Researchers Combine AI & Quantum Mechanics To Solve Renewable Energy Problems

The sun doesn’t always shine and to store energy[batteries] in its absence is a costly affair. So, with all this talk about going green, how is it possible to incentivise people towards futuristic yet unsustainable and expensive goals?

To address this challenge, Facebook AI and the Carnegie Mellon University (CMU) have announced the Open Catalyst Project, a collaboration intended to use AI to accelerate quantum mechanical simulations by 1,000x in order to discover new electrocatalysts needed for more efficient and scalable ways to store and use renewable energy.

The goal of the Open Catalyst Project, stated the Facebook AI team, is to discover low-cost catalysts in order to find solutions that are good alternatives of current solutions that are inefficient or rely on rare and expensive electrocatalysts like platinum, limiting their practicality.

FB and CMU have even released the Open Catalyst 2020 (OC20) dataset and are also providing baseline models for the community to benchmark approaches against state-of-the-art and compare progress…

#developers corner #carnegie mellon university #ai

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Researchers Combine AI & Quantum Mechanics To Solve Renewable Energy Problems
Vincent Lab

Vincent Lab

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How to do Problem Solving as a Developer

In this video, I will be talking about problem-solving as a developer.

#problem solving skills #problem solving how to #problem solving strategies #problem solving #developer

Researchers Combine AI & Quantum Mechanics To Solve Renewable Energy Problems

The sun doesn’t always shine and to store energy[batteries] in its absence is a costly affair. So, with all this talk about going green, how is it possible to incentivise people towards futuristic yet unsustainable and expensive goals?

To address this challenge, Facebook AI and the Carnegie Mellon University (CMU) have announced the Open Catalyst Project, a collaboration intended to use AI to accelerate quantum mechanical simulations by 1,000x in order to discover new electrocatalysts needed for more efficient and scalable ways to store and use renewable energy.

The goal of the Open Catalyst Project, stated the Facebook AI team, is to discover low-cost catalysts in order to find solutions that are good alternatives of current solutions that are inefficient or rely on rare and expensive electrocatalysts like platinum, limiting their practicality.

FB and CMU have even released the Open Catalyst 2020 (OC20) dataset and are also providing baseline models for the community to benchmark approaches against state-of-the-art and compare progress…

#developers corner #carnegie mellon university #ai

Researchers Find A New Way To Make AI 1000X More Energy-Efficient

While it comes with extreme benefits, artificial intelligence has time and again proved to be notoriously energy-consuming when implemented with traditional methods. According to data, training a single AI model can produce the same amount of CO2 as done by five cars in their whole span. In an attempt to create energy-efficient AI, researchers at UCL have created a system that can enhance the performance of a brain-inspired computing system by reducing the AI’s carbon footprint.

In a recent study, published in a paper, the researchers and engineers of UCL have suggested the application of ‘committee machines’ on the memristor-based neural networks. Physically implemented neural networks undergo issues like device faults, random telegraph noise, variability in device-to-device interaction, and line resistance. Thus, it was noted that the accuracy of the AI system could be vastly improved by replacing the transistors with memristors for all devices. Deploying simulations and experimental data from memristive devices would further allow them to enhance their performance without increasing the number of memristors.

With the ever-increasing demand for data, it can cause difficulties in increasing data transmission capacity after a certain point; thus, this study can be extremely beneficial for the current era.

#opinions #1000x energy efficient ai #ai carbon foot print #energy efficient ai #ai

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Aileen  Jacobs

Aileen Jacobs

1597245602

Researchers Claim Inconsistent Model Performance In Most ML Research

The process of benchmarking is considered to be one of the most crucial assets for the progress of AI and machine learning research. The benchmark datasets are usually fixed sets of data, which are manually, semi-automatically as well as automatically generated to form a representative sample for these specific tasks to be solved by a model.

Recently, researchers from the Institute for Artificial Intelligence and Decision Support, Vienna claimed that the considerable part of metrics currently used to evaluate classification AI benchmark tasks might be inconsistent. It may result in a poor reflection in the performance of a classifier, especially when used with imbalanced datasets.

For the research, they analysed the present aspect of performance metrics that are based on data covering more than 3500 ML model performance results from a web-based open platform.

#developers corner #ai research benchmark #ai research papers #benchmark #benchmarking ai #bias in ml research #inconsistent benchmark