Mythcœur

Tag: algorithms

  • The Algorithmic Priesthood – Power, knowledge, and control

    The Algorithmic Priesthood: Power, Knowledge, and Control

    In recent years, algorithms have emerged as the new architects of our digital reality, shaping everything from the news we consume to the products we buy. This phenomenon has given rise to what some are calling an “algorithmic priesthood”—a class of experts wielding enormous power and control over the mechanisms that govern our digital lives. In this article, we explore the implications of this newfound power, examining its impact on knowledge, societal control, and the responsibilities that accompany such influence.

    The Rise of Algorithms

    Algorithms, at their core, are sets of instructions designed to perform specific tasks. In the digital age, these algorithms are employed to manage and make decisions based on vast amounts of data. They influence the content we see on social media platforms, determine our search engine results, and even guide financial markets. As described by John McCarthy, a pioneer of Artificial Intelligence, “As soon as it works, no one calls it AI anymore.”[1]

    The Algorithmic Elite

    The individuals who design and control these algorithms are part of a growing class that holds significant sway over digital infrastructures. This “algorithmic elite” includes data scientists, computer engineers, and mathematicians who possess the skills necessary to shape and operate complex systems. Michael Schrage, a research fellow at MIT Sloan School, points out, “They are, in fact, the new masters of the universe in the digital domain.”[2]

    Knowledge and Control

    • Information Gatekeepers: By controlling algorithms, this elite group becomes de facto gatekeepers of knowledge. They decide which information is prioritized, suppressed, or amplified. Eli Pariser, author of “The Filter Bubble,” warned that algorithms can create a world of “ides” that make democracy itself the first casualty of the digital world.[3]
    • Economic Power: Companies utilizing sophisticated algorithms often dominate their respective markets, such as Google in search or Amazon in online retail. This monopolistic power affects economies and innovation. As Tim Berners-Lee, the inventor of the World Wide Web, suggests, “The web has become a tool for interests to maximize their power and interests.”[4]
    • Behavioral Influence: Algorithms not only reflect but also shape societal norms and behaviors. For instance, social media algorithms incentivize certain behavior through likes and shares, influencing how people interact and express themselves online.

    Societal Implications

    The power wielded by this algorithmic priesthood poses numerous societal implications. Foremost among these is the potential erosion of privacy. Algorithms analyze personal data to provide tailored experiences, but this data can be exploited for commercial gain without adequate consent.

    Shoshana Zuboff, in her seminal work “The Age of Surveillance Capitalism,” argues that, “It’s not just about selling ads. It’s not just about invasions of privacy, data, or the internet. It’s really about that internal migration online ultimately works in favor of power concentration.”[5]

    Another concern is the lack of transparency in how algorithms operate. This opacity can exacerbate biases and inequalities, as the decisions and guidelines programmed into these systems are often not publicly scrutinized or understood. As Cathy O’Neil articulates in “Weapons of Math Destruction,” algorithms can sometimes “codify the past” and perpetuate existing prejudices.[6]

    The Responsibility of the Algorithmic Elite

    With significant power comes the responsibility to ensure that algorithms are used ethically and transparently. There is a growing movement advocating for algorithmic accountability, which calls for critical assessments of the societal impacts of algorithms.

    • Auditing Algorithms: Creating processes to regularly audit and evaluate algorithms can help identify biases and ensure fairness. Initiatives such as the Algorithmic Accountability Act have been proposed to improve the transparency of algorithmic decision-making processes.
    • Ethical Design: Incorporating ethical considerations and diverse perspectives in the design of algorithms can help mitigate biases and promote more equitable outcomes.
    • Public Education: Educating the public about the role and function of algorithms can empower individuals to make informed decisions and advocate for their digital rights.

    Conclusion

    As algorithms continue to drive the digital revolution, the power and influence of the algorithmic priesthood will inevitably expand. It is crucial to navigate this era with consciousness and accountability, ensuring that the technologies that shape our world are wielded in ways that are fair, transparent, and equitable. Only by doing so can we harness the full potential of algorithms to foster a more just digital future.

