The Impact of AI on Business - Assessing Production Readiness

AI is probably the hottest topic nowadays in the IT landscape. What could be more impressive than reproducing our cognitive capabilities into a hardware-powered infrastructure that can sometimes outperform its creators? This is true on the level of principles - however, details and practical factors of applicability together make the puzzle complete.  When we take a look at AI from an implementation business perspective, the most common question we will face is:

"Is it production ready?"

We will try to answer this question in the following article.

Nothing new…

Mathematical models for neural networks deduced from the synaptic connections found in the human neural system have been around for decades. Their early implementations as computer software also emerged at least 40 years ago. Yet their progress was limited by technological and financial barriers - considering the unit cost of computational capability. In recent years hardware technology and the appearance of hyperscalers / public clouds practically eliminated this obstacle. It is enough if we think about Microsoft's designation of Azure cloud solutions as "supercomputing for everyone" - and this works in practice! So we can say, that there are very valuable relics from our past that can finally shine.

These are the following:

• Neural network

Neural networks draw inspiration from the intricate workings of the human brain. They comprise interconnected nodes, referred to as neurons, which process information through weighted connections and activation functions. In the past, the training of deep neural networks demanded substantial computational power and time, rendering them impractical for many applications. However, today, the landscape has evolved. Graphics Processing Units (GPUs) have revolutionized neural network training. This breakthrough has paved the way for the widespread adoption of deep learning, enabling its use across a broad spectrum of applications, from image and speech recognition to natural language processing.

• Machine learning algorithms

Diverse machine learning algorithms, encompassing decision trees, support vector machines, and k-nearest neighbors, hinge on mathematical principles to discern patterns and classify data. Historically, these algorithms grappled with the processing of extensive datasets and complex models, largely due to computational limitations. However, contemporary solutions, such as distributed computing, parallel processing, and cloud-based platforms, have ushered in an era where big data can be handled with remarkable efficiency. This transformation has not only enhanced the scalability of machine learning algorithms but has also bolstered their accuracy, rendering them applicable to real-world challenges.

• Reinforcement learning

Reinforcement learning finds its foundation in the Markov decision process and dynamic programming, with agents learning optimal actions through iterative trial and error. In the past, training reinforcement learning agents within intricate environments proved computationally intensive and time-consuming. Nevertheless, the advent of advanced simulation environments and the power of distributed computing have catalyzed a paradigm shift. This has led to groundbreaking achievements in fields such as autonomous robotics and AI-driven game-playing.

• NLP (Natural Language Processing)

NLP algorithms rely on probabilistic models, context-based analysis, and linguistic rules to comprehend and generate human language. Historical limitations in NLP, including challenges in tasks like machine translation and sentiment analysis, were attributed to the complexities of language and the substantial data volumes required for effective training. Presently, the confluence of vast text corpora and hardware acceleration has wrought transformative change. This synergy has markedly augmented the accuracy and efficiency of NLP models, unlocking a multitude of applications.

• Computer Vision

Computer vision algorithms leverage mathematical techniques, notably convolutional neural networks (CNNs), to scrutinize and interpret visual data. The real-time analysis of images and videos once posed daunting computational challenges. However, the advent of high-performance GPUs and specialized hardware has rendered real-time computer vision applications, such as facial recognition, autonomous vehicles, and object detection, not only viable but also highly reliable.

Or maybe there is something…

Of course, we cannot ignore the latest developments in this field. Some of these innovations are listed below:

• Intersection of quantum computing and AI

Combining the power of quantum computing with AI is a growing area of exploration. This synergy is expected to significantly boost computational capabilities, enabling quicker resolution of complex problems compared to traditional computing methods.

• Explainable AI (XAI)

Efforts are being made to develop AI that is clear and comprehensible. The aim is to produce AI systems that people can easily interpret and understand, an important factor in building trust and making ethical choices.

• Edge AI

This involves processing AI tasks directly on local devices. This approach minimizes the need to transfer data to remote servers, thus enhancing security and quickening processing speeds, which is particularly beneficial for IoT devices.

