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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

Published

2 years ago

July 12, 2021

TwinzTech

Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

Table of Contents

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

AI Tools

Achieving Business Agility with AI-Integrated ERP Implementation

Let’s explore some common hurdles that businesses might face during AI-Integrated ERP implementation and discuss strategies to overcome them:

Published

1 week ago

August 2, 2023

TwinzTech

Achieving Business Agility with AI-Integrated ERP Implementation

In today’s fast-paced and fiercely competitive business world, adapting quickly and effortlessly is more critical than ever. This is where the concept of business agility comes into play. Business agility is all about being nimble, flexible, and responsive to changes in the market, customer demands, and industry trends. It’s like being a swift and graceful dancer who effortlessly adjusts their moves to match the rhythm of the music while everyone else struggles to keep up.

Now, how can a business achieve such agility? This is where Enterprise Resource Planning (ERP) systems step in. Imagine an ERP system as the ultimate conductor of a well-orchestrated business symphony. It combines various departments and processes under one roof, harmonizing their efforts and enabling seamless collaboration. ERP systems streamline operations, optimize workflows, and provide a comprehensive view of business data, allowing decision-makers to stay on top of their game.

ERP examples include Oracle Cloud Enterprise Resource Planning – NetSuite, Microsoft Dynamics 365, and others.

Table of Contents

1. The Role of ERP in Enhancing Agility

Let’s dive deeper into the role of ERP systems in enhancing agility and the benefits they offer in centralizing data, automating processes, reducing operational inefficiencies, and fostering data-driven decision-making.

Centralizing Data: One of the key ways ERP systems contribute to organizational agility is by centralizing all essential data. Instead of having data scattered across different departments and systems, an ERP system acts as a single source of truth, providing a unified and comprehensive view of business information. This centralized data repository enables faster access to critical information, facilitating better communication and collaboration between departments. With everyone working from the same data, it becomes easier to respond swiftly to changes, make informed decisions, and adapt strategies as needed.

Automating Processes: ERP systems streamline and automate various business processes, from finance and accounting to inventory management and human resources. By automating routine tasks and workflows, businesses can save valuable time and resources, which can be directed toward more strategic activities. This automation increases operational efficiency and minimizes the risk of human errors, ensuring that processes run smoothly and consistently. This level of automation is crucial in maintaining agility, especially when responding quickly to market fluctuations and customer demands.

Reducing Operational Inefficiencies: Process inefficiencies can act as roadblocks to business agility. ERP systems identify and address these inefficiencies by standardizing processes and eliminating redundant tasks. By streamlining operations, businesses can significantly improve their overall efficiency and responsiveness. For example, ERP systems can optimize inventory management, reducing stockouts and overstocking, improving customer satisfaction and cost savings. Businesses can better adapt to market changes and seize new opportunities by minimizing wastage and maximizing resource utilization.

Fostering Data-Driven Decision-Making: In today’s data-driven world, making decisions based on accurate and up-to-date information is crucial for business success. ERP systems facilitate data-driven decision-making by providing real-time insights and analysis. Managers and executives can access key performance indicators, financial data, and operational metrics at their fingertips, allowing them to make informed decisions swiftly. Quick and data-driven decision-making is vital in an agile organization to capitalize on emerging opportunities and navigate challenges effectively.

2. Integrating AI into ERP Systems

Let’s shift our focus to how AI can specifically enhance ERP systems.

Advanced Analytics: AI empowers ERP systems with advanced analytics capabilities, enabling businesses to extract valuable insights from their data. With AI-driven analytics, businesses can uncover patterns, trends, and correlations that might be overlooked. These insights can help identify new market opportunities, understand customer behavior, and optimize operational processes.

Predictive Capabilities: AI equips ERP systems with predictive analytics, allowing businesses to anticipate future outcomes based on historical data and current trends. For example, AI can forecast product demand, helping companies adjust their inventory levels accordingly and avoid stockouts or overstocking. Predictive maintenance is another valuable application where AI can detect potential equipment failures before they occur, minimizing downtime and maintenance costs.

