A Record Year of AI Investments and Rising Expectation – SwissCognitive AI Investment Radar

The AI investment landscape continues to reshape industries at a global scale, with both emerging startups and tech giants driving growth in this transformative sector. This week’s AI Investment Radar as every week, we highlight for you the most significant developments in AI funding. Startung from Zhipu AI’s state-backed investment boost in China to Celestica’s upgrade amid strong Ethernet-related AI growth.

Despite investor concerns about profitability timelines, the drive toward generative AI remains strong, with companies like Walliance embedding AI into real estate platforms and Claro Colombia preparing its infrastructure for AI applications with a $200M fiber network investment.

Even as some segments of the tech sector experience slowdowns, all-in-all AI investments continue to surge. AI startups have raised $48.4 billion so far this year, surpassing 2023’s totals, hitting its record and experts predict that China’s AI industry could see $1.4 trillion in investments over the next six years. As AI continues to evolve, balancing opportunity with risk management is becoming essential for companies across the globe.

Join us, once again, and explore with us these key shifts in AI funding and innovation.

Previous SwissCognitive AI Radar: Investment Horizons – SwissCognitive AI Investment Radar.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag A Record Year of AI Investments and Rising Expectation – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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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AI Market Updates and Investor Insights – SwissCognitive AI Investment Radar

Welcome to the SwissCognitive AI Investment Radar, your go-to source for the latest updates on AI investments worldwide.

Our journey begins in Malaysia, where ByteDance is set to establish a $2.1 billion AI hub, marking a significant expansion in Southeast Asia. Despite a surge in the S&P 500, investors are becoming more selective with AI stocks, reflecting a nuanced market sentiment. IBM’s AI advancements are poised to boost its market share, earning a “buy” rating from Goldman Sachs.

Masayoshi Son of SoftBank reveals ambitious plans for AI, describing previous investments as merely a “warm-up” for his goal of creating artificial superintelligence. NATO’s $1 billion fund is making its first moves in AI and robotics, emphasizing defense innovation.

Navigating the complex landscape of AI ethics and regulation is crucial for investors, as transparency and explainability become paramount. Nvidia’s stock valuation sparks debate, while Baader Bank prepares to launch Europe’s first AI-enhanced ETF.

Oracle’s $1 billion commitment to AI and cloud services in Spain, alongside AI’s transformative potential in emerging markets, showcases the technology’s global impact. Japan’s AI-friendly regulations aim to attract top talent, and AI is revolutionizing real estate from investor opportunities to tenant experiences.

Samsung hints at entering the GPU market, potentially challenging industry giants, while companies worldwide grapple with AI strategy and implementation for 2024.

Join us as we delve into these compelling narratives and explore the dynamic world of AI investments.

Previous SwissCognitive AI Investments Radar: Transformative AI Investments and Market Leaders.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI Market Updates and Investor Insights – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Article: Ayman Dayekh – “AI Success in GCC Countries: A Framework for Digital Transformation”

The more DIGITAL a company is, the higher the likelihood that their digital transformation–embedded AI projects will succeed.

In the high-stakes arena of digital transformation, Artificial Intelligence (AI) stands as both a herald of innovation and a beacon of caution. The narrative of AI’s potential in driving business transformation is well-documented, yet the road is littered with tales of both remarkable achievements and cautionary tales of ambition meeting reality. The academic discourse around these outcomes has given rise to frameworks aimed at guiding successful AI integration, among which the DIGITAL framework emerges as a beacon for managerial action in the tumultuous seas of digital transformation (Brock & Wangenheim, 2019).

Understanding the DIGITAL Framework

At its core, the DIGITAL framework identifies seven critical areas for managerial action and implementation in AI-driven digital transformation projects:

• Data: The lifeblood of AI, where relevance, accuracy, and timeliness of data dictate the success of analytical problem-solving.
• Intelligence: extends beyond operational efficiency, encapsulating the strategic integration of AI into business models, backed by managerial and technical prowess.
• Grounded: Anchoring projects in reality, ensuring alignment with current business needs, and mapping out a clear implementation roadmap.
• Integral: Digitalizing core business processes and ensuring seamless integration of technology into the organizational fabric.
• Teaming: Identifying and collaborating with strategic partners to bolster the ecosystem and enhance the project’s success.
• Agility: Maintaining organizational flexibility to swiftly adapt to market changes and emerging challenges.
• Leadership: Cultivating a leadership mindset that embraces change, actively supports digital initiatives, and communicates progress effectively.

