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EVP of Development & Acquisitions The Trump Organization, Father, Outdoorsman, In a past life Boardroom Advisor on The Apprentice
Son of Former President of the United States Donald J. Trump.
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Defensibility amidst AI automating AI.
The vast majority of applied AI use cases currently in place across sectors stem from machine learning algorithms that have been in place for decades.
Decision trees, boosting models, neural networks - these foundational algos from the past are now widespread
The key underpinning for the rise in applying these algos is the standard formula of i) increased data availability and ii) exponentially scaling compute power of the past two decades.
But what's next?
Crafting ML models is not hard. Downloading open-source libraries and plugging structured/unstructured datasets into them is of task category that is ripe for automation by GenAI.
This is the ultimate irony of what the world of AI is undergoing internally post-ChatGPT.
Unfortunately, the vast majority of 'AI startups' I have come across are simply recycling open source ML models. There is no technical defensibility in this approach - in the long-term most definitely.
One can argue on a data advantage - but the increasingly mass availability + accessibility of open source data is shaking the core of such arguments.
This paradox is raison d'etre for our move to build a new ML architecture suited specifically to risk assessment in financial services at zypl.ai.
We selected synthetic data - a novel concept in optimizing credit risk - as a domain that we want to fundamentally alter from a technical standpoint.
The driving intuition behind zGAN - our version of generative adversarial networks (GANs) - is to replace the traditional data distribution replication of GANs with a more nuanced approach rooted in black swans.
By generating new data that fits alternative, 'fat tail' distributions, we are building from scratch a new ML pipeline to build macro-resilient credit decision models.
The intention? Defensible algo that will provide us a competitive moat against AI automating AI.
zypl прошел в Hub71 — главный стартап-хаб Абу-Даби. Два крупнейших банка Абу-Даби (читайте всего региона) в нашем пайплайне на 2024. Ну скажите, что же еще zypl может сделать в MENA?) ?? действительно стал нашим вторым домом. Благодарен этой фантастической…
zypl прошел в Hub71 — главный стартап-хаб Абу-Даби.
Два крупнейших банка Абу-Даби (читайте всего региона) в нашем пайплайне на 2024.
Ну скажите, что же еще zypl может сделать в MENA?)
?? действительно стал нашим вторым домом. Благодарен этой фантастической стране за все эти возможности.
?
— девальвация рубля, конечно же)
877,316,240 сомони - портфель zypl.score на рынке Таджикистана.
Лауреат Нобелевской премии Кристофер Писсаридес посоветовал абитуриентам не стремиться изучать естественные науки, технические дисциплины и математику. Он считает, что в будущем будут гораздо выше цениться «эмпатические» и творческие навыки, которые помогут процветать в мире доминирования искусственного интеллекта. Айтишники, по его словам, рискуют собственными руками посеять «семена саморазрушения», продвигая искусственный интеллект, который в конечном итоге займёт их же рабочие места. @bankrollo
Uncensored posts from the Office of Donald J. Trump
Reserved for the 45th President of the United States
https://donaldjtrump.com
Last updated 2 months, 2 weeks ago
Government of India's official channel on Telegram for communications and citizen engagement
MyGov homepage: mygov.in
MyGov COVID19 page : corona.mygov.in
MyGov Hindi Newsdesk: https://t.me/MyGovHindi
Last updated 8 months, 3 weeks ago
EVP of Development & Acquisitions The Trump Organization, Father, Outdoorsman, In a past life Boardroom Advisor on The Apprentice
Son of Former President of the United States Donald J. Trump.
DonJr.com
Last updated 3 months, 1 week ago