Woman and Robot thinking

AI and the expansion of the way we think and relate

In the era of Artificial Intelligence (AI), conversations around innovation often focus on ethical challenges, practical benefits, and the impact on process automation. However, there is a fascinating and transformative aspect of AI that, in my opinion, deserves a central place in reflection: its impact on the structuring of thought and relationships at all levels of organizational life, both socially and in business.

In this article, we will explore AI from a different angle. We won’t focus on ethical issues or obvious practical benefits. Instead, we’ll examine how AI can expand our thinking, modify relationships between individuals and structures, and transform our individual and collective communications. Overcoming resistance to change and acquiring skills thus becomes a transformative experience that organizations are already leveraging, and from which society could benefit.

This is a unique opportunity to lead change, as preparing for the future requires more than just understanding the basics of AI. It requires embracing its transformative potential and incorporating it into our way of communicating, thinking, and relating.

I propose this exploration by navigating through a few points:

Prompt engineering and the expansion of the way we think

Artificial Intelligence is not just a means to automate processes or solve complex technical problems. It is much more.

One aspect that fascinates me, especially about Large Language Models (LLMs), such as OpenAI’s ChatGPT, is how our thinking and creativity are stimulated in potentially revolutionary ways when interacting with them.

What are Large Language Models (LLMs)?

LLMs are considered one of the most advanced and promising fronts in Artificial Intelligence. They represent significant progress in natural language processing, trained on vast text corpora from various sources, allowing them to acquire in-depth knowledge of grammar, syntax, and other linguistic elements.

During training, these models become capable of generating coherent and meaningful texts from provided input. The practice of “prompt engineering,” which I’ll delve into later, becomes an art that harnesses the power of these models to stimulate creativity and expand thought.

How do LLMs work?

LLMs are a category of artificial intelligence that uses deep neural networks to learn from extensive textual datasets, understanding and generating coherent and relevant texts on any topic. Their pre-training phase relies on deep learning techniques, allowing computers to learn from large amounts of data, similar to how the human brain functions.

Deep learning is a technology that leverages artificial neural networks inspired by the human brain, enabling models to capture nuances of language, grammar, and context. For instance, OpenAI’s GPT-4 can generate texts on any topic and in any style, making its power accessible through an API for language-based application development.

In the field of artificial intelligence, natural language involves studying and applying techniques that enable computers to understand, generate, and use human language. Natural language processing, a branch of AI that combines linguistic knowledge and machine learning, becomes crucial for prompt engineering.

It might seem like we’re discussing actions only accessible to IT professionals. However, that’s not the case at all. Soft skills such as empathy, clear and conceptual communication, continuous learning, cognitive agility, and virtual collaboration become essential pillars for mastering the art of prompt engineering, especially when interacting with Large Language Models. Ethical awareness is equally critical to responsibly managing these powerful AI resources.

The practice of prompt engineering thus becomes a crucial exercise, a true training for our brains and language. Let’s delve into what it entails.

About Prompt Engineering

Prompt engineering is the ability to write precise instructions for AI algorithms, guiding them in generating innovative content and ideas.

These prompts are not just a random series of words but a way to leverage AI as a powerful tool for advanced brainstorming. AI professionals are harnessing this capability to discover new ideas, creative solutions, and unexpected perspectives. The truth, however, is that you can do it too (without being an AI professional).

The key point is that prompt engineering requires not only technical skills but also a deep understanding of the specific problems and challenges of the scenario, along with excellent language skills. It’s the opposite of giving orders to a machine. We are in a new dimension of technology that offers unique opportunities to surpass the limits of conventional ideas.

A clarifying example of this concept is the substantial difference between a generic prompt and a well-structured one. While a common prompt might be “Create something artistic,” a well-formulated prompt could be “Generate an idea for a modern painting representing the connection between humanity and technology in the context of contemporary art.” Precision in language is crucial here to achieve meaningful results and stimulate AI responses that meet expectations. Imagine a scenario where the machine provides an unsatisfactory initial response: the person writing the prompt must be able to reformulate their instruction, taking into account the feedback from the LLM, creating a real dialogue with the machine through three key actions: 1. refining, 2. shaping, and 3. sculpting.

