Kozminski Techblog

Reading Time: 3 minutes

In the ever-evolving landscape of AI companionship, ElliQ has taken a substantial leap forward with the release of ElliQ 2.0. Crafted by the Israeli startup Intuition Robotics, this latest version builds upon its initial limited release in March, introducing a host of enhanced features and experiences that redefine the interaction between AI and users.

Unveiling ElliQ 2.0: A New Era of Companionship

ElliQ, distinguished by its unique design featuring a digital display and an animated “bobble head,” has emerged as a proactive solution to tackle the growing issue of loneliness, particularly among the elderly. The upgraded ElliQ 2.0 is not merely a voice-operated device; it is an empathetic companion that goes beyond typical functionalities to express compassion, foster meaningful relationships, and improve the overall well-being of its users.

Elevated Experiences and Advanced Capabilities

One of the key features of ElliQ 2.0 is the introduction of “Elevated Experiences,” a suite of new conversation prompts and virtual encounters that elevate the user’s engagement. These experiences range from a “virtual café” showcasing images of different cities with accompanying local sounds to an “art exhibition” featuring famous artworks discussed by a narrator. Additionally, a virtual road trip allows users to accompany ElliQ to iconic destinations.

ElliQ’s capabilities extend to initiating conversations with users, prompting them to share personal stories and memories. These interactions, recorded by ElliQ, can be transformed into a digital journal, offering a unique way for users to preserve their experiences.

User-Centric Improvements and Enhanced Interaction

ElliQ 2.0 introduces several user-centric improvements, including a simplified tablet charging mechanism, an enhanced display, and improved far-field microphone performance. Priced at $249.99 for the initial purchase, with a monthly subscription fee of $29.99 for ongoing support, the device aims to provide an enriched and seamless user experience.

Empathy in Action

The Tel Aviv and US-based company behind ElliQ, Intuition Robotics, asserts bold claims backed by user experiences. According to the company’s website, “95 percent of users find ElliQ useful in reducing their loneliness and improving their well-being, and 90 percent report that ElliQ has improved their quality of life.” These numbers underline the transformative power of AI when designed to express empathy and foster genuine connections.

A Beacon of Comfort for Seniors

As ElliQ becomes an integral part of users’ lives, it goes beyond the realm of a functional AI assistant. ElliQ is positioned as a friend—a companion that understands, learns, and adapts to the user’s unique personality and preferences. The device becomes a source of comfort, especially for the 14 million Americans over the age of 65 living alone, offering a solution to the pervasive issue of senior loneliness.

Mixed Reactions and Future Prospects

The growing integration of robotics in elderly care elicits mixed reactions. While advocates see these machines as pragmatic solutions addressing the needs of aging populations, critics express concerns about potential social and emotional repercussions. As technology continues to evolve, Intuition Robotics remains committed to assessing user responses and refining ElliQ’s capabilities.

ElliQ 2.0 signifies a significant stride in leveraging AI for meaningful companionship, promising to address the complex and evolving needs of an aging demographic. As this revolutionary technology unfolds, ElliQ stands at the forefront, ushering in a new era of empathetic and proactive AI companionship.

Resources:

• ElliQ, the sidekick for healthier, happier aging
• Kontrowersyjny sposób na samotność. Robot dla staruszka – rp.pl
• Robot towarzyszący ElliQ może pytać użytkowników o ich dzieciństwo, aby stworzyć cyfrowe „wspomnienia” – Cyberfeed.pl
• ELLiQ Robot: The AI-enabled chatty companion robot for the elderly (interestingengineering.com)
• ElliQ, a new robot for the elderly, uses AI to provide companionship – The Washington Post

AI used: Chatgpt 4

Reading Time: 4 minutes

As we all know, analyzing a player’s technique in professional sports is very important and common. My post was based on quite old articles related to AI in sports. To this day, Kohonen’s maps are not used very often, but this blog aims to show us how in 1995 people were aware that artificial intelligence would have an impact even in this field. However, it is worth stopping at this post and realizing how, despite a few accurate observations, technology surprises us and overtakes our speculations, and the question arises: Will what we imagine now really look like this in our future?

Introduction

Over the last decade, the integration of Artificial Intelligence (AI) in sports biomechanics has witnessed significant strides, with advancements in Expert Systems, Artificial Neural Networks (ANNs), and Evolutionary Computation. In that post I will reflect on the developments in the field, comparing the use of Expert Systems in gait analysis with their limited presence in sports biomechanics. I will also delve into the applications of ANNs, specifically Kohonen self-organizing maps, and explores the emerging role of Evolutionary Computation in optimizing sports techniques.

