AI & Machine Learning News. 09, 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?


CloudQuant Nominated for Benzinga Fintech Award!

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NSF $1billion for 12 New AI Institutes!

Feds Investing $1B to Fund 12 New AI Institutes

The federal government is increasing its investment in AI research, with the announcement on August 26 of over $1 billion of awards to establish 12 new AI and quantum information science (QIS) research institutes nationwide.

The announcement was from the White House Office of Science and Technology Policy, the National Science Foundation (NSF) and the US Department of Energy (DOE), in a release issued from the Brookhaven National Laboratory of Upton, N.Y.

The $1 billion will go toward NSF-led AI Research Institutes and DOE QIS Research Centers of five years, establishing 12 multi-disciplinary and multi-institutional national hubs for research and workforce development. The goals are to spur innovation, support regional economic growth and advance American leadership in strategic industries.

2020-09-03 19:53:58+00:00 Read the full story…
Weighted Interest Score: 4.0495, Raw Interest Score: 1.6336,
Positive Sentiment: 0.2193, Negative Sentiment 0.1973

CloudQuant Thoughts : This is big news, now will the money go to education institutions or to private businesses?

Google’s $5 Mn Funded AI Institute Will Explore Human-AI Interactions

Google recently the launch of new Artificial Intelligence Institute for research aimed at boosting R&D on interaction between people and AI. Launched in collaboration with the US National Science Foundation (NSF), the National AI Research Institute for Human-AI Interaction will focus on areas such as speech, written language and gestures to make it more effective.

Google will provide $5 million as funding for supporting the institute, along with offering AI expertise, research collaborations and cloud support for the researchers to conduct advanced AI research in the field.

“Research projects will engage a diverse set of experts, educate the next generation and promote workforce development, and broaden participation from underrepresented groups and institutions across the country,” said the company in a blogpost.
2020-09-03 08:08:26+00:00 Read the full story…
Weighted Interest Score: 3.9225, Raw Interest Score: 1.6473,
Positive Sentiment: 0.2422, Negative Sentiment 0.0000

CloudQuant Thoughts : Well that didn’t take long to find out!

Backed by $12.5M in federal funding, Univ. of Washington leads new data science institute

With $12.5 million in federal funding, the University of Washington will lead a cohort of institutions tackling foundational challenges in the field of data science.

The UW is teaming up with interdisciplinary researchers from University Wisconsin-Madison, University California-Santa Cruz and University of Chicago to form the Institute for Foundations of Data Science (IFDS). The effort will be led by Maryam Fazel, a UW electrical and computer engineering professor.

The institute marks the culmination of three years of work supported by the National Science…
2020-09-01 23:59:00+00:00 Read the full story…
Weighted Interest Score: 3.3509, Raw Interest Score: 1.5326,
Positive Sentiment: 0.2395, Negative Sentiment 0.2874

CloudQuant Thoughts : Ah, and academics!


How Does The Data Size Impact Model Accuracy?

In machine learning, while building predictive models we often come to a situation where we have fewer data. What to do in such scenarios? Do we need a very strong predictive model or more data to build our model? It is often said more data will always result in good performance of a model. But is it correct?

Through this article, we will experiment with a classification model by having datasets of different sizes. We will build a model with less no of data samples and then more no of data samples and then check their accuracy scores. For this, we are going to use the Wine Dataset that is available on Kaggle.

What we will learn from this?

  • How the size of the data impacts the accuracy of a classification model?
  • Comparison of model accuracy with less and more number of data samples

2020-09-08 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.3981, Raw Interest Score: 1.8304,
Positive Sentiment: 0.1854, Negative Sentiment 0.0000

CloudQuant Thoughts : The article lacks any kind of detailed experiment but the idea is sound. The age old question, how important is size? Data scientists unfortunately find little spare time to revisit a completed ML task to see how well it would have predicted the results using less data. Yet the benefits from this small test could be enormous.

We’re entering the AI twilight zone between narrow and general AI

With recent advances, the tech industry is leaving the confines of narrow artificial intelligence (AI) and entering a twilight zone, an ill-defined area between narrow and general AI.

