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AI Innovations: Highlights of Ai4 2023| The Xperts Pakistan

AI Innovations: Highlights of Ai4 2023| The Xperts Pakistan

Artificial intelligence (AI) continues to fascinate, excite, and even frighten businesses. Generative AI tools such as ChatGPT, DALL-E, and Synthesia have demonstrated remarkable abilities to generate human-like text, images, and even videos. And many executives are directed to explore and maximize opportunities for generative AI systems like GPT-4 in their organizations.

UiPath has always seen AI as an asset and enabler of more and more advanced automation. AI-powered automation frees people from slow, monotonous work so they can focus on realizing their creative and strategic potential. So we are happy to join and sponsor it. Ai4 2023One of the United States’ most influential gatherings of AI leaders, thinkers, and data practitioners, including experts United States Coast Guard, United States Air Force, Takedaand quest evaluation.

On stage, UiPath Head of AI Strategy Ed Challis described the UiPath AI roadmap and strategy. He explained how AI is integral to the UiPath business automation platform, including our long-standing investment in AI computer vision that enables our robots to see and understand screens like humans. Now, we’re embedding generative AI across the entire UiPath platform, connecting generative AI with the data, context, governance, and controls it needs to operate securely and successfully across the enterprise.

AI without automation is like a mind without a body. The UiPath platform combines powerful generative AI with purpose-built specialized AI models and the power of automation. When generative AI complements the expertise of specially trained models built on real business data, it empowers automation robots to understand, reason, and create.

Ed Challis presenting to a full house at the Ai4 2023 conference.

Ai4 attendees saw how AI-powered automation offers countless new opportunities for businesses to increase profits, transform costs, multiply productivity and enhance the customer experience.

At Ai4 we also announced that Project “Wingman” is coming to Private Preview this month. Project “Wingman” will reduce the learning curve for new automation users, as well as support experienced automation developers. Project “Wingman” also enables users to build automations that span an ecosystem, creating end-to-end automated workflows that connect disparate business systems. We’re always pioneering new ways to build automation, and this preview will allow you to test and provide feedback on a set of features that help build, generate feedback, and create API workflows. So Generative uses the power of AI and natural language processing. your automation. Register for our insider program today. And learn more about what the future holds for UiPath Studio.

UiPath offers the best of generative AI and specialized AI through the UiPath Platform—a platform that’s open, flexible, and enterprise-ready. UiPath Chief Product Officer Graham Sheldon recently spoke about this. At the UiPath TOGETHER London event.

During the same event, we announced the general availability of our OpenAI and Azure OpenAI connectors. Attendees saw Clipboard AI, which combines generative AI and our exclusive AI computer vision models to easily transfer data between documents, spreadsheets and apps. And they heard how we’re expanding UiPath’s document understanding and communications mining capabilities with powerful new GPT-4 integration.

Thanks to everyone who was a part of the Ai4 conference.

We have more exciting updates and AI announcements coming! Join us this October at FORWARD VI at the MGM Grand in Las Vegas and be the first to learn about our biggest AI announcements. Register for FORWARD VI + TechEd Day. Before August 15 to take advantage of special summer pricing.

AI for Good: Three Principles for Using AI Responsibly at Work| The Xperts Pakistan

AI for Good: Three Principles for Using AI Responsibly at Work| The Xperts Pakistan

The impact of AI at work has been impressive. We are inspired by the stories of Artificial intelligence (AI) Working together with people and automation to achieve greater business efficiency — accelerating the creative process, enhancing human capabilities, and increasing productivity. But we also cannot ignore the potential of AI to be used for harm.

The growing popularity of generative AI models has raised many questions on their safety, security and especially data privacy. According to the results obtained from MIT Sloan Management Review and Boston Consulting Group84% of AI experts and implementers believe responsible AI management should be a top priority. However, more than half (56%) believe it is being taken seriously enough by business leaders.

Consider the damage that can be caused by biased training data that hinders AI decision-making, or the ability of these systems to spread misinformation on a large scale. There is a lack of transparency in how these systems work, and the risk that the data you share with them could be used for training and exposed to another user.

To ensure the ethical use of AI, there is an urgent and pressing need for proper governance. AI evolves too quickly for policymakers, so the onus falls on creators and users to ensure their use of AI is beneficial and ethical. As an AI innovator that enables the implementation of AI on our platform, UiPath is no exception to this rule.

