AI & Machine Learning News. 13, April 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?
Spring Break Fort Lauderdale vs COVID19 : Mobile Phone tracking Secondary locations
CloudQuant Thoughts: Fantastic analysis, again, as long as you do not stop to think of a) how do they do it and b) that is some sophisticated software so someone is paying a lot of money for this data. Mashable, amongst others, have an article on the concerns raised by this specific video.
3D Photography using Context-aware Layered Depth Inpainting
5 ways to maximize the value of the AI Solutions
When we start talking about AI, it intrigues many, but then they lose interest at the moment we start discussing the technical details. Let me try to keep this piece a more light-hearted to highlight what are the top 5 considerations I keep in mind when I try to find a workable and deployable solution for a problem that business or an end-user face.
Please bear in mind, some of these approaches I picked up over the past 16 years from outstanding software designers as the best practices of when they design a solution, and some I had to improvise myself just by facing unexpected issues in my projects. Many of such projects were the pioneer projects in companies we were trying to do for the first time. I usually focus more on the bigger picture than an immediate challenge a developer faces while developing the solution.
I am translating the considerations I put into an AI / ML solution in the form of these five questions that I ask myself when I build a solution/application that includes an AI / ML focus component.
- Is the use case ripe for an AI / ML solution?
- What benefit the use case is targeting, and what’s the ROI?
- How many source systems are involved in sourcing the data?
- What business process(es) these models would facilitate?
- Who is the target audience of the results coming out of the models?
2020-04-13 07:31:31 Read the full story…
Weighted Interest Score: 2.8275, Raw Interest Score: 1.4146,
Positive Sentiment: 0.1505, Negative Sentiment 0.2709
CloudQuant Thoughts : A very nice sensible article obviously written from the point of view of experience!
10 Must-read Machine Learning Articles (March 2020)
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.
While COVID-19 is dominating headlines across the world, it’s important to note that in the world of machine learning, many companies are operating business as usual. Of course almost everyone by now has taken some measures to fight the spread of the Coronavirus. However, many researchers are working hard to keep up progress and innovation in the world of AI.
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.
- Google launches Cloud AI Platform Pipelines
- AI Implant Gives Amputees Control Over Prosthetic Hands
- AI is Changing the Video Game Industry
- AI Breakthrough Could Significantly Improve Oculus Quest
- Intro to FastAI
- What is the Difference Between CNN and RNN?
- The Future of Data Analytics
- Social Media Data Mining Techniques
- Google Dataset Search
- Coronavirus Datasets from Every Country
2020-04-10 00:00:00 Read the full story…
Weighted Interest Score: 4.9777, Raw Interest Score: 1.9291,
Positive Sentiment: 0.3140, Negative Sentiment 0.0224
CloudQuant Thoughts : If you are finding yourself with time on your hands (lucky you!!) then some of these articles may while away the hours!
Mark Cuban: Here’s how to give your kids ‘an edge’
The way to set your children up for success in this day and age is to ensure they learn about artificial intelligence, according to the billionaire tech entrepreneur Mark Cuban. “Give your kids an edge, have them sign up [and] learn the basics of Artificial Intelligence,” Cuban tweeted on Monday.
Cuban, who is a star on the hit ABC show “Shark Tank” and the owner of the Dallas Mavericks NBA basketball team, was promoting a free, one-hour virtual class his foundation is teaching an introduction to artificial intelligence in collaboration with A.I. For Anyone, a nonprofit organization that aims to improve literacy of artificial understanding.
“Parents, want your kids to learn about artificial intelligence while you’re stuck in quarantine,” Cuban says on his LinkedIn account. In the hour-long virtual class, “you’ll learn what AI is, how it works, its impact on the world, and how you can best prepare for the future of AI,” Cuban says on his LinkedIn account about the class. At the end of the hour-long online class, participants will receive a list of Cuban’s foundation’s best recommendations for AI learning resources.
2020-04-11 00:00:00 Read the full story…
Weighted Interest Score: 2.7574, Raw Interest Score: 1.4680,
Positive Sentiment: 0.2447, Negative Sentiment 0.1398
CloudQuant Thoughts : Poorly written article but great advice from Cuban!
Data Labeling For Natural Language Processing – Why Does Training Data Matter?
Machine Learning has made significant strides in the last decade. This can be attributed to parallel improvements in processing power and new breakthroughs in Deep Learning research. Another key reason is the abundance of data that has been accumulated. Analysts estimate humankind sits atop 44 zettabytes of information today. The headline-grabbing OpenAI paper GPT-2 was trained on 40GB of internet data. These algorithms have advanced at a phenomenal rate and their appetite for training data has kept pace.
Methods of feeding data into algorithms can take multiple forms. Unsupervised learning takes large amounts of data and identifies its own patterns in order to make predictions for similar situations. Unsupervised learning has been applied to large, unstructured datasets such as stock market behavior or Netflix show recommendations. This article will focus on supervised learning, in which humans apply their own set of labels to data in order to better understand and classify other data. Supervised learning requires less data and can be more accurate, but does require labeling to be applied. The dataset along with its associated label is referred to as ground truth. We will cover common supervised learning use cases below.
