vision, retail is the one to watch out for. Computer vision simplifies tasks in the retail industry by performing inventory scans, notifying stock-outs, and helping people self-checkout, which is ultimately improving customer experience.

Edge Computing

Edge computing is a concept of distributed computing frameworks bringing computing and the source of data closer to each other. Edge, here, means processing data at or near its source – which enables faster speed and results. With edge computing, data is processed in real-time, locally, and closer to where it is generated. This approach reduces the latency and bandwidth required for transmitting data to a centralized location for processing.

It has become a huge market now, and its global revenue is expected to reach $59,633 million by 2030, at a CAGR of 21.2%. Automation in retail and autonomous robots are the common use cases of edge computing.

Manufacturing

For manufacturers, edge computing is leveraged to analyze and filter data, sending only the relevant information to the server in a cloud or on-site. This enables manufacturers to monitor all the information and assets. Microsoft Azure IoT Edge is a widely used platform that helps manufacturers run AI and machine learning algorithms on IoT devices using edge computing.

Remote Workspaces

Edge computing is widely used for remote working arrangements to increase efficiency and bandwidth. Especially after the COVID-19 pandemic, many companies are using platforms like the Google Cloud Platform, ADLINKS, etc., to leverage edge computing functionalities.

Oil and Gas

Oil and gas industry is the one where massive amounts of data are produced by oil rigs. But when it comes to analysis, 99% of data remains unused, which leads to the lack of real-time access. By the time the data is analyzed, it may not be relevant anymore.

With time, machines have evolved, and the volume of data and information has increased significantly. It all boils down to one major demand: keeping up with the pace and efficiency. Edge computing is helping industries achieve the same.

Deep Learning

Deep learning, a subsection of machine learning, refers to a machine learning technique that helps machines perform tasks like humans. The technology is based on artificial neural networks (networks with multiple layers of processing) that extract more accurate features from complex data.

Deep learning is garnering popularity lately for many reasons, primarily because of its multiple (even hundreds) processing layers. These models bring about accuracy that can even surpass that of humans at times.

Deep learning has changed the way humans think, decide, and act, given the privileges it provides. And that’s why businesses are enjoying a good time unleashing their offerings. Here are some of the most prevalent ones:

Autonomous Driving

Self-driving vehicles largely employ machine learning models based on CNNs (convolutional neural networks). These models identify and classify objects, like zebra crossing, road signs, etc., and learn from them. Using this learning, they develop programs for autonomous driving vehicles.

E-commerce

E-commerce platforms provide tailored experiences to customers based on their past purchases and browsing history. Alibaba, the largest e-commerce marketplace, uses deep learning to recommend products to customers as per their browsing history.

Entertainment

OTT platforms are thriving, and easy accessibility is the major factor contributing to their success. To boost user experience, streaming apps are implementing deep learning. Netflix, one of the leading streaming platforms in the world, uses deep learning algorithms to analyze the tastes and preferences of viewers.

Explainable AI

While AI gives you the output, Explainable AI gives reasoning behind it. Defined as a set of methods/ processes. Explainable AI makes the results created by machine learning algorithms of AI understandable and reliable to users. It is interpretability that allows humans to understand the information a model offers, what it is learning, and why it is generating certain results.

Explainable AI has a stronghold in today’s market space as businesses are indulging in AI and ML and want these models to be transparent and trustworthy.

Explainable AI enhances transparency and fairness and also improves the accountability of AI systems. It helps the user understand the explanation for a particular prediction or reasoning behind the decision made by ML models. Here are some of the common use cases of explainable AI that exemplify its usage in different sectors:

Healthcare

In healthcare, explainable AI can help medical professionals explain the diagnosis to the patient and help them understand how a treatment plan will work. It can also be helpful for medical imaging data for diagnosis.

Autonomous Vehicles

Autonomous vehicles are trained with the help of explain ability techniques, which incorporate human-readable descriptions in order to explain the reasoning behind a prediction.

HR

Another prevalent example of this is in the Human Resource domain; explainable AI can be helpful in explaining the reason behind a particular status of the job application.

Moreover, Explainable AI systems in the banking sector help with explanations for the approval or rejection of loan applications. These systems are useful in every AI-driven business that involves factors like accountability and reliability.

So, these are the fierce and amazing trends in AI and machine learning in 2023. Let us now take a look at the top industries that are extracting the best out of these advancements.

