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.

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SwissCognitive Guest Blogger: Zachary Amos – “How AI Will Impact 2024 Elections”

Generative artificial intelligence — models that can create images, videos, audio or text — have become incredibly popular because they’re widely available, easy to use and fast. Unfortunately, their greatest features are also threats. Will this technology permanently improve elections or unfairly sway the polls in one candidate’s favor?

AI’s Impact on Elections Is Global

2024 is a pivotal election year — not just for the United States but the world. Residents of over 50 countries will visit the polls this year alone, including Mexico, South Korea, the United Kingdom, India, South Africa, Taiwan and the European Union.

While most voters have come to expect — and know how to spot — attack ads, online trolling and misinformation around election time, AI has brought the world into uncharted waters. Generative models can create convincing images and videos with only one minute of audio or a few lines of text.

An AI-generated deepfake — real content that has been digitally manipulated with AI — is another massive concern. This technology replaces one person’s likeness or voice with a synthetic alternative.

According to one recent survey, about 78% of people believe bad actors will use AI to influence the U.S. presidential election outcome, with 70% thinking they’ll generate fake information and 62% assuming they’ll convince people not to vote.

AI’s Negative Impacts on Elections

There’s no downplaying AI’s negative impacts on elections.

Disinformation

Most people learn about candidates and current events from social media and internet headlines. In the United States, 82% of adults get their daily news from a digital device. This is an issue in an age where bad actors can create AI-generated disinformation almost instantly.

The Center for Countering Digital Hate, a British nonprofit, recently tested six of the leading AI voice cloning tools. Each produced fake audio snippets of high-profile politicians, with 80% of the tests generating a convincing clip.

Algorithmic Bias

AI systems can learn to make biased decisions if their training data contains skewed or inaccurate information or variables like gender, age, race or sexuality. Algorithms could act with prejudice if governments use this technology to accelerate vote counting or check voter eligibility.

Deepfakes

Divyendra Singh Jadoun is known as the “Indian Deepfaker” for his work on Bollywood clips and TV commercials. Recently, he claimed hundreds of Indian politicians sought his services ahead of the country’s elections, with 50% making unethical requests like defamation or deception. He says he denied them but doesn’t doubt others would accept their offers.

A deepfake can place a politician’s likeness over any body, face and voice to make it seem like they said or did something they never have. Politicians can — and have — used fake videos to make their opponents less likable. They even use AI on themselves to cast doubt on any real wrongdoings that might surface, giving them plausible deniability.

Fake Accounts

AI-powered social media bots spread misinformation and subconsciously influence voters by posting comments, sharing articles, and liking posts about certain politicians or upcoming elections.

Examples of AI Impacting Elections

AI’s effect on elections isn’t just hypothetical — it’s already happening. Of the 112 national elections in the United Kingdom between 2023 and 2024, 19 show signs of AI interference so far. When considering the evidence of AI-generated disinformation, that figure increases.

In Slovakia, days before the election — which was to determine who would lead the country — an audio clip of one of the leading candidates spread online. In it, he bragged about rigging the election. His opponent ended up defeating him.

In the United States, a former political consultant robocalled New Hampshire voters with an AI-generated voice meant to mimic President Biden, directing them not to vote. It reached thousands of people just ahead of the presidential primary. The man faces criminal and felony charges, along with a steep $6 million fine issued by the Federal Communications Commission.

Although the number of voters influenced in these situations remains unclear, one thing is certain — they were affected by AI interference. Going forward, cases like these aren’t going to be outliers. Instead, they may become as routine as attack ads and fake news posts.

AI’s Positive Impacts on Elections

It turns out AI might not be all bad — it still stands to positively impact the election.

Heightened Awareness

People aware of AI’s capabilities may be more likely to approach social media posts, news articles and viral clips with greater skepticism. Their newfound tendency to fact-check content can protect them from disinformation.

Election Administration

AI-powered systems could help administer elections, accelerating the time it takes to count votes, register voters or remind the general public of upcoming election dates. Considering these processes are typically so time-consuming, streamlining and automating them could be substantially beneficial.

Voter Education

Governments can offer AI tools to help voters stay informed. A machine learning model can pull up the latest news, fact-check social media posts, summarize news articles or identify AI-generated content.

AI-Generated Content Will Influence Elections

While rampant disinformation around election time isn’t new, it was obvious to those who could spot the telltale signs of Photoshop or traditional digital manipulation tactics. Now, generative models have muddied the waters. How can people tell what’s real anymore? What happens when politicians shrug off real scandals as some AI-generated hoax?

