Industry News

Industry News

AI ML News October 2020 : Whisper’s hearing aids use AI to boost speech and reduce noise : Microsoft Is Enabling Its AI-Based Technology To Be Disability-Inclusive : A radical new technique lets AI learn with practically no data : This New Semi-Supervised Learning Method Is Gaining Traction : AI’s next big leap : IBM Spins off AI : CloudQuant attending The Trading Show Europe

AI & Machine Learning News. 19, October 2020

AI & Machine Learning News. 19, October 2020

AI and Machine Learning Newsletter

The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?

Whisper’s hearing aids use AI to boost speech and reduce noise

Whisper, a startup developing AI-powered hearing aids that self-tune over time, emerged from stealth today with $50 million. CEO Dwight Crow says the funds will be put toward the company’s go-to-market efforts as it ramps up production of its first product, the Whisper Hearing System.
The U.S. National Institute on Deafness looked at adults aged 70 and older who have hearing loss and could benefit from hearing aids and found that fewer than one in three (30%) had ever used them. Even fewer (approximately 16%) of the adults aged 20 to 69 who might benefit from hearing aids had ever considered trying them.
The reasons for this are myriad, but Crow says typical hearing aids require frequent adjustments, which puts most wearers off. “Technology should be used to improve people’s lives,” he said. “Many of the problems people face in hearing — whether hearing in a loud restaurant or having a device that quickly gets outdated — are solvable with recent advancements in consumer electronics and artificial intelligence.”

2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 2.0569, Raw Interest Score: 1.1635,
Positive Sentiment: 0.2909, Negative Sentiment 0.2909

CloudQuant Thoughts : One of the lowest rated articles that our system recommended this week was one of the most interesting for me. As populations in the west age, hearing loss and hearing aids become more of a priority. Everyone wears earpods now, so the stigma of wearing something in your ears has gone and AI can step in to bring hearing aids into the 21st, adaptively filtering out background noise and emphasizing human speech frequencies. This is a very cool product!

Microsoft Is Enabling Its AI-Based Technology To Be Disability-Inclusive

The lack of machine learning datasets that include people with disabilities has proved to be a major roadblock for developing technological solutions customised to their needs. This is often referred to as ‘data desert’. It is a common practice for organisations building technology products and services to use data at an aggregate level, which leads to stereotyping and being exclusionary in the process.
Earlier this week, Microsoft, in a lengthy blog, revealed its roadmap to deal with this data desert which has become a major hindrance in making artificial intelligence accessible to people with disability. The tech giant Microsoft has revealed its various collaborations to ‘shrink this data desert’ as discussed below.

2020-10-18 04:30:57+00:00 Read the full story…
Weighted Interest Score: 4.7638, Raw Interest Score: 1.2572,
Positive Sentiment: 0.1397, Negative Sentiment 0.1796

CloudQuant Thoughts : I partake in an App called “Be my eyes” where a blind person anywhere in the world can call for assistance from a sighted person facilitated by using their phone camera. Microsoft’s incredibly inclusive drive whether it be through AI apps like “Seeing AI” which would replace the Humans in Be My Eyes with an AI based system or more traditional barrier busters like the Adaptive Controller games controller are to be applauded!

A radical new technique lets AI learn with practically no data

“Less than one”-shot learning can teach a model to identify more objects than the number of examples it is trained on.
Machine learning typically requires tons of examples. To get an AI model to recognize a horse, you need to show it thousands of images of horses. This is what makes the technology computationally expensive—and very different from human learning. A child often needs to see just a few examples of an object, or even only one, before being able to recognize it for life.
In fact, children sometimes don’t need any examples to identify something. Shown photos of a horse and a rhino, and told a unicorn is something in between, they can recognize the mythical creature in a picture book the first time they see it.

2020-10-16 00:00:00 Read the full story…
Weighted Interest Score: 3.3524, Raw Interest Score: 1.4774,
Positive Sentiment: 0.2130, Negative Sentiment 0.0799

CloudQuant Attending The Trading Show Europe this Thursday – October  22nd 2020

CloudQuant will be attending The Trading Show Europe this Thursday, October 22nd. We will be showing our latests Alternative Data analysis and all of our dataset. Head over to the Trading Show website to register, it is Free and then swing by to say hi!
The Trading Show Europe Website.

This New Semi-Supervised Learning Method Is Gaining Traction

Deep neural networks are the most used model for computer vision applications, largely because of their scalability. Deep neural networks generally derive their superior performance through underlying supervised learning mechanisms.
Supervised learning is a type of deep learning methods which uses labelled datasets. While supervised learning offers superior performance benefits, it comes at a high cost, as labelling data requires human labour. Further, the cost is significantly higher when a data labelling has to be done by an expert, such as a medical practitioner.
In such a scenario, semi-supervised learning (SSL) proves to be a powerful alternative. SSL is a method where learning takes place with a small number of labelled data and a relatively larger set of unlabelled data. This method mitigates the need for labelling all the data as in the case of supervised learning.

2020-10-19 11:30:34+00:00 Read the full story…
Weighted Interest Score: 3.3689, Raw Interest Score: 1.5666,
Positive Sentiment: 0.2611, Negative Sentiment 0.1899

AI’s next big leap

The unlikely marriage of two major artificial intelligence approaches has given rise to a new hybrid called neurosymbolic AI. It’s taking baby steps toward reasoning like humans and might one day take the wheel in self-driving cars.
A few years ago, scientists learned something remarkable about mallard ducklings. If one of the first things the ducklings see after birth is two objects that are similar, the ducklings will later follow new pairs of objects that are similar, too. Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same color, even if they are blue, over two spheres that are each a different color. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. They can imprint on the notion of dissimilarity too.
What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.
2020-10-14 15:02:00 Read the full story…
Weighted Interest Score: 2.8465, Raw Interest Score: 1.3943,
Positive Sentiment: 0.1643, Negative Sentiment 0.4612

CloudQuant Thoughts : These three articles, combined with articles in previous weeks where we have had machine learning with “very small datasets” are what fascinate me about ML and AI.  The idea that we can go from needing big data, to small data, to “Less Than One”-shot object identification and finally to trying to emulate the in built imprinting mechanisms of young animals is astonishing.