• AI-driven hardware acceleration

Advancements in creating hardware specifically tailored for AI tasks are ongoing. Innovations include the design of unique processors such as TPUs and FPGAs, which are designed to efficiently handle AI operations, thereby increasing processing speed and overall efficiency.

• Generative AI

Beyond NLP and computer vision, generative AI is making strides, especially in generating new digital content like text, images, and videos. This aspect of AI holds considerable potential for creative sectors.

• Evolution of MLOps (Machine Learning Operations)

The evolution of MLOps (Machine Learning Operations) represents the integration of machine learning (ML) models into the broader context of IT operations and software development. MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. This practice includes automation and scaling of ML deployments, along with improved efficiency, quality, and consistency in ML model development and deployment.

The growing footprint of AI is also proven by the fact that there are emerging areas and fields of science. One of these is the science of producing synthetic data for teaching AI engines for specific tasks. This approach is very interesting considering its fundamentals: to succeed, the generated datasets should resemble the reality of the actual problem we are trying to treat by using AI. This means that the closer to reality our model is, the more successful we are – however this also means, that if we can succeed, we are already in possession of the model, we are trying to teach to the AI. The equation is not so simple in this case, meaning that the reliance on synthetic data alone presents several major challenges and potential threats to the efficacy and reliability of AI models:

• Lack of Real-World Complexity

Synthetic data may lack the diverse range and intricacies present in real-life data. Consequently, AI models trained on such data might excel in predictable, simulated settings but struggle in real, more complex, and changeable environments. This issue, often referred to as domain shift, highlights a gap between simulated and actual scenarios.

• Bias and Representation Issues

When synthetic data is generated using algorithms based on incomplete or biased real-world data, it could embed these biases into the AI models. Such models might then fail to accurately represent the diverse needs and situations of the wider population they're designed to assist.

• Overfitting Risks

Training AI models solely on synthetic data can lead to overfitting, where the models become overly optimized for the specific characteristics of this data. This over-specialization can diminish the models' effectiveness in dealing with actual, varied data.

• Validation and Verification Challenges

Ensuring the accuracy and relevance of AI models is difficult when their training is restricted to synthetic data. For a comprehensive assessment, real-world data is crucial to ascertain the accuracy and applicability of the models' outputs in practical scenarios.

• Ethical and Legal Concerns

Relying exclusively on synthetic data for training AI models brings forth ethical and legal issues. This is particularly critical in sectors like healthcare, finance, and law, where AI decisions carry a significant impact. Questions arise regarding the ethical integrity and legal validity of decisions made by such AI models.​​​​

From the perspective of the implementator, AI has endless usages. Numerous methods exist for categorizing the solutions we offer in the field of AI. Practically, we typically employ the following approaches:

• Connecting the digital realm with the analog world
• Decision support / prediction / forecasting
• Decision making
• Generating content

The first group includes both analog to digital transfer of information like speech to text, image recognition, and stream recognition (in any given spectrum of electromagnetic waves), and the combination of these. In this field, AI opens completely new perspectives, with gradually reduced implementation costs. Qualysoft offers such systems – currently, these solutions are primarily used in manufacturing for Quality Assurance purposes.

Related portfolio elements and technical stack are the following:

• Speech Recognition Technologies: Advanced speech-to-text solutions, which are crucial for converting spoken language into digital text. This includes technologies like Google's speech recognition APIs or IBM Watson Speech to Text, offering high accuracy in diverse languages and environments.
• Image Recognition and Computer Vision Systems: Tools like OpenCV or TensorFlow are fundamental for processing and analyzing visual data. These systems can identify, classify, and interpret images, crucial for automated inspection and quality control in manufacturing.
• Electromagnetic Spectrum Analysis Tools: Specialized software and algorithms capable of analyzing data across various electromagnetic spectra. This includes infrared, ultraviolet, or X-ray spectrum analysis, which can be vital in material inspection or quality assurance.
• Deep Learning Frameworks: Frameworks like PyTorch or TensorFlow are essential for building and training AI models that handle the complexities of converting analog data to digital. These frameworks support the development of custom models tailored to specific Quality Assurance tasks.
• Automated Quality Control Systems: AI-driven systems like machine vision cameras integrated with software like Detectron or MASK-RCNN for advanced object detection and quality assessment. These systems are highly efficient in identifying defects and ensuring product quality.
• Data Processing and Analytics Engines: Technologies like Apache Kafka or Spark for processing large streams of data in real-time, which is critical in manufacturing environments where immediate data analysis is required for quality control.

