Articles about Machine Learning

In the past six months, COVID-19 has evolved from a speck on the world radar to a full-blown pandemic. While it has claimed the lives of many and shed a massive spotlight on some of the major issues in healthcare, it has also served as a catalyst for innovation.

As with nearly every element of the healthcare system, applications of machine learning and artificial intelligence (AI) have also been transformed by the pandemic. Although the power of machine learning and AI was being put to significant use prior to the Coronavirus outbreak, there is now increased pressure to understand the underlying patterns to help us prepare for any epidemic that might hit the world in the future.

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When progressive technologies start to deliver on their potential, we can expect a wholesale shift of vendors looking to get on the bandwagon. First the technology enthusiasts and early adopters will come to validate the promises of the newest technology and hone its potential into something viable for the mainstream. Once that is done, the early majority, late adopters and finally, even the skeptics jump in as well.

Finally the time is here for Artificial Intelligence and Machine Learning (AIML) in cyber. There is a widespread move out of the early adopter stage and into the early majority stage of adoption. We need to get onboard if we are going to thwart cybercriminals. The good news is that the industry is recognizing the power and the value of AIML and is finally making investments in this space.

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If you’re looking for a strategy to get ahead when it comes to customer acquisition, machine learning can be your secret weapon. While machine learning does fall under the larger category of artificial intelligence (AI), it’s a bit more specific and can be extremely effective technology to pair with your customer and prospect database. True AI can think for itself like Lieutenant Commander Data from Star Trek. Machine learning, however, can automate tasks and apply predictive analytics that drive meaningful growth.

Machine learning is the AI focal point for your customer relationship management (CRM) tool and can be the key to boosting your customer acquisition.

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The UK government has announced plans to spend £1.2 billion ($1.6 billion) on what it describes as "the world's most powerful weather and climate supercomputer".

The investment will make weather forecasts faster and more accurate, and the computer will make it easier to predict the impact of climate change. It will be managed by the Met Office, and will be used to help communities and government agencies better prepare for severe weather.

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Over the last decade we've seen significant advances in AI and machine learning. But there's more on its way, with ML set disrupt almost every industry sector in the decade to come.

We spoke to Eric Loftsgaarden, VP of Data Science and co-founder of consulting services company Atrium to find out how ML can be used to set businesses apart from their competitors and give start ups an edge in traditional industries.

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Machine learning development is still in its early stages in many enterprises but investment in the technology is on the increase according to a new report.

The report from Algorithmia shows 22 percent of respondents say their companies have been in production with machine learning for a year. However, 50 percent say they spend between eight and 90 days deploying a single machine learning model.

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Microsoft has announced that it is introducing new filters that will enable Xbox Live players to avoid language they may find offensive or unacceptable. It is hoped that the AI-powered system will help to reduce trolling and bullying.

The system will be optional and will offer three levels of language filtering, as well as the option of remaining unfiltered. It's an expansion of Microsoft's family settings, and the company says it recognizes "that while some adults use profanity without any ill intent while gaming, parents with small children likely won't find this same experience acceptable".

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Every year, 22 percent of eCommerce customers abandon their shopping carts due to website errors. Every year, insurance companies discharge up to 10 percent of their claims cost on fraudulent claims. Network outages cost up to $5,600 per minute. These and other failures represent anomalies that are detectable by machine learning in ways that human-powered monitoring can’t replicate.

When it comes to deploying a machine learning anomaly detection system, companies have the choice of either purchasing a ready-made system or developing their own. No matter what they choose, however, the resulting system should be based on criteria that account for their company’s size, needs, use cases and available resources. Here are the six principles that companies should pay attention to:

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As artificial intelligence and machine learning become more widespread, it's essential that developers have access to the latest models and data sets.

Today at the OSCON 2019 open source developer conference, IBM is announcing the launch of two new projects for developers.

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Data center environments must stay consistent regarding things like humidity and temperature. Otherwise, the costly equipment inside them could begin malfunctioning. Moreover, data center clients want assurances that the valuable information stored within a facility will be available whenever they need it, and maintaining consistency comes into play there, too.

Here are four ways machine learning and artificial intelligence (AI) are upending data center management.

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For many organizations, AI and machine learning are seen as a route to greater efficiency and competitive advantage.

But according to a new study conducted by Dimensional Research for Alegion  almost eight out of 10 enterprise organizations currently engaged in AI and ML report that projects have stalled, and 96 percent of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence.

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When you consider all the machine learning (ML) algorithms, you’ll find there is a subset of very pragmatic ones: neural networks. They usually require no statistical hypothesis and no specific data preparation except for normalization. The power of each network lies in its architecture, its activation functions, its regularization terms, plus a few other features.

When you consider architectures for neural networks, there is a very versatile one that can serve a variety of purposes -- two in particular: detection of unknown unexpected events and dimensionality reduction of the input space. This neural network is called autoencoder.

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Google has announced that it is closing down its artificial intelligence ethics council following controversy about board members. The Advanced Technology External Advisory Council (ATEAC) was formed just a week ago, but there was strong criticism of the decision to appoint Heritage Foundation president Kay Coles James to the board.

Rightwinger James has a history of opposing LGBTQ rights, and dozens of Google employees signed a petition in protest at her board membership. In response, Google has said that it is "going back the drawing board" and is ending the council.

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The biggest problem for security teams is often too much data and many are addressing this by turning to analytics and machine learning, according to a new report.

The study from CyberEdge Group surveyed 1,200 IT security decision makers and practitioners and finds 47 percent intend to deploy advanced analytics solutions in the next year.

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The use of more artificial intelligence to improve security has been touted for a while. New research from Webroot reveals that a majority of business are now actively exploring the technology.

It finds 71 percent of businesses surveyed in the United States plan to use more artificial intelligence and machine learning in their cybersecurity tools this year. However, a worrying 58 percent say that aren't sure what that technology really does.

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