IBM Spins off AI

The Impact Of IBM’s Move To Split On Its AI Initiatives

In one of the biggest news of the year, IBM recently announced that it is splitting its IT services business into a new company, temporarily named NewCo. The move headed by its CEO, Arvind Krishna, will lead to diversification of the world’s first big computing firm away from its legacy businesses to focus on high-margin cloud computing and AI business. The company believes that with this move, both the companies will be on an improved growth trajectory with more remarkable ability to partner and capture new opportunities.
With this, IBM becomes the big first computing firm to split up from its legacy business to focus on the new tech. The brand new IBM will accelerate and focus on the $1 trillion hybrid-cloud opportunity, whereas the NewCo. will focus on its services business with a revenue of around $19 billion. The NewCo will provide technical support to 4,600 clients in 115 countries, and is expected to record an expense of nearly $5 billion to be incurred in the separation and operational changes.
Krishna believes that this move will help the company revive growth after almost a decade of shrinking revenue, with a strategic focus on each of the businesses. “Now is the right time to create two market-leading companies focused on what they do best”, the company stated.
2020-10-18 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5108, Raw Interest Score: 1.3590,
Positive Sentiment: 0.3209, Negative Sentiment 0.0566

IBM Reports Earnings Today as Investors Digest Spinoff Plans

The company has already provided preliminary results, but it will probably have a lot to say about its doubling down on hybrid cloud computing.
It didn’t take long for International Business Machines (NYSE:IBM) CEO Arvind Krishna to shake up the century-old tech giant. Just six months after Krishna took the helm, IBM announced that it would spin off its gigantic managed infrastructure services provider business into a new company. The move will allow IBM to concentrate its efforts on hybrid cloud computing, artificial intelligence, and other growth areas.
IBM will report its third-quarter results after the market closes today. The numbers won’t be a surprise — along with announcing the spinoff, IBM provided preliminary third-quarter results. The company expects to produce revenue of $17.6 billion and adjusted earnings per share of $2.58. That revenue estimate is slightly ahead of analyst expectations, while the EPS estimate was in line.

2020-10-19 00:00:00 Read the full story…
Weighted Interest Score: 3.2178, Raw Interest Score: 1.6389,
Positive Sentiment: 0.1944, Negative Sentiment 0.1667

Living Lettuce, Vertical Gardening: This Startup Is Using AI For Organic Farming

The interest and popularity of organic and sustainable farming are increasing drastically. While the consumers are often sceptical about the food products that they consume, Dubai and New Delhi-based Barton Breeze is growing safe, delicious and healthy food while relying on analytics and AI. It offers top-quality products that are grown locally in nutrient-rich water without pesticides. The crops are harvested weekly and delivered to sales outlets within a couple of hours.
Following the principle of ‘living lettuce’, it follows a method where roots are left intact, which makes it last longer. The startup also follows vertical gardening where it uses vertically stacked growing beds, up to five levels high using less than 1% of the space required by the conventional growing, a precious commodity in densely populated urban areas.
After graduating from IIM Ahmedabad, Singh started working on a pilot project around hydroponics and set up two container farms in Dubai. “During this time I thought, a country like India with profound climate changes needs this technology more than anyone else,” he says. Soon after, Barton Breeze was established in 2015 in Dubai, UAE, with a mission for technology innovation in agriculture. As Singh recalls, the journey initially was challenging and well expected, but with the right vision, it became unstoppable.
2020-10-13 Read the Full Story…

Working with NLP datasets in Python

Tutorial: Comparing the new HuggingFace Datasets library with the TensorFlow Datasets library and other options

In the field of Deep Learning, datasets are an essential part of every project. To train a neural network that can handle new situations, one has to use a dataset that represents the upcoming scenarios of the world. An image classification model trained on animal images will not perform well on a car classification task.
2020-10-19 12:19:18.940000+00:00 Read the full story…
Weighted Interest Score: 6.8248, Raw Interest Score: 1.8462,
Positive Sentiment: 0.0726, Negative Sentiment 0.0726

RBC Capital Markets Launches AI-powered Trading Platform

RBC Capital Markets launches Aiden® – a new AI-powered electronic trading platform
Traders and AI scientists at RBC and Borealis AI collaborate to deliver a real-world AI solution to help improve trading results and insights for clients in a measurable and explainable way
TORONTO, October 14, 2020 – RBC Capital Markets today announced the launch of Aiden®, an AI-based electronic trading platform that uses the computational power of deep reinforcement learning in its pursuit of improved trading results and insights for clients.
The Aiden® platform was developed jointly by RBC Capital Markets and Borealis AI, a world-class AI research center created by RBC, as traders and AI scientists worked side-by-side to create the initial bold concept and deliver a real-world solution. In doing so, both organizations undertook one of the biggest challenges in the field of AI today – applying deep reinforcement learning into a constantly changing environment like equities trading, with measurable and explainable results for its users.

2020-10-14 13:15:08+00:00 Read the full story…
Weighted Interest Score: 6.4714, Raw Interest Score: 2.3261,
Positive Sentiment: 0.3292, Negative Sentiment 0.1756

CloudQuant Thoughts : Whilst much of the time one should take AI and Trading with a massive pinch of salt, one must not forget that IEX founders are ex-RBC people and THOR was an incredibly popular algo created by RBC to very effectively thwart HFTs and their “disappearing quotes”.

AI Governance Rises to the Top of the Stack

Artificial intelligence (AI) is running amok, or so that’s the general perception these days. AI governance is important because the stakes are so high for getting AI right and consequences so dire if we screw it up. Governance must be approached from a risk management perspective. AI’s principal risk factors are in the following areas:

  • Can we prevent AI from invading people’s privacy?
  • Can we eliminate socioeconomic biases that may be baked into AI-driven applications?
  • Can we ensure that AI-driven processes are entirely transparent, explicable, and interpretable to average humans?
  • Can we engineer AI algorithms so that there’s always a clear indication of human accountability, responsibility, and liability for their algorithmic outcomes?
  • Can we build ethical and moral principles into AI algorithms so that they weigh the full set of human considerations into decisions that may have life-or-death consequences?
  • Can we automatically align AI applications with stakeholder values, or at least build in the ability to compromise in exceptional cases, thereby preventing the emergence of rogue bots in autonomous decision-making scenarios?
  • Can we throttle AI-driven decision making in circumstances where the uncertainty is too great to justify autonomous actions?
  • Can we institute failsafe procedure so that humans may take back control when automated AI applications reach the limits of their competency?
  • Can we ensure that AI-driven applications behave in consistent, predictable patterns, free from unintended side effects, even when they are required to dynamically adapt to changing circumstances?
  • Can we protect AI applications from adversarial attacks that are designed to exploit vulnerabilities in their underlying statistical algorithms?
  • Can we design AI algorithms that fail gracefully, rather than catastrophically, when the environment data departs significantly from circumstances for which they were trained?

