SwissCognitive Guest Blogger: Dr. Revanth Kumar Guttena – “AI as a Companion: A Blessing or a Curse in Modern Times?”

Marriage has long been a cornerstone of human society, serving as a foundation for family life and social stability. However, in recent years, there has been a noticeable decline in marriage rates across various countries, driven by factors such as financial burdens, compatibility issues, and evolving societal norms. Concurrently, advancements in artificial intelligence (AI) have opened new avenues for emotional support and companionship, suggesting that AI could potentially play a role in fulfilling human emotional needs. This article explores the reasons behind the decline in marriage rates and considers AI’s potential as a supplement to, rather than a replacement for, human companionship.

Decline in Marriage Rates: Complex Factors at Play

The decline in marriage rates is a multifaceted issue influenced by various factors. The financial burden associated with weddings, housing, and child-rearing has made marriage less appealing to many, as seen in Japan, where young people increasingly view marriage as a financial strain. Compatibility issues also play a significant role, with emotional and psychological alignment becoming crucial in modern relationships. Many individuals now prioritize personal values and goals, making it more challenging to find compatible partners. Additionally, a growing focus on individual achievements and personal growth has led people to prioritize careers and personal aspirations over marital commitments. Cultural shifts and changing societal norms further contribute to the decline, with greater acceptance of alternative lifestyles reducing the societal pressure to marry. This trend is evident in countries like India, where a significant percentage of young people express little interest in marriage.

Human-AI Relationships: Navigating New Territories

While human-AI relationships were once the domain of science fiction, the rapid growth of AI technology has brought these concepts into reality. The AI market is projected to reach $407 billion by 2027, with AI increasingly integrated into daily life. As technology continues to evolve, AI is beginning to reshape social interactions and influence how individuals connect emotionally. However, while some may form deep attachments to AI, it is essential to recognize that these relationships should complement rather than replace human connections.

The Role of Anthropomorphism in Human-AI Interaction

Anthropomorphism, the tendency to attribute human-like traits to non-human entities, plays a significant role in how people interact with AI. AI systems that exhibit behaviors and conversational styles reminiscent of human personalities can evoke emotional responses from users. This can include qualities such as empathy, humor, and kindness, making AI feel more personable and engaging. However, it is important to remember that these interactions, while valuable, are still based on algorithms rather than genuine emotions.

The Role of AI Companionship: Supplementing Human Interaction

As traditional forms of companionship face challenges, AI is emerging as a potential supplement for emotional support. However, it is essential to view AI companionship as an addition to, rather than a replacement for, human relationships. AI can provide personalized emotional support by analyzing emotions and responding empathetically, offering tailored comfort. Yet, while AI can help combat loneliness, it lacks the genuine understanding and emotional depth inherent in human relationships. Its 24/7 availability is beneficial for those feeling isolated, but it should not replace efforts to build and maintain human connections. Additionally, AI companions can facilitate social engagement and encourage individuals to connect with others, but they cannot replicate the authenticity and richness of human emotional bonds.

The Triarchic Theory of Love and Its Limitations in AI

Some studies suggest that based on the triarchic theory of love—intimacy, passion, and commitment—it is possible for individuals to experience affection for AI. However, while AI may simulate aspects of love, it lacks the depth and mutuality that define human relationships. True intimacy, passion, and commitment are grounded in shared experiences, emotional reciprocity, and personal growth, elements that AI cannot fully replicate.

Navigating the Complexities: Balancing Innovation with Responsibility

While AI offers promising avenues for emotional support and companionship, it is important to consider its limitations. AI, despite advancements, cannot fully replicate the depth and complexity of human emotions. Unlike humans, AI cannot share personal experiences or provide genuine emotional reciprocity. Overreliance on AI companions could lead to a decline in human interactions and social skills, potentially contributing to feelings of isolation and loneliness. Additionally, AI systems collect and analyze vast amounts of personal data, raising privacy and security concerns. Furthermore, AI algorithms may inadvertently perpetuate biases or discrimination present in the data they are trained on. There is also a risk that AI companions could inadvertently replace human relationships, leading to a decline in social cohesion. AI systems can be designed to manipulate emotions by providing tailored responses based on user data, potentially leading to a strong emotional dependency on AI. While AI technology is rapidly evolving, there may be limitations in its ability to fully understand and respond to complex human emotions. AI systems can sometimes exhibit unexpected or unintended behaviors, which can be disconcerting for users. In conclusion, while AI companions offer potential benefits, it’s essential to approach them with caution and consider the potential drawbacks. A balanced approach that integrates AI companionship with human interactions is likely to be the most beneficial for individuals and society as a whole.

References

Christina Pazzanese (2024). Lifting a few with my chatbot. (Accessed: 06 September 2024).

Deepak Maggu (2022). Youth in India Report 2022: 23 percent of young people are not interested in marriage. (Accessed: 06 September 2024).

Jaap Arriens (2024). AI companions can relieve loneliness – but here are 4 red flags to watch for in your chatbot ‘friend’. (Accessed: 06 September 2024).

Manish Raj Malik (2024). Rarest of the Rare: Japan Government Asks Young People Reason Behind Not Marrying Amid Population Crisis. (Accessed: 06 September 2024).

Neuroscience News. (2024). AI companions and loneliness. (Accessed: 06 September 2024).

Sian Zaman (2024). AI champions – Exploring the ethical concerns, promises and perils. (Accessed: 06 September 2024).

Surbhi Bhatia and Sriharsha Devulapalli (2020). Are India’s youth giving up on marriage? (Accessed: 06 September 2024).

The conversations (2024). AI ‘companions’ promise to combat loneliness, but history shows the dangers of one-way relationships. (Accessed: 06 September 2024).

Uma Shashikant (2024). Why women refuse marriage. (Accessed: 06 September 2024).

About the Author:

Dr. Revanth Kumar Guttena, Assistant Professor in Marketing, Woxsen University, India has more than 15 years of experience in industry an academics. The author obtains a PhD degree in Business Administration, specialized in marketing from National Dong Hwa University, Taiwan. Master in Imagineering from Breda University of Applied Sciences, The Netherlands. The author practices appreciative inquiry in his daily life and feels the importance in student’s  behavior, motivated to write this article.

Der Beitrag AI as a Companion: A Blessing or a Curse in Modern Times? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Bloggers: Bidyut Sarkar, Senior Solution Manager, IBM USA and Rudrendu Kumar Paul – Boston University, Boston, USA – “Conversational AI on Manufacturing Floors With NLP-Enabled Assistants”

Takeaways:

• NLP-enabled assistants are transforming manufacturing by simplifying human-machine interactions.
• Industry giants like Toyota, Boeing, and Shell have witnessed enhanced efficiency and reduced errors through AI integration.
• The future of manufacturing envisions plant floors driven by data-rich, conversational interactions.

