AI & Machine Learning News. 21, September 2020

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?


Snowflake IPO

Snowflake Pops in ‘Largest Ever’ Software IPO

Snowflake’s stock more than doubled today during its initial public offering, and the volatility was so great that stock market officials briefly halted trading in what the mainstream press is calling the largest ever IPO for a software company.

The fact that Snowflake is not a software company but a cloud services company would seem to make little difference to investors who racked up a big gains with the new equity, which trades on the New York Stock Exchange under the symbol SNOW.

The San Mateo, California-based company elected to price its stock debut at $120 per share, which was significantly higher than the $75 to $85 per share range that it proposed last week, according to a story on CNBC. The company planned to sell 28 million shares priced at $120 apiece, raising $3.4 billion and valuing it at over $33 billion.

2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 2.9375, Raw Interest Score: 1.3958,
Positive Sentiment: 0.2917, Negative Sentiment 0.0417

Snowflake IPO Raises $3.36 Billion, The Biggest So Far In 2020

Sydney-headquartered cloud data platform, Snowflake raised $3.36 billion in its initial public offering, which is also this year’s biggest US listing so far. The IPO was priced at $120.00 per share of Class A common stock. The company, according to its blog post, was aiming to sell it for 28 million.

This has surpassed the IPO of Royalty Pharma RPRX.O, which was the biggest so far, for the year 2020. Snowflake IPO comes as a rebound for the US stock market, as many companies had put a hold on IPO due to COVID-19 pandemic.
2020-09-17 11:42:10+00:00 Read the full story…
Weighted Interest Score: 2.6158, Raw Interest Score: 1.6894,
Positive Sentiment: 0.1635, Negative Sentiment 0.0000

Forget Snowflake: 3 Cloud Stocks I’d Rather Own

This has been a topsy-turvy year for Wall Street and the investment community. Though volatility never disappears from the stock market, we’ve borne witness to the wildest vacillations in history. The broad-based S&P 500 sank like a stone during the first quarter and lost 34% of its value in under five weeks. It also rebounded aggressively from its March 23 low, taking less than five months to regain all that was lost.

But the records just keep …
2020-09-21 00:00:00 Read the full story…
Weighted Interest Score: 2.0116, Raw Interest Score: 1.1687,
Positive Sentiment: 0.1992, Negative Sentiment 0.1726


Top 50 FREE Artificial Intelligence, Computer Science, Engineering and Programming Courses from the Ivy League Universities

We’ve decided to make a larger list of courses related to AI, CS, and Programming from the Ivy League. The Ivy League has the best courses in the world, and we feel that free courses from this caliber can help you a lot. Most of the courses are FREE to attend to, some of them may have some sort of certificate that may require some sort of payment, but you will be NOT required to pay, since the certificate does not represent your level of knowledge, but your work does.

So, our advice is, if you can’t afford to pay for the certificates, then don’t, the most important thing is to learn something from these courses, then later you can use it in your projects. From our experience so far, the certificates cost around $400-$500, and you will get a couple of them, which makes them cheaper than your local web design academy, but as we’ve said, those are not important, focus on learning.

2020-09-20 00:00:00 Read the full story…
CloudQuant Thoughts : Train on an Ivy league course for “cheaper than your local web design academy…” can’t say better than that!

Palantir is going public after 17 years — here’s what it does and why it’s been controversial

After 17 years on the private market, data analytics company Palantir is making its public market debut. This long-awaited news, along with its recent announcement that it will be moving its headquarters from Palo Alto to Denver, has put the software company in the spotlight.

Co-founded by Peter Thiel back in 2003, Palantir’s tech helps detect patterns in large datasets. The company is best known for its work with U.S. government agencies like the CIA (which was an early investor through its non-profit venture arm, In-Q-Tel), the Department of Defense, and — most controversially — Immigration and Customs Enforcement.

While these contracts have come under increased scrutiny during the Trump administration, in recent years Palantir has increasingly courted commercial customers too, which now make up almost half of the company’s revenue.
2020-09-20 00:00:00 Read the full story…
Weighted Interest Score: 2.3601, Raw Interest Score: 1.3112,
Positive Sentiment: 0.1748, Negative Sentiment 0.0874

CloudQuant Thoughts : Palantir’s history has been controversial, if they can pivot their expertise in pattern matching into other business environments with similar success then they will be  one to watch!