The GCC Context: A Tale of AI Ambition and Reality

The Gulf Cooperation Council (GCC) countries[1], often celebrated for their bold strides in AI adoption, present a unique backdrop for the DIGITAL framework’s application. According to the 2023 Government AI Readiness Index, several GCC countries notably outperform global averages[2], signaling a regional commitment to embracing AI (Government AI Readiness Index, 2023). The first ministry of Artificial Intelligence (AI was established in October 2017 in United Arab Emirates (UAE). According to McKinsey research, Artificial intelligence (AI) has the potential to deliver real value in the Middle East’s Gulf Cooperation Council (GCC) countries—as much as $150 billion (Berglind, Fadia, & Isherwood, 2022).

However, this enthusiasm is not immune to the pitfalls of digital transformation, as evidenced by high-profile AI project challenges. Real-life examples, such as the ambitious yet ultimately recalibrated AI initiatives in projects across the region, highlight the critical need for a structured approach to AI integration. These examples underscore the importance of not just ambition but strategic, well-grounded planning in leveraging AI’s potential.

Evolving the DIGITAL Framework for the GCC

Within the dynamic and diverse context of the Gulf Cooperation Council (GCC) countries, both existing elements of the DIGITAL framework and additional considerations assume enhanced significance due to the unique cultural, economic, and technological landscapes of the region.

Let’s explore some of these elements:

1. Data sovereignty and localization are crucial in the GCC due to stringent data protection regulations and the emphasis on national security. Ensuring data is stored and processed within national borders aligns with regulatory requirements and builds trust in digital systems.
2. The integration of cultural intelligence into AI systems is vital to ensure that technology solutions are culturally appropriate and sensitive. This is particularly important in a region with deep cultural and religious roots, ensuring technology adoption is smooth and respectful of local norms.
3. Projects need to be grounded in the economic and social realities of the GCC, where there is a strong push towards diversification away from oil-dependency. This means AI and digital projects should support broader national visions, which focus on sustainable development, innovation, and knowledge-based economies.
4. Public-private partnerships (PPPs) gain enhanced significance in the GCC, where the government often plays a pivotal role in economic initiatives. Collaboration between the public and private sectors can accelerate the adoption of AI and digital transformation, leveraging the strengths of both to achieve national development goals.
5. Incorporating Islamic ethical considerations into AI development and deployment is significant for the GCC. Ensuring that AI applications comply with Islamic ethical standards can enhance societal acceptance and support.
6. The GCC is home to a diverse population, including a significant number of expatriates. AI and digital solutions must be designed to cater to a wide range of cultural backgrounds and languages, ensuring inclusivity and accessibility.
7. As AI and automation change job landscapes, focusing on education, reskilling, and upskilling programs becomes crucial. The GCC’s investment in education technology (EdTech) and vocational training can support a smooth transition for the workforce into the digital economy.

In summary, the unique context of the GCC not only amplifies certain aspects of the DIGITAL framework but also introduces additional considerations that are critical for the successful implementation of AI projects in the context of Digital Transformation. These adaptations reflect the region’s ambitions, cultural values, and socio-economic goals, ensuring that digital transformation efforts are both effective and aligned with the GCC’s vision for the future.

Conclusion: The Imperative for an Adapted Framework

The necessity of a tailored approach to AI integration in the GCC is highlighted by the region’s ambitious AI roadmap juxtaposed against the global backdrop of AI project outcomes. The disparity between the envisioned impact of AI projects and their actual realization calls for a structured framework that not only accommodates but thrives on the unique challenges and opportunities presented by the GCC market.

This discourse invites practitionairs, professionals, and scholars alike to engage in a critical evaluation of the DIGITAL framework’s applicability in the GCC. Through academic rigor and professional insights, this research aims to contribute a contextualized adaptation of the framework, ensuring that the GCC’s AI ambitions are met with success.