Therefore, the ability to formulate sophisticated prompts requires not only technical skills but also a deep understanding of language and its potential influence on the AI’s creative process, ultimately requiring humanistic and soft skills.

AI and Digital Soft Skills: A Complex Symphony

In the advanced era of Artificial Intelligence, the concept of prompt engineering emerges as a sophisticated discipline that requires not only a deep technical understanding but also a refined set of digital soft skills. We are moving along the same lines as the paradigm shift towards a human-centric, sustainable, and resilient production and business system, often referred to as “Industry 5.0” (see my in-depth exploration here – only Italian available at the moment).

Possessing the right social, emotional, and cognitive skills is essential for actively engaging with Artificial Intelligence, especially in dialogue with LLMs.

Empathy and Effective Prompt Creation At the core of prompt engineering lies the ability to write instructions that are not only technically correct but also understandable and in harmony with the nuances of the machine’s language we are interacting with, and ultimately, our language. Here, empathy plays a crucial role. Having a deep understanding of linguistic subtleties enables the creation of effective prompts that resonate authentically. To delve deeper into the theme of digital empathy, you can read my article here (only Italian available at the moment), co-written with my colleague Caterina Giannottu.

Clear and Conceptual Communication Clarity in communication is a fundamental pillar of the soft skills necessary for prompt engineering. The ability to express complex concepts clearly and directly is crucial for creating instructions that AI can interpret accurately. Conceptual communication allows the translation of abstract ideas into tangible instructions, fueling the predictive capability of language models.

Continuous Learning and Cognitive Agility As the AI landscape is continuously evolving, continuous learning and cognitive agility are essential. Digital soft skills enable professionals to adapt quickly to new technological developments, modifying prompt engineering strategies in response to new challenges and opportunities. Open-mindedness to continuous learning is what sets true prompt engineering masters apart.

Virtual Collaboration and Digital Teamwork Prompt engineering is often a collaborative practice, requiring the ability to work as a team through digital means. Soft skills related to virtual collaboration are crucial to ensure that instructions are constructed from the perspective of diverse teams. Effective management of digital team dynamics contributes to efficiency in creating sophisticated prompts.

Ethics in AI and Digital Soft Skills Finally, ethical awareness is a critical component. Digital soft skills help navigate complex ethical issues related to AI. The ability to consider the ethical implications of instructions given to AI is a sign of maturity in prompt engineering. If you want to delve deeper into the topic, I strongly recommend Luciano Floridi’s book “Ethics of Artificial Intelligence” (2022).

In summary, prompt engineering is much more than a technical practice. It is an art based on a harmonious synergy of human and digital skills (see a comprehensive list of suggested soft skills for full AI utilization according to Codemotion here). Possessing the right digital soft skills enhances the creation of effective prompts and also contributes to defining a future where humanity and AI coexist ethically and effectively.

A true relationship

In light of all this, it becomes essential to consider our interaction with machines not just as a technological process but as a true relationship. This approach opens up interesting perspectives. For instance, we could apply principles and techniques of coaching to enhance our interaction with AI, leveraging both technical expertise and soft skills to maximize machine performance.

This perspective not only concerns optimizing interaction with machines but also provides the opportunity to transform human relationships. By applying coaching principles to the use of AI, we refine our communication skills and develop a deeper awareness of our thoughts and interactions.

Applying coaching techniques in our dialogue with AI.

Active Listening
After receiving a response from AI, the user employs active listening, carefully analyzing the information provided. They identify any gaps or errors and rephrase the subsequent request based on this comprehensive understanding.

Rephrasing
If the AI provides an ambiguous or not entirely relevant response, the user employs the rephrasing technique. They modify the request using more specific language or adding details to obtain a more accurate response.