2020-04-09 15:33:40+00:00 Read the full story…
Weighted Interest Score: 2.7208, Raw Interest Score: 1.3694,
Positive Sentiment: 0.2008, Negative Sentiment 0.2111
CloudQuant Thoughts : Is that a serious question? “Why does training data matter?”.
Big Data Is Fundamentally Altering the Future of File Transfer Security
File transfer security has become a major concern for many organizations thanks to increased cybersecurity threats, skyrocketing costs associated with data breaches, as well as more compliance standards and privacy requirements (e.g., HIPAA, PCI DSS, Sarbanes-Oxley, Gramm-Leach- Bliley.) Due to COVID-19 the number of employees working remotely has exploded. This creates a cyber threat with employees sharing potentially sensitive data from their home offices. As such, they need a file transfer solution that can handle the movement of large files around the world and comply with various security standards without putting a strain on their IT resources.
Meanwhile, companies are realizing that their legacy file transfer solutions, such as FTP, are lacking the capacity and security measures they need to stay compliant and competitive. These basic file transfer tools don’t have the flexibility for handling multiple sources and targets, nor can they support business-to-business interactions among partners, vendors, and suppliers. Also, these legacy systems often don’t include provisions for data encryption. Sensitive data is easily exposed in transit, making it a prime target for cybercriminals.
To stay competitive in today’s global business environment, you need a file transfer strategy that can scale up with your business without adding substantial costs. Here are 11 key considerations when you’re designing a big data file transfer strategy and selecting file transfer solutions for your organization…
2020-04-08 14:05:58+00:00 Read the full story…
Weighted Interest Score: 1.7666, Raw Interest Score: 1.0528,
Positive Sentiment: 0.2677, Negative Sentiment 0.2677
How Much Data Quality is Good Enough?
Ask the question “How much Data Quality is good enough?” and see some very puzzled and alarmed looks. Data Quality, comprising all activities making data fit for consumption, plays a fundamental role in trust, security, privacy, and competitiveness. Good Data Quality is critical because it fuels a surviving and thriving business.
While it would be nice to have 100 percent Data Quality for all data all the time, this goal will remain elusive. For starters, companies do not have an infinite supply of money, people, and time. Additional reasons, in-depth, have been listed by Phil Teplitzky in a talk at the Fourth MIT Information Quality Industry Symposium.
However, ignoring Data Quality until an issue arises is not financially viable. Forethought, action, and measurement are necessary. Understanding Data Quality risks, how these impact business processes, and how to proceed given this information will lead to good-enough Data Quality, allowing a business to profit without overrunning time or money.
2020-04-07 07:35:18+00:00 Read the full story…
Weighted Interest Score: 2.7000, Raw Interest Score: 1.6435,
Positive Sentiment: 0.3419, Negative Sentiment 0.2537
Data Firm Says Its AI Predicts Where Next COVID-19 Spike Will Be
The system was able to predict coronavirus outbreaks in 14 US states.
An artificial intelligence created by New York-based risk detection firm Dataminr was able to predict where the next spike in coronavirus cases will be in the UK and US by analyzing social media posts, The Next Web reports.
According to the company’s website, “growth in clusters of eyewitness, on-the-ground, first-hand public social media posts on COVID-19” allowed their algorithm to detect “hotspots 7-15 days prior to exponential growth in COVID-19 official case count.” These social media posts include “posts ranging from people indicating they tested positive, people indicating they are experiencing symptoms, people indicating they have been exposed but not tested, first-hand accounts of confirmed cases” and so on. Dataminr also predicted future spikes in 14 different US states. Seven days later, all 14 states were hit hard by the coronavirus, TNW reports.
2020-04-09 Read the full story…
The 5 Components Towards Building Production-Ready Machine Learning Systems
The biggest issue facing machine learning is how to put the system into production. Machine learning systems differ from traditional software in two fundamental ways:
- Machine learning is never fully deterministic; therefore, the performance of an ML system can’t be evaluated against a strict specification. Instead, it should always be evaluated against application-specific metrics (false positives/negatives, churn rates, sales).
- The behavior of a machine learning system is determined more by the data used for training than the model used for inference. Therefore, data collection, data wrangling, pipeline management, model retraining, and model deployment are tasks that will never go away.
2020-04-07 15:12:24+00:00 Read the full story…
Weighted Interest Score: 2.7100, Raw Interest Score: 1.6480,
Positive Sentiment: 0.1889, Negative Sentiment 0.1779
Neo4J Creates Platform for Graph Data Science
Neo4j, a provider of graph technology, is launching Neo4j for Graph Data Science, a data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science helps data scientists leverage highly predictive, yet largely underutilized relationships and network structures to answer unwieldy problems.
Examples include user disambiguation across multiple platforms and contact points, identifying early interventions for complicated patient journeys and predicting fraud through sequences of seemingly innocuous behavior. Neo4j for Graph Data Science combines a native graph analytics workspace and graph database with scalable graph algorithms and graph visualization for a reliable, easy-to-use experience.