2020-10-13 00:00:00 Read the full story…
Weighted Interest Score: 5.5953, Raw Interest Score: 2.2791,
Positive Sentiment: 0.1057, Negative Sentiment 0.2702

Alation Partners with Dataiku to Improve Governance of Sensitive Data for AI Models

Alation, a provider of enterprise data intelligence solutions, has formed a partnership with Dataiku, an enterprise AI and machine learning platform, to ensure that sensitive data used to create AI and machine-learning models is properly classified and governed.
According to Alation, data science teams depend on its technology for critical insight into the right data to use, enabling them to find and understand trusted data with deep context for data modeling. With the Alation and Dataiku integration, data scientists have immediate access to curated data ingested directly into Dataiku. Models trained in Dataiku can then be governed and shared within Alation, making data science insights available to a broader user group throughout the organization. The integration will result in decreased time spent searching for data and subject matter experts, while also ensuring data quality.

2020-10-13 00:00:00 Read the full story…
Weighted Interest Score: 5.2168, Raw Interest Score: 2.7095,
Positive Sentiment: 0.1890, Negative Sentiment 0.1260

Generating Suitable ML Models Using LazyPredict Python Tool

While building machine learning models we are not sure which algorithm should work well with the given dataset, hence we end up trying many models and keep iterating until we get proper accuracy. Have you ever thought about getting all the basic algorithms at once to predict for model performance?
LazyPredict is a module helpful for this purpose. LazyPredict will generate all the basic machine learning algorithms’ performances on your model. Along with the accuracy score, LazyPredict provides certain evaluation metrics and the time taken by each model.
Lazypredict is an open-source python package created by Shankar Rao Pandala. Development and contribution to this are still going.

2020-10-18 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.2334, Raw Interest Score: 1.6213,
Positive Sentiment: 0.0523, Negative Sentiment 0.1918

CompassRed to Create a Data Innovation Lab to Address COVID-19

CompassRed, a data analytics and artificial intelligence company, will use its $2 million grant from the CARES Act to fund a Data Innovation Lab to accelerate the use of data insights and intelligence to address COVID-related issues in the Mid-Atlantic region.
“This grant recognizes the tremendous potential of data innovations to create jobs, stimulate the economy and build on the existing regional strengths we have already in the analytics space,” said Patrick Callahan, CEO, CompassRed. “Funding will develop new discoveries and technologies to help companies and the data industry evolve together. Potential applications include improved automation and transformation for businesses impacted by COVID-19, including enhanced pandemic analytics and data visualization strategies.”
The goal of the Data Innovation Lab will be to engage local and global organizations in an ongoing conversation around the use of advanced data analytics and artificial intelligence to fast forward research ideas out of the lab and into the marketplace.

2020-10-16 00:00:00 Read the full story…
Weighted Interest Score: 4.0354, Raw Interest Score: 2.0476,
Positive Sentiment: 0.6641, Negative Sentiment 0.0553

Dataloop Drives Labeling Into the DataOps Pipeline

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it’s received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities.
Today’s computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide.

2020-10-16 00:00:00 Read the full story…
Weighted Interest Score: 4.0329, Raw Interest Score: 2.0719,
Positive Sentiment: 0.1268, Negative Sentiment 0.1268

BMO Increases Match Rate With Liquidity Awareness Signal

BMO Capital Markets has developed an algorithm which the bank said can achieve a nearly 500% improvement in hit rates for midpoint orders by increasing the probability of finding liquidity for a specific order type at different venues.
Ray Ross, co-head of electronic trading at Bank of Montreal (BMO Capital Markets), told Markets Media that the firm developed the Liquidity Awareness Signal because of the fragmentation of liquidity in the US equity market. Three new US equity exchanges launched last month taking the total to 16. In addition, there are more than 30 dark pools, as well as single-dealer venues.
Ross explained that the signal uses machine learning to analyse patterns amongst millions of trades. “Different orders behave differently on different venues,” he added. “They also have different decay rates – the length of time for which the signal is useful for future trading.” The bank said that using the dynamic signal means it is possible to achieve a nearly 500% improvement in hit rates for midpoint orders. “We knew hit rates would go up but it was a question of working out how to leverage the data,” said Ross. “The more trading is done, the more powerful the signal becomes.”

2020-10-13 09:35:35+00:00 Read the full story…
Weighted Interest Score: 3.9989, Raw Interest Score: 1.6903,
Positive Sentiment: 0.4362, Negative Sentiment 0.0545

Data Governance in Operations Needed to Ensure Clean Data for AI Projects

Organizations relying on AI and machine learning applications need to have a plan for data governance, to bridge operations and strategic vision. (Credit: Getty Images)
By AI Trends Staff
Data governance in data-driven organizations is a set of practices and guidelines that define where responsibility for data quality lives. The guidelines support the operation’s business model, especially if AI and machine learning applications are at work.
Data governance is an operations issue, existing between strategy and the daily management of operations, suggests a recent account in the MIT Sloan Management Review.
“Data governance should be a bridge that translates a strategic vision acknowledging the importance of data for the organization and codifying it into practices and guidelines that support operations, ensuring that products and services are delivered to customers,” stated author Gregory Vial is an assistant professor of IT at HEC Montréal.