Alternative Data Sources: How to Improve Your Models

Picture this: You’ve been working hard on a project at work. You’ve run several algorithms, tuned the necessary hyperparameters, performed cross validation and exhausted the checks required to ensure you’re not overfitting. Yet, the performance metric isn’t where you would like it to be; or worse, isn’t where the business needs it to be. You take a hard look at your data science pipeline and don’t see any room for improvement. What do you do? Go back to the source; specifically, go to an alternative source.

FinTechs working in the credit space differentiate themselves by their ability to muster alternative data sources and put them through their analytics pipeline. These companies aim to predict a person’s default probability, i.e. how likely they won’t pay their loan. However, to get a competitive advantage from the established household names (e.g., Transunion, Equifax), they need to find uncharted information, clean it and finally, use it as input in their models.

2020-09-17 12:21:09-05:00 Read the full story…
Weighted Interest Score: 4.0917, Raw Interest Score: 1.6426,
Positive Sentiment: 0.2206, Negative Sentiment 0.1961

CloudQuant Thoughts : Alternative Data has taken root in the trading world and CloudQuant has one of the best data discovery/data delivery platforms out there.  Check out this link showing how one Alternative Data set may have signaled the recent dramatic $TSLA move. Head over to our Catalog to find out more or book a demo.

(Array programming in) Numpy paper published by Nature Magazine

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

2020-09-16 Read the full story…

CloudQuant Thoughts : The wornderful Numpy covered in a prestigious publication like Nature! Check out Cupy, like Numpy but for computation on a GPU.

This Programming Language Could Land You a Goldman Sachs Job

If you want a technology job at Goldman Sachs, you might not want to focus too hard on learning how to code in Python; despite the language’s rising ubiquity in everything from finance apps to machine learning, it’s not the firm’s favorite programming language. You probably want to focus on coding in Java instead.

In the past six months, data from Burning Glass indicates that Goldman Sachs advertised 134 new jobs mentioning Java, compared to 89 mentioning Python, 52 mentioning C++, and 46 mentioning JavaScript.

Goldman’s affinity for Java is nothing new. Developers who’ve worked for the bank say it’s known for being a Java house. “Goldman is all about Java and Slang,” said one, referring to the proprietary programming language that underpins the historic SecDB risk and pricing system.”There wasn’t much Python when I was there.”

2020-09-21 00:00:00 Read the full story…
Weighted Interest Score: 3.6425, Raw Interest Score: 2.4793,
Positive Sentiment: 0.1458, Negative Sentiment 0.0486

Data.world Aims to Rethink Data Catalogs

What is a data catalog? If you answered that it’s simply an index that tells you where to find data, then Brett Hurt would like a word with you. As the co-founder and CEO of data.world, Hurt is looking to redefine what is a data catalog. And with a fresh $26 million raised in a round of funding announced today, he’s on his way to doing just that.

“We really want to redefine what a data catalog actually means,” Hurt tells Datanami in a Zoom call last week from his Austin, Texas home. “It’s one thing to just have a library of your data assets and your analytics. It’s a whole other thing to actually use the data.”

Data.world does provide an index to customers’ data, as all data catalogs do. But by building the catalog atop a knowledge graph and extending it with the ability to execute federated queries through hooks with popular BI tools, the data.world offering goes beyond what most people think a data catalog is.

According to Hurt, who is a prolific tech investor and also a co-founder of a company called Coremetrics that was acquired by IBM in 2010, these additional capabilities in the cloud-based data catalog allows customers to make greater use of their data.

2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 2.7945, Raw Interest Score: 1.5243,
Positive Sentiment: 0.0794, Negative Sentiment 0.2382

Hands-On Guide To Darts – A Python Tool For Time Series Forecasting

Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasets, stock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.

In this article, we will learn about Darts, implement this over a time-series dataset.

Introduction to Darts : For a number of datasets, forecasting the time-series columns plays an important role in the decision making process for the model. Unit8.co developed a library to make the forecasting of time-series easy called darts. The idea behind this was to make darts as simple to use as sklearn for time-series. Darts attempts to smooth the overall process of using time series in machine learning.