To date, all the capabilities attributed to machine learning and AI have been in the category of narrow AI. No matter how sophisticated – from insurance rating to fraud detection to manufacturing quality control and aerial dogfights or even aiding with nuclear fission research – each algorithm has only been able to meet a single purpose. This means a couple of things: 1) an algorithm designed to do one thing (say, identify objects) cannot be used for anything else (play a video game, for example), and 2) anything one algorithm “learns” cannot be effectively transferred to another algorithm designed to fulfill a different specific purpose. For example, AlphaGO, the algorithm that outperformed the human world champion at the game of Go, cannot play other games, despite those games being much simpler.
2020-09-03 00:00:00 Read the full story…
Weighted Interest Score: 4.6652, Raw Interest Score: 1.7951,
Positive Sentiment: 0.2949, Negative Sentiment 0.0898

CloudQuant Thoughts : I do not think we are close to taking the first steps away from Narrow AI.

AI In Banking: Detecting Fraudulent Transactions

AI in Banking: CEO of Fraud Management Solution Speaks About Working with 15 Top US Banks

Visa unveiling its powerful AI tool that approves/denies card transactions clearly reflects the growing use of AI in banking. As we turn to deep-learning applications to makes more accurate decisions on behalf of banks experiencing network disruptions, DataVisor, an advanced fraud management solution who is working with 15 of the top banks in the US shares his thoughts.

2020-09-04 22:30:01+00:00 Read the full story…
Weighted Interest Score: 4.4756, Raw Interest Score: 1.6742,
Positive Sentiment: 0.2262, Negative Sentiment 0.6335

CloudQuant Thoughts : I have had the misfortune of having my credit details stolen on a few occasions, despite being technologically aware and on guard. But on of the occasions demonstrated such a blitheringly dumb set of security protocols that I was left fuming. The sooner AI takes over these menial initial checks the sooner we can put these fraudsters behind us.

Embracing AI – Key to Futuristic Org Strategy

In the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report, 9 out of 10 respondents agree that AI represents a business opportunity for their company.

While at the same time when they were asked: “What if competitors, particularly unencumbered new entrants, figure out AI before we do?”

In 2019, 45% perceived some risk from AI, up from an already substantial 37% in 2017. More and more leaders are viewing AI as a risk if they are behind in adoption.

2020-09-07 10:30:40+00:00 Read the full story…
Weighted Interest Score: 4.3234, Raw Interest Score: 1.7978,
Positive Sentiment: 0.1498, Negative Sentiment 0.1124

CloudQuant Thoughts : It does not matter what business you are in, not baking AI into your OrgChart is foolish at this point in time.

DOE Announces ‘First Five Consortium’ to Fight Natural Disasters with AI

As wildland fires tear across California and hurricane season starts to warm up, natural disasters are top-of-mind for many Americans. Predicting and managing these disasters is an ongoing challenge, and researchers are leveraging technology from supercomputing to big data analytics to try to bridge these gaps. Now, the Department of Energy (DOE) has announced the First Five Consortium: a group of leaders in the AI space determined to use intelligent tools to combat natural disasters in the United States.

The consortium, co-chaired by the DOE and Microsoft, was formed in response to a January White House forum focused on disaster responses and is named after the “critical first five minutes in responding to a disaster.” The areas it hopes to tackle include wildfire prediction and fire line containment; damage assessment; search and rescue; and natural disasters like hurricanes and tornadoes.

In support of the consortium, Microsoft has established a “critical infrastructure team” that will use AI, confidential computing, advanced communications and more to improve disaster resilience.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 1.9334, Raw Interest Score: 1.0149,
Positive Sentiment: 0.3172, Negative Sentiment 0.6026

Air Force Expands Predictive Maintenance

The U.S. Air Force is expanding its embrace of predictive analytics tools to keep pace with maintenance demands for its huge fleet of fighters, bombers, tankers, transports and helicopters.

There is no shortage of U.S. military aircraft, with estimates ranging as high as 5,400 for the Air Force alone. The problem has been keeping that air armada flying. According to Air Force Times, aircraft readiness as measured as a percentage of planes able to fly has steadily decreased over the past decade.

Hence, the service has been enlisting analytics and AI software companies to help get a handle on maintaining increasingly complex aircraft loaded with electronics gear. Those and other modernization efforts have been spearheaded by the Defense Innovation Unit (DIU), the Silicon Valley-based Pentagon unit established in 2015 to accelerate the transfer of commercial technologies to the military services.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 3.3042, Raw Interest Score: 1.5006,
Positive Sentiment: 0.1699, Negative Sentiment 0.6229

Google Joins the MLOps Crusade

Machine learning developers face an expanded set of management issues beyond merely getting the code right, including the testing and validation of data used in ML models while handling an additional set of infrastructure dependencies. After deployment, those models will degrade over time as use cases evolve.