2020-10-15 14:10:52+00:00 Read the full story…
Weighted Interest Score: 3.9963, Raw Interest Score: 2.0944,
Positive Sentiment: 0.1286, Negative Sentiment 0.1286

Tellius is Now Available on the AWS Marketplace

Tellius, the decision intelligence company, announced that the Tellius platform is available in AWS Marketplace, allowing customers to try, purchase, and deploy Tellius within their AWS account. Tellius offers a decision intelligence platform for business and analytics teams to make smarter decisions from their enterprise data using AI-driven Guided Insights.
“Tellius’ purpose is to accelerate data-driven decision making and make it accessible to every organization,” says Ajay Khanna, Founder and CEO, Tellius. “Offering Tellius in AWS Marketplace simplifies the procurement and deployment process so customers can focus on generating the insights from all their data to give them strategic advantage.”
Tellius’ listing in AWS Marketplace gives organizations a faster way to understand ‘what’ is driving business performance and uncover the reasons ‘why’ metrics change with machine learning automation and enhanced data scalability.

2020-10-13 00:00:00 Read the full story…
Weighted Interest Score: 3.8172, Raw Interest Score: 2.2212,
Positive Sentiment: 0.0945, Negative Sentiment 0.1890

Top 100 Banks Using Social Media for the Third Quarter of 2020

The top banks using social media usage ranked by their overall Power 100 score for the third quarter of 2020.

2020-10-12 00:14:48+00:00 Read the full story…
Weighted Interest Score: 3.6822, Raw Interest Score: 2.1318,
Positive Sentiment: 0.5814, Negative Sentiment 0.2907

FinTech Futures Jobs: five exciting fintechs that are hiring now

As the world begins to settle into what we’re calling the “new norm”, many companies are starting to hire once more, and talent has started to take notice.
The fintech sector is one that is thriving. In fact, the global fintech market is predicted to grow at a rate of almost 25% annually over the next couple of years, making it one of the most exciting industries to be involved in at the moment.
There are so many companies doing interesting things in the fintech sphere and looking for talent – and we’ve put five of them in the spotlight (do check out our job portal for all available vacancies!):

  1. Cleo AI
  2. BNY Mellon
  3. Monzo
  4. OakNorth
  5. Chip

2020-10-15 17:28:34+00:00 Read the full story…
Weighted Interest Score: 3.6632, Raw Interest Score: 1.5662,
Positive Sentiment: 0.2660, Negative Sentiment 0.0296

IBM Launches Artificial Intelligence Centre In Brazil

Introduced in 2019, by IBM, Brazil has launched the largest research facility, that focuses on artificial intelligence, through a collaboration between the private and public sector.

The Artificial Intelligence Center (C4AI) is supported by investments made by IBM along with the São Paulo Research Foundation (FAPESP) and the University of São Paulo (USP).

This AI centre — C4AI has been established to tackle five significant challenges that are related to health, the environment, the food production chain, the future of work and the development of NLP technologies in Portuguese. Along with this, it will also aid in projects relating to human wellbeing improvement as well as initiatives focused on diversity and inclusion.

2020-10-15 11:33:41+00:00 Read the full story…
Weighted Interest Score: 3.5759, Raw Interest Score: 1.4191,
Positive Sentiment: 0.2208, Negative Sentiment 0.0946

IIT-Jodhpur Launches Undergraduate Programme in AI & Data Science

he Indian Institute of Technology Jodhpur is launching a new BTech programme in artificial intelligence and data science from the academic session 2020-21. The new undergraduate programme will have courses in computer science, mathematics, artificial intelligence, machine learning, data science, and their applications in various domains.
According to the institute, students, once opted for the course, will be able to take a specialisation in various areas including visual computing, socio-digital realities, language technologies, robotics, and artificial intelligence and others. In IIT-Jodhpur’s official release, it has been mentioned that, with the course, enrolling students will also have the option to take up MBA (tech) in their fifth year as a dual-degree option in the School of Management and Entrepreneurship.
Prof Santanu Chaudhury, Director, IIT-Jodhpur, said that, with the vision of creating AI for everything, “students belonging to the academic programmes in artificial intelligence, data and computational sciences will be part of scientific innovations for addressing local and global engineering and social problems in close collaboration with industry.”
2020-10-12 Read the Full Story…

Automation and AI: Challenges and Opportunities

Businesses across the globe are fascinated with the idea of AI and automation because this advanced technology promises operational efficiency, enhanced processes, and substantial cost savings. However, AI and its allied technologies have also created uncertainties, confusion, and doubts about the human capability for adopting, deploying, and executing these magical systems in actual business situations — simply because the business leaders and operators are still all humans.
Today, it is widely acknowledged that automation and AI technologies will gradually transform the global workplace, with intelligent machines performing human tasks in some cases and aiding the human in other cases. The presence of robotic machines in the workplace will ultimately increase efficiency and reduce costs. As a result, many human occupations will disappear, while others will adapt to technology-enabled roles
Although businesses have shown a recent trend of hiring AI developers at a breakneck speed to fulfill their in-house automation needs, few understand the fundamental challenges that this technology brings with it. As a result, the “AI comfort zone” is still missing in enterprise business circles, and business operators are still doubtful about the cost benefits associated with AI.
2020-10-13 Read the full story…

Watson AI is Debatable

An AI system under development by IBM researchers seeks to move its Watson platform beyond chess matches and game shows, combining natural language processing (NLP), listening comprehension and the ability to model human dilemmas to create an agile debating machine.
Project Debater also uses sentiment analysis, deep neural networks and machine learning techniques to “mine” the claims and evidence behind an argument. The debater can tackle subjects it has not been trained on, instead scanning text and key sentences in minutes, selecting the strongest evidence for its position, then delivering an open statement in debates with humans.
It then listens to an opponent’s response before formulating a rebuttal. Rather than an academic exercise, the AI research is intended to augment human decision makers with tools that will inform their decisions. The six-year-old AI project has so far generated 45 technical papers and benchmarked data sets on subjects ranging from NLP and “argument mining” to “weak supervision” deep neural networks. In a reference to the rise of “Fake News,” IBM researchers note the need for better decision-making tools in a “world awash in information, misinformation and superficial thinking.”

