Lifeboat Foundation

In short, data-driven solutions themselves are only part of the overall approach. It is the effective integration of this fast-evolving technology into existing workflows and processes that leads to successful business outcomes.

The first step to integrating AI is identifying places and processes where it can help increase efficiency or accuracy. Businesses should step back and identify their pain points, creating a list of processes that are slow, tedious, cumbersome or suffering from a lack of staff. They should also analyze where additional data or information could help make better decisions.

In the pharma industry, data-driven AI solutions have been widely adopted in sales and marketing processes. For example, by analyzing patient and physician data, electronic medical records and demographic information, AI algorithms can identify trends, patterns and insights that help sales representatives tailor their messaging and presentations to specific HCPs.

AI isn’t programming’s future, it’s its present.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

Summary: Researchers leveraged a tracking algorithm from video games to study molecules’ behavior within live brain cells.

They adapted the fast and accurate algorithm used to track bullets in combat games for use in super-resolution microscopy. The innovative approach enables scientists to observe how molecules cluster together to perform specific functions in space and time within the brain cells.

The data obtained could shed light on molecular functions’ disruption during aging and disease.

Ultra-high pressure can have strange effects in physics and chemistry, and in a new study, high-pressure modeling has led to the prediction of four new compounds: compounds that don’t form in normal ways, have crystal structures we’ve never seen before, and can even act as superconductors in certain temperatures.

Those compounds are Li₁₄ Cs, Li₈Cs, Li₇Cs, and Li₆Cs, and they’re all formed from lithium (Li) and cesium (Cs) – though not in a conventional way. All four are superconductors, which means electricity can flow through them without resistance or energy loss.

The scientists behind the study used a special crystal structure prediction algorithm called USPEX (Universal Structure Predictor: Evolutionary Xtallography) to find these new compounds. It’s known as an evolutionary algorithm, using a range of methods to figure out the probability of how atoms will link together.

Dr. Ben Goertzel shares his thoughts on where we are at the end of 2021, beginning of 2022 — how progress toward AGI looks in retrospect, and looking into the future — updates on the ecosystem…

And the importance of the SingularityNET Community 🥰
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Continue reading “Ben Goertzel — 2021 Reflection and Update on SNET, Ecosystem and Path to AGI” »

Understanding the behavior of nuclear matter—including the quarks and gluons that make up the protons and neutrons of atomic nuclei—is extremely complicated. This is particularly true in our world, which is three dimensional. Mathematical techniques from condensed matter physics that consider interactions in just one spatial dimension (plus time) greatly simplify the challenge.

Using this two-dimensional approach, scientists solved the complex equations that describe how low-energy excitations ripple through a system of dense nuclear matter. This work indicates that the center of neutron stars, where such dense nuclear matter exists in nature, may be described by an unexpected form.

Being able to understand the quark interactions in two dimensions opens a new window into understanding neutron stars, the densest form of matter in the universe. The approach could help advance the current “golden age” for studying these exotic stars. This surge in research success was triggered by recent discoveries of gravitational waves and electromagnetic emissions in the cosmos.

Computer scientists have, for decades, been optimizing how computers sort data to shave off crucial milliseconds in returning search results or alphabetizing contact lists. Now DeepMind, based in London, has vastly improved sorting speeds by applying the technology behind AlphaZero — its artificial-intelligence system for playing the board games chess, Go and shogi — to a game of building sorting algorithms. “This is an exciting result,” said Emma Brunskill, a computer scientist at Stanford University, California.

The system, AlphaDev, is described in a paper in Nature¹, and has invented faster algorithms that are already part of two standard C++ coding libraries, so are being used trillions of times per day by programmers around the world.

One nebulous aspect of the poll, and of many of the headlines about AI we see on a daily basis, is how the technology is defined. What are we referring to when we say “AI”? The term encompasses everything from recommendation algorithms that serve up content on YouTube and Netflix, to large language models like ChatGPT, to models that can design incredibly complex protein architectures, to the Siri assistant built into many iPhones.

IBM’s definition is simple: “a field which combines computer science and robust datasets to enable problem-solving.” Google, meanwhile, defines it as “a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.”

It could be that peoples’ fear and distrust of AI comes partly from a lack of understanding of it, and a stronger focus on unsettling examples than positive ones. The AI that can design complex proteins may help scientists discover stronger vaccines and other drugs, and could do so on a vastly accelerated timeline.