Continuous Feedback
After each AI response, the user provides specific feedback on the accuracy and relevance of the answer. For example, “Your response is correct, and you could further elaborate on point X.”

SMART Goals
The user sets a SMART goal such as “Write an article on AI. My goal is to obtain a comprehensive English text on the practical application of AI in oncological medical research as of January 2022, with a length of approximately 800 words.”

Mirroring
If the user employs a formal tone, the AI responds similarly. If the user takes a more informal approach, the AI adapts its communication style accordingly.

Emotional Intelligence
Even though AI lacks emotions, human emotional intelligence can be applied to interpret AI responses. Understanding possible nuances and interpreting the (albeit simulated) emotional tone of AI enhances mutual understanding.

These examples illustrate how a coaching approach to LLM responses can guide the generation process towards more targeted and goal-centered outcomes. The application of such techniques allows for the refinement and progressive improvement of AI responses to achieve more effective and satisfying results. The integration of these methodologies also contributes to creating a more harmonious dialogue adaptable to human needs.

The transformation of communications in organizations

In its potential, AI is not just a driving force for automation but also a valuable ally in improving relationships within and outside organizations.

Considering the interaction with AI as a relationship paves the way for a positive transformation of human dynamics. Applying coaching principles, for instance, not only enhances machine performance but also contributes to developing broader thinking and more effective communication. In this way, Artificial Intelligence becomes not just an advanced technological tool but an ally in the development of our human capabilities and the creation of more meaningful relationships.

LLMs, in particular, with their unique ability to understand and generate natural language, are setting a new standard for advanced, inclusive, and efficient corporate communication. Today, they can play a crucial role in shaping a new paradigm in business communication.

A virtual bridge for multilingual communication

One area where AI proves particularly effective is in facilitating intercultural and multilingual communication. LLMs, thanks to their ability to understand and generate texts in different languages, act as a virtual bridge that overcomes linguistic barriers. Imagine a team spread across different regions of the world, each member communicating in their own language. LLMs can instantly translate conversations, enabling smooth and seamless communication.

This not only increases operational efficiency but also contributes to creating an inclusive environment where linguistic diversity is an asset rather than a barrier. The result is a cohesive and collaborative team capable of tackling complex challenges regardless of linguistic diversity.

Identifying needs and improving efficiency

Another crucial aspect is how AI, through LLMs and related tools, can improve efficiency in identifying the needs of customers and employees. Advanced algorithms can analyze large amounts of data, including feedback, requests, and behaviors, to identify patterns and trends.

Imagine a customer service department supported by an AI system capable of understanding the emotional context of customers, anticipating their needs, and responding empathetically. Similarly, in internal business dynamics, AI can analyze employee interactions, identify potential issues, and provide preventive solutions.

The result is a more responsive and needs-oriented organization where technology seamlessly integrates with human dynamics to enhance relationship quality.

A new level of engagement and participation

When strategically deployed, AI can take business relationships to a new level of engagement and participation. AI tools can analyze interaction patterns within teams and projects, suggesting optimizations and improvements. For example, they can recommend more effective communication approaches or identify areas requiring increased engagement.

Furthermore, the ability of LLMs to generate engaging and relevant content can be leveraged to enrich internal and external communication. Creating personalized content based on predictive analysis of interest and preferences enhances interest and participation.

Responsibilities for gender equality and inclusivity

woman programming AI

In the context of Artificial Intelligence (AI), particularly Large Language Models, a crucial issue emerges regarding gender equality and diversity inclusion. LLMs, as products of natural language generated by models trained on extensive datasets, may reflect biases and inequalities present in the source texts. However, it is essential to recognize that these models are not autonomous in shaping content—they reflect the information provided to them by humans. It is crucial to remember that despite the power of Artificial Intelligence, it lacks ontological autonomy: every result is a reflection of information provided by humans.

This places significant responsibility on the global community that uses and interacts with AI. Combating gender biases requires collective commitment, not only from professionals in the IT and communication sectors but from everyone influencing the information flow that fuels these models.