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 6.1314, Raw Interest Score: 2.5841,
Positive Sentiment: 0.1950, Negative Sentiment 0.2925
5 Artificial Intelligence Trends Changing The eCommerce Industry
eCommerce companies have always been at the forefront of technological innovation. Even they are surprised by the sudden and wonderful disruption of big data.
Artificial Intelligence (AI) is changing the way that eCommerce companies do business. AI is being implemented in systems across the eCommerce sector. From generating leads to gathering information, AI has improved multiple facets of the industry. Algorithmic bots have revolutionized customer facing services. Automated systems are the driving force behind improvements in back-end eCommerce software. eCommerce AI is a data-driven trend that allows companies to manage and analyze consumer information easily. Using these automated systems andAI robot machines, companies are better able to meet their sales goals. Here are some artificial intelligence trends changing the eCommerce industry.
2020-04-10 16:46:20+00:00 Read the full story…
Weighted Interest Score: 4.9110, Raw Interest Score: 2.2442,
Positive Sentiment: 0.5798, Negative Sentiment 0.2244
On AI: Perfume with a hint of AI (Video)
Swiss fragrance manufacturer Givaudan has infused their perfume creation process with a small dose of artificial intelligence. “Carto,” a computer coupled with a robot, allows perfume makers to imagine, combine and test their ideas more quickly and efficiently, marking a big step in the industry.
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 4.5584, Raw Interest Score: 1.9943,
Positive Sentiment: 0.2849, Negative Sentiment 0.0000
How using predictive analytics and big data in Forex Trading can enhance your success
With an average daily turnover of approximately 5.1 trillion USD, the Forex market is the most liquid market on earth. With technological advancements, it has become easier for investors to trade currencies through online Forex trading platforms. But it isn’t easy to make money trading this market, as there are so many things that need to be taken into consideration in order to make smart trading choices.
First thing to know: you need to think about your personality, your trading knowledge, your financial goals and the level of risk you’re willing to bear. How do you want to improve your goals? What can you do to better pursue your dreams? – all of these factors contribute to determining your trader personality profile, as well as your overall strategy. If you don’t know exactly where to start, don’t worry, we’ll cover a few tips about different ways to enter the trading world. While day trading has a steep learning curve, it’s one of the most challenging and stimulating types of active trading. From there, you’ll need to go on a research to decide which kind of trading pairs works best with your trading method, which type of news to follow and how to understand market behavior. To that end, predictive analytics and big data can help you save time and help you obtain useful and actionable insights about the FX market, as well as the general mood of market participants, all of which will help you achieve better trading results.
2020-04-08 04:30:02+00:00 Read the full story…
Weighted Interest Score: 4.4360, Raw Interest Score: 1.9747,
Positive Sentiment: 0.5023, Negative Sentiment 0.1039
Talend Extends Partnership with Databricks
Talend, a provider of in cloud data integration and data integrity, is bolstering its partnership with Databricks.
“Talend is an important addition to our new partner ecosystem, which was built to speed data ingestion access for our customers,” said Michael Hoff, SVP business development and partners at Databricks. “Talend provides both a powerful integration platform for data engineers and a simple-to-use data ingestion tool for business analysts. This not only helps our customers get started fast, but also gives them a path forward for enterprise data management.” With the Winter ’20 release of Talend Data Fabric, including Stitch Data Loader for data ingest, Talend now supports Delta Lake. The comprehensive support enables data ingestion into lakehouse environments where data warehouse management features are combined with low-cost storage.
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 4.0017, Raw Interest Score: 2.1519,
Positive Sentiment: 0.6751, Negative Sentiment 0.0000
BTON Financial And genesis Automate Buy-side Trading
BTON Financial, the independent outsourced dealing desk for asset managers and genesis, the Low Code Application Platform for Capital Markets, are pleased to announce their partnership to automate trading workflows, which in turn drives greater trading performance. The partnership helps drive front office transformation, bringing together genesis’ ability for agile software development and BTON Financial’s independent technology and data driven approach to outsourced dealing in the form of their award winning ‘Smart Broker Router’.
Following a competitive due diligence process, covering both vendors and consultancies, BTON Financial selected genesis as their technology partner because of their deep market expertise and Low Code Application Platform built specifically for capital markets. By using the genesis Low Code Application Platform, BTON are able to create solutions quickly without having to write substantial lines of code, making the development and deployment of these solutions much faster, simpler and much easier to support.
2020-04-08 09:44:10+00:00 Read the full story…
Weighted Interest Score: 3.9922, Raw Interest Score: 1.9140,
Positive Sentiment: 0.5270, Negative Sentiment 0.0832
AI Transparency, Fairness Get Boost with Naming of Prof. Judea Pearl of UCLA
Efforts to further AI transparency and fairness got a boost recently with the naming of Prof. Judea Pearl of UCLA as the World Leader of 2020 by the AI World Society, a joint effort with the Boston Global Forum that calls for AI to be developed and deployed in ways that benefit all mankind. In presenting the award to Prof. Pearl, former Gov. Michael Dukakis, chairman of the institute bearing his name, stated, “I am inspired by your watershed work in establishing cause-and-effect relationships as a statistical and mathematical concept, most especially as we strive to more completely understand the rapidly-evolving impact of AI and machine learning on society.”
