Sunday, February 25, 2024

AI and design



The current landscape of AI development encompasses various formulations worldwide, targeting crucial design domains: form, function, solution, and communication. These advancements underscore a critical realisation about the future of design—AI threatens to marginalise the profession if it fails to adapt and reinvent its foundational principles. Despite design's relatively brief history, emerging alongside the Industrial Revolution and craft automation, AI's strides in these domains pose a significant challenge.

The potential impact of AI on design goes beyond mere displacement; it could fundamentally redefine the profession, relegating it from a vital industrial or corporate career to a romanticised aesthetic craft pursuit. This shift is exemplified by personal experiences, such as a senior management figure deeming my role as an artist lacking market value compared to a design professional's. Four years down the lane, the design profession finds itself at a crossroads, overshadowed by AI's prowess in problem-solving—a core aspect of its genealogy.
Unlike art, which retains its intrinsic value despite technological advancements, design's reliance on research and process-driven methodologies for industry, renders it vulnerable to AI's encroachment. Thus, any meaningful change in the approach to design must begin with a comprehensive overhaul of design education. However, examining the current state of design colleges and their pedagogy reveals a concerning misalignment with the rapid pace of AI developments in the industry.
This disconnect extends beyond design education alone, highlighting broader issues within university curricula. The glaring disparity between technological progress and academic structures underscores a significant gap, with technological advancements outpacing educational reforms by 15 to 20 years. Fortunately, technical education, grounded in pure science and mathematics, aligns better with its curriculum and industry demands, ensuring its continued relevance and effectiveness.
Following are a few crucial types of developments taking place in AI that will challenge the design practice and its relevance.
Artificial Intelligence (AI) manifests in various forms, each tailored to specific functions. Narrow AI, often termed Weak AI, targets precise tasks like Siri or Alexa's virtual assistants, recommendation systems, and email spam filters. Conversely, General AI, or Strong AI, remains theoretical, aspiring to human-like cognition, complete with self-awareness and consciousness.
Machine learning (ML) is a pivotal subset of AI, facilitating computers to glean insights from data without explicit programming. It encompasses supervised and unsupervised Learning alongside reinforcement learning. Deep Learning, an ML offshoot, hinges on intricate neural networks to assimilate vast datasets, proving adept at tasks spanning image and speech recognition, natural language processing, and autonomous driving.
Reinforcement Learning constitutes an ML paradigm wherein an agent refines decision-making through interaction with its environment. Via trial and error, guided by rewards or penalties, it adapts actions iteratively—an approach pervasive in gaming, robotics, and autonomous vehicles.
Natural Language Processing (NLP) underpins AI's ability to decipher, interpret, and generate human language. It spans text translation, sentiment analysis, language generation, and comprehension. Simultaneously, Computer Vision empowers machines to fathom the visual realm, facilitating object detection, image classification, facial recognition, and segmentation.
Evolutionary Algorithms epitomise AI-driven optimisation, borrowing insights from natural selection to tackle multifaceted challenges. These algorithms iteratively refine candidate solutions over generations. Robotics integrates AI, ML, and other disciplines to craft autonomous entities capable of independently sensing, deciding, and executing tasks. Applications range from industrial automation to healthcare and eldercare, encompassing autonomous vehicles, drones, and assistive robots.
(image courtesy: copyright free image from net for representational purpose only)

Decolonial studies should ground their arguments in democracy.

                                                        (I am not scared to love, 2009, digital )