Participate in the Survey Now

Your experience, insights, and participation in this survey will not only enrich our understanding but also contribute to shaping a framework that is robust, adaptable, and reflective of the GCC’s unique digital landscape.

Participate in the Survey Now

Your expertise can help guide the future of AI integration in the GCC, ensuring that the region’s AI initiatives in the context of digital transformation are both ambitious and achievable.

References

Brock, J. K.-U., & Wangenheim, F. v. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), 110-134.

(2023). Government AI Readiness Index. Oxford Insights.

Berglind, N., Fadia, A., & Isherwood, T. (2022). The potential value of AI—and how governments could look to capture it. McKinsey.

[1] The Cooperation Council for the Arab States of the Gulf, also known as the Gulf Cooperation Council, is a regional, intergovernmental, political, and economic union comprising Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

[2] UAE (18), KSA (29), Qatar (34), Oman (50), Bahrain (56), Kuwait (69)

About the Author:

Ayman Dayekh is the founder and Managing Director of AZRE Consulting LLC and Board President of American Institute of Business & Technology (AIBT). With 20+ years of experience across multiple industries, he has experienced tremendous success, and gained some valuable skills and experiences along the way. Throughout those years, Ayman was accountable for delivering a wide portfolio of projects in government, real estate, freight management, education, healthcare, agriculture, manufacturing, and banking.

Der Beitrag AI Success in GCC Countries: A Framework for Digital Transformation erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Ethan Millar – “Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail?”

Gaining deep insight with artificial intelligence tools is the trend for businesses to operate. Both small businesses and large enterprises are compelled to use AI technologies. AI chatbots communicate with more complex sessions. Companies that have already completed digital transformation should be moving towards a new generation of chatbots. SMEs can also take advantage of this new trend.

The new generation of AI chatbots comes with complex neural connections to have conversations. It is scalable as developers use deep learning tools. It eventually helps enterprises to bankroll AI-based intents with a high-tech approach. Unless the developer knows the pros, cons, and effects of deep learning tools on training chatbots, the very purpose of accurate deliverables gets lost in translation.

This 10-minute reading material is a virtual assistant for the developer to understand how deep learning tools maximize their potential. Moreover, it is also aimed at leadership with companies to understand why a bot-building project has met with failure. How can be brought back to work?

As both are inter-connected, this post focuses on IT developers in large enterprises and lean departments of small businesses.

Lessons to learn from the developer’s perspective

Have you just met with a failure in an AI Chatbot-based project, recently? It is not success that teaches. Failure adds a valued experience while dealing with different approaches to creating chatbots. Many companies fail initially in their efforts. It becomes the ideal base for understanding how a CRM developer can help an enterprise monetize through deep learning tools.

Three things count:

1. Deep learning does not involve or solve everything for business solutions. Some applications can do without it.
2. All enterprises cannot deal with specialized tools unless they have the requirement.
3. All developer tools are not meant for monetizing.

If an enterprise uses only deep learning tools, then only about 1/3^rd of its potential will be realized and the rest will remain untapped. The developer needs an overall understanding to tap it.

Two systems for learning

An IT team of a company) will need to research AI Chatbots and their specific requirements. It will avoid aberrations related to conversations with humans and machines. Earlier virtual assistants like Cortana, Siri, and Alexa set the bar for new bots. They still work with smartphones, appliances, and other home-based devices. They work on 2 systems – Supervised learning and unsupervised learning which require natural language processing capabilities. Since 2020, 85% of customers have been dealing with chatbots by making inquiries. The human connections have reduced.

Supervised Learning

The software is developed after getting data from real-world requests. Correlations are established between ‘tags’ and ‘user-intents’ which are marked for learning and engaging the customer. In such a case, deep learning tools achieve a high level of accuracy. Specialized tools are developed for this purpose. The only hitch here is if the data collected is insufficient or not suitable then the functionality and success are trapped.

Unsupervised learning

Again, in this case, too, a good database is required to understand the customer intent of the chatbot. When it is not supervised, it works independently. There is no need for human supervision while it functions nor does it require specific tags to prompt it to work.