In response to growing calls for standardization of machine learning operations, cloud and tool vendors are promoting new services aimed at making life a bit easier for data scientists and machine learning developers. Among them is Google Cloud, which this week dropped a batch of cloud AI tools that include data pipelines, metadata and a “prediction backend” for automating steps in the MLOps workflow.

“Creating an ML model is the easy part—operationalizing and managing the lifecycle of ML models, data and experiments is where it gets complicated,” Craig Wiley, director of product management for Google’s cloud AI platform, noted in a blog post unveiling the MLOps services.

The “MLOps foundation” is perhaps the most compelling of the cloud AI tools unveiled this week by the public cloud and AutoML vendor (NASDAQ: GOOGL).
2020-09-01 00:00:00 Read the full story…
Weighted Interest Score: 4.0931, Raw Interest Score: 2.4103,
Positive Sentiment: 0.0841, Negative Sentiment 0.1682

Demonstration Of What-If Tool For Machine Learning Model Investigation

Machine learning era has reached the stage of interpretability where developing models and making predictions is simply not enough any more. To make a powerful impact and get good results on the data it is important to investigate and probe the dataset and the models. A good model investigation involves digging deep into the understanding of the model to find insights and inconsistencies in the developed model. This task usually involves writing a lot of custom functions. But, with tools like What-If, it makes the probing task very easy and saves time and efforts for programmers.

In this article we will learn about:

  • What is the What-If tool?
  • What are the features of this tool?
  • Walkthrough with a sample dataset.

2020-09-08 11:30:33+00:00 Read the full story…
Weighted Interest Score: 3.9518, Raw Interest Score: 1.8024,
Positive Sentiment: 0.1220, Negative Sentiment 0.1084

SAX And Other Big Data Advances Revolutionize Stock Future Trading

The financial industry is incredibly dynamic. One of the reasons is its incredible resilience and dependence on rapidly changing technology. A prime example is the growing use of big data for stock future trading.

Predictive analytics models have proven to be remarkably effective with the stock futures market. One company that uses big data to forecast stock prices has found that its algorithms outperform similar forecasts by 26%.

Big data is changing the tide with stock futures trading : How do these algorithms work so effectively? They build complex machine learning models that rely on numerous pieces of information. Of course, they have to understand the basics of stock futures first. Some of the data that is incorporated into these algorithms is listed below.

2020-08-31 17:56:13+00:00 Read the full story…
Weighted Interest Score: 5.8973, Raw Interest Score: 2.3142,
Positive Sentiment: 0.5520, Negative Sentiment 0.0425

Low-Code Can Lower the Barrier to Entry for AI

Organizations that want to get started quickly with machine learning may be interested in investigating emerging low-code options for AI. While low-code techniques will never completely replace hand-coded systems, they can help accelerate smaller, less experienced data science teams, as well as help with prototyping for professional data scientists.

First of all, what is low-code? Well, the phrase can mean different things to different people, and its applicability to AI is not entirely nailed down. Mainstream developers have been using low-code (or no-code) approaches to creating business and consumer applications for years, and that largely forms the basis for low-code approaches in AI.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 4.9505, Raw Interest Score: 2.2462,
Positive Sentiment: 0.2438, Negative Sentiment 0.1219

Top 8 Ways To Manage Imbalanced Classes In Your Dataset

Imbalanced classes in a dataset are often usual among classification problems in machine learning. Balancing an imbalanced class is crucial as the classification model, which is trained using the imbalanced class dataset will tend to exhibit the prediction accuracy according to the highest class of the dataset. Researchers have proposed several approaches to deal with this problem as well as improve the quality of the classifiers.

Below here, we listed down the top eight ways you can manage the imbalanced classes in your dataset.
2020-09-08 05:30:21+00:00 Read the full story…
Weighted Interest Score: 4.5884, Raw Interest Score: 1.4982,
Positive Sentiment: 0.2239, Negative Sentiment 0.2066

3 Steps for Making High-Performance BI Work Directly with Cloud Data Lake Storage

No longer do you have to move data from cloud data lake storage into proprietary data warehouses—or create cubes, aggregation tables or BI extracts—in order to perform BI or data science analytics upon it.

