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“The lack of transparency regarding training data sources and the methods used can be problematic. For example, algorithmic filtering of training data can skew representations in subtle ways. Attempts to remove overt toxicity by keyword filtering can disproportionately exclude positive portrayals of marginalized groups. Responsible data curation requires first acknowledging and then addressing these complex tradeoffs through input from impacted communities.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“For businesses, it is vital to embed ethical checkpoints in workflows, allowing models to be stopped if unacceptable risks emerge. The apparent ease of building capable LLMs with existing foundations can mask serious robustness gaps. However unrealistic the scenario may seem under pressure, responsible LLM work requires pragmatic commitments to stop if red lines are crossed during risk assessment.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Many presume that integrating more advanced automation will directly translate into productivity gains. But research reveals that lower-performing algorithms often elicit greater human effort and diligence. When automation makes obvious mistakes, people stay attentive to compensate. Yet flawless performance prompts blind reliance, causing costly disengagement. Workers overly dependent on accurate automation sleepwalk through responsibilities rather than apply their own judgment.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“It is critical to recognize the limitations of LLMs from a consumer perspective. LLMs only possess statistical knowledge about word patterns, not true comprehension of ideas, facts, or emotions. Their fluency can create an illusion of human-like understanding, but rigorous testing reveals brittleness. Just because a LLM can generate coherent text about medicine or law doesn’t mean it grasps those professional domains. It does not. Responsible evaluation is essential to avoid overestimating capabilities.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Every piece of data ingested by a model plays a role in determining its behavior. The fairness, transparency, and representativeness of the data reflect directly in the LLMs' outputs. Ignoring ethical considerations in data sourcing can inadvertently perpetuate harmful stereotypes, misinformation, or gaps in knowledge. It can also infringe on the rights of data creators.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“LLMs represent some of the most promising yet ethically fraught technologies ever conceived. Their development plots a razor’s edge between utopian and dystopian potentials depending on our choices moving forward.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Automation promises to execute certain tasks with superhuman speed and precision. But its brittle limitations reveal themselves when the unexpected arises. Studies consistently show that, as overseers, humans make for fickle partners to algorithms. Charged with monitoring for rare failures, boredom and passivity render human supervision unreliable.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Responsible data and complete transparency are the unbreakable shields in the realm of large-scale Ethical AI for protecting humanity and fostering growth.”
― Sri Amit Ray, Ethical AI Systems: Frameworks, Principles, and Advanced Practices

“While we strive to understand the universe, the true nature of reality might be as much about the observer as the observed.”
― Enamul Haque, The Ultimate Modern Guide to Artificial Intelligence: Including Machine Learning, Deep Learning, IoT, Data Science, Robotics, The Future of Jobs, Required Upskilling and Intelligent Industries

“Dive deep into AI, but always keep a lifeline tethered to human empathy.”
― Enamul Haque, The Ultimate Modern Guide to Artificial Intelligence: Including Machine Learning, Deep Learning, IoT, Data Science, Robotics, The Future of Jobs, Required Upskilling and Intelligent Industries

“Open source philosophies once promised to democratize access to cutting-edge technologies radically. Yet for AI, the eventual outcome of the high-stakes battle between open and closed systems remains highly uncertain.
Powerful incentives pull major corporate powers to co-opt open source efforts for greater profit and control, however subtly such dynamics might unfold. Yet independent open communities intrinsically chafe against restrictions and centralized control over capacity to innovate. Both sides are digging in for a long fight.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“One of the main AI challenges lies in conjugating safety and efficiency. Equilibrium between AI ethics and performance will forge our future.”
― Stephane Nappo

“On generative AI, LLMs, etc.

Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to acquiring knowledge, humans tend to rely on transparent, trusted, and structured sources.

In contrast, ChatGPT and other large language models (LLMs) use a vast array of opaque, unattested sources of raw, unfiltered, and unstructured content as their means of language and communication training and as the source of information used in their responses.

While this approach has proven to be effective in generating natural language, it has also been inconsistent and, at times, significantly lacking in integrity in its responses. While it may provide information, it does not necessarily provide knowledge.

To be truly useful, generative AI must be able to separate language and communication training from the acquisition of knowledge to be used in its responses. This will allow LLMs to not only generate coherent and fluent language but also to provide accurate and reliable information to users. However, in a culture that values self-proclaimed influencers where transparency and accuracy is secondary, it has become increasingly challenging to separate reliable information from misinformation and knowledge from ignorance. This poses a significant obstacle for AI algorithms that strive to provide accurate and trustworthy responses.”
― Tom Golway

“As generative AI becomes a core component of products, processes, and services, use case development shifts from a tactical step to a strategic capability. Organizations must invest in framing use cases rooted in customer needs, ethical principles and pragmatic execution. Only then can generative AI be leveraged for sustainable shared value.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“As AI continues its rapid evolution, the path forward seems increasingly to lie in hybrid systems. These innovations—RAG, PAL, and ReAct—are emblematic of this trend, melding traditional neural network strengths with other methods to push AI's capabilities further. For business leaders, an understanding of these advancements isn't just beneficial; it's essential for staying ahead in the AI-driven future.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“Benchmarks should aid rather than substitute multifaceted, human-centric assessment focused on benefiting diverse populations. We must see behind the leaderboard, upholding wisdom over metrics. Tools like model cards and datasheets support responsible benchmark practices. But comprehensive governance requires collaboration at all levels of society.”
― I. Almeida, Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype

“One can, to be sure, program a digital machine in such a way as to be able to carry on a conversation with it, as if with an intelligent partner. The machine will employ, as the need arises, the pronoun “Iâ€� and all its grammatical inflections. This, however, is a hoax! The machine will still be closer to a billion chattering parrots—howsoever brilliantly trained the parrots be—than to the simplest, most stupid man. It mimics the behavior of a man on the purely linguistic plane and nothing more.”
― StanisÅ‚aw Lem, A Perfect Vacuum

“Once upon a time, knowledge was king. The person who could provide the answers, solve the most problems and remember the most facts was considered the smartest in the room. Being intellectually sharp was synonymous with possessing vast stores of information and the ability to recall it at a moment's notice. However, with the rise and widespread use of tools like ChatGPT and other Generative AI technologies, this paradigm has shifted.”
― Aymen El Amri, Generative AI For The Rest Of US: Your Future, Decoded

“In the crucible of data-driven models, we forge a power unprecedented in human history—a force that illuminates the unknown and reshapes our understanding of the possible.”
― Emmanuel Apetsi