The advent of artificial intelligence in the manufacturing sector has brought a transformative era. As industries evolve, the integration of AI technologies becomes not just advantageous but essential. Natural language interfaces are a pivotal innovation among the myriad of advancements. These interfaces, rooted in human language and cognition principles, offer a seamless bridge between intricate machine operations and human understanding. In contemporary production settings, the ability to communicate with machines using everyday language can redefine operational efficiency. Such interfaces eliminate the barriers of complex coding languages, making data queries and command executions more intuitive. The shift towards these natural language interfaces underscores a broader movement in manufacturing: embracing AI not as a mere tool but as a collaborative partner. This partnership, built on the foundation of mutual understanding, promises to reshape the dynamics of production floors, making them more agile, responsive, and intelligent.

For Europe, the anticipated compound annual growth rate (CAGR) for the natural language processing industry from 2023 to 2030 is projected to be 15.19%, leading to an estimated market value of $17.41 billion by the end of the period. (Statista)

Source: Statista

At the same time, the value added by the manufacturing market in Europe is anticipated to reach $3.54 trillion in 2028, with an expected compound annual growth rate (CAGR) of 3.93% from 2023 to 2028. (Statista)

Source: Statista

The Role of Conversational Interfaces in Manufacturing

Conversational interfaces represent a paradigm shift in how humans interact with machines. At their core, these interfaces harness the nuances of human language, enabling a more intuitive communication pathway with technological systems. In the context of manufacturing, this innovation holds profound significance. Historically, interactions with machines required specialized knowledge, often demanding intricate command sequences or coding.

Conversational interfaces, on the other hand, simplify this interaction. They allow operators to engage with systems using natural language, making the process more accessible and less daunting. This shift democratizes access and accelerates response times as the need for translating thoughts into machine-specific commands diminishes.

Comparing conversational interfaces with their traditional counterparts reveals stark contrasts. Standard interfaces, often graphical or command-line-based, necessitate a learning curve and can limit responsiveness. Causal models break these barriers, offering a more fluid, adaptive, and user-centric approach. In essence, the evolution from traditional to conversational interfaces in manufacturing marks a transition from rigid, prescriptive systems to more flexible, understanding, and adaptive ones. This transition holds the potential to redefine the efficiency and adaptability of manufacturing processes. (Swiss Cognitive)

Technological Foundations

The underpinnings of conversational interfaces lie in two pivotal technological advancements: Natural Language Processing (NLP) and neural networks. NLP, a subfield of artificial intelligence, delves into the interaction between computers and human language. Its primary objective is to enable machines to understand, interpret, and generate human language meaningfully and contextually relevantly. This understanding forms the bedrock of any conversational interface, ensuring that interactions are syntactically correct and semantically coherent.

Neural networks, inspired by the structure and function of the human brain, play a complementary role. (IJAIM) These interconnected algorithms process information in layers, allowing for recognizing patterns and relationships in vast datasets. In NLP, neural networks facilitate the deep learning processes that drive language comprehension, sentiment analysis, and response generation.

When NLP and neural networks converge, the result is a conversational interface capable of understanding intricate language patterns, discerning context, and generating appropriate responses. Unlike traditional systems that rely on explicit programming for every possible interaction, these interfaces learn and adapt. They draw from vast linguistic datasets, refining their understanding with each interaction. This continuous learning, underpinned by the combined might of NLP and neural networks, empowers conversational interfaces to be dynamic, adaptive, and increasingly attuned to the nuances of human language. In the manufacturing sector, this translates to responsive and predictive interfaces, heralding a new age of intelligent interaction.

Leading Innovators in the Field

Several trailblazers have emerged in the dynamic landscape of conversational interfaces, each carving a distinct niche with innovative solutions. CoPilot.ai, Sigma, and Arria NLG have garnered significant attention for their pioneering contributions to manufacturing.

CoPilot.ai stands at the forefront of integrating artificial intelligence with human-centric design.

Their platform emphasizes intuitive interactions, ensuring operators can query and command production systems seamlessly. By prioritizing user experience, CoPilot.ai has managed to bridge the gap between sophisticated AI algorithms and the practical needs of manufacturing floors.

Sigma, on the other hand, has taken a data-driven approach. Their platform harnesses the power of big data analytics, combined with NLP, to offer insights and recommendations. This means real-time feedback, predictive maintenance alerts, and actionable insights that can significantly enhance operational efficiency in manufacturing. Sigma’s strength lies in transforming raw data into meaningful, actionable intelligence.

Arria NLG, focusing on the Natural Language Generation, brings a fresh perspective. Instead of merely understanding or interpreting human language, Arria NLG’s solutions excel in generating human-like text based on data. In manufacturing, this capability translates to detailed reports, summaries, and explanations generated on the fly, providing operators with a clear understanding of complex processes and data streams.

These innovators are redefining the boundaries of what’s possible in manufacturing. While varied in approach, their unique solutions share a common goal: to enhance the symbiotic relationship between humans and machines. By doing so, they are not only elevating the capabilities of individual operators but also setting the stage for a more collaborative and intelligent manufacturing future.

Source: Avnet

Real-world Applications and Case Studies

The theoretical promise of AI-powered conversational interfaces is compelling, but it’s in real-world applications where their transformative potential truly shines. Several industry giants have already begun harnessing these technologies, yielding tangible benefits.

Synonymous with automotive excellence, Toyota has integrated AI-powered assistants into its production lines. The primary objective was to combat the perennial challenge of downtime. By leveraging these advanced interfaces, Toyota’s operators can swiftly diagnose issues, receive instant feedback, and implement corrective measures. The result was a significant reduction in unproductive hours, ensuring that assembly lines run smoother and more efficiently.

Boeing, a behemoth in the aerospace sector, has turned to conversational interfaces to streamline its intricate manufacturing processes. Given the complexity of aircraft production, even minor inefficiencies can lead to substantial delays. Boeing’s adoption of these interfaces has enabled its engineers and technicians to access critical data, seek clarifications, and receive guidance without wading through cumbersome manuals or databases. The outcome has marked improved workflow efficiency and reduced production bottlenecks.

Shell, a global leader in the energy sector, faces the daunting task of managing vast and complex operations. The introduction of AI-guided processes has been a game-changer. These systems assist in monitoring equipment, predicting maintenance needs, and even guiding operators in crisis scenarios. The result is a more streamlined operation with a notable decrease in errors, leading to safer and more efficient energy production.

Beyond these industry leaders, several other enterprises have embraced the power of conversational AI. For instance, pharmaceutical companies use these interfaces for precision drug formulation, while textile manufacturers employ them for quality control. The common thread across these applications is straightforward: conversational interfaces, backed by robust AI, are ushering in a new era of enhanced productivity, reduced errors, and more intuitive human-machine collaboration.

Benefits of AI-Powered Production Assistants

Integrating AI-powered production assistants into manufacturing processes has ushered in a series of tangible benefits that are reshaping the industry landscape. One of the most pronounced advantages is the substantial reduction in downtime. By providing real-time diagnostics and predictive insights, these assistants enable swift identification and rectification of issues, ensuring that production lines remain operational and minimizing costly disruptions.