Expert Systems: A Slow Start

In 1995, Lapham and Bartlett predicted a promising future for Expert Systems in sports biomechanics. However, a decade later, these systems, essentially a combination of a database, knowledge base, reasoning, and a user interface, are still underutilized. Unlike in gait analysis, where Expert Systems are employed for diagnostic purposes, their implementation in sports biomechanics has been scarce. The reluctance may stem from the complexity of technique analysis, the lack of a strong developmental motivation, and the challenges of dealing with fuzzy, imprecise data.

Expert Systems in Sports Biomechanics

Expert Systems, powerful knowledge databases, hold immense potential in transforming sports biomechanics. In a cricket context, a hypothetical expert system for fast bowling might use rules like: IF “shoulder-axis counter-rotation” is high; THEN “technique” is mixed (p = 0.8). Handling the vagueness in biomechanical data, exemplified in Figure 1 for fast bowling, showcases the challenge. These systems act as robust diagnostic tools, offering valuable insights and aiding in error identification for flexible sports techniques.

Utilizing expert systems, possibly integrated with video analysis tools like SiliconCOACH’s ‘wizards,’ holds promise for developing diagnostic tools to identify technique errors. This aligns with the optimistic view on the utility of expert systems in sports biomechanics expressed by Lapham and Bartlett in 1995.

Artificial Neural Networks (ANNs): Mapping Movement Patterns

In contrast to Expert Systems, ANNs, especially Kohonen self-organizing maps, have found a niche in sports biomechanics. ANNs mimic the brain’s neural network, allowing computers to learn from experience and analyze complex movement patterns. Studies utilizing Kohonen maps have shown promise in discerning patterns in discus throws, javelin throws, soccer kicks, and more. Despite their successes, challenges remain in deciphering the output map nodes and determining their relevance to movement characteristics.

Evolutionary Computation: Predicting Optimal Techniques

Evolutionary Computation, incorporating genetic algorithms and evolutionary strategies, has made a notable appearance in optimizing sports techniques. In a soccer throw-in scenario, an evolutionary strategy successfully predicted an optimal technique aligning with coaching literature. This application showcases the potential of Evolutionary Computation in refining sports skills.

Future Perspectives

As technology evolves, automatic marker-tracking systems enable the collection of vast and precise human movement data. This may pave the way for the development of fuzzy Expert Systems for diagnosing faults in sports techniques. Kohonen mapping is expected to become commonplace, provided researchers can identify the specific technique elements captured by these maps. Multi-layer ANNs are anticipated to play a crucial role in technique analysis, building on their success in biomechanics and gait analysis. Evolutionary Computation and hybrid systems are likely to feature prominently in optimizing sports techniques and skill learning.

Conclusion

While recent years have seen remarkable progress in integrating artificial intelligence into sports biomechanics, challenges and untapped potential remain. The optimism expressed in 1995 by Lapham and Bartlett has not yet been fully realized, but with continued progress and a growing understanding of the applications of artificial intelligence in traffic analysis, the future appears promising. The synergy between artificial intelligence and sports biomechanics can open new dimensions in optimizing sports performance, improving sports techniques and avoiding injuries. It is worth noting, however, that considering the great development in the world when it comes to artificial intelligence, sports are still a niche direction in which technology is developing.

LET’S REMEMBER THAT THERE ARE MANY UNDERESTIMATED AREAS THAT ARE JUST WAITING TO DEVELOP- one of them is sport

Resources:

• Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope? – PMC (nih.gov)
• The science and medicine of cricket: an overview and update: Journal of Sports Sciences: Vol 21, No 9 (tandfonline.com)
• The use of artificial intelligence in the analysis of sports performance: A review of applications in human gait analysis and future directions for sports biomechanics: Journal of Sports Sciences: Vol 13, No 3 (tandfonline.com)
• AI in Biomechanics: The Key to Unlocking Human Movement Potential (ts2.pl)
• Self-Organizing Maps for the Analysis of Complex Movement Patterns | Neural Processing Letters (springer.com)

AI used: ChatSonic

Reading Time: 3 minutes

In a groundbreaking study published in Nature Electronics, researchers in the United States have introduced “Brainoware,” an innovative AI hardware approach that harnesses adaptive reservoir computation of brain organoid neural networks (ONNs). This development showcases the system’s practical potential in tasks ranging from nonlinear equation prediction to speech recognition.