Now there’s a way to eliminate that data pipeline complexity, and enable both BI users and data scientists to easily search, curate, accelerate and share datasets on their own.

By doing so, you can empower any data consumer in your company to self-serve accurate answers to their most pressing business questions directly from data residing in cloud data lake storage….
2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 5.3819, Raw Interest Score: 2.4306,
Positive Sentiment: 0.5208, Negative Sentiment 0.3472

Data Visualization 101: How to Choose a Chart Type

When working on any data science project, one of the essential steps to explore and interpret your results is to visualize your data. At the beginning of the project, visualizing your data helps you understand it better, find patterns and trends.

At the end of the project, after you’ve done your analysis and applied different machine learning models, data visualization will help you communicate your results more efficiently.

Humans are visual creatures by nature; things make sense to us when it’s represented in an easy to understand visualization. It’s way easier to interpret a bar chart than it is to look at massive amounts of numbers in a spreadsheet.

2020-09-08 03:40:13.321000+00:00 Read the full story…
Weighted Interest Score: 5.1922, Raw Interest Score: 1.8740,
Positive Sentiment: 0.3581, Negative Sentiment 0.0955

Reducing the Cost of Cloud Data Analytics: 3 Architecture Choices

c uncertainty, forcing technology leaders to find ways to accomplish more with less. With data and technology at the heart of the business, it is not possible to simply shut down cloud migrations and data analytics projects. Furthermore, in some verticals such as financial and health services, higher volatility and volume is leading to increasing amounts of data that needs to be processed and analyzed.

In this paper we look at three popular architecture choices for cloud data analytics, then describe how Dremio can help you accelerate projects and productivity at a fraction of the cost of cloud data wareho…
2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 4.2151, Raw Interest Score: 1.7442,
Positive Sentiment: 0.4360, Negative Sentiment 0.2907

What Does Building a Fair AI Really Entail?

Artificial intelligence (AI) is rapidly becoming integral to how organizations are run. This should not be a surprise; when analyzing sales calls and market trends, for example, the judgments of computational algorithms can be considered superior to those of humans. As a result, AI techniques are increasingly used to make decisions. Organizations are employing algorithms to allocate valuable resources, design work schedules, analyze employee performance, and even decide whether employees can stay on the job.

This creates a new set of problems even as it solves old ones. As algorithmic decision-making’s role in calculating the distribution of limited resources increases, and as humans become more dependent on and vulnerable to the decisions of AI, anxieties about fairness are rising. How unbiased can an automated decision-making process with humans as the recipients really be?
2020-09-03 12:25:42+00:00 Read the full story…
Weighted Interest Score: 4.0792, Raw Interest Score: 1.3172,
Positive Sentiment: 0.2544, Negative Sentiment 0.2362

The Essential Guide to Feature Selection (Register to Download)

Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong. The wrong features will give you inaccurate answers and may impact your ML models’ efficiency in ways you can’t predict. This guide focuses on establishing a reliable feature selection process that will pay dividends when you move your models into production.

Register to download the paper.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 4.0449, Raw Interest Score: 2.9345,
Positive Sentiment: 0.2257, Negative Sentiment 0.2257

Data Quality in Machine Learning.

We regularly see and hear phrases like “data is the life blood of an organisation” or “the world’s most valuable resource is no longer oil, but data”. There is no denying that data is an incredibly valuable resource. But a theme that is overlooked in many articles or only mentioned in passing is the importance of data quality.

Technology by itself is not a panacea. You can have any technology you like, and you can have much data as you like but if you don’t have high quality data you are taking an immense risk.

This short paper starts by looking at different types of data: quantitative, qualitative, and then looks the challenges of using this data in Machine Learning applications.

2020-09-07 11:10:02 Read the full story…
Weighted Interest Score: 3.8817, Raw Interest Score: 2.0438,
Positive Sentiment: 0.3371, Negative Sentiment 0.4003

Going Beyond Data-Driven: The Three Pillars of Data Analytics

The push for digital transformation is nothing new. Yet the accelerated adoption of digital transformation inspired by the coronavirus pandemic is unlike anything we have ever seen before. Companies have been forced to quickly ramp up their digital strategies in order to survive in our new world of virtual business. Those that could not quickly pivot and reset their business strategy did not survive.