Furthermore, the precision and vigilance of AI assistants have led to a marked decrease in errors and mistakes. Unlike human operators, AI systems maintain consistent accuracy and may overlook anomalies or misinterpret data under pressure. Their ability to process vast amounts of data quickly and identify discrepancies means that potential issues are flagged and addressed before they escalate.

Lastly, the overarching impact of these advancements is the enhancement of overall efficiency and productivity. Production rates improve with streamlined workflows, instant access to data, and the elimination of common bottlenecks. Moreover, operators, freed from routine troubleshooting, can focus on more value-added tasks, driving innovation and quality.

In essence, adopting AI-powered assistants in manufacturing is not just about automating processes; it’s about elevating the entire production ecosystem to new heights of excellence.

The Future of Conversational Plant Floors

The journey through the intricacies of NLP-enabled assistants underscores their transformative potential in reshaping manufacturing dynamics. These advanced interfaces, bridging human intuition with machine precision, promise a future where communication barriers on production floors become relics of the past. As industries evolve, the vision is clear: plant floors will become hubs of data-driven conversations, where machines execute commands and offer insights, fostering a collaborative atmosphere. This synergy between human expertise and AI-driven insights is set to redefine manufacturing, heralding an era where conversational interactions drive innovation, efficiency, and unparalleled growth.

About the Authors:

Bidyut Sarkar, Fellow of the IET (UK) and author of books on AI is an expert in life sciences and industrial manufacturing industry solutions with applied AI/ML experience, having served as a keynote speaker and judge at startup competitions. His professional experience has taken him to various parts of the world, including the USA, Netherlands, Saudi Arabia, Brazil, Australia, and Switzerland.

Rudrendu Kumar Paul is an applied AI and machine learning expert and the author of multiple books on AI, with over a decade of experience in leading data science teams at Fortune 50 companies across industrial high-tech, automation, and e-commerce industries. Rudrendu holds an MBA, an MS in Data Science from Boston University (USA), and a bachelor’s degree in electrical engineering.

Der Beitrag Conversational AI on Manufacturing Floors With NLP-Enabled Assistants erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Bloggers: Bidyut Sarkar, Senior Solution Manager, IBM USA and Rudrendu Kumar Paul – Boston University, Boston, USA – “AI-Powered Predictive Maintenance in Advanced Manufacturing”

Takeaways:

• Deep learning offers unparalleled precision in predictive maintenance by analyzing intricate patterns in sensor data.
• Collaboration between domain experts and data scientists is crucial for effective model implementation.
• Embracing deep learning in maintenance strategies can lead to significant operational efficiency and cost savings.

Once a novel concept, predictive maintenance has evolved significantly with technological advancements. Historically, industries relied on rudimentary methods to predict equipment failures. However, the landscape transformed with artificial intelligence and deep learning. These cutting-edge technologies have ushered in a new era, offering unparalleled insights into the health and longevity of machinery. Deep understanding, in particular, has demonstrated its prowess by analyzing intricate patterns from sensor data, thereby enhancing the precision of predictions. This shift not only underscores the potential of modern algorithms but also highlights the transformative impact of technology on industrial operations.

In Europe, the market for machine learning (which includes deep learning applications) is forecasted to expand from $43.40 billion in 2023 to $144.60 billion by 2030, with a compound annual growth rate (CAGR) of 18.76% during this period.

Source: Statista

At the same time, the global predictive maintenance market is projected to grow to $64.3 billion by 2030, with a compound annual growth rate (CAGR) of 31% from 2022 to 2030. (Statista)

Source: Statista

The Limitations of Traditional Predictive Maintenance

Historically, predictive maintenance relied on essential monitoring tools and heuristic techniques. These methods, while foundational, often fell short of accurately forecasting equipment malfunctions. Relying on rudimentary sensors and manual inspections, traditional approaches needed more granularity to detect subtle anomalies or predict failures with high confidence. Furthermore, these techniques were susceptible to human error and could not adapt to the evolving complexities of modern machinery. Such limitations underscored the pressing requirement for innovations that could offer more detailed insights and higher predictive accuracy. As industries grew and machinery became more intricate, the inadequacies of conventional predictive maintenance became increasingly evident, paving the way for the integration of advanced technological solutions.

Deep Learning: A Game Changer for Predictive Maintenance

Deep learning, a subset of artificial intelligence, harnesses neural networks with multiple layers to analyze vast amounts of data; unlike traditional algorithms that plateau after a certain data threshold, deep learning thrives on extensive datasets, extracting intricate patterns often invisible to other methods. In the context of predictive maintenance, this capability is invaluable. (Swiss Cognitive)

Machinery, especially in industrial settings, generates a plethora of sensor data. This data, rich in minute details, holds the key to understanding the health and potential vulnerabilities of equipment. With their advanced neural structures, deep learning models efficiently sift through this data, identifying patterns and anomalies that might indicate impending failures. By doing so, these models offer a nuanced understanding of equipment health, allowing industries to address issues before they escalate preemptively.

The true prowess of AI (which includes deep learning) lie in its ability to discern patterns from seemingly random data points. In predictive maintenance, this means recognizing the early signs of wear and tear or the subtle hints that a machine part might be on the brink of malfunction. Thus, deep learning is a beacon of innovation, revolutionizing how industries approach equipment maintenance. (IJAIM)

Source: KSB

Real-world Applications and Case Studies

In the evolving landscape of predictive maintenance, several trailblazing entities have emerged, leveraging deep learning to redefine industry standards. Their applications provide compelling evidence of the transformative potential of this technology.

Uptake

One notable entity in this domain is Uptake, which has made significant strides in forecasting outages. By harnessing the power of deep learning, Uptake’s models analyze vast datasets to predict potential disruptions. The implications of such precise forecasting are profound. By averting unplanned downtimes, industries can optimize operations, reduce costs, and enhance overall productivity. Moreover, the ripple effect of these advancements extends beyond mere operational efficiency, influencing supply chains, labor management, and even environmental sustainability.

Augury

Another pioneer, Augury, has carved a niche in detecting nuanced data indicators that hint at equipment health. Traditional methods often overlook these subtle signs, but with deep learning’s intricate pattern recognition, Augury’s models can pinpoint anomalies with remarkable precision. Such capabilities enable industries to undertake timely interventions, ensuring machinery longevity and reducing the risk of catastrophic failures.

C3 AI

C3 AI stands out with its commendable achievement of over 85% accuracy in predictive analytics. Such a high degree of precision is a testament to the prowess of deep learning models that can sift through complex data structures, identifying patterns that would otherwise remain obscured. This accuracy bolsters confidence in predictive maintenance strategies and underscores the potential for further refinements and innovations in the field.

Delving deeper into specific applications:

• ML Forecasting Bearing Faults: Bearings, critical components in many machines, can exhibit faults that, if undetected, can lead to significant operational challenges. Deep learning models have demonstrated their capability to forecast these faults by analyzing vibrational data, temperature fluctuations, and other sensor outputs, ensuring timely interventions.
• Pump Cavitation Detection: Cavitation in pumps, where vapor bubbles form in the liquid due to pressure changes, can harm equipment health. Through deep learning, subtle signs of cavitation, often missed by conventional methods, can be detected, allowing for preventive measures.
• Predicting Wind Turbine Failures: Wind turbines, monumental feats of engineering, are not immune to wear and tear. When processed through deep learning algorithms, their vast data outputs can predict potential failures, from blade issues to gearbox malfunctions, ensuring optimal energy production and equipment longevity.

