From Words to Wisdom: Unleashing the Potential of Language Models for Human-Centered Data Exploration

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Presenter: Paul Watts, Director, Product Management at Forsta With the rapid adoption of AI technologies like ChatGPT into insight platforms, companies face the challenge of finding the right balance between harnessing the power of generative AI and navigating the well-documented pitfalls associated with a technology that is still in its infancy. In this session, Paul provides valuable insights on the potential benefits and downsides of this technology, and he will explore how a hybrid model can effectively leverage the strengths of both humans and technology. By embracing a human-centered approach, we showcase a compelling use case from the Forsta labs, demonstrating how insight professionals can effortlessly explore their datasets using natural-language questioning. Furthermore, we discuss the future implications of this technology and the exciting possibilities it holds for data exploration. To access the recording of this presentation, visit NewMR.org/Play-Again

From Words to Wisdom:
Unleashing the Potential of Language Models
for Human-Centered Data Exploration
NewMR Webinar August 2023
Paul Watts
Director, Product Management at Forsta

From words to wisdom:
Unleashing the potential of Large Language Models (LLMs)
for human-centred data exploration
Paul Watts, Product Director AI and ML, Forsta
August 2023

Supporting the human
vs.
Replacing the human
AI = Augmented Intelligence?
5
© 2023 Forsta. All rights reserved.
Challenge plausibility using
your own expertise
(or you may end up in a pickle!)

AI and ML use cases for Insights teams
6
Gather
– Conversational
surveys
– Response quality
(e.g. Fraud)
– Survey Design
Analyse
– Theme prediction
– Signal Detection
– NLP/Text Analytics
Visualize
– Exploratory analysis
– Summarization and
report generation
Act
– Recommended
actions
© 2023 Forsta. All rights reserved.

Example:
Assisted response generation
7
© 2023 Forsta. All rights reserved.

The art and science of prompt engineering
vs.
WHAT you ask and HOW you ask matters
8
© 2023 Forsta. All rights reserved.

Using AI and ML for exploratory analysis
ML/AI technologies used:
– LLM Prompt Design and
Tuning
– Text-to-SQL
– Text Similarity
– Text Summarization
Goal: Generate qualitative and quantitative
insights by asking questions in a human manner
9
© 2023 Forsta. All rights reserved.
Describe what you’d like to see…
Show the breakdown of responses from men vs women for 2022
Press Ctrl+Enter to apply
Visualize & Summarize

Returning vizualisations from a natural language question
10
© 2023 Forsta. All rights reserved.

Comment segmentation and summaries
11
© 2023 Forsta. All rights reserved.

Model innovation
We are still at the start of the journey…
Legislation and privacy Domain-specific applications
© 2023 Forsta. All rights reserved. a PGForsta company. 12

Q & A
Ray Poynter
NewMR
Paul Watts
Forsta

In the rapidly evolving world of ChatGPT and Large Language Models (LLMs), businesses are understandably apprehensive. Numerous potential hazards and hurdles exist such as: Unrealistic expectations of LLMs as a magic solution to managing corporate content without requisite human involvement Difficulty distinguishing between creative outputs and fabricated responses (hallucinations) Decisions around training models: balancing usefulness with the threat of exposing trade secrets or other proprietary knowledge Absence of clear audit trails and citation sources The risk of generating responses misaligned with company policies or brand image Potential financial burden of proprietary LLMs and related enterprise software platforms In this webinar, we will examine a structured approach to harvest, utilize, and protect corporate knowledge resources. We will explore how both commercial and open-source large language models can be leveraged to deliver precise conversational responses without jeopardizing intellectual property. Learn how your organization can effectively use LLM based applications for competitive advantage. Using a general LLM will provide efficiency, but through standardization. Differentiation using your corporate terminology and knowledge will allow for competitive advantage. You don’t have to deploy ChatGPT to benefit from these approaches. They will improve the information metabolism of the enterprise and pave the way for advanced AI applications.

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CzechDreamin

We're living the AI revolution and Salesforce is adapting and bring new value to their customers. Einstein products are evolving rapidly and navigating their limitations, language support, and use cases can be challenging. Let's make review of what Einstein product are available currently, what are the capabilities and what can be used for in CEE region and how Rossie.ai can help to learn Salesforce speak Czech. We will explore the Einstein roadmap and I will make a short live demo (based on your vote) of some Einstein feature.

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arbar https://www.slideshare.net/Solutionman/charles-caldwell-improve-your-li...

