human-computer interaction

Herbert Alexander Simon, a renowned American polymath, profoundly influenced a broad range of fields, including economics, psychology, and computer science. His significant contributions to Artificial Intelligence (AI) and cognitive psychology have shaped the understanding of human decision-making processes and problem-solving, embedding these concepts deeply into the development of AI.

The Quest for Understanding Human Cognition

Simon's work in AI was driven by his fascination with the processes of human thought. He sought to understand how humans make decisions, solve problems, and process information, and then to replicate these processes in machines. His approach was interdisciplinary, combining insights from psychology, economics, and computer science to create a more holistic view of both human and machine intelligence.

Pioneering Work in Problem Solving and Heuristics

Alongside Allen Newell, Simon developed the General Problem Solver (GPS) in the late 1950s, an early AI program designed to mimic human problem-solving strategies. This program was groundbreaking in its attempt to simulate the step-by-step reasoning humans employ in solving problems. Simon's work in this area laid the groundwork for later developments in AI, especially in symbolic processing and heuristic search algorithms.

Bounded Rationality: A New Framework for Decision-Making

Simon's concept of 'bounded rationality' revolutionized the understanding of human decision-making. He argued that humans rarely have access to all the information needed for a decision and are limited by cognitive and time constraints. This idea was pivotal in AI, as it shifted the focus from creating perfectly rational decision-making machines to developing systems that could make good decisions with limited information, mirroring human cognitive processes.

Impact on AI and Cognitive Science

Simon's contributions to AI extend beyond his technical innovations. His theories on human cognition and problem-solving have deeply influenced cognitive science and AI, particularly in the development of models that reflect human-like thinking and learning. He was also instrumental in establishing AI as a legitimate field of academic study.

A Legacy of Interdisciplinary Influence

Herbert Simon's legacy in AI is one of interdisciplinary influence. His work not only advanced the field technically but also provided a conceptual framework for understanding intelligence in a broader sense. He was awarded the Nobel Prize in Economics in 1978 for his work on decision-making processes, underscoring the wide-reaching impact of his ideas.

Conclusion: A Visionary's Contribution to AI

Herbert Alexander Simon's contributions to AI are marked by a deep understanding of the complexities of human thought and a commitment to replicating these processes in machines. His interdisciplinary approach and groundbreaking research in problem-solving, decision-making, and cognitive processes have left an indelible mark on AI, paving the way for the development of intelligent systems that more closely resemble human thinking and reasoning. His work continues to inspire and guide current and future generations of AI researchers and practitioners.

Kind regards Schneppat AI & GPT 5

Allen Newell, an American researcher in computer science and cognitive psychology, is renowned for his substantial contributions to the early development of Artificial Intelligence (AI). His work, often in collaboration with Herbert A. Simon, played a pivotal role in shaping the field of AI, particularly in the realm of human cognition simulation and the development of early AI programming languages and frameworks.

Pioneering the Cognitive Approach in AI

Newell's approach to AI was deeply influenced by his interest in understanding human cognition. He was a key proponent of developing AI systems that not only performed intelligent tasks but also mimicked the thought processes of the human mind. This cognitive perspective was fundamental in steering AI research towards exploring how intelligent behavior is structured and how it could be replicated in machines.

The Development of Information Processing Language (IPL)

One of Newell's significant contributions was the development of the Information Processing Language (IPL), one of the first AI programming languages. IPL was designed to facilitate the manipulation of symbols, enabling the creation of programs that could perform tasks akin to human problem-solving. This language laid the groundwork for subsequent developments in AI programming and symbol manipulation, crucial for the field's advancement.

Logic Theorist and General Problem Solver

In collaboration with Herbert Simon and John C. Shaw, Newell developed the Logic Theorist, often considered the first artificial intelligence program. The Logic Theorist simulated human problem-solving skills in the domain of symbolic logic. Following this, Newell and Simon developed the General Problem Solver (GPS), a program designed to mimic human problem-solving techniques and considered a foundational work in AI and cognitive computing.

Unified Theories of Cognition

Throughout his career, Newell advocated for unified theories of cognition - comprehensive models that could explain a wide range of cognitive behaviors using a consistent set of principles. His vision was to see AI not just as a technological tool, but as a means to understand the fundamental workings of the human mind.

Legacy and Influence

Allen Newell's work significantly influenced the direction of AI, especially in its formative years. His emphasis on understanding and simulating human cognition has had lasting impacts on how AI systems are developed and studied, particularly in fields like natural language processing, decision-making, and learning systems.

Conclusion: A Visionary's Contribution to AI

Allen Newell's contributions to AI were not just technological but also conceptual. He helped shape the understanding of AI as a field that bridges computer science with human cognitive processes. His work continues to influence contemporary AI research, echoing his belief in the potential of AI to unravel the complexities of human intelligence. Newell's legacy is a testament to the profound impact of interdisciplinary approaches in advancing technology and understanding cognition.