The basic principles of darts are:

  1. There are two types of models in darts :
    • Regression models: these predict the output based on a set of input time-series.
    • Forecasting models: these predict a future output based on past values.
  2. They have a class called TimeSeries which is immutable like strings.
  3. The TimeSeries class can either one single dimensional or multi-dimensional. Some models like neural networks need multiple dimensions while other simple models work with just 1 dimension.
  4. Methods like fit() and predict() are unified across all models from neural networks to ARIMA.

2020-09-19 10:30:10+00:00 Read the full story…
Weighted Interest Score: 5.4003, Raw Interest Score: 2.3697,
Positive Sentiment: 0.1422, Negative Sentiment 0.0474

Adaptive computing platforms deliver efficient AI acceleration

AI has begun to change many facets of our lives, creating tremendous societal advancements. From self-driving automobiles to AI-assisted medical diagnosis, we are at the beginning of a truly transformative era.

But with opportunity, comes challenge. AI inference, the process of making predictions based on trained machine learning algorithms, requires high processing performance with tight power budgets, regardless of deployment location — cloud, edge, or endpoint. It’s generally accepted that CPUs alone are not keeping up and some form of compute acceleration is needed to more efficiently process AI inference workloads.

At the same time, AI algorithms are evolving rapidly, faster than the speed of traditional silicon development cycles. Fixed-silicon chips like ASIC implementations of AI networks risk becoming quickly obsolete due to the rapid innovation in state-of-the-art AI models.

2020-09-17 00:00:00 Read the full story…
Weighted Interest Score: 4.6898, Raw Interest Score: 1.6776,
Positive Sentiment: 0.3860, Negative Sentiment 0.2227

Workshop: Natural Language Processing (NLP) From Scratch

The Association of Data Scientists, the premier global professional body of data science & machine learning professionals, has announced a full-day workshop on Natural Language Processing (NLP) on the 26th of September, Saturday.

Natural Language Processing (NLP) is one of the key frontiers of Artificial Intelligence and has been in trend since the rise in popularity of conversational AI. When it comes to communicating with machines, NLP offers some of the best tools and techniques …
2020-09-14 09:25:08+00:00 Read the full story…
Weighted Interest Score: 4.5788, Raw Interest Score: 2.4757,
Positive Sentiment: 0.2653, Negative Sentiment 0.0000

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial i…
2020-09-16 17:41:29+00:00 Read the full story…
Weighted Interest Score: 4.3021, Raw Interest Score: 2.3211,
Positive Sentiment: 0.1675, Negative Sentiment 0.1675

Nasdaq Launches AML Investigation Technology

Nasdaq Automated Investigator to address gap in anti-money laundering (AML) investigations process

Today Nasdaq (Nasdaq: NDAQ) announced the launch of the cloud-deployed Nasdaq Automated Investigator for AML, the first automated solution for investigating anti-money laundering (AML) for retail and commercial banks and other financial institutions. Designed, built and offered in partnership with UK-based Caspian, Nasdaq Automated Investigator for AML further expands Nasdaq’s global efforts in combatting financial crime and promoting market integrity in the capital markets and beyond.

“The financial industry is making a structural shift to more intelligent technologies and real-time adaptive analytics based on much larger, more diverse data pools to detect and investigate financial crime,” said Valerie Bannert-Thurner, SVP and Head of Sell-side and Buy-side Solutions, Market Technology, Nasdaq. “As both a market operator and technology provider, we have a commitment to the capital market ecosystem to keep markets healthy and safe to fight financial crime. Through the years of expertise we have gained as an industry leader in trade surveillance, we are both moving beyond our alerting capabilities to investigation, and expanding our solutions to help eradicate illegal money transactions.”

2020-09-16 10:59:26+00:00 Read the full story…
Weighted Interest Score: 4.1467, Raw Interest Score: 1.7927,
Positive Sentiment: 0.1530, Negative Sentiment 0.5247

How Big Data Impacts The Finance And Banking Industries

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. These terms are fundamentally tied predominantly to matters involving digital transformation as well as growth in companies. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved.

Through this tool, gaining better insight has become much easier now. It doesn’t matter whether the decision being considered has huge or minimal impact; businesses have to ensure they can access the right data to move forward. Typically, this approach is essential, especially for the banking and finance sector in today’s world.