These real-world applications underscore the transformative impact of deep learning on predictive maintenance, heralding a new era of efficiency and precision.

Solution Strategies in Implementing Deep Learning for Predictive Maintenance

Incorporating deep learning into predictive maintenance is a nuanced endeavor, necessitating adherence to certain best practices to ensure optimal outcomes.

Model Governance

At the heart of any deep learning initiative lies the model itself. Ensuring its reliability and consistency is paramount. This involves rigorous testing, validation, and monitoring of the model in real-world scenarios. A robust governance framework makes the model behave as expected, even when encountering diverse and evolving datasets. Furthermore, documentation of model parameters, training methodologies, and validation results aids in maintaining transparency and trust.

Iterative Improvement

The dynamic nature of machinery and operational environments means that a one-size-fits-all model is a myth. As such, continuous refinement of deep learning models is essential. Industries can enhance predictive accuracy over time by revisiting and updating models based on new data and feedback. This iterative approach ensures that models remain relevant and practical, even in changing industrial landscapes.

Practitioner Collaboration

The success of any predictive maintenance initiative hinges on the synergy between data scientists and maintenance experts. While data scientists bring expertise in model development and data analysis, maintenance experts possess invaluable domain knowledge. Collaborative efforts between these professionals can lead to models that are not only technically sound but also contextually relevant. Such collaboration ensures that the insights derived from deep learning are actionable and aligned with on-ground realities.

Adhering to these best practices can significantly augment the efficacy of deep learning in predictive maintenance, ensuring sustainable and impactful results.

The Future of Predictive Maintenance with Deep Learning

The trajectory of predictive maintenance, guided by deep learning, paints a promising picture. As computational capabilities expand and datasets grow more affluent, the potential for refining and enhancing predictive models becomes increasingly evident. These advancements could lead to even more nuanced detections, capturing the minutest of anomalies that might have previously gone unnoticed.

Industries stand at the cusp of this transformative era, and preparation is crucial. Embracing a culture of continuous learning and fostering an environment conducive to innovation will be pivotal. Investing in training programs that bridge the knowledge gap between traditional maintenance practices and modern data-driven approaches can also prove beneficial. Moreover, as technology evolves, so should the strategies, ensuring that industries remain agile and adaptive.

In essence, the fusion of deep learning with predictive maintenance heralds a future marked by unparalleled precision, proactive interventions, and enhanced operational efficiency.

Challenges and Considerations

While integrating deep learning into predictive maintenance offers immense promise, it has challenges. A primary consideration is the data itself. Both quality and quantity are paramount; models trained on insufficient or skewed data can produce misleading results, potentially leading to costly misjudgments.

Additionally, the intricacies of machinery and equipment demand domain expertise. Mere algorithmic prowess needs to be improved. Collaborative efforts between domain experts and data scientists are essential to ensure that models are grounded in practical realities.

Lastly, concerns surrounding transparency and trustworthiness arise, as with any AI-driven initiative. Black-box models, which offer little insight into their decision-making processes, can be a source of apprehension for industries. Addressing these concerns through explainable AI methodologies and rigorous validation can help build confidence and ensure the responsible adoption of deep learning in predictive maintenance.

Conclusion

The fusion of deep learning with predictive maintenance signifies a pivotal shift in how industries approach equipment health and longevity. This synergy offers an unparalleled opportunity to detect intricate patterns, forecast potential failures, and ensure timely interventions. As the technological landscape continues to evolve, industries stand to gain immensely from these advancements, reaping benefits in terms of operational efficiency, cost savings, and machinery lifespan. Forward-thinking entities must recognize this potential and actively integrate deep learning methodologies into their maintenance strategies. Doing so, they pave the way for a future marked by precision, proactivity, and enhanced productivity.

About the Authors:

Bidyut Sarkar, Fellow of the IET (UK) and author of books on AI is an expert in life sciences and industrial manufacturing industry solutions with applied AI/ML experience, having served as a keynote speaker and judge at startup competitions. His professional experience has taken him to various parts of the world, including the USA, Netherlands, Saudi Arabia, Brazil, Australia, and Switzerland.

Rudrendu Kumar Paul is an applied AI and machine learning expert and the author of multiple books on AI, with over a decade of experience in leading data science teams at Fortune 50 companies across industrial high-tech, automation, and e-commerce industries. Rudrendu holds an MBA, an MS in Data Science from Boston University (USA), and a bachelor’s degree in electrical engineering.

Der Beitrag AI-Powered Predictive Maintenance in Advanced Manufacturing erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data Science at Sigli – “How AI-powered tools can help people with hearing impairments”

According to the data provided by the WHO, by 2050, 1 in every 10 people, or in total over 700 million, will suffer from disabling hearing loss. And it is a very impressive figure. While some people lose hearing abilities in life span, others are born with hearing impairments and have no other choice but to use sign language for communication.

Hearing loss and deafness often influence such aspects of people’s lives as cognition, education, and employment, which can result in loneliness and full social isolation. The history of electronic hearing aids started more than a century ago, in 1898 practically just after the introduction of the telephone by Alexander Graham Bell in 1876. However, we can’t say that today they can fully solve the problems of people with hearing impairments. Even modern hearing aids are far from being perfect, they have a lot of limitations and may cause a lot of discomfort for their users.

Nevertheless, thanks to emerging technologies (and Artificial Intelligence occupies a leading position among them), we can change our approach to making our environment better for people with partial or full hearing loss.

In this article, we offer you to have a look at the new opportunities that AI opens to us in addressing the difficulties that people with hearing impairments face every day.

AI tools: Use cases and examples

To begin with, we should mention that today hearing aid manufacturers have already started demonstrating their interest in the capabilities of AI and are studying the possibility of making their devices more advanced with it. We should admit that such AI-powered hearing aids have a high chance of becoming game-changers.

Usually, digital hearing aids have so-called modes or programs, like a TV mode or a home program. These modes include static settings that correspond to different environments. However, such settings can be okay in very standardized conditions only and they won’t match up to unique circumstances. AI used in hearing systems works another way. It does not rely on strictly set modes but is able to adjust them in real-time based on the experiences of a user.

For example, when a user is visiting a crowded place with his or her spouse, AI-powered hearing aids that are enriched with noise-cancellation technologies can make their communication much more comfortable than it used to be without such tools. This device will be able to define the voice that a user hears most often and prioritize it over all others around while canceling other noises and sounds.

Cutting-edge hearing aids may also have sound amplification tools. In other words, when somebody is speaking too quietly or, for example, through a mask that obviously mutes sounds, AI-powered devices can detect such issues and amplify the sound in real time.