2020-10-13 00:00:00 Read the full story…
Weighted Interest Score: 3.1660, Raw Interest Score: 1.6075,
Positive Sentiment: 0.1005, Negative Sentiment 0.4689


The future of the “agency” is being shaped by technology. The belief that artificial intelligence and machines will displace human labor is pervasive across many industries. For agencies, embracing artificial intelligence (AI) and intelligent automation (IA) comes with trepidation that the human side of creativity will be forever lost. Even though AI tantalizes marketers with a fantasy of computer creators that won’t push back and enamors agency executives with the prospect of offsetting outmoded labor models, there’s a problem with this narrative: AI won’t demolish the agency. It will enhance them.
Our future is a human plus machine, not a machine or human alone. When asked whether humans or computers will dominate future chess competitions, world champion Garry Kasparov said neither: A human with a computer will dominate both. The inimitable spark of human creativity and intuition shines through to complement the computers. The same is true for agencies. AI and intelligent automation combined with agencies’ experts will transform the work agencies do. For instance, data science has taken control of the insights role, producing near-real-time understanding of audiences and objectives. The velocity of content production is propelled by the speed and scale of technology plus data. AI and automation are pushing the boundaries of creativity by optimizing copy and dynamically compiling digital advertising. Media plans and channels are selected with the assistance of algorithms. And bots are administering the HR, IT, finance, and ad ops tasks.
2020-10-15 15:10:38-04:00 Read the full story…
Weighted Interest Score: 3.1069, Raw Interest Score: 1.6638,
Positive Sentiment: 0.6278, Negative Sentiment 0.2511

Five Ways Your Business Can Transform into a Data Innovator

My company, Splunk, recently partnered with ESG Research to uncover the true value of data to businesses. With the COVID-19 pandemic, the lessons from this research have become more important: Companies that have a grasp of their data and can innovate will be able to open their doors and get back to business faster than those who don’t.
Our research (which you can access here) identified three distinct stages of data maturity, defined by a company’s sophistication in discovering and operationalizing all data. We called them Data Deliberators, Data Adopters, and Data Innovators.
Data Deliberators are the least mature. Often, data silos and isolated information exists within these organizations, rendering it “dark” to the rest of an organization. They know that they need to start their digital transformation, but often don’t know how to accelerate those initiatives to create business outcomes.
Two-fifths of companies surveyed fall into the next category: Data Adopters. For Data Adopters the mission is clear: uncovering data is their organization’s most important IT priority. Better yet, they’re dedicating the resources necessary to make the most of their data, and 80% of Adopters have a chief data officer (CDO) or equivalent leading the charge.
Finally, the most sophisticated data organizations are Data Innovators.

2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 3.0268, Raw Interest Score: 1.5713,
Positive Sentiment: 0.7443, Negative Sentiment 0.2316

What are the important principles of data visualization?

The architecture of the data visualization must be focused on the empowerment of your company. To do so, there are important principles that need to be practiced. In this article, you can explore Data Visualisation, what visual encoding techniques should be considered, how you can refine the visualization and, what do you mean by narrative visualization?
What do you mean by Data Visualization? : Data visualization is the process of translating raw data into graphs, images that explain numbers and allow us to gain insight into them. It shifts the way we make use of the knowledge to build meaning out of it, to find new patterns, and to identify trends.
We, as humans, quickly comprehend information by visualization. In layman’s terms, data visualization is the graphical representation of the data procured. This allows decision-makers to move more effectively on the basis of the evidence visualized and presented. Visualization of data can help businesses.

2020-10-19 12:30:37.644000+00:00 Read the full story…
Weighted Interest Score: 3.0211, Raw Interest Score: 1.4253,
Positive Sentiment: 0.1705, Negative Sentiment 0.1584

Guided Labeling Episode 5: Blending Knowledge with Weak Supervision

Welcome to the fifth episode of our Guided Labeling Blog Series.In the last four episodes, we introduced Active Learning and a practical example with body mass index data, which shows how to perform active learning sampling via the technique “exploration vs exploitation”. This technique employs label density and model uncertainty to select which rows should be labeled first by the user of our active learning application.
The other episodes are here:

  1. An Introduction to Active Learning
  2. Label Density
  3. Model Uncertainty
  4. From Exploration to Exploitation

2020-10-16 07:35:31+00:00 Read the full story…
Weighted Interest Score: 2.9318, Raw Interest Score: 1.5822,
Positive Sentiment: 0.1868, Negative Sentiment 0.1319

Three Necessities for a Modern Analytics Ecosystem

Now, more than ever, enterprises need speed, agility, and insight to navigate today’s rapidly-changing business environments. Fast, actionable intelligence is a universal goal. However, making the right data available to the right people at the right time is an ongoing challenge. To cover the full spectrum of enterprise data — and the diverse needs of enterprise data users — traditional data warehousing and analytics systems need to be reexamined.
Join us for a special webinar on October 15th that dives into the three necessities for a modern analytics ecosystem today:

  • A Public Cloud Strategy: Public clouds enable a new era of application and data management while freeing companies from costly infrastructure administration and resource constraints necessary for data warehouses to reach their full potential.
  • An Integrated Data and Analytics Ecosystem: Modernizing the analytics ecosystem may begin with cloud data lakes or data science teams, but it is necessary to have a data warehouse within the cloud environment for integrated and defined data hubs and subject areas to draw upon.
  • A Streaming Data-First Strategy: Embracing a paradigm whereby all data flows in streams resets the common denominator for all analytics applications to leverage easily and faster.

2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 2.9044, Raw Interest Score: 1.5523,
Positive Sentiment: 0.3005, Negative Sentiment 0.2003

Image Classification in Python with Keras

Have you ever stumbled upon a dataset or an image and wondered if you could create a system capable of differentiating or identifying the image?
The concept of image classification will help us with that. Image Classification is one of the hottest applications of computer vision and a must-know concept for anyone wanting to land a role in this field.
In this article, we will see a very simple but highly used application that is Image Classification. Not only will we see how to make a simple and efficient model classify the data but also learn how to implement a pre-trained model and compare the performance of the two.
By the end of the article, you will be able to find a dataset of your own and implement image classification with ease.
Prerequisites before you get started:

  • Python programming
  • Keras and its modules
  • Basic understanding of Image Classification
  • Convolutional Neural Networks and its implementation
  • Basic understanding of Transfer learning

2020-10-16 08:10:24+00:00 Read the full story…
Weighted Interest Score: 2.8991, Raw Interest Score: 1.3928,
Positive Sentiment: 0.2659, Negative Sentiment 0.2089

Building an End to End Image Classification

In the recent years, face recognition applications have been developed on a much larger scale. Image classification and recognition has evolved and is being used at a number of places. I recently read an article where a face recognition application has been deployed at one of the airports for a completely automated check in process.
This will alleviate the need for manual intervention and provide a seamless end to end check in process via technology. It looks like a magical application for normal human beings but I will be talk about what is required for you to build an application of this kind on your own mobile phone.

