Copyright: news.mit.edu – “Need a Research Hypothesis? Ask AI.”

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New PhD candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?

MIT researchers have created a way to autonomously generate and evaluate promising research hypotheses across fields, through human-AI collaboration. In a new paper, they describe how they used this framework to create evidence-driven hypotheses that align with unmet research needs in the field of biologically inspired materials.

Published Wednesday in Advanced Materials, the study was co-authored by Alireza Ghafarollahi, a postdoc in the Laboratory for Atomistic and Molecular Mechanics (LAMM), and Markus Buehler, the Jerry McAfee Professor in Engineering in MIT’s departments of Civil and Environmental Engineering and of Mechanical Engineering and director of LAMM.

The framework, which the researchers call SciAgents, consists of multiple AI agents, each with specific capabilities and access to data, that leverage “graph reasoning” methods, where AI models utilize a knowledge graph that organizes and defines relationships between diverse scientific concepts. The multi-agent approach mimics the way biological systems organize themselves as groups of elementary building blocks. Buehler notes that this “divide and conquer” principle is a prominent paradigm in biology at many levels, from materials to swarms of insects to civilizations — all examples where the total intelligence is much greater than the sum of individuals’ abilities.

“By using multiple AI agents, we’re trying to simulate the process by which communities of scientists make discoveries,” says Buehler. “At MIT, we do that by having a bunch of people with different backgrounds working together and bumping into each other at coffee shops or in MIT’s Infinite Corridor.”[…]

Read more: www.news.mit.edu

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Copyright: intelligencebriefing.substack.com – “Making AI Adoption Less Uncomfortable – For All”

In October, Gartner reported that 74% of CEOs believe AI will transform their industry in 2024. Yet, 70-80% of AI projects still fail. But that’s hardly because of technology. Slack’s recent Fall 2024 Workforce Index sheds more light on a growing disconnect between executives and employees. It’s especially one data point that I can’t stop thinking about. (More on that in a second.)

In conversations with CIOs and VPs of IT, I hear that their companies already use AI—often developed and implemented by their IT teams. These are real business scenarios in which AI adds value or helps the business team do a task they were unable to do before, from supply chain optimization to product descriptions and document processing. But it’s not without struggles. Organizational dynamics, politics, and resistance to change come up more often as adoption barriers. So, how does all that fit together?

A Common Perception: Using AI Makes You Seem “Lazy”

According to Slack’s Fall 2024 Workforce Index report, 48% of respondents feel that admitting they use AI at work will make them seem “lazy.” They will also likely end up with just more work due to their newly unlocked efficiency.

Nearly half (48%) of all desk workers would be uncomfortable admitting to their manager that they used AI for common workplace tasks. The top reasons for workers’ discomfort are 1) feeling like using AI is cheating 2) fear of being seen as less competent and 3) fear of being seen as lazy.

But here’s the thing: it says more about the state of corporate culture than about AI technology if the most innovative employees—who are evidently creating new efficiencies with AI(!)—are afraid to share that they know how to use AI (and do it). By the way, they still use AI but they just won’t tell you.[…]

Read more: www.intelligencebriefing.substack.com

Der Beitrag Making AI Adoption Less Uncomfortable – For All erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Livia Spiesz – “AI in Diplomacy: Can Technology Foster a More Peaceful World?”

There are currently 56 ongoing conflicts globally involving 96 countries (Vision for Humanity, 2024; Salhani, 2024). The issue to mitigate this lies in the increasing norm of using force. Leaders are opting for peace-undermining military solutions, believing they can succeed through force rather than diplomacy. I would even challenge that as a reason. War seems to be the new status quo, disregarding also the thousands of lives lost in the process. What does it really mean? I let numbers speak: in 2023, the global expenditure on military operations reached $2.44 trillion USD, while peacebuilding and peacekeeping operations were at $34.1 billion. (International Peace Institute, 2023; and Dyvik, 2024). Result? 110 million people are displaced globally as we speak, and in 2023, more civilians were killed or injured by airstrikes, bombs, and artillery than in any year over the past decade. (International Rescue Committee, 2024; and Sabbagh, 2024)

Humanity struggles to learn from history, as violence remains a persistent and ineffective approach to resolving conflicts. With Artificial Intelligence (AI) being increasingly applied across industries and domains, I wonder, can this technology offer a solution to these escalating tensions and “save our world” from falling apart? Can it transfer diplomacy? Is the field ready at all?