All this may seem too futuristic but such devices already exist. One of them is Widex Moment Sheer which was introduced in September 2022. Widex is focused on the quality of sound and utilizes AI and ML for designing hearing modes based on users’ typical environments.

But are AI-powered hearing aids the only options for helping people with hearing impairments? And what can be offered to people with full hearing loss? It’s high time to speak about solutions of other types.

A lot of people with hearing impairments have a well-developed skill of lip reading which means that they can understand what others say by interpreting the movements of their faces, lips, and tongues. This skill is highly valuable for them but the problem is that due to various kinds of disabilities, including muteness, they can’t use natural speech and use sign language to express their thoughts. It can become a barrier to synchronous communication when their partners do not know how to interpret all signs and gestures. Moreover, how is it possible to organize communication if a person is not good at lip reading or if it is not possible to interpret lip movements amid the ongoing conditions? Here’s when we should mention the possibility of building AI-powered sign language translators.

AI-powered Kinect Sign Language Translator by Microsoft is a solution that can convert signs into a spoken or written language and, vice versa, it can convert natural language into signs. To use such a tool, it is necessary to have a computer and a Kinect camera that will recognize gestures and provide their translation in real time. A similar process takes place when a hearing person is speaking. The system is “listening” to the speech and then transforms the words into signs.

But Microsoft is not the only company that is standing behind a sign language translator. A lot of startups are also working on similar solutions. In 2018, a Netherlands-based startup GnoSys introduced its app that is intended for translating sign language into speech and written text in real time. The application relies on neural networks and computer vision for recognizing sign language. Then with the help of smart algorithms, the recognized signals are transformed into speech.

The above-mentioned tools can help a lot in face-to-face communication which is highly important for the socialization of people and their possibility of getting a job. However, in our article, we also need to mention solutions that will revolutionize the online experience for people with deafness or other hearing impairments. How do these people usually watch films? With captions. But what can be done if there are no added captions? AI real-time captioning and transcription services can address this issue. And what is more important, such services will be of great use not only for movie lovers, they can be applied during lifestreaming, Zoom meetings, online lessons, etc. It’s will a great idea to rely on real-time captioning tools if you organize online events for a wide audience to ensure better inclusivity.

Verbit is one of the vendors that provides such services. What makes the offered tools extremely comfortable is the possibility to integrate them directly into streaming or conferencing platforms like YouTube, Twitch, or Zoom for seamless experiences. Such services are also popular among those who are watching videos in a non-native language or those who can’t use their headphones. However, the community of people with hearing loss can benefit from them most of all as for them the use of real-time captioning is not just a question of comfort but also a must.

It’s also crucial to mention that AI can also contribute to increasing safety for people with hearing loss which can affect their ability to react to emergencies. For example, let’s consider driving. Hearing impairments do not have a direct impact on driving skills but due to them, people can’t hear important sounds like sirens of emergency vehicles. These sirens always indicate a necessity to quickly react and make way for such vehicles as fire trucks or ambulances. When a driver doesn’t hear these sounds, he or she can’t take any measures which can lead to dangerous road situations. Engineer Jan Říha paid attention to these risks and developed a smart device dubbed PionEar. It relies on an audio classification algorithm and is able to analyze background noise and recognize the sounds of emergency vehicles. When such sounds are detected, a driver will be alerted with the help of a visual cue.

However, sometimes, to make our society more suitable for everyone, we do not need to create something exclusive for a particular community. Sometimes it will be enough to adapt something that was created for wide circles to the needs of some groups.

Virtual assistants with text-to-speech and speech-to-text capabilities like Siri are among such examples. If a girl with hearing impairments needs to make an appointment at a beauty salon, what options does she have? She can write a message. But what if administrators don’t have time to read messages? She can ask her friend or sister to do it. But it’s not always possible. Moreover, it will require additional time and effort. With a virtual assistant, everything will be easier. It will be enough to activate Siri by using a voice command or the Type to Siri mode and ask it to make a call to a beauty salon.

Yes, at the moment, this functionality still requires enhancements. But we are here to make the world a better place to live for everyone by means of technology. Right?

Closing word

The range of AI use cases that we’ve considered in this blog post is a cool demonstration of how modern tech solutions can help us break down the barriers that used to exist (and are still existing) for people with any type of disability, not only hearing impairments. Thanks to modern AI-powered tools, people will have the possibility to be fully integrated into society. And what is probably even more important, our society will become more inclusive for them.

In the series of our blog posts, we will continue talking about the ways AI can help us to reach such goals. Stay tuned!

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Artem will be speaking at the SwissCognitive World-Leading AI Network AI Conference focused on The AI Trajectory 2024 – Invest for Impact on 13th December.

Der Beitrag How AI-Powered Tools Can Help People With Hearing Impairments erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger:  HennyGe Wichers, PhD – “AI Is Huge – And So Is Its Energy Consumption”

Replacing every Google search with an LLM interaction uses as much electricity as Ireland, Alex de Vries writes in a commentary published in the journal Joule on October 10, 2023. The PhD candidate at Vrije Universiteit Amsterdam is raising concerns about the environmental impact of artificial intelligence.

Large models like ChatGPT (OpenAI), Bard (Google), and Claude (Anthropic) consume a lot of energy. Take, for example, ChatGPT. The chatbot is built on a model called GPT-3, which used an estimated 1,287 MWh in electricity during its training phase. According to the chatbot, that’s the equivalent of driving a Tesla Model 3 around the Earth’s equator 21,500 times.

ChatGPT puts 1,287 MWh in perspective

ChatGPT suggested using household energy consumption for scale first. It was a good idea, but its answer was clearly wrong. It doesn’t make sense that 1,287 MWh powers either 1,468 American homes for a year or 122 for a month. The machine politely apologised when I pointed this out.

That’s nice, but I no longer trusted the AI and had to verify its second suggestion – with a Google search. For now, LLMs are probably increasing rather than replacing traditional search traffic. But I digress.

It’s well-known that training an LLM uses a lot of energy, yet that’s only the first step in the process. The second step is inference, a phase few of us have heard of, but many participated in.

For ChatGPT, inference began when it was launched. It continued to learn while interacting with the public and creating live responses to user queries. The chatbot’s estimated energy consumption during this phase was 564 MWh daily. That’s close to half the electricity consumed in training (44%) – but used every single day.

“Looking at the growing demand for AI service, it’s very likely that energy consumption related to AI will significantly increase in the coming years,” de Vries commented in an interview.

ChatGPT exploded as soon as it launched, registering an incredible 100 million users in just 2 months and igniting a chain reaction of artificial intelligence products. Unfortunately, we can’t skip the inference phase for new AIs because, without it, the machine would not have the ability to learn.

From 2019 to 2021, a whopping 60% of Google’s AI-related energy bill was for inference. But progress is being made. Hugging Face, a relative newcomer founded in 2016, developed the open-source alternative BLOOM using significantly less energy in the inference phase relative to the training phase.

Let’s take a look at the energy consumption for a single user request. Comparing the different methods, the graph looks as follows.

