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to adapt to constant changes in customers’ homes, Astro uses a deep-learning model to produce invariant representations of visual data, estimates sensor reliability to guide the fusion of sensor data, and prunes and compresses map representations: https://amzn.to/36qQUZo
A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
#consc… See more.
Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
Source: CORDIS
New research supported by the EU-funded HBP SGA3 and DoCMA projects is giving scientists new insight into human consciousness.
Led by Korea University and projects’ partner University of Liège (Belgium), the research team has developed an explainable consciousness indicator (ECI) to explore different components of consciousness.
Teledyne FLIR Defense has announced the partnership with MFE Inspection Solutions to integrate the FLIR MUVE C360 multi-gas detector on Boston Dynamics’ Spot robot and commercial unmanned aerial systems (UAS). The integrated solutions will enable remote monitoring of chemical threats in industrial and public safety applications.
The compact multi-gas detector can detect and classify airborne gas or chemical hazards, allowing inspection personnel to perform their job more safely and efficiently with integrated remote sensing capabilities from both the air and ground.
MUVE C360 is designed to operate on Boston Dynamics‘Spot mobile robot, which can autonomously inspect dangerous, inaccessible, or remote environments. It is also compatible with common commercial UAS systems, which allow operators to fly the C360 into a scene to perform hazard assessments in real-time.
Next, we aimed to determine whether the model type, i.e., a linear regression vs. a neural network, would significantly impact the performance. We, therefore, compared the aforementioned linear models with the neural network AltumAge using the same set of features. AltumAge outperformed the respective linear model with Horvath’s 353 CpG sites (MAE = 2.425 vs. 3.011, MSE = 32.732 vs. 46.867) and ElasticNet-selected 903 CpG sites (MAE = 2.302 vs. 2.621, MSE = 30.455 vs. 39.198). This result shows that AltumAge outperforms linear models given the same training data and set of features.
Lastly, to compare the effect of the different sets of CpG sites, we trained AltumAge with all 20,318 CpG sites available and compared the results from the smaller sets of CpG sites obtained above. There is a gradual improvement in performance for AltumAge by expanding the feature set from Horvath’s 353 sites (MAE = 2.425, MSE = 32.732) to 903 ElasticNet-selected CpG sites (MAE = 2.302, MSE = 30.455) to all 20,318 CpG sites (MAE = 2.153, MSE = 29.486). This result suggests that the expanded feature set helps improve the performance, likely because relevant information in the epigenome is not entirely captured by the CpG sites selected by an ElasticNet model.
Overall, these results indicate that even though more data samples lower the prediction error, AltumAge’s performance improvement is greater than the increased data effect. Indeed, the lower error of AltumAge when compared to the ElaticNet is robust to other data splits (Alpaydin’s Combined 5x2cv F test p-value = 9.71e−5).
Discovering a system’s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.
A research team from DeepMind, Mila – University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper Learning to Induce Causal Structure, proposing a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs. The team’s proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.
The team summarizes their main contributions as:
And an AI could generate a picture of a person from scratch if it wanted or needed to. its only a matter of time before someone puts it all together. 1. AI writes a script. 2. AI generates pictures of a cast (face/&body). 3. AI animates pictures of the cast into scenes. 4. it cant create voices from scratch yet, but 10 second audio sample of a voice is enough for it to make voices say anything; AI voices all the dialog. And, viola, you ve reduced TV and movie production costs by 99.99%. Will take place by 2030.
Google’s PHORUM AI shows how impressive 3D avatars can be created just from a single photo.
Until now, however, such models have relied on complex automatic scanning by a multi-camera system, manual creation by artists, or a combination of both. Even the best camera systems still produce artifacts that must be cleaned up manually.
With the massive degree of progress in AI over the last decade or so, it’s natural to wonder about its future – particularly the timeline to achieving human (and superhuman) levels of general intelligence. Ajeya Cotra, a senior researcher at Open Philanthropy, recently (in 2020) put together a comprehensive report seeking to answer this question (actually, it answers the slightly different question of when transformative AI will appear, mainly because an exact definition of impact is easier than one of intelligence level), and over 169 pages she lays out a multi-step methodology to arrive at her answer. The report has generated a significant amount of discussion (for example, see this Astral Codex Ten review), and seems to have become an important anchor for many people’s views on AI timelines. On the whole, I found the report added useful structure around the AI timeline question, though I’m not sure its conclusions are particularly informative (due to the wide range of timelines across different methodologies). This post will provide a general overview of her approach (readers who are already familiar can skip the next section), and will then focus on one part of the overall methodology – specifically, the upper bound she chooses – and will seek to show that this bound may be vastly understated.
Part 1: Overview of the Report
In her report, Ajeya takes the following steps to estimate transformative AI timelines:
Aulos Biosciences is now recruiting cancer patients in Australian medical centers for a trial of the world’s first antibody drug designed by a computer.
The computationally designed antibody, known as AU-007, was planned by the artificial intelligence platform of Israeli biotech company Biolojic Design from Rehovot, in a way that would target a protein in the human body known as interleukin-2 (IL-2).
The goal is for the IL-2 pathway to activate the body’s immune system and attack the tumors.