  • Face Recognition – Phone cameras use face recognition for unlocking the phone. Face recognition systems could be deployed at entry gates of office buildings.
  • Image Classification – It is used for distinguishing between multiple image sets. Industries like automobile, retail, gaming etc. are using this for multiple purposes.
  • Image Recognition – Security companies use image recognition for detecting various things in bags at the airports, image scanners etc.

Steps to Build the App:

  • Obtain the Data
  • Data preparation
  • Data Modelling
  • Design the User Interface
  • Integrate User Interface and Modelling

2020-10-14 14:37:56+00:00 Read the full story…
Weighted Interest Score: 2.8226, Raw Interest Score: 1.4113,
Positive Sentiment: 0.1008, Negative Sentiment 0.0576

Enabling DataOps for Analytics

Modern enterprises need to quickly deliver the right data to a growing data consumer audience to drive strategic initiatives, often encompassing data science and machine learning, and thereby create competitive advantage. But many of these projects are failing because yesterday’s processes and systems can no longer meet today’s analytics requirements. Traditional data pipelines are breaking, and data quality is suffering.
We know well that data consumers’ expectations are rising. Analytics within the lines of business are demanding ever-higher volumes, variety and velocity of data, as well as rapid data transformation for analytics. Their SLAs are increasingly difficult to meet. Data managers within IT, meanwhile, are struggling with legacy systems and processes that were built for longer, batch-oriented cycle times. These two groups tend to speak different languages, further complicating efforts to collaborate.
DataOps seeks to fix these imbalances and put data-driven initiatives back on a sustainable footing. This emerging discipline encompasses processes and technologies that improve the speed, efficiency and flexibility of data pipelines. It incorporates agile development methodology, rapid response to user feedback and continuous data integration. Picture the lean manufacturing process, with data as the product.

2020-10-19 00:00:00 Read the full story…
Weighted Interest Score: 2.7977, Raw Interest Score: 1.5665,
Positive Sentiment: 0.4366, Negative Sentiment 0.2054

Modern Data Warehousing: Enterprise Must-Haves

THURSDAY, NOVEMBER 19, 2020 – 11:00 am PT / 2:00 pm ET
To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.

2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.7871, Raw Interest Score: 1.7877,
Positive Sentiment: 0.1117, Negative Sentiment 0.0000

Informatica Likes Its Chances in the Cloud

Quick: Name a company that made its name in the 1990s and 2000s by providing data integration tools for enterprise analytics running in on-prem data centers, but has since pivoted the cloud and was even named Snowflake’s partner of the year? If you said “Informatica,” then give yourself a green checkmark.
Informatica was born at the dawn of the data warehousing age, when Fortune 500 firms employed millions of people and spent billions of dollars to ensure their operational data was thoroughly cleansed and rationalized and stored in third-normal firm so that SQL-loving analysts write build reports describing what just happened, and also what to expect.

2020-10-13 00:00:00 Read the full story…
Weighted Interest Score: 2.6719, Raw Interest Score: 1.5773,
Positive Sentiment: 0.2191, Negative Sentiment 0.0876

Why the focus on risk weighted asset optimisation is increasing

The ongoing pandemic continues to impact banks and financial institutions globally. Regulatory bodies have reduced interest rates and provided moratoriums to ease the financial pressures on the end consumers, but the impact of such moves have resulted in a severe liquidity crunch for the financial institutions thereby impairing their profitability.
To reduce the liquidity pressure on the industry, regulators and central banks have also taken certain steps such as:

  • The US Federal Reserve purchasing $700 billion of longer-dated bonds to help markets function smoothly,
  • Bank of England putting its stress test on hold this year and advising banks to tap into £23 billion from their countercyclical capital buffers, reducing the same to 0% for next 12 months,
  • In Europe, EU-wide stress test exercises have been postponed to 2021.

While the above directives have provided some relief to banks these institutions are also assessing alternative avenues to help them free-up capital that can enable them to seamlessly continue with business operations. One such opportunity lies in optimising any risk weighted assets (RWAs), which is an integral part of the capital adequacy ratio (CAR) calculation process.

2020-10-16 00:00:26+00:00 Read the full story…
Weighted Interest Score: 2.6297, Raw Interest Score: 1.3866,
Positive Sentiment: 0.2152, Negative Sentiment 0.2630

Top Technology Jobs, Skills That Google Is Hiring For (clue – Python programmers!)

Work at Google is undergoing some big, systemic changes. At the end of September, Google CEO Sundar Pichai (who’s also CEO of Alphabet, parent company of Google) announced that employees could adopt a “hybrid” schedule, working from home for part of the week if they so desired.
Pichai announced that decision after Google’s internal surveys showed that a majority of employees only wanted to come into the office on some days. By offering that flexibility, Google stays competitive with Microsoft and other firms that are also adopting hybridized schedules for employees.
As Google moves into this new era, what kinds of jobs is it hiring for, and what skills does it need? We’ve noted before how the bulk of Google’s hiring seems focused on the fundamentals—popular programming languages such as Python and Java, and roles such as software developers. That hasn’t changed in our most recent analysis of data from Burning Glass, which collects and analyzes millions of job postings from across the country.
Here’s the breakdown of tech jobs that Google has hired for over the past 60 days:

2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 2.6044, Raw Interest Score: 1.9859,
Positive Sentiment: 0.2482, Negative Sentiment 0.0414

The Top Trends in Data Management for 2021

THURSDAY, DECEMBER 10, 2020 – 11:00 am PT / 2:00 pm ET

From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relational and non-relational databases, transactional and analytical systems, and on-premises and cloud sites.
Join us for a special expert panel on December 10th to dive into the key technologies and strategies to keep on your radar for 2021.