(Considering democracy's demonstrated ability in the 20th century to effectively displace colonialism both socio politically and culturally, this note argues that decolonial studies should begin with an understanding that "a critical approach in the study of decolonisation should center its argument on power dynamics and their associated terminologies in contemporary societies rather than solely focusing on historical contexts, drawing insights from democracy in contemporary societies and its diverse perspectives.)
Colonial legacies have left indelible imprints on sociocultural and political systems characterised by dominance, appropriation, and description. These imprints not only shape the contemporary worldview but also influence the perceived historical relevance of societies. Consequently, impacted societies undergo an extended period of introspection to define themselves and others, often leading to a reductionist portrayal of culture akin to superheroes from Marvel comics in need of reinvention from a specific historical period. A recent example of this phenomenon is observed in India, where narratives surrounding the "Ram Temple story" were seen as emblematic of the decolonisation of both mind and history by some.
The concept of the Renaissance as a catalyst for social reinterpretation is recurrent across cultures and history, often mirroring colonial structural approaches. However, caution is warranted when considering the Renaissance as a means of decolonisation, as it frequently intersects with the linguistic and historical decolonisation methods employed in critical studies. Furthermore, defenders of historical relevance often leverage rituals and myths intrinsic to rootedness and locational identities, which are also pivotal in critical decolonisation discourses.
Critical decolonisation studies must detach themselves from parameters such as location, dislocation, and migration, as these are frequently appropriated by proponents of cultural hegemony, perpetuating colonial socio-political histories. Instead, emphasis should be placed on examining contemporary marginalisation to develop a nuanced understanding of social hierarchy, a hallmark of colonisation.
Colonisation can be understood as a language construct reflecting the hegemony of power. Unlike revisionists and reformists who focus on rootedness, dislocation, and migration, a critical approach should centre its argument on power dynamics and their associated nomenclatures, acknowledging the intrinsic role of rootedness, dislocation, and migration within these power structures.
By adopting this critical discourse, we gain insight into the hegemony of marginalisation and exclusionism, whether based on caste, class, race, or religion, which are intertwined with the colonial and socio-political constructs of the cultural or historical Renaissance.
As democracy has proven effective in challenging the dominance of colonial powers, it is crucial to employ democratic methods in critically assessing contemporary colonialism and its manifestations in society. So, democracy becomes especially pertinent when revivalist movements attempt to utilise democratic frameworks to perpetuate colonial legacies under the guise of a renaissance.
For these revivalists, figures like the Marvel comic hero Ram with its Western colonial hegemony, or ISIS or Taliban decree of Islam through technological marvel guns, or events such as the Gaza genocide perpetrated by descendants of Holocaust survivors or the disdain of certain immigrant groups by born-again Christian immigrants in the USA may be dismissed as non-issues, akin to the caste, creed, race, religious, and class divides within their societies. They fail to recognise or acknowledge that the mindset and power dynamics they uphold constitute colonialism. Instead, they often present a simplistic view of history detached from its colonial context.
Therefore, it is imperative to diverge from the revivalist perspective on colonialism to engage with the complexities of colonialism and decolonialism critically.
In linguistics, it is widely accepted that words do not possess inherent meanings, nor do their definitions retain historical permanence; both evolve over time, influenced by context and usage. Acknowledging this fluidity of language is essential for departing from a revivalist stance on decolonisation; critical engagement with decolonisation must transcend a mere examination of the geographical shifts and alterations in language and its terms. Language, in this context, extends beyond textual expression to encompass sociocultural and political artefacts, where the aesthetics of communication are integral.
As previously emphasised, a thorough exploration of decolonisation must transcend a narrow focus on the Renaissance or its correlated elements like rootedness, dislocations, and migrations. Given the demonstrated ability of democracy in the 20th century to effectively displace colonialism both sociopolitically and culturally, the de-colonial studies should instead commence with an understanding that "a critical approach in the study of decolonisation should center its argument on power dynamics and their associated terminologies in contemporary societies rather than solely focusing on historical contexts, drawing insights from democracy in contemporary societies and its diverse perspectives."
Art work: I am not scared to love(changed title), 2009

Wednesday, February 7, 2024

Take away the label "Artificial" from Artificial Intelligence, and all will be well. There's nothing artificial about it; AI is just another ritual we employ to navigate our lives.


Rituals are fundamental to our lives, serving as the foundational routines that shape our existence. From the simplest acts to the most complex endeavours, rituals provide structure, comfort, and a sense of security.
Every aspect of the human experience is punctuated by rituals. From the journey of falling in love to the establishment of a family, from the pursuit of knowledge to the milestones of success and failure, rituals are omnipresent. They guide our interactions, govern our institutions, and define our societal norms.
Even the most mundane tasks, such as daily conversations or meal times, are governed by rituals. Our language, behaviours, and social hierarchies are all steeped in ritualistic practices.
Beyond the realm of personal experience, rituals extend into every facet of society. They dictate the workings of politics, law, commerce, and warfare. From the rituals of governance to the rituals of conflict resolution, human civilization operates within a framework of ritualistic behaviour.
Yet, rituals are not confined to the human realm alone. They permeate the natural world as well, influencing agricultural practices, ecological systems, and even the cycles of life and death. From the planting of seeds to the harvesting of crops, from the maintenance of hygiene to the rituals of mourning, nature itself is governed by ritualistic patterns.
In essence, rituals are the building blocks of life, shaping our world and defining our existence. To understand and master these rituals is to achieve success in the eyes of society. Whether consciously or unconsciously, we are all participants in this grand ritual of life, following the preordained algorithms that govern our existence.
Consciousness involves grasping and enacting various rituals aligned with life's priorities, and it can comprehend and adopt these technological rituals too.As our understanding of rituals expands, so too does our acceptance of artificial intelligence as simply another expression of the algorithm that governs all existence.
In the end, it is the acknowledgement and embrace of ritual that allows us to find meaning and fulfilment in our lives. As the saying goes, "Remove the word artificial from AI, and everything will be alright."

"Empathy is not merely stepping into someone else's shoes but developing the understanding that there are no others"

Take away the label "Artificial" from Artificial Intelligence, and all will be well. There's nothing artificial about it; AI is just another ritual we employ to navigate our lives.


Rituals are fundamental to our lives, serving as the foundational routines that shape our existence. From the simplest acts to the most complex endeavours, rituals provide structure, comfort, and a sense of security.

Every aspect of the human experience is punctuated by rituals. From the journey of falling in love to the establishment of a family, from the pursuit of knowledge to the milestones of success and failure, rituals are omnipresent. They guide our interactions, govern our institutions, and define our societal norms.

Even the most mundane tasks, such as daily conversations or meal times, are governed by rituals. Our language, behaviours, and social hierarchies are all steeped in ritualistic practices.

Beyond the realm of personal experience, rituals extend into every facet of society. They dictate the workings of politics, law, commerce, and warfare. From the rituals of governance to the rituals of conflict resolution, human civilization operates within a framework of ritualistic behaviour.

Yet, rituals are not confined to the human realm alone. They permeate the natural world as well, influencing agricultural practices, ecological systems, and even the cycles of life and death. From the planting of seeds to the harvesting of crops, from the maintenance of hygiene to the rituals of mourning, nature itself is governed by ritualistic patterns.

In essence, rituals are the building blocks of life, shaping our world and defining our existence. To understand and master these rituals is to achieve success in the eyes of society.
We are all participants in this grand ritual of life, following the preordained algorithms that govern our existence.