This article will help explain the different ways we implement AI at work, along with the controls we put in place to protect users who use them. As always, our approach is based on our core AI and automation principles: open, flexible, and responsive. As highlighted here..

1. Creating an open ecosystem for AI excellence

We cannot believe that any one company will ever have a monopoly on the best AI capabilities. After all, ‘AI’ refers to a diverse range of different tools and technologies. There is, of course, generative AI, but there is also specialized AI, which involves models that are trained for a specific business task or process, such as document processing or sentiment analysis. Each has value to the enterprise and no one company can be the best at all of them. Our goal is to maintain this diversity for the benefit of our customers and the AI ​​industry.

This is why UiPath follows an open approach to AI. We want our customers to be able to combine the best generative and specialized AI models. We enable them to choose from a list of advanced AI models built by UiPath, or they can take full control of their AI governance and user-contributed models, or models from external providers such as OpenAI. can also be used.

Special AI models have long been developed by UiPath. Our platform provides multiple offerings viz UiPath Task Mining, Process miningAnd Document understanding. Then there is UiPath Communications Miningwhich gives businesses access to the latest Large Language Model (LLM), a foundational technology for generative AI, to understand and automate their business communications.

Still, we recognize the immense value of introducing features with generative AI capabilities. We announced recently that UiPath Document Understanding and UiPath Communications Mining will leverage generative AI to accelerate model training for users. Our coming Project “Wingman”now available in private preview, enables users to create powerful automations using only natural language cues.

In all cases, AI capabilities are clearly marked to ensure UiPath users know what their options are. It enables users to make informed and ethical decisions about the use of technology.

2. Flexible AI that adapts to the user—not the other way around.

As creators of AI solutions it is vital that our models can adapt to the needs of users. When a customer has no choice but to choose an off-the-shelf model that doesn’t meet their use case, accidents happen. Accuracy means the world when you’re using AI to make decisions that directly affect your customers, or that need to be tailored.

That’s why we take model flexibility seriously. The UiPath Business Automation Platform is a flexible platform designed to meet the exact needs of our customers. The UiPath platform enables users to create customized workflows through multiple AI models, user interfaces, and application programming interfaces. UiPath Integration Service And Activities. These offerings make it easy for customers to integrate the model of their choice directly into the UiPath platform.

Moreover, users can customize our models according to their specific tasks and domains. Consider how different and unique each enterprise is. There is no room for a ‘one size fits all’ solution. Thankfully, tailored and accurate automations can be created with the fine-tuning features of communications mining and document understanding. This results in improved accuracy, efficiency and safety for everyone.

3. Guardrails for Responsible AI

Data is needed to improve AI systems. But consumers deserve the right to know their data is safe. UiPath has a responsibility to ensure that the data used to build our tools is of good quality, obtained legally, and managed securely. We do this in different ways:

  • Legal and Compliant Data Collection

  • Appropriate measures to ensure data processed in the UiPath platform are secure.

  • Validate and balance data in UiPath models and algorithms to remove bias in training data

  • GDPR compliance, including data deletion

The platform is actively reviewed for privacy, bias, and data security concerns, helping to develop trusted AI. We even got some innovation. In general, incorrect or biased models are the result of poor training rather than bad intentions. Especially for typical business users, it can be difficult to know when their AI model is sufficiently trained and balanced. UiPath Communications Mining solves this problem through its model rating feature. This capability largely automates the model validation process, assigning it scores for efficiency, accuracy and balance. The UiPath platform then provides users with a fully guided improvement experience, showing them what steps they should take to improve the model.

UiPath promotes human-centricity and transparency in AI. We’ve given users the tools to control their automations directly into it. UiPath Studio And Robot through Workflow Analyst And Automation operations. We have also developed robust criteria for evaluating third-party AI and LLM technologies. We aim to ensure measures are taken to reduce bias, malicious intent and other risks.

Read the full list of responsible AI principles.

To create ethical AI, first enable ethical humans.

to provide The foundation of innovation™ means embracing new technologies that move the world forward. In our approach to AI in the workplace, UiPath enables customers to implement these powerful capabilities in multiple ways that fit their automation needs as well as their security, governance, and risk posture. . It is important that these powerful technologies are used in ways that are as responsible and ethical as the people they serve.

Additionally, we believe there is a need to democratize emerging technologies and put the tools in the hands of users to understand and use accordingly. This is why we are developing dedicated AI courses as part of the curriculum. UiPath Academy.