An offshoot of the Boston Global Forum, the Michael Dukakis Institute for Leadership and Innovation was born in 2015 with the mission of generating ideas, creating solutions and deploying initiatives to solve global issues, especially focused on cybersecurity and AI. Prof. Pearl is the author of the recent, “The Book of Why: The New Science of Cause and Effect,” published in 2018, a study of cause and effect that helps answer difficult questions such as whether a drug cured an illness. Dukakis stated that the book “provides us with the new tools needed to navigate the uncharted waters of causality for students of statistics, economics, social sciences, mathematics and most urgently today, epidemiology.”
2020-04-09 21:30:48+00:00 Read the full story…
Weighted Interest Score: 3.8871, Raw Interest Score: 1.3880,
Positive Sentiment: 0.1453, Negative Sentiment 0.3712
Buy-Side AI Platform Gains Traction
Exabel, which provides a simple-to-use artificial intelligence and machine learning platform to active investment managers and financial analysts, has gained clients in the UK and aims to expand into the US.
Neil Chapman, chief executive of Exabel, told Markets Media: “We help the buy side to use more data and become more quantitative. We can provide artificial intelligence and machine learning as a platform to non-technical users to allow asset managers to squeeze more value from data.” Exabel announced that Chapman had joined as chief executive in January this year from ForgeRock, which develops develops commercial open source identity and access management products. For the majority of active asset managers, developing an in-house AI capability is prohibitively expensive, especially as data scientists are a scarce resource. Chapman explained that there is a bewildering variety and quality of data, so asset managers need help in determining which have value for their strategies.
2020-04-06 17:27:13+00:00 Read the full story…
Weighted Interest Score: 3.8380, Raw Interest Score: 1.8031,
Positive Sentiment: 0.1061, Negative Sentiment 0.0424
How Asset Managers Can Drive Returns Amid Margin Spikes
The last four weeks have seen massive disruption due to the ongoing Coronavirus pandemic, with huge market swings and a dramatic increase in volatility across the globe. This has caused margin rates, both Initial Margin and Variation Margin, to jump dramatically for fund managers at a time when they should be freeing up as much capital as possible.
To understand why margin rates have spiked so significantly, we need to look back a few decades. The process of globalisation has been accelerating now for over 30 years, creating a situation where today supply chains for virtually all industries span many countries and operate on ‘Just in Time’ inventories. The inherent vulnerability this presents to cross-border disruption has long been seen as a worthwhile price to pay for the benefits such an approach brings to production agility and price control.
COVID-19 has exposed the dark side of this vulnerability, with three decades’ worth of increasingly globalised supply chains being abruptly cut by the rapid closure of nation after nation’s borders. Meanwhile, demand is simultaneously being disrupted as consumers disappear into self-isolation with less money to spend and less incentive to spend it in the face of an uncertain future.
2020-04-08 01:56:35+00:00 Read the full story…
Weighted Interest Score: 3.7916, Raw Interest Score: 1.6343,
Positive Sentiment: 0.0617, Negative Sentiment 0.4625
How Will The Cloud Impact Data Warehousing Technologies?
How will the cloud make an impact on the development of advanced data warehousing technologies? Here is what to know about this.
The recent years have seen a tremendous surge indata generation levels, characterized by the dramatic digital transformation occurring in myriad enterprises across the industrial landscape. The amount of data being generated globally is increasing at rapid rates. In fact, studies by the Gigabit Magazine depict that the amount of data generated in 2020 will be over 25 times greater than it was 10 years ago. Furthermore, it has been estimated that by 2025, the cumulative data generated will triple to reach nearly 175 zettabytes.
Demands from business decision makers for real-time data access is also seeing an unprecedented rise at present, in order to facilitate well-informed, educated business decisions. In order to make data useful, actionable and scalable for their business, enterprises need an efficient and cost-effective way to store, label, and interpret this data. One of the most lucrative ways to do this is through data warehousing.
2020-04-08 16:52:36+00:00 Read the full story…
Weighted Interest Score: 3.7668, Raw Interest Score: 2.1376,
Positive Sentiment: 0.2694, Negative Sentiment 0.0719
Talend Accelerates the Journey to Lakehouse Paradigm with Expanded Databricks Partnership
A recent press release reports, “Talend, a global leader in cloud data integration and data integrity, announced today its continued partner momentum with Databricks. With the Winter ’20 release of Talend Data Fabric, including Stitch Data Loader for data ingest, Talend now supports Delta Lake. The comprehensive support enables data ingestion into lakehouse environments where data warehouse management features are combined with low-cost storage. The additional support for Delta Lake combined with the enhanced integration and integrity capabilities in Talend Data Fabric enable the fast ingest and optimal processing of reliable, high-quality data for Databricks users to inform machine learning workloads and quickly unlock insights for their business.”