In addition to our AI solutions, we offer an extensive portfolio of services that culminate in a comprehensive service package solution.

• Internet of Things (IoT) and Sensors
• Robotic Arms Physical Implementation
• Augmented Reality (AR) for Inspection and Maintenance
• IT infrastructure solutions
• Cybersecurity solutions
• Custom development
• Facility management solution
• RPA

When there is sufficient teaching data available, AI opens up new frontiers for decision-support model creation. With the corresponding technologies, we have a chance to go on a „brute force” approach in creating a decision support model. Its efficiency and accuracy greatly depend on the quantity and quality of teaching data.

Related portfolio elements and technical stack are the following:

• Transformers (PLD: DETR): These are pivotal for processing sequential data, and the Detection Transformer (DETR) model, in particular, is essential for decision support systems that involve time-series data or context-rich data sequences.
• Auto-encoders: Utilized for unsupervised learning of data representations, these are crucial for dimensionality reduction and feature extraction in decision support systems, especially when handling large and complex datasets.
• BERT (Bidirectional Encoder Representations from Transformers): This model plays a significant role in understanding and processing natural language, making it a cornerstone for NLP-driven decision support systems.
• GPT (Generative Pre-trained Transformer): Known for its advanced natural language understanding and generation, GPT models are indispensable in decision support systems that rely on intricate language processing.
• CLIP (Contrastive Language–Image Pretraining by OpenAI): CLIP's ability to understand visual tasks through learning from images and their corresponding text descriptions makes it a valuable tool for decision support models that integrate both visual and textual data.
• Detectron and MASK-RCNN: These technologies are critical for precise object detection and segmentation in images, enhancing decision support in areas requiring detailed visual analysis, like manufacturing quality control.
• HuggingFace: Offering a wide range of pre-trained models, HuggingFace is a key resource for deploying and customizing AI models quickly for diverse decision support applications.
• Google and Azure AI's Tools: Google and Azure AI provides a variety of tools, including advanced analytics and image/speech recognition capabilities, essential for multifaceted decision support systems.

One of the use cases delivered by Qualysoft is the 'Wasabi solution.' Wasabi is a popular restaurant chain in Hungary, known for its unique conveyor belt system where customers pick their desired dishes as they pass by. Our custom solution is designed to segment each plate, detect the specific type of food on it, and store this information. This enables real-time monitoring of consumption and aids in future inventory management. This particular use case highlights the power and potential of AI solutions.

Moving one step ahead, we do not use AI only to recommend a decision, we use it to make decisions and execute them.  Just think about the higher-degree self-driving car AIs – they are already here. Their quality, just as stated before exclusively depends on the quality and quantity of teaching data. Nowadays computational capabilities mean no real limit.

Related portfolio elements and technical stack are the following:

• Machine Learning Algorithms for Autonomous Decision-Making: This includes advanced algorithms like reinforcement learning and deep learning, which are essential for training AI systems to make decisions in dynamic environments, similar to those used in self-driving car AI.
• Real-Time Data Processing Technologies: High-speed data processing tools, such as Apache Kafka or real-time processing features in cloud services like AWS or Azure, are critical for handling the continuous stream of data required for real-time decision-making.
• Computer Vision and Image Processing: Technologies like OpenCV, Yolo, and TensorFlow are crucial for enabling AI systems to interpret and understand visual information from the environment, a key aspect in autonomous vehicles and other decision-making AI applications.
• Sensor Fusion and IoT Technologies: Integrating data from various sensors - LIDAR, radar, GPS, cameras - is crucial for a comprehensive understanding of the surrounding environment. Sensor fusion technology synthesizes this data, providing a cohesive picture for the AI to base its decisions on.
• Robust Neural Networks: Frameworks like PyTorch and TensorFlow for designing and training complex neural networks that can process large amounts of data and learn from it, essential for AI systems that make independent decisions.
• Simulation and Testing Software: Tools for creating simulated environments, such as CARLA or Unity’s Simulation, are necessary for testing and refining AI decision-making in a controlled, risk-free setting, especially important for autonomous vehicles.
• Natural Language Processing (NLP): For AI systems that interact with humans or need to process human language inputs, NLP technologies like BERT or GPT-3 are vital. They enable AI systems to understand and respond to voice commands or textual information.
• Predictive Analytics and Forecasting Tools: Using historical data to predict future scenarios, tools like TensorFlow Time Series or Facebook Prophet assist in making informed decisions by anticipating future conditions or events.
• Cloud Computing Platforms: Platforms such as Google Cloud AI, AWS, and Microsoft Azure provide the necessary computational power and storage capabilities, allowing AI systems to function efficiently and scale as needed.