Now that we are moving beyond the initial rush to adapt to the digital workplace, what did we learn from that first phase of the pandemic? Companies that can evolve and retune their business strategy endure. This has never been more evident than in today’s rapidly changing business landscape and uncertain economy.

2020-09-04 07:35:14+00:00 Read the full story…
Weighted Interest Score: 3.6650, Raw Interest Score: 1.7419,
Positive Sentiment: 0.2488, Negative Sentiment 0.0995

Opinion: Integrating artificial Intelligence into how we live, work, and communicate

Artificial intelligence touches some form of our life every day. Not only does it change the way we see and interact with brands, it also improves the way we manage brands. Efficiency, accuracy, and automation are currently the key advantages of working with AI technologies so it is imperative for brands to understand AI and how it can enhance the overall customer experience journey.

When using technology we sometimes forget where AI is operating in everyday moments. For example, Facebook uses facial recognition to recommend who to tag when you upload a photo. Facebook is now claiming that its AI DeepFace program has a 97 per cent success rate in recognizing whether two images are of the same person or not – compared to 96 per cent for humans.

When on Google, AI uses deep learning to rank our search results. Netflix uses machine learning to personalise our recommendations. Amazon uses natural language processing to give us the news delivered by Alexa. The Sydney Morning Herald’s website uses AI to write data-driven articles to support our daily editorial consumption. From smarter web searches to e-commerce recommendations to voice assistants, AI is integrated into how we live, work, and communicate in the world.

2020-09-03 02:30:10+00:00 Read the full story…
Weighted Interest Score: 2.9876, Raw Interest Score: 1.3330,
Positive Sentiment: 0.5237, Negative Sentiment 0.1904

dunnhumby Speeds Time-to-Insight for Data Scientists with New Tool on Microsoft Azure

dunnhumby, a provider of software for customer data science, has launched a new web-based application on Microsoft Azure, enabling data scientists to deliver customer insights faster. dunnhumby Model Lab is designed to solve complex retail challenges, such as understanding customer churn and predicting propensity to purchase and in what channel, in store versus online. With these capabilities, the tool helps retailers and brands build loyalty and profitability by focusing on the shopper experience.

By automating many of the repetitive, time-consuming tasks, data scientists can focus on the modeling that delivers greatest value. The application uses machine-learning technology, hosted in Azure to achieve high performance, reduce run time, and allow data scientists to quickly explore many algorithms.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9412, Raw Interest Score: 1.6376,
Positive Sentiment: 0.5459, Negative Sentiment 0.0546

5 Step Process For Insightful Data Driven Business Decision Making

Big data is becoming increasingly important in business decision-making. The market for data analytics applications and solutions is expected to reach $105 billion by 2027.

However, big data technology is only a viable tool for business decision-making if it is utilized appropriately. Google has shown how to use big data effectively for decision-making, but many other companies don’t understand the principles to follow. Far too many businesses fail to develop a sensible data strategy, so their ROI from their data collection methodologies is often subpar.

A lot of companies are still struggling to develop data-driven cultures. One poll cited by Harvard Business Review found that 72% of companies had not achieved this goal yet. Fortunately, there are steps that can be taken to address this.

2020-09-04 16:19:49+00:00 Read the full story…
Weighted Interest Score: 2.9201, Raw Interest Score: 1.4869,
Positive Sentiment: 0.3404, Negative Sentiment 0.1612

How A Data Mining Approach For Search Engine Optimization Works

Data mining in Search Engine Optimization is a new concept and has gained importance in the digital marketing field. It can be understood as a process that can be used for extracting useful information from a large amount of data. In other words, data mining is a process that can be used by companies for converting raw data into useful data with the help of a software.

In this article, we would be diving into the details of data mining, its role in business decisions, the importance of SEO as well as how it is changing SEO in today’s digital world.

What is Data Mining? Data Mining can be understood as a set of methodologies that are used in analyzing data from different perspectives and dimensions for finding out previously unknown hidden patterns. This helps in classifying and grouping the data to create a summary of the identified relationships. Data mining tasks are divided into two parts:

  • Creating predictive power: It involves using the features of the software to predict any unknown or future values of a similar feature.
  • Creating a descriptive power: This step helps in finding interesting and human-interpretable patterns that are used for describing the data.