Kind regards Schneppat AI & GPT5

Using math to improve photographs, with expert guest Gordon Wetzstein. Such methods have exploded in recent years and have wide-ranging impacts from improving your family photos, to making self-driving cars safer, to building ever-more-powerful microscopes. Somewhere in between hardware and software, he says, is the field of computational imaging, which makes cameras do some pretty amazing things. Wetzstein and host Russ Altman bring it all into focus on this episode of Stanford Engineering’s The Future of Everything podcast.

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Chapters:

(00:00:00) Introductions 

Host Russ Altman introduces the guest, Gordon Wetzstein as well as the concept of non-line-of-sight imaging.

(00:02:58) Computational Imaging 

Gordon Wetzstein explains the concept of computational imaging and the way it integrates hardware and software for optimal image capture.

(00:04:22) High Dynamic Range (HDR) Imaging  & Focal Stacking

An explanation of what HDR is and how it captures high-contrast scenes, and the similar process of focal stacking, using multiple images to create depth. 

(00:09:56) Non-Line-of-Sight Imaging 

(00:15:51) Optical Computing: Extending Hardware Capabilities 

Insights into optical computing, explaining how specially designed hardware can preprocess data for AI algorithms.

(00:18:08) Applications of Optical Computing 

Exploration of applications for optical computing in power constraint systems and increased efficiency in data centers.

(00:23:07) The Intersection of AI, Physics, and Computer Graphics 

Synergy between AI, physics, and computer graphics in creating 3D content and models. 

(00:25:47) Generative AI to Create 3D from 2D 

Exploring the challenge of generating 3D digital humans from unstructured 2D images using algorithms

(00:32:02) Challenges & Advancements in VR and AR Design 

Connect With Us:

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Host: Hutch

On ITSPmagazine  👉 https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/hutch

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Episode Introduction

In this episode, we are going to discuss a unique way that the Transformer architecture can be used for physical robotics.

In this episode, we will be examining how the many disparate areas of AI are beginning to converge into a single architecture -- namely transformers. And we will also look at how Google researchers have proposed that this same architecture may be useful within the world of physical robotics.

References

https://www.quantamagazine.org/will-transformers-take-over-artificial-intelligence-20220310/

https://github.com/google-research/robotics_transformer

https://ai.googleblog.com/2022/12/rt-1-robotics-transformer-for-real.html

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Resources

______________________

For more podcast stories from Cyber Cognition Podcast with Hutch, visit: https://www.itspmagazine.com/cyber-cognition-podcast

Watch the video podcast version on-demand on YouTube: https://www.youtube.com/playlist?list=PLnYu0psdcllS12r9wDntQNB-ykHQ1UC9U

In the land of artificial intelligence, there’s no shortage of systems seeking to help us make better decisions. From a list of products that “you might also like” to an analytics dashboard letting you know the best time to market to your ideal customer. AI-generated advice is everywhere.

But, what are the social consequences of taking algorithmic advice, such as a robo-advisor for investment choices or a robo-lawyer for legal help? What judgments do we make about people who listen to their advice over actual humans? Well, we know little about how people view others who take algorithm advice.

That sparked the curiosity of my guests, Emir Efendić and Philippe van de Calseyde. Their recent paper, “Taking Algorithmic vs Human Advice Reveals Different Goals to Others,” in the International Journal of Human-computer Interaction looks closely at this topic.

In this episode, I talk with them about what they learned about the social implications of taking advice from algorithms instead of humans. And if you, like most people, are guided by algorithms in your personal or professional life, I think you’ll find this conversation enlightening.

Music by Eggy Toast

Host: Hutch

On ITSPmagazine  👉 https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/hutch

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Episode Introduction

Examines the implicit occupational imperative that, to be economically competitive, we must continue to integrate with emerging technology -- and the dangerous precedent that this sets.

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Resources

Elon Musk Code Conference 2016 Interview - https://www.youtube.com/watch?v=wsixsRI-Sz4

FDA Greenlights Neuralink Human Trials - https://www.reuters.com/science/elon-musks-neuralink-gets-us-fda-approval-human-clinical-study-brain-implants-2023-05-25/

______________________

For more podcast stories from Cyber Cognition Podcast with Hutch, visit: https://www.itspmagazine.com/cyber-cognition-podcast

Watch the video podcast version on-demand on YouTube: https://www.youtube.com/playlist?list=PLnYu0psdcllS12r9wDntQNB-ykHQ1UC9U

Host: Hutch

On ITSPmagazine  👉 https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/hutch

______________________

Episode Sponsors

Are you interested in sponsoring an ITSPmagazine Channel?