2020-04-10 07:15:19+00:00 Read the full story…
Weighted Interest Score: 3.7249, Raw Interest Score: 2.1252,
Positive Sentiment: 0.4021, Negative Sentiment 0.0000
Hedge funds rising to the challenge
Last year was challenging for hedge fund managers. Although the market registered a dimension of recovery, regulation and fee pressure continued to ramp up while performance did not always to live up to expectations. However, hedge fund managers are resilient and are being pushed to innovate, finding ways to rise above these difficulties. This flexibility is bound to prove vital in the year ahead as the industry braces itself for the expected turbulence.
Operational concerns : “We anticipate that hedge funds will continue to face increased infrastructure, reporting and regulatory requirements by institutional investors, a tough capital raising climate and continued fee compression. As such, managers will have to take a serious look at how they are currently operating their business,” says Greg Farrington, President of Constellation Advisers.
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 3.6490, Raw Interest Score: 1.9107,
Positive Sentiment: 0.2092, Negative Sentiment 0.2789
Machine Learning: Making Sense of Unstructured Data and Automation in Alt Investments
Institutional investors are buckling under the operational constraint of processing hundreds of data streams from unstructured data sources such as email, PDF documents, and spreadsheets. These data formats bury employees in low-value ‘copy-paste’ workflows and block firms from capturing valuable data. Here, we explore how Machine Learning (ML) paired with a better operational workflow, can enable firms to more quickly extract insights for informed decision-making, and help govern the value of data.
According to McKinsey, the average professional spends 28% of the workday reading and answering an average of 120 emails – on top of the 19% spent on searching and processing data. The issue is even more pronounced in information-intensive industries such as financial services, as valuable employees are also required to spend needless hours every day processing and synthesizing unstructured data. Transformational change, however, is finally on the horizon. Gartner research estimates that by 2022, one in five workers engaged in mostly non-routine tasks will rely on artificial intelligence (AI) to do their jobs. And embracing ML will be a necessity for digital transformation demanded both by the market and the changing expectations of the workforce.
2020-04-08 01:29:51+00:00 Read the full story…
Weighted Interest Score: 3.5386, Raw Interest Score: 2.1870,
Positive Sentiment: 0.2604, Negative Sentiment 0.3541
Your Friendly Neighborhood AutoML-Empowered Data Scientist
Automation-focused machine learning (AutoML) has the power to dramatically upscale AI at your organization. With AutoML tools, organizations can unlock valuable new business insights, embed advanced AI capabilities in applications, and empower data scientists and nontechnical experts alike to build predictive models rapidly.
Faster than a speeding GPU, more powerful than a neural network, your AutoML-empowered data scientist can save the day.
AutoML automates repetitive, tedious, and time-intensive tasks that eat up a lot of data scientists’ time. Endowed with this technology, your super data scientists can iterate faster, try more features and algorithms, and tackle more priority projects. New superpowers, like the ability to build deep learning models for image recognition and natural language understanding, once the exclusive purview of a select few data scientists, will be in reach for the many.
Organizations around the world see the appeal. In the Forrester Analytics Global Business Technographics® Data And Analytics Survey, 2019, 61% of data and analytics decision makers whose firms are adopting AI said they had implemented, were in the process of implementing, or were expanding/upgrading their implementation of automation-focused machine-learning solutions. Another 25% planned to implement within the next year.
2020-04-07 17:14:02-04:00 Read the full story…
Weighted Interest Score: 3.4633, Raw Interest Score: 1.9381,
Positive Sentiment: 0.3126, Negative Sentiment 0.1250
Mercari price recommendation for online retail sellers using Machine learning
Regression experiments and secondary research on the mercari dataset in Kaggle as part of self case study — Applied AI Course using Python
- Business problem
- Use of Machine learning / Deep learning to solve the business problem
- Evaluation metric (RMSLE)
- Exploratory data analysis
Product pricing gets even harder at scale, considering just how many products are sold online. Clothing has strong seasonal pricing trends and is heavily influenced by brand names, while electronics have fluctuating prices based on product specs. Mercari, Japan’s biggest community-powered shopping app, knows this problem deeply. They’d like to offer pricing suggestions to sellers, but this is tough because their sellers are enabled to put just about anything, or any bundle of things, on Mercari’s marketplace. In this competition, Mercari’s challenging you to build an algorithm that automatically suggests the right product prices. You’ll be provided user-inputted text descriptions of their products, including details like product category name, brand name, and item condition.
2020-04-12 14:20:54.198000+00:00 Read the full story…
Weighted Interest Score: 3.3613, Raw Interest Score: 1.5756,
Positive Sentiment: 0.1838, Negative Sentiment 0.0263
BlackRock’s Aladdin Hosted On Microsoft Azure Cloud
BlackRock and Microsoft have formed a strategic partnership to host BlackRock’s Aladdin infrastructure on the Microsoft Azure cloud platform, bringing enhanced capabilities to BlackRock and its Aladdin clients, which include many of the world’s most sophisticated institutional investors and wealth managers.