2020-09-05 03:44:59+00:00 Read the full story…
Weighted Interest Score: 2.8296, Raw Interest Score: 1.8104,
Positive Sentiment: 0.3755, Negative Sentiment 0.0805

The Top Trends in Data Management for 2021 (Registration for Webinar)

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.

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

How Adobe is using an AI chatbot to support its 22,000 remote workers

When the COVID-19 shutdown began in March throughout the United States, my team at Adobe had to face a stark reality: Business as usual was no longer an option. Suddenly, over just a single weekend, we had to shift our global workforce of over 22,000 people to working remotely. Not surprisingly, our existing processes and workflows weren’t equipped for this abrupt change. Customers, employees, and partners — many also working at home — couldn’t wait days to receive answers to urgent questions.

We realized pretty quickly that the only way to meet their needs was to completely rethink our support infrastructure.

Our first step was to launch an organization-wide open Slack channel that would tie together the IT organization and the entire Adobe employee community. Our 24×7 global IT help desk would front the support on that channel, while the rest of IT was made available for rapid event escalation.

2020-09-05 00:00:00 Read the full story…
Weighted Interest Score: 2.5723, Raw Interest Score: 1.3303,
Positive Sentiment: 0.2565, Negative Sentiment 0.2084

Modern Data Warehousing: Enterprise Must-Haves (Registration Webinar)

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

Breakingviews – SMIC selloff downplays U.S.-China trade friction

A researcher plants a semiconductor on an interface board during a research work to design and develop a semiconductor product at Tsinghua Unigroup research centre in Beijing, China, February 29, 2016. REUTERS/Kim Kyung-Hoon

HONG KONG (Reuters Breakingviews) – Chinese investors may be downplaying U.S.-China trade tensions. The Hong Kong shares of chipmaking champion Semiconductor Manufacturing International (SMIC) plunged some 23% on Monday on n…
2020-09-08 05:12:59+00:00 Read the full story…
Weighted Interest Score: 2.4601, Raw Interest Score: 1.4680,
Positive Sentiment: 0.0432, Negative Sentiment 0.0432

Top 10 R Packages For Natural Language Processing (NLP)

R is one of the popular languages for statistical computing among developers and statisticians. According to our latest report, R is the second most-preferred programming language among data scientists and practitioners after Python. The language ruled the preference scale, with a combined figure of 81.9 percent utilisation for statistical modelling among those surveyed.

Below is the list of top ten packages for NLP in R language one must know.

  1. koRpus
  2. lsa
  3. OpenNLP
  4. Quanteda
  5. RWeka
  6. Spacyr
  7. Stringr
  8. Text2vec
  9. TM
  10. Wordcloud

2020-09-07 12:30:12+00:00 Read the full story…
Weighted Interest Score: 2.4461, Raw Interest Score: 1.6945,
Positive Sentiment: 0.1432, Negative Sentiment 0.0716

China tech veterans to launch ‘domestic replacement’ fund amid U.S. sanctions

SHANGHAI (Reuters) – Chinese tech veterans, including former executives at Huawei and SMIC, are planning to launch a “domestic replacement” fund by the end of the year to help create China’s next tech giant and support Chinese companies sanctioned by Washington.

Venture capital firm China Europe Capital aims to raise 5 billion yuan ($731.46 million) for the fund which will invest in start-ups specialising in technologies including semiconductor,…

2020-09-08 11:35:05+00:00 Read the full story…
Weighted Interest Score: 2.4328, Raw Interest Score: 1.3072,
Positive Sentiment: 0.1452, Negative Sentiment 0.1089

UK sees tech jobs recovery as vacancies grow by third

Vacancies in the tech sector have grown by more than a third over the past two months as restrictions on hiring begin to ease, new figures show.

In the months before lockdown there were more than 150,000 jobs in the industry advertised each week, according to data from jobs site Adzuna. With job ads plummeting during lockdown and other restrictions some recovery has been cited in the tech sector.

By August 9, tech job ads had increased by 36pc …
2020-09-07 00:00:00 Read the full story…
Weighted Interest Score: 2.4267, Raw Interest Score: 1.5132,
Positive Sentiment: 0.1892, Negative Sentiment 0.0315

Sisu Adds New Tools to Augment Data Analytics Workflows

Sisu Data has announced two new ways to augment data preparation: a shared query repository and an Athena connector for Amazon S3 data. This product expansion is part of Sisu’s focus on augmenting every part of the analytic workflow. The new capabilities were announced in a Sisu blog post by Davide Russo, product manager of Sisu.

