Its the Meaning That Counts: The State of the Art in NLP and Semantics KI Künstliche Intelligenz

Semantic processing is about understanding the meaning of a given piece of text. Part-of-speech tagging or POS tagging is the task of assigning a POS tag to each word depending upon its role in the sentence. Assigning the correct POS tags helps us better understand the intended meaning of a phrase or a sentence and is thus a crucial part of syntactic processing. The next step after the lexical analysis is to extract more information from the sentence, by using its syntax this time. Instead of only looking at the words, we look at the syntactic structures, i.e., the grammar of the language to understand what the meaning of given sentence is.

In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also. The data presented in Table 2 elucidates that the semantic congruence between sentence pairs primarily resides within the 80–90% range, totaling 5,507 such instances. Moreover, the pairs of sentences with a semantic similarity exceeding 80% (within the 80–100% range) are counted as 6,927 pairs, approximately constituting 78% of the total amount of sentence pairs.

This ends our Part-9 of the Blog Series on Natural Language Processing!

The first category consists of core conceptual words in the text, which embody cultural meanings that are influenced by a society’s customs, behaviors, and thought processes, and may vary across different cultures. These recurrent words in The Analects include key cultural concepts such as “君子 Jun Zi, 小人 Xiao Ren, 仁 Ren, 道 Dao, 礼 Li,” and others (Li et al., 2022). A comparison of sentence pairs with a semantic similarity of ≤ 80% reveals that these core conceptual words significantly influence the semantic variations among the translations of The Analects. The second category includes various personal names mentioned in The Analects. Our analysis suggests that the distinct translation methods of the five translators for these names significantly contribute to the observed semantic differences, likely stemming from different interpretation or localization strategies. Through the analysis of our semantic similarity calculation data, this study finds that there are some differences in the absolute values of the results obtained by the three algorithms.

• A major drawback of statistical methods is that they require elaborate feature engineering.
• All the subsequent parsing techniques which we are going to see later in the post use the part-of-speech tags to parse the sentence.
• Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs.
• A key component in information extraction systems is Named-Entity-Recognition (NER).
• This study obtains high-resolution PDF versions of the five English translations of The Analects through purchase and download.

To learn more about such techniques to represent words as vectors and understand the context by analyzing the neighborhood in which the word is used refer to the post, Distributional Semantics | Techniques to represent words as vectors. As with most machine learning problems, the lack of labeled data is a common problem in building disambiguation models. Supervised techniques for word sense disambiguation require the input words to be tagged with their senses. The sense is the label assigned to the word by maximizing posterior probability. Our brain can process sentences meaningfully because it can relate the text to other words and concepts it already knows, such as football, FIFA, etc.

Exploring NLP Semantics with Natural Language Processing

The similarities and dissimilarities among these five translations were evaluated based on the resulting similarity scores. The Jennings’ translation considered the readability of the text and restructured the original text, which was a very reader-friendly innovation at the time. Despite this structural change slightly impacting the semantic similarity with other translations, it did not significantly affect the semantic representation of the main body of The Analects overall data analysis. This study employs natural language processing (NLP) algorithms to analyze semantic similarities among five English translations of The Analects. To achieve this, a corpus is constructed from these translations, and three algorithms—Word2Vec, GloVe, and BERT—are applied to assess the semantic congruence of corresponding sentences among the different translations. Analysis reveals that core concepts, and personal names substantially shape the semantic portrayal in the translations.

These models, including BERT, GPT-2, and T5, excel in various semantic analysis tasks and are accessible through the Transformers library. Semantic processing uses a variety of linguistic principles to turn language into meaningful data that computers can process. By understanding the underlying meaning of a statement, computers can accurately interpret what is being said.

Meaning representation can be used to reason for verifying what is true in the world as well as to infer the knowledge from the semantic representation. Natural language processing can help customers book tickets, track orders and even recommend similar products on e-commerce websites. Teams can also use data on customer purchases to inform what types of products to stock up on and when to replenish inventories.

It promises to reshape our world, making communication more accessible, efficient, and meaningful. With the ongoing commitment to address challenges and embrace future trends, the journey of semantic analysis remains exciting and full of potential. It specializes in deep learning for NLP and provides a wide range of pre-trained models and tools for tasks like semantic role labelling and coreference resolution. Transformers, developed by Hugging Face, is a library that provides easy access to state-of-the-art transformer-based NLP models.