Business Agility with AI-Integrated ERP Implementation

Intelligent Automation: AI brings a new level of automation to ERP systems, making processes even smarter and more efficient. Intelligent automation enables ERP systems to handle complex tasks autonomously, reducing the need for manual intervention. For instance, AI-powered bots can process invoices, perform data entry, and generate reports automatically. This frees up employees’ time and allows them to focus on higher-value tasks that require creativity and critical thinking.

Personalized User Experience: AI can enhance the user experience within ERP systems by providing personalized recommendations and interfaces. The system can learn from individual users’ behaviors and preferences to tailor the user interface, making it more intuitive and efficient for each user.

Businesses can achieve greater agility and competitiveness by integrating AI into ERP systems. The combination of AI’s advanced analytics, predictive capabilities, and intelligent automation empowers organizations to make data-driven decisions faster, optimize their operations proactively, and respond rapidly to changing market dynamics.

3. Overcoming Challenges in AI-Integrated ERP Implementation

Integrating AI with existing ERP systems can be a transformative journey for any organization, but it comes with its fair share of challenges. Let’s explore some common hurdles that businesses might face during AI-Integrated ERP implementation and discuss strategies to overcome them:

Data Compatibility: One of the significant challenges is ensuring that AI algorithms can seamlessly work with the data stored in the ERP system. Legacy ERP systems might sometimes need the data structures or formats required for AI applications. To address this, businesses should invest in data integration solutions that bridge the gap between AI and ERP systems. Data cleansing, transformation, and normalization may be necessary to make the data AI-friendly.

System Complexity: Integrating AI into an existing ERP system can introduce additional layers of complexity. The interactions between AI modules and the ERP system must be carefully designed to avoid conflicts or disruptions. This requires a thorough understanding of AI technologies and the ERP system’s architecture. Engaging experienced ERP consultancy consultants or working closely with the ERP and AI vendors can help navigate this complexity.

Change Management: Introducing AI often means introducing significant changes to existing workflows and processes. Resistance to change is a common challenge during AI-Integrated ERP implementation. To address this, businesses should prioritize change management efforts. Communicate the benefits of AI and how it will improve employees’ work experience and productivity. Involve key stakeholders early and encourage open dialogue to address concerns and build buy-in.

Employee Training: Implementing AI requires employees to learn new tools, techniques, and processes. Training is crucial in helping employees embrace AI and use it effectively. Develop comprehensive training programs tailored to different user groups, from end-users to administrators. Provide hands-on training, workshops, and documentation to help employees feel confident and competent using the AI-enabled ERP system.

Data Security and Privacy: Integrating AI into ERP systems involves handling sensitive business data. Businesses must address concerns related to data security and privacy. Implement robust security measures to protect data from breaches and unauthorized access. Ensure compliance with data protection regulations and consider anonymizing or aggregating data when applying AI algorithms to protect individual privacy.

Performance and Scalability: AI applications can be computationally intensive, and ERP systems must be able to handle the increased workload. Businesses should assess their ERP infrastructure’s performance and scalability to ensure it can accommodate AI without sacrificing system responsiveness. Upgrading hardware, leveraging cloud-based solutions, or optimizing existing infrastructure might be necessary.

Continuous Monitoring and Maintenance: AI-Integrated ERP solutions require ongoing monitoring and maintenance to perform optimally. Implement processes for regular model validation, data quality checks, and algorithm updates. Establish a dedicated team responsible for AI maintenance and support and empower them with the necessary tools and resources.

4. The Takeaway

Embrace the power of AI-Integrated ERP to fuel your business’s agility and stay ahead in the competitive landscape. Leap data-driven decision-making, seamless automation, and enhanced efficiency. Start your journey to success with AI-ERP integration today and shape a future of endless possibilities. Don’t wait; act now!