Dr. Ben Goertzel with Philip K. Dick at the Web Summit in Lisbon 2019.
Ben showcases the use of OpenCog within the SingularityNET enviroment which is powering the AI of the Philip K. Dick Robot.
This month, the UW team upped their game.
Tapping into both AlphaFold and RoseTTAFold, they tweaked the programs to predict which proteins are likely to tag-team and sketched up the resulting complexes into a 3D models.
Using AI, the team predicted hundreds of complexes—many of which are entirely new—that regulate DNA repair, govern the cell’s digestive system, and perform other critical biological functions. These under-the-hood insights could impact the next generation of DNA editors and spur new treatments for neurodegenerative disorders or anti-aging therapies.
The year is almost at an end, and so it is once again time for the obligatory trends articles (Trends for 2022). We already know that AI is impacting every industry. In past articles I have covered “The tipping point”, the fact that AI is already immersed in our daily lives and with no end in sight. Here I outline seven areas where we can expect a greater involvement of AI in the lives of all of us, in 2022.
Data marketplaces.
AI thrives on data, and the rise and ubiquity of AI has placed a yet greater emphasis on the value of data as both a competitive advantage and a core asset to companies and countries alike. This in turn has risen to privacy laws and efforts to educate the public on how their data can be used. These efforts are geared towards giving individuals agency in exercising their data rights. The confluence of these factors is already leading to data marketplaces. Data marketplaces are online venues where individuals and corporations can buy and sell data. Data marketplaces have the potential to combine democratized access, privacy controls and monetization models to enable data owners to benefit from data use.
REE Automotive has revealed Leopard, its autonomous concept vehicle based on a brand new ultra-modular EV platform design. The full-scale concept is intended for customers, including last-mile autonomous and electric delivery companies, delivery fleet operators, e-retailers, and technology companies seeking to build fully autonomous solutions.
Developed with leading global delivery and technology companies focused on autonomous delivery and Mobility as a Service (MaaS) fleets, the Leopard concept vehicle measures 3.4 meters in length and just 1.4 meters in width. It is built on a home-brewed platform that contains the batteries, along with REEcorner units, front-wheel-steer, rear-wheel-drive, steering, suspension, motor, gearbox, and braking components.
Leopard is powered by a 50 kWh battery of unspecified range and an undisclosed type of electric motor that provides a top speed of 60 mph (96 km/h). It has a cargo capacity of 180 cubic feet (5 cubic meters) and a gross vehicle weight rating of 2 tonnes (2.2 tons). The vehicle is also designed to carry significantly more cargo due to REE’s low, flat floor.
A robot-assisted surgery was completed in Israel using the Da Vinci surgical system, by which the surgeon sits at a console and controls the robot.
Qualcomm is diversifying from mobile phones, to supplying chips for BMW’s self-driving cars.
#News #Reuters #BMW #Qualcomm #SelfDriving.
Continue reading “Qualcomm to supply BMW with self-driving car chips” »
An artificial intelligence-based method for identifying patients who are at risk for atrial fibrillation has been developed by a team led by researchers at Harvard-affiliated Massachusetts General Hospital and the Broad Institute of MIT and Harvard.
Atrial fibrillation — an irregular and often rapid heart rate — is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. The study was published in Circulation.
The investigators developed the artificial intelligence-based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH.
Ad Astra School is the experimental school that Elon Musk started in one of SpaceX’s factories to give an education to his own children and selected children of SpaceX employees. The future of work will require a set of skills that are not taught in schools today. The future of work will involve robots and Artificial Intelligence collaborating with humans. The Astra Nova School’s pillars include caring about community, focusing on student experiences, and sharing the work they do with the world.
Here students learn about simulations, case studies, fabrication and design projects, labs, and corporate collaboration. In general, school systems are rigid. They are more system-centric than student-centric. Astra Nova is changing that by creating a philosophy of student centricity, a value for individual abilities, praising curiosity, and encouraging problem-solving and critical thinking.