2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6729,
Positive Sentiment: 0.0929, Negative Sentiment 0.0929

Google Analytics 4 Released: Key AI/ML-Based Enhancements

Google has announced an overhauled version of Google Analytics. In one of the major revamps of the platform, in a decade, the new Google Analytics is built on the foundation of App+Web property, whose beta was introduced in 2019. The new Google Analytics has machine learning at its core and allows integration between analytics and Google Ads. The company claims that this would help customers manage their data better and can bear industry disruptions.
Among the most significant changes, the new analytics will alert the user of the significant trends in their data. Further, one can also anticipate actions that customers may take in the future. Other features include the addition of new predictive metrics. The company claims that such insights can help users and business owners achieve high-value customers and improve results by taking steps like analysis of customer expenditure patterns.

2020-10-17 10:30:57+00:00 Read the full story…
Weighted Interest Score: 2.3831, Raw Interest Score: 1.5000,
Positive Sentiment: 0.2500, Negative Sentiment 0.1136

Deep Learning based Recommender Systems

Traditionally, recommender systems are based on methods such as clustering, nearest neighbor and matrix factorization. However, in recent years, deep learning has yielded tremendous success across multiple domains, from image recognition to natural language processing. Recommender systems have also benefited from deep learning’s success. In fact, today’s state-of-the-art recommender systems such as those at Youtube and Amazon are powered by complex deep learning systems, and less so on traditional methods.
Why this tutorial? While reading through the many useful tutorials here that covers the basics of recommender systems using traditional methods such as matrix factorization, I noticed that there is a lack of tutorials that cover deep learning based recommender systems. In this notebook, we’ll go through the following:

  • How to create your own deep learning based recommender system using PyTorch Lightning
  • The difference between implicit and explicit feedback for recommender systems
  • How to train-test split a dataset for training recommender systems without introducing biases and data leakages
  • Metrics for evaluating recommender systems (hint: accuracy or RMSE is not appropriate!)

2020-10-19 02:56:35.148000+00:00 Read the full story…
Weighted Interest Score: 2.3627, Raw Interest Score: 1.0133,
Positive Sentiment: 0.1779, Negative Sentiment 0.0774

Artificial Intelligence And Africa: The Case For Investing In African Telecoms

Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa’s large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa’s telecom and data backbone.
Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent’s relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy. However, by setting their sights on participating in the ongoing fourth industrial revolution, developing nations in Africa may be able to chart a navigable course to rapidly raising living standards. With the window for pursuing labor led industrial development narrowing, Africa can’t afford to take a gradual approach towards rapidly matching prevailing technological standards. Several opportunities are open to Africa within the corridors of the coming age of hyperconnectivity and automation. Africa focused investors will be well served by a bold approach to the continent’s digital infrastructure.

2020-10-19 00:00:00 Read the full story…
Weighted Interest Score: 2.3583, Raw Interest Score: 1.2828,
Positive Sentiment: 0.3057, Negative Sentiment 0.1928

BMC’s 2020 Mainframe Survey Reveals New Strategic Priorities

BMC has announced the results of its 15th Annual Mainframe Survey. The findings reveal strong support for mainstreaming the mainframe, new strategic priorities, and a resurgence of next-generation mainframe talent.
The survey drew responses from more than 1,000 executives and practitioners on their priorities, challenges, and growth opportunities for the platform.
Insights from the survey results include the following:

  • 90% of respondents see the mainframe as a platform for growth and long-term applications.
  • 78% of respondents want to be able to update mainframe applications more frequently than currently possible.
  • 68% expect MIPS, the mainframe’s measure of computing performance, to grow.
  • 63% of respondents say security and compliance were their top mainframe priorities.
  • More than half of respondents (54%) reported an increase in transaction volume and 47% reported an increase in data volumes.

2020-10-19 00:00:00 Read the full story…
Weighted Interest Score: 2.2866, Raw Interest Score: 1.3486,
Positive Sentiment: 0.3678, Negative Sentiment 0.0409

Charts speak a thousand words – Cuemacro

I probably haven’t had as many burgers in recent months as usual. However, I am of endeavouring to make up for that in the coming months! The thing with burgers is, that it’s not just about the taste. It’s also about how good it looks. Even the tastiest burger in the world isn’t going to appeal quite as much if it doesn’t exactly look the part. Presentation is important with burgers, just as it is with food more broadly. It’s just that in general presentation might seem like a bit of an afterthought, compared with the actual cooking itself.
When it comes to analysing financial markets and data science more broadly, all the buzzwords seem to be about machine learning, artificial intelligence and so on, and for good reason. However, what’s even more important? Being able to understand and communicate your results not just to yourself but others. Obviously, in recent months the coronavirus crisis has really highlighted how important it is to be able to communicate what data is to the general public and not purely to statisticians.
Having tables and tables of numbers isn’t really that optimal for presenting your analysis, particularly if your audience isn’t technical. Even if they are technical, no one really wants to go through pages and pages of tables anyway. Having effective visualisation is key to presenting your results. Of course, we could opt for a simple line chart if we are presenting a time series. Or we can go one further step and use a candlestick chart if we are looking at P&L? Or maybe we can use some box plots?
2020-10-17 00:00:00 Read the full story…
Weighted Interest Score: 2.2856, Raw Interest Score: 1.0455,
Positive Sentiment: 0.2261, Negative Sentiment 0.0848

8 Powerful Hacks to Ace Data Science Hackathons

Data science hackathons can be a tough nut to crack, especially for beginners. Here are 12 powerful tips to crack your next data science hackathon!
Introduction : Like any discipline, data science also has a lot of “folk wisdom”. This folk wisdom is hard to teach formally or in a structured manner but it’s still crucial for success, both in the industry as well as in data science hackathons.
Newcomers in data science often form the impression that knowing all machine learning algorithms would be a panacea to all machine learning problems. They tend to believe that once they know the most common algorithms (Gradient Boosting, Xtreme Gradient Boosting, Deep Learning architectures), they would be able to perform well in their roles/organizations or top these leaderboards in competitions. Sadly, that does not happen!
If you’re reading this, there’s a high chance you’ve participated in a data science hackathon (or several of them). I’ve personally struggled to improve my model’s performance in my initial hackathon days and it was quite a frustrating experience. I know a lot of newcomers who’ve faced the same obstacle.
So I decided to put together 12 powerful hacks that have helped me climb to the top echelons of hackathon leaderboards. Some of these hacks are straightforward and a few you’ll need to practice to master.
If you are a beginner in the world of Data Science Hackathons or someone who wants to master the art of competing in hackathons, you should definitely check out the third edition of HackLive – a guided community hackathon led by top hackers at Analytics Vidhya.
The 12 Tips to Ace Data Science Hackathons