2020-04-08 11:14:54+00:00 Read the full story…
Weighted Interest Score: 3.2729, Raw Interest Score: 1.8410,
Positive Sentiment: 0.6721, Negative Sentiment 0.1169
AI Based Financial Modeling Firm Daloopa Partners with Analyst Hub • Integrity Research
Daloopa uses artificial intelligence (AI) to build fundamentally oriented financial models that enable buy-side analysts to make better predictions of company performance. Daloopa’s proprietary technology automatically ingests and reads hundreds of company financial reports and then identifies thousands of key performance indicators (KPIs) for each company. Daloopa presents this information in text and tables, with linked citations for each data point, enabling analysts to accurately enter required data and produce their financial models in a fraction of the time it currently takes. Daloopa models update automatically, with data from earnings announcements incorporated as soon as the financial reports are filed.
The platform currently covers all US publicly traded technology media and telecommunications (TMT) companies, and plans to cover all publicly listed US companies by the end of 2020. Daloopa’s data can be integrated into a Microsoft Excel spreadsheet or accessed through an analyst’s application programming interface (API), making the data instantly available whether clients create their own financial models or download prepopulated models.
2020-04-06 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1319, Raw Interest Score: 1.7848,
Positive Sentiment: 0.1711, Negative Sentiment 0.0244
Beating the Pandemic and Data Protection Are Not Mutually Exclusive
Tracking the spread of COVID-19 with precise data is self-evidently one of the key tools needed to slow the spread of the pandemic. Unless health experts know when, where and how cases are contracted, they are effectively fighting an enemy with one hand tied behind their back.
But that doesn’t mean we throw the baby out with the bathwater. Privacy rights and the protection of personal data, in particular health information, are also important. Sadly as many civil liberties have been trampled in the name (only) of fighting the pandemic — looking at you, Viktor Orban — a backlash has also occurred claiming that data protection laws are preventing health workers and authorities from doing what is necessary.
But this is not the case. “Contrary to many reports, there is no general conflict between data protection and the use of personal data in the fight against an epidemic. Statements claiming that data protection must be ‘waived’ seem to be based on a false understanding of law,” said Max Schrems, privacy rights activist and chair of digital rights group NOYB.
2020-04-10 16:00:00+00:00 Read the full story…
Weighted Interest Score: 2.9183, Raw Interest Score: 1.7131,
Positive Sentiment: 0.0692, Negative Sentiment 0.3115
Prometeia’s PFTPro was named a leader among Digital Wealth Management Platforms by a leading independent research firm
We’re thrilled to announce that PFTPro, our wealth management digital platform, has been named a market leader in the latest Forrester WaveTM: Digital Wealth Management Platforms, Q1 2020.
The independent research firm has evaluated the 13 most important providers of Digital Wealth Management platforms and how they stack up in terms of strategy and offering. Prometeia received the highest possible score in 14 evaluation criteria and scored highest in the current offering category.
According to the report, Prometeia has designed a platform that differentiates with its ability to solve wealth management-specific challenges, such as wealth cash flow projections, risk management analysis, tax planning, intelligent product picking and understanding client behavior.
PFTPro Suite’s strengths include AI/machine learning and advanced analytics that reduce the cost to provide wealth management services to the affluent and mass affluent customer segments.
2020-04-07 00:00:00 Read the full story…
Weighted Interest Score: 2.8231, Raw Interest Score: 2.4637,
Positive Sentiment: 0.3790, Negative Sentiment 0.0632
Latent Dirichlet Allocation(LDA): A guide to probabilistic modelling approach for topic discovery
Latent Dirichlet Allocation(LDA) is one of the most common algorithms in topic modelling. LDA was proposed by J. K. Pritchard, M. Stephens and P. Donnelly in 2000 and rediscovered by David M. Blei, Andrew Y. Ng and Michael I. Jordan in 2003. In this article, I will try to give you an idea of what topic modelling is. We will learn how LDA works and finally, we will try to implement our LDA model.
What is Topic Modelling?
2020-04-13 03:01:19.189000+00:00 Read the full story…
Weighted Interest Score: 2.5707, Raw Interest Score: 1.0047,
Positive Sentiment: 0.0591, Negative Sentiment 0.0394
The Insights Beat: Spring Has Sprung — Get Your Data And Analytics Tools In Order
The time of year has finally arrived when the sun lingers on the horizon longer and longer, life creeps back into the trees, and weather forecasts look much more favorable. But this normally welcome period of seasonal change coincides with unprecedented change in how we are forced to conduct business. In the midst of the global pandemic, businesses must connect with their customers in novel ways and find smarter and more efficient methods of using their data.
In this month’s Insights Beat, we feature some of these new ways to manage, analyze, and monetize your data. In particular, data commercialization efforts become even more important, as companies will continue to look for new revenue sources.
Make Customer Analytics A Perennial, Not A Seasonal : As the ongoing pandemic disrupts businesses, now is the time to continue to invest in cutting-edge customer analytics technologies to increase lifetime value, increase customer loyalty and retention, and bolster the customer experience. This means pivoting customer analytics practices in the short term but also adopting customer analytics technologies to position your company to come through this period of change in the longer term.
2020-04-10 20:54:48-04:00 Read the full story…
Weighted Interest Score: 2.5566, Raw Interest Score: 1.5953,
Positive Sentiment: 0.2519, Negative Sentiment 0.0840
The Next Great Frontier: Automating Data and Application Deployments
DevOps, DataOps, AI, and containers all lead to one important innovation for enterprises seeking to be more data-driven—and that is greater automation. Data-driven enterprises cannot function if data resources and applications are in any way being manually administered, deployed, remediated, or upgraded.