The Role of Big Data : Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. They have to utilize Big Data to its full potential to stay in line with their specific security protocols and requirements. Banking institutions actively use the data within their reach in a bid to keep their customers happy. By doing so, these institutions can limit fraud cases and prevent any complications in the future.

2020-09-17 21:29:59+00:00 Read the full story…
Weighted Interest Score: 4.0569, Raw Interest Score: 2.2376,
Positive Sentiment: 0.5019, Negative Sentiment 0.1673

Minio Archives

The rapid growth of data and the changing nature of data applications is challenging established architectural concepts for how to store big data. Where once organizations may have first looked to large on-premise data lakes to centralize petabytes of less-structured data, they now are considering scale-out file and object storage systems that give them greater flexibility to store data in a way that meshes with the emerging multi-cloud and hybrid paradigm. Read more……
2020-09-15 00:00:00 Read the full story…
Weighted Interest Score: 3.6017, Raw Interest Score: 1.6949,
Positive Sentiment: 0.2119, Negative Sentiment 0.2119

Virtualization Startup Varada Streamlines Data Ops

Varada, a data virtualization startup targeting big data query acceleration, announced a $12 million funding round this week as it ramps up its “zero data-ops” platform designed to prioritize analytics workloads via proprietary indexing technology

The Series A round announced on Tuesday (Sept. 15) was led by MizMaa Ventures, and early stage investors in Israeli technology startups. Gefen Capital joined seed investors F2 Venture Capital, Lightspeed and StageOne Ventures.

Tel Aviv-based Varada is readying a data virtualization platform for accelerating big data workloads via its patented indexing technology. The platform is intended to help prioritized workloads to “balance performance and cost.”

2020-09-15 00:00:00 Read the full story…
Weighted Interest Score: 3.4346, Raw Interest Score: 2.2817,
Positive Sentiment: 0.0661, Negative Sentiment 0.1323

It Might Be Too Late To Bet On Nvidia

Nvidia NVDA has been one of the hottest and most beloved Tech names over the last several years. It has been one of the biggest winners of the COVID pandemic, and its products and services have never been in more demand as they are right now. The stock has been so beloved for so long now, that Jim Cramer of CNBC even named his dog Nvidia.

With its recent acquisition of Softbank’s Arm, and with the increasing demand for its chips and processors, Nvidia is forecasted to grow even more in 2020. However, has the stock moved too high and too fast for its own good? Can we strongly and unequivocally recommend buying Nvidia right here and right now?

The stock has skyrocketed this year, but the picture is not all rosy. Tech names have been crushed over the last 3 weeks, and Nvidia’s technical indicators are not great. The macro economic indicators are questionable at the moment as well, and trade relations with China could be crucial for the stock’s performance. There are some indicators out there as well that point out to a possible tech bubble, similar to that of the dotcom crash in 2000. With the way Nvidia’s stock has moved, they would surely be affected by this.
2020-09-18 00:00:00 Read the full story…
Weighted Interest Score: 3.3919, Raw Interest Score: 1.3624,
Positive Sentiment: 0.2306, Negative Sentiment 0.2096

Charts: Splunk Has Further to Fall

In his first “Executive Decision” segment during Friday night’s Mad Money program, Jim Cramer spoke with Doug Merritt, president and CEO of Splunk Inc. (SPLK) , the big data company with shares down 11.5% over the past month as money managers make room for new offerings such as Snowflake (SNOW) .

Merritt explained that with more and more devices connecting to the Internet, companies need firm’s like Splunk to help them capture all of this new data and make sense of it all. Soon, he said, every company will become a data company as they digitize their operations.

When asked about the flood of new companies in the big data space, like Snowflake, Merritt explains that while there are a lot of players, Splunk has been around for 15 years and is still among the fastest growing companies at its size.
2020-09-21 07:58:50-04:00 Read the full story…
Weighted Interest Score: 3.2621, Raw Interest Score: 1.4378,
Positive Sentiment: 0.0719, Negative Sentiment 0.2876

AI ethics groups are repeating one of society’s classic mistakes

International organizations and corporations are racing to develop global guidelines for the ethical use of artificial intelligence. Declarations, manifestos, and recommendations are flooding the internet. But these efforts will be futile if they fail to account for the cultural and regional contexts in which AI operates.