The Brainoware Genesis

At the core of this cutting-edge technology is Brainoware, a hybrid biocomputer that marries laboratory-grown human brain tissue with conventional electronic circuits. Described on December 11 in Nature Electronics, Brainoware not only demonstrates capabilities in voice recognition but also holds the promise of integration into artificial intelligence (AI) systems, presenting improved models for neuroscience research.

Crafting Brainoware’s Architecture

Brainoware leverages brain organoids—clusters of tissue-mimicking human cells that emulate organ structures. To build this revolutionary system, researchers placed a single organoid onto a plate equipped with thousands of electrodes. This intricate connection between brain tissue and electric circuits facilitated the conversion of input information into patterns of electric pulses. The neural responses were then decoded using machine learning algorithms.

Unveiling Brainoware’s Capabilities

One of the primary applications tested for Brainoware was voice recognition. The system, trained on 240 recordings of eight speakers, showcased an impressive 78% accuracy in identifying speakers by interpreting unique neural activity patterns for each voice. This breakthrough demonstrates the potential of Brainoware in enhancing the efficiency and energy-conscious nature of AI systems.

Beyond Voice Recognition: A Superior Brain Model

Brainoware’s impact extends beyond voice recognition. By combining organoids with circuits, researchers open up new frontiers in studying the brain’s intricacies. This technology replicates the architecture and functionality of a working brain, providing an advanced model for neurological research. Brainoware’s potential applications include studying disorders like Alzheimer’s disease and testing treatment effects, aiming to replace traditional animal models in these studies.

Challenges and Future Prospects

While the fusion of living cells for computing showcases immense potential, challenges must be addressed. Sustaining the vitality of organoids, especially as they grow larger, remains a significant hurdle. The ongoing challenge involves investigating how brain organoids can handle more complex tasks and enhancing their stability for seamless integration into existing AI computing technologies.

Conclusion: Navigating the Future of Biocomputing and AI

As Brainoware takes center stage, it signifies a monumental step in the computational revolution. The convergence of human brain cells with AI opens unprecedented possibilities. While challenges persist, Brainoware propels us into uncharted territories, urging researchers to explore the limitless potential that lies at the intersection of biological and artificial intelligence. This hybrid system sets the stage for transformative advancements, reshaping the future of biocomputing and AI hardware.

Resources:

• Brainoware: A Revolutionary Biocomputer System using Brain Organoids and AI (medriva.com)
• ‘Biocomputer’ combines lab-grown brain tissue with electronic hardware (nature.com)
• Brainoware: A breakthrough AI approach using brain organoids for advanced computation (news-medical.net)
• Sztuczna inteligencja z żywych komórek (mlodytechnik.pl)
• ‘Brainoware’ fuses human biology and silicon | Information Age | ACS

Ai used: Chatgpt 3.5

Reading Time: 4 minutes

A Journey into the Creative Potential of Artificial Intelligence

In the ever-evolving tapestry of human creativity, a new chapter is being written at the intersection of artificial intelligence and art. As we stand on the precipice of a technological revolution, the traditional boundaries of artistic expression are expanding, giving rise to a fascinating synergy between human ingenuity and machine intelligence. Welcome to “Artificial Imagination,” where pixels meet algorithms, and the canvas becomes a playground for both human and artificial creativity.

In this blog, we embark on a journey through the enchanting realms where AI and art converge, exploring the profound impact of technology on the creative process. From AI-generated masterpieces to collaborative dialogues between artists and algorithms, we delve into the innovative landscapes where brushes are guided not just by human hands but by the intricate dance of ones and zeros.

Join us as we unravel the mysteries of AI in art, witnessing the birth of new forms, the redefinition of inspiration, and the ethical considerations that arise in this symbiotic relationship. “Artificial Imagination” invites you to ponder the limitless possibilities and the uncharted territories where imagination and computation entwine, reshaping the very essence of what it means to create. Are you ready to traverse the digital brushstrokes and explore the unexplored horizons of artificial creativity?

Introduction art point of view:

Role of art

Art traditionally serves as a profound expression of the human experience, encapsulating emotions, beliefs, and societal narratives. It acts as a cultural cornerstone, preserving heritage, reflecting social and political contexts, and shaping identity. Over the centuries artists painted wars, battles and other important events in history as you have for sure learned in high school and primary schools. Nevertheless, they did not forget about enjoying little moments in life as in many works we can notice day to day activities. Artists contribute to the collective dialogue and challenging norms. Art fosters empathy, it also have part in breaking down cultural barriers and enabling global exchange.
As my art teacher used to say, art should evoke emotions in us, whether good or bad – just emotions.

