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Miss Jinnah gave a hint of what those dark days must have been like when she told a reporter in 1947: “There has been absolutely no lighter side of my life during the past 12 years. Now that we have achieved what we wanted, our work will be of a different nature. . In shortly over a year Mr. Jinnah was dead, and Fatima, who had been his most trusted confidante for nearly 20 years, withdrew from public life. People still visited her at home in Karachi and reported her bitter comments about the series of men who succeeded her brother as Pakistan's leaders. Even today, when similarities of personality, between her and Jinnah are pointed out, Miss Jinnah accepts them with the terse comment, “I was brought up by my brother. Like her brother, Miss Jinnah was born in Karachi. Her birthdate is unknown, but for official records she uses July 3, 1893. Her parents were Bombay people who had embraced Islam only two or three generations before, and who had migrated to Karachi in the mid? 9th century when the British began developing the city as a port for the Indus Valley. Her father was a trader in gum arabic and at one time had been sufficiently prosperous to send his eldest son, Mohammad Ali, to London for a legal education, but by the time Fatima, the eighth child, was born the family money was gone. Soon afterward Mohammad Ali, 17 years Fatima's senior, returned from England and went to Bombay to begin the practice of law. AT the age of 8, she was orphaned and her brother, already a rising young Bombay barrister, sent for her. She was enrolled as a boarder in a Roman Catholic convent school at nearby Bandra, and she later attended a Church of England boarding school at Khandala, a hill station near Bombay, from which she was graduated in her mid? eens.

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The creative development of this persona was a cartographic experiment in transgressive human enterprise but as it does not present itself as a bridge to the collective psyche, the persona and all the individuals who found solace in its withdrawn territory are no friend to humanity. They constitute a suicidal hoard of unconscious, faithless semiotic-nodes. It is human-beings as such which perpetuate mimetic individualism in compensation for an ability to know themselves. When subjects are alienated by a state of panic and overwhelming competition one might try and delude oneself into thinking that they have no relation to the collective whatsoever, even though by distinguishing oneself from the collective they become inseparably dependent on it as the only entity which can grant the subject a reflection. The individual who only knows himself through transgression with the collective attests to the existence of the collective while submitting to the order of the collective relations as outside of it’s own control. That being said, GG Allin almost caught the wave of cognitariat existence, as an individual who labors towards the extension of human enterprise but because he could not re-incorporate his carnal existence he only contributes to the outdated phenomenon of the American No Wave which is a reactionary movement to industrial-era alienation where the worker is out of touch with himself and the other workers, their interaction muted by machinery. In the contemporary, the workers have left the factory but the noise has only grown more insufferable- the contemporary subject must create intentional refrains to remove themselves from this infinite war on our attention. In 1962 Mort Heilig produced the Sensorama, this first proposal of an immersive system encountered soon the evolution of computer graphics, led by the endeavour of simulate reality in the controllable surface of computers. The translation of images to data packages coded by complex algorithms produced the interactive interfaces and the dystopian worlds inhabited by human surrogates, but also the training systems where the bodies and movements of pilots become data, which are sensed and translated to simulated landscapes of warfare. Rendering these data as images, computers learned how to read any image of our world as data. Computer vision and the pervasiveness of networked computing has augmented the perception of the everyday world with communication capabilities and digital information which is contextualised and delivered in real-time. Virtual and Augmented Reality had contributed to developing the systems that allow abstract our world into data readable by computers towards the constitution of a ubiquitous medium in which our daily interactions are under the commanding of the standards and protocols of unperceived platforms (Bratton, 2016). Postdigital Art (Bishop et al, 2016) goes beyond the interfaces to encounter the materiality of this whole machine and build critical assemblages, which can disrupt it embodying the computer mediated processes shaping our daily lives and repurposing them for the proposal of potential futures. This text will introduce new artistic strategies which take advantage of the virtual capabilities of these systems to disrupt the immediacy and real-time tracking and unveil a deep time. The complex processes from where emerge our current techno-social milieu and its capability for the design of speculative futures. She has published in refereed journals such as the Technoetics Art Journal (Intellect) and Artnodes and has participated in international congresses including CAC 2016 (Paris), ISEA 2015 (Vancouver), Re-new 2013 (Copenhagen) and the CR13 International Research Conference as part of the “Consciousness Reframed: Art and Consciousness in the Post Biological Era” series (Cairo).

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If all of our data lived in a secure, durable black box, we could breathe easy. But how we access the data can be just as important as its storage. Every database has its query language, designed to access the contents as efficiently as possible. Keep that in mind because as we mentioned earlier, time series data is special. Think of the army of Skynosaurs sending data to Skynosaurus Rex headquarters again. There are millions of data points to search, but now, we have a query language that is built for the task at hand: not to view data as it relates to other pieces of the schema but to view data in the context of time in order to aggregate, set windows, or see trends. This isn't about whether other databases are capable of doing such a thing, it's about how we choose to spend our resources. Database architecture is about trade-offs and priorities. Do you need speed or accuracy or volume or predefined schemas. Measure everything. Don't choose a tool or a product-choose a solution to your problem. Specialty tools are made for special problems, so time series databases are optimized for time series problems. The importance of these technologies can't be overemphasized, as all three are opening up innovation, uncovering opportunities, and optimizing businesses. Machine learning isn't a brand new concept, simple machine learning algorithms actually date back to the 1950s — though today, it's subject to large-scale datasets and applications. Today, I want to run through some step-by-step videos that will teach you how to use Talend's machine learning capabilities with Microsoft Azure to help pinpoint errors in large datasets for cleansing before entering the analytics pipeline. Machine learning techniques bring tremendous opportunity to better target customers and improve operations.

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