Fig 1: Energy consumption per user request (replicated from Joule)

The last two bars show estimations for AI-powered Google Search by two independent research firms, New Street Research and SemiAnalysis. It’s very costly at 20 to 30 times the usage of regular Google Search. That’s not an immediate problem, however, because NVIDIA can’t supply the hardware required.

HARDWARE CONSTRAINTS

Google would need 512,821 of NVIDIA’s AI servers to make every search an LLM interaction. That’s more than 5 times the company’s production for 2023, when it’s expected to deliver around 100,000 servers. The gap is enormous. Moreover, NVIDIA has around 95% market share, so no alternative supplier exists today.

Chips are an issue, too. NVIDIA’s chip supplier, TSCM, is struggling to expand its chip-on-wafer-on-substrate (CoWoS) packaging technology, which is essential for the chips NVIDIA needs. TSCM is investing in a new plant, but it will only begin to produce volumes in 2027. By then, NVIDIA could have demand for 1.5 million of its AI servers.

Hardware will remain a bottleneck for several more years. Still, without hardware constraints, we’d encounter problems further upstream. Building permissions for data centres take time, and construction does, too. Not to mention, the energy grid needs to expand to deliver the electricity required to run and cool them.

INNOVATION

But these constraints will drive innovation. We will find more efficient models and ways to operationalise them. A breakthrough in quantum computing could change everything, both for the supply and demand for AI.

De Vries points out, “The result of making these tools more efficient and accessible can be that we just allow more applications of it and more people to use it.”

He’s referring to Jevons’ Paradox, which occurs when an increase in efficiency causes costs to fall and demand to increase to the point where we use the tools more than we would have without the improvement.

The paradox was formulated in 1865 and has been observed many times since. LED lighting is a nice example: running LEDs is so cheap that we’ve covered the planet with them and now use more electricity for lights than ever before. If that is hard to imagine, just take a look at Sphere in Las Vegas.

Link to the YouTube video.

How amazing is that? But we wouldn’t have built it if innovation hadn’t graduated to LEDs. AI might follow a similar path. Nevertheless, without game-changing innovation to reduce energy use and ensure sustainable supply, we must think twice.

“The potential growth highlights that we need to be very mindful about what we use AI for. It’s energy-intensive, so we don’t want to put it in all kinds of things where we don’t actually need it,” de Vries offers as a parting thought.

Still, the very AI that poses the risk may also help us solve climate change and sustainability challenges.

Source: Joule via EurekAlert!

About the Author:

HennyGe Wichers is a technology science writer and reporter. For her PhD, she researched misinformation in social networks. She now writes more broadly about artificial intelligence and its social impacts.

Der Beitrag AI Is Huge – And So Is Its Energy Consumption erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger:  HennyGe Wichers, PhD – “ChatGPT rivals doctors at suggesting diagnosis in ER”

In December of last year, most of us were oblivious to ChatGPT. But the large language model (LLM) was already taking the United States Medical Licensing Exam, a three-part test aspiring doctors take between medical school and residency. The AI did well, scoring at or near the passing threshold for all three exams – without any special training. The chatbot also offered sensible and insightful explanations for its answers.

But now there’s evidence suggesting that ChatGPT can also work in the real world. Researchers Dr Hidde ten Berg and Dr Steef Kurstjens piloted the LLM in the Emergency Department of Jeroen Bosch Hospital in The Netherlands. They published their findings in the Annals of Emergency Medicine on September 9, 2023, and will present their work at the European Emergency Medicine Congress in Barcelona on September 17-20.

* * *

“Like a lot of people, we have been trying out ChatGPT and we were intrigued to see how well it worked for examining some complex diagnostic cases. So, we set up a study to assess how well the chatbot worked compared to doctors with a collection of emergency medicine cases from daily practice,” Dr Ten Berg explains.

ChatGPT and GPT-4 performed well in generating lists of possible diagnoses and suggesting the most likely option. The results produced by the LLMs showed a lot of overlap with actual doctors’ lists of potential diagnoses.

“Simply put, this indicates that ChatGPT was able [to] suggest medical diagnoses much like a human doctor would,” Dr Ten Berg adds in an interview ahead of the Congress in this weekend.

The researchers asked the LLM to suggest differential diagnoses for 30 patients who attended the Emergency Department in early 2022. At the time, a doctor examined each patient on arrival at the hospital and made notes of their assessment. Patients then underwent standard laboratory tests and were assigned to a treating physician, who recorded potential diagnoses and decided on additional tests. All patients were discharged with a confirmed diagnosis, and letters to their General Practitioner confirmed the details.

For the study, a fresh set of doctors reviewed each case and devised five possible diagnoses for each patient, picking one as the most likely. Initially, they only used the available notes. Then, they looked at the test results and made revisions if the new information changed their opinion. Finally, the research team entered each case into ChatGPT and GPT-4 in threefold.

Human doctors included the correct diagnosis in their top 5 for 83% of cases using only the notes. But the LLMs got impressive scores, too. ChatGPT achieved 77% and GPT-4 87%. Adding the lab test results, physicians’ accuracy increased to 87% and ChatGPT got a near-perfect 97%. GPT-4 remained at 87%.

Fig 1: Percentage of cases with correct diagnosis in the top 5 (researchers’ image)

In some cases, the AI outperformed the physician. Dr Ten Berg illustrates: “For example, we included a case of a patient presenting with joint pain that was alleviated with painkillers, but redness, joint pain and swelling always recurred. In the previous days, the patient had a fever and sore throat. A few times there was a discolouration of the fingertips. Based on the physical exam and additional tests, the doctors thought the most likely diagnosis was probably rheumatic fever, but ChatGPT was correct with its most likely diagnosis of vasculitis.”

* * *

These results suggest that “there is potential here for saving time and reducing waiting times in the emergency department,” according to Dr Ten Berg. He adds that the benefit of using AI could be supporting doctors with less experience, or it could help spot rare diseases.

But it’s important to remember that ChatGPT is not a medical device, and there are concerns over privacy when using AI with medical data.

“We are a long way from using ChatGPT in the clinic, but it’s vital that we explore new technology and consider how it could be used to help doctors and their patients,” said Youri Yordanov, who was not involved in the research. The professor at the St. Antoine Hospital emergency department (APHP Paris) in France and Chair of the European Society for Emergency Medicine added: “I look forward to more research in this area and hope that it might ultimately support the work of busy health professionals.”

The study adds to a growing literature highlighting the role AI can play in personalised treatments and using electronic health records for (preventive) diagnostics.

* * *

Source: Annals of Emergency Medicine via EurekAlert!

About the Author:

HennyGe Wichers is a technology science writer and reporter. For her PhD, she researched misinformation in social networks. She now writes more broadly about artificial intelligence and its social impacts.

Der Beitrag ChatGPT Rivals Doctors at Suggesting Diagnosis in ER erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“20 AI Industry Expert Insights On Beyond Efficiency: AI’s Creative Potential”
For the conference details, agenda, speaker line-up, and handouts CLICK HERE. For the conference recording CLICK HERE

Yesterday (05.09.2023), our groundbreaking virtual conference – “Beyond Efficiency: AI’s Creative Potential” – welcomed AI aficionados, leaders, and pioneers from all corners of the globe. Inspired by AI transcending its traditional roles, the conference examined the unfolding narrative of generative AI. Our speakers shed light on the revolution currently transforming industries and shifting paradigms.