The evolution of artistic media over time

Through diverse media such as painting, sculpture, literature, and performance, artists articulate the nuances nuances of the human condition. The canvas becomes a mirror, mirroring the joys, struggles, and complexities of life. Following this trend humans continued to explore various mediums such as marble sculpture, mosaics, oil painting on canvas, photography, architecture, film even till this day when we challenge the traditional boundaries of art by using AI.

How do you even create ai art?

Creating AI art involves leveraging advanced algorithms to transform textual descriptions or simple geometric shapes into visually compelling images. One popular approach is the generation of images based on textual prompts, where users input descriptive phrases or sentences, and the AI interprets and visualizes these prompts, bringing them to life on the canvas. This process, often powered by generative models like OpenAI’s GPT-3, enables the generation of diverse and contextually relevant images, although the outcomes may vary in quality and interpretation. Another intriguing method involves translating basic geometric shapes into intricate artworks. Artists or users input simple shapes, and through the magic of AI, these rudimentary forms are transformed into sophisticated and aesthetically pleasing compositions. The fascinating interplay between human input and AI interpretation opens up a realm of creative possibilities, showcasing the evolving landscape of artistic collaboration in the digital age. If you’re curious to witness the transformation unfold, stay tuned for an illustrative video demonstration that will shed light on the enchanting process.

Generative adversarial networks (GAN):

In the realm of artificial intelligence, Generative Adversarial Networks (GANs) are akin to a creative dance between two entities – the “Generator” and the “Discriminator.” Think of the Generator as an artist armed with a blank canvas, but instead of wielding a paintbrush, it utilizes mathematical algorithms and random concepts to craft something entirely new. On the other side, the Discriminator plays the role of an art critic, initially tasked with deciding whether the Generator’s creations are genuine or artificial. The game unfolds as the Generator produces art, and the Discriminator endeavors to distinguish between the authentic and the generated. Through this iterative process, they engage in a learning exchange. The Generator refines its techniques, creating art so realistic that it challenges the Discriminator’s ability to discern what is real and what is not. Simultaneously, the Discriminator hones its skills, becoming increasingly adept at differentiating between genuine and computer-generated art. This dynamic interplay results in the continual improvement of both entities, culminating in the generation of remarkably lifelike synthetic data that closely mirrors the nuances of the training data. GANs, with their collaborative competition, exemplify the captivating synergy between artificial intelligence and creativity, revolutionizing the landscape of generative models in machine learning.

This video is a shortened version of the film (it presents the most important content to understand how Ai works in art): (640) AI art, explained – YouTube

Ethical concerns surrounding AI in art:

The integration of artificial intelligence in the realm of art is not without its ethical considerations. One prominent concerns revolves around the notion of authorship and ownership. As AI algorithms play an increasingly active role in the creative process, questions arise about who holds the rights to the resulting artworks – the original artist, the programmer, or the machine itself? Additionally, ethical dilemmas surface in cases where AI-generated art might inadvertently reproduce biases present in the training data, reflecting and perpetuating prejudices. Transparency and accountability in the development and deployment of AI in art become crucial to addressing these concerns. Artists, programmers, and AI developers must navigate a delicate balance between innovation and responsibility, ensuring that the creative potential of AI is harnessed ethically and with a keen awareness of the societal impact. Striking this balance will be essential for fostering a harmonious collaboration between human ingenuity and artificial intelligence in the artistic domain.

Resources:

• Art and AI: A Creative Partnership Unleashing the Boundless (genai.tv)
• The Relationship Between Art and AI | by Jennifer Aue | IBM Design | Medium
• (640) AI art, explained – YouTube
• YSYS Tobi Master Landscape CTA Lr | Adobe Creative Cloud (youtube.com)
• Art Trend of 2022: How AI Art Emerged and Polarized the Art World (mymodernmet.com)

AI Chat was not used in that blog post

Reading Time: 4 minutes

EEG-Based Measurement System for Monitoring Student Engagement

My blog contains only simple and basic information about EEG-based measurement system in education, but it is worth delving deeper into this topic, because a lot of works have been created on its basis (personal recommendation: https://www.nature.com/articles/s41598-022-09578-y what we can read in the text: This text introduces a wearable EEG-based system for personalized detection of cognitive and emotional engagement during learning activities. The proposed system aims to enhance adaptability in Intelligent Teaching Systems, focusing on both cognitive and emotional aspects of engagement. The method is validated through experiments involving cognitive and motor skills tasks with 21 students. The proposed system showcases an average accuracy of almost 77% in detecting both cognitive and emotional engagement. He also explains the operation and construction of EEG equipment.), which perfectly describe the technology that may await us in a few years.