For all the amazing progress of the past few years, the AI ​​story has only just begun. It is a story that will largely be told by its developers and users, who have a responsibility to share accountability and advance the ethical development of the technology. UiPath is behind them. We are committed to building trusted AI properties, giving our customers the tools and information they need to use AI responsibly.

If you’d like to learn more about the principles that guide our AI development, and see what’s coming next, be sure to Reserve your spot on FORWARD VI..

Extending the benefits of automation with AI in insurance| The Xperts Pakistan

Extending the benefits of automation with AI in insurance| The Xperts Pakistan

In our ever-evolving digital age, it’s no surprise that every industry is undergoing significant change. And the insurance industry is right in the middle of this revolution.

Automation and artificial intelligence (AI) are not distant dreams. They are ubiquitous, radically changing insurers’ operations and customer interactions. At the UiPath AI Summit 2023, key industry players, Andrea Simpson, IT Manager – PMO Robotic Process Automation at USI, and Thach Nguyen, Director of Digital Innovation at Hub International, shared their insights, highlighting that these technologies How are you. Reshaping the Insurance Landscape and What’s Next on the Horizon

From basic automation to AI compatibility

Prior to 2018, automation was largely understood as a tool under the influence of robotic process automation (RPA) aimed at managing discrete, isolated tasks. As Nguyen reflects, “Historically, automation in insurance has appeared as a tool where every problem has a nail on its hammer.” This description captures the early stage, which is characterized by a bottom-up approach, and prioritizes more fundamental tasks.

However, around 2022 the winds changed direction. Unprecedented workforce shifts and the “Year of the Great Resignation,” with skyrocketing operational costs, forced a paradigm shift. Industry now uses automation for dual purposes: cost efficiency and elevating the customer journey. What were once modest efficiency targets of 8-12% have reached 15-20%. This highlights the growing focus on human-AI collaboration in strategic decision-making.

Powering efficiency and growth with AI integration

USI’s automation journey started in 2019 with the support of UiPath. Starting with the automation of standard, repetitive accounting functions, they gradually scaled the automation, eventually adopting AI to further streamline operations.

Faced with challenges such as increased staff turnover during the Great Resignation, Simpson’s team leveraged AI-powered automation, witnessing a profound shift in results. He highlighted the immense value gained from automated functions such as direct bill commission and underwriting data entry.

His top achievements include:

  • Using AI and machine learning to interpret different templates from multiple carriers for direct bill commission, saving over a million dollars annually

  • Fully automating complex underwriting data entry, ensuring real-time client application updates

  • Streamlining and automating the remittance email process, freeing up 800 hours per year

From adoption to expansion

Nguyen laid out a complex map of Hub International’s journey into the realm of AI-powered automation. The initial phase involved taking baby steps by adopting basic automation in accounting. This was a critical step not only in terms of operational tweaks, but as a proof of concept that helped secure the significant investment needed to take our projects to the next level.

Once funding was secured, Nguyen’s team set out to scale their program. The focus was not only on increasing the volume of automated tasks but also on diversifying the types of tasks being automated. He extended automation beyond the limited scope of accounting to diverse business departments throughout the company.

Summarizing the transformation, Nguyen commented, “Automation started out as just a tool for us—a mechanism to streamline repetitive tasks. However, its role in our organization has evolved. It is now an enabler, a catalyst that gives us the power to achieve more than we initially thought See it as just another resource to become business partners.”

For Nguyen and his team, the journey wasn’t just about automating manual labor. Their vision is more ambitious. He is interested in the potential of automation and AI to improve decision-making processes within a company. For example, sophisticated algorithms can analyze large data sets to offer insights that were previously either too complex or too time-consuming to obtain manually. This depth of analytics is a game changer in strategic planning and operational efficiency.

Additionally, the team is focused on reducing the complexity of existing workflows, particularly in high-stakes, complex sectors such as brokerage. Here, automation has proven incredibly useful in handling large data sets, especially related to broker information management. It has also been an invaluable asset in facilitating mergers and acquisitions. By automating due diligence and data integration processes, a company is able to make more informed choices faster, thereby gaining a competitive edge in the fast-paced world of mergers and acquisitions (M&As).