Consciousness involves grasping and enacting various rituals aligned with life's priorities, and it can comprehend and adopt these technological rituals too.As our understanding of rituals expands, so too does our acceptance of artificial intelligence as simply another expression of the algorithm that governs our existence.

In the end, it is the acknowledgement and embrace of ritual that allows us to find meaning and fulfilment in our lives. As the saying goes, "Remove the word artificial from AI, and everything will be alright."

Monday, January 22, 2024

"Why God? (why art?)

 "Why God? (why art?)

Upon awakening this morning, a peculiar question lingered from a dream. While the exact context and details of the dream escape me, the inquiry persisted:
Why God?
Beyond the nature of what constitutes a deity, the essence of the question was rooted in the rationale behind the existence of God.
Years ago, I delved into writings centred on my purported companionship with death and its associate, God. I posited that without the impermanence or mortality inherent in the world, the concept of God would remain undiscovered. Envision a realm where entities do not perish, mortality is absent, and immortality prevails!
Such a world would be stagnant, devoid of change, arrested in time, and absent the fundamental concepts of birth, life, death, and motion. I theorized that death or perishability sculpted our dynamic world, and the fear of loss or death birthed God—a concept capable of deferring the inevitability of change. I concluded that the one possessing the power to postpone mortality must transcend mortality, and the force halting change cannot itself undergo change. Essentially, I believed that an immortal God is a construct moulded by death, mortality, or change.
A static and immutable God reigning over an ever-mortal world.
In Advaita philosophy, this static God is called Nirguna Brahman—an inert god. According to its proponents, this concept embodies the ultimate realization for the faithful. I marvelled at this revelation, considering myself enlightened.
However, as the sands of time trickled through the hourglass of my life, subsequent enlightenment dawned upon me. If the God capable of deferring change or mortality is the nirguna Brahman (inert God), then death, mortality, and change must also be static—immutable, unchanging entities. I grappled with confusion. While an immortal, changeless God remains a conceptual construct, the notion of death, mortality, or change transcends mere abstraction—it is an action.
The subsequent illumination dawned upon me: to preserve the sanctity of the static God, these erudite philosophers crafted the idea of an immutable soul within us all—a soul that neither dies nor changes.
Clever intellectuals and an enlightened version of myself, I thought. Simultaneously, existing as a static soul within a perishable body became my newfound enlightenment.
Static God, static mortality (in the unchanging guise of death or change), and a mortal body—but regrettably, I equated the inert God with immortal death or change. As mentioned earlier, death is an enduring reality. Shankara, the proponent of Advaita, astutely posited that if God embodies immortality, it cannot coexist within mortality or a mortal body. In simpler terms, he argued that a vessel cannot remain static in a flowing river—something constantly changes its position. Thus, he asserted that our mortal and ever-changing world must be an illusion, Maya, incapable of actual existence. Only the static, inert God can endure.
My enlightenment encountered a setback, a fate shared by many enlightened individuals of that era. They challenged, 'How can an illusion, incapable of containing immortality or inertia, house an immortal or inert God if the body, its changes, or mortality are mere illusions?'
Advaitha crumbled, making way for Dwaitha. The new philosophy advocated a clear separation between the immortal God and its mortal container—the body. Advaithis, being epistemologists, engaged in logical arguments, utilizing tools and techniques to construct their case.
Responding to dualists, they borrowed from Zarathustra's argument that the shadow does not exist in the fire; it is a product of objects in the path of fire. Static and immortal fire renders the object and its shadow ephemeral. The material world, like fire, is deemed an illusion or Maya. Similarly, the atman exists in all forms, incapable of birth or death, making it inert or nirguna Brahman. Those questioning this inference were likened to those confusing a rope for a snake due to ignorance. The proper knowledge, they asserted, would dispel the confusion.
Advaithis cautioned others, especially Buddhists, against mistaking a rope for a snake out of ignorance. The atman within is the Brahman everywhere, pure and universal. Nothing adheres to or alters its immortal and versatile nature like fire. Ignorance, they argued, creates the illusion of the body as a container for the atman (fire). Only the atman (fire) is true; the rest are mere Maya, products of ignorance.
However, some questioned Adi Shankara's conceptual framework. If everything other than Brahman is an illusion, they asked, who does the 'ignorant' need knowledge about Brahman? Isn't ignorance itself an illusion? Shankara's attempt to bridge the gap between material and metaphysical worlds with the Maya concept faced heightened contradictions.
Shankara's evasion of questions led to the establishment of intellectually militant Shankar mutts and physically militant akharas to protect his Maya hypothesis in his illusional universe.
I realized my error, thinking I had uncovered a flaw in the non-dualist Advaita's denial of the material form (body) in the art of form (God). Painting on a canvas, I realized, is not art; art resides in the act of painting. Similarly, the body (canvas) is neither the painting (atman) nor the art (Brahman). Once the painting is finished, the canvas (body) becomes an illusion (Maya). Only painting and art exist, according to non-dualistic logic, and realizing this logic represents jeevanmukhti or ultimate liberation.
Rather than feeling enlightened, confusion set in. How could art theory or history replace the canvas, and can understanding the logic of art theory or history lead to liberation in art? My artist friend, Ravindra Gutta, argued, 'Knowing theory, history, and principles of electricity would not explain electric shock or its feel. Art is that electric shock.'
I discerned an inherent bias by examining Adi Shankara's prescribed method of Jeevan Mukti to attain non-dualism (Advaita). His rejection of physical reality in favour of conceptual approximation revealed a deep-rooted adherence to the Chaturvarna caste concept of Hindu society. Shankara's application of the same logic to erase the farmer from the food, the craftsman from the pot, or the one doing physical work from societal visibility reflected his rejection of physical reality for conceptual approximation.
I exclaimed, 'My God! Why, God!' As Shankara turned God into a theory or language-dependent cognitive estimation, his non-dualistic Advaita reduced God to a logical/conceptual approximation, excluding the rest of the universe. He missed the electric shock my friend was talking about.
If Bajagovindam was indeed authored by him, the poem advocating faith in God rather than critical interrogation of God through logical perspectives suggests a shift in his perspective. Later, he grasped the challenges inherent in his Advaita hypothesis.
In the initial stanza of the poem, the appeal to pray to the Lord without reliance on rules or grammar during the separation of the body (death) reflects a departure from his earlier intellectual pursuits. This sentiment, however, is not unique to him; Nagarjuna, a luminary in Madhyamic Buddhist philosophy, concluded his profound exploration with a declaration of having nothing more to say.
During the aftermath of the First and Second World Wars, where Europe witnessed unprecedented devastation and traditional facets of human life faltered, DADA artists asserted that no meaning was essential, initiating an anti-art movement. Marcel Duchamp, a prominent conceptual artist, further rejected the notion of problems and solutions, dismissing the relevance of God and critical intelligence.
My engagement with Advaithic Brahman concludes, at least partially. I experienced enlightenment, akin to going back to sleep, described as sushupthi by Adi Shankara. Turia, the stage of super consciousness where Atman and Brahman unite, is not a concern given the identified pitfalls in the basic concepts of Advaita.
Concerns about God no longer trouble me. Belief, whether in God or not, is subjective, defined as an acceptance without proof. Analogous to the way water doesn't make the river but rather the flow does, the unseen rhythm of life's flow prompts contemplation—isn't art (God) present when art is there?