The failure rate increases if the database does not provide a wide range of variables. The quality is not good enough for it to be released in the public domain. Even if it does come out, it will have limited success. The data volumes required are large for deep learning tools to be effective. And, it goes without saying that poor data does not give the required results and also affects business.

Chatbots will continue to grow

Despite the failure rate, AI chatbots will grow and many companies experiment with their capabilities. Consumers are already hooked on them and enjoy the services of such virtual assistants. They find an opportunity to add value to their routine tasks. Every public company wants to reduce customer care efforts, and this is a solution that has promise in the real world.

The only reason why it fails is due to the data required for tags and the user intent in each company is diverse. In some cases, it is limited to a certain extent. Hence, deep learning tools need careful deployment by the developer. They require a well-structured database and good examples for training the system. Getting advanced systems to work requires a good degree of inference latency, interpretability, and reproducibility to understand the data and train the program.

Developer’s skills are tested

A complex toolset may not be the answer for a training program to converse. It took years and several failed tests for Siri or Alexa to reach the stage where they are now. E-commerce giants using machine learning tools have survived as they have a constant flow of data to test and train. In the final analysis, a complete overview of components is required before they can be channeled and ready for public use or limited enterprise utility.

If developers choose hybrid systems, advanced NLP, and AI algorithms and do not rely on the 2 main systems there are bright chances of creating the right chatbot.

Now we turn our focus on the functionality and advantages of AI bots for real-time business needs.

AI chatbots are the new Jeeves

Your wish is my command!

Are you still confused about the diverse functions of AI deep learning? chatbots? Here is a simple description of the new automated ‘Jeeves’ in the corporate world. They are computer programs that communicate with the user as messengers. Some are advanced enough to handle instructions in the absence of the programmer.

It may sound like sci-fi but it is gaining traction as it is a time saver and do various tasks efficiently for different departments. For small businesses, it reduces overheads while multi-tasking.

How can it be deployed?

Most people are used to texting messages to each other as their main form of communication on social media or FB messenger even for work. This is the way even customer care is handled worldwide. Now chatbots are designed to take over.

Once you are familiar with deep learning and how it influences business processes the possibilities of its uses are unlimited. For example, they can be embedded in websites to answer 24×7 any customer queries. It is a live chat and once the user signs up on the website, the chatbot is functional.

Where is it most influential and popular? In businesses where customer services need to be handled with care. Today, pharma, real estate, and financial companies also use AI chatbots successfully.

Smart business advantage

Ai Chatbots are more common than you think. Google Assistant, Apple’s Siri and Amazon’s Alexa are all chatbots serving various functions. They are not only useful but are extremely popular. A smart chatbot increases your company’s visibility thereby boosting sales.

Earlier it was possible only for large companies to invest in AI deep learning. Now more avenues have opened up for small businesses to take advantage of this feature. Chatbots can be integrated into many areas of a company’s business. Chatbots use natural language processing in combination with machine learning to respond accurately to a customer’s requests.

They have been created to recognize an inquiry and provide an appropriate answer. With advances in the tool and features, they record previous questions and answers. They are geared to offer a personal experience to the user. As a service, it upgrades the company’s overall profile to settle disputes and provide customer satisfaction.

Ideal social media tools

AI chatbots have proven to be excellent social media marketing tools. Their efficiency is only set to increase in the coming years. AI provides personalized, real-time content targeting that produces 20 percent more sales opportunities. It can also be utilized for behavioral targeting methods for specific buyers. This is a sleek advantage for small companies that cannot hire expensive marketing managers.

Using this technology data and statistics prove to be useful to make decisions through predictive analysis. Machine learning can be applied in marketing to optimize for successful campaigns. Automaton reduces time gaps for performances and many sectors are turning towards bots to increase productivity and interactions.

Evolving innovation

With new developments, the way conversations are perceived is changing. This platform has already introduced voice bots and crypto tokens, messengers for blockchain.  Companies like Google, Apple, and Amazon are already developing new conversational platforms for better customer interaction. Perhaps this evolution will help solutions to be more forthcoming.