AI-powered automation as the new standard

Whether it’s streamlining routine duties such as sending remittance emails or handling complex tasks directly in the bill commission process, it’s clear that automation and AI have transitioned from optional add-ons to becoming indispensable components of insurance operations. Who is

As the sector evolves, adopting and mastering these transformative technologies will dictate not only success, but survival. Insights shared by industry experts at the UiPath AI Summit 2023 only reiterate the magnitude and urgency of this digital transformation.

To access the full recording of this session and explore other must-see sessions on AI-powered automation, visit the AI ​​Summit on-demand recording.

Be sure to join us at the Financial Services Summit UiPath Forward VI. The Financial Services Summit will begin on Wednesday, October 11, 2023 at 3:30 pm PDT.

7 Ways Clipboard AI is Simplifying Finance Operations| The Xperts Pakistan

7 Ways Clipboard AI is Simplifying Finance Operations| The Xperts Pakistan

Editor’s note: UiPath Clipboard AI™ was named one of TIME’s Best Inventions of 2023. Read Why TIME Recognized Clipboard AI.

In the evolving landscape of financial operations, the synergy between AI-powered automation and workplace efficiency has emerged as a driving force. It’s not just about implementing artificial intelligence (AI), it’s about turning its vast potential into concrete, actionable results. Applying AI to the workplace adds a deeper level of intelligence to daily operations, reshaping entire industries by automating knowledge-driven tasks. UiPath seamlessly integrates machine learning, natural language processing, and generative and specialized AI capabilities, paving the way for rapid business transformation for the AI ​​era.

Building on this momentum, we’re excited to unveil the public preview. UiPath Clipboard AI.

UiPath Clipboard AI - AI for the rest of us

Since no one in 2023 should copy from one sheet just to paste it into another, Clipboard AI handles it for you. Clipboard AI is a no-code app that makes it easy to transfer data between different documents, spreadsheets and applications by intelligently understanding screens and accurately placing the necessary data. It is designed to improve workflow across a spectrum of use cases. Our initial venture is in the world of finance. Financial tasks often require greater accuracy, faster data transfer, and fewer manual errors—challenges that Clipboard AI is perfectly equipped to address. Here are seven reasons to integrate it into your work:

1. User friendly for all

Incorporating new technology can be complicated, especially if you’re not tech-savvy. Clipboard AI breaks this barrier. With its user-friendly interface, all it takes is one click to download and you’re ready to get started. No coding skills required.

2. The virtue of saving time

With clipboard AI taking care of time-consuming tasks like data entry or transfers, employees have more bandwidth for analytical and strategic roles.

3. Integration across platforms

Finance often means navigating between different software platforms. Clipboard AI makes this easy, ensuring fluid data movement and consistency across systems, from Microsoft Excel to many popular enterprise resource planning (ERP) tools including SAP Fiori, Oracle NetSuite, and more. Is.

4. Mastering unstructured data

Emails, informal transaction records, or customer requests often come in disparate forms and lack consistent order or structure. Clipboard AI excels at distilling that data, interpreting it, and putting it where it belongs.

5. Cognitive powers

With its advanced cognitive capabilities, Clipboard AI excels at tasks such as classification, summarization, content reasoning, and context interpretation. For example, it can understand the meaning of a word and categorize it appropriately. If a document mentions “violet,” but the input form only offers the “purple” option, Clipboard AI recognizes the similarity between the two colors and classifies it accordingly.

6. Increasing data accuracy

In the financial sector in particular, human errors can have significant consequences. Clipboard AI significantly reduces such errors, ensuring that data, whether from invoices or financial statements, is transferred across systems accurately.

7. Adaptive and learning oriented

The more you use Clipboard AI, the more it understands your unique needs, evolving to better serve you with each interaction. This adaptation ensures that Clipboard AI becomes more compatible with your workflow, making your tasks smoother and more efficient over time.

UiPath Clipboard AI for Finance Source Destination Example

Clipboard AI in action.

Let’s explore the many financial use cases Clipboard AI offers:

  • Invoice processing: Instantly extract invoice details received via email and populate them into an Excel spreadsheet or a platform like NetSuite.

  • Data migration to ERP systems: Seamlessly copy data from Excel and insert it into ERP systems like SAP Fiori, creating comprehensive customer profiles without manual data entry.

  • Unstructured applications are being processed.: Imagine an email from a client requesting a bank transfer. Clipboard AI can interpret this, extract the necessary details, and update them in systems like SAP, making operations efficient and error-free.