👉 https://www.itspmagazine.com/sponsor-the-itspmagazine-podcast-network

______________________

Episode Introduction

Examines the implicit occupational imperative that, to be economically competitive, we must continue to integrate with emerging technology -- and the dangerous precedent that this sets.

______________________

Resources

Elon Musk Code Conference 2016 Interview - https://www.youtube.com/watch?v=wsixsRI-Sz4

FDA Greenlights Neuralink Human Trials - https://www.reuters.com/science/elon-musks-neuralink-gets-us-fda-approval-human-clinical-study-brain-implants-2023-05-25/

______________________

For more podcast stories from Cyber Cognition Podcast with Hutch, visit: https://www.itspmagazine.com/cyber-cognition-podcast

Watch the video podcast version on-demand on YouTube: https://www.youtube.com/playlist?list=PLnYu0psdcllS12r9wDntQNB-ykHQ1UC9U

Host: Hutch

On ITSPmagazine  👉 https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/hutch

______________________

Episode Sponsors

Are you interested in sponsoring an ITSPmagazine Channel?

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Episode Introduction

In this episode, we are going to discuss the ELIZA effect -- a common psychological bias that emerges from interactions with language models.

The "ELIZA effect" refers to a phenomenon in human-computer interaction, wherein people attribute understanding and emotion to a computer program that simply follows predefined rules. This concept is named after an early natural language processing computer program developed at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum in the mid-1960s.

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Resources

https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html

https://www.rfc-editor.org/rfc/rfc439

https://arxiv.org/pdf/2212.03551.pdf

https://medium.com/nerd-for-tech/eliza-the-chatbot-who-revolutionised-human-machine-interaction-an-introduction-582a7581f91c

https://www.theatlantic.com/technology/archive/2014/06/when-parry-met-eliza-a-ridiculous-chatbot-conversation-from-1972/372428/

______________________

For more podcast stories from Cyber Cognition Podcast with Hutch, visit: https://www.itspmagazine.com/cyber-cognition-podcast

Watch the webcast version on-demand on YouTube: (coming soon)

Host: Hutch

On ITSPmagazine  👉 https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/hutch

______________________

Episode Sponsors

Are you interested in sponsoring an ITSPmagazine Channel?

👉 https://www.itspmagazine.com/sponsor-the-itspmagazine-podcast-network

______________________

Episode Introduction

In this episode, we are going to discuss the ELIZA effect -- a common psychological bias that emerges from interactions with language models.

The "ELIZA effect" refers to a phenomenon in human-computer interaction, wherein people attribute understanding and emotion to a computer program that simply follows predefined rules. This concept is named after an early natural language processing computer program developed at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum in the mid-1960s.

______________________

Resources

https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html

https://www.rfc-editor.org/rfc/rfc439

https://arxiv.org/pdf/2212.03551.pdf

https://medium.com/nerd-for-tech/eliza-the-chatbot-who-revolutionised-human-machine-interaction-an-introduction-582a7581f91c

https://www.theatlantic.com/technology/archive/2014/06/when-parry-met-eliza-a-ridiculous-chatbot-conversation-from-1972/372428/

______________________

For more podcast stories from Cyber Cognition Podcast with Hutch, visit: https://www.itspmagazine.com/cyber-cognition-podcast

Watch the webcast version on-demand on YouTube: (coming soon)

Sound-bytes are our shorter episodes about what we’ve been up to recently. Jalada got a chance to try out a kitted out sim-racing setup in VR, and that got us thinking...

Following on from last episode we're moving from the realms of desktop computer to mobile computing with a discussion of mobile operating systems. How they work, how they differ, and what we think the future holds for them.

Gary Bolles, entrepreneur, venture advisor, and best-selling author, is a deep thinker who established roots in Silicon Valley in the 80s to pursue his joint passions for technology and exploring what he calls the three boxes of life - learning, work, and leisure. He’s the author of The Next Rules of Work which was published August 31. He’s also the chair for the Future of Work at Singularity University and the founder of eParachute among other companies. Oh, and his LinkedIn courses have helped train more than 800,000 students. 

Listen and learn...

1. How the son of a laid off minister became one of the foremost authorities on the future of work
2. What opportunities are being created by "The Great Reset"
3. How technology is redefining work... and redefining our identity as humans
4. What it means that we're moving from a "workforce" to a "worknet"
5. About the $10 million exercise... and why it's the best way to find your passion
6. Why living the acronym "PACE" is the best way to ensure future career success

References in today's episode...

