Lifeboat Foundation

Machines lace almost all social, political cultural and economic issues currently being discussed. Why, you ask? Clearly, because we live in a world that has all its modern economies and demographic trends pivoting around machines and factories at all scales.

We have reached the stage in the evolution of our civilization where we cannot fathom a day without the presence of machines or automated processes. Machines are not only used in sectors of manufacturing or agriculture but also in basic applications like healthcare, electronics and other areas of research. Although, machines of varying types had entered the industrial landscape long ago, technologies like nanotechnology, the Internet of Things, Big Data have altered the scenario in an unprecedented manner.

The fusion of nanotechnology with conventional mechanical concepts gives rise to the perception of ‘molecular machines’. Foreseen to be a stepping stone into nano-sized industrial revolution, these microscopic machines are molecules designed with movable parts that behave in a way that our regular machines operate in. A nano-scale motor that spins in a given direction in presence of directed heat and light would be an example of a molecular machine.

New biomarkers for aging is good news for researchers!

“Given the high volume of data being generated in the life sciences, there is a huge need for tools that make sense of that data. As such, this new method will have widespread applications in unraveling the molecular basis of age-related diseases and in revealing biomarkers that can be used in research and in clinical settings. In addition, tools that help reduce the complexity of biology and identify important players in disease processes are vital not only to better understand the underlying mechanisms of age-related disease but also to facilitate a personalized medicine approach. The future of medicine is in targeting diseases in a more specific and personalized fashion to improve clinical outcomes, and tools like iPANDA are essential for this emerging paradigm,” said João Pedro de Magalhães, PhD, a trustee of the Biogerontology Research Foundation.

The algorithm, iPANDA, applies deep learning algorithms to complex gene expression data sets and signal pathway activation data for the purposes of analysis and integration, and their proof of concept article demonstrates that the system is capable of significantly reducing noise and dimensionality of transcriptomic data sets and of identifying patient-specific pathway signatures associated with breast cancer patients that characterize their response to Toxicol-based neoadjuvant therapy.

Continue reading “BGRF scientists publish seminal paper and announce project to develop biomarkers of aging” »

In Brief:

• Researchers have created a heuristically trained neural network that outperformed conventional machine learning algorithms by 160 percent and its own training by 9 percent.
• This new teaching method could enable AI to make correct classifications of data that’s previously unknown or unclassified, learning information beyond its data set.

Machine learning technology in neural networks has been pushing artificial intelligence (AI) development to new heights. Most AI systems learn to do things using a set of labelled data provided by their human programmers. Parham Aarabi and Wenzhi Guo, engineers from the University of Toronto, Canada have taken machine learning to a different level, developing an algorithm that can learn things on its own, going beyond its training.

Internet users leave a data trail of over 2.5 billion gigabytes of data daily, and this tool gives a look at what could be done with this kind of big data.

Great to see more quality aging biomarkers arriving for researchers. Well done to Alex Zhavoronkov and his team for this brilliant work.

Pathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification.

Google has built machine learning systems that can create their own cryptographic algorithms — the latest success for AI’s use in cybersecurity. But what are the implications of our digital security increasingly being handed over to intelligent machines?

Google Brain, the company’s California-based AI unit, managed the recent feat by pitting neural networks against each other. Two systems, called Bob and Alice, were tasked with keeping their messages secret from a third, called Eve. None were told how to encrypt messages, but Bob and Alice were given a shared security key that Eve didn’t have access too.

Continue reading “Battle of the Bots: How AI Is Taking Over the World of Cybersecurity” »

To better understand how the brain identifies patterns and classifies objects — such as understanding that a green apple is still an apple even though it’s not red — Sandia National Laboratories and the Intelligence Advanced Research Projects Activity are working to build algorithms that can recognize visual subtleties the human brain can divine in an instant.

They are overseeing a program called Machine Intelligence from Cortical Networks, which seeks to supercharge machine learning by combining neuroscience and data science to reverse-engineer the human brain’s processes. IARPA launched the effort in 2014.

Sandia officials recently announced plans to referee the brain algorithm replication work of three university-led teams. The teams will map the complex wiring of the brain’s visual cortex, which makes sense of input from the eyes, and produce algorithms that will be tested over the next five years.

Continue reading “Building the foundation for AI-enabled computer vision” »

Quantum computing is about to get more complex. Researchers have evidence that large molecules made of nickel and chromium can store and process information in the same way bytes do for digital computers. The researchers present algorithms proving it’s possible to use supramolecular chemistry to connect “qubits,” the basic units for quantum information processing, in Chem on November 10. This approach would generate several kinds of stable qubits that could be connected together into structures called “two-qubit gates.”

“We have shown that the chemistry is achievable for bringing together two-qubit gates,” says senior author Richard Winpenny, Head of the University of Manchester School of Chemistry. “The molecules can be made and the two-qubit gates assembled. The next step is to show that these two-qubit gates work.”

What if we could find one single equation that explains every force in the universe? Professor Michio Kaku explores how physics could potentially shrink the science of the big bang into an equation as small as E=mc².