ben brown

July 7, 2023

Business

Top Process Automation Tools For Businesses in 2023

ben brown

July 7, 2023

Business

Process automation is a big topic on everyone’s mind in the face of AI. What jobs will be overtaken by computers? How can companies cut costs by leveraging business process automation tools (BPA)? How can I run with technology instead of away from it?

There are a lot of questions and speculation, but the ultimate question is; are companies putting their money where their mouth is when it comes to integrating AI? Well, research is showing us that of about 2600 companies surveyed globally, more than 94% believe AI is critical to success, almost 80% have begun implementing a variety of AI solutions, and 82% found a boost in job satisfaction from AI tools.

If you’re curious about the direction of AI's future with software, watch this video.

Integrating AI is something companies must do, but how they execute this integration will vary. Of the use cases for process automation, the biggest one we’re seeing is end-to-end visibility, which essentially is what allows companies to track their entire workflow from start to finish. Why would they want to do that? It’s quite simple: by having end-to-end visibility, companies can identify bottlenecks and proactively address them.

Among many reasons for incorporating process automation, efficiency and cost-cutting are two of the most important factors. Let’s use the example of a plastics manufacturing company that uses inventory management, production scheduling, and quality control software as its main time-consuming, repetitive tasks. BPA in this case would automate all of these tasks, and now the staff’s role shifts its focus to oversight, creativity, and decision-making. For most companies today, that is the goal; streamline and optimize operations end-to-end.

In 2023, there are several tools that are most commonly used across industries to make this happen. Here are some to pay attention to:

1) UiPath

UiPath is a robotic process automation (RPA) platform that takes over repetitive tasks (as most of these tools do) at scale. It has a visual drag-and-drop interface for designing automation workflows and integrates with various applications and systems.

For instance, a human resources department can use UiPath to automate the employee onboarding process, where the software automatically generates employee contracts or updates employee records in HR systems, and notifies the relevant stakeholders, reducing manual effort. Now you have a system that can be scaled.

2) Pega

Pega is a platform that combines business process management (BPM) and intelligent automation. Pega is a comprehensive platform that offers a unified view of the entire business process and ideally leads companies to a solution for end-to-end automation. For example, a retail organization can use Pega to automate its order management process. The platform can allocate resources, track inventory levels, and then adjust production schedules based on current demand as well as forecasted demand.

3) Blue Prism

Blue Prism's RPA software can automate rule-based tasks (Data entry, processing invoices, QC, etc) across different departments. For instance, think of a healthcare organization, that’ll use Blue Prism to automate something like claims processing, where the software first validates claims, then checks for errors, and initiates payment processes.

Blue Prism is best utilized for repetitive tasks that ideally can be scaled. For instance, we used examples from healthcare, but email marketing is another common task for companies that would benefit from scalable RPA.

4) Appian

This is a low-code development platform that’s meant for companies to design and automate workflows. Appian connects with data sources and external applications, supporting standard protocols and APIs like REST, SOAP, and JDBC, which makes integration easy. This is what’s going to attract something like a big manufacturing company that would use this to speed up their approval process or integrate it with systems for inventory management.

5) Automation Anywhere

Automation Anywhere is one of the top RPA platforms that are great for enterprise automation. This is another platform with a drag-and-drop interface (easy to use) and end-to-end process automation. This is one that a FinTech (or other various large-scale entities) could leverage to ultimately reduce manual effort and scale the operation.

The Value In BPA

Think about a 30-year multi-billion dollar business with tens of thousands of employees. How can they leverage process automation across the board? Ultimately, it comes down to recognizing what can be optimized and what’s in the best interest of the product or service's long-term sustainability. An easy one is Amazon— if tomorrow they decided to get rid of warehouse workers and fully leverage RPAs like automated guided vehicles (AGVs), and intelligent warehouse management systems, their inventory management would streamline.

These are the kind of gaps that companies need to be looking for in the coming years. It’s less about what you do and all about how efficiently you do it.

The Takeaway

Finding gaps in your current processes can be difficult without a thorough analysis and understanding of your operations. This is where AI consulting comes in. By leveraging this level of expertise, companies will identify latent pains and receive the most suitable automation solutions for their specific needs. It’s not a cookie cutter; it’s a comprehensive approach tailored to the unique challenges and goals of each business.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

July 4, 2023

Business

Top 9 Java Libraries For Machine Learning

ben brown

July 4, 2023

Business

In 5 years, the machine learning (ML) market size is projected to top $31 billion. This growth is mainly due to the advancements we’re seeing in AI, but right behind that is the increasing need companies have to reduce costs and streamline processes. Machine learning at its most basic level is a data management tool that retains information and improves from experience which is something every company wants from employees. The difference now is that it’s scalable and has less margin for error in addition to its work capacity which continues to improve over time. Managing data is among the highest in-demand skills globally for businesses right now.