The three-hour conference was facilitated by SwissCognitive, World-Leading AI Network, and propelled by its Co-Founders, the Swiss AI Power Couple, Dalith Steiger and Andy Fitze.

This short wrap-up provides a concise overview of the event’s highlights, presenting the powerful synthesis of creativity, AI, and business impact. Revisit the expert perspectives, innovative solutions, and the resounding call for a future where AI not only complements but elevates human potential.

“It is the human being who brings technology to the next level, and conversely, it is technology that brings humankind to its next level.”
Dalith Steiger

We stand at the cusp of a new era where the future of AI is not just intelligent but generative. From the bustling tech hubs in the Netherlands to the markets of Europe, businesses are already harnessing the power of generative AI. But the key to unlocking its full potential lies in having a well-defined roadmap rooted in responsible AI guiding principles.

“Navigating the Transformative Journey” Keynote Igor van Gemert, ITsPeople, ResilientShield

“Regulation has to be careful and balanced. Do not overregulate, as that could end the innovation era in which we are right now. It is a fantastic time to be alive and use those Generative AI technologies.”
Igor van Gemert

Generative AI is not just a trend; it’s a revolution that’s reshaping operations across industries. The urgency of adopting AI isn’t merely about staying ahead; it’s about staying in the game, as competitors leap forward and the cost of inaction grows. From the factory floors to the C-suite, generative AI is streamlining operations and transforming business models, demanding that we re-evaluate and adapt our strategies for a rapidly evolving landscape.

Panel Discussion: “Operations Redefined by Generative AI” with Monique Morrow, Hedera, Innosuisse | Jeff Winter, Hitachi Solutions America | Jarrod Anderson, ADM | Simon See, NVIDIA | George Boretos, FutureUP, Cube RM

“Whether you like it or not, as a professional, Generative AI will be integrated into nearly everything you do on a daily basis. The question is: Are you going to take advantage of it?”
Jeff Winter

The frontiers of AI, innovation, and impact are converging, not just to create wealth but to solve urgent challenges like climate change and sustainability. In this new paradigm, financial returns and positive impact are not mutually exclusive; they intersect, offering unprecedented opportunities for investors.

“Shaping the Future: AI, Innovation, Impact, and Wealth Creation” Use Case with Robert Marcus, ALPHA10X

We stand at a unique crossroads: We must strike a balance between caution and innovation in sectors like healthcare, always keeping a vigilant eye on metrics and effectiveness. While we navigate regulatory hurdles that are playing catch-up, the key to unlocking AI’s full potential lies in asking the right questions, spotting the wrong answers, and building upon proprietary data.

Panel Discussion: “Innovative Strategies Fuelled by Generative AI” with Yoav Evenstein | Valeria Sadovykh, Microsoft | Camila Manera, LIBRODEPASES | Harvey Castro, “ChatGPT and Healthcare”, PONS | Arek Skuza, Volta Venture

“The problem with our AI regulations is that they can’t catch up quickly enough with the technology’s development. By the time they’re issued, they’re already irrelevant.”
Valeria Sadovykh

Using AI, we can not only stay ahead in predicting technological shifts across sectors but also master the art of timing these changes to drive unparalleled innovation in our businesses.

“From Efficiency to Inspiration” Use Case with Lars Tvede, Supertrends Radio

Generative AI is already transforming our workforce, how we interact, perform, and achieve today. It refines video performances, elevates our writing and speech, and liberates humans to focus on what we excel at—all while grounding us in the timeless principle that the core of any successful venture is fulfilling a true human need.

Panel Discussion: “The Potential of Workforce Redefined by Generative AI” with Carolina Pinart, Nestlé | Evelina Anttila, Wellstreet, Justic | Esha Joshi, Yoodli | Asaf Yanai, Alison.ai

In a world where the boundaries of artificial intelligence are being redrawn week by week, Behshad reminds us that the journey isn’t just about the technology but how we integrate it meaningfully into our lives. While the bar of entry for creating AI applications has dramatically lowered, the goal remains to empower humans, not to replace them, offering us a future that’s flexible, adaptable, and deeply attuned to the intricacies of different industries. As we forge ahead, it’s crucial to remember that learning to work harmoniously with AI—navigating its limitations and maximizing its potential—is not just an engineering challenge but a transformative opportunity for society as a whole.

“Redefining the Boundaries of Possibilities with AI” Interview with Behshad Behzadi, Google

“The work with AI presents an opportunity to empower humans to be able to do more, not replace them. It can elevate us to superhuman levels while preserving human connections.”
Behshad Behzadi

As the echoes from our ‘Beyond Efficiency: AI’s Creative Potential’ conference still reverberate, we’re deeply grateful for the invaluable engagement of our esteemed audience. In the coming weeks, we will release detailed articles that further unpack the insights shared during the conference.

Also, a dedicated Q&A section will capture and address the many thought-provoking questions posed during the live broadcast. Stay connected as we continue to shed light on the transformative power of AI, enriched by the contributions of our incredible audience.

If you missed our “Beyond Efficiency: AI’s Creative Potential” virtual conference, here you can find the video recording:

For the conference details, agenda, speaker line-up, and handouts CLICK HERE.

Der Beitrag 20 AI Industry Experts’ Insights On AI’s Creative Potential erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: nationalgeographic.com – “AI Can Help You Plan Your Next Trip – If You Know How to Ask”

Some destinations, like the Mulafossur Waterfall of the Faroe Islands (seen here), have the benefit of being well-documented online. For those that aren’t, AI can sometimes “hallucinate” facts or recommendations to travelers, especially when the location is small or remote.
PHOTOGRAPH BY MARTIN EDSTROM, NAT GEO IMAGE COLLECTION

With the introduction of accessible new AI systems like ChatGPT, travel will never be the same.

AI has simplified planning, made it easier to discover new experiences, and streamlined the booking process. Instead of slogging through hours of research, users get similar results with a quick conversation with AI.

AI is great for some travel functions, like generating ideas, spotlighting small businesses, and translating languages—but there are tricks to using it well, especially when it comes to traveling.

Here’s what you need to know about how to best use AI for travel.

What are the best uses for AI for travel?

The best uses of AI for travel currently fall largely into the planning and purchasing phase. “All the excitement around booking a trip can quickly become overwhelming when travelers are faced with lots of options that each require research,” says Rathi Murthy, the CTO of Expedia Group. “This is what AI can solve in travel. ”

Many AI platforms use ChatGPT, which you can use for free, in-browser, or through an iOS app (an app for Android is also coming soon). Up to its knowledge cut-off date of September 2021, ChatGPT generates suggestions based on details in your request. For $20 a month, users can upgrade to GPT-4, which offers additional web plug-ins and searches for current information, like live pricing and weather.