How Emerging Trends are Impacting Businesses and Industries

From self-driving cars to virtual makeup try-on, the most exciting technological events are happening in this century! These emerging trends in AI and machine learning in 2023 are “revolutionary” by all standards – no matter the industry. They are helping businesses scale and are opening the door to more opportunities. Moreover, they are eliminating the distance between the workforce and efficiency.

54% of executives claim that AI has brought increased productivity to their desks. Because why not?

Automation, streamlining, tracking, and a lot of terms have become prevalent in the business world with the help of AI and ML. While there is a behemoth of advancements – every sector has its own leverage to make out of trends in AI and ML in 2023.

AI and ML in Healthcare

Healthcare systems have the potential to make a significant change for people, save lives, and save money. That said, it is one of the major hubs where AI and ML trends are to thrive. Several business giants, including Microsoft, and startups, have already commenced the development of healthcare tools and processes using deep learning, natural language processing, and explainable AI to aid the system. Research predicts that the global AI market in healthcare will flourish at a CAGR of 37.5% between 2023 and 2030.

In healthcare, diagnosis is the most notable use case of AI and ML in 2023. Technology is helping doctors identify diseases and interpret diagnoses. Machines can now read reports and diagnostic tests to identify the issue. Healthcare professionals also take a hand from wearable technology to gather real-time data. Another prevalent use case of AI and ML in healthcare is personalized treatment. By interpreting large sets of data, the technology helps professionals get precise prescriptions for the patient.

Real-World Examples of AI and ML in Healthcare

Here are a few examples of AI and ML in action in the healthcare industry.

  1. The world-famous Mayo Clinic undertakes robotic surgeries in its urology and gynecology departments. They use the da Vinci System with robot-assisted devices.
  2. The Hospital for Sick Children, Toronto, uses an AI-based MendelScan tool to analyze historical patient data.

AI and Machine Learning in Finance

Banks and financial institutions have a lot to gain from the current AI and ML trends. The technology will not only help boost customer experience but will also allow the industry to reduce costs. According to research by Autonomous Next, banks will be able to minimize costs by 22% by 2030 with the help of artificial intelligence technologies, which will help them save up to $1 trillion.

The credit score report is a common use case of AI and ML in the finance sector. The technology has simplified the entire journey of a user checking their credit score online. Every day, millions of individuals want to know the whereabouts of their credit health, and with a mathematical model, it is no longer a challenge. Another predominant use case is a personalized experience. Natural language processing is helping banks and financial institutions to improve customer experience by providing them with tailored services, such as personalized offers, chatbot services, etc.

Real-world Examples of AI and ML in Finance

Here are some examples of AI and ML in the banking and financial sector.

  1. The TransUnion Bank utilizes an ML-powered budgeting tool called Mint. This tool offers advice on raising credit scores.
  2. Wells Fargo, a famous commercial bank, uses AI-powered chatbots to provide account information.

AI and Machine Learning in Retail

In retail, success is mostly a matter of pace. The industry is employing techniques and implementing AI and ML solutions to boost productivity and stay ahead of the competition. AI and ML solutions are helping this sector with operations and costs by optimizing business processes. The stronghold of technology is such that AI services in retail are forecast to amount from $5 billion to over $31 billion by 2028.

Real-world Example of AI and ML in Retail

Taco Bell introduced a seamless way to order food through Tacobot. This AI-driven solution allows customers to order in larger quantities through a simple step – texting. The bot is integrated with Slack, which makes it super easy for customers to type and order!

AI and Machine Learning in Manufacturing

The manufacturing industry is yet another arena where the rising trends in AI and ML bring significant contributions. In fact, 43% of manufacturers have employed data scientists in their workforces, and 35% are planning to do it within the next five years. Moreover, a study by McKinsey reveals that manufacturing companies implementing AI have welcomed revenue and cost savings. While 16% of the companies surveyed witnessed 10 to 19% drop in costs, and 18% noticed up to 10% boost in their revenue.

The trends in AI and machine learning in 2023 are also redefining the management standards for the manufacturing industry. First and foremost, manufacturers can now monitor the areas of their operation in real-time – it solves many challenge spots, including resource allocation.

Real-world Example of AI and ML in Manufacturing

The BMW Group mobilized image recognition to perform inspections and run quality tests. At the crux, the emerging trends in AI and machine learning in 2023 are paving the way to effectiveness, traceability, and monetary relief for manufacturers.