To view each demo, please Check out the Clipboard AI Finance page.

Our venture into the finance sector is just the beginning, and we couldn’t be more excited. With every step of growth and adaptation, we aim to empower everyone, from individuals to entire industries. With AI that goes beyond mere functionality, we aim to elevate every aspect of work. Join us in this journey of change. Try Clipboard AI., and please share your feedback. Your insights drive our innovation.

Unleashing Enterprise Productivity: The UiPath Blueprint for AI at Work| The Xperts Pakistan

Unleashing Enterprise Productivity: The UiPath Blueprint for AI at Work| The Xperts Pakistan

Generative AI has sparked one. Increase personal productivityHelping us do all sorts of things more efficiently. Yet, in the enterprise, that productivity has hit a wall. While business leaders are eager to deploy AI in their companies, a number of challenges, particularly around trust, are giving them pause. In a Workday survey of more than 2,300 business executives, 43% of respondents “Concerned about reliability of AI and ML.”

Fortunately, new developments are helping companies overcome these obstacles and pave the way for enterprise-wide productivity improvements.

Meanwhile, these developments were discussed in detail. “The UiPath Blueprint for AI at Work” keynote On the forward VI. The keynote featured AI experts Professor David Barber, Director of the UCL Center for Artificial Intelligence and UiPath Distinguished Scientist, Dr. Ed Challis, Head of AI Strategy at UiPath, and Luke Palmara, VP of AI Product Management at UiPath.

The experts gave an interesting talk on the history of AI and its implications. Latest developments Meaning for enterprise users.

The era of AI

As AI has dominated the headlines, many believe that it is a brand new technology. However, researchers have been working on it for at least half a century. And, according to Barber, “the desire has always been to make these systems work like humans can.”

To get closer to replicating human intelligence, researchers have, in recent decades, built artificial neural networks that mimic how our brains work.

The idea made sense, but figuring out how to connect artificial neural networks “is a very complex problem,” Barber said. “The amount of computation… and the amount of data you need to determine these connections is very, very difficult. So, while the field has been around for a long time, it’s relatively recent,” he explained. That we’ve seen rapid progress in the sense that these systems are becoming very, very useful.

This rapid development came in 2012 with a wide variety of graphics processing units (GPUs). GPUs have provided a much-needed increase in processing power that has greatly accelerated the time it takes to train AI.

As the graph above shows, today’s GPU-powered AI outperforms the average human in object recognition, speech, and even reading comprehension. But these cognitive functions are only half of what makes human intelligence so profound.

Making AI a partner rather than just an advisor

As AI’s cognitive abilities grew, researchers began to explore its creative potential. In 2019, they had the bold idea to train an LLM on a huge chunk of the Internet and ask the LLM to generate the next word in a sentence.

As we have seen. LLMs like ChatGPT, the results have been surprising. These systems don’t just generate code or snippets of text—given the vast linguistic data available online, they’ve achieved a depth of cultural understanding previously thought unattainable.

But while the creativity of these systems is amazing, they are far from perfect.

The good thing is. [LLMs] are built like the human brain…so they are good at cognitive tasks. The bad thing is that they are designed like the human brain in the sense that they are also fallible.

David Barber, Director of the UCL Center for Artificial Intelligence and UiPath Distinguished Scientist

Understanding when we can trust AI, and when we can’t, is a huge roadblock to wider enterprise adoption (more on that soon).

Current AI Challenges

When it comes to today’s enterprise AI, “things are kind of distributed,” according to Palmara.

On the one hand, you have Special AIHighly optimized models that are fast, cheap, and have a detailed understanding of business data. They are highly effective in specific tasks, such as extracting information from communications and documents, including invoices, bills and receipts. However, they are less efficient when faced with conflicts other than trained examples.

Then you have the LLMs, which underpin generative AI. They are extremely powerful, but mainly for individual tasks such as summarizing information, analyzing data, etc. They also have other limitations, such as providing false information.

For AI to move beyond personal tasks and increase enterprise productivity, it needs to be more than an advisor—it needs to actually do the work. How can we make it real?

According to Palmara, there are three main components.

Context Action Trust Slide from UiPath AI Blueprint Forward VI Keynote

idea, context

“AI is only as good as you make it. Even if you have the best customer service agent in the world, they won’t be able to help customers if they don’t know company policies,” Palmara said. Is.