After exchanging with hundreds of AI experts globally in the last over 6 years, I see great potential for AI in the world of diplomacy. This great potential is however wrapped in peril that needs the human mind, heart and soul to remove. On one hand, AI can revolutionize diplomacy and streamline negotiations (for instance through AI-driven conflict analysis tools and tailored approaches to negotiations), and also enhance global security (monitoring local news, tracking down misinformation, identifying potential conflicts, and anticipating trends (Delcker, 2023)). On the other hand, it can exacerbate existing disparities, fuel an arms race, increase sophisticated cyber threats, and deepen distrust among nations. This contrast makes me wonder: Are we prepared to handle this double-edged sword wisely, or will we let it cut deeper into the fabric of international relations?

AI readiness will likely determine future economic growth, potentially widening the prosperity gap between AI-ready and non-AI-ready countries (Georgieva, 2024). The United Nations (UN) has a critical role in promoting international cooperation and addressing this challenge posed by AI. The question is: are global leaders also prepared to create policies that ensure AI benefits everyone or will they simply reinforce existing inequalities?

Experts emphasize proactive AI governance to prevent risks from an unchecked race driven by strategic advantage and profit​ (Pasquini, 2024). Proposals range from informal agreements to initiatives like the WEF’s AI Governance Alliance and the UN Secretary-General’s AI Advisory Body​. These bodies aim to provide technical assessments and promote international cooperation. However, the absence of major players like China and Russia, along with the underrepresentation of developing nations, poses significant challenges​ (Zhou, 2024).
Article continues below.

The centralization of AI development in the Global North also creates power imbalances, leaving the Global South in a consumer role, sometimes with AI models that do not even fit local contexts (Antony et al., 2024). This problem is further complicated by the geopolitical impacts of AI, which potentially undermines the autonomy of countries that import these technologies (World Economic Forum, 2024). Governments face the dilemma of attracting investments from tech giants while protecting public interests. The lack of diversity in AI development leads to biased outcomes, disproportionately affecting marginalized communities. Are we considering the global impacts of our AI advancements, or are we perpetuating existing inequalities?

We are currently in the “inter-AI years,” a brief window to influence the trajectory of AI development before norms, values, and standards become entrenched (Cohen & Lee, 2023). Decisions made today will shape the future of AI and its impact on global power dynamics. This period is crucial for determining the path of AI, and it is vital that we get it right. AI’s potential to boost economic growth is enormous, with estimates suggesting that widespread AI adoption could increase global GDP by nearly $7 trillion over ten years (Cohen & Lee, 2023). However, realizing these benefits depends on the availability of energy, computing power, data, and models, not even mentioning AI literacy.

To leverage AI for global peace, we must move beyond mere regulatory frameworks and embrace a collaborative and inclusive approach. The UN’s role in promoting international cooperation is crucial. As AI continues to evolve, the question remains: Will we harness AI to create a more peaceful and just world, or will short-sighted national interests undermine its potential and allow it to further destabilize global relations? Time will tell. I personally see great potential for a better world where we augment our human abilities and capabilities with cognitive technologies. However, I would also like to emphasize that diplomacy thrives on the art of empathy, personal connection, and building trust – qualities I can’t imagine machines ever truly mastering.

References and Resources Used

Anthony, A., Sharma, L.; and Noor, E. (2024). Advancing a More Global Agenda for Trustworthy Artificial Intelligence. Link

Cohen, J. and Lee, G. (2023). The generative world order: AI, geopolitics, and power. Link

Dyvik, E., H. (2024). Global military spending from 2001 to 2023. Link

Garcia, E., V. (2020). Multilateralism and Artificial Intelligence: What Role for the United Nations? Link

Georgieva, K. (2024). AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity. Link

International Peace Institute. (2023). A Measure of Peace: Key Findings from the 2023 Global Peace Index. Link

