It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the \textit{"significance"} of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11\%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale.

Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber is proposed, providing guidance as to what computational processes one should hypothesize if one is provided constraints to work within. It is conjectured that, under suitable assumptions, computational processes obeying algorithmic Markov conditions will maximize algorithmic caliber. It is proposed that in accordance with this, real-world cognitive systems may operate in substantial part by modeling their environments and choosing their actions to be (approximate and compactly represented) algorithmic Markov networks. These ideas are suggested as potential early steps toward a general theory of the operation of pragmatic generally intelligent systems.

This story was originally published by Next Avenue. Read all of Next Avenue's COVID-19 coverage geared toward keeping older generations informed, safe and prepared. Job hunting is never easy. But the coronavirus pandemic is creating challenges unlike any we've ever seen, with unemployment expected to hit 16% or higher and employers laying off or furloughing millions. The job search engine site Indeed says job postings in late April were more than a third lower than a year ago.

While working on different Machine Learning techniques for Data Analysis, we deal with hundreds or thousands of variables. Most of the variables are correlated with each other. Principal Component Analysis and Factor Analysis techniques are used to deal with such scenarios. Principal Component Analysis (PCA) is an unsupervised statistical technique algorithm. PCA is a "dimensionality reduction" method.

Sign in to report inappropriate content.

With each passing day, it just gets a little crazier as a result of COVID-19. As each day goes by, I hear about another person who has contracted COVID-19, or sadly, someone else who knows someone who has passed as a result. And all I can hope is that we are getting closer to seeing an end to this pandemic. Obviously, it's a stressful time and a lot of terrible things are happening. But I also want to acknowledge that so many people are stepping up and being kind to one another.

As with nearly every element of the healthcare world, applications of artificial Intelligence and machine learning have been reshaped by the COVID-19 pandemic. Whole business models and strategies became outdated and entirely new approaches have replaced them. Three industry leaders who compared notes on a recent HIMSS20 Digital presentation all agree: AI and machine learning technologies are key to responding to the coronavirus pandemic, and how they can aid patients and providers. They will also have an additional role in helping healthcare businesses recover financially while also identifying and serving those most in need when it has ended, the group said. Their online conversation, Reactions from the Field: Artificial Intelligence Machine Learning, centered on how AI is being deployed right now to help care for patients, aid researchers in the hunt for therapeutics and vaccines – and how financially battered healthcare systems can use machine learning insights to help optimize their business models moving forward.

Archaeologists have uncovered scores of long-abandoned settlements along coastal Madagascar that reveal environmental connections to modern-day communities. They have detected the nearly indiscernible bumps of earthen mounds left behind by prehistoric North American cultures. Still other researchers have mapped Bronze Age river systems in the Indus Valley, one of the cradles of civilization. All of these recent discoveries are examples of landscape archaeology. They're also examples of how artificial intelligence is helping scientists hunt for new archaeological digs on a scale and at a pace unimaginable even a decade ago.

I'm pleased to announce the results of our open-source Coronavirus Drug Discovery Competition! In just 2 weeks, hundreds of developers from around the world signed up to join the fight against the novel coronavirus, using publicly available datasets and algorithms to come up with relevant solutions. The top 3 submissions, winning $3500 in prizes, stood out from the rest in terms of their algorithmic and reporting quality. In this episode, I'm going to announce each of their backgrounds, as well as dive into the various machine learning techniques they used to predict a suitable treatment for Coronavirus. The top submission identified a compound called Remdesivir as the the most promising treatment for COVID-2019, due to its high scoring inhibitory potential when docked against the Coronavirus main Protease.

The new report on the global Artificial Intelligence in the Telecommunication market is an extensive study on the overall prospects of the Artificial Intelligence in the Telecommunication market over the assessment period. Further, the report provides a thorough understanding of the key dynamics of the Artificial Intelligence in the Telecommunication market including the current trends, opportunities, drivers, and restraints. The report introspects the micro and macro-economic factors that are expected to nurture the growth of the Artificial Intelligence in the Telecommunication market in the upcoming years and the impact of the COVID-19 pandemic on the Artificial Intelligence in the Telecommunication . In addition, the report offers valuable insights pertaining to the supply chain challenges market players are likely to face in the upcoming months and solutions to tackle the same. The report suggests that the global Artificial Intelligence in the Telecommunication market is projected to reach a value of US$XX by the end of 2029 and grow at a CAGR of XX% through the forecast period (2019-2029).