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Introducing Baskerville (waf!)

#artificialintelligence

Baskerville is a machine operating on the Deflect network that protects sites from hounding, malicious bots. It's also an open source project that, in time, will be able to reduce bad behaviour on your networks too. Baskerville responds to web traffic, analyzing requests in real-time, and challenging those acting suspiciously. A few months ago, Baskerville passed an important milestone – making its own decisions on traffic deemed anomalous. The quality of these decisions (recall) is high and Baskerville has already successfully mitigated many sophisticated real-life attacks.


5 Must-Read Data Science Papers (and How to Use Them) - KDnuggets

#artificialintelligence

Data science might be a young field, but that doesn't mean you won't face expectations about having an awareness of certain topics. This article covers several of the most important recent developments and influential thought pieces. Topics covered in these papers range from the orchestration of the DS workflow to breakthroughs in faster neural networks to a rethinking of our fundamental approach to problem solving with statistics. The team at Google Research provides clear instructions on antipatterns to avoid when setting up your data science workflow. This paper borrows the metaphor of technical debt from software engineering and applies it to data science.


Council Post: How Is Big Data Analytics Using Machine Learning?

#artificialintelligence

Chithrai is the Chief Technology and Innovation Officer (CTIO) for InfoVision. It is no longer a secret that big data is a reason behind the successes of many major technology companies. However, as more and more companies embrace it to store, process and extract value from their huge volume of data, it is becoming a challenge for them to use the collected data in the most efficient way. That's where machine learning can help them. Data is a boon for machine learning systems.


To Operationalize AI, Invest in Humans - InformationWeek

#artificialintelligence

IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.


Neo4j Announces New Version of Neo4j for Graph Data Science

#artificialintelligence

Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.


Council Post: How Is Big Data Analytics Using Machine Learning?

#artificialintelligence

Chithrai is the Chief Technology and Innovation Officer (CTIO) for InfoVision. It is no longer a secret that big data is a reason behind the successes of many major technology companies. However, as more and more companies embrace it to store, process and extract value from their huge volume of data, it is becoming a challenge for them to use the collected data in the most efficient way. That's where machine learning can help them. Data is a boon for machine learning systems.


"How AI will power the next-gen applications in Connected Industries (CI)"

#artificialintelligence

Hiroshige Seko, the minister of Economy Trade and Industry (METI) of Japan introduced a new concept for their roadmap to realize'Society 5.0' the future urbanism as the next big thing in industries. He mentioned that we require another industrial revolution using advanced technological innovations including, AI, IoT, and Big Data; this would be'Connected Industries.' This was the inception of'Connected Industries' as introduced by Hiroshige with the impact on future lives. Artificial Intelligence or AI will be on a next-level role in this development, with a more significant impact on each ecosystem entity. Before moving ahead to understand the role of AI in the'Connected Industries', let's first understand AI and its applications.


The insideBIGDATA IMPACT 50 List for Q4 2020 - insideBIGDATA

#artificialintelligence

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. Our in-box is filled each day with new announcements, commentaries, and insights about what's driving the success of our industry so we're in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they're impacting the enterprise through leading edge products and services. We're happy to publish this evolving list of the industry's most impactful companies! The selected companies come from our massive data set of vendors and industry metrics.


How To Be A Fantastic Data Scientist: An Expert Shares His Secrets

#artificialintelligence

In the latest episode of our podcast, Machine Learning that Works, I had a great pleasure to talk to Gabriel Preda, a Lead Data Scientist at Endava and a Kaggle Grandmaster. For those of you who want to see the full interview, here is the video version. If, on the other hand, you prefer to read, I prepared a summary as well. It's not a faithful transcript of our conversation, but a structured and rephrased version of the interview, that includes the key points and observations. Without further ado, let's meet Gabriel Preda! I work for Endava, which is a software service company, and our projects are actually our clients' projects.


Microsoft Teams will use artificial intelligence to better reduce background noises in video conferences

USATODAY - Tech Top Stories

Always worried about the potential for embarrassing background noises at home during video meetings? Microsoft is working on an update that could save you from future videoconferencing faux pas. The company's Microsoft 365 roadmap lists as in development "AI-based real-time noise suppression," which is scheduled for release in November 2020. The feature, spotted by news site Windows Latest, "will automatically remove unwelcome background noise during your meetings." Artificial intelligence technology is used to analyze a user's audio and "specially trained deep neural networks" will filter out noises and keep the person's voice, the software giant's planning document says.