Word Sense Disambiguation (WSD)

As semantic analysis advances, it will profoundly impact various industries, from healthcare and finance to education and customer service. Future NLP models will excel at understanding and maintaining context throughout conversations or document analyses. This will result in more human-like interactions and deeper comprehension of text.

Neri Van Otten is a machine learning and software engineer with over 12 years of Natural Language Processing (NLP) experience. As NLP models become more complex, there is a growing need for interpretability and explainability. Efforts will be directed towards making these models more understandable, transparent, and accountable. Future trends will address biases, ensure transparency, and promote responsible AI in semantic analysis. Sentiment analysis is a tool that businesses use to examine consumer comments about their goods or services in order to better understand how their clients feel about them. Companies can use this study to pinpoint areas for development and improve the client experience.

Statistical NLP (1990s–2010s)

Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots.

So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. For comparative analysis, this study has compiled various interpretations of certain core conceptual terms across five translations of The Analects. Evaluating translated texts and analyzing their characteristics can be achieved through measuring their semantic similarities, using Word2Vec, GloVe, and BERT algorithms.

The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. The analysis of sentence pairs exhibiting low similarity underscores the significant influence of core conceptual words and personal names on the text’s semantic representation. The complexity inherent in core conceptual words and personal names can present challenges for readers. To bolster readers’ comprehension of The Analects, this study recommends an in-depth examination of both core conceptual terms and the system of personal names in ancient China. By doing so, readers can greatly improve their cognitive abilities during the reading process.

Read more about https://www.metadialog.com/ here.

How to Increase Sales With Artificial Intelligence Tools

Artificial intelligence not only helps you make your sales process more efficient, but it also helps you uncover new insights that help you close more sales. Sales AI tools can help you analyze sales calls to gain better insights into understanding conversations with prospects. These tools can identify key moments within your sales calls, note mentions of competitors, and more.

Advanced analytics, gathered automatically for optimal efficiency, show you the big picture before making a sales forecast. Find out in this Dialpad for Sales guide, which walks through how sales leaders are using Dialpad to solve challenges like rep onboarding, tedious activity logging, and gathering customer intelligence. Armed with this insight, a sales leader can easily keep an eye on tens (or even hundreds) of active calls and quickly see which ones have negative sentiment.

Use conversational AI to identify trends and patterns in sales calls

Your AI tool will continue to learn and adapt, getting increasingly more accurate as time goes on. With the rise of prominent programs like ChatGPT, artificial intelligence (AI) is becoming increasingly integral in the digital landscape. If you want to improve your sales process, consider investing in sales AI. This analytical prowess empowers sales managers with actionable insights to tailor coaching strategies, allocate resources effectively, and measure the impact of coaching initiatives over time. It aggregates and analyzes vast amounts of this call data to identify patterns and trends, uncovering strengths and areas that need enhancement for individual salespeople and the team.

AI-driven tools are much more advanced in scoring and ranking leads than typical sales representatives. With AI, sales teams can automate those important tasks for which they neglected to allocate resources due to their high labor intensity. AI algorithms can improve sales forecasting and predict revenue streams with greater accuracy than people. The integration of AI into sales processes offers a plethora of advantages, transforming traditional methods and driving efficiency. The top use case for AI in sales is to help representatives understand customer needs, according to Salesforce’s State of Sales report. Your knowledge of a customer’s needs informs every decision you make in customer interactions — from your pitch to your sales content and overall outreach approach.

Real-time decision-making with sales enablement:

As an introvert, artist, and woman of letters, please believe me when I confide that sales didn’t come to me naturally. Over the years I have greeted tech advances with open arms because I need all the help I can get. Luckily, process automation has been building upon itself for decades, cutting out the need for various types of expertise to make meaningful moves. Plecto is a data visualization software that helps you motivate your employees to reach new limits and stay on top of your business. So, in the battle against churn, AI is your trusted ally, helping you not only identify at-risk customers but also providing the insights you need to keep them engaged and satisfied. AI tools can swiftly compose personalized messages based on prospect data, ensuring each interaction resonates with recipients.

