Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 seconds making planning across varied product types easier. Introduction Accurate production scheduling is a cornerstone of efficient manufacturing. In practice, schedules are generated based on estimates of processing times required for each step in the production process. However, when these estimates deviate from actual conditions--due to missing or outdated data - the generated schedules quickly become obsolete.

The global use and further development of AI continued to grow in 2021 as enterprises found more ways to deploy it and developers discovered new ways to capture its possibilities for business users. So, what might 2022 bring for AI and a wide range of related IT fields from MLOps to security, cloud and edge computing, open source, the metaverse and more? To answer that question, we received a wide range of predictions from IT industry experts who shared their thoughts with EnterpriseAI. We are publishing them here, edited for clarity and brevity, to give our readers an early look at what may come in 2022 in enterprise AI and related technologies. Rodrigo Liang, the CEO and co-founder of AI platform vendor SambaNova Systems, said he sees companies moving away from DIY AI and linking up with vendors who can help them better reach their business goals.

By Unique Kumar Mental Health: There is a sincere need to improve mental health, As per the reports available, it is estimated by the WHO World health organization, the burden of mental health to the tune of 2443 disability-adjusted life years (DALYs) per 100, 000 population, and the age-adjusted suicide rate is 21.1. Between 2012 & 2030, the health conditions are pegged at 1.03 trillion dollars. Similarly, the mental health survey, 2016 estimated that over 85% of people with common mental disorders such as depression or anxiety disorder and 73.6% of people with mental disorders such as psychosis or bipolar disorder. Interestingly the ideal psychiatrist rate is 1:8000 to 10,000 but actual figures stand at 1:2,00,000 and this is a huge gap. It is not possible to fill the vacancies by employing more and more people & the way to address the problem is only possible by leveraging technology and use Artificial Intelligence, Automation with help of Mobile apps to improve mental health.

By 2025, the lines separating culture, information technology and health will be blurred. Engineering biology, machine learning and the sharing economy will establish a framework for decentralising the healthcare continuum, moving it from institutions to the individual. Propelling this forward are advances in artificial intelligence and new supply chain delivery mechanisms, which require the real-time biological data that engineering biology will deliver as simple, low-cost diagnostic tests to individuals in every corner of the globe. As a result, morbidity, mortality and costs will decrease in acute conditions, such as infectious diseases, because only the most severe cases will need additional care. Fewer infected people will leave their homes, dramatically altering disease epidemiology while decreasing the burden on healthcare systems.

The legal sector is quickly moving to embrace digital transformation and leaning towards innovation as it recognises the opportunity to improve customer services, drive productivity and adhere to the raft of compliance checks that all law firms have to meet. In fact, in feedback from legal professionals in our recent Advanced Trends Survey Report 2019/2020, only 40 per cent felt their law firm wasn't acting fast enough to keep up with the pace of technology innovation – so that means 60 per cent are acting with pace and are certainly well ahead on that journey. To encourage greater innovation, one technology that we predict will have a transformative effect on the industry is Artificial Intelligence (AI). Although AI is still in its relative infancy, it is already helping to change the way many industries operate and the legal sector is increasingly recognising its potential benefits. For example, a recent Deloitte study estimated 100,000 legal roles will be automated by 2036, leaving legal professionals to concentrate on higher value, client facing tasks.

Last week, I started a blog series, looking at some of the loftiest technology predictions for 2020 and beyond, and how it will impact construction specifically. Last week's topic was the IoT (Internet of Things), and this week I would like to dive into AI (artificial intelligence) and ML (machine learning). Now, before we look at predictions for where the market is headed, let's take a quick minute to define these terms. Too often, people use the acronyms, IoT, AI, and ML interchangeably, but they are in fact very different. Quite simply, AI is the data or the intelligence that tells a machine what to do, while ML studies previous datasets and makes predictions for future datasets.

With 2018 out the door, it's important to take a look at where we've been over these past twelve months before we embrace the possibilities of what's ahead this year. It has been a fast-moving year in enterprise technology. Modern data management has been a primary objective for most enterprise companies in 2018, evidenced by the dramatic increase in cloud adoption, strategic mergers and acquisitions and the rise of artificial intelligence (AI) and other emerging technologies. Continuing on from my predictions for 2018, let's take out the crystal ball and imagine what could be happening technology-wise in 2019: Such mixed-use architectures will be essential in driving machine learning operationalization. By adoping centralized cross-functional AI departments, organizations will be able to produce, share and reuse AI models and solutions to realize rapid return on investmentt (ROI).

We are living in interesting times. Multiple technologies, improving exponentially, are converging. I have been chronicling this convergence for several years in my newsletter, Exponential View. As Bill Gates said, "Most people overestimate what they can do in one year and underestimate what they can do in ten years." Likewise, most annual predictions overestimate what can occur in a year, and underestimate the power of the trend over time.

Big Data, artificial intelligence (AI) and Internet of Things (IoT) are currently three of the biggest business trends. Today, if someone is not using these in their operations, they are likely losing the competitive edge. Scalability will be the key word for the year as enterprises continue to seek insight from their growing pools of data but also attempt to limit the costs of cloud growth. Cloud has always been linked with scalability. Earlier, enterprises used to rent a part of a server (shared hosting) and host their website.

For the new year, we asked 18 of our researchers and staff members from around PARC to share some of their technology predictions for 2018 and beyond. "Deep learning and deep reinforcement learning has revolutionized data analytics, but the amount of data required is still relatively huge. It's also not how humans learn – typically a few examples are enough to form a mental model. I think this is going to change as research shifts from feasibility and accuracy to scalability and transparency. There's already some work in this area of slow learning, including from PARC, and this will only get more prominent."