According to Russo, while SQL is the preferred method for analyzing data stored in data warehouses, analysts write and rewrite queries across applications so queries can quickly become convoluted. To accelerate the processes, Sisu is announcing the two ways to augment data preparation.

Rather than writing a query once and throwing it away, Russo says, the new query repository creates a central place for collaborative data teams to write and share queries in Sisu, allowing them to accelerate data preparation and deliver faster answers to the business. The new library stores saved queries for each data source so users can select the best one, or write their own custom query. As they are writing, Sisu auto-completes queries and provides the ability to preview a new table before saving and running the analysis.

2020-09-02 00:00:00 Read the full story…
Weighted Interest Score: 2.4259, Raw Interest Score: 1.7251,
Positive Sentiment: 0.3774, Negative Sentiment 0.0539

How Financial Institutions Can Overcome Conflict Around Data

Which one of the following strategic priorities do you think produces the most conflict at banks and credit unions: branch initiatives, advocacy initiatives, mobile banking initiatives, data utilization initiatives, or AI-driven initiatives?

The answer is data utilization initiatives. A survey of industry leaders at a mix of financial institutions ranging from less than $500 million in assets to more than $10 billion found that respondents overwhelmingly said such initiatives produce the most conflict in their organization. This is among finding in the “Ultimate Guide to AI, Data, and Personalized Financial Automation.”

What’s particularly surprising is just how much more these initiatives around data utilization produced conflict compared to the other options: More than 20 percentage points higher than conflict around branch initiatives and nearly 40 percentage points higher than mobile banking initiatives.

2020-09-08 00:01:53+00:00 Read the full story…
Weighted Interest Score: 2.3955, Raw Interest Score: 1.2359,
Positive Sentiment: 0.2441, Negative Sentiment 0.3662

The Future of Data Science

I spend a lot of time consulting with a diverse set of companies about their data science strategies. I also regularly teach courses on topics in data science. I’m witnessing a change in the way companies are thinking about the role of data science and its position within their corporate structures. I believe these changes have been slowly taking place for the past few years, but the onset of COVID-19 and the Russia-Saudi Arabia oil price war this year have accelerated the shift.

What’s changing? There are many roles necessary to succeed in data science, but this change is primarily targeting the role of the data scientist itself.
Data Science work falls into two distinct camps. One group is focused on the more academic aspects of data science like models and algorithms. The other group is more focused on the pragmatic work of helping make business decisions. This latter discipline is commonly referred to as applied data science.

2020-09-08 03:38:03.661000+00:00 Read the full story…
Weighted Interest Score: 2.3923, Raw Interest Score: 1.3336,
Positive Sentiment: 0.1527, Negative Sentiment 0.1425

Deliveroo backer leads £4m investment into AI skin cancer detection start-up

A British start-up using artificial intelligence (AI) to detect skin cancer has raised investment from a venture capital fund which previously backed food delivery start-up Deliveroo.

Cambridge-headquartered Skin Analytics uses AI to analyse people’s skin to detect skin cancer as well as pre-cancerous and benign lesions.

The business has raised £4m in a funding round led by Hoxton Ventures, the early stage investment fund which has backed lar…
2020-09-08 00:00:00 Read the full story…
Weighted Interest Score: 2.3810, Raw Interest Score: 1.0209,
Positive Sentiment: 0.0972, Negative Sentiment 0.2431

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

Sketching out the design for a house communicates much more clearly than trying to describe it with words. The same thing is often true for data — and that’s where data visualization with ggplot2 comes in!

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2.

2020-09-02 14:39:03+00:00 Read the full story…
Weighted Interest Score: 2.2871, Raw Interest Score: 1.1744,
Positive Sentiment: 0.1360, Negative Sentiment 0.0371

Snowflake’s Upcoming IPO: Does This Signal A Strong Future Of Cloud Data Warehousing?

Snowflake is probably one of the most exciting Initial Public Offerings taking place this year. Analysts have given favorable estimates for the upcoming IPO and even said that the soon to be publicly traded company could be the next big cloud firm that can provide great returns to investors.