• Gary's site
• Singularity University
• AI and the Future of Work with Charlene Li

In the 21st Century, science is a team sport played by humans and computers, both. Social science in particular is in the midst of a transition from the qualitative study of small groups of people to the quantitative and computer-aided study of enormous data sets created by the interactions of machines and people. In this new ecology, wanting AI to act human makes no sense, but growing “alien” intelligences offers useful difference — and human beings find ourselves empowered to identify new questions no one thought to ask. We can direct our scientific inquiry into the blind spots that our algorithms find for us, and optimize for teams diverse enough to answer them. The cost is the conceit that complex systems can be fully understood and thus controlled — and this demands we move into a paradigm of care for both the artificial Others we create and human Others we engage as partners in discovery. This is the dawn of Social Computing: an age of daunting risks and dazzling rewards that promises to challenge what we think we know about what can be known, and how…

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

In this episode, I speak with SFI External Professor James Evans, Director of the University of Chicago’s Knowledge Lab, about his new work in, and journal of, social computing — how AI transforms the practice of scientific study and the study of scientific practice; what his research reveals about the importance of diversity in team-building and innovation; and what it means to accept our place beside machines in the pursuit of not just novel scientific insight, but true wisdom.

If you value our research and communication efforts, please consider making a donation at santafe.edu/podcastgive — and/or rating and reviewing us at Apple Podcasts. You can find numerous other ways to engage with us at santafe.edu/engage. Thank you for listening!

Join our Facebook discussion group to meet like minds and talk about each episode.

Podcast theme music by Mitch Mignano.

Follow us on social media:
Twitter • YouTube • Facebook • Instagram • LinkedIn

Key Links:

• James Evans at The University of Chicago

• Knowledge Lab

• Google Scholar

• “Social Computing Unhinged” in The Journal of Social Computing

Other Mentioned Learning Resources:

• Melanie Mitchell, “The Collapse of Artificial Intelligence”

• Alison Gopnik’s SFI Community Lecture, “The Minds of Children”

• Hans Moravec, Mind Children

• Ted Chiang, “The Life Cycle of Software Objects”

• Re: Recent CalTech study on interdisciplinarity and The Golden Age of Science

• Yuval Harari, “The New Religions of the 21st Century”

• Melanie Mitchell & Jessica Flack, “Complex Systems Science Allows Us To See New Paths Forward” at Aeon

• Complexity Episode 9 - Mirta Galesic (on Social Science)

• Compexity Episode 20 - Albert Kao (on Collective Behavior)

• Complexity Episode 21 - Melanie Mitchell (on Artificial Intelligence)

Links:

• Charlie's website
• Look mum - no hands: A talk about brain-controlled interfaces
• Atlassian's career site
• Atlassian's team playbook
• Brain-sensor startup Neurosity
• Google Dev Expert
• Mozilla Tech Speaker

Computers are everywhere and humans are engaging with them in nearly everything they do. Knowing this, the question becomes: How do we design a world around us so that technology makes life better, not worse? James Landay, an expert in human-computer interaction, says the key to thoughtfully integrating humans with digital technology is to put people first.

This perspective draws on a philosophy known as human-centered or user-centered design. Within this approach, the first priority is to understand the problem vexing a particular population by observing, interviewing, and working with that population. Only once the problem is clear does the development of a solution begin. Typically, engineers and technologists have done the opposite. They’ve worked to develop the coolest technology they can think of, and then once it’s ready look around for a way to use it.

With human needs at the forefront, Landay’s research focuses on finding ways to use artificial intelligence technology to augment human performance. His current projects range from leveraging technology to encourage positive behavior change, to enabling kids to stay engaged in their education, to helping professionals stay healthy while feeling more connected to their co-workers and workplace.

Tune in to this episode of The Future of Everything to hear more about how Landay draws on user-centered design to develop technology that supports human needs. You can listen to The Future of Everything on Sirius XM Insight Channel 121, iTunes, Google Play, SoundCloud, Spotify, Stitcher or via Stanford Engineering Magazine.

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While billions scroll their merry ways through Facebook and Twitter each day, behind the scenes are legions of reviewers scanning photos and video to prevent graphic content from making the newsfeeds of unsuspecting users.

Elsewhere, the faceless armies of the gig economy are making movies, building homes, driving Uber and working piecemeal to caption innumerable images for people too busy to do it for themselves.

Welcome to the future of crowdsourcing. While the collective actions of those on the frontlines of crowdsourcing save millions of others from drudgery and from psychological trauma, the ascension of automation is raising questions that human society has never had to deal with before. These are the “wicked problems” — questions in which success cannot be determined with certainty or where multiple, mutually exclusive goals must be delicately balanced to create an optimal outcome.

These are questions that Stanford's Michael Bernstein, an assistant professor of computer science and an expert on Human-Computer Interaction (HCI), grapples with on a daily basis. What is the optimal organizational structure for such crowdsourcing communities? What are the ethical implications of the gig economy? And, who are the right people to answer these questions?

On The Future of Everything radio show, host Russ Altman and Bernstein discuss those question and explore what our increasingly automated future will look like.

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Episode Transcripts >>> The Future of Everything Website

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