The question isn’t whether the AI’s positive impacts outweigh its negatives — it’s how to combat bad actors using this technology. Generative and machine-learning models are here to stay, so voters, governments and politicians should work together to figure out how to handle them. Swift, collaborative action may soon be the only thing ensuring fair elections.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Could Impact The 2024 Elections erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Dalith Steiger and Andy Fitze, Co-Founders of SwissCognitive, World-Leading AI Network – “Understanding the Weather Better – With Pangu-Weather, a 3D High-Resolution System for precise forecasting”

For centuries, the weather has fascinated mankind, as we have always been directly dependent on the elements. The awareness for the importance of weather and the preparation for various weather phenomena enters our minds once again when natural disasters occur. Recently, this has been the case with increasing frequency, and we must acknowledge that we are reliant on predictable weather conditions.

The importance of weather forecasting for business

For those earning their living in agriculture, this realization may sound almost laughable, as they constantly keep themselves informed on the forecast to take the best possible precautions for their livestock, crops and other produce. However, it is not only agriculture which is affected. Weather also has a direct impact on the energy industry, the construction industry, insurance companies and many more. But for all industries applies: Preparations are only as accurate and useful as the forecasts they are based on.

The accuracy of weather forecasts: Understanding complex systems

Unfortunately, weather forecasts are not as accurate as the global community wants them to be. This is exactly why the HUAWEI CLOUD AI team chose to focus on weather predictions. In the words of Dr. Tian Qi, Chief Scientist of HUAWEI CLOUD AI Field, an IEEE Fellow, and Academician of the International Eurasian Academy of Sciences: “Weather forecasting is one of the most important scenarios in the field of scientific computing because meteorological prediction is a very complex system, yet it is difficult to cover all aspects of mathematical and physical knowledge. […] (Huawei 2023). Let’s find out together, how existing forecasting systems can be strengthened.

Methods to predict weather

At present, the weather is predicted by numerical methods (NWP). Numerical methods are highly sensitive to the initial time point from which a model assumes because there are many uncertainties no matter which starting point is chosen. Therefore, large errors can be embedded in forecasts after only a few days of predicting. This is why meteorologists use ensemble forecasts. They include several starting points, which are based on slightly different conditions. Thus, the error rate is reduced (LMU – Faculty of Physics o.J.). Even if the error rate is lowered, it cannot be completely eliminated. According to Bi et al. (2023, S. 541), one reason for this is the required computational overhead of NWP which “limits the amount of ensemble members that may be included in a model, hence weakening the diversity and accuracy of probabilistic weather forecasts.” The solution for this and other challenges in weather forecasting is Pangu-Weather, an AI-based system.

Pangu-Weather, an AI-based system

Pangu-Weather trains deep networks for fast and accurate numerical weather forecasting (Bi et al. 2023, S. 537). The use of Pangu-Weather allows meteorologists for example to apply their expertise to the model in order to control noise and as a result improve ensemble forecast while simultaneously reducing costs (Bi et al. 2023, S. 537).

The concept of using AI-based systems in weather forecasting is not entirely new to the field. Deep learning methods assume that complex relationships between input and output date can be conceptualized by abundantly training a model with data sets without fully understanding the underlying physical procedures. Methods of deep learning were first applied to problems of precipitation forecasting and based on radar data or satellite data (Bi et al. 2023, S. 541). But according to Bi et al. (2023, S. 533–535) 3D models like Pangu-Weather herald a breakthrough in terms of speed and accuracy in numerical weather forecasting. This is because they train deep networks in a revolutionizing way.

How does Pangu-Weather work?

Like other models based on deep learning methods, Pangu- Weather captures relationships between two points in time. The Pangu-Weather model is trained on 39 years of global weather data (Bi et al. 2023, S. 537). New about 3D models is, that they can capture these relationships in three dimensions instead of only two. This additionally allows relationships between atmospheric states at different pressure levels to be detected. (Bi et al. 2023, S. 533) This results in Pangu-Weather being at times more accurate and also faster compared to two-dimensional models in forecasting the weather. A prime use case is, that Pangu-Weather is more than 10,000-times faster than the world’s best NWP, the operational IFS of ECMWF when it comes to producing deterministic forecast results on reanalysis data. In this case, it achieves greater accuracy as well (Bi et al. 2023, S. 535–537). These findings apply to sustained forecasts, but also to the prediction of extreme weather phenomena (Bi et al. 2023, 533-334).

Let’s read from Dr. Tian Qi again and find out, what the future might hold in terms of possibilities: “At present, Pangu-Weather mainly completes the work of the forecast system, and its main ability is to predict the evolution of atmospheric states. Our ultimate goal is to build next-generation weather forecasting framework using AI technologies to strengthen the existing forecasting systems (Huawei 2023).