While LLMs can replicate and amplify gender stereotypes, on the other hand, they offer a unique opportunity to correct and mitigate such biases. The key lies in guiding the training of these models towards inclusive language, promoting diversity, and gender equity.

I even theorize a militant use of it: withdrawing the use of AI from an elite of (male) technicians and experts in STEM fields and claiming its use outside of those contexts becomes a contribution to making AI more inclusive. In this regard, I recommend reading the report “Shaping the future of work for women in AI” (2022 Edition) produced by the initiative Women in AI.

Choosing to embrace AI becomes a significant act and a responsibility for anyone wishing to positively influence the global narrative on equity issues. Using LLMs consciously, and encouraging training practices that embrace diversity, can be a significant step towards creating respectful and gender-equal content. This way, AI becomes an ally in the fight against biases, representing a positive force for change and equality.

Investing in Continuous Learning

In light of all this, the need to invest in continuous learning emerges as a technical and strategic challenge. Rapid transformations in the IT, communication, and innovation sectors require a structured and focused approach to keep skills up-to-date.

Companies and all organizations, especially those with the responsibility for community well-being, must design and invest in advanced training paths. What are these paths? I attempt to list some of the skills that I consider indispensable for living through this transformation:

  • Programming languages for AI: a focus on languages like Python and TensorFlow, essential for understanding and implementing machine learning algorithms and language models.
  • Training on Cloud Environments: with AI increasingly based on cloud services, mastering platforms such as AWS, Azure, or Google Cloud becomes crucial for the effective integration of AI algorithms.
  • Development of Technological Soft Skills: In addition to technical skills, continuous learning should include paths on effective communication, empathy, and collaborative work in the digital environment—essential skills for success in writing AI prompts.
  • Data Management Strategies: With an increasing emphasis on the importance of data in shaping AI algorithms, understanding data collection, cleaning, and management strategies becomes a fundamental element.
  • Security and ethics in AI: given the growing concerns about security and ethics in the use of AI, specific pathways on how to ensure data security and maintain an ethical approach in the training and implementation of algorithms.

Major e-learning platforms such as Coursera, Udacity, or edX now offer countless opportunities for remote training in AI programming languages and technical skills suitable for all levels. Here is a selection of beginner-level AI courses currently available on Coursera (filter activated for level and language in English and Italian).

Companies and organizations leading change

Investing in technical training paths allows professionals to acquire the necessary skills and prepares them to tackle the growing specific challenges of the industry. The ability to write effective prompts thus becomes an integral part of a broader approach that encompasses technical knowledge, soft skills, and a deep understanding of AI dynamics.

In this scenario, the company/organization positions itself as a facilitator of change through continuous learning, providing access to specialized courses, workshops, and online resources. Customizing training paths based on specific needs then becomes essential to maximize the impact of the investment in continuous learning.

However, it is crucial to emphasize that training should not be conceived only for professionals but should also extend to the general public, which currently uses and will continue to use AI in various aspects of daily life. Without accessible and widespread training, there is a risk that a significant portion of the population will be overwhelmed by the wave of change, with negative and divisive consequences on a social and cultural level.

Organizations have the responsibility to make AI training accessible to every individual and field, thereby contributing to bridging gaps and ensuring that technological progress becomes a driver of inclusion, awareness, and sustainable development, rather than exclusion, manipulation, and social disconnect.

A crucial role is also played by institutions tasked with the challenging responsibility of regulating the development of artificial intelligence. As we write, the European Union is in the process of debating the approval of the so-called “AI Act,” a memorandum that, if passed, would regulate AI advancements to ensure that systems used within member countries fully comply with EU rights and values (human oversight, safety, privacy, transparency, non-discrimination, social and environmental well-being).

Never more than now, companies, organizations, and institutions can assume a guiding role in shaping the future. This inclusiveness in training is crucial to building a resilient and prepared society to face the challenges of the future. It is up to us to choose how to steer this process of change, ensuring that it becomes a driver of growth and not a cause of harm to individuals and communities.

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