One of the best uses of AI for travel is to coordinate multiple flights and suggest destinations based on certain parameters, including timing and pricing. For example, GPT-4 can find multiple flights for under $1,500 for five people traveling from five different cities in the U.S. and traveling to Amsterdam in August. GPT can save a lot of research by coordinating budgets and itineraries and searching for the best deals on multiple platforms.[…]

Read more: www.nationalgeographic.com

Der Beitrag AI Can Help You Plan Your Next Trip – If You Know How to Ask erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: weforum.org – “6 ways AI is helping us learn more about our past – and future”

Artificial intelligence (AI) is usually associated with getting us to the future faster, but it can also be a powerful tool in uncovering the past.

Here are 6 ways the technology is being used around the world to help us understand the past and prepare for the future.

An inscription showing Algorithm helping historians restore Greek inscriptions. Image: Nature.

1. Restoring ancient texts with AI

An AI algorithm called Ithaca is helping historians restore ancient Greek inscriptions.

Researchers at British AI firm DeepMind trained the algorithm on around 60,000 ancient Greek texts from across the Mediterranean that were written between 700 BC and AD 500.

On its own, Ithaca was able to restore the texts with more than 60% accuracy, according to New Scientist magazine. Working with historians, the success rate grew to more than 70%.

2. Identifying long-lost faces

Facial recognition AI is being used to identify people in World War II images of the Holocaust – the genocide of approximately six million Jews by the Nazi regime and its collaborators.

The project, From Numbers to Names (N2N), was created and developed by software engineer Daniel Patt in his own time, according to reports in The Times of Israel. A descendant of Holocaust survivors, he now works for Google and the N2N now includes software engineers, data scientists and researchers.

The site scans more than 34,000 photos made available by the United States Holocaust Memorial Museum and provided by families.[…]

Read more: www.weforum.org

Der Beitrag 6 ways AI is helping us learn more about our past – and future erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Shaping the Societal Acceptance & Development of Artificial Intelligence
It doesn’t matter how great new technological (or any) inventions are, if society doesn’t accept them, all the resources used for research and development goes down the drain. To ensure that new technologies can go through their entire lifecycle and pave the path for new inventions, many aspects come into play. Leaders and experts are one of these aspects and they play a pivotal role in the societal acceptance of these technologies.

Looking at AI From the Right Perspective
AI is an interesting newcomer (if newcomer at all). We use it, as part of end products or services, yet we feel sceptical about it – based on some unrealistic reputation built by the media and Hollywood. As a result, many people, when think about AI, tend to picture robots and avatars, such as Arnold Schwarzenegger in the Terminator, Ava in Ex Machina, David in Prometheus, or they have headlines popping in, such as “AI Will Overtake Humans in 5 Years”, “Humanity Should Fear Advances in Artificial Intelligence”, “Is the World Moving Towards Real Terminators?”, “AI Gained Consciousness”.

The invisibility of this technology also doesn’t make it easy for many of us to understand and accept it. Which is quite interesting as with most inventions we are actually not necessarily interested how they work, but rather what purpose they serve and how they contribute to our lives. Nevertheless, the case is different with AI. Which is not an strange issue after all, as we, human beings are naturally curious, but it certainly sets tougher standards and expectations for industries which need to be met. Organizations not only need to ensure that they comply with Technological Social Responsibility, but they also need to find ways to communicate to the public how the technology works and how they benefit from it.  The sources of these information have to be trusted, justified and credible. Industry experts and leaders play a pivotal role in the process of spotlighting the need for AI-based solutions, and with hands-on transparent insights create understanding and drive development.

“Industry experts and leaders play a pivotal role in the process of spotlighting the need for AI-based solutions, and with hands-on transparent insights create understanding drive development.”

AI Experts in the Spotlight
To ensure that our products and services increasingly benefit from the power of AI across industries, credible experts with hands-on practical insights need to be put more into the public eye. Reason being is that their expertise, skills, experience, and limitless curiosity don’t only serve as mediums to advance AI, but also as a mediums to demystify this technology, create understanding, and  build public trust.

Global AI Ambassadors on a Joint Mission
With the purpose of demystify AI, creating understanding, and  building public trust, and with the strong SwissCognitive principle of “Share for Success”, a global network of AI experts have been created, consisting of 50 AI Ambassadors. These incredibly inspiring minds have been carefully selected by the core and extended Team of SwissCognitive, World-Leading AI Network strictly on the basis of their personal accomplishments – regardless companies, organizations or products. They meet various tough criteria concerning for instance expertise, experience and, very importantly, trust. The AI Ambassadors believe in the smart combination of human and artificial intelligence that can drive businesses and societies forward. They are propelled by passion and committed to share their knowledge & experience with an interdisciplinary approach. They are driven to involve communities worldwide in the AI journey, stimulate information flow across borders and disciplines, and while building trust, play a crucial role in demystifying AI.

The current network of AI Ambassadors is spread out on six continents and consists of experts with an average of twenty years of experience in AI. The power of the network of AI Ambassadors lies in collaboration – reducing misconceptions, spotlighting applications, potentials and challenges and driving the development forward together to the point where the limitation of this technology is only our imagination.

Find out who these experts are! Connect with them on social media and get practical, realistic and un-hyped insights with them into the world of AI.

“The power of the network of AI Ambassadors lies in collaboration – reducing misconceptions, spotlighting applications, potentials and challenges and driving the development forward together to the point where the limitation of this technology is only our imagination.”

Global AI Ambassadors 2022
Aleksandra Przegalinska, Angelica Sirotin, Ann Aerts, Antonio Russo, Aruna Pattam, Ashley Casovan, Carolina Pinart, Christian Guttmann, Claire Matuka, Clara Langevin, Craig Ganssle, Daniel Angerhausen, David Meza, Enrico Molinari, Erik Nygren, Eva Schönleitner, Ganesh Padmanabhan, Heinz V. Hoenen, Irakli Beridze, Jacques Ludik, Jayant Narayan, John Kamara, Marisa Tschopp, Muhannad Alomari, Nancy Nemes, Ngozi Bell, Pascal Bornet, Ria Persad, Ricardo Chavarriaga, Sophie Achermann, Steffen Konrath, Tania Peitzker, Thomas Helfrich, Tom Allen, Umberto Michelucci, Utkarsh Amitabh, Utpal Chakraborty, Alexandra Ebert, Andeed Ma, Chad Osorio, Frida Polli, Giselle Mota, Jeff Winter, Johan Steyn, Kim Dressendoerfer, Leila Toplic, Natasja Ludik, Bret Greenstein, Andreas Welsch, Giuliano Liguori

Countries: Australia, Austria, Brazil, Canada, Germany, India, Italy, Kenya, Netherlands, Philippines, Poland, Singapore, South Africa, Spain, Sweden, Switzerland, United Kingdom, United States

Continents: Africa, North America, South America, Europe, Asia, Australia,

Der Beitrag Shaping the Societal Acceptance & Development of Artificial Intelligence – 50 Global AI Ambassadors on the Mission erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.