The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

From very limited usage in the business world before 2012, machine learning dependency has gone up exponentially since the boom. Today there are 9k machine learning startups and companies according to Crunchbase. Here are the top machine learning start-ups that aim to excel in 2022. Algorithmia – Algorithmia's expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development, and leverages existing SDLC and CI/CD practices.

Robotic process automation (RPA) is gaining traction among enterprises, as RPA tools have proven they can streamline repetitive processes and save lots of time. But as more companies implement RPA, they're also finding they maximize ROI when they pair it with Intelligent Process Automation (IPA). RPA software revenue grew 63.1% in 2018 to $846 million, according to Gartner, making it the fastest-growing segment of the global enterprise software market. RPA tools are used in all industries, although Gartner says the biggest adopters are banks, insurance companies, telcos and utility companies. Such firms typically have many legacy systems and use RPA to help integrate data among them.

In our dealings with customers, one of the most challenging aspects of launching intelligent process automation (IPA) projects is a seemingly simple one: where to start. Whether it's a company that has hit the limits of what robotic process automation can do or is starting from scratch with IPA, in this post we offer three actionable steps to get your project underway. Intelligent process automation, in a nutshell, helps companies automate workflows and processes that involve unstructured content, including text and images found in documents of various formats (PDF, Word, etc.). It enables companies to automate more workflows than they can address with RPA alone, which can only deal with structured content -- like information found in a database. IPA achieves this by applying AI technologies such as machine learning and natural language processing, bringing powerful capabilities to bear.

In the technology industry, it's not unusual for vendors to want to latch on to the latest trend and claim to have a product or service that fits the category. Artificial intelligence is no exception, which means customers need to be vigilant about querying vendors to ensure their technology can really be classified as AI at all, if not intelligent process automation (IPA) specifically. The London-based venture capital firm MMC found that of 2,830 startups in Europe that were classified as AI companies, only 1,580 – about 56% – actually offered AI technology. "We looked at every company, their materials, their product, the website and product documents," David Kelnar, head of research for MMC, told Forbes. "In 40% of cases we could find no mention of evidence of AI."

Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the'My Profile' page under the'HTTP API' and update the iCalendar links afterwards.

BPM, which has been around for quite some time, is focused on improving business processes. But it doesn't necessarily require automation; it could be as simple as improving an entirely manual process by streamlining it to remove unnecessary steps. In short, it's about using best practices to ensure processes are as effective and efficient as possible. Robotic process automation involves automating repetitive tasks to make a process less labor-intensive for humans. It works well with deterministic business processes that involve structured data; in other words, where the process is exactly the same every time and where data is in well-defined fields, such as a spreadsheet. The process also must not require any human judgment, working strictly on "if/then" scenarios. Example use cases for RPA include aggregating data for financial reports, such as at the end of a quarter. So long as you know where the reports' data has to come from, and where in the report it's located, RPA can automate the gathering and aggregation process and get it done far faster than a human.

This spring I presented a talk entitled "Effective Transfer Learning for NLP" at ODSC East. The talk was intended to demonstrate how surprisingly effective pre-trained word and document embeddings are at low training data volumes, and to lay out a set of practical recommendations for applying these techniques to your own tasks. Thanks to some excellent research by Alec Radford and the team at OpenAI, our recommendations are beginning to change. To explain why the tides are shifting, let's first walk through the rubric we use at Indico to evaluate whether or not a novel machine learning method is viable for industry use. Let's see how well pre-trained word document embeddings satisfy these requirements: In short, using pre-trained embeddings is computationally cheap and performs well at the lower extremes of training data availability, but using static representations imposes an unfortunate cap on the benefit gained from additional training data.

Newswire) indico, a provider of enterprise machine learning solutions for unstructured content, today announced $4 million in new equity seed funding led by Osage Venture Partners. Ventures, Boston Seed, and Hyperplane also participated. The funding follows a $1.5 million round of convertible debt financing announced in October, and the addition of veteran CEO Tom Wilde in September. Artificial intelligence and machine learning offer big opportunities for businesses, but many organizations struggle to find practical business applications for the technology due to limited access to required skillsets, insufficient data and infrastructure, and poorly designed pre-trained offerings. Philadelphia-based Osage Venture Partners focuses on early stage, business-to-business technology companies including analytics and artificial intelligence-based startups such as Automated Insights, BAInsight, SevOne, and Sidecar.

Technology companies like IBM have arguably overhyped the capabilities of their artificial intelligence technologies over the past few years. But some executives believe the field is turning a corner, and businesses are starting to deliver practical applications of A.I. "There's some reality setting in," says Boston software executive Tom Wilde. "Some proof points need to show up. That said, the dollars being spent on [A.I. products] are pennies of what they're going to be" in the future. Wilde will try to deliver on the promise of A.I. in the business world as CEO of Indico, a small Boston-based machine learning and data analytics startup.