Toggle light / dark theme

Like this article; there is 2 more pieces missing from the roadmap for 2010 & beyond and that is Biocomputing & Singularity. Biocomputing will provide the financial industry (banks, trading firms, accounting & audit firms, bond insurers, etc.) the ability to expand information/ data storage and transmission capacities like we have never see before just look at what Microsoft, Google, Amazon, etc. have done with DNA storage. And, the much loved Singularity enables boosting of knowledge and insights as well as more mobility and access to information as they need it. BTW — Biometrics is NOT the same as Biocomputing; biocomputing goes well beyond security/ identity management.


The influential non-profit rates these technologies alongside the PC, the internet, and smartphones in terms of their potential to transform financial…

Read more

A prototype chip with large arrays of phase-change devices that store the state of artificial neuronal populations in their atomic configuration. The devices are accessed via an array of probes in this prototype to allow for characterization and testing. The tiny squares are contact pads used to access the nanometer-scale phase-change cells (inset). Each set of probes can access a population of 100 cells. There are thousands to millions of these cells on one chip and IBM accesses them (in this particular photograph) by means of the sharp needles (probe card). (credit: IBM Research)

Scientists at IBM Research in Zurich have developed artificial neurons that emulate how neurons spike (fire). The goal is to create energy-efficient, high-speed, ultra-dense integrated neuromorphic (brain-like) technologies for applications in cognitive computing, such as unsupervised learning for detecting and analyzing patterns.

Applications could include internet of things sensors that collect and analyze volumes of weather data for faster forecasts and detecting patterns in financial transactions, for example.

Read more

Luv this.


The University of Bristol’s Quantum Technology Enterprise Centre (QTEC) is looking to recruit its first cohort of Enterprise Fellows that will be the next generation of quantum technology entrepreneurs.

Merging training in systems thinking, quantum engineering and entrepreneurship, QTEC will provide the necessary skills for budding innovators to develop their own business ideas and for them to branch out into the emerging field of quantum technologies.

The Centre, which is the first of its kind in the world, was funded as part of the UK’s £270 million investment into quantum technologies. These technologies exploit the laws of quantum mechanics to create practical and useful technologies that will outperform their classical rivals and that have the potential to transform artificial intelligence, healthcare, energy, finance, cyber security and the internet.

Read more

Sad for Russia.


President Vladimir Putin and other Russian officials dream of a technological leap that could immediately close the gap between Russia and more advanced economies, as Sputnik did for the Soviet Union. The hyperloop, a kind of train in a tube that can reach speeds of up to 700 mph, fits that dream, and a well-connected Russian businessman has invested in it — only to see the project become embroiled in a lawsuit involving a Silicon Valley startup’s founders and claims of financial mismanagement.

Elon Musk, Tesla’s chief executive, proposed the hyperloop four years ago. This “fifth mode of transport” would involve a system of practically airless tubes through which magnetically levitated pods could carry passengers and cargo. Musk has not set up a company to bring the project to reality, but others have. For example, Hyperloop Transportation Technologies, wants to build a system in Slovakia. Another, Hyperloop One, offered a public demonstration of some elements of its technology in May.

Hyperloop One has seemed the most advanced project, and Russian investors showed an interest from the start. The state-owned Russian Direct Investment Fund took a small stake in the company, but Ziyavudin Magomedov, head of Summa Capital, was the most enthusiastic Russian investor, putting up money for both of the company’s funding rounds.

Nice chime on QC.


Manoj Saxena is the executive chairman of CognitiveScale and a founding managing director of The Entrepreneurs’ Fund IV (TEF), a $100m seed fund focused exclusively on the cognitive computing space.

Saxena is also the chairman of Federal Reserve Bank of Dallas, San Antonio branch and Chairman, SparkCognition an Austin based cognitive security and safety analytics company.

Prior to joining TEF, Saxena was general manager, IBM Watson, where his team built the world’s first cognitive systems in healthcare, financial services, and retail. Earlier he founded, built and sold two Austin based software startups.

Nice.


Networks are mathematical representations to explore and understand diverse, complex systems—everything from military logistics and global finance to air traffic, social media, and the biological processes within our bodies. In each of those systems, a hierarchy of recurring, meaningful internal patterns—such as molecules and proteins interacting inside cells, and capacitors and resistors operating within integrated circuits—determines the functions or behaviors of those systems. The larger and more intricate a system is, however, the harder it is for current network modeling techniques to uncover these patterns and represent them in organized, easy-to-understand ways.

Researchers at Stanford University, funded by DARPA’s Simplifying Complexity in Scientific Discovery (SIMPLEX) program, have made progress in overcoming these challenges through a framework they have developed for identifying and clustering what mathematicians call “motifs”: essential but often obscure patterns within systems that are the building blocks of mathematical modeling and that facilitate the computational representation of complex systems.

A research paper describing the team’s achievement was published in Science (“Higher-order organization of complex networks”). At the heart of the team’s success was the creation of algorithms that can automatically explore and prioritize the hidden patterns in data that are fundamental to explaining network structure and function.