International Crisis Group. (2024). 10 Conflicts to Watch in 2024. Link

International Rescue Committee (2024). 110 million people displaced around the world: get the facts. Link

Pasquini, N. (2024). Proactive AI Policy. Link

Sabbagh, D. (2024). More civilian casualties recorded in 2023 than any year since 2010. Link

Salhani, J. (2024). Iran’s response to Israel looms. What are the possible scenarios? Link

Vision of Humanity. (2024). Highest number of countries engaged in conflict since World War II. Link

World Economic Forum. (2024). Artificial Intelligence: The Geopolitical Impacts of AI. Link

Zhou, L. (2024). Russia and China compare notes on ‘military use of artificial intelligence’. Link

About the Author:

With a diverse background in human behavior, criminal psychology, leadership, diplomacy, development, and peacebuilding, Livia is committed to fostering understanding and growth on both personal and societal levels. As a strategic partnerships and communications expert with over 10 years of international experience, she has spent the last 6+ years in the AI industry, collaborating with hundreds of AI experts and leaders globally. Livia seeks out less-traveled paths and embraces challenges with a realist-idealist approach, living by the motto: “Reach for the sky, and you will get to the stars.”

Der Beitrag AI in Diplomacy: Can Technology Foster a More Peaceful World? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zhaklina Zhikova – “2024 – The Year of Big Learning – AI Skills”

How can organizations most effectively implement training in AI skills to engage and retain their people and achieve the goals and business impact they have set for themselves? How to tailor AI training to be most effective, targeting specific professional skills and organizational levels? Along with professional technology training, the overall culture in the organization and the “feel” and “sense” of AI are essential, as well as building trust in AI embedded in an organization’s strategy and communication.

These are some of the most seemingly “clear” issues for which there is already a large amount of information, detailed comparisons, and assessments from reputable and professional sources, but behind which practical, methodical, and long-term efforts that are worth making. The purpose of this overview is to save time and effort on initial research and to share experiences from experts in AI training, which can be another direction in the decision tree on this topic in organizations.

AI training is a crucial component in organizations’ strategies, playing a significant role in shaping their future.

According to McKinsey, among the top eight key priorities of CEOs for 2024 are – The challenges of leveraging generative AI in business operations, scaling its applications, and providing a competent advantage through technology to extract maximum value from digital transformations.  Over 80% of companies globally are aware of the current and future skills gap related to AI. For over 60% of employees, training in new skills or upskilling related to analytical and creative thinking using AI and big data will be required. Organizations need to prioritize these skill areas to develop employees to their full potential.

Where is AI training used in organizations’ strategies?

Providing broad AI training at all levels – from managers and department heads to CEOs through to all employees – to get a “sense” and knowledge of AI is a crucial step for a successful AI transformation in the company. This goes hand in hand with starting and executing pilot projects early on, creating an internal AI team, developing an AI strategy, and developing external and internal communication with all stakeholders in the company.

The areas of AI training are vast, and most platforms and training opportunities are focused on directions like data scientists, machine learning engineers, AI researchers, and AI enthusiasts.

Image: Datacamp

What route in AI training should we take?

To start our journey in AI learning, it is good to have clarity and a prepared “roadmap,” even when starting from scratch. Examples like this are well-developed with most external online learning platforms. The basic roadmap is prepared according to the learner’s interests and pace for an initial period of 3 to 9-12 months. Aligning the program with the professional goals and depth of learning in the different domains is essential.

It starts with Basics/Fundamentals, building up with specialization and advanced options, developing special AI skills, and learning essential AI tools and packages – tools for various business functions such as marketing, sales, data analytics, customer service, and their practical application. It follows training in additional specializations according to the professional field, certification, constant updating of information and trends, joining professional communities, exchange of ideas and experiences with AI professionals, and last but not least, applying the ethical aspect of AI. It is important to note that an introductory course on AI Ethics and an ISO/IEC 42001:2023 – Artificial Intelligence Management System (AIMS) course, which teaches how to apply AI responsibly and by the new ISO standard, are now included in almost each platform. ISO/IEC 42001 is the first international AI Management System Standard that specifies the requirements for creating, implementing, maintaining, and continuously improving an Artificial Intelligence Management System (AIMS) in organizations.