    In the words of legendary computer scientist Donald Knuth, “Science is what we understand well enough to explain to a computer. Art is everything else we do.” It is by understanding and bridging these two domains that we can actually shape an inclusive algorithmic society.[7]

  • From Oracle to Algorithm – What we lost when prediction became automated

    From Oracle to Algorithm – What we lost when prediction became automated

    From Oracle to Algorithm: What We Lost When Prediction Became Automated

    Throughout history, humans have sought ways to predict the future. From ancient oracles to sophisticated algorithms, our methods may have evolved, but the underlying desire remains the same: to gain insight into what lies ahead. However, as prediction has become more automated, we’ve lost something profound—an element of mystique, humanity, and cultural richness.

    The Age of Oracles

    In ancient times, oracles were revered as sacred intermediaries between the divine and mortal worlds. The Oracle of Delphi, one of the most famous oracles in ancient Greece, served as a powerful and central figure. The Pythia, the high priestess of the Temple of Apollo, would deliver cryptic prophecies believed to be divinely inspired.

    “Know thyself,” reads the inscription at the Temple of Apollo at Delphi, underpinning the oracular tradition—self-awareness as a path to enlightenment.

    Oracular traditions were not confined to Greece alone. Across the globe, various cultures turned to their own forms of prophecy:

    • The I Ching: A divination text in China that involves the casting of coins or yarrow sticks to reveal insights about the future.
    • The Druids: In Celtic societies, these priestly figures used natural signs and symbols to interpret future events.
    • Shamans: In indigenous cultures, shamans would enter trance states to communicate with spirits to divine the future.

    These traditions were not just about predicting the future; they were rituals deeply embedded in cultural and religious meanings. An oracle’s pronouncement was a communal event, a shared experience that brought people together.

    Prediction in the Modern Era

    With the advent of the Age of Enlightenment, reliance on mystical oracles began to decline. The rise of scientific reasoning encouraged empirical methods and a rational understanding of the world. However, the human need to foresee the future only shifted form, leading to the development of early statistical methods.

    Fast forward to the 20th century, and prediction entered the realm of computation. Algorithms, initially simple in structure, started to shape how we approached forecasting everything from weather patterns to economic trends. The introduction of machine learning and artificial intelligence pushed predictive capabilities to unprecedented heights, enabling the continuous analysis of massive data sets beyond human scope.

    The Age of Algorithms

    Today, algorithms touch every part of our lives. Recommendation systems suggest what we should watch next on streaming platforms. Financial algorithms decide on creditworthiness. Google’s Machine Learning Algorithms track user behavior to predict search trends, as explored in detail in a piece on Wired.

    “We are in the midst of a golden age of machine learning and artificial intelligence. Algorithms can now accurately predict when we’re sick, where we’re going, what we’ll buy next, and even whom we might date,” states Wired Magazine.

    The Pitfalls of Automation

    While automation has its advantages, predicting outcomes faster and more accurately than any human oracle could, it also comes with inherent downsides:

    • Loss of Context: Algorithms rely heavily on data input, but they often miss the nuanced understanding of human behavior and cultural contexts.
    • Ethical Concerns: Automated predictions can lead to biased outcomes if the algorithms are trained on skewed data sets, as examined in a study published in PNAS.
    • Lack of Human Touch: While algorithms are efficient, they lack empathy and a personal touch that ancient oracles imbued into their prophecies.
    • Reduction of Shared Experience: Predictions have become private and individual, stripping away the communal aspect that once played a significant role in interpreting prophecies.

    The Spiritual and the Secular

    The transition from oracles to algorithms reflects a broader cultural shift from the spiritual to the secular. While algorithms present a scientific approach to prediction, some argue that this transition has contributed to disenchantment—where the mystical and the mysterious are overshadowed by the empirical and the calculated.

    Yet, even in an era dominated by technology, the allure of the mystical persists. Astrology, tarot readings, and other forms of divination continue to thrive, signifying a persistent human yearning for spiritual insight.

    A Future Synthesis?

    Rather than viewing oracles and algorithms as opposing entities, perhaps the future lies in a synthesis of the two—a blend of automated prediction grounded in human values and ethical considerations. The technology firm IBM Research suggests integrating AI’s analytical prowess with human empathy to create more balanced predictive models that account for human complexities.