3D AI-based models: A breakthrough in weather forecasting

For us at SwissCognitive, this is fantastic news. The breakthroughs in AI are happening even faster than they were predicted! One can even say that the potential of AI models in weather forecasting has been vastly underestimated. Therefore it has not been taken seriously by many scientists around the world according to Sven Titz a journalist at the renowned newspaper NZZ (2023). This also had to do with the fact that developments in this area usually tend to be incremental and progress is made rather slow. So, people were just not expecting results of such magnitude! The fact that scientists speak of a “possible paradigm shift” or “imminent breakthrough” (Titz 2023), can be considered a great success for the HUAWEI CLOUD AI team. However, the success story continues: It has been the first time that employees of a Chinese technology company are the sole authors of a Nature paper, according to the publication’s own index (Huawei 2023).

Pangu- Weather: A competitive model

In the estimate of SwissCognitive, Pangu- Weather shows that 3D weather forecasting models are relevant real-world application cases. It is a competitive model with vast potential. An example from May of 2023 illustrates this in an outstanding way. Remember, when Typhoon Mawar caught the world’s attention as the strongest tropical cyclone of the year thus far? According to the China Meteorological Administration, Pangu-Weather accurately predicted the trajectory of Typhoon Mawar five days before it changed course in the eastern waters of the islands of Taiwan (Huawei 2023). The difference in terms of error rate compared to the IFS of ECMWF is impressively visualized across the data points, as shown in the figure below:

Source: (Bi et al. 2023)

Pangu- Weather: The future of weather forecasting

Not only the model’s accuracy is impressive, but also the way the findings are distributed. Commenting on the significance and quality of the research by HUAWEI CLOUD, the academic reviewers from Nature explained that not only is Pangu-Weather very easy to download and run, but that it is executed quickly on even a desktop computer. “This means that anyone in the meteorological community can now run and test these models to their hearts’ desire. What a great opportunity for the community to explore how well the model predicts specific phenomena. That’s going to help with progress in the field.” Another reviewer noted that “the results themselves are a significant step beyond previous results. This work will, in my opinion, make people reevaluate what forecasting models might look like in the future” (Huawei 2023).

It is precisely this open approach, making insights available to all and providing the necessary food for thought on how to rethink the future in a field, that determines the success of an approach in SwissCognitive’s view. This is impressively demonstrated by Pangu- Weather. This case is clear proof that when researched and used with ethical standards considered, AI can lead to the improvement of living conditions and, most notably, the economic upliftment of global communities.

References

Bi, Kaifeng; Xie, Lingxi; Zhang, Hengheng; Chen, Xin; Gu, Xiaotao; Tian, Qi (2023): Accurate medium-range global weather forecasting with 3D neural networks. In: Nature 619 (7970), S. 533–538. DOI: 10.1038/s41586-023-06185-3.

Huawei (2023): Prestigious science journal Nature publishes paper about Pangu Weather AI Model authored by HUAWEI CLOUD researchers. Meteorological model shows strong performance when compared with traditional prediction in speed and accuracy. Online verfügbar unter https://www.huawei.com/en/news/2023/7/pangu-ai-model-nature-publish, zuletzt aktualisiert am 06.07.2023, zuletzt geprüft am 28.07.2023.

LMU – Faculty of Physics (o.J.): Ensemble Vorhersagen und Vorhersagbarkeit. Online verfügbar unter https://www.meteo.physik.uni-muenchen.de/DokuWiki/doku.php?id=arbeitsgruppen:ensemble_vorhersagen, zuletzt geprüft am 22.07.2023.

Titz, Sven (2023): Umsturz bei der Wettervorhersage: Modelle mit künstlicher Intelligenz holen die herkömmlichen Vorhersagemodelle ein 2023, 05.07.2023. Online verfügbar unter https://www.nzz.ch/wissenschaft/wettervorhersage-mit-kuenstlicher-intelligenz-wird-konkurrenzfaehig-ld.1745565?reduced=true, zuletzt geprüft am 28.07.2023.