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SVT Robotics, a provider of software that orchestrates robots in warehouses and factories, has raised $25 million in series A funding led by Tiger Global with participation from Prologis Ventures, the company announced this morning. SVT says that it’ll use the new capital to bolster its product R&D and expand its customer outreach efforts.
According to cofounder and CEO A.K. Schultz, SVT’s platform helps customers to solve the growing “interoperability problem” in industrial automation. The industry is severely limited by its capacity to execute, he says. Integrations are typically custom-coded, translating to long, complex development cycles. A recent piece in Industry Today finds that factors ranking among the top concerns of manufacturers adopting automation include a lack of experienced workers to operate the machines, high transition expenses, and safety concerns.
“It’s expensive, and companies wait as much as a year or more for new automation to go live,” Schultz said in a statement. “Solving that problem with [SVT’s platform] empowers the market to grow at its full potential.”
Machine learning (ML) models are powerful tools to study multivariate correlations that exist within large datasets but are hard for humans to identify16,23. Our aim is to build a model that captures the chemical interactions between the element combinations that afford reported crystalline inorganic materials, noting that the aim of such models is efficacy rather than interpretability, and that as such they can be complementary guides to human experts. The model should assist expert prioritization between the promising element combinations by ranking them quantitatively. Researchers have practically understood how to identify new chemistries based on element combinations for phase-field exploration, but not at significant scale. However, the prioritization of these attractive knowledge-based choices for experimental and computational investigation is critical as it determines substantial resource commitment. The collaborative ML workflow24,25 developed here includes a ML tool trained across all available data at a scale beyond that, which humans can assimilate simultaneously to provide numerical ranking of the likelihood of identifying new phases in the selected chemistries. We illustrate the predictive power of ML in this workflow in the discovery of a new solid-state Li-ion conductor from unexplored quaternary phase fields with two anions. To train a model to assist prioritization of these candidate phase fields, we extracted 2021 MxM ′yAzA ′t phases reported in ICSD (Fig. 1, Step 1), and associated each phase with the phase fields M-M ′-A-A′ where M, M ′ span all cations, A, A ′ are anions {N3−, P3−, As3−, O2−, S2−, Se2−, Te2−, F−, Cl−, Br−, and I−} and x, y, z, t denote concentrations (Fig. 1, Step 2). Data were augmented by 24-fold elemental permutations to enhance learning and prevent overfitting (Supplementary Fig. 2).
ML models rely on using appropriate features (often called descriptors)26 to describe the data presented, so feature selection is critical to the quality of the model. The challenge of selecting the best set of features among the multitude available for the chemical elements (e.g., atomic weight, valence, ionic radius, etc.)26 lies in balancing competing considerations: a small number of features usually makes learning more robust, while limiting the predictive power of resulting models, large numbers of features tend to make models more descriptive and discriminating while increasing the risk of overfitting. We evaluated 40 individual features26,27 (Supplementary Fig. 4, 5) that have reported values for all elements and identify a set of 37 elemental features that best balance these considerations. We thus describe each phase field of four elements as a vector in a 148-dimensional feature space (37 features × 4 elements = 148 dimensions).
To infer relationships between entries in such a high-dimensional feature space in which the training data are necessarily sparsely distributed28, we employ the variational autoencoder (VAE), an unsupervised neural network-based dimensionality reduction method (Fig. 1, Step 3), which quantifies nonlinear similarities in high-dimensional unlabelled data29 and, in addition to the conventional autoencoder, pays close attention to the distribution of the data features in multidimensional space. A VAE is a two-part neural network, where one part is used to compress (encode) the input vectors into a lower-dimensional (latent) space, and the other to decode vectors in latent space back into the original high-dimensional space. Here we choose to encode the 148-dimensional input feature space into a four-dimensional latent feature space (Supplementary Methods).