When an individual creates a learning plan, it defines an indicative timeline, skill goals, activities, programs, and resources needed to achieve those skills. What is our level of knowledge about AI beginner? Do we have a foundation in math and statistics skills? Are we familiar with basic concepts? What is our goal for the training – a new job, a career, or to complement and build on our current career? How much time can we devote to training? What budget can we spend? Do we want to invest in a boot camp, enroll in free courses and videos, or participate in professional online courses? How do we want to learn – in a comprehensive certification program, a Boot Camp, self-paced learning, or various online courses?

How should we prepare to learn best and apply AI learning?

• To choose a directed professional focus, what has been required for the

professional role, what skill set is needed, and what level in AI learning do we start from;

• To arm ourselves with patience and an understanding that this training is a life-

long process, with time to study each concept for as long as it takes to understand it and move on to the next;

• To apply the skills in practical projects to gain practical experience according to

the specific field;

• To join the AI community, professional groups, and specialized meetings online

and offline;

• To keep learning and building on what enhances our qualifications

and transforms us from novice to expert. To be persistent, patient, and practice;

• To review and evaluate the results of AI tools as they;

are there to help us, not decide for us, and to be aware of the ethical aspect of AI.

How do organizations train their employees in AI skills?

According to a Deloitte study, the shift in skills requiring employee training and upskilling concerns the increased value and importance of technology and human-centric skills.

Image: Deloiite

GenAI is to increase the value of some technology-centric and people-centric skills as follows – of the technology-centric abilities, the most significant increase in value is expected for data analysis (70%), prompt engineering (60%), information research (59%), and software engineering/coding (57%). Of the human-centered skills, the most outstanding value is in critical thinking and problem-solving (62%), creativity (59%), flexibility/resilience (58%), and the ability to work in teams (54%).

According to experts, some of the challenges in AI training are adapting training to the extremely rapid and dynamic development of technology, engaging and retaining employees and overcoming resistance to new technologies, respecting ethics and privacy, fostering a culture of continuous learning, and providing hands-on testing and simulations.

Some companies rely on e-learning and outside sources, and also create small internal learning and training communities that allow employees in a supportive atmosphere to experiment, make mistakes, and practice under the guidance of technology-savvy colleagues and instructors, with time set aside for internal discussions on the topic. Before specifying the training itself, it is necessary to identify the areas where GenAI has the most benefit and impact, identify the fully automated tasks, train Managers early, and focus on the application of GenAI so that they can confidently lead their teams and prepare for the risks and opportunities in implementing AI in the company. Best practices include creating Prompt libraries and resources, templates, and successful case studies’ lessons.

To help their employees, some organizations have created so-called “AI Playgrounds”, which contain software, domain-specific data, and policies that enable technically and non-technically oriented employees to experiment with GenAI in a safe environment. This avoids the risks of using external computer servers or violating copyright law. When using externally or internally developed resources for GenAI training in organizations, experts think that e-learning courses have an essential role to play. Still, they may have a limitation because they become obsolete with the rapid dynamics in GenAI development. This type of courses can be the basis for specifying own training in the company and according to industry needs. Collaboration with universities that develop programs for employees of a specific organization that creates GenAI boot camps is often very fruitful.

Many companies take advantage of the vast GenAI learning resources available from external providers with a huge interest in tech courses and training, such as Udemy and edX. For a better learning experience, it is good to combine training in different environments – digital and in-person – with an instructor.

Big tech companies are actively promoting free resources and certification programs for tech and non-tech beginners and experts. Amazon created and launched a free program, “AI Ready” to support professionals in the workplace. Eight new, free AI and generative AI courses will train 2 million people by 2025. The program is dedicated to beginners and experts, covering foundational to advanced AI skills. Microsoft and LinkedIn offered an extensively free Certification and AI skills training program via the LinkedIn platform, with free access until 2025. The initiative is dedicated to training employees globally to understand and be up-to-date with AI, work successfully and confidently with AI, and use GenAI to increase productivity and efficiency in companies while using technology responsibly and ethically. Google provides many free AI Skills courses and AI Skills courses on the Coursera platform.