Allowing automation to take over is the key to uncovering revelations like these in the future. Nearly 62% of top sales pros say that this kind of guided-selling will become essential moving forward. For Instance, the data might suggest that the director or vice president of sales is your best bet at companies within a certain range of revenue. However, companies in that range might have dramatically different go-to-market models.

But these tools often augment human salespeople rather than replace them. In fact, AI tools are increasingly taking over work that human salespeople don’t have the ability or the time to do. AI analyzes customer data and social media posts to guide sales reps on the right approach. AI bridges the gap between sales and marketing teams, aligning their workflows and strategies.

• Its mission is to accelerate the content generation process for diverse marketing endeavors, including campaigns, drips, newsletters, and more.
• Beyond commercial partnerships, coop-etition will be a powerful force as it solves data availability and accessibility challenges for specialized use cases.
• Ultimately, the goal of AI in sales is to boost efficiency and effectiveness while reducing costs.
• One effective way to use AI for sales is to personalize your audience’s experience.

As long as you’re looking to prospect the types of people who tend to be on LinkedIn, you can use Clay to create dynamic prospect profiles. Clay integrates with LinkedIn to compile spreadsheet-style lead lists customized to meet your data needs. When you start a new list, you’re prompted to enter details about your target leads, like job title, experience level, bio/headline keywords, and education. With your parameters set, you can select the depth of information you want to pull. I’m not overly impressed with Postaga’s interface, which is intuitive but less polished than higher-end tools.

Our research suggests that a fifth of current sales-team functions could be automated. Operating as a browser extension, Lavender syncs with your inbox to feed a suite of sweet AI email tools. Premium users can use the Start My Email feature to turn bullet points into new messages, and even free plan users can tap the integrated ChatGPT bot to compose them on the fly. Lavender even offers suggestions for the output copy within the bot’s conversation feed, which you can apply with the click of a button and have it regenerated.

The community is a hidden gem filled with exclusive prompts, designed specifically for various AI tools. You can think of it like a potluck – everyone brings something unique to the table. You can share your prompts, try out the prompts shared by others, and discuss the fascinating results. Don’t forget to check out this SaaS lifetime deal website if you’re looking for the latest lifetime deals on SaaS software. It’s the perfect spot to not just discover amazing deals but to also stay updated on the fast-paced world of SaaS. Moreover, the huge database of tools is hosted on Retable, a fantastic platform that effortlessly turns spreadsheets into smart database apps, making navigating this vast sea of AI tools a breeze.

Decide on key performance indicators (KPIs)

Traditional forecasting relies on what happened last year or season and perhaps a handful of current variables to anticipate what will happen in the future. On the other hand, AI-driven sales analytics tools consider complex algorithms and data sets for a more accurate outlook. AI in sales can be used to help manage and predict customer behavior, identify cross-selling and upselling opportunities, automate repetitive tasks, and improve forecasting accuracy. Ultimately, the goal of AI in sales is to boost efficiency and effectiveness while reducing costs.

This receptionist makes decisions and provides original responses (virtually) the way a human would. Furthermore, this platform must offer crystal transparency to your sales reps and foster transparency of the whole sales process. Dialpad supercharges the process with its AI-powered sales coach, which offers real-time coaching and sales recommendations. Live Coach™ helps new sales assistants get up to speed quickly, but is also great for continuous learning. For example, you can use sales artificial intelligence tools that tell you how often your competitors are coming up on sales calls.

AI-driven contextual selling

Fortunately, AI is here to help us in this crucial task by analyzing customer behavior and spotting early signs of churn. Our research indicates that players that invest in AI are seeing a revenue uplift of 3 to 15 percent and a sales ROI uplift of 10 to 20 percent. Such trailblazers are already realizing the potential of gen AI to elevate their operations. Currently based in Albuquerque, NM, Bryce Emley holds an MFA in Creative Writing from NC State and nearly a decade of writing and editing experience. When he isn’t writing content, poetry, or creative nonfiction, he enjoys traveling, baking, playing music, reliving his barista days in his own kitchen, camping, and being bad at carpentry.

• Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.
• In a world where quality matters more than quantity, Gen AI is a game-changer.
• It isn’t the most efficient way to process your data and is time-consuming on top of it all.
• The benefit of using Exceed is that it will engage with the leads entering your pipeline by using a human touch in communication.
• In fact, the highest performing sales teams were 2.3X more likely to use AI guided selling.
• A car dealership decided to leverage an AI tool to optimize its lead generation process.

Read more about How To Use Artificial Intelligence (AI) For Sales? here.

12 key benefits of AI for business

Chatbots can be integrated with social media platforms to assist in social media customer service and engagement by responding to customer inquiries and complaints in a timely and efficient manner. For example, it is very common to integrate conversational Ai into Facebook Messenger. Deliver personalized service and save time with AI built directly into your flow of work. Utilize our trusted AI for customer service to create seamless conversational, predictive, and generative AI experiences for your agents and customers.

AI can summarize each conversation using natural language processing and do sentiment analysis which will help support agents prioritize issue based on severity, urgency, customer data and sentiment. This can save a few seconds per customer issue and greatly improve the customer service experience. By implementing generative AI in customer support systems, your business can create more streamlined and intuitive customer experiences. Moreover, generative AI can play the vital role of a virtual assistant for customer support teams, boosting a team’s productivity and response times. Sentiment analysis helps businesses understand customer emotions and perceptions through their interactions. This AI technology scans customer feedback, detecting positive, negative, or neutral sentiments.

Analyze Content and Context of Customer Feedback Using Natural Language Processing (NLP)

Customers often voice their opinions, feedback, and complaints on social media, expecting quick and effective responses. AI tools can help businesses monitor social media platforms for mentions of their brand, products, or services, enabling them to respond promptly and proactively engage with their customers. Moreover, voice recognition technology can also improve the efficiency of customer service representatives.

Traditionally, customer support solely relied on human has limited ability and availability. That may lead to delays in response, restricted operating hours, and a lack of knowledge. This can help businesses save money, as they won’t need to hire extra staff to handle increased demand. AI has the capability to provide professional, error-free responses by using advanced rephrasing and grammar-check features. This improves the quality of communication with customers and ensures that the brand’s voice and tone remain consistent across all channels.

Striking the Right Balance: Human and AI Collaboration:

No matter when, where, and how urgently they require assistance, they will get it quickly and efficiently. Such speed combined with the competence of your human support team can help turn your website visitors into your loyal customers. Artificial intelligence for customer service is getting more and more advanced. There are plenty of advanced tools, and many able to learn from each conversation they have with visitors. There’s a variety of AI software that can help businesses from any industry partially or fully automate the customer communication tasks. These include responding to customer inquiries, welcoming new customers, recovering abandoned carts, answering FAQs, and more.

This capability helps businesses tailor their products, services, and customer experiences. Customers benefit from more personalized and relevant experiences, as businesses can use these insights to improve product offerings and customer service strategies. One limitation of chatbots is their lack of human touch, including empathy, which may make them unsuitable for all customer interactions. NLP is a subset of AI that enables computers to understand human language (including idioms, slang, and regional variations), while conversational AI systems engage in natural language conversations. Conversational AI uses NLP as a foundation to interpret human language but also involves other technologies, such as machine learning and speech recognition, to enable human-like interactions.

Choose the right AI tools

This not only enhances the customer experience but also increases customer satisfaction. AI in customer service offers several benefits, such as improved efficiency, personalization, and round-the-clock support. However, it also faces challenges, including the lack of human touch and potential job displacement.

Read more about Key Benefits of AI-Powered Customer Service and Support here.

Salesforce Announces Public Sector Compliance Certifications, AI and Automation Tools to Accelerate Mission Impact

In order to avoid the siloing of best practices and lessons learned within each department, agencies should place a priority on publishing their efforts openly and communicating findings outside of usual intra-agency pathways. In addition to a technical focus on securing models, research attention should also focus on creating testing frameworks that can be shared with industry, government, and military AI system operators. In a similar manner to how automobiles are tested for safety, testing frameworks for the security of models can be established and used as a core component alongside the traditional testing methods used for vehicles, drones, weapon systems, and other systems that will adopt AI. From a technological standpoint, an additional difficulty is created by the fact that the field and technology itself is rapidly changing.