About the Authors:

Dalith Steiger is a serial entrepreneur and a global AI Strategist and Thought-Leader. She belongs to the top pioneering women in cognitive technologies and one of the top digital shapers and leading voices in the global AI ecosystem. Dalith was featured in Onalytica’s Who’s Who in AI report as a global key opinion leader. She was born in Israel, grew up in Switzerland, and studied mathematics and business informatics at the University of Zurich. With Andy Fitze she co-founded the award-winning AI start-up SwissCognitive, and the CognitiveValley Foundation. Dalith is a global AI-strategy advisor and speaker, sharing her extensive knowledge and experience in the field of AI around the world. She sits in several boards and juries, is leading the Swiss IT Leadership Forum, advises various companies in their AI journey, mentors young women and girls in tech, and teaches AI & Machine Learning in a CAS module at the Applied University of Luzern. Besides her drive for cognitive technologies, she is also a loving mother of two young women, a passionate mountain biker and a big fan of high-heel shoes.

Andy Fitze is a serial entrepreneur, digital cognitive strategist, AI influencer, and top global AI and digital transformation advisor for start-ups and enterprise boards. Andy was featured in Onalytica’s Who’s Who in AI report October 2021 and is one of the top digital shapers. With Dalith Steiger he co-founded the award-winning start-up SwissCognitive, and the CognitiveValley Foundation. He is president of the Swiss IT Leadership Forum, member of the Board of Directors of SwissICT. Andy sits in several Boards of Directors of various companies. Andy is a lecturer and Member of the Strategic Advisory Board at Bern University of Applied Sciences and is a lecturer at the ETH for CAS Architecture Digitization. Previously Andy worked as Group CIO of RUAG, and at PostFinance he was responsible for IT governance. He holds a degree in electrical engineering (HTL), an Executive MBA from the University of St. Gallen, and received the Swiss CIO Award for Best IT Manager in Switzerland in 2015.  To share his 30 years of extensive knowledge and experience, Andy is often seen on global stages. He is also a passionate skipper on the oceans – providing him with a great balance for head and soul.

Photo credit: Andy Fitze

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Der Beitrag Here’s how opinions on the impact of AI differ around the world erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SARS-COV-2, also known as coronavirus has shocked  the world with an outbreak that is at least 10 times more lethal than influenza, and much more communicable. Our knowledge of the virus and its potential risk factors is still limited, even if growing day by day. Some states are struggling to facilitate the population’s health needs forcing health providers to prioritize patients most urgently need in care, also called triage. On the other hand nations such as Taiwan, managed to avoid mass casualty scenarios by detecting infected individuals early through deployment of automatic, digital diagnostics and tracking through mobile devices of infected individuals. Many countries went on to develop such triage systems, however, limited SARS-COV-2 clinical data restricts the systems to rule based solutions mostly derived from information of the general SARS-COV family.

A team of scientists at IBM is set to address this limitation by developing a solution informed by our experience in building leading medical triage systems with Medgate https://www.cnnmoney.ch/shows/big-picture/videos/ibm-and-medgate-andy-fischer-creating-chatbot-diagnose-your-aches-and and real-world medical data from IBM’s explorys database https://www.ibm.com/watson-health/about/explorys, currently counting around 18’000 SARS-COV-2 infected individuals.

Accurate risk assesments are crucial for resource priorization. We hope to help government entities better predict the capacity of ICUs, number of beds or critical instruments such as ventilators by using machine learning models and real-wold-clinical data.

Beyond medical triage IBM is accelerating drug discovery by providing AI tools and computing resources in the hope of finding a vaccine to end this pandemic. https://venturebeat.com/2020/04/13/what-privacy-preserving-coronavirus-tracing-apps-need-to-succeed/  

Copyright by Alexander Büsser, IBM

Alexander Büsser

Manging Data Science Consultant | Data Science Lead
IBM

Virtual Global AI Conference
Co-Hosted by AI Capital & SwissCognitive

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In this profile series, we interview AI innovators on the front-lines – those who have dedicated their life’s work to improving the human condition through technology advancements. This time, meet Damian Borth.

Damian Borth, chair in the Artificial Intelligence & Machine Learning department at the University of St. Gallen (HSG) in Switzerland, and past director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI). He is also a founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian’s background is in research where he focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams.

Damian talks about his realization in deep learning and shares why integrating his work with deep learning is an important part to help prevent future natural disasters.

What has your journey been like in deep learning? How did you end up at DFKI?

I spent two years in Taiwan, went to the University of Kaiserslautern, Germany for my PhD while having a stopover at Columbia University, and did my post-doctoral at UC Berkeley and the International Computer Science Institute in Berkeley. In Berkeley, I spent my time on deep learning network architectures and got really into it. That was a really great time. After my stay in the US, I went back to the DFKI to found the Deep Learning Competence Center. Now, I am helping the University of St. Gallen to establish a lab in Artificial Intelligence and Machine Learning and hopefully soon the buildup of a new computer science faculty.

What made you become a DL believer?

