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Engineering intelligence for everyday life

Explore how engineers design AI systems for real-world use, balancing intelligence, responsibility, and human interaction.

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Podcast • The Tech Between Us

When AI becomes personal

Raymond and Marisa discuss how trust in AI forms and what that means for engineers designing systems people rely on in daily life.

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Host: Raymond Yin Mouser Director of Technical Content

For over a decade, Raymond Yin has worked at Mouser as its resident Director of Technical Content. During his tenure, he has collaborated with many manufacturer brands, industry leaders and technology experts to develop the latest content on technology and applications. Managing and expanding Mouser's portfolio of content for new product introductions and technology trends, Raymond stays immersed in the electronic component industry and well-versed in the latest industry developments impacting Mouser's customers.

Earning his Bachelor of Science degree in Electrical Engineering from Southern Methodist University in 1986, Raymond's 20 plus years in the electronics distribution industry, and 7 years as a design engineer, has given him a deep understanding of the capabilities of the latest components as well as the companies that produce them.

Guest: Dr. Marisa Tschopp Psychologist, scip AG

Dr. Marisa Tschopp is a researcher at scip AG, a cybersecurity company based in Zurich and a long-time supporter of Women in AI NPO. She explores AI from a psychological perspective, addressing a range of questions related to psychological phenomena with a particular interest in ethical implications. Her research focuses on human-AI relationships, trust in AI, agency, behavioral performance assessment of conversational systems (A-IQ), and gender issues in AI.

As an organizational psychologist, Marisa has experience in social and educational institutions and has studied and taught at various universities in Germany, Canada, and Switzerland. She has published media articles, book chapters, and scientific papers, and is a frequent speaker at conferences and events (including TEDx) worldwide.

Marisa holds a Master's degree in Psychology for Excellence in Business and Education from Ludwig-Maximilians-Universität München, as well as a Bachelor's degree in Business Psychology with a focus on market and consumer psychology. Her PhD (Dr. rer. nat) from the University of Tübingen, examined the perceived relationships between humans and conversational AI and the influence of human-AI relationships on behavior.

Man using stylus on smart presentation board

Foundation Models & System Architecture

Engineered with intent

85% of surveyed organizations cite architecture and deployment as primary drivers of performance and cost.

Today's AI experiences rely on engineered platforms that pair large language models (LLMs) with intuitive user interfaces to help enhance human capabilities. With constraints such as cost, materials, latency, security, and trust to consider, engineers shape how these systems perform and scale in real-world environments.

Source: O'Reilly AI Adoption in the Enterprise (2024 Survey)

Woman with an AI device in her ear having a conversation with a man in front of her

ChatGPT (OpenAI), Gemini (Google), Copilot (Microsoft)

Conversational AI: LLM interfaces

More than 50% of U.S. adults report having used an LLM-powered AI tool at least once through 2025.

Conversational LLMs enable users to interact with natural language interfaces for help completing tasks. Engineers integrate features such as search, summarization, translation, and dialogue across consumer and enterprise workflows to produce better, collaborative outcomes.

Source: Elon University Survey on AI (2025)

Woman using an AI voice assistant

Speech + LLM hybrid pipelines (e.g., Alexa, Siri, Google Assistant)

Voice & assistant systems

Nearly 50% of U.S. households use voice assistant platforms.

Voice assistants powered by AI combine speech recognition patterns with LLMs to support real-time, hands-free interactions across platforms and devices. Engineers weigh trade-offs such as latency, privacy, on-device processing, and cloud dependency to build AI companions that predictively streamline human inputs to desired outcomes.

Source: Adobe Consumer Voice Assistant Use Trends (2024 Report)

Woman reading a paper while wearing AI glasses

Gemini (multimodal), Claude (reasoning), Perplexity (research)

Multimodal AI & contextual reasoning

Multimodal AI models outperform single-modality baselines by ~15–25% on combined text + image reasoning benchmarks.

Multimodal AI models integrate a vast knowledge base with real-time analysis of visual and language inputs to enable contextual interpretation. Engineers optimize these systems for context window size, compute cost, and task complexity.

Source: VQA Benchmark Results (2024) and Multimodal Learning Survey (2023)

Woman wearing smart ring while walking on a treadmill

Edge + embedded systems with predictive models (e.g., wearable health sensors, smart rings, fitness trackers)

Sensor-driven & health-aware AI

Around 40% of U.S. adults use wearable devices with health sensors.

In sensor-rich environments, AI blends local inference like wearables to recognize health patterns and share insights with the human wearer. Engineers designing these systems strive for a balance of signal accuracy, safety thresholds, data privacy, and ease of use.

Source: Pew Research Center, Wearables and Health Tracking (2024)

Explore how AI-driven technologies transform daily life

Man using stylus on smart presentation board

Foundation Models & System Architecture

Engineered with intent

85% of surveyed organizations cite architecture and deployment as primary drivers of performance and cost.

Today’s AI experiences rely on engineered platforms that pair large language models (LLMs) with intuitive user interfaces to help enhance human capabilities. With constraints such as cost, materials, latency, security, and trust to consider, engineers shape how these systems perform and scale in real-world environments.

Source: O’Reilly AI Adoption in the Enterprise (2024 Survey)

Woman with an AI device in her ear having a conversation with a man in front of her

ChatGPT (OpenAI), Gemini (Google), Copilot (Microsoft)

Conversational AI: LLM interfaces

More than 50% of U.S. adults report having used an LLM-powered AI tool at least once through 2025.

Conversational LLMs enable users to interact with natural language interfaces for help completing tasks. Engineers integrate features such as search, summarization, translation, and dialogue across consumer and enterprise workflows to produce better, collaborative outcomes.

Source: Elon University Survey on AI (2025)

Woman using an AI voice assistant

Speech + LLM hybrid pipelines (e.g., Alexa, Siri, Google Assistant)

Voice & assistant systems

Nearly 50% of U.S. households use voice assistant platforms.

Voice assistants powered by AI combine speech recognition patterns with LLMs to support real-time, hands-free interactions across platforms and devices. Engineers weigh trade-offs such as latency, privacy, on-device processing, and cloud dependency to build AI companions that predictively streamline human inputs to desired outcomes.

Source: Adobe Consumer Voice Assistant Use Trends (2024 Report)

Woman reading a paper while wearing AI glasses

Gemini (multimodal), Claude (reasoning), Perplexity (research)

Multimodal & contextual reasoning

Multimodal AI models outperform single-modality baselines by ~15–25% on combined text + image reasoning benchmarks.

Multimodal AI models integrate a vast knowledge base with real-time analysis of visual and language inputs to enable contextual interpretation. Engineers optimize these systems for context window size, compute cost, and task complexity.

Source: VQA Benchmark Results (2024) and Multimodal Learning Survey (2023)

Woman wearing smart ring while walking on a treadmill

Edge + embedded systems with predictive models (e.g., wearable health sensors, smart rings, fitness trackers)

Sensor-driven & health-aware AI

Around 40% of U.S. adults use wearable devices with health sensors.

In sensor-rich environments, AI blends local inference like wearables to recognize health patterns and share insights with the human wearer. Engineers designing these systems strive for a balance of signal accuracy, safety thresholds, data privacy, and ease of use.

Source: : Pew Research Center, Wearables and Health Tracking (2024)

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