Lifeboat Foundation

The lab will work in areas such as quantum computing, explainable artificial intelligence (AI) that presents data in a manner that can be understood by humans, and Metaverse, a virtual world where people can connect through their digital avatars.

Tech Mahindra already has 10 Makers Lab across the world and the new unit at Mahindra University will be the 11th facility globally and second in Hyderabad, said the company.

“With the launch of Makers Lab, we will provide many talented and skilled individuals, with the opportunity to progress for a greater cause,” said CP Gurnani, MD & CEO, Tech Mahindra.

IT major Tech Mahindra (TechM) and Mahindra University have signed a memorandum of understanding (MoU) to set up a new ‘Makers Lab’ for research and development. The lab will work in areas such as quantum computing, explainable artificial intelligence (AI) that presents data in a manner that can be understood by humans, and Metaverse, a virtual world where people can connect through their digital avatars.

Tech Mahindra already has 10 Makers Lab across the world and the new unit at Mahindra University will be the 11th facility globally and second in Hyderabad, said the company.

“With the launch of Makers Lab, we will provide many talented and skilled individuals, with the opportunity to progress for a greater cause,” said CP Gurnani, MD & CEO, Tech Mahindra.

What do we know about cortical columns? A neuroscience (and artificial intelligence) discussion with Jeff Hawkins of Numenta.

To read about all our work on solving protein folding, go to deepmind.com/AlphaFold or read a timeline of the breakthrough here.

It’s been one year since we released and open sourced AlphaFold, our AI system to predict the 3D structure of a protein just from its 1D amino acid sequence, and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world. Proteins are the building blocks of life, they underpin every biological process in every living thing. And, because a protein’s shape is closely linked with its function, knowing a protein’s structure unlocks a greater understanding of what it does and how it works. We hoped this groundbreaking resource would help accelerate scientific research and discovery globally, and that other teams could learn from and build on the advances we made with AlphaFold to create further breakthroughs. That hope has become a reality far quicker than we had dared to dream. Just twelve months later, AlphaFold has been accessed by more than half a million researchers and used to accelerate progress on important real-world problems ranging from plastic pollution to antibiotic resistance.

Continue reading “AlphaFold reveals the structure of the protein universe” »

The Army will focus on light robotic combat vehicles.

MRI, electroencephalography (EEG) and magnetoencephalography have long served as the tools to study brain activity, but new research from Carnegie Mellon University introduces a novel, AI-based dynamic brain imaging technology which could map out rapidly changing electrical activity in the brain with high speed, high resolution, and low cost. The advancement comes on the heels of more than thirty years of research that Bin He has undertaken, focused on ways to improve non-invasive dynamic brain imaging technology.

Brain electrical activity is distributed over the three-dimensional brain and rapidly changes over time. Many efforts have been made to image brain function and dysfunction, and each method bears pros and cons. For example, MRI has commonly been used to study brain activity, but is not fast enough to capture brain dynamics. EEG is a favorable alternative to MRI technology however, its less-than-optimal spatial resolution has been a major hindrance in its wide utility for imaging.

Continue reading “Advancing dynamic brain imaging with AI” »

Researchers at the Francis Crick Institute have developed an imaging technique to capture information about the structure and function of brain tissue at subcellular level—a few billionths of a meter, while also capturing information about the surrounding environment.

The unique approach detailed in Nature Communications today (25 May), overcomes the challenges of imaging tissues at different scales, allowing scientists to see the surrounding cells and how they function, so they can build a complete picture of neural networks in the brain.

Various imaging methods are used to capture information about tissue, cells and subcellular structures. However, a single method can only capture information about either the structure or function of the tissue and looking in detail at a nanometer scale means scientists lose information about the wider surroundings. This means that to gain an overall understanding of the tissue, imaging techniques need to be combined.

The human brain is less accessible than other organs because it is covered by a thick, hard skull. As a result, researchers have been limited to low-resolution imaging or analysis of brain signals measured outside the skull. This has proved to be a major hindrance in brain research, including research on developmental stages, causes of diseases, and their treatments. Recently, studies have been performed using primary neurons from rats or human-derived induced pluripotent stem cells (iPSCs) to create artificial brain models that have been applied to investigate brain developmental processes and the causes of brain diseases. These studies are expected to play a key role to unlocking the mysteries of the brain.

In the past, artificial brain models were created and studied in 2D; however, in 2017, a research team from KIST developed a 3D artificial brain model that more closely resembled the real brain. Unfortunately, due to the absence of an analytical framework for studying signals in a 3D brain model, studies were limited to analyses of surface signals or had to reform the 3D structure to a flat shape. As such, tracking neural signals in a complex, interconnected artificial network remained a challenge.

The Korea Institute of Science and Technology (KIST) announced that the research teams of Doctors Il-Joo Cho and Nakwon Choi have developed a analysis system that can apply precise non-destructive stimuli to a 3D artificial neural circuit and measure neural signals in real-time from multiple locations inside the model at the cellular level.

The table also shows the average normalized rank of transfer learning approaches. Hyperparameter transfer learning uses evaluation data from past HPO tasks in order to warmstart the current HPO task, which can result in significant speed-ups in practice.

Syne Tune supports transfer-learning-based HPO via an abstraction that maps a scheduler and transfer learning data to a warmstarted instance of the former. We consider the bounding-box and quantile-based ASHA, respectively referred to as ASHA-BB and ASHA-CTS. We also consider a zero-shot approach (ZS), which greedily selects hyperparameter configurations that complement previously considered ones, based on historical performances; and RUSH, which warmstarts ASHA with the best configurations found for previous tasks. As expected, we find that transfer learning approaches accelerate HPO.

Our experiments show that Syne Tune makes research on automated machine learning more efficient, reliable, and trustworthy. By making simulation on tabulated benchmarks a first-class citizen, it makes hyperparameter optimization accessible to researchers without massive computation budgets. By supporting advanced use cases, such as hyperparameter transfer learning, it allows better problem solving in practice.