Monday, January 8, 2024

Kathal - film review

 


While initially hesitant to delve into another Mammootty or Mohanlal film, the irresistible allure of "Kathal," a Malayalam movie headlined by Mammootty, ultimately drew me in. The film artfully weaves a family narrative around gender issues within the confines of orthodox societies, harking back to the long-lost era of Malayalam cinema during MT Vasudevan nair's time.

Embracing the narrative style of that bygone period, "Kathal" poignantly captures the suppressed emotions of human suffering through the lens of a gender narrator. The storyline revolves around four oppressed personas in a religiously "pious" household – a wife, a queer husband, his lover, and an "aged" father (considering old age as a distinct gender). Social compulsions exert a profound influence in this setting, and the film unfolds the tale of individual triumph over oppression, where bold steps lead to liberation.
While gender issues take center stage, the film also delves into patriarchy, religious orthodoxy, money, power, family dynamics, and other oppressive factors with subtle references. In our own lives, we encounter or are part of such countless stories of social oppression, often witnessing individuals succumb without ever having an escape route. As the narrative progresses, the film leaves a haunting imprint on the heart, marked by memorable shots but offering a breath of relief by the end.
Unlike narratives like "Brokeback Mountain" or "Philadelphia," where the entanglement of queerness and family is explored with heightened complexity or a quest for dignity, "Kathal" eloquently takes a straightforward approach to the human lives ensnared in these issues. Omana's story is not centered on sex or sexuality but rather on social entrapment within an oppressive system dictated by societal norms, affecting not just her but also her partner. Unlike characters in "Charulatha" or "Paroma," Omana doesn't succumb to emotional impulses or rebel against society for freedom like Aparnasen's Paroma. The director's brilliance lies in creating a character attempting a personal "coming out" by helping her partner embrace openness.
Mammootty's subtle yet standout performance takes center stage, overshadowing Jyothika's portrayal. The film, in the end, unfolds more from a feminist perspective than a queer narrative. "Kathal" emerges as a brilliant cinematic piece, rekindling the essence of a lost format cherished in Malayalam films, where human vulnerabilities and subjectivities were the true "kathal" (essence) of the movies. Special commendation goes to Joe Baby for his directorial craft and Mammootty for his acting and production. Undoubtedly, a must-watch film.

Tuesday, November 28, 2023

Time in Western painting (part 2)


While sorting through my hard disk today, I stumbled upon a PDF file containing materials from the second class of an art appreciation course I once taught at Srishti during my tenure there. This particular course aimed to delve into the exploration of how artists, throughout history, approached the concept and reality of Time. The way artists intricately dealt with time in their creations has always captivated me. It stands as a profound aspect of their artistic expression. Interestingly, this comprehensive 10-part course was intended to be transformed into a paper. As is customary with my projects, it eventually slipped my mind.
Time in Western painting (part 2) : enjoy













 

Monday, November 27, 2023

Within Human Society, Do We Share a Common Identity?"



Certainly not. That would be a discerning response when one maintains a conscientious perspective. Allow me to construct an argument using insights from my three workshops involving children from diverse economic backgrounds.