For those who are not aware of Snowflake, it is a cloud-based data warehouse founded in 2012 by three data warehousing experts who previously worked at Oracle. Since its inception, the company has acquired thousands of customers around the globe and even raised about half a billion from private venture capital firms at a valuation of about $12 billion.

2020-09-07 06:30:00+00:00 Read the full story…
Weighted Interest Score: 2.2503, Raw Interest Score: 1.2998,
Positive Sentiment: 0.2632, Negative Sentiment 0.0329

How AI will automate cybersecurity in the post-COVID world

By now, it is obvious to everyone that widespread remote working is accelerating the trend of digitization in society that has been happening for decades.

What takes longer for most people to identify are the derivative trends. One such trend is that increased reliance on online applications means that cybercrime is becoming even more lucrative. For many years now, online theft has vastly outstripped physical bank robberies. Willie Sutton said he robbed banks “because that’s where the money is.” If he applied that maxim even 10 years ago, he would definitely have become a cybercriminal, targeting the websites of banks, federal agencies, airlines, and retailers. According to the 2020 Verizon Data Breach Investigations Report, 86% of all data breaches were financially motivated. Today, with so much of society’s operations being online, cybercrime is the most common type of crime.

Unfortunately, society isn’t evolving as quickly as cybercriminals are. Most people think they are only at risk of being targeted if there is something special about them. This couldn’t be further from the truth: Cybercriminals today target everyone. What are people missing? Simply put: the scale of cybercrime is difficult to fathom. The Herjavec Group estimates cybercrime will cost the world over $6 trillion annually by 2021, up from $3 trillion in 2015, but numbers that large can be a bit abstract.

A better way to understand the issue is this: In the future, nearly every piece of technology we use will be under constant attack – and this is already the case for every major website and mobile app we rely on.

2020-09-06 00:00:00 Read the full story…
Weighted Interest Score: 2.1578, Raw Interest Score: 0.9825,
Positive Sentiment: 0.2911, Negative Sentiment 0.7642

Diffblue launches a free community edition of its automated Java unit testing tool – TechCrunch

Diffblue, a spin-out from Oxford University, uses machine learning to help developers automatically create unit tests for their Java code. Since few developers enjoy writing unit tests to ensure that their code works as expected, increased automation doesn’t just help developers focus on writing the code that actually makes a difference but also lead to code with fewer bugs. Current Diffblue customers include the likes of Goldman Sachs and AWS.

Diffblue previously only offered its service through a paid — and pricey — subscription. Today, however, the company also launched its free community edition, Diffblue Cover: Community Edition, which doesn’t feature all of the enterprise features in its paid versions, but still offers an IntelliJ plug-in and the same AI-generated unit tests as the paid editions.

The company also plans to launch a new lower-cost “individual” plan for Diffblue Cover, starting at $120 per month. This plan will offer access to support and other advanced features, as well.

2020-09-08 00:00:00 Read the full story…
Weighted Interest Score: 2.0697, Raw Interest Score: 1.3384,
Positive Sentiment: 0.1768, Negative Sentiment 0.1768

Using Machine Learning to Predict Car Accidents

oad accidents constitute a significant proportion of the number of serious injuries reported every year. Yet, it is often challenging to determine which specific conditions lead to such events, making it more difficult for local law enforcement to address the number and severity of road accidents. We all know that some characteristics of vehicles and the surroundings play a key role (engine capacity, condition of the road, etc.). However, many questions are still open. Which of these factors are the leading ones? How much are the external factors to blame, compared to the driver skills?

We leveraged Machine Learning and the United Kingdom’s road accidents database to clarify these questions and specifically provide impact on two major areas:

  • First, we developed a risk score that quantifies the likelihood of a driver having a fatal/serious accident solely based on inputs gathered from individual and vehicle data. This score can be used both to influence driving rules and regulation and inform drivers on the factors that increase their accident risk.
  • Second, we analysed situational information (such as road type, weather conditions, etc.) to estimate the severity of an accident. Such insights would help governments to better understand the sources of accidents and act to reduce them.

2020-09-07 14:47:22.878000+00:00 Read the full story…
Weighted Interest Score: 2.0024, Raw Interest Score: 1.2066,
Positive Sentiment: 0.1931, Negative Sentiment 0.7601


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