SOLTUIONSpeople, THINKubators, THINKathons

Welcome to my SlideShare presentation on ChatGPT, a powerful language model based on the GPT-3.5 architecture. In this presentation, I will introduce you to ChatGPT and explore its features and capabilities. ChatGPT is a state-of-the-art language model that can understand natural language and generate responses that are highly relevant and accurate. I will discuss the underlying technology behind ChatGPT, including its neural network architecture and training process. I will also highlight the benefits of using ChatGPT, such as its ability to understand complex language and its potential applications in various industries. Additionally, I will share examples of how ChatGPT can be used to improve customer service, create conversational interfaces, and generate human-like responses in various applications. In conclusion, ChatGPT is a powerful tool for businesses and individuals looking to enhance their communication capabilities. Its advanced language understanding and generation capabilities make it an ideal solution for a variety of use cases. I hope this presentation has been informative and has given you a better understanding of the capabilities of ChatGPT.

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Neo4j

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State of AI Report 2023 - ONLINE.pptx When conducting a PEST analysis for the Syrian conflict, it's important to consider the political, economic, socio-cultural, and technological factors that have influenced and continue to impact the situation in Syria. Here's a high-level overview of a PEST analysis for the Syrian conflict: 1. Political Factors: - Government Instability: Ongoing civil war and conflict have led to political instability and a complex power struggle between various factions and international players. - Foreign Intervention: Involvement of external powers and regional actors has exacerbated the conflict and added geopolitical complexities to the situation. - International Relations: Relations with global powers like the United States, Russia, and regional players like Iran and Turkey significantly impact the conflict dynamics. 2. Economic Factors: - Humanitarian Crisis: The conflict has resulted in a severe humanitarian crisis, causing widespread displacement, destruction of infrastructure, and economic decline. - Sanctions and Trade Barriers: International sanctions and disrupted trade have further worsened the economic situation in Syria, affecting the livelihoods of the population. - Resource Depletion: Conflict-driven resource depletion, including loss of agricultural lands and disruption of industries, has weakened the economy. 3. Socio-cultural Factors: - Civilian Suffering: The conflict has led to a significant loss of life, displacement of populations, and severe trauma among civilians, impacting social cohesion and community structures. - Ethnic and Religious Divisions: Deep-seated ethnic and religious divisions have fueled the conflict, leading to sectarian tensions and societal fragmentation. - Refugee Crisis: The conflict has triggered a massive refugee crisis, with millions of Syrians seeking asylum in neighboring countries and beyond, straining regional stability. 4. Technological Factors: - Communication and Propaganda: Technology, including social media, has been used for communication, mobilization, and spreading propaganda by various actors in the conflict. - Warfare Technology: Advancements in warfare technology and the use of drones, cyber warfare, and other advanced weaponry have transformed the nature of conflict in Syria. - Cybersecurity Concerns: The conflict has also raised concerns about cybersecurity threats, misinformation campaigns, and digital vulnerabilities in the region. This analysis provides a broad understanding of the multifaceted nature of the Syrian conflict, highlighting the diverse factors at play and the complex challenges facing Syria and the international community.

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Speech Recognition Datasets: A Cornerstone for Innovation

GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED

In the realm of artificial intelligence, speech recognition stands as a transformative technology that has revolutionised the way we interact with our devices. From virtual assistants like Siri and Alexa to voice-controlled home automation systems, speech recognition has become an integral part of our daily lives. At the heart of this technological marvel lies a critical component: the speech recognition dataset. What is a Speech Recognition Dataset? A speech recognition dataset is a collection of audio recordings and corresponding transcriptions that are used to train and evaluate speech recognition models. These datasets are meticulously curated to include a diverse range of voices, accents, dialects, and speaking styles to ensure that the resulting models are robust and versatile. Importance of Speech Recognition Datasets The quality and diversity of a speech recognition dataset directly influence the performance of the speech recognition system. A well-curated dataset enables the development of models that can accurately understand and transcribe speech from a wide array of speakers, including those with different accents or speech impediments. Moreover, speech recognition datasets are pivotal in advancing research and development in the field. They provide a benchmark for comparing the effectiveness of different algorithms and approaches, fostering innovation and continuous improvement in speech recognition technology. Challenges in Creating Speech Recognition Datasets Creating a comprehensive speech recognition dataset is not without its challenges. It requires the collection of vast amounts of audio recordings, which must then be accurately transcribed. Ensuring the diversity of the dataset is also crucial, as it must represent various demographics, languages, and speaking conditions (such as noisy environments). Privacy and consent are additional concerns, as the collection and use of voice recordings must adhere to ethical guidelines and regulations to protect individuals' personal information. Conclusion Speech recognition datasets are the unsung heroes behind the seamless voice interactions we enjoy with our technology today. They are the foundation upon which speech recognition systems are built, enabling them to understand and respond to our spoken commands accurately. As we continue to push the boundaries of what's possible with speech recognition, the role of these datasets will only grow in importance, driving innovation and shaping the future of human-computer interaction.

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