Goto

Collaborating Authors

Machine Learning


How Having Bigger AI Models Can Have A Detrimental Impact On Environment

#artificialintelligence

The COVID crisis has skyrocketed the applications of artificial intelligence -- from tackling this global pandemic, to being a vital tool in managing various business processes. Despite its benefits, AI has always been scrutinised for its ethical concerns like existing biases and privacy issues. However, this technology also has some significant sustainability issues – it is known to consume a massive amount of energy, creating a negative impact on the environment. As AI technology is getting advanced in predicting weather, understanding human speech, enhancing banking payments, and revolutionising healthcare, the advanced models are not only required to be trained on large datasets, but also require massive computing power to improve its accuracy. Such heavy computing and processing consumes a tremendous amount of energy and emits carbon dioxide, which has become an environmental concern. According to a report, it has been estimated that the power required for training AI models emits approximately 626,000 pounds (284 tonnes) of carbon dioxide, which is comparatively five times the lifetime emissions of the average US car.


Top S&P 500 Stocks Based on Genetic Algorithms: Returns up to 75.82% in 3 Months

#artificialintelligence

This top S&P 500 stocks forecast is designed for investors and analysts who need predictions for the whole S&P 500. Package Name: Top S&P 500 Stocks Recommended Positions: Long Forecast Length: 3 Months (4/1/2020 – 7/1/2020) I Know First Average: 32.03% The greatest return came from ABMD at 75.82%. NVDA and ETFC also performed well for this time horizon with returns of 44.61% and 42.98%, respectively. The overall average return in this Top S&P 500 Stocks package was 32.03%, providing investors with a 11.47% premium over the S&P 500's return of 20.56% during the same period.


Stock Scanner Based on Genetic Algorithms: Returns up to 486.89% in 3 Months

#artificialintelligence

This stock scanner is part of the Risk-Conscious Package, as one of I Know First's equity research solutions. We determine our aggressive stock picks by screening our algorithm daily for higher volatility stocks that present greater opportunities but are also riskier. Package Name: Aggressive Stocks Forecast Recommended Positions: Long Forecast Length: 3 Months (4/1/2020 – 7/1/2020) I Know First Average: 100.66% The highest trade return came from NVAX, at 486.89%. NLS and DPW followed with returns of 259.77% and 213.26% for the 3 Months period.


Options Outlook Based on Pattern Recognition: Returns up to 63.69% in 1 Month

#artificialintelligence

This forecast is part of the Options Package, as one of I Know First's algorithmic trading tools. Package Name: Options Recommended Positions: Long Forecast Length: 1 Month (5/31/2020 – 7/1/2020) I Know First Average: 13.93% I Know First's State of the Art Algorithm accurately forecasted 8 out of 10 trades in this Options Package for the 1 Month time period. OSTK was the highest-earning trade with a return of 63.69% in 1 Month. Additional high returns came from DVAX and RH, at 35.78% and 19.72% respectively.


Artificial Intelligence Market Research Report (2020-2025) by Future Trend, Growth rate …

#artificialintelligence

Artificial intelligence uses the techniques such as natural language processing, machine learning, adaptive learning, deep learning, and computer vision …


Causal AI & Bayesian Networks

#artificialintelligence

We are all familiar with the dictum that "correlation does not imply causation". Furthermore, given a data file with samples of two variables x and z, we all know how to calculate the correlation between x and z. But it's only an elite minority, the few, the proud, the Bayesian Network aficionados, that know how to calculate the causal connection between x and z. Neural Net aficionados are incapable of doing this. Their Neural nets are just too wimpy to cut it.


Build AI you can trust with responsible ML

#artificialintelligence

As AI reaches critical momentum across industries and applications, it becomes essential to ensure the safe and responsible use of AI. AI deployments are increasingly impacted by the lack of customer trust in the transparency, accountability, and fairness of these solutions. Microsoft is committed to the advancement of AI and machine learning (ML), driven by principles that put people first, and tools to enable this in practice. In collaboration with the Aether Committee and its working groups, we are bringing the latest research in responsible AI to Azure. Let's look at how the new responsible ML capabilities in Azure Machine Learning and our open-source toolkits empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process.


Why IBM Decided to Halt all Facial Recognition Development

#artificialintelligence

In a letter to congress sent on June 8th, IBM's CEO Arvind Krishna made a bold statement regarding the company's policy toward facial recognition. "IBM no longer offers general purpose IBM facial recognition or analysis software," says Krishna. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." The company has halted all facial recognition development and disapproves or any technology that could lead to racial profiling. The ethics of face recognition technology have been in question for years. However, there has been little to no movement in the enactment of official laws barring the technology.


4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

#artificialintelligence

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for transfer learning (4S-DT) model.4S-DTencourages Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar.4S-DThas


Top 10 Courses to Learn AI, Machine Learning and Deep Learning

#artificialintelligence

Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques.