Industry News

Industry News

ML/AI News : Kinsa Smart Internet Connected Thermometer helps track the Spread of Coronavirus : Making Use of the Explosion of Data Available to Organizations with Self-Service Data Preparation (Video) : FIX Aims To Reduce Pain Points in ESG Data : Pandas Tricks

AI & Machine Learning News. 30, March 2020

AI & Machine Learning News. 30, March 2020

March 30, 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

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?

Kinsa Smart Internet Connected Thermometer helps track the Spread of Coronavirus

US Health Weather Map by Kinsa
The map above shows you how much influenza-like illness above the normal expected levels we have detected since March 1.
The time series chart allows you to compare Kinsa’s observations of the influenza-like illness level in the U.S., in orange and red, against where we’d expect them to be, in blue, and see how that relationship has changed over the past few weeks
2020-03-23 17:45:21+00:00 Read the full story…
CloudQuant Thoughts : Video game sales up (even though “essential service” Game Stop have finally closed their stores), Travel and lodging are down, Costco sales up (16%), Liquor store sales up (60%), so much alternative data being spewed out as a result of this pandemic but the data from Internet connected thermometer company Kinsa caught my eye. They are actually seeing fewer fevers year on year overall (as the self isolation has also cut the ability of the season Flu to spread) but we can also see the “hotspots”. People are working from home, pollution is down because traffic is down dramatically hence auto fatalities are also down.  Effects and their data are rippling through our entire economy.

Making Use of the Explosion of Data Available to Organizations with Self-Service Data Preparation

We’ve seen a monumental shift in the way we collect, store, process and analyze data. The first video in Trifacta’s new series The Data School with Professor Joe Hellerstein, looks at the why, what, and how of the digital transformation taking place before our eyes, and introduces the series which will be an ongoing educational resource for professionals who work with data, people who work with data systems, and managers who define data strategies.
What Ignited This Shift? In times past, organizations had only limited information they could work with to analyze their own performance. It wasn’t too long ago that companies were starting to convert paper records, most commonly transactional records, into digital data and set up systems that collected every new transactional record and stored that information in their data centers. The volume of data was small by today’s standard, and could be Extracted, Transformed, and Loaded (ETLed) to an on-premise data warehouse where business intelligence tools would pick up the data to measure historical performance. Fast forward to 2020, and every day every one of us interacts with devices, websites, systems and more that constantly generate data at each point of contact. The volume of data collected has skyrocketed.
2020-03-23 00:00:00 Read the full story…
Weighted Interest Score: 2.9579, Raw Interest Score: 1.9258,
Positive Sentiment: 0.2140, Negative Sentiment 0.0000

CloudQuant Thoughts : An interesting and charismatic intro to a course about handling “the Data Boom”. Hopefully the future episodes with keep up this excellent quality.

FIX Aims To Reduce Pain Points in ESG Data

FIX Trading Community, the non-profit standards body, aims to make it easier for the financial industry to use environmental, social and governance data as asset managers could spend roughly $554m ($515m) for ESG data next year.
Rebecca Healey, global head of market structure & strategy at Liquidnet and co-chair of the FIX Trading Community’s EMEA regulatory subcommittee, told Markets Media that the group can provide a backbone of how new data fields are defined, the assumptions that are made and the reports that end-investors find useful so each market participant does not have to reinvent the wheel.
“The organisation provided the same service for MiFID II and saved the industry hundreds of millions of dollars,” she added. “FIX helped solve the pain points between data providers and the buy side.”
She continued that ESG as an investment strategy is a rapid success story and so is moving from niche to mainstream, requiring the use of more ESG data.
“It is becoming a consideration in every investment decision and as a result the industry’s data consumption will be on steroids,” said Healey. “Data consumption on the investment process will change as managers turn to alternative data sources and techniques such as artificial intelligence and natural language processing.”

2020-03-23 17:45:21+00:00 Read the full story…
Weighted Interest Score: 3.4131, Raw Interest Score: 1.7881,
Positive Sentiment: 0.1601, Negative Sentiment 0.0801

CloudQuant Thoughts : Nice to see that they are “aiming”, here at CloudQuant we have already shot and hit the bullseye. One of our goals is to seek out, quality check and test alternative data sets so you don’t have to. In the process we ETL the data into a clean useable format, we write a simple model to test the data efficacy, then we write a white paper (which includes the code and access to the data to confirm results!) . With our CloudQuant Explorer (data visualization), CloudQuant AI (Jupyter Lab environment) and CloudQuant Mariner (US Equities backtesting Engine) we have all the facilities required by both data vendors (to promote and protect their data) and data scientists (to quickly and easily identify valuable data sets). Head over to our data catalog for more information.

Pandas Tricks Not Known By Many

Pandas is a fast, powerful and easy to use open-source data analysis and manipulation tool which is designed on top of the Cytron, C, and Python programming language. It is an amalgamation of two different terms, i.e. panel and data. From combining data frames to reshaping them, Pandas comes with a host of advanced features. For example, it lets a user input a URL in the place of a file name. One can also scrape data from a webpage using its “read_html” function. Although Pandas is one of the most popular libraries among data scientists, due to its wide range of applications, it contains methods that not everyone is familiar with.
The list of functionalities Pandas have are too long and broad to be pointed here, but its vast nature amazes the users from time to time. However, there are a number of lesser-known Pandas tricks which one could further use to be more productive.
2020-03-28 07:30:59+00:00 Read the full story…
Weighted Interest Score: 3.2425, Raw Interest Score: 1.5276,
Positive Sentiment: 0.1410, Negative Sentiment 0.0940

CloudQuant Thoughts : I love Pandas and Numpy Tricks and Tips.. You can always guarantee to discover something that you didn’t know and which has the potential to speed up your throughput! For example, Nearest Merge in this article,.

Google releases Semantic Reactor for natural language understanding experimentation

Google today released Semantic Reactor, a Google Sheets add-on for experimenting with natural language models. The tech giant describes it as a demonstration of how natural language understanding (NLU) can be used with pretrained, generic AI models, as well as a means to dispel intimidation around using machine learning.
“Companies are using NLU to create digital personal assistants, customer service bots, and semantic search engines for reviews, forums and the news,” wrote Google AI researchers Ben Pietrzak, Steve Pucci, and Aaron Cohen in a blog post. “However, the perception that using NLU and machine learning is costly and time-consuming prevents a lot of potential users from exploring its benefits.” Semantic Reactor, then, which is currently a whitelisted experiment in the Google Cloud AI Workshop, allows users to sort lines of text in a sheet using a range of AI models.
2020-03-27 00:00:00 Read the full story…
Weighted Interest Score: 2.9529, Raw Interest Score: 1.8818,
Positive Sentiment: 0.0330, Negative Sentiment 0.1651

‘Inclusive’ Approach Seen as Key to Trusted AI

rustworthy AI for enterprise applications remains elusive, frequently due to poor-quality or siloed data. The result is little confidence in early AI deployments, a vendor survey found.
Dataiku, the enterprise AI platform specialist, used the results its survey of about 400 data scientists, analysts and AI application users to make the case for “inclusive AI.” That approach not only democratizes data but improves quality and, with it, the potential for scaling AI projects into application users’ trust.
The survey found that that 52 percent of respondents have frameworks in place to help ensure data quality in hopes of developing trusted AI applications that scale. Along with trusted data, key considerations for machine learning developers include AI explainability and ethical use of algorithms.

2020-03-25 00:00:00 Read the full story…
Weighted Interest Score: 5.1437, Raw Interest Score: 2.0941,
Positive Sentiment: 0.2731, Negative Sentiment 0.3035

Strongest Demand for AI Talent Comes from Non-IT Departments, says Gartner

For the past four years, the strongest demand for talent with artificial intelligence (AI) skills has not come from the IT department, but rather, from other business units in the organisation, according to Gartner.
Gartner Talent Neuron data shows that although the IT department’s need for AI talent has tripled between 2015 and 2019, the number of AI jobs posted by IT is still less than half of that stemming from other business units.
“High demand and tight labour markets have made candidates with AI skills highly competitive, but hiring techniques and strategies have not kept up,” said Peter Krensky, research director at Gartner. “In the recent Gartner AI and Machine Learning Development Strategies Study, respondents ranked ‘skills of staff’ as the number one challenge or barrier to the adoption of AI and machine learning.”
Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management.