AI Training – top priority training in organizations

To successfully train and implement AI Skills in an organization, we need to have a focused strategy and policy and internal standards on AI implementation; have goals and be able to measure the impact of AI and its benefits; share successes across our organization; ensure ethical use of AI; develop training plans across the organization and all departments – for those early AI adopters, as well as for those who will be trained later, for department heads to develop plans for their teams.

Demand for AI skills will continue to grow, so organizations need to train and “sell” their people on AI skills training and practice by building a long-term AI strategy across the organization, a continuous learning setup, effective communication, and the right training programs – a roadmap, a mix of established external providers, and dedicated internal learning communities, according to specific tasks and roles within the company. AI training is an investment that will prepare and equip people for the future. Like any investment, it also needs to be measured for its effect on the organization according to the KPIs set for AI’s impact on the business.

About the Author:

Zhaklina Zhikova, PMP, Business professional, with valuable experience in managing dynamic business projects (B2B – Sales, Manufacturing, BPO DACH) in EU companies with the highest technological and business standards, active “agent of change” dedicated to the synergy of AI Success and Business Models.

Der Beitrag 2024 – The Year of Big Learning – AI Skills erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Unfolding The AI Investment Landscape – SwissCognitive AI Investment Radar

We delve into a compelling array of AI investments that are not only enhancing the technological landscape, but also knitting together the global economic fabric. As Artificial Intelligence continues to be a lighthouse of innovation, its tendrils extend from the established tech oases of Silicon Valley to the emerging markets of Southeast Asia and beyond, indicating a robust intercontinental commitment.

This vibrant narrative begins in the bustling tech hubs of the UK, with significant funding driving the development of autonomous vehicles, sweeps through the strategic corridors of the European Commission, promoting AI and quantum research, and extends to the ambitious shores of Singapore, with Amazon’s massive expansion of cloud services.

Each of these movements is an example of how AI investment is increasingly becoming the lynchpin not only of individual company agendas, but also of the shaping of national and regional technology strategies.

From Microsoft’s extensive commitments in Malaysia to the burgeoning AI scene in the Middle East, the world is witnessing an unprecedented alignment of economic foresight with technological prowess.

This week, we see how the promise of AI is mobilising capital, influencing policy frameworks and redefining what it means to invest in the future.

Join us as we explore these developments, their implications and the exciting potential they hold for the global landscape.

Previous SwissCognitive AI Investments Radar: AI Investment Initiatives and Market Dynamics.

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 Unfolding The AI Investment Landscape – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI Investment Initiatives and Market Dynamics – SwissCognitive AI Investment Radar

This week, we span continents to uncover how AI is not only reshaping traditional industries but also forging new pathways in green technology and infrastructure development.

From Jeff Bezos championing AI for ecological resilience to Microsoft amplifying its AI footprint in the Middle East, the scope of AI investments continues to expand dramatically. We see Elon Musk channeling a significant $10 billion into AI to elevate Tesla’s autonomous capabilities, while Europe’s commitment to AI strengthens amidst a complex regulatory landscape.

As tech giants like Google, Microsoft, and Alphabet reveal robust AI-driven financial outcomes, it’s clear that AI investments are becoming central to corporate growth strategies. This global rush towards AI is not just about enhancing current systems but is a forward-looking endeavor to mold the future of digital and environmental governance.

Stay tuned as we explore how these strategic investments are knitting a new narrative in AI development, from enhancing enterprise solutions to tackling pressing global challenges.

Join us in tracking the transformative journey of AI investments that are setting new benchmarks across industries and regions.

Previous SwissCognitive AI Investments Radar: Worldwide And Local AI Investments and Innovations.

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 Investment Initiatives and Market Dynamics – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: bmmagazine.co.uk – “Bosch’s €2 billion gamble: Investing in employee retraining amid the AI revolution”

What is ‘quiet hiring?’ – Organizations that invest in reskilling and upskilling can fortify their workforces for the coming seismic changes wrought by technology, globalization, and markets.