As 2024 is underway, the use of AI chatbots is no longer a luxury. It has become essential. With ChatGPT, Gemini, Bing, and Claude making an influential impact, it is hard to ignore them for business operations. Leaders require content generation and customization to streamline. AI bots can reason with limited inaccuracies with the user.

Closing thoughts

If you have failed once, now with experience take advantage of the new ‘Jeeves’ and its sophisticated commands. It’s time your developers take a fresh take on creating the right chatbot and reduce operational challenges.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.

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Here is the million-dollar question!
What does weather, email marketing, art, science, the real estate industry, sport, and geochemistry have in common?

AI

Supposedly, it doesn’t come as a surprise.
Let us take you into the world of AI again, and send you the latest cross-industry news, that we think, you should not miss.

These are very interesting times, and there’s so much more to come! Tell us how you use and perceive AI HERE, and we share your story with our global community. We are looking forward to reading from you.

Sunny regards,

The Team of SwissCognitive

Der Beitrag Understanding the Weather Better erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

With another week and even a month behind us, we collected a great selection of news for you from the AI ecosystem. Scroll down to find out our the joint mission of Global AI Ambassadors, how AI & ML are transforming the real estate industry, how AI is shaking up dating apps, and some food for thought – how AI is reducing waste in restaurants.

Happy reading and sharing.

Kind regards,

The Team of SwissCognitive

Der Beitrag Featured News | Collaboration for AI-Propelled Innovation | AI in Real Estate | Dating Apps | Reducing Food Waste erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Harvard Graduate School of Design Executive Education – “Beyond ChatGPT: How Artificial Intelligence and Machine Learning are Transforming the Real Estate Industry” – regarding the A.I., Machine Learning, and the Built Environment: Fundamentals & Proptech Applications three-day intensive online course.

ChatGPT has taken the world by storm, accumulating users faster than any consumer application. But while ChatGPT has dominated the headlines, real estate has already been transformed by AI and machine learning.

In residential real estate, proptech giants such as Zillow and Redfin are powered by AI. First, they used algorithmic valuation models to generate values for every home in the US. Then, they allowed homeowners to generate floorplans to help them sell their homes. Finally, they moved on to “iBuying”, where they used algorithms to attempt to “flip” houses for a profit — buying low and selling high after some cosmetic renovations. Their successes and failures in these AI-driven initiatives have been well-documented, and their stock prices have risen and fallen in lockstep.

Meanwhile, in multifamily real estate, major property owners now use AI-powered chatbots to speak with tenants directly to support the sales process and ensure satisfaction by receiving and vetting maintenance requests. These chatbots work round the clock – so if a light bulb breaks at 2 am on a Friday, that repair request is received and prioritized accordingly – reducing ownership costs. Owners also use AI-powered tools such as RealPage’s YieldStar to maximize revenue by setting rental unit prices based not on historical data and gut instinct but real-time insights from lease transaction data for more than 13 million units across the country. This product may have contributed to unprecedented rapid increases in US rents in 2022.

AI is already an ever-present force in real estate – but its full transformation has yet to happen. Our Lecturer in Architectural Technology Jose L. García del Castillo y López posits that Machine Learning is poised to transform architectural design, altering the role of the architect forever. He even states that “the AI revolution may have an even deeper impact in unraveling the untapped creative potential of an even larger community of end users by harnessing one of the greatest affordances of neural networks: their capacity to be trained without writing a computer code.” In fact, he believes that “the predictive and suggestive capacities of AI agents as creative companions may result in a global augmentation of our intellectual capacities.”

This March, Jose Luis Garcia del Castillo y Lopez teaches A.I., Machine Learning, and the Built Environment: Fundamentals & Proptech Applications. The three-day online course helps you to gain the knowledge and practical know-how to assess and harness AI for use in your everyday work – and better understand the transformation already underway in real estate.

Are you interested in learning first-hand advice on how to harness AI in the Real Estate Industry?

Intensive 3-day course offered by Harvard Graduate School of Design.
Application deadline: 29 March 2023!

Visit here for more details.

