the natural process that follows Digitization. Once both digital culture and data systems have settled, the differentiation of companies boils down to their algorithms. Most of current largest tech companies were digital native and their competitive edge became their much protected algorithms - Google, LinkedIn, Netflix, Amazon... A good example of non digital natives that are becoming algorithms - i.e. they are evolving via Algorithmization - are hedge funds. Banks are expected to follow closely. B
Banking Technology Awards: one of the most prestigious awards in Banking.
CogX: one of the largest events on AI at the UK.
DeepTech: highly advanced startups in terms of science and engineering innovation. Often require large investment to reach commercial success and its IP is largely protected by barriers to entry.
Digitalization: there is no consensus on the differentiation between digitalization and digitization. We refer to digitalization as the process of converting information into a digital format - e.g. from a physical letter to a .pdf document.
Digitization: a process of transformation followed by most industries to become more effcient. It has become a trend once Moore's Law implied an affordable cost of data storage and management - which gave birth to Big Data. The further democratization of the exploitation of such data (via open source libraries for Machine Learning) gave room to the first isolated algorithmic projects - not to be confused with Algorithmization, where the exploitation is not isolated but orchestrated.
Digital: a digital company can be natively so or can become it after a digitization process. Note that companies can go through digitization and not become digital - e.g. raw material companies that optimize their business through IoT, satellite imaginery, price predictions, etc can perfectly keep their physical interaction with their clients and suppliers for a long while, still.
Digital strategies: from the data (standard or alternative) stimulus (ones or those from a third party) to the reaction in the markets (multiquoting across assets) through a transparent rationale (easy to understand, change and audit).E
statistical distribution that represents an increasing growth. One of the most realistic ways to achieve exponential growth is by exploiting synergies as opposed to isolating efforts. A large part of Algorithmization is focused on synergies exploitation across departments, vendors and entire disciplines.
Himitsu Tech ®: it is our founders' DeepTech platform. It is based on their HybridTech research and it is often described as the evolution of the machine within Machine Learning, stating that in order to shift the Frontier of Knowledge through further advances on the learning side more flexible machines and realistic ecosystems are needed.
Moore's Law: I
HybridTech: theory on bleeding edge software design that was born at UCL in the overlap between micro-servicing, algorithmics, IP protection-and-sharing, regulation and cybersecurity. Startups whose inner kernel is based on HybridTech theory are native DeepTech. As a result, HybridTech is theory on advanced DeepTech platforms' design; Himitsu Tech ® is our founders' code instance of such theory; SW FRACTAL ® is SW's commercial name for Himitsu Tech ® in Finance; SW's platform comprehends SW FRACTAL ® plus apps in Finance; and SW belongs to both FinTech from a domain perspective, and Deep tech from a technology perspective. As Deep tech companies are versatile, in order to have a leaner market approach, the usage of Himitsu Tech ® in other verticals has been orderly isolated in sister companies.
Investment Cycle: currently, the financial industry isolates different aspects from the whole Investment Cycle. As a result, exponentiallity cannot be entirely exploited and that has an effect on the prices to the final client - i.e. there is a risk of losing competitivity. By Investment Cycle we refer to all the pieces that, independently on where they are embedded now, should be integrally considered: 1) prop or market making steps such are portfolio construction, execution, strategy management and portfolio management, 2) new must-have processes: backtest and simulation, 3) advanced risk management tracking: near time indicators of live strategies, 4) advanced risk management tests such are those from SW VR Sandbox ® and 5) advanced risk management actions such is SW Algo Risk Strats ®.
a pattern followed by the hardware evolution which basically says that the capacity of the machines double every two years. As a consequence, the same tech halves in price every two years. It has been fairly accurate for the last decades, however a decay is expected soon as it shall resemble a Sigmoid shape. Recommender systems (RecSys):
algorithms focused on giving recommendations to users based on features of the products to be recommended along with the features of the user and its peers' preferences. Netflix launched a competition to improve the RecSys they had and the community came up with a methodology that was very interesting for their specific problem. Such methodology inspired numerous applications by consultants for different clients' challenges. But too often, the nature of the Netflix problem did not match that of their clients. Hence, it became a major flag of a risky combination (trends & practitioners) that could add expensive noise to the industry evolution.
statistical distribution with the shape of an S. It is often used to note that there is a top to the way things can increase - first slow, then fast and then, slow again.
UK Digital Economy Networks:
UCL: University College London. In this website we always refer to its Computer Science Department - highly regarded for its Machine Learning innovation and place where DeepMind was born.
one of the most relevan initiatives set out by the UK to connect industry and academia around digital transformation.
UK EPSRC: UK Engineering and Physical Sciences Research Councils - one of the most influentials research councils globally.