The ability to move fast, make decisions in real time, and respond quickly to events requires automated processes for ingesting and managing data. Organizations that fail to effectively leverage and deploy their data assets will find themselves falling behind. Data managers are turning to automation and autonomous databases and platforms, a recent survey of 217 data managers by Unisphere Research, a division of Information Today, Inc., found. According to the research, three in four DBAs feel that applications can be deployed faster with increased database management automation, and seven in 10 expect increased database automation to boost the impact of their roles (“2019 IOUG Autonomous Database Adoption Survey”).
Already, database functions such as backup and recovery are highly automated, and plans are underway to automate such day-to-day functions as monitoring, provisioning, and maintenance. Data managers welcome the advance of automation of these tasks and see greater roles for themselves in higher-level business decision making.
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 2.2915, Raw Interest Score: 1.4076,
Positive Sentiment: 0.1207, Negative Sentiment 0.2815
e-Billboarding and AI Autonomous Cars
Why might billboards become a lost art and die off? Here’s why.
If we are all ensconced in our AI self-driving cars, it is believed that we will sleep in them, we will work while inside them, and that otherwise we will be visually entertained and our focus will be nearly exclusively on the interior of the self-driving car. There is no particular reason to look out the car windows when you are in a true Level 5 self-driving car because the AI is doing the driving and you don’t need to pay attention to the roadway (that’s the theory of it). In fact, you probably don’t really want windows at all and instead would use that same area to have LED displays. This would allow you to have your favorite online video streaming on one of the “windows” (now a display), while maybe doing a Skype-like session via the use of the space on another “window” and so on.
2020-04-09 21:30:05+00:00 Read the full story…
Weighted Interest Score: 1.9149, Raw Interest Score: 0.6122,
Positive Sentiment: 0.0924, Negative Sentiment 0.1432
Learn data science while practicing social distancing
How I am helping my friends make the most of their time at home, Chilling out doing some data science with my mates
Adjusting to the new normal of living during a global pandemic is challenging. In Australia we have it a lot better than many other countries, at least at the moment. However, isolation can still get you down. Adhering to …
2020-04-12 13:28:12.114000+00:00 Read the full story…
Weighted Interest Score: 1.8979, Raw Interest Score: 1.2001,
Positive Sentiment: 0.2512, Negative Sentiment 0.1814
COVID-19 deaths still growing exponentially in U.S. hot spots, Seattle startup finds in new data analysis
Reflecting a sentiment being conveyed in some COVID-19 hotspots, Gov. Phil Murphy of New Jersey tweeted this week that the “curve is flattening” in the state’s COVID-19 crisis. But he cautioned that it was too early to celebrate — saying that it was “no time to spike any footballs or to take our foot off the gas.”
However, it is time to sharpen our pencils. And it turns out the math agrees with all of Murphy’s metaphors.
Daily deaths in New York, New Jersey, California, Michigan and Washington state “are still on an exponential growth curve,” according to a new analysis from Seattle health data startup MDMetrix. The company says it’s using artificial intelligence combined with control charts to distinguish genuine trends from less-than-significant changes in data sets that vary widely from day-to-day.
2020-04-09 00:28:30+00:00 Read the full story…
Weighted Interest Score: 1.7907, Raw Interest Score: 1.1265,
Positive Sentiment: 0.1477, Negative Sentiment 0.1477
Microsoft’s CTO wants to spread tech’s wealth beyond the coasts
Microsoft CTO Kevin Scott and I share a few things in common. We both grew up in small American towns in the ’70s and ’80s—he in Virginia, me in Nebraska. We both now live and work in the Bay Area. We both make fairly frequent trips back to rural America to see family and friends.
And we’ve both watched as two extremely important trends have taken shape in the first part of the 21st century. The tech industry’s wealth, influence, and relevance to daily life have steadily increased, and will likely accelerate with the further application of automation, robotics, and AI. Big West Coast tech companies such as Facebook and Uber have celebrated IPOs on the floor of the NASDAQ, minting millionaires in the process.
Meanwhile, rural America struggled through a painfully slow recovery from the last recession, exacerbated by the continued exporting of jobs to cheap labor in China and Mexico, and by the destruction of jobs by automation. Largely ignored by the media, the symptoms of that distress began to show, first in the Tea Party movement, then in Occupy, then in the 2016 victory of Donald Trump, the politician most skilled at weaponizing rural America’s growing anger over a “rigged” system.
2020-04-06 07:00:50 Read the full story…
Weighted Interest Score: 1.7280, Raw Interest Score: 0.8922,
Positive Sentiment: 0.2771, Negative Sentiment 0.2217
Avoiding the DIY route to cybersecurity
With the emergence of public cloud services from vendors like AWS and Azure, fund managers may be tempted to take a do-it-yourself approach to technology and cybersecurity systems. However, service providers warn against this tactic as managers may find themselves exposed to risk they would have neither intended nor foreseen. George Ralph managing director at RFA explains: “Everything is available at the click of a button. However, there are risks associated with deploying new services that haven’t been properly configured to ensure appropriate levels of security. I’d urge clients to engage a specialist to support them with their cloud deployments.”