Not long ago, Bosch announced a staggering plan to invest €2bn in retraining a portion of its 400,000 employees. As Europe’s largest car parts supplier, Bosch aimed to mitigate further job losses as the automotive industry transitions from traditional combustion engines to electric vehicles. The issue extends far beyond car-making.

McKinsey & Company forecasts that by 2030, one in 16 workers – totaling over 100 million across eight economies – may need to change occupations. This underscores the pressing need for reskilling and upskilling initiatives, driven primarily by rapid technological advancements automating jobs and generating demand for new skills.

Additionally, globalization and shifting market dynamics necessitate workers to adjust to new industries and roles. This interconnectedness has boosted trade, communication, and mobility across borders, often resulting in the outsourcing of jobs to countries with lower labor costs, displacing or rendering jobs in traditional sectors obsolete.

This process is also driven by shifts that occur within markets over time, due to changes in consumer preferences or regulatory overhauls. Tasks within industries tend to become more complex as new procedures, tools, and regulations emerge.

In the financial services industry, the proliferation of complex financial products like collateralized debt obligations (CDOs) and credit default swaps (CDS) has heightened the complexity of risk management. Assessing credit, market, and liquidity risk for these instruments poses unique challenges, demanding specialized knowledge and skills from risk managers. Continuous learning and skill development are essential for these professionals to remain relevant in their field, a necessity that extends beyond banking.[…]

Read more: www.bmmagazine.co.uk

Der Beitrag Investing In Employee Reskilling Amid The AI Revolution  erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: eweek.com – “AI Model Optimization: 6 Key Techniques”

The process of optimizing an AI model is crucial to create AI models that are high performing, consume reasonable amounts of resources, and are highly applicable to complex real-world scenarios. From model optimization strategies like model pruning to regularization, it’s possible to fine tune models to not only perform more accurately in rigorous use cases but also leverage the full potential of AI.

Model optimization in artificial intelligence is about refining algorithms to improve their performance, reduce computational costs, and ensure their fitness for real-world business uses. It involves various techniques that address overfitting, underfitting, and the efficiency of the model to ensure that the AI system is both accurate and resource-efficient.

However, AI model optimization can be complex and difficult. It includes challenges like balancing accuracy with computational demand, dealing with limited data, and adapting models to new or evolving tasks. These challenges show just how much businesses have to keep innovating to maintain the effectiveness of AI systems.

Here are some of the strategies that enable us to optimize AI models.

Retraining on Better Data

The quality of the AI model is an amplified reflection of the quality of the data. Therefore, retraining AI models on enhanced datasets — datasets rich in quality, diversity, and relevance — is foundational for optimization. These enhanced datasets have minimal noise and errors and represent a wide range of scenarios and outcomes. They are also closely aligned with the current dynamics of their problem spaces, such as trends and scenarios.

This ensures that models are updated with the latest information, which means that they can make more accurate predictions and adapt to not only changing data landscapes but also evolving use cases. It also makes sure models are adaptable to new trends, which is indispensable in fast changing fields like social media trend analysis and market forecasting.[…]

Read more: www.eweek.com

Der Beitrag AI Model Optimization: 6 Key Techniques erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwssCognitive Guest Blogger: Felipe Castro Quile – “Rethinking Investments in the Age of AI Towards Economic Thinking”

In today’s technological advancement, the rapid progression of artificial intelligence stands out as a transformative force reshaping our societies from the ground up. In the heart of this storm of innovation, traditional concepts like money and investment find themselves under the microscope, their roles and meanings challenged against the backdrop of profound societal shifts.

Money, at its core, is a social construct—a medium of exchange infused with value by collective agreement. Its purpose extends beyond mere transactions, serving as a symbol of trust and a store of wealth. Yet, in the realm of investment, money takes on a different guise, becoming a tool for allocating resources with the expectation of future returns. This relationship between money and investment is deeply entrenched in economic and social systems, shaping the fabric of our societies.

However, the advent of AI introduces new complexities into this age-old equation. The relentless march of automation and machine learning algorithms disrupts traditional notions of value creation, employment, and wealth distribution. As AI permeates financial systems, it redefines the dynamics of decision-making in investments and challenges established valuation paradigms.