Governments today depend on AI algorithms to improve their decision-making as regards citizens. Sensitive data could find its way into the illegal hands or be used for iniquitous purposes. This calls for urgent government intervention in ensuring sufficient safekeeping of individuals’ private information. We are fostering industry support for SAIF with partners and customers, hosting SAIF workshops with practitioners and publishing AI security best practices. We partnered with Deloitte on a whitepaper on how organizations can use AI to address security challenges.

Where do AI regulations go from here ?

In addition, challenges that concern transparency and accountability are of importance in a government driven by AI. As AI systems grow into more complex and independent forms, individuals find it more difficult and worrisome to understand how and what their data is being used for, and if these algorithmic decisions remain fair. Governments need to proliferate mechanisms that support a transparent, accountable, and harm-free automated decision-making process. Google will continue to build and share Secure AI Framework resources, guidance, and tools, along with other best practices in AI application development. SAIF is our framework for creating a standardized and holistic approach to integrating security and privacy measures into ML-powered applications.

Second, the military’s unique domain necessitates the creation of similarly unique datasets and tools, both of which are likely to be shared within the military at-large. Because these datasets and systems will be expensive and difficult to create, there will be significant pressures to share them widely among different applications and branches. However, when multiple AI systems depend on this small set of shared assets, a single compromise of a dataset or system would expose all dependent systems to attack. A final avenue to poison a model is to simply replace a legitimate model with a poisoned one. Once trained, a model is just a file living within a computer, no different than an image or PDF document.

Data Privacy Hub

“By promoting accountability, data privacy, equitable solutions, and human review, we help our customers identify valuable use cases for generative AI and strike the right balance between human and AI,” said Gretchen Peri, managing director of Americas Public and Social Impact Industry at Slalom. Make the most of your live LEXI captions with LEXI Library – our easy-to-use cloud caption archiving and search tool. Via an intuitive web portal, LEXI Library allows you to view, search, edit and archive your live captions as raw text.

Taken together, these weaknesses explain why there are no perfect technical fixes for AI attacks. These vulnerabilities are not “bugs” that can be patched or corrected as is done with traditional cybersecurity vulnerabilities. From this understanding, we can now state the characteristics of the machine learning algorithms underpinning AI that make these systems vulnerable to attack. Third, the report contextualizes AI vulnerabilities within the larger cybersecurity landscape. It argues that AI attacks constitute a new vertical of attacks distinct in nature and required response from existing cybersecurity vulnerabilities. Given time, researchers may discover a technical silver bullet to some of these problems.

Using AI to Improve Security and Compliance

Commercial applications that are using AI to replace humans, such as self-driving cars and the Internet of Things, are putting vulnerable artificial intelligence technology onto our streets and into our homes. Segments of civil society are being monitored and oppressed with AI, and therefore have a vested interest in using AI attacks to fight against the systems being used against them. A secure cloud fabric is a powerful tool that can help the federal government meet its evolving data management and processing needs. It provides a secure and private multi-cloud connection that supports both data lakes and AI infrastructure.

It is necessary to hold those developing and deploying AI accountable to standards that protect against unlawful discrimination and abuse, including in the justice system and the Federal Government. Only then can Americans trust AI to advance civil rights, civil liberties, equity, and justice for all. Harmful consequences from AI systems can ensue for several reasons, even if neither the user nor the developer intends harm. First, it is difficult to precisely specify what we want deep learning-based AI models to do, and to ensure that they behave in line with those specifications. In other words, reliably controlling AI models’ behavior remains a largely unsolved technical problem.

Managed Cloud Infrastructure

(l)  The term “Federal law enforcement agency” has the meaning set forth in section 21(a) of Executive Order of May 25, 2022 (Advancing Effective, Accountable Policing and Criminal Justice Practices To Enhance Public Trust and Public Safety). (c)  The term “AI model” means a component of an information system that implements AI technology and uses computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs. Some companies have countered this, saying they will face high compliance costs and disproportionate liability risks. The world has learned a number of painful lessons from the unencumbered and reckless enthusiasm with which technologies with serious vulnerabilities have been deployed. Social networks have been named as an aide to genocide in Myanmar and the instrument of democratic disruption in the world’s foremost democracy.