And of course, the applied solutions mentioned above, can be combined. Le tus stay with our example of the self driving car. Image, Lidar, Radar signatures are being recognized by AI, the result is sent to decision making model that produces the commands for the actuators.

Generative AI is probably the latest product of this field of science. This is no surprise, as it requires an immense amount of teaching data, and processing capability. The most widespread solutions use large partitions (or all) of the data accessible on the Internet. To bring these solutions into action it took all the achievements of the CPU, memory, and storage industry.

Related portfolio elements and technical stack are the following:

• Generative Adversarial Networks (GANs): GANs are pivotal in image generation and modification. They work by using two neural networks (a generator and a discriminator) that work against each other to produce highly realistic images. This technology is central to creating art, enhancing photographs, and generating realistic training data.
• Transformers in NLP: Technologies like OpenAI's GPT-4 and Google's BERT are revolutionizing the field of natural language processing. These transformer models are capable of generating coherent and contextually relevant text, making them ideal for applications like content creation, chatbots, and language translation.
• Deep Learning Frameworks: TensorFlow, PyTorch, and other deep learning frameworks are essential for building and training the complex neural networks used in generative AI. They provide the necessary tools and libraries to handle the large-scale data processing required.
• Cloud Computing Platforms: Platforms such as AWS, Google Cloud, and Microsoft Azure are vital in providing the computational power and scalability needed to process large datasets and run complex AI models. These platforms enable the deployment of generative AI applications at scale.
• NLP Preprocessing Tools: Tools for text normalization, tokenization, and vectorization are crucial in preparing textual data for processing by NLP models, ensuring the data is in a usable format for model training.
• Edge Computing Solutions: For real-time generative AI applications, edge computing allows for data processing to be done closer to the source, reducing latency and making applications more responsive.

Some of the most common uses of gen AI categorized by industries:

Manufacturing

• Design and Prototyping: Generative AI can be used to create new product designs or improve existing ones. It can rapidly generate and evaluate multiple design prototypes, speeding up the innovation process.
• Predictive Maintenance: By generating simulations of machinery wear and tear, generative AI can predict when maintenance is needed, thus reducing downtime and maintenance costs.
• Project Planning and Simulation: Generative AI can be used to simulate different construction scenarios, aiding in efficient project planning and risk management.
• Automated Design Compliance: AI can automatically generate construction designs or modify existing plans to ensure compliance with regulations and standards.
• Inventory Management: AI can predict inventory requirements, helping businesses manage stock more efficiently and reduce waste.

Telecommunications (Telco)

• Network Optimization: Generative AI can simulate network conditions and generate optimal configurations, improving efficiency and reducing costs.
• Fraud Detection: By generating patterns of fraudulent activities, AI can help in identifying and preventing telecom fraud.

Banking and Finance

• Personalized Financial Products: Generative AI can be used to create customized financial products and services tailored to individual customer needs.
• Risk Assessment Models: AI can generate various financial scenarios to aid in more accurate risk assessment and decision-making.
• Algorithmic Trading: Generative AI can be used to simulate various market scenarios and generate trading strategies.
• Fraud Detection and Prevention: By generating models of fraudulent transactions, AI can enhance the ability to detect and prevent financial fraud.