About the Author:

The mission of Executive Education at the Harvard Graduate School of Design is to educate leaders to build smarter, healthier, greener, more beautiful, and overall better cities. Their work spans design, architecture, real estate, technology, finance, city planning, leadership, economic development, and all the key sectors that shape life in cities present and future.

Der Beitrag Beyond ChatGPT: How Artificial Intelligence and Machine Learning are Transforming the Real Estate Industry erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

With the week going by, we collected a new bouquet of news for you from the cross-industry and cross-domain global AI ecosystem. Scroll down to see how AI will influence real estate, how this technology can help businesses in demand forecasting, why airlines need to use AI to survive recession, and more.

Happy reading and sharing.

Kind regards,

The Team of SwissCognitive

Der Beitrag Featured News | AI in Real Estate | Accurate Demand Forecasting with AI | Airlines to use AI to Survive Recession erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Gregg Barrett, Chief Executive Officer, Cirrus – “A meaningful use of Artificial Intelligence – In Africa – AI Trajectory 2023+”

It brings to mind the Financial Modelers’ Manifesto. The manifesto was a proposal calling for greater fiscal and risk management responsibilities in the wake of the US housing market collapse and the subsequent financial crisis of 2008-2009. It was created by two financial engineers Paul Wilmott and Emanuel Derman. Both Paul and Emanuel understood that models are not reality. A model describes the likeness of something. To describe the likeness of something is an approximation. It is not that something exactly. AI as it exists today is a model of something. Models contain an error – that needs to be accounted for.

That said over the last decade our capacity to model vast amounts of data with better function approximators has improved significantly. It is these improvements in machine learning that we now term AI. Critically, the proper application of these capabilities has the potential to save lives and impact society in positive ways that are not artefacts of marketing spin.

Societal impact in an African context

Governance and governing institutions in Africa are generally not of the same calibre as those found in the Western world, and law enforcement institutions do not have the same level of capabilities, specifically technological capabilities. With limited capabilities of law enforcement undesirable activities can and do proliferate. All too often societies in Africa are heavily reliant on the work of NGO’s to fill the void. Yet these NGO’s themselves are no panacea as the requisite technological platforms are in most instances beyond their reach impeding the effectiveness of their operations.

As an example, a leading anti-human trafficking organisation operating at one of South Africa’s busiest airports, while intercepting trafficked persons uncovered systemic corruption and organised crime involved in the trafficking networks. Their airport security clearance was revoked preventing them from operating in the airport. Their cost per intercept is now around 300 USD which needs to be reduced to around 50 USD to be in-line with funding – requiring the adoption of AI methods which can be scaled without a resulting scaling in cost and headcount. While realising the need for AI in this context is easy, bringing it to pass is anything but. Indeed, the challenges of the undertaking are more than an excuse to do nothing, but to do nothing is costing lives – an unacceptable situation given that it is solvable

Anti-human trafficking

Working with data management and AI platform providers there is now an undertaking to place anti-human trafficking organisations in Africa onto the same technological footing as their Western counterparts. In parallel are efforts to secure anonymous mobility data from the major telcos to help in identifying choke points for placing new stations with human monitors to intercept trafficked persons. Following the implementation of the requisite technology platform, the strategy is to start work with the South African banking sector on a Financial Intelligence Unit. The purpose of this unit will be to train financial services and anti-money laundering staff on a new platform that allows these professionals to share knowledge, information, and best practices in real-time. Further, this unit is intended to help survivors of human trafficking get access to banking services that they would not otherwise qualify for because of poor credit and other issues related to their trafficking experience. Relatedly, there is work to significantly bolster the data science / machine learning of the anti-human trafficking organisations by establishing collaborations with academia and industry, and the formation of a data hub to provide key data to researchers, academics, law enforcement officers and others seeking to deepen knowledge and understanding in the fight against human trafficking.

Beyond the anti-human trafficking the intention is to extend the data management and AI platform to other areas, including: water and environmental resource management; wildlife trafficking and environmental crimes; arms trafficking and conflict finance; exploitation of natural resources (illegal mining, logging and deforestation, illegal and unregulated fishing). An undertaking to produce synthetic rhino horn that is contaminated to cause issues like indigestion when consumed is also in the early stages of formation. The data management and AI platform applicable to anti-human trafficking are needed to provide insights into the illicit supply networks into which the synthetic contaminated product will be introduced.