Rather than saving cost in this area, Ralph suggests managers implement technology to automate key tasks and workflows. These can help reduce the number of back office staff and maintain a lean headcount. Looking ahead, Ralph expects to see a greater focus on technology risk management among clients: “Really understanding the level of risk using risk assessments and planning for mitigations is going to be critical this year. The cost of not managing risk is too high for firms in this sector; fines from the regulators, the information commissioner, loss of investor trust and possible lost investment, reputational damage and actual lost earnings while systems malfunction or are breached. With data as the new currency, our clients cannot afford to take any risks.”
2020-04-08 00:00:00 Read the full story…
Weighted Interest Score: 1.6507, Raw Interest Score: 1.0620,
Positive Sentiment: 0.1976, Negative Sentiment 0.2964
Aisera Seed Funding Success is a Good Omen for AI-Based Customer Service
few years ago, many companies were skeptical about the benefits of artificial intelligence. The opportunities that it provides have become a lot clearer these days. A growing number of companies are exploring the benefits of artificial intelligence in customer service.
You can see the sudden demand for AI customer service options by analyzing the companies that create the solutions that they are predicated on. The growing demand for their services and the sizable investments in the companies that deliver them shows that there is a growing need for these solutions.
Aisera Demonstrates the Demand for Machine Learning in Customer Service
Aisera is an excellent example of this. This is a company that uses machine learning to automate a number of customer service tasks. The platform has a number of features that have proven to be invaluable to countless businesses. A lot of the applications are reserved for handling internal processes to help a company run more efficiently. However, the features also enable companies to streamline the processes involved with engaging with outside stakeholders. This has made it considerably easier for businesses to carve out a competitive edge by delivering a sound customer service.
2020-04-09 04:30:33+00:00 Read the full story…
Weighted Interest Score: 1.3745, Raw Interest Score: 1.0677,
Positive Sentiment: 0.3125, Negative Sentiment 0.1823
Machine Learning Offers New Opportunities with the Evolution of Branding Signatures
Artificial intelligence has been one of the most disruptive new technologies to affect the marketing profession in the last 50 years. One study found that53% of marketers plan to use machine learning in some capacity. At Smart Data Collective, we have discussed many of the ways that AI and machine learning have changed the face of performance marketing. However, brand marketing is also evolving with new technological advances.
Machine learning is changing the way that companies position their brand image. Some experts are debating the long-term impact on the marketing profession, but others are focusing on integrating new machine learning tools into their branding strategies. They are using machine learning to improve the designs of everything from their logos to the channel letters of their literature.
Mostafa Elbermawy, an author with Single Grain, wrote a very interesting article on the importance of AI in branding. Other experts have shared similar insights.
2020-04-10 18:51:56+00:00 Read the full story…
Weighted Interest Score: 1.3691, Raw Interest Score: 1.0889,
Positive Sentiment: 0.6769, Negative Sentiment 0.0589
What to do when you didn’t get any medal in a Kaggle competition?
Be like gradient descent — learn from the errors!
Several weeks ago one more Kaggle Competition has ended — Bengali.AI Handwritten Grapheme Classification.
Bengali is the 5th most spoken language in the world. This challenge hoped to improve on approaches to Bengali recognition. Its alphabet has 49 letters and 18 diacritics, which means there are a lot of possible graphemes (the smallest units in a written language).
2020-04-11 16:17:43.298000+00:00 Read the full story…
Weighted Interest Score: 1.2383, Raw Interest Score: 0.7755,
Positive Sentiment: 0.2877, Negative Sentiment 0.3127
Can AI Slash the Costs of Accounting Errors in 2020?
Machine learning is helping companies in every sector optimize their business models. Machine learning advances are helping companies solve some of their most obvious problems. However, they are also helping businesses deal with more mundane issues, such as accounting problems. While these problems seem less important at first glance, they are actually very important to address. Companies need to take appropriate steps to address them.
Every business has a number of challenges that it needs to overcome. Business owners often focus on some of the more pressing concerns, such as identifying their target market and refining the designs of the product. Unfortunately, some often overlooked problems can become very expensive if they are not addressed. Accounting errors are a prime example of a problem that may not seem like a big deal initially. However, they can cause tremendous problems for your brand down the road. We want to cover the costs of both job scheduling and appointment scheduling issues in this post. Machine learning can help with both.
The IRS imposed $29.3 billion in civil penalties in the last reporting year. Of course, there are a lot of other ways accounting mistakes can be costly for SMEs. Obviously, there are a number of causes of accounting mistakes. Fortunately, new big data technology can address all of them. Companies that use machine learning tools can solve many accounting problems. The same logistical principles apply for all of these issues. Big data has made creating custom accounting software easier than ever.
2020-04-10 15:24:16+00:00 Read the full story…
Weighted Interest Score: 1.0312, Raw Interest Score: 1.0407,
Positive Sentiment: 0.2662, Negative Sentiment 0.6050
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