RPA is an application of technology, governed by business logic and structured inputs, aimed at automating business.
RPA“Autonomics,” as IBM, and other organizations call it, gives any work process that is definable, repeatable, and rules-based the ability to map out a business process and assign a software robot to manage the execution of that process, just as a human would. RPA technology is not a part of a company’s information automate. quickly and efficiently without altering existing infrastructure and systems. Another way to look at RPA technology is to consider that it is not designed to be a business application, but designed to be a proxy for a human worker to operate business applications.
In hopes of improving the efficiency of the queue review process, the bank began to explore other options. It worked with a robotic process automation provider to implement an automation procedure within a few short months. This new procedure employs twenty virtual robotic employees that complete the queue review process exactly as a human would. With the implementation of this new software, the efficiency and accuracy of the process have increased dramatically.
The task of recognizing textual entailment, also known as natural language inference, consists of determining whether a piece of text (a premise), can be implied or contradicted (or neither) by another piece of text (the hypothesis).
Deep LearningWhile this problem is often considered an important test for the reasoning skills of machine learning (ML) systems and has been studied in depth for plain text inputs, much less effort has been put into applying such models to structured data, such as websites, tables, databases, etc. Yet, recognizing textual entailment is especially relevant whenever the contents of a table need to be accurately summarized and presented to a user, and is essential for high fidelity question answering systems and virtual assistants.
In “Understanding tables with intermediate pre-training”, published in Findings of EMNLP 2020, we introduce the first pre-training tasks customized for table parsing, enabling models to learn better, faster and from less data. We build upon our earlier TAPAS model, which was an extension of the BERT bi-directional Transformer model with special embeddings to find answers in tables. Applying our new pre-training objectives to TAPAS yields a new state of the art on multiple datasets involving tables. On TabFact, for example, it reduces the gap between model and human performance by ~50%. We also systematically benchmark methods of selecting relevant input for higher efficiency, achieving 4x gains in speed and memory, while retaining 92% of the results. All the models for different tasks and sizes are released on GitHub repo, where you can try them out yourself in a colab Notebook.
The task of textual entailment is more challenging when applied to tabular data than plain text. Consider, for example, a table from Wikipedia with some sentences derived from its associated table content. Assessing if the content of the table entails or contradicts the sentence may require looking over multiple columns and rows, and possibly performing simple numeric computations, like averaging, summing, differencing, etc.
The success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community’s understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem?
Artificial IntelligenceHow deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others. In recent years, AutoML algorithms have emerged [e.g., 1, 2, 3] to help researchers find the right neural network automatically without the need for manual experimentation. Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space. With the proper setup, these techniques have demonstrated they are capable of delivering results that are better than the manually designed counterparts.
But more often than not, these algorithms are compute heavy, and need thousands of models to train before converging. Moreover, they explore search spaces that are domain specific and incorporate substantial prior human knowledge that does not transfer well across domains. As an example, in image classification, the traditional NAS searches for two good building blocks (convolutional and downsampling blocks), that it arranges following traditional conventions to create the full network.
The Model Search system consists of multiple trainers, a search algorithm, a transfer learning algorithm and a database to store the various evaluated models. The system runs both training and evaluation experiments for various ML models (different architectures and training techniques) in an adaptive, yet asynchronous, fashion. While each trainer conducts experiments independently, all trainers share the knowledge gained from their experiments. At the beginning of every cycle, the search algorithm looks up all the completed trials and uses beam search to decide what to try next. It then invokes mutation over one of the best architectures found thus far and assigns the resulting model back to a trainer.
The performance of machine learning (ML) models depends both on the learning algorithms, as well as the data used for training and evaluation.
Machine LearningThe performance of machine learning (ML) models depends both on the learning algorithms, as well as the data used for training and evaluation. The role of the algorithms is well studied and the focus of a multitude of challenges, such as SQuAD, GLUE, ImageNet, and many others. In addition, there have been efforts to also improve the data, including a series of workshops addressing issues for ML evaluation. In contrast, research and challenges that focus on the data used for evaluation of ML models are not commonplace. Furthermore, many evaluation datasets contain items that are easy to evaluate, e.g., photos with a subject that is easy to identify, and thus they miss the natural ambiguity of real world context. The absence of ambiguous real-world examples in evaluation undermines the ability to reliably test machine learning performance, which makes ML models prone to develop “weak spots”, i.e., classes of examples that are difficult or impossible for a model to accurately evaluate, because that class of examples is missing from the evaluation set.
To address the problem of identifying these weaknesses in ML models, we recently launched the Crowdsourcing Adverse Test Sets for Machine Learning (CATS4ML) Data Challenge at HCOMP 2020 (open until 30 April, 2021 to researchers and developers worldwide). The goal of the challenge is to raise the bar in ML evaluation sets and to find as many examples as possible that are confusing or otherwise problematic for algorithms to process. CATS4ML relies on people’s abilities and intuition to spot new data examples about which machine learning is confident, but actually misclassifies.
Hyperautomation – A step beyond Robotic Process Automation (RPA)
Automation has become one of the most important technologies for digital enterprises. Robotic process automation (RPA) tools have made its way to almost every organization to make process automation easier.
Prior to RPA, organizations have relied on complex code for automation, but RPA has made it easier to implement and adopt. RPA bots work like a human using user interfaces (UIs) of common application like SAP, Salesforce, Service Desk and Microsoft Office.
But RPA is limited to automate only simple tasks with predefined rules and structured data. This leads to the automation in silos and limited to repetitive tasks. There is a need of more mature and advanced solution to automate critical tasks. Gartner’s report on Top 10 Technology Trends for 2020 has mentioned one such technology, Hyperautomation.
What is Hyperautomation?
According to Gartner, “Hyperautomation refers to an effective combination of complementary sets of tools that can integrate functional and process silos to automate and augment business processes.”
We at AutomationEdge are excited about Hyperautomation as it aligns with our vision of work.
Hyperautomation is built on few key technologies like RPA, process mining, process discovery, iBPMS, iPaaS and low code. Hyperautomation suggests achieving digital supremacy with process optimization, integration and automation.
Digital businesses need to scale automation enterprise-wide to remain competitive. Gartner sees it as an unavoidable state for businesses and need a change in strategy to achieve it.
Scope of automation:
With Hyperautomation, scope of automation changes from rule-based repetitive tasks to more complex, long-running process. Businesses need to connect more dots to achieve end-to-end automation.
Range of tools:
RPA is not enough anymore, companies need to integrate more tools and technologies like machine learning and artificial intelligence to achieve greater results.
Cross-functional initiative and integration is needed to achieve an end-to-end automation. Departmental tools and their integrations will play an important role.
Hyperautomation will explode the automation capabilities of the organizations. It will enable organizations breaking the inter-functional boundaries and achieve end-to-end automation. Hyperautomation will automate the processes which thought to be the work of domain experts only. It will enable adaptive and intelligence processes which will select the next best course of action instead of same repetitive activities.