The initial two workshops transpired in a semi-urban locale encompassing a blend of economic strata, cultures, and religions. During these sessions, we tasked children with illustrating "the pathway from school to home." The first group hailed from the lower-middle or below-middle class, depicting a vibrant panorama featuring dogs, cats, plants, flowers, snakes, frogs, birds, cycles, cars, buses, roads, and people traversing the journey. Conversely, the second group, comprised of upper-middle and affluent class children, illustrated a starkly different scenario. Their drawings solely comprised buildings – shopping malls, theaters, religious sites – interconnected by lines, reflecting their lack of firsthand experience walking to school. Despite inhabiting the same city, these groups had limited or no exposure to each other's ecosystems and habitats.

The third workshop, conducted in collaboration with the Hume Foundation in Wayanad, involved Tribal children. Given that these children were relocated to urban government hostels during school days due to a dearth of educational facilities in their native communities, the exercise shifted to depicting their habitat. Although their drawings included trees, birds, and people, similar to urban affluent kids, animals were notably absent due to a historical backdrop of man-animal conflicts. Furthermore, unlike urban children's drawings, there were minimal representations of the sky. To glimpse the sky amidst the towering trees in the forest, they had to tilt their heads 90 degrees, an unconventional routine. In contrast, when I conducted a similar exercise with metropolitan children in the college where I taught, I discerned no distinctions between urban children and their connection to ecology and habitat.

Presently, individuals from villages or lower-middle to poor economic backgrounds are the only ones with some semblance of connection to the natural or chaotic facets of our world and its ecology. The urban and metropolitan middle class and above inhabit a distinctly disparate ecology and habitat. Their experiences, fears, joys, desires, and perspectives stand in stark contrast.

Village and urban lower-middle-class children still exert physical effort in their daily lives. They walk, run to catch buses, and engage in activities that necessitate physical exertion. Conversely, the upper class has systems in place that obviate such physical efforts. Even a trip to the store is a luxury for the urban affluent, with gardens tended to by gardeners, water flowing from taps, and transportation arranged through cars and services. Their lives have transitioned from physical effort to finger inputs, thanks to technology.

Our world now exists in two distinct ecologies – one tethered to the physical effort and the other ensconced in technological comfort. The urban affluent no longer need to physically connect with nature, except perhaps during recreational pursuits or vacations. They constitute a new breed that believes money and technology acquired through wealth define their existence.

In the realm of academic research, I find it intriguing that, armed with resource aggregator software, scholars produce papers justifying their research conducted in one corner of the world by citing findings from a distant continent with which they have minimal connection or understanding. No, we are not all the same. We are not universal citizens. Despite seemingly inhabiting the same world, regional disparities manifest, evident in differing contexts, habitats, and circumstances.

As a human species, we now reside in two distinct ecologies – one rooted in physical efforts and the other steeped in technological comfort. From nomenclature to modalities, these ecologies diverge. Of course, there exists a cohort in transition between these two realms, navigating migration and displacement. Current estimates indicate that 56% of the global population resides in cities, a figure projected to rise to seven out of ten people by 2050.

In one of his essays in the American Rural and Agrarian Ideologue, Wendell Berry deliberates on the global phenomenon of rapid urbanization and its attendant challenges. He contends that a rural villager, farmer, or craftsman who migrates to a city experiences not only physical displacement but also severs ties with the knowledge and skills intrinsic to farming, crafts, or physical labor that hold little relevance in urban environments. Such individuals become a new breed, uprooted from their traditional moorings and communal living. In villages, mutual acquaintanceship and interdependence prevail, whereas in cities, anonymity reigns, and human connections diminish. The primary objective in the urban milieu becomes the pursuit of financial gains, channeled into banks, stocks, and insurance under the belief that these mechanisms safeguard and define one's life. Those who once derided villagers for their "primitive belief systems in natural forces" find themselves ensnared in the belief of an artificial financial system – uprooted, disconnected, and disoriented.

Determining which lifestyle is superior is not within my purview. However, one certainty prevails: in answering the question of whether we are all the same, the resounding response is no. We are distinct. These two ecologies are different and can not be compared with one another."

Wednesday, November 15, 2023

Demise of Design: The Erosion of a Profession

The profound impact of AI algorithms is not on language, as many apprehend, but rather on the method and profession of design. Its decline seems imminent.

Following the establishment of the EU and the subsequent need for universalised protocols to standardise societal and state functions, the inception of ERP (entrepreneurial resource planning) and the first AI protocol signalled the impending demise of the design profession. As the design field diminished human activities and cognitive engagements into deductible patterns, displacing essential soft skills such as observation, representation, experience, and imagination with standardised tools and lemmatisation processes, it inadvertently paved the way for its own demise.
Human nature tends to believe in deducing standard principles, formulas, and tools from our experiences to predict and perfect human lives. Throughout history, various knowledge systems, including mathematics, science, philosophy, astrology, history, language, and grammar, attempted to standardise interactions through identifiable patterns, tools, techniques, and methods. In this historical progression, more equipped knowledge systems, armed with tools and processes, displaced lesser-equipped ones into redundancy, especially those attempting to replace the soft skills of human observation, representation, imagination, and the crucial ability to make mistakes, dissent, protest, empathise, sympathise, and fail.
Regrettably, the design and technology fields are structured around the pursuit of perfecting resilient structures rather than embracing the potential for mistakes or failures. As mentioned earlier, the inclination has been to replace human soft skills with mechanical tools and models. Today, AI is diligently perfecting this trait and model, particularly in areas where design, as a profession and method, applies tools, techniques, and strategies to resolve problems, address flaws, or enhance resilience.
The crucial juncture for design has arrived. With a relatively short history of just over a century, it must either redefine or reinvent itself to withstand the advancing AI models or face extinction in the near future. Design's survival hinges on adopting the methods of art, which, despite evolutionary changes in human lives, have endured by keeping soft skills at the core and not succumbing to becoming mere operatives of tools, techniques, and methods for predicting and perfecting structures.
For design to endure, it must redefine its approach, displacing tools, techniques, and methods with soft skills such as love, compassion, failure, dissent, and protest. Shifting its focus from a problem-solving method to, similar to art, creating a space for "breathing life" is imperative.
Should design fail to undertake this redefinition and reorientation, the demise of this profession at the hands of AI will occur sooner than anticipated.
(Painting - KK Hebbar's work) both images are drawn from internet

