The viral awareness and adoption of artificial intelligence foundation models such as OpenAI LP's ChatGPT have created both an opportunity and threat to automation platforms generally and robotic process automation point tools specifically. On the one hand, large language models can reduce complexity and accelerate the adoption of enterprise automation platforms. The flip side is that software robots are designed to improve human productivity through intelligent automation and GPT models could cannibalize some, if not many, use cases initially targeted by RPA vendors. This reality is causing customers to rethink their automation strategies and vendors to evolve their messaging rapidly to position foundation models as an accelerant to their platforms. In this Breaking Analysis, we provide you with a perspective on how foundation models could have an impact on automation platforms. We review Enterprise Technology Research data that quantifies the ascendency of OpenAI.

It's no secret that the commercial application of NLP technologies has exploded in recent years. From chatbots and virtual assistants to machine translation and sentiment analysis, NLP technologies are now being used in a wide variety of applications across a range of industries. With the increasing demand for technologies that can process human language, investors have been eager to get a piece of the action. In this article, we look at NLP start-up funding over the past year, identifying the applications and domains that have received investment. A version of this article will appear in the Journal of Natural Language Engineering in early 2023.

Given the rapid growth that robotic process automation leader UiPath Inc. has shown over the last few years, it's no wonder that the small robotics process automation pond isn't enough for the company to swim in. Instead, it wants to be the big fish in the expanding enterprise automation lake. I covered last year's Forward IV conference, where UiPath cemented its leadership position in RPA, while looking to expand into the platform-centric enterprise automation space. At that time, its vision was ahead of its product execution in a clear example of growing pains for the recently public company. In spite of UiPath's slowing growth (a common phenomenon as companies mature), this year's Forward 5 conference doubled in size from last year's pandemic-constrained Forward IV – and the increased excitement among customers, partners and UiPath employees was palpable.

The opportunities to automate within an organization are broad and deep -- including within IT, finance, HR, supply chain, and customer services, and interest in automation continues to deepen. Automation remains one of the fastest growing enterprise software categories, with a recent report by research firm Gartner stating it expects global robotic process automation (RPA) software revenue to increase nearly 20% over last year. While RPA solutions traditionally focused on automating tasks via screen scraping, today's modern RPA solutions focus on an API-first approach for process automation. Meanwhile, RPA vendors are continuously enhancing their API integration capabilities to offer both user interface-based screen scraping and API-first integration capabilities to their customers. "Vendors are focusing on evolving their RPA offerings into a broader automation platform with complementary technologies, such as process mining and task mining for process automation, monitoring and improvement, and wide range of integration capabilities," says Gartner senior market research specialist Varsha Mehta.

Size-based separation of bioparticles/cells is crucial to a variety of biomedical processing steps for applications such as exosomes and DNA isolation. Design and improvement of such microfluidic devices is a challenge to best answer the demand for producing homogeneous end-result for study and use. Deterministic lateral displacement (DLD) exploits a similar principle that has drawn extensive attention over years. However, the lack of predictive understanding of the particle trajectory and its induced mode makes designing a DLD device an iterative procedure. Therefore, this paper investigates a fast versatile design automation platform to address this issue. To do so, convolutional and artificial neural networks were employed to learn velocity fields and critical diameters of a wide range of DLD configurations. Later, these networks were combined with a multi-objective evolutionary algorithm to construct the automation tool. After ensuring the accuracy of the neural networks, the developed tool was tested for 12 critical conditions. Reaching the imposed conditions, the automation components performed reliably with errors of less than 4%. Moreover, this tool is generalizable to other field-based problems and since the neural network is an integral part of this method, it enables transfer learning for similar physics. All the codes generated and used in this study alongside the pre-trained neural network models are available on https://github.com/HoseynAAmiri/DLDNN.

Replacing computer-assisted coding (CAC) and robotic process automation (RPA) with an AI-powered automation platform will deliver greater accuracy, cut turnaround time, save significant dollars and drastically improve operational efficiency.

Nitin Rakesh, a distinguished leader in the IT services industry, is the Chief Executive Officer and Director of Mphasis. For centuries, banks demonstrated expertise in keeping, lending and saving money. In return, customers followed a bank's regulations. This included how banks stipulated interest rates for lending, identified creditworthy cohorts and facilitated banking transactions. Fast-forward to 2020, and banks are now viewed under the same lens as customer-facing organizations like movie theatres, restaurants and hotels.

The life of a clinical study relies on data from documentation, meetings, emails and calls; all of which can be overwhelming for patients, clinical trial teams and associates. Although mundane, documenting, executing and collecting data is crucial to move a trial from phase to phase. Clinical trial teams face a multitude of competing priorities, from evaluating hundreds of potential patients to maintaining compliance and recording patient progress. No aspect or step can be neglected for a trial to succeed, especially regarding patient recruitment and retention. To help streamline these facets, AI-powered support automation platforms provide clinical trial management teams an interactive and informative interface with integrated cloud storage, intelligent document processing and a centralized knowledge base.

Organizations are devoting more resources to deploying intelligent document processing, particularly as they embark on digital transformations. As Deloitte explains, intelligent document processing automates the processing of data contained in documents -- understanding what the document is about, what information it contains, extracting that information, and sending it to the right place. According to Everest Group, the global market for intelligent document processing was worth between $700 million and $750 million in 2020. Leveraging a blend of AI, including computer vision and natural language processing, intelligent document processing can help to automate tasks like invoice processing, insurance claims, patient records, proof of delivery, and order forms. A range of startups offer these types of services, including Mindee, Zuva, Rossum, PandaDoc, and Anvil.

EVP of products at Kofax, a supplier of intelligent automation software to digitally transform end-to-end business operations. Artificial intelligence (AI) is anything but new. It's been a major part of our everyday lives for some time now. But when it comes to the business world, AI hasn't fully arrived yet -- at least not for most companies. While many organizations consider AI to be the next frontier in digital transformation, they just can't shift into the next gear and further their investment in AI-powered processes.