AI systems have repeatedly been shown to cause problems that disproportionately affect marginalized groups while benefiting a privileged few. The global AI ethics efforts under way today—of which there are dozens—aim to help everyone benefit from this technology, and to prevent it from causing harm. Generally speaking, they do this by creating guidelines and principles for developers, funders, and regulators to follow. They might, for example, recommend routine internal audits or require protections for users’ personally identifiable information.
2020-09-14 00:00:00 Read the full story…
Weighted Interest Score: 3.1073, Raw Interest Score: 0.9753,
Positive Sentiment: 0.2495, Negative Sentiment 0.5217

Executive Interview: Steve Bennett, Director Global Government Practice, SAS

Using AI and analytics to optimize delivery of government service to citizens

Steve Bennett is Director of the Global Government Practice at SAS, and is the former director of the US National Biosurveillance Integration Center (NBIC) in the Department of Homeland Security, where he worked for 12 years. The mission of the NBIC was to provide early warning and situational awareness of health threats to the nation. He led a team of over 30 scientists, epidemiologists, public health, and analytics experts. With a PhD in computational biochemistry from Stanford University, and an undergraduate degree in chemistry and biology from Caltech, Bennet has a strong passion for using analytics in government to help make better public better decisions. He recently spent a few minutes with AI Trends Editor John P. Desmond to provide an update of his work.

AI Trends: How does AI help you facilitate the role of analytics in the government?

2020-09-17 23:06:42+00:00 Read the full story…
Weighted Interest Score: 2.9715, Raw Interest Score: 1.4261,
Positive Sentiment: 0.2875, Negative Sentiment 0.2424

How to Build a Fair AI System

AI is being rapidly deployed at companies across industries, with businesses projected to double their spending in AI systems in the next three years. But AI is not the easiest technology to deploy, and even fully functional AI systems can pose business and customer risks. One key risk highlighted by recent news stories on AI in credit-lending, hiring, and healthcare applications is the potential for bias. As a consequence, some of these companies are being regulated by government agencies to ensure their AI models are fair.

ML models are trained on real-world examples to mimic historical outcomes on unseen data. This training data could be biased for several reasons, including limited number of data items representing protected groups and the potential for human bias to creep in during curation of the data. Unfortunately, models trained on biased data often perpetuate the biases in the decisions they make.

Ensuring fairness in business processes is not a new paradigm. For example, the U.S. Government prohibited discrimination in credit and real-estate transactions in the 1970s with fair lending laws like Equal Credit Opportunity Act (ECOA) and The Fair Housing Act (FHAct). In addition, the Equal Pay Act, Civil Rights Act, Rehabilitation Act, Age Discrimination in Employment Act, and Immigration Reform Act all provide broad protections against discrimination towards certain protected groups.

Building a fair AI requires a two-step process: (1) Understand Bias and (2) Address Potential Bias. In this article, we’re going to focus on the first topic.

2020-09-15 00:00:00 Read the full story…
Weighted Interest Score: 2.9372, Raw Interest Score: 1.3125,
Positive Sentiment: 0.1790, Negative Sentiment 0.2386

The Top Trends in Data Management for 2021 (Register)

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

Goldman Sachs is taking what it learned from a $100 million acquisition to upgrade the Marcus app

The bank has just released the first version of a personal finance management tool that gives customers of its Marcus retail brand a top-down view of all their financial accounts, as well as insights into spending and a monthly snapshot of their budget, according to Adam Dell, a Goldman partner and head of product at Marcus.

The feature, called Marcus Insights, is the latest step that Goldman — known for most of its 151-year history as a bank for the wealthy and powerful — is taking into the financial lives of ordinary consumers. The bank hopes that by helping users get a handle on their finances with a simple, clean interface, they will be more inclined to trust Goldman — and try some of the firm’s existing and upcoming products. “We want to make understanding your financial health approachable and easy,” Dell said in a phone interview. “What did you spend this month, and where did you spend it, and how much do you have left? And is there any extra that you could set aside for an emergency fund, or just put in a high-yield savings account?”

Insights is bundled in an update to the bank’s Marcus app and will be available at first only to those who have a loan or deposit account with the bank. By year-end, anyone who wants to download the Marcus app will be able to make use of the tools, which are free. Dell, an entrepreneur and brother of billionaire Michael Dell, came to Goldman in 2018 after selling a start-up called Clarity Money to the bank for $100 million. That app, which is still a separate offering run by Goldman, is a personal finance tool that uses machine learning to nudge users into better habits.