Tuesday, October 31, 2023

Noam Chomsky's problematic article on AI in New York Times



Lately, many within our social circle are sharing an excerpt from Noam Chomsky's article, which was recently published in The New York Times, either as a meme or a significant critique of AI. However, it's quite shocking that Noam Chomsky has chosen to pen an article of this particular nature. 

I apologise, Professor, but your recent article in The New York Times has not cast doubt on the advancements in language processing AI or its foundational constructs. In fact, it has regrettably cast a shadow over the extensive linguistic theories you have diligently cultivated, which have standardised language construction.

Your contributions are far-reaching, particularly the development of the syntactic structure that served as a cornerstone for cognitive science, paving the way for numerous postmodern developments in linguistics. Your introduction of theories such as generative grammar, which dissects sentences into constituent parts using phrase structure rules, and the meticulous application of recursive rules in the creation of Hebrew grammar, as well as the transformation generative grammar breaking down sentences into patterns of relationships among their components—these are all testaments to your commitment to standardising language and its processes. However, these very theories have been dismissed in your effort to support your assumption regarding AI's supposed inability to mitigate language bias through its standardised methods of pattern discovery within language. It is worth noting that AI language processing research heavily relies on these linguistic theories to comprehend the abstract.

Suppose one discredits the potential for bias mitigation in pattern discovery through data analysis. What purpose does Chomsky's hierarchy theory serve? What about the regular grammar, context-free grammar, context-sensitive grammar, and recursively enumerable grammar that you have painstakingly developed for language automation?


Furthermore, could you provide insights into your Descriptive Adequacy Theory, which intricately specifies rules for accounting for all observed data arrangements and, in turn, defines the rules responsible for generating well-formed constructs within the protocol space?

Let us pause here; it is indeed regrettable that in your endeavour to discredit AI language processing, you inadvertently question the fundamental theories you have ardently constructed and imparted over the years as a linguistics scholar.


It's worth noting that linguistic studies, theories, rules, and methods have played an integral role in developing AI data and language processing, underscoring the symbiotic relationship between AI and linguistics.


Should one seek to present a compelling argument against AI, it may be more prudent to explore humanitarian concerns. AI systems are founded on principles of standardisation and a precision-focused approach. In stark contrast, the human mind defies standardisation, echoing Shakespeare's famous words, "To err is human." Furthermore, human imagination, extending beyond the confines of certitude, commences where certainty ceases. Art, representing the abstract musings of the human mind in its quest to comprehend and extrapolate reality, inherently diverges from AI's standardised approach to patterns.


One is still unsure if AI will ever supplant the human mind, just as machines have never entirely replaced human labour. The limitations imposed on the mechanised process were not a consequence of the machinery itself. They were the outcome of decisive interventions through legislation to protect the dignity of labour and human effort by establishing labour rules within the production process. In AI, the trajectory would likely follow a similar path. However, it is crucial not to dismiss the research and academic rigour that underpins AI language processing. To provide a glimpse of the diverse realms of academic research taking place in AI, here are a few key areas. It is essential to acknowledge that these represent just a fraction of the research areas shaping AI, and the field is in a constant state of evolution. 


(the following list is prepared with the help of ai)


Language processing :

  1. Multimodal AI: Language processing models are increasingly being combined with computer vision to create more comprehensive models capable of understanding and generating both text and images. This is particularly important in applications like image captioning and visual question-answering.
  2. Conversational AI: Chatbots and virtual assistants continue to improve regarding natural language understanding and generation. They are used in customer support, virtual companions, and more.
  3. Content Generation: AI generates various types of content, including articles, reports, and creative writing. It has applications in journalism, marketing, and content creation.
  4. Translation and Language Localisation: Language processing AI has improved translation quality, enabling real-time translations in various languages. This is valuable for international business, travel, and content localisation.
  5. Sentiment Analysis: AI analyses social media and customer feedback for sentiment analysis. This helps businesses understand public opinion about their products and services.
  6. Healthcare: In the healthcare sector, AI is used for medical transcription, clinical documentation, and extracting information from medical records. It's also used to assist in diagnosing and monitoring health conditions.
  7. Legal and Compliance: AI can review legal documents and contracts for compliance, reducing the time and effort required for legal professionals.
  8. Content Recommendations: AI is used to recommend content on various platforms, such as streaming services, e-commerce websites, and news outlets, based on user preferences.
  9. Academic Research: AI aids researchers in processing and analysing large volumes of text-based academic literature for insights and trends.
  10. Accessibility: Language processing AI makes digital content more accessible to disabled people. This includes speech recognition for those with mobility impairments and text-to-speech for the visually impaired.
  11. Education: AI-powered language processing tools are used in online education for grading essays, providing personalised feedback, and assisting with language learning.
  12. Academic Conferences: ACL and EMNLP