    In conclusion, while algorithms provide efficiencies and advancements in prediction, acknowledging the spiritual and communal dimensions of ancient practices could lead to a more holistic future of forecasting. By recognizing what we have lost, we may yet find a way to regain it in a contemporary context.

    “The greatest danger of the future is that we will turn our imaginations over to machines,” philosopher Jaron Lanier warns in his discussion about the ethics of AI.

    The journey from oracle to algorithm tells us much about our collective passage through time, a narrative that holds as much about our future as it does about our past. As we stride forward, let us carry with us the wisdom of both worlds.

  • Before Data, There Was Meaning – What algorithms cannot inherit

    Before Data, There Was Meaning – What algorithms cannot inherit

    From the rise of artificial intelligence to the ubiquitous data-driven narratives that dominate our technological landscape, it often seems that algorithms are the new arbiters of reality. Yet, behind the bloom of data and the sophistication of machine learning models, there lies an essential human dimension that machines still struggle to grasp: meaning. In a world where data tries to dictate meaning, it’s crucial to ask: What can’t algorithms inherit from us?

    The Primacy of Human Context

    Human understanding is deeply rooted in context and experience. While algorithms excel at pattern recognition and prediction based on vast datasets, they often miss the nuances that only context can provide. Philosopher Hubert Dreyfus, in his critique of artificial intelligence, famously argues that human intelligence and skills are fundamentally tied to our embodied experiences and social contexts—a concept he elaborated in Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division I. As Dreyfus puts it, “Only a being with the sort of body and social upbringing we have could have the kinds of expertise we have.” [Source]

    The Complexity of Language

    Natural language processing applications have made impressive advances, yet the task of deriving meaning from language remains inherently complex. Language is not just a string of words or sentences but a rich tapestry woven with culture, intention, and emotion. Linguist Noam Chomsky highlighted the challenges of computational understanding in his numerous works, emphasizing the intricacies of syntax and semantics that go beyond algorithmic computation. Chomsky once noted, “The infinite use of finite means—language remains a defining species characteristic.” [Source]

    Understanding Subtlety and Emotion

    Emotions are a profound aspect of human life that shape our interpretations and decisions. While sentiment analysis and affective computing are emerging fields aiming to bridge this gap, they often fail to capture the subtleties of human emotions. As Rosalind Picard, a pioneer in affective computing, states, “It’s not that computers are emotional; it’s that they can help people be emotionally insightful.” [Source]

    The Ethical Dimensions

    Algorithms, by their nature, lack ethical reasoning. They follow instructions, learn from data, and predict outcomes, but do not possess a moral compass. This limitation is particularly apparent in complex ethical scenarios where human values play critical roles. As the field of AI ethics explores these limitations, a popular stance holds that ethical reasoning involves “imagination and seeing all sides,” which are outside current machine capabilities. [Source]

    “While machines can simulate human behavior, they cannot replace human judgment, which is often guided by wisdom, empathy, and insight,” remarks ethicist Shannon Vallor. [Source]

    The Role of Creativity

    Creativity stands as one of the ultimate tests of any claim about machine intelligence. While algorithms can produce art, music, and even poetry, they do so by recombining existing data based on set parameters. True creativity, as seen in human works, often involves breaking boundaries, defying logic, and crossing conventional expectations in a way that machines can only mimic, not originate.

    MIT’s renowned professor, Marvin Minsky, illustrated this in his exploration of AI, stating, “You can’t learn to be creative just by recording data—it requires breaking the mold.” [Source]

    Concluding Thoughts

    As we drive forward in this digital age, it’s important to remember that while data can inform insights and algorithms can enhance efficiencies, the authentic leap from data to meaning, from calculation to comprehension, is a distinctly human trait. As we embrace technology’s potential, nurturing the irreplaceable aspects of human intelligence—our context, emotions, ethics, and creativity—is not just beneficial, but essential.

    In doing so, we can ensure that as we rely on the growing tide of algorithms, we do not lose sight of the deeply human elements that imbue our data with true meaning.