2020-09-14 00:00:00 Read the full story…
Weighted Interest Score: 2.5658, Raw Interest Score: 1.5672,
Positive Sentiment: 0.1667, Negative Sentiment 0.0000

Modern Data Warehousing: Enterprise Must-Haves (Register)

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: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000

How COVID-19 Permanently Changed American’s Financial Habits

The unexpectedly rapid increase in use of digital banking channels as a result of the COVID-19 lockdowns, and the ongoing desire for social separation and less contact, has left banks and credit unions with a two-fold challenge.

Dahlgren points out that these worries are strong and that consumers support financial institution action on multiple fronts. For example, previous BAI studies have found that 60% of consumers would feel more secure if financial institutions used factors like fingerprints, retina scans and speech patterns to identify them. And many also support use of artificial intelligence and analysis of big data to verify who they are.

2020-09-16 00:05:53+00:00 Read the full story…
Weighted Interest Score: 2.4920, Raw Interest Score: 1.2411,
Positive Sentiment: 0.2602, Negative Sentiment 0.3103

The CDO’s Role in Leading Data-Driven Transformation

The evolution demanded of companies today is propelled by the blurring of lines between the physical and digital worlds. With faster internet connections, the adoption of mobile and cloud technologies, as well as the advancement and ubiquity of technology, companies have had to rapidly adapt to a digital, data-driven world. Businesses at the forefront of this transformation – such as Amazon, Google and Salesforce – have challenged traditional business models with their out-of-the box thinking to maximize on the potential and new avenues revealed by digital and data.

Faced with this disruption, CEOs are asking themselves: How do I pivot my organization to put data and digital at the forefront of our future? Who on the executive team will lead the charge? And so, we see the rise of the Chief Data Officer (CDO). Even though the role has existed for almost two decades, it is still largely misunderstood and rapidly evolving.

Let’s dive into the reality of why the CDO role is one of the most important positions in an organization, and how CDOs can prove their worth and lead their organizations to digital and data success.

2020-09-18 00:00:00 Read the full story…
Weighted Interest Score: 2.3559, Raw Interest Score: 1.2942,
Positive Sentiment: 0.3392, Negative Sentiment 0.3392

The Essential Guide to Analytic Process Automation

What drives digital transformation success? In “The Essential Guide to Analytic Process Automation,” discover how the convergence of analytics, data science, and process automation is accelerating successful digital transformation and fueling business outcomes.

Learn how Analytic Process Automation platforms:

  • Widen accessibility to data and analytics with hundreds of code-free building blocks
  • Automate repetitive and complex analytic processes to accelerate insights and actions
  • Scale analytics across the organization and amplify human output
  • Transform business outcomes and workforces including top-line growth, bottom-line return, efficiency gains, and perpetual upskilling

2020-09-17 00:00:00 Read the full story…
Weighted Interest Score: 2.2508, Raw Interest Score: 1.9293,
Positive Sentiment: 0.6431, Negative Sentiment 0.0000

Stream Processing Is a Great Addition to Data Grid, Hazelcast Finds

In-memory data grids (IMDGs) historically have exceled in applications that require the fastest processing times and the lowest latencies. By adding a stream processing engine, called Jet, to its IMDG, Hazelcast is finding customers exploring new use cases at the cutting edge of high-performance computing.

Hazelcast Jet is a stream processing framework designed for fast processing of big data sets. Originally released in 2017, the open source engine runs in a distributed manner atop the Hazelcast IMDG, which it leverages for high availability and redundancy, as well as a source and a sink for data. Developers use directed acyclic graph (DAG) development paradigm to develop real-time and batch applications with Jet, which also supports Apache Beam semantics.
2020-09-14 00:00:00 Read the full story…
Weighted Interest Score: 2.1593, Raw Interest Score: 1.5546,
Positive Sentiment: 0.1636, Negative Sentiment 0.1800

Merge Arm with Graphcore, says co-founder

The co-founder of Arm has urged ministers to back a merger between the chip designer and one of Britain’s most promising start-ups instead of allowing a US takeover.