A central technology behind conversational AI is natural language processing (NLP), which enables machines to understand, interpret, and generate human language. Many U.S. agencies such as the Securities Exchange Commission, Internal Revenue Service, and the Department of the Treasury use algorithms to help direct enforcement resources for potential fraud cases. These programs are detailed further in Government by Algorithm, a 2020 report from ACUS. Our Enterprise license is ideal for government bodies who require multiple teams, as it leverages our unique Hub & Spoke architecture. Each team operates their GRC activities from a dedicated Spoke, ensuring data and operational separation with unrestricted access to modules, users, content, audits and a powerful AI engine, all connected to a central Hub for centralized administration, content management, and aggregate reporting.

Other AI Initiatives at the Department of State

Finally, local governments should regularly review their generative AI security policies to remain up-to-date and aligned with evolving security threats and best practices. Its features include a Content Library for turn-key compliance obligations and controls, an AI-enhanced controls builder, and actionable control task creation and linkages. The system simplifies evidence gathering for control effectiveness, and auto-maps controls to compliance needs, leveraging our AI engine and eliminating manual mapping. Integrated audit modules and a Trust Portal make auditing, sharing with stakeholders and proving compliance easy. Managing controls and policies is inefficient due to rapid regulatory changes, the labor-intensive process of developing and implementing high-quality controls, and the lack of a unified system.

Read more about Secure and Compliant AI for Governments here.

18 Best AI Scheduling Assistants for Boosting Productivity

This proactive approach to time management allows you to approach your studies with a sense of control and confidence. Use Syntea as your project management tool, and pick up valuable project management skills as you progress through your studies. It’s like having your personal study management software, crafting a bespoke study plan perfectly suited to your academic needs and ambitions. As artificial intelligence-based systems and applications became integrated with consumer devices, so too did its proliferation as an everyday addition to people’s digital activities. These systems are now able to access titanic reams of information and data in seconds, which in turn means that we have that information at our disposal too. CalendarHero offers a dynamic and scalable meeting scheduling solution designed to streamline the process for teams and businesses.

Where the opportunity lies is using AI to optimize this piece of the puzzle. Not only does it benefit the productivity of your team, but it maximizes your output as a business, creating more opportunities. There’s no need for today’s project teams to schedule anything manually. With solutions like ALICE, you can streamline the scheduling fundamentals, creating a positive domino effect. Artificial Intelligence (AI) and machine learning (ML) can help many aspects of health care. Patient scheduling has significant implications benefits of improved technology.

What is Clockwise AI Scheduler?

Together, you can make positive changes that will help you both be more successful. Tools like Base’s Analyze examine the calendar patterns of the executive you support to uncover insights into how her/his time is spent. In seconds, you can get a visual representation of how your executive’s day, week, or year is divided between meetings, travel, value-add activities and more. With this information at your fingertips, you can more easily correct inefficiencies, set goals, and take advantage of strategic opportunities. AI algorithms can analyse data and make predictions about future workforce needs, enabling more efficient scheduling and staffing decisions.

• Reclaim says their personalized schedule can save 40% of your time, while Motion claims that their AI scheduling assistant can help you do 25% more tasks.
• Buffer’s AI Assistant is a fantastic feature that makes social media scheduling faster and easier by suggesting post ideas and repurposing existing content.
• You can have your entire team added to the platform in one meeting, and the AI will instantly begin engaging your inbound leads, routing, and  safeguarding your reps  meetings.

All logs are also visible on the Gantt timeline in real-time, making it effortless to stay on track with production. Machine malfunctions, changes in lead times, changes in material availability, and priority orders happen daily in production. SkyPlanner’s built-in Arcturus AI addresses all issues with production scheduling and personnel in a matter of seconds. SkyPlanner APS ensures that your facility works to its fullest capacity. Factories that are using SkyPlanner APS save an enormous amount of time and resources.

Time tracking analytics to optimize your workweek

AI-based scheduling assistants aim to provide a solution to address these challenges. Before AI, it would be almost impossible to reach this personalized schedule level without manually adding information to your calendar. If all permissions are set, it will then check both users calendars to see if there any prexisting conflicts with any of the users calendars. This is limited to 3 free times per day, and will check up to 7 business days ahead of time for these free times. It will check for free times for the duration of the meeting, if provided.

Read more about How To Use AI For Scheduling here.