There is a way to come up with this, within the UiPath Business Automation Platform. UiPath Integration Service. It acts as a bridge between AI and related data sources, giving AI the context it needs to operate.

the process

“What’s knowledge if you can’t practice it?” Palmara asked the crowd. To be productive at the enterprise level, AI needs to do more than generate text and images. It needs to perform tasks such as transferring data between systems, responding to customers and placing orders automatically.

That’s what UiPath is all about… it’s about putting AI to work. It’s about bringing that rich set of capabilities that allow AI to take action and not just be an advisor.

Luke Palmara, VP, AI Product Management, UiPath


Many company leaders are eager to embrace AI, but do not yet have a sufficient level of trust in it. “Trust is the foundation. If we can’t trust these systems, ultimately we can’t use them,” Dr. Challis said.

Dr. Challis described three primary challenges standing in the way of trusting AI: information security, lack of specialized knowledge, and illusion.

Three Challenges Standing in the Way of Trusting AI

UiPath is working hard to address these challenges. new UiPath AI Trust Layer The model provides transparency, admin controls, and usage auditing to assure users that their data is secure in UiPath applications.

There are a few ways to overcome the lack of specialized knowledge. First, you can give accurate clues. “UiPath is very strong in getting this information into the LLM,” said Dr. Challis.

You can also fine-tune models with special information. This has historically been expensive, but active learning is a new way to train models more efficiently.

How active learning improves performance.

Essentially, active learning enables AI to recommend its own algorithms instead of relying on manual data labeling. For example, traditionally, training a model for cat recognition requires manually labeling cat images. However, with active learning, AI can learn by itself to recognize a cat in an image and validate its identity with human feedback, greatly reducing the bottleneck of manual data labeling.

Replace cats with documents, and the enterprise value becomes clear. Dr. Challis said that he “looks at active learning almost as a conversation between two peers. You just ask questions about things you’re not sure about, you ask the same question over and over again. Don’t ask.

AI still needs a human in the loop.

As powerful as AI is with active learning, it still needs a human to ensure trust and accuracy. Having a human in the loop is “one of the most important capabilities when it comes to making enterprise AI productive in business,” Palmara said.

While correcting mistakes is an important human task, they also build trust. That’s why we’ve built the UiPath Action Center to allow humans to approve AI decisions and override them when necessary.

The road ahead for AI

Dr. Challis concluded the session by asking Barber what he expects from AI in the next several years. Barber expects her to improve in logic and calculation, which, when combined with her current abilities, will be “a giant step forward for humanity”.

While AI has its fair share of problems, Dr. Challis reminded the audience that “we have the components to make it safe, to make it responsible, to make it manageable. The onus is on us.” That we build the systems on which we want to base our business.

Read related.: Top 5 AI Announcements by UiPath FORWARD VI

Get the best of FORWARD VI delivered to your inbox when you Register for the ‘Best Bits’.

Active learning: How to speed up AI model training.| The Xperts Pakistan

Active learning: How to speed up AI model training.| The Xperts Pakistan

Let’s face it: training an AI model isn’t much fun.

Imagine you are training an intern who has just joined your company. This intern is extremely bright and can work at lightning speed for 24 hours without a break. It should be a dream come true, right? Plus, they don’t know anything about your business. They can’t tell the difference between a ‘thank you’ email and a quick customer complaint. They lack common sense and misunderstand even the most basic things.

Anyone who has ever started training an AI model for their business can probably relate to this example. The good news is that AI can be trained to accurately understand your business and execute your most important processes. But it takes time and effort, and, in general, a lot of data interpretation.

Data interpretation constraints

Simply put, data interpretation helps AI understand and safely manage the data that drives your business processes.

Data annotation, also known as data labelling, is the manual process of tagging raw data with relevant classifications or ‘labels’. In business, this is an important part of the process of training AI models to accurately recognize patterns in your data and respond appropriately to them. For example, helping the model distinguish between a ‘thank you’ email and a quick complaint. Or helping to correctly extract important data from a message, such as a delivery address or customer number, which can be important for many valuable automations.

You could say that interpretation has become the new programming. Increasingly, instead of programming what we want machines to do, we use examples to replicate them. But that doesn’t stop it from being a long and boring process for those who do!