Hermann Hauser, part of the team that established Arm in Cambridge in 1990, writes in The Sunday Telegraph that rather than approve Nvidia’s $40bn (£30bn) acquisition, the Government should engineer a combination with Graphcore, based in Bristol.
2020-09-19 00:00:00 Read the full story…
Weighted Interest Score: 2.1459, Raw Interest Score: 1.2876,
Positive Sentiment: 0.2575, Negative Sentiment 0.0000

COVID-19 Stokes The Chatbot Hype In Financial Services

COVID-19 and its associated containment measures are accelerating digital transformation and automation in financial services. Customer service has been under enormous pressure, and financial services firms such as Nationwide Building Society in the UK and the Royal Bank of Canada have launched chatbots to deal with the unusually high volume of requests. However, digital teams in financial services firms should remain wary of deploying chatbots and voice assistants faster than their customers are ready for them, or than their systems can support.
2020-09-18 07:47:04-04:00 Read the full story…
Weighted Interest Score: 2.1125, Raw Interest Score: 1.2971,
Positive Sentiment: 0.1255, Negative Sentiment 0.1255

Why the Pro Medicus pathway is just beginning

Pro Medicus’ flagship Visage software lets radiologists view reports and large image files generated by X-rays and other medical scans on-the-go from their mobile devices, enabling them to make diagnostic decisions remotely.

Unlike competitors’ software, the images can be streamed from their archive thanks to Pro Medicus’ proprietary streaming platform, allowing multi-gigabyte files to display almost instantly, rather than requiring lengthy down…
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 2.1046, Raw Interest Score: 1.0602,
Positive Sentiment: 0.1668, Negative Sentiment 0.0596

Importance Of Using Data Analytics To Optimize Lead Pipelines

Big data is utilized in many facets of business. One of the most important benefits of data analytics with lead generation and optimization.

Many experts agree that big data is reinventing the art of lead generation. There are a number of benefits of integrating data analytics into the lead pipeline. You need to know how to leverage your data resources to your full advantage.

What Are the Benefits of Us…
2020-09-17 17:52:04+00:00 Read the full story…
Weighted Interest Score: 2.1041, Raw Interest Score: 1.4120,
Positive Sentiment: 0.2769, Negative Sentiment 0.1107

Illumina to Acquire Bezos-Backed Grail for $8 Billion

Illumina says it will acquire the remaining stake in Jeff Bezos-backed gene-sequencing company Grail that it doesn’t already own for $8 billion in cash and stock.

Illumina (ILMN) – Get Report said Monday it would acquire a remaining majority stake in Jeff Bezos-backed gene-sequencing company Grail that it doesn’t already own for $8 billion in cash and stock.

The early stage cancer-detection healthcare company, backed by Amazon’s (AMZN) – Get Re…
2020-09-21 11:47:39+00:00 Read the full story…
Weighted Interest Score: 2.0790, Raw Interest Score: 1.4812,
Positive Sentiment: 0.1378, Negative Sentiment 0.1378

Predictiv AI and Sigfox Canada strike a global sales channel and tech integration partnership for ThermalPass

In addition to the tech integration, Sigfox Canada will be a global non-exclusive sales distributor and will promote ThermalPass to other Sigfox operators in over 70 countries

Predictiv AI CEO Michael Lende said the company is “very excited” to partner with Sigfox Canada to provide a more “robust and flexible fever detection device which will help more customers around the world combat the spread of coronavirus”

( ) (OTCMKTS:INOTD), a software …
2020-09-18 00:00:00 Read the full story…
Weighted Interest Score: 2.0702, Raw Interest Score: 1.3181,
Positive Sentiment: 0.3549, Negative Sentiment 0.0253

Data Analytics Solutions To HIPAA Compliance During Quarantine

Data analytics has created new opportunities for employers and workers around the world. However, a growing emphasis on data has also created a slew of challenges as well.

One of the biggest issues in healthcare is patient privacy. You can learn some insights from the study Patient Privacy in the Era of Big Data.

Allowing employees to work remotely helps them set their own schedules and work from home. Though…
2020-09-17 17:57:14+00:00 Read the full story…
Weighted Interest Score: 2.0130, Raw Interest Score: 1.1854,
Positive Sentiment: 0.1566, Negative Sentiment 0.2237


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