How to Stay Ahead of the Curve with AI and Machine Learning

It can be tempting to take the plunge for a full-fledged AI and ML implementation. But more often than not, businesses find themselves encountering ambiguity in planning and road mapping. The most important parameters that can make or break a plan are: onboarding the right people, identifying and addressing the challenges, and keeping operations in alignment with ethics and responsibilities.

Hiring and Training for AI and Machine Learning

Before you make those AI and machine learning trends in 2023 work for you, find the right people who know how to make them work.

The most popular and in-demand job roles in AI and ML include data scientists, machine learning engineers, and big data engineers. The expertise and the number of people a business needs to hire depend on the project and what it is that it seeks to achieve or solve.

Businesses must also emphasize training new hires for AI and machine learning. It is crucial to ensure that the team is both innovative and analytical. Apart from that, it is imperative to have a dynamic AI and ML culture within the business environment. It means being open to creating a diverse team and getting familiar with the data-driven culture and a flock of tools.

Challenges and Solutions in Implementing AI and Machine Learning

Companies planning to introduce AI and ML to their functions are faced with unexpected challenges and encounters. These challenges include the identification of the right data, budget requirements, data, and privacy. Moreover, hiring the right people, integration with existing systems, and complex AI/ML algorithms also pose a roadblock for companies.

In order to overcome these challenges, businesses need to define their goals and priorities. It is critical to be familiar with different technologies that fall under the umbrella of AI and machine learning and how to use them. Here’s how businesses are using these technologies:

Facebook

The social media giant uses DeepText to understand and interpret the sentiments of posts. It also uses DeepFace technology that helps the platform automatically identify your face in a photo.

IBM

IBM has always been bold with the implementation of new technologies in AI. The introduced Project Debater. It is the first AI system that is capable of debating complex subjects and can help people make arguments.

Tencent

The company means it when it states, “AI in All.” Tencent is all into incorporating AI in its operations to develop products catering to a variety of customer segments, including gaming, live streaming, and payments.

Ethics and Responsibility in AI and Machine Learning

AI ethics refers to a system intended to reinforce moral values, accountability, and responsible use of technology. With AI and ML spanning a whopping space in various operations, companies are participating in the development of ethics and responsibilities that aid any decisions regarding the appropriate use of AI. The system focuses on four areas: Responsibility, Explainability, Fairness, and Misuse.

Ever since its emergence, the technology has intrigued the world in some way or another. At the same time, there have been some landmark cases where AI went wrong and sparked a big question about its future.

Microsoft

The tech giant made headlines with Tay, a cool-headed chatbot that could go on with casual chats on Twitter. The chatbot turned out to be mayhem when it shared offensive tweets commenting on historical episodes like Hitler and 9/11. The chatbot was programmed to interact with humans so it could communicate like them. However, the idea went downhill and added to an AI gone wrong event.

Uber

Another real-world example of AI gone wrong comes from Uber, which became newsworthy when its self-driving car hit a pedestrian in Arizona. A lawsuit was filed against the autonomous car, which was no less than a beacon warning the world about the mindful use of technologies.

Amazon

Bias was one of the major contributors to bringing AI to the moral court. Amazon introduced an AI recruitment tool, which was supposed to shortlist the top resumes out of thousands. What began with an approach to efficiency in HR management boiled down to bias against women. The data had been trained using the applications of the last ten years, among which most were men. Thus, it ended up finding men more suitable for the roles in the tech industry.
AI and ML may be more efficient, but they are not humans. Businesses across all industries much consider ethical concerns and abide by the safeguards to minimize any collateral damage.

Conclusion

Today, artificial intelligence is nearly a $100 billion market, which will be twenty times bigger by 2030. These emerging trends in AI and machine learning in 2023 are setting the trail of automation, accuracy, and experience that businesses can thrive on. If we talk about mainstream technologies, then deep learning and NLP have already established a stronghold, decking up customer experience and allowing businesses to scale more. These fiercely burgeoning trends in AI and ML in 2023 are not far from cracking into more businesses in the coming years.

It’s only a matter of the right knowledge and the right implementation at the right time.

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About TimeLinx
TimeLinx delivers innovative project & service management software as a complete solution that perfects the sell-track-manage-support-bill cycle that services organizations must have to delight their customers; TimeLinx brings the cycle together in a single application that offers less frustration, better project management, complete reporting, and improved profitability – all specially designed for Infor and Sage.

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