Data analysis, pattern recognition, and decision-making:

  1. Explainable AI (XAI): One of the critical advancements in analytical models is the push for transparency and interpretability. XAI techniques aim to make AI models more understandable and explain their decisions, which is crucial in healthcare, finance, and law.
  2. Federated Learning: This approach enables analytical models to be trained across decentralised data sources while keeping the data localised. It's essential for privacy-sensitive applications like healthcare and finance.
  3. AutoML: Automated Machine Learning (AutoML) tools are becoming more sophisticated, allowing non-experts to create, train, and deploy analytical models without in-depth machine learning knowledge. This democratises AI and expands its use.
  4. Graph Analytics: With the growth of network data, graph analytics has gained prominence. It's used in social network analysis, recommendation systems, and fraud detection.
  5. Reinforcement Learning: In analytical models, reinforcement learning is used for optimisation problems, such as supply chain management and autonomous systems like self-driving cars and robotics.
  6. Anomaly Detection: Improved analytical models for anomaly detection are used in various applications, from cybersecurity to predictive maintenance in industrial equipment.
  7. Natural Language Processing (NLP): Analytical models integrate NLP for text analysis, sentiment analysis, and information extraction from unstructured data sources.
  8. Time Series Analysis: There are ongoing developments in time series analysis for forecasting, resource planning, and trend analysis in various domains.
  9. AI in Finance: Analytical models in finance are evolving for risk assessment, fraud detection, algorithmic trading, and customer service.
  10. AI in Healthcare: In the healthcare sector, analytical models are used for medical imaging, disease diagnosis, patient management, and drug discovery.
  11. AI in Supply Chain and Logistics: AI is increasingly used for optimising supply chains and logistics operations, including demand forecasting, route optimisation, and inventory management.
  12. AI in Energy and Sustainability: Analytical models optimise energy consumption, monitor environmental data, and improve sustainability practices.
  13. AI in Marketing: AI analytics are used for customer segmentation, personalised marketing, and recommendation systems, improving the effectiveness of marketing campaigns.
  14. AI Ethics and Fairness: There is growing attention to ensuring that analytical models are ethical and fair, addressing issues related to bias and discrimination.


Cultural expression processing :

  1. Generative Art: AI has been used to create generative art, producing paintings, music compositions, and even poetry. The technology often relies on neural networks to generate original pieces inspired by different artistic styles or cultural contexts.
  2. Language and Literature: AI is used to analyse and generate literary works, helping authors and researchers explore new narratives, genres, and styles. Chatbots and AI-driven virtual authors are also being developed.
  3. Music and Creativity: AI is used in music composition, generating melodies and harmonies in various genres. This is particularly useful for assisting musicians, scoring films, and creating background music for video games.
  4. Design and Fashion: AI tools can assist designers by generating fashion designs, offering recommendations, and predicting trends. Virtual try-on applications use AI to enhance the online shopping experience.
  5. Cultural Preservation: AI is helping in the preservation and restoration of cultural heritage. This includes the repair of the damaged artwork and the digitisation of historical texts and artefacts.
  6. Language Translation and Localisation: AI-powered language translation tools have improved significantly, making it easier to translate cultural expressions like literature, films, and music.
  7. Recommendation Systems: AI-driven recommendation systems, such as those used by streaming platforms, suggest culturally relevant content to users based on their preferences and viewing history.
  8. Digital Museums and Galleries: AI technology creates immersive digital experiences in museums and art galleries, enhancing visitor engagement and education.
  9. Creative Collaboration: AI tools facilitate collaboration between human creators and AI systems. Artists, writers, and musicians are experimenting with AI as a creative partner.
  10. Cultural Understanding and Interpretation: AI can analyse and interpret artistic expressions, providing insights into the meaning and significance of art, literature, and music within different cultural contexts.
  11. Personalised Content: AI-driven platforms offer personalised content experiences based on individual preferences, allowing users to explore and engage with their preferred cultural expressions.
  12. Digital Storytelling: AI-driven chatbots and narrative generation tools create interactive and immersive digital storytelling experiences.


Governance and ethics:

  1. Ethical AI Frameworks: Organisations and governments have been developing ethical frameworks and guidelines for AI development. These frameworks focus on responsible data usage, fairness, transparency, and accountability in AI systems.
  2. Data Privacy Regulations: Implementing data privacy regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has forced organisations to pay more attention to data governance and ensure data purity.
  3. AI Ethics Committees: Some companies have established AI ethics committees or boards responsible for reviewing and ensuring the ethical use of AI technologies, including data governance and purity.
  4. Data Quality Tools: There has been a growing emphasis on data quality tools and platforms that help organisations clean, validate, and maintain high-quality data. These tools are essential for ensuring data purity.
  5. Data Anonymisation Techniques: To protect sensitive information, AI developers use advanced data anonymisation techniques to minimise the risk of data breaches while preserving data utility.
  6. Bias Mitigation: Researchers and organisations are developing algorithms and strategies to reduce bias in AI systems. This includes identifying and addressing discrimination in training data and algorithms.
  7. Explainable AI (XAI): Developments in XAI are making it easier to understand how AI models make decisions. This helps ensure that AI decisions are justifiable and unbiased.
  8. Blockchain for Data Governance: Some organisations are exploring using blockchain technology to improve data governance and enhance data purity, ensuring that data remains tamper-proof and transparent.
  9. Data Catalogs and Metadata Management: The development of data catalogues and metadata management solutions makes it easier to discover, understand, and manage data assets, which is crucial for data governance.
  10. AI Auditing and Compliance Tools: AI auditing tools are being developed to monitor AI system behaviour, assess compliance with regulations, and identify issues related to data governance and purity.
  11. AI in Regulatory Compliance: AI systems help organisations comply with data governance and purity regulations by automating compliance tasks, data monitoring, and reporting.
  12. Data Stewardship and Data Ownership: Organisations are establishing clear roles and responsibilities for data stewardship and data ownership to ensure accountability for data governance.
  13. Collaboration and Knowledge Sharing: Initiatives to foster cooperation and knowledge sharing in the AI community, such as AI ethics conferences and research collaborations, promote best practices in data governance and purity.



