Grammarly

Grammarly introduces the Effective Communication Score (ECS) to measure AI's impact on organizational communication. It aims to assess overall communication quality through metrics like correctness, clarity, and inclusivity. ECS provides real-time insights for Enterprise customers, helping them understand communication effectiveness compared to industry benchmarks. The scoring method gauges the number of unaddressed writing suggestions, reflecting communication quality. The initiative seeks to enhance insights on communication across platforms and prove Grammarly's ROI, helping organizations improve their writing and productivity.

https://www.grammarly.com/blog/engineering/effective-communication-score/

Customer support teams face high ticket volumes and constant communication demands, leading to burnout; 62% of agents feel pressured by the expectation to always be connected. Despite AI's potential to alleviate some of this burden, adoption is low, with only 68% using it. Barriers include lack of resources, privacy concerns, and preference for human interaction. AI can enhance agent communication, productivity, and brand compliance, but customer support tools must evolve to meet growing demands.

https://www.grammarly.com/business/learn/cx-productivity-shift/

Writing involves direct and reported speech. Direct speech conveys exact words (quotes), while reported speech summarizes without quoting verbatim. Both enhance communication in narratives, journalism, and academia. This guide covers definitions, differences, common errors, and usage, including when to opt for each type. Direct speech uses quotation marks, retains the original tense and pronouns, while reported speech paraphrases, adjusts tense, and omits quotation marks. Proper usage affects clarity and tone in writing. Understanding these forms is crucial for effective expression across various contexts.

https://www.grammarly.com/blog/grammar/direct-and-reported-speech/

Dr. Brian Harfe from the University of Florida addresses AI's impact on student writing assessments by incorporating AI into coursework. Instead of banning AI, he redesigned assignments for students to create drafts using AI, then critique and revise them, promoting transparency and critical engagement. He utilized Grammarly Authorship for accurate attribution of AI versus student-written content, enhancing the assessment process. The initiative resulted in high student engagement and insights into their writing processes. Harfe's experiments signal a shift in teaching strategies to adapt to AI's role in education while maintaining academic integrity.

https://www.grammarly.com/blog/writing/modernizing-the-traditional-writing-assignment/

AI integration in customer experience (CX) faces unique challenges requiring precision and empathy. Key insights from a recent discussion include:

1. Focus on Outcomes: Redefine productivity from activity metrics to meaningful customer experiences.
2. Cautious Adoption: Fear of errors slows AI adoption; clear guidelines and supportive environments can help.
3. Reduce Tedious Tasks: Automate routine work to enable human-focused customer interactions.
4. Build Trust: Governance and clear policies foster adoption; confidence in tool usability is critical.
5. Simplicity in AI: Ensure AI tools simplify workflows rather than complicate them.

The ultimate goal is enhancing team effectiveness through clarity and reduced workload, meeting rising customer expectations with limited resources.

https://www.grammarly.com/business/learn/real-talk-on-cx/

Semantics, a branch of linguistics, studies sentence meaning derived from grammar, word choice, and context, distinguishing it from syntax, which focuses on sentence structure. It enables meaningful communication and understanding beyond literal interpretations, encompassing pragmatics to address context and intent. Semantics is crucial in various fields, including technology and daily communication, aiding clarity and effective expression.

https://www.grammarly.com/blog/grammar/semantics/

Grammarly is developing a compact, offline model for spelling and grammar correction, addressing challenges such as limited device memory and processing. By creating a 1B-parameter model, they aim to provide real-time, quality suggestions while maintaining the user's voice. Llama was chosen as the base model for its efficiency and capability to handle diverse writing styles. Synthetic training data was created to capture various writing contexts and error types. Optimizations improved processing speed to ~210 tokens/second. Despite successes in accuracy and preserving meaning, the model struggles with proper nouns, article placement, and tense consistency. Future work will refine training data and enhance model performance.

https://www.grammarly.com/blog/engineering/efficient-on-device-writing-assistance/