Cross-Sector Applications

• Customer Service: AI-generated chatbots and virtual assistants can provide personalized customer service across sectors.
• Marketing and Advertising: Generative AI can create personalized marketing content, including text, images, and videos, tailored to specific audiences.
• Training and Simulation: Across all sectors, generative AI can be used for creating realistic training simulations for employees, helping in skill development and decision-making training.
• Document and Report Generation: In sectors like banking and finance, AI can automate the generation of financial reports, compliance documents, and personalized client communications.

Both ways are feasible.

At Qualysoft, we are capable of building an AI solution from the ground up or implementing it using AIaaS (Artificial Intelligence as a Service) providers. The question arises: which method of implementation is recommended, and for what use cases? The answer is the familiar 'it depends,' because we need to consider various factors including the specific use case, the desired outcomes, the underlying data layer (both the quantity and quality of the data), the volatility of the use case (frequency of domain changes), time to market, maintenance and support costs.

Implementing AI solutions, whether from the ground up in a custom manner or by using AIaaS (Artificial Intelligence as a Service) providers, comes with its own set of pros and cons. Understanding these can help in making informed decisions based on the specific needs and context of your project.

Custom AI Solution Implementation (From the Ground Up)

Pros

• Tailored Solutions: Custom AI development allows for solutions that are precisely tailored to specific business needs and use cases, offering a high degree of customization.
• Control and Ownership: You have complete control over the development process, data, and the algorithms used, which is crucial for sensitive or proprietary applications.
• Competitive Advantage: A custom AI solution can provide a competitive edge by offering unique capabilities not available through standard platforms.
• Flexibility and Scalability: Custom solutions can be designed to be highly scalable and flexible to adapt to changing business needs.

Cons

• Higher Cost and Time Investment: Building an AI solution from scratch requires significant investment in terms of time, resources, and expertise, which can be costly.
• Maintenance and Updates: Ongoing maintenance, updates, and improvements require dedicated resources and can be challenging to manage.
• Risk of Failure: There is a higher risk of failure or inefficiency, especially if the development team lacks experience in building robust AI systems.
• Resource Intensive: Requires a team of skilled AI professionals, including data scientists, machine learning engineers, and developers.

Using AIaaS Providers

Pros

• Cost-Effective: Generally more cost-effective than building from scratch, especially for small to medium-sized businesses or for projects with limited budgets.
• Quick Deployment: AIaaS solutions can be deployed quickly, as they often require less customization and development time.
• Less Technical Expertise Required: Businesses don’t need to have extensive AI expertise in-house, as the AIaaS provider manages the complexities of the AI model.
• Regular Updates and Support: AIaaS providers typically offer ongoing support and regular updates, ensuring the AI solution stays current with the latest technologies.

Cons

• Limited Customization: While some customization is possible, AIaaS solutions may not fully meet the unique requirements of every specific use case.
• Dependence on Provider: There is a dependence on the AIaaS provider for continued service, updates, and support, which may impact long-term strategic flexibility.
• Potential for Vendor Lock-in: There's a risk of becoming locked into a specific provider's ecosystem, which can limit future technology choices and negotiation leverage.

In summary, the choice between building a custom AI solution and using AIaaS depends on various factors like budget, time constraints, specific business needs, in-house expertise, and long-term strategic goals. Often, a hybrid approach may be the most effective, leveraging the strengths of both methodologies.

At Qualysoft, since we are partners with Microsoft and AWS, we prefer using Azure Cognitive Services and AWS AI Services when it comes to AIaaS providers. For custom AI implementations, our technical palette is very varied which was detailed in the previous sections.

• The most crucial aspect of any AI implementation is to assess and understand the project's requirements and data-related prerequisites;
• AI implementations do not necessarily require a huge amount of investment compared to custom development projects. However, as always, the complexity of a project can increase the budget. It's crucial to have a clear understanding of the project's specific needs and constraints to accurately estimate the budget;
• The largest portion of the budget is often allocated to generating a teaching dataset with the necessary metadata and finding the best model solution that fits the particular use case. By using synthetic data as teaching data, we can speed up the time to market and leverage potential cost optimizations;
• At any company we can identify potential areas for improvement by using AI and we are always open for discussions. ​​​​​