Menstrual health

Menstrual health and related areas have not been effectively addressed in developing markets, and there is a need to unite the academic / research community, medical practitioners, NGO’s and enterprises with women in the developing world to deliver effective outcomes.

The groundwork is now being laid for the development of a women’s health application under a public benefit organisation that is freely available to all women. Importantly, the undertaking is architected from the start to enable data generation and where the data generated is properly governed and used to support research and guide interventions.

Data management and AI platforms now provide us with the capabilities to better address data and intelligence asymmetries where diverse collaborative efforts can be united under a single application. Some high-level targets include: development of a cost-effective test for endometriosis; cost-effective hormonal cycle tracking; provision of sanitary vending in corporate workplaces; encouraging the use of and provision of menstrual cups and applicators; provision of expert help to those struggling with menstrual health and related issues (at no cost) like PMS, PMDD, fertility etc.

It is only through cross-sector collaborations bringing together even broader coalitions to facilitate analysis of data, that we can have a bigger impact on menstrual health. Helping to inform stakeholders of who needs help and what help they need, resulting in more effective resources allocation. This contrasts with blindly giving funding to stakeholders who in turn purchase more resources. The importance of these collaborations cannot be overstated when addressing social issues, rather than a landscape in which organizations each carve out their own territory.

Philanthropic engineering

The implementation of the social impact strategy requires the establishment of a philanthropic engineering operation funded through revenues from commercial engagement with industry.  The philanthropic engineering operation is intimately connected to the commercial operation, and develops deep, hands-on, and often long-term relationships with NGOs and social sector organisations. This includes regularly sending engineering teams to work with these organisations and collaboration on developing solutions. The on-site engineering helps to develop foundational understanding of the respective fields enabling deeper relationships and the establishment of broader collaborations of players to work on solving social problems.

In the context of anti-human trafficking and menstrual health in Africa, there is the capability to drive on-the-ground action with partners that have the capacity to use utilise data management and AI platforms for maximum impact, with organisations that have: sources of data; will benefit from machine learning analysis of the data; have staff in place to act on the insights to drive action.

While the application of AI through a philanthropic engineering operation will directly improve and save human lives, the engagements will also have a powerful impact internally, helping to attract, retain, and engage employees. The challenges being tackled require world class engineers and in Africa there is a dire need to create opportunities to cultivate this talent – to offer meaningful work and the chance to make a difference. Simply, there is no shortage of people to train, and no shortage of opportunities for them to make an impact.

Leadership

No multi-continental corporation has the capacity to impact anti-human trafficking or menstrual health when acting alone. However, when leaders in industry, academia, and NGO’s share data to address the problem collaboratively, combined with AI, real progress can be made.

Notably the anti-human trafficking effort now underway was inspired by an incredible woman who sadly passed away in 2020 – and her sister is the catalyst for the menstrual health work. It serves as a reminder that while technology enables new capabilities, it is only out of the vision of a heart for others that lives are transformed. Without the leadership of these two incredible women the anti-human trafficking and menstrual health work in Africa would not be where it is today, and the application of AI to this work would not be possible.

A reminder that heart to transform lives is something no AI model will ever replicate.

Gregg will be speaking at the SwissCognitive World-Leading AI Network AI Conference focused on The AI Trajectory 2023+ on 13 December on this and other topics, including:

• AI and the North – South divide
• AI as a technology ecosystem
• AI benchmarks and environmental impacts

About the Author:

Gregg Barrett is a seasoned executive with extensive and diverse experience in strategy, building and managing relationships, deal-making, communication, developing high-performance teams, organisational leadership, and problem-solving across a range of areas. Over the last decade, Gregg has led work in data science, machine learning, corporate research, and corporate venture capital. This includes the establishment and management of data science, machine learning, corporate research, and corporate venture capital operations, working across people, processes, and technology, integrating structured and unstructured data to direct research, business, and investment strategy.

Der Beitrag A meaningful use of Artificial Intelligence – In Africa – AI Trajectory 2023+ erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.