According to a report from Cision, the 10-year period up to 2030 will double the market size of global enterprise data management. What this means, in a broad sense, is that companies across various industries, investors, and especially tech leaders are recognizing the value of effectively managing and utilizing data. Ultimately they know something others don’t, or they’re just accepting something others won't which is that data holds the most potential for business growth in the future. They’re putting their money where their mouth is by investing in and utilizing systems that leverage machine learning and ultimately make this process more accessible.

Where Java Comes In

Java, being the versatile programming language it is, offers tons of libraries and frameworks that facilitate the development of machine learning. These libraries have pre-built algorithms and tools that simplify the implementation of machine learning models and make the development process way more efficient. In this blog, we’re looking at some of the top Java libraries for machine learning that can help developers to leverage it effectively in their applications.

Before we get to that, here’s what you want to be thinking about when selecting a Java machine learning library:

Algorithm support: Assess the library's support for different machine learning algorithms, like linear regression, decision trees, support vector machines, and of course neural networks.

Ease of use and improvement: Look for libraries that offer easy-to-use APIs and utilities for training machine learning models. Consider the availability of tools for cross-validation, hyperparameter tuning, and model evaluation.

Feature engineering and data preprocessing: Does the library have functionalities for feature extraction, transformation, and normalization? Look for utilities that simplify common data preprocessing tasks, such as handling missing values, categorical encoding, and feature scaling.

Support for big data processing: This is a big one. If you're working with large-scale datasets, you’ll likely want libraries that seamlessly integrate with distributed computing frameworks like Apache Spark.

Visualization and interpretation: Check if the library offers tools for visualizing data, or interpretability. Visualizations are going to help with understanding your data and model behaviour, while interpretability tools help you gain insights into the factors driving your model's predictions.

Deployment and integration: Evaluate how easily the library can be integrated into your existing software stack and deployed in production environments. Look for libraries that offer options such as model import/export or support for common deployment frameworks like TensorFlow Serving or Apache Kafka.

Performance optimizations: Consider libraries that offer optimizations like parallel computing, GPU acceleration, or distributed training.

There’s a lot to consider when choosing an ideal framework, these variables will help guide your choice but ultimately your unique variables will be the key factors such as project requirements, team expertise, and the overall goals you want to achieve.

With that, here are some of the top options that Java offers for ML libraries:

1) Deeplearning4j (DL4J):

DL4J is a Java library that specializes in deep learning. It provides sets of tools and algorithms for building and training deep neural networks. With its integration with Apache Spark and Hadoop, DL4J enables distributed deep learning on big data platforms. It also supports various neural network architectures, such as convolutional networks (CNNs) and recurrent networks (RNNs).

2) Smile:

Smile, or Statistical Machine Intelligence and Learning Engine, specializes in a range of AI tasks. When it comes to machine learning model integration and data analysis, Smiles's interface is user-friendly and has a ton of algorithms for classification, regression, clustering, dimensionality reduction, and so on.

3) Weka:

Weka, an open-source Java library, has been a go-to among machine-learning enthusiasts for many years. It offers a vast collection of machine learning algorithms and tools for data preprocessing, classification, regression, clustering, and especially association rule mining.

Weka's graphical user interface, called the Weka Explorer, lets users try out different algorithms. It also provides extensive support for data visualization, which makes it easier to understand and interpret the patterns in the data.

4) MOA:

Massive Online Analysis (MOA) is an open-source Java framework designed specifically for online learning and mining big data streams. It offers a variety of machine-learning algorithms that can handle consistent data streams in real-time. For developers, MOA allows them to build scalable and efficient models that adapt to changes in data over time.

Like the last two, it also includes algorithms for classification, regression, clustering, and additionally anomaly detection. MOA's focus on online learning makes it a great tool for applications where data arrives continuously and needs to be processed immediately.

5) DL-Learner:

DL-Learner focuses on machine learning with description logic (DL). It specializes in knowledge extraction from structured data and supports creating logical knowledge bases. DL-Learner includes algorithms for ontology learning, rule induction, and concept learning. It can be used to build intelligent systems that not only extract knowledge from data but also reason with logical rules.

DL-Learner is particularly useful in domains where say for instance formal representation and reasoning are essential, so things like semantic web applications and knowledge-based systems for instance.

6) Apache Mahout:

Apache Mahout is a scalable machine learning library that has algorithms for the typical clustering and classification, but also recommendation mining. It integrates with big data platforms like Apache Hadoop and Apache Spark, which allows developers to leverage a more distributed computing landscape.

Apache Mahout supports various machine-learning techniques, including collaborative filtering, clustering, and classification. It’s suitable for large-scale data analysis which is why it’s widely used in industries like e-commerce, social media, and anything that leverages personalized recommendations.

7) ADAMS:

Advanced Data mining And Machine Learning System (ADAMS), is a data-driven workflow engine, and an open-source, modular framework. When it comes to machine learning, ADAMS is great for data preprocessing and feature engineering to model training, evaluation, and deployment.

8) JSAT:

JSAT includes popular algorithms such as k-nearest neighbours, support vector machines, decision trees, neural networks, and more. One of the notable features of JSAT is its emphasis on parallel computing and performance optimizations. It leverages multi-core processors and implements parallel algorithms to speed up computations, making it ideal for managing large datasets.