Introduction:

In the ever-evolving world of education, making students more engaged is a top priority.  As technology continues to advance, new and creative solutions are emerging to understand students better. One remarkable development is the use of EEG-based systems to track student engagement. They are used to provide feedback to teachers on how to improve their teaching methods. In this blog, we will explore the potential of EEG technology in education and its impact on understanding and improving student engagement.

Understanding EEG:

Electroencephalography (EEG) is a non-invasive technique that measures electrical activity in the brain. By placing electrodes on the scalp, researchers can capture and analyze the brain’s electrical signals. Traditionally used in medical settings, EEG has found a new application in education, offering a unique window into students’ engagement during a learning activity.

Monitoring Student Engagement:

Traditional methods of figuring out if students are engaged, such as surveys and observation, have limitations in providing real-time and objective data. But, EEG technology is different. It gives a more detailed and quick look at what’s going on in students’ minds. By analyzing brainwave patterns, educators can identify when students are focused, bored, confused, or engaged. This feedback can be used to adjust teaching methods and improve student engagement.

Advantages	Disadvantages
• Real-Time Insights: Advantage: EEG provides immediate, real-time data on students’ engagement, allowing educators to adapt their teaching methods on the spot.	• Privacy Concerns: Disadvantage: Recording and analyzing brainwave data raises privacy concerns, necessitating careful consideration of how this sensitive information is collected, stored, and used.
• Objective Assessment: Advantage: Unlike subjective measures like self-reporting, EEG provides an objective assessment of engagement levels, reducing bias in evaluating student participation and understanding.	• Ethical Considerations: Disadvantage: The use of EEG in education raises ethical questions about consent, particularly when applied to minors. There is a need for clear guidelines and ethical standards to protect students’ rights.
• Early Detection of Challenges: Advantage: EEG can help identify early signs of learning difficulties or challenges, enabling timely interventions to support struggling students.	• Cost and Accessibility: Disadvantage: EEG equipment can be expensive, limiting its widespread adoption in resource-constrained educational settings. This raises issues of accessibility and equity.
• Individualized Learning: Advantage: EEG data can be used to create personalized learning paths, tailoring educational content to individual students’ needs and preferences.	• Interpretation Challenges: Disadvantage: Interpreting EEG data requires specialized knowledge, and misinterpretation can lead to inaccurate conclusions about a student’s engagement level or cognitive state.
• Enhanced Feedback for Educators: Advantage: Educators gain deeper insights into the effectiveness of their teaching methods, allowing for continuous improvement and refinement of instructional strategies.	• Comfort and Wearability: Disadvantage: EEG headsets may be uncomfortable for some students, potentially affecting the quality and reliability of the data collected. This discomfort could introduce confounding variables in the educational environment.
• Research Opportunities: Advantage: EEG technology opens up new avenues for educational research, facilitating the exploration of cognitive processes during learning and the development of more effective teaching practices.	• Limited Understanding of Long-Term Effects: Disadvantage: The long-term effects of prolonged use of EEG-based systems in educational settings are not fully understood. Research is needed to assess any potential impact on students’ well-being, cognitive development, or attitudes towards learning.

In conclusion, while EEG-based measurement systems offer promising advantages for understanding and enhancing student engagement in education, it is crucial to address the associated challenges, particularly regarding privacy, ethics, cost, and interpretation of data. Balancing the potential benefits with these considerations will be key to the responsible and effective integration of EEG technology in educational settings.

In my opinion, there is another significant problem. Education has always been a lesson in independence, persistence and self-knowledge. When everything is handed to us on a plate, will we lose the benefits of self-knowledge or not?

Resources:

• https://techietonics.com/tag/neuroscience
• https://www.nature.com/articles/s41598-022-09578-y
• https://eric.ed.gov/?id=ED602495
• Electroencephalography – Wikipedia
• EEG-Based Tool for Prediction of University Students’ Cognitive Performance in the Classroom – PMC (nih.gov)

AI resources: AI-powered chat mode of Microsoft Bing