  1. Understand the Problem Statement
  2. Build your Hypothesis Set
  3. Team Up
  4. Create a Generic Codebase
  5. Feature Engineering is the Key
  6. Ensemble (Almost) Always Wins
  7. Discuss! Collaborate!
  8. Trust Local Validation
  9. Keep Evolving
  10. Build hindsight to improve your foresight
  11. Refactor your code
  12. Improve iteratively

2020-10-12 00:00:00 Read the full story…
Weighted Interest Score: 2.1856, Raw Interest Score: 1.1777,
Positive Sentiment: 0.3103, Negative Sentiment 0.1340

Silent Eight leverages AI to detect and solve financial fraud

Silent Eight, a cybersecurity startup leveraging AI to combat crime, today closed a $15 million funding round. The company says the funds will be used to accelerate current hiring efforts and fuel customer acquisition as it expands to new geographies.
While technologies like embedded chip cards and two-factor authentication have helped reduce financial fraud, the problem remains widespread. According to a report from Javelin, the number of consumers falling victim to identity fraud exceeded 14 million in 2018. At least 3.3 million of those were held partially liable for fraud committed against them, with out-of-pocket costs hitting a record $1.7 billion.
Silent Eight’s platform claims to avert fraud by learning how to conduct investigations from past alerts. It recognizes anomalous behavior by drawing on databases and watchlists and provides a degree of transparency regarding financial decisions.

2020-10-18 00:00:00 Read the full story…
Weighted Interest Score: 2.1787, Raw Interest Score: 1.2322,
Positive Sentiment: 0.0948, Negative Sentiment 0.6319

An AWS exec says that it is ‘urgency that has replaced perfectionism’ as financial institutions double-down on cloud amid the pandemic

The financial industry has a reputation for being willing to try out emerging technology, like cloud or artificial intelligence.
JPMorgan Chase, for example, is already investing in quantum computing despite the tech being years away from robust commercial use and Bank of America and Nasdaq were well into their cloud migrations before this year, only to have the projects validated by the coronavirus pandemic. HSBC also expanded its cloud investments during the outbreak.
While others remain skeptical out of fear of housing sensitive client data in public servers, among other concerns, those that did make the investments may have had an easier time pivoting after the pandemic forced them to require employees to work from home — including call center workers.
Amazon Web Services managing director Scott Mullins said that the pandemic has also created a record-breaking volume of trading for its Wall Street customers, which AWS supported through enabling them to quickly scale their services to “peak processing demands at a moment’s notice.”

2020-10-17 00:00:00 Read the full story…
Weighted Interest Score: 2.1647, Raw Interest Score: 1.1828,
Positive Sentiment: 0.2901, Negative Sentiment 0.1116

Real-Life Angel Investing Returns 2012–2016

I have been doing angel investing since 2012 after the Facebook IPO. Since then I have invested in more than 150+ startups. 50+ of them are direct investments where my name is on the cap table and 100+ are through SPV (special purpose vehicles) and crowd funding platforms like AngelList, FundersClub and MicroVentures. When I first started, there was not a lot of public data about the returns of angel investing. 8 years later, there is still not a lot of public data about angel investing returns. Chances are returns vary a lot since the top 10% of the investments determine the performance of an angel portfolio. Therefore, the variance of the returns is quite high.
After all these years, I believe stronger that angel investments as an investment class is worth pursuing if you have the money and the time. When you invest in a startup, the money directly goes to the economy to build up a business, to create jobs and to actually contribute to the trickle down economy. On the contrary, investing in the public market is simply buying stocks from other investors. The money doesn’t directly go into the economy. In addition, I believe angel investors can earn better returns than the public market through diversification. We will get to that later but let me start with sharing my real-life angel investing returns.
2020-10-18 23:34:38.244000+00:00 Read the full story…
Weighted Interest Score: 2.1576, Raw Interest Score: 1.1177,
Positive Sentiment: 0.2257, Negative Sentiment 0.1182

Lightweight Kubernetes Pushes Orchestrator to the Edge

Kubernetes, the evolving cluster orchestrator, has gone on a diet, stepping off the scales as a lightweight, resilient clustering tool that switches to autopilot once three or more nodes are clustered.
The slimmed-down version dubbed MicroK8s automatically migrates stored data between nodes to maintain a “quorum” in the event of a production failure, Canonical said this week in unveiling the micro-version of Kubernetes. The Ubuntu OS publisher aims MicroK8 at production workloads increasingly running in cloud and server deployments.
Given the complexities of deploying Kubernetes in production, Canonical is stressing its lightweight version as a “zero-ops” alternative for maintaining cloud-based microservices and micro datacenters used for edge computing applications.

2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 2.1339, Raw Interest Score: 1.1170,
Positive Sentiment: 0.0986, Negative Sentiment 0.2300

AtScale 2020.4 Extends OLAP Capabilities to Snowflake

AtScale, a software provider for advanced analytics, is releasing the AtScale 2020.4 platform, extending Cloud Online Analytical Processing (OLAP) capabilities for cloud data platforms, including Snowflake and analytics applications like Microsoft Excel and Tableau.
In addition, the AtScale 2020.4 platform includes new functionality for accelerating feature engineering in artificial intelligence (AI) and machine learning (ML) workflows while ensuring consistency and governance of the business inputs.
“We are proud of our long-term partnerships with Snowflake, Microsoft, and Tableau,” said Christopher Lynch, executive chairman and CEO, AtScale. “Our joint customers have experienced unparalleled performance and cloud adoption while accelerating the time to realize return on investment. We continue to invest jointly in our customers and our 2020.4 release is no exception.”

2020-10-16 00:00:00 Read the full story…
Weighted Interest Score: 2.1170, Raw Interest Score: 1.5800,
Positive Sentiment: 0.2312, Negative Sentiment 0.0000

This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. We used natural language processing (NLP) to determine an interest score, and to calculate the sentiment of the linked article using the Loughran and McDonald Sentiment Word Lists.

If you would like to add your blog or website to our search crawler, please email We welcome all contributors.

This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as investment advice or as a recommendation regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.