It’s also great in scenarios where data is high-dimensional and contains many zero values - which is something text-based applications, particularly natural language processing will benefit from.

9) JavaML

JavaML emphasizes two things: scalability and efficiency. It uses incremental learning which is particularly useful in scenarios where new data arrives consistently or when resources are limited. In addition to that, it integrates with the distributed computing framework Apache Hadoop, which enables the system to handle large datasets.

What’s Next?

A solid infrastructure is pivotal for organizations to get the most out of machine learning. In 2023, Java is a staple in the machine learning landscape, with ongoing advancements and developments. As we look to the future, integration with emerging technologies, expansion of libraries and frameworks, and collaboration and interoperability will shape the evolution of machine learning in Java.

Written By Ben Brown

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ben brown

June 30, 2023

Business

Artificial Intelligence’s David vs Goliath: Comparing Big and Small Generative AIs

ben brown

June 30, 2023

Business

There’s always going to be the notion with technology that bigger is better. The more powerful, the more capable, the more sophisticated – these are the qualities tied to larger and more complex systems. Yet, when it comes to generative AI models, the story isn’t as straightforward. There’s an interesting David versus Goliath dynamic at play between big and small generative AIs. Let me explain…

In recent years as we know, big generative AI models have garnered tons of attention and acclaim. Models like ChatGPT-3, with its 175 billion parameters, GPT 4 with an estimated 170 trillion parameters, or Midjourney with its large language and diffusion models as well as its comprehensive list of parameters; these models have demonstrated remarkable capabilities and can pretty much generate anything you want. They’re trained on massive amounts of data, that allow them to capture complex patterns and produce the outputs we value so much. Why is that important to know? Well, it’s instances like this that have earned big generative models such a grand reputation.

On the other hand, we have smaller generative AIs. Right off the bat, these models have fewer parameters and less computational power, which might make them seem like underdogs compared to their larger counterparts. But make no mistake; they possess unique advantages that make them fierce competitors in the AI landscape.

Generative AIs in Action

One of the most notable advantages when it comes to small generative AIs is their efficiency. Due to their smaller size, they require less computational resources and can be deployed on devices that have limited processing power. This makes them ideal for applications that require real-time generation or that have strict resource constraints.

Think about a mobile app that generates customized images based on user prompts. Since a small generative AI doesn’t have to depend on a remote server, it can process prompts directly on the user's device. This eliminates the need for constant internet connectivity and reduces latency, resulting in a super responsive user experience. When you compare this to a big generative AI, it’s not as powerful in terms of the sheer scale and volume of outputs. However, a small generative AI model, in this case, embedded in a mobile app is independent, efficient, secure, and highly customizable which makes it a versatile tool, especially for something such as personalized image generation.

Where One Compliments the Other

Using a big generative AI, like that of ChatGPT does offer a lot more in terms of capabilities. If a multi-billion dollar corporation sat down and decided they wanted to develop an AI system with immense scale and resources that was going to revolutionize healthcare (For instance) a big generative AI model combined with a small generative AI model would be the ideal solution.

First off, the big generative AI model would be trained on vast amounts of medical data. With its scale and resources, it will capture complex patterns and relationships within the data, enabling it to provide advanced diagnostic support, predict outcomes, and assist in drug discovery and development.

However, deploying a system like this is going to require a lot of computational power and an infrastructure that can handle the sheer scale of data being processed. This is where the small generative AI model comes into play.

The small generative AI model is now embedded within medical devices, wearables, and mobile applications, which again, enables data processing in real-time. With that, it now analyzes patient-specific data, such as vital signs, symptoms, and lifestyle factors, to provide immediate feedback, personalized recommendations, and continuous monitoring.

Don't Compete - Balance and Complete

To break this down simply, the big model is the brain that processes and stores the information, and the small model is the hands that carry out the actionable. Achieving a balance between the two is simply leveraging the strengths of each and coordinating to ensure data can be exchanged easily between models. How do you enforce this? By following these 4 guidelines:

Each model has clear tasks
Protocols are in place to facilitate data exchange
Workloads are distributed based on computational requirements
The system is constantly monitored and being improved on

These actionables are very general and could be applied to any industry but they give you a sense of what it takes to achieve balance and coordination between big and small generative AI models. While the specific implementation may vary across industries, these general guidelines provide a framework for companies to start with.

The Results Generative AIs Deliver in a Business

Big or small, generative AIs deliver results, however, the size and complexity of the model will be a big factor in determining the quality of results attained. From what we know about big generative models, we know that they excel at generating high-quality content, predicting trends, optimizing systems, and driving innovation. By that same token with small models, we know their efficiency offers agility, responsiveness, and personalized experiences. They’re awesome for tasks such as personalized recommendations, interactive applications, and ultimately enhancing customer engagement.

The Takeaway

While big generative AI models have garnered attention for their remarkable capabilities and ability to generate high-quality content, small generative AI models shouldn't be underestimated. Businesses have a lot to gain by leveraging each but ultimately it comes down to the strategy you put behind them.

Written By Ben Brown

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