Lifeboat Foundation

Instead of relying on a fixed catalogue of available materials or undergoing trial-and-error attempts to come up with new ones, engineers can turn to algorithms running in supercomputers to design unique materials, based on a “materials genome,” with properties tailored to specific needs. Among the new classes of emerging materials are “transient” electronics and bioelectronics that portend applications and industries comparable to the scale that followed the advent of silicon-based electronics.

In each of the three technological spheres, we find the Cloud increasingly woven into the fabric of innovation. The Cloud itself is, synergistically, evolving and expanding from the advances in new materials and machines, creating a virtuous circle of self-amplifying progress. It is a unique feature of our emerging century that constitutes a catalyst for innovation and productivity, the likes of which the world has never seen.

Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning. Now they can reduce quantum computing errors using custom machine learning algorithms.

Fundamental constants like e and π are ubiquitous in diverse fields of science, including physics, biology, chemistry, geometry, and abstract mathematics. Nevertheless, for centuries new mathematical formulas relating fundamental constants are scarce and are usually discovered sporadically by mathematical intuition or ingenuity.

Our algorithms search for new mathematical formulas. The community can suggest proofs for the conjectures or even propose or develop new algorithms. Any new conjecture, proof, or algorithm suggested will be named after you.

The potential of quantum computers to solve problems that are intractable for classical computers has driven advances in hardware fabrication. In practice, the main challenge in realizing quantum computers is that general, many-particle quantum states are highly sensitive to noise, which inevitably causes errors in quantum algorithms. Some noise sources are inherent to the current materials platforms. de Leon et al. review some of the materials challenges for five platforms for quantum computers and propose directions for their solution.

Science, this issue p. eabb2823.

Historical accounts of the mortality outcomes of the Black Death plague pandemic are variable across Europe, with much higher death tolls suggested in some areas than others. Here the authors use a ‘big data palaeoecology’ approach to show that land use change following the pandemic was spatially variable across Europe, confirming heterogeneous responses with empirical data.

Smart cities are supposed to represent the pinnacle of technological and human advancement. They certainly deliver on that promise from a technological standpoint. Smart cities employ connected IoT networks, AI, computer vision, NLP, blockchain and similar other technologies and applications to bolster urban computing, which is utilized to optimize a variety of functions in law enforcement, healthcare, traffic management, supply chain management and countless other areas. As human advancement is more ideological than physical, measuring it comes down to a single metric—the level of equity and inclusivity in smart cities. Essentially, these factors are down to how well smart city administrators can reduce digital exclusivity, eliminate algorithmic discrimination and increase citizen engagement. Addressing the issues related to data integrity and bias in AI can resolve a majority of inclusivity problems and meet the above-mentioned objectives. make smart cities more inclusive for people and communities from all strata of society, issues related to digital exclusion and bias in AI need to be addressed by public agencies in these regions.

Hundreds of millions of users. No algorithm. No ads. Courage in the face of autocracy. Sound like a dream? Careful what you wish for.

The algorithms spot and classify synthetic-material objects based on the distinctive manner in which they reflect polarized light. Polarized light reflected from human-made objects often differs from natural objects, such as vegetation, soil, and rocks.

The researchers tested such a camera, both on the ground and from a US Coast Guard helicopter, which was flying at the altitude at which the polarimetric-camera-equipped drones will fly.

Once fully operational, data collected by the drone-based machine learning system will be used to make maps that show where marine debris is concentrated along the coast to guide rapid response and removal efforts. The researchers will provide NOAA Marine Debris Program staff with training in the use of the new system, along with standard operating procedures manual.

Quantum computing and machine learning are two of the most exciting technologies that can transform businesses. We can only imagine how powerful it can be if we can combine the power of both of these technologies. When we can integrate quantum algorithms in programs based on machine learning, that is called quantum machine learning. This fascinating area has been a major area of tech firms, and they have brought out tools and platforms to deploy such algorithms effectively. Some of these include TensorFlow Quantum from Google, Quantum Machine Learning (QML) library from Microsoft, QC Ware Forge built on Amazon Braket, etc.

Students skilled in working with quantum machine learning algorithms can be in great demand due to the opportunities the field holds. Let us have a look at a few online courses one can use to learn quantum machine learning.

In this course, the students will start with quantum computing and quantum machine learning basics. The course will also cover topics on building Qnodes and Customised Templates. It also teaches students to calculate Autograd and Loss Function with quantum computing using Pennylane and to develop with the Pennylane.ai API. The students will also learn how to build their own Pennylane Plugin and turn Quantum Nodes into Tensorflow Keras Layers.