I was actually a “non-believer” in deep learning, until I started my post-doc at UC Berkeley. It’s very hard to train a neural network efficiently without sufficient data and at the time that I started by PhD, neural networks were not trusted as the go-to method. Instead, we looked at support vector machines for classification. But then AlexNet came along and showed neural networks do, in fact, work consistently. Then people began to download the Caffe framework, use it, improve it, and outperform other architectures.

What did you do in Berkeley?

I continued the work we have started at Columbia in sentiment analysis for pictures. It could classify objects like e.g. animals such as a dog or a cat. We attached adjectives to the noun and made the analysis differentiate between a scary dog or a cute dog. The vocabulary was roughly 2,000 adjectives noun pairs (ANP). By conditioning the noun with an adjective, we were able to move a very objective judgement to a subjective assessment. Doing so we were able to derive a link from this mid-level representation to a higher level of sentiment representation. The positive image of a cute dog or a laughing baby could flip to a negative sentiment when it saw a dark street or a bloody accident. This mid-level representation proved to be also very successful beyond sentiment analysis and was applied to aesthetics and emotion detection. It created a bridge between the objective world and the subjective world of visual content. In Berkeley I was also part of the team creating the YFCC100m dataset the largest curated image dataset at that time. Having such a dataset with 100 million creative common images and videos from Flickr helps if you want to train a very deep neural network architecture.

Did you continue your sentiment analysis work with DFKI?

We call it Multimedia Opinion Mining (MOM), because we want it to consider different modalities such as video and audio. Currently we’re extending deep learning architectures towards multi-model signal processing. The goal is to take different modalities as an input and move them all into one architecture. If you have a self-driving car, you’re not only detecting the visual signal of the camera, but also the radar data from an audio signal and others in one network. Working with different architectures such as late fusion, infusion, and in some work on early fusion demonstrated to improve system performance. In particular early fusion has been successfully used in satellite image analysis for remote sensing where a lot of information is multi-model. This is really a game changer for disaster recovery. Using this information, we can help with flooding and wildfires disasters where emergency response teams on the ground can get immediate information from satellites to find where the fire is, what the flooding looks like, or how many buildings can be affected and is it accessible by road or by boat.

Can you elaborate on the disaster response case? How can your work help these first responders?

We were analyzing data collected from a wildfire case at Fort McMurray. When we looked at the data, initially we saw that the area around the fire, in particular the vegetation and already burned area was a strong indicator for the direction of the fire spread. Once the wind changed the fire changed its course as well which caused more damage. This analysis would have predicted that change of how the fire develops much earlier. Such information is very valuable to the first responders and their work on the ground. Another case we’re currently working on is with flooding. We started a benchmark challenge to foster collaboration to build up a community with MediaEval Satellite Task. In the first year 16 teams from around the world have been participating. The teams submit their neural networks results and we compare the performance on the test data set to figure out which one provides the best predictions. This way we know very quickly which approaches work and which not.

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Telecom Asia , a press medium that specializes in Asian telecom industry, recently reported that Asia’s smart home market will grow to US$115 billion by 2030, accounting for 30% of the global share, citing a report by global management consulting firm AT Kearney.

Smart homes on the rise

Smart home is a system that allows you to control and monitor different devices in the home including the heating, lighting, security, and entertainment, automatically and sometimes remotely via the Internet. The report, “The Battle for the Smart Home: Open to All,” forecasted that the growth will be driven mainly by China and Japan, as well as highly-connected countries such as Singapore, South Korea and Taiwan. Japan is leading the smart home market. “Japan, which is already among the top five global markets in terms of smart home penetration, will see continued growth driven by an ageing population enticing households to install health and wellness solutions,” said Nikolai Dobberstein, partner and Asia-Pacific head of communications for media and technology at AT Kearney. “The opportunity in China is great with a phenomenal number of households seeing increased incomes and a strong local manufacturing and technology ecosystem,” he added. Singapore, South Korea, and Taiwan are all expected to have a high penetration of smart homes due to the large proportion of high-income households and the data connectivity in these countries.

What is driving the change?

According to the report, there are several major changes in Asia that are accelerating the expansion of the Asian smart home market. The first is the advancements in technology and smartphone functions along with the addition of big data and artificial intelligence (AI) are rapidly improving the convenience of smartphone application. Second is the expanding interoperability among products from different manufactures. Advancement of application programming interfaces (APIs) and efforts to standardize communication of applications are increasing rapidly. In addition, availability of related product has expanded and prices have become cheaper. At home, automated products are already being used in almost all respects and nearly 80% of them are available in the market in “smart” form. Also, cost reduction of key technology components are making smart home applications more cheaper […]

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