Uses about data interpretation 80% of the time Dedicated to any AI project. Subject matter experts (SMEs), often involving teams of employees, will typically spend hundreds of collective hours labeling thousands of individual instances. But add human error to the mix. Some labels will inevitably be incorrect, affecting the AI’s understanding of the data and potentially requiring more time to fix the damage.

Many AI projects struggle to get off the ground because employees are often reluctant to interpret data. People even pay to train AI models now. Turning to AI to interpret the data for them. And that’s actually not a bad idea. After all, the main reason we use AI in business is to free ourselves from work we don’t enjoy.

However, there is a much better way to train AI quickly and accurately…

Active learning: Better AI in less time and less cost

Data interpretation is an essential part of supervised learning—one of the most popular AI training methods. In supervised learning, AI learns from a pre-labeled dataset and uses this learned information to process new data in a desired way. This contrasts with unsupervised learning, where the AI ​​is given unlabeled data and left to identify patterns independently.

Supervised learning produces models that perform more consistently and reliably. This is the only type of model that should be used in a real business environment without supervision. Supervised learning is the key to building specialized AI models, designed to understand and perform a specific task. Yet data interpretation constraints mean that these models are slower to train and deploy than unsupervised learning.

But what if we could combine the accuracy of supervised learning with the speed of unsupervised learning?

Active learning is a mature training method for AI, but it has only recently been used to train enterprise AI models. It combines elements of supervised and unsupervised learning to build better AI models in less time.

Similar to supervised learning, active learning requires annotated samples for model training. However, instead of just learning from the dataset, the model makes unsupervised decisions about what it wants to learn next.

It then actively queries the SME but, importantly, only asks them to interpret examples that it is not sure about or thinks are necessary for its training. will be more effective. Just like in unsupervised learning, the model itself identifies patterns and decides what information it needs to learn better.

Active learning helps create an intelligent workflow for annotation. Remember the AI ​​intern from our example at the beginning of this blog post? With active learning they can complete most of the training on their own, deciding what they should learn next and asking for help when they get stuck. Active learning is much closer to human learning patterns, and means much less hand-holding and work from the SME.

What makes active learning useful for businesses struggling to train their AI? You need very few annotated examples to train a model from start to finish. AI takes a lot of weight in terms of training and will work with your SMEs to improve its understanding — both when you build the model and later when you deploy and refine it.

AI models built with active learning can be trained faster, with fewer labeled examples, and without sacrificing accuracy or performance. Another advantage of active learning is that it leaves opportunities to reduce human error and bias. That’s why it’s the ideal way to help entrepreneurs train specialized AI models that are reliable and get up and running quickly.

Putting AI to work — fast

What is the secret of AI success? Are these the models you use? Or how many data scientists and SMEs you hire to train?

What really separates the AI ​​leaders from the laggards is how quickly they can ‘operationalize’ the technology. How quickly they can deploy AI in their business and how quickly it starts creating value for them. Traditionally, this has been a real challenge for Intelligent Document Processing (IDP). Training AI models to reliably understand and process documents and messages typically demands a large investment of time and effort.

That’s why UiPath uses active learning to accelerate time-to-value for users using our leading AI capabilities for IDP.

Understanding UiPath documentation And Communication Mining (both available through the UiPath platform) enable users to rapidly build custom AI models that can understand and automate documents and business communications. Thanks to active learning, these UiPath platform capabilities start training with just a few illustrative examples. The SMEs and AI then work together to improve understanding of the model by labeling only the most informative and valuable instances.

Our active learning approach—combined with the no-code, fully guided user interface of the UiPath platform—generates accurate, high-performing AI models in hours rather than weeks or months. For example, the introduction of active learning in UiPath document understanding has seen 80% faster model training according to our internal tests. Models that used to take a week to train now only need a day before they’re ready for business.


In business and in life, time is the most valuable thing we have. And, right now, data interpretation is taking a lot more than that. Taking time for value and putting pressure on your people. Fortunately, active learning offers a better approach. By using both supervised and unsupervised methods, active learning reduces data interpretation to focus only on the most important instances.

Active learning drastically reduces the labeling effort required to train and deploy accurate, high-performing AI that truly understands your business. This means less labeling, happier employees, and faster time to value for AI.

UiPath is a pioneer of active learning for the enterprise, reducing time and effort while increasing the efficiency and accuracy of specialized AI models. To learn more about the latest UiPath developments in active learning, Join the UiPath Insider Program. And try building an advanced document understanding project using the preview capabilities in UiPath Automation Cloud.