Communication, articulation and expression :

  1. Conversational AI: Conversational AI systems, including chatbots and virtual assistants, have improved their ability to engage in more context-aware and natural conversations. These systems can handle multi-turn dialogues, offer personalised responses, and provide better user experiences.
  2. Natural Language Understanding (NLU): AI models have advanced in understanding the nuances of human language, including slang, idioms, and regional dialects. This allows AI systems to comprehend user queries more accurately.
  3. Multimodal AI: Integrating language with other modalities like images and videos is becoming more prevalent. AI systems can describe visual content and generate text-based descriptions for multimedia data.
  4. Emotion Recognition and Sentiment Analysis: AI is better at recognising and understanding human emotions from text and speech, which is essential for personalised and emotionally intelligent interactions.
  5. Voice Assistants: Voice-based AI assistants, such as Siri, Google Assistant, and Alexa, improve their ability to understand and respond to natural voice commands, making them more user-friendly.
  6. Text Summarisation and Generation: AI models have made significant progress in summarising long texts and generating coherent and contextually relevant text, which is beneficial for content creation and knowledge extraction.
  7. Content Recommendation: AI-driven recommendation systems are becoming more accurate in suggesting content, products, and services based on user's preferences and behaviours.
  8. Language Translation: Machine translation has improved, making it easier for people to communicate across different languages and access content in their preferred language.
  9. Speech Synthesis: Text-to-speech (TTS) technology has advanced, producing more natural-sounding and expressive synthesised speech.
  10. Creative Writing Assistance: AI assists writers by suggesting ideas, helping with plot development, and providing grammar and style recommendations.
  11. Cultural and Regional Adaptation: AI systems are trained to adapt their language and expressions to specific cultural and regional contexts, making interactions more relatable and respectful of cultural differences.
  12. Accessibility: AI is being used to improve accessibility for individuals with disabilities, including speech recognition for those with mobility impairments and text-to-speech for the visually impaired.
  13. Voice Cloning and Personalisation: AI enables voice cloning for personalised voice assistants and applications, making interactions more individualised and engaging.
  14. Ethical and Bias Mitigation: Efforts are being made to ensure that AI communication is honest and unbiased, with developments in responsible AI to reduce harmful or discriminatory language in AI systems.


Ethics and moral comprehension:

  1. Ethical AI Frameworks: Organisations and researchers are developing ethical frameworks and guidelines for AI development. These frameworks promote responsible AI behaviour and address ethical considerations.
  2. Ethical Decision-Making Models: AI systems are designed with ethical decision-making models, enabling them to assess moral dilemmas and make choices that align with established ethical principles.
  3. Explainable AI (XAI): XAI is gaining importance to make AI's decision-making processes transparent and understandable. This is critical for identifying and addressing ethical biases in AI systems.
  4. Bias Mitigation: Researchers are developing algorithms and strategies to reduce bias in AI systems, particularly concerning gender, race, and other sensitive attributes. Addressing discrimination is a fundamental aspect of ethical AI.
  5. Value Alignment: AI developers focus on aligning AI systems with human values and ethics. This involves training AI models to understand and prioritise moral values in their actions.
  6. Moral Philosophy Integration: AI systems incorporate moral philosophy to better understand and navigate complex ethical dilemmas. They can use established ethical theories to make decisions.
  7. Ethical Chatbots and Virtual Assistants: Chatbots and virtual assistants are being designed to provide honest guidance and adhere to ethical guidelines, especially in contexts where moral decisions are involved.
  8. Moral Reasoning and Explanation: AI is trained to provide moral reasoning and explanations for its decisions, helping users understand why a particular ethical choice was made.
  9. Cross-Cultural Ethics: AI systems are becoming more adaptable to cross-cultural ethical considerations, recognising that ethics can vary across different societies and cultures.
  10. AI Ethics Committees and Boards: Some organisations have established AI ethics committees or boards responsible for reviewing AI system behaviour, addressing ethical concerns, and ensuring compliance with ethical guidelines.
  11. Fairness and Accountability: Efforts are being made to hold AI systems accountable for their actions and to ensure that they operate fairly and justly.
  12. Legal and Regulatory Compliance: Ethical AI development includes adherence to legal and regulatory requirements, such as data protection and privacy laws.
  13. Education and Training: Training data for AI models is sourced from diverse and ethical sources, and AI practitioners receive education on ethical considerations.
  14. Ethical AI Auditing: There's a growing focus on auditing AI systems for ethical compliance to identify and rectify any ethical issues.
  15. Public Awareness and Engagement: Ethical AI initiatives aim to raise public awareness about AI ethics and involve the public in discussions about the moral and ethical aspects of AI development and deployment.