The fusion of AI and finance ushers in a new era—one where algorithms parse through vast troves of data, identifying patterns and making split-second decisions that elude human cognition. This shift not only streamlines processes but also introduces a level of efficiency and accuracy previously unimaginable. Despite this, accountability, transparency, and the human element in financial decision-making still remain at risk of being diminished.

The evolving landscape prompts a reevaluation of the relationship between money, investment, and societal value. As AI-driven technologies reshape industries and redefine labor markets, the traditional metrics of economic success—GDP growth, profit margins, and shareholder value—seem increasingly inadequate. In this transformative era, the pursuit of sustainability, social impact, and equitable wealth distribution takes center stage, challenging the status quo and demanding a recalibration of our economic priorities.

In fact, the intersection of AI and finance underscores the urgent need for adaptable frameworks capable of accommodating emerging technologies and evolving societal values. This calls for a paradigm shift in economic thinking—one that embraces complexity, prioritizes inclusivity, and fosters resilience in the face of uncertainty.

One promising avenue for exploration lies in decentralized finance (DeFi)—an emerging paradigm that leverages blockchain technology to democratize access to financial services and redefine the nature of trust in economic transactions. By eliminating intermediaries and empowering individuals to directly participate in financial markets, DeFi holds the potential to usher in a more equitable and transparent financial ecosystem.

Additionally, as AI continues to evolve, so too must our governance frameworks; governance in the sense of reevaluating power dynamics and AI regulation before it becomes too entrenched with vested interests that hinder innovation or worst. Ensuring that AI-driven financial systems are accountable and aligned with the societal values of a new era requires proactive measures to safeguard against potential risks and mitigate unintended consequences. How this is achieved is just a matter of strategy.

Ultimately, the convergence of AI and finance signals a new frontier of possibilities, where innovation and disruption go hand in hand with benefit and foresight. As we navigate this uncharted territory, let us pay attention to the lessons of the past while embracing the opportunities of the future. By embracing adaptable frameworks that prioritize sustainability (which is only reached through mindful consumption and allocating the same resources we discuss above), social impact, and decentralized finance, we can pave the way for a more resilient and inclusive economic paradigm—one that truly serves the needs of all. AI is the key to unlocking unprecedented efficiency and insight, but it’s our responsibility to ensure that its benefits are distributed equitably and its risks are mitigated effectively, empowering us to transform.

About the Author:

Felipe Castro Quile is an accomplished international entrepreneur, leads as CEO at Emerging Rule and GENIA Latinoamérica. With dual MBA credentials and expertise in deep learning, blockchain technology, and virtual learning, he excels in crafting and deploying AI solutions to address intricate business dilemmas and drive societal progress. Felipe’s fervor for leveraging technology to enrich education, coupled with his adeptness as a virtual teaching specialist, is matched by his reputation as a pioneering expert in blockchain technology, particularly in transforming supply chain management. Renowned for his innovative thinking and meticulous execution, he is a sought-after consultant and mentor within the tech industry, leaving a significant imprint on global technological advancements.

Der Beitrag Rethinking Investments in the Age of AI Towards Economic Thinking erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Next-Gen AI Innovations And Investments – SwissCognitive AI Investment Radar

In this edition, we embark on a comprehensive journey through the heart of global innovation, from AI-powered startups transforming e-commerce analytics to massive funding rounds setting the pace for future technology.

With AI technologies spearheading unprecedented advancements across industries, we delve into the dynamic shifts marking AI’s significant impact. From the bustling tech hubs of Pittsburgh to the visionary investments of Saudi Arabia, we uncover the pivotal role AI plays in shaping our world. Notable rounds like FundGuard’s $100 million Series C and strategic insights for board members navigating the GenAI boom signal a pivotal moment in technological growth and investment strategies.

As the world tunes into the potentials unlocked by AI in accounting, stock markets, short-haul trucking, and beyond, we explore the blend of innovation, and strategic foresight guiding investments.

Join us on the latest updates of investment and innovation in the AI sphere, spotlighted in this edition of SwissCognitive’s AI Investment Radar.

Previous SwissCognitive AI Investments Radar: Shaping Tomorrow’s Tech Today.

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 Next-Gen AI Innovations And Investments – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.