2020-03-27 10:31:26+11:00 Read the full story…
Weighted Interest Score: 4.8288, Raw Interest Score: 1.7158,
Positive Sentiment: 0.1320, Negative Sentiment 0.0880

Klarrio and UBIX Announce AI and Data-Science Partnership

According to a recent press release, “Klarrio and UBIX have entered into a consulting partnership agreement, in which both firms will collaborate on a number of data science and artificial intelligence (AI) initiatives. Under the agreement, Klarrio, one of the world’s leading providers of real-time data-streaming solutions, will provide services and delivery capabilities to enhance the deployment of the UBIX platform of AI-on-demand tools to enterprises. ‘This partnership will help strengthen our companies’ collective capabilities in data streaming, data science, AI and IoT,’ said Doug Barton, …
2020-03-30 07:10:19+00:00 Read the full story…
Weighted Interest Score: 4.4033, Raw Interest Score: 1.7880,
Positive Sentiment: 0.7663, Negative Sentiment 0.0000

Data and the Buy-Side Trading Desk

How are buy-side trading desks gathering, organizing and utilizing data?

Hopefully in a structured, consistent way! Data tells a story: in the first chapter we write about what we want to achieve, and the rest of the story just writes itself. The ending might not be one we like, but we can draw valuable insights from it to make our next story a better one.
To make gathering and organizing data as seamless as possible, there should be connectivity and consistency between the order management system (OMS), the execution management system (EMS), and the pre/post trade analytics module. Storing the data in one location is ideal but not always possible. Data should be organized in such a way that it is easy to run several iterations from different perspectives. However, the most difficult part is probably deciding what to store and how much.
What are the main data challenges/ pain points for the buy side?
2020-03-30 01:22:20+00:00 Read the full story…
Weighted Interest Score: 4.2266, Raw Interest Score: 1.6346,
Positive Sentiment: 0.5786, Negative Sentiment 0.2459

Planixs Launches its Global Solution Provider Partner Programme

Planixs, the leading provider of real-time, intraday cash, collateral and liquidity management solutions, today announced that it has launched its global solution provider partner programme and invites financial services software providers to join.

With the significant increase in demand seen across the world for real-time liquidity solutions within banks, non-bank financial institutions and corporates, Planixs has launched its partner program…
2020-03-23 00:00:00 Read the full story…
Weighted Interest Score: 4.0663, Raw Interest Score: 2.3092,
Positive Sentiment: 0.5020, Negative Sentiment 0.0502

EDM Council Report Reveals Major Shifting of Data Management Landscape

The EDM Council, the cross-industry trade association for data management, has released its 2020 Global Data Management Benchmark Report that has uncovered numerous new trends in data, analytics, and responsible data management strategies that are evolving across a variety of industry verticals. The study examines the shifts in data management priorities, drivers and operational activities that have shaped the international data management landscape since 2017 when its last report was published.
Previously focused largely on the financial sector, 30% of the participants in EDM Council’s 2020 study represent multiple industry sectors such as manufacturing, software, services, consultancies, and others. The report has also expanded geographically, with 38% of responses coming from the Americas, 27% from EMEA, and 35% from APAC regions.
2020-03-27 01:42:52+00:00 Read the full story…
Weighted Interest Score: 3.8131, Raw Interest Score: 2.2556,
Positive Sentiment: 0.0537, Negative Sentiment 0.1074

AI and Machine Learning Shine a New Light on Data Management

AI and the machine learn­ing that underpins it are surging as top technology initiatives. Yet, the ques­tion is this: Are data enterprises ready for the changes it will bring?
For data managers, AI and machine learning not only offer new ways of delivering rapid insights to business users but also the promise of improving and adding intel­ligence to their own operations. While many AI and machine learning efforts are still works in progress, the technol­ogies hold the potential to deliver more enhanced analytic capabilities through­out enterprises.
For starters, the emergence of AI and machine learning is bringing greater autonomy to databases—but indus­try experts caution that more complete autonomy is still a distance away. This is “an exciting emerging area,” said Ger­rit Kazmaier, executive vice president of SAP HANA and Analytics. “But trusting AI and machine learning solutions to take full responsibility for the management of database systems across all profiles—from low-risk to enterprise-critical appli­cations—will take time.”
2020-03-23 00:00:00 Read the full story…
Weighted Interest Score: 3.7573, Raw Interest Score: 1.8599,
Positive Sentiment: 0.2803, Negative Sentiment 0.2293

Addressing Drawbacks Of AutoML With AutoML-Zero

Automated machine learning – or AutoML – is an approach that cuts down the time spent in doing iterative tasks concerning model development. AutoML tools help developers build scalable models with great ease and minimal domain expertise.
AutoML is one of the most actively researched spaces in the ML community. AutoML studies have discovered ways to constrain search spaces to isolated algorithmic aspects. This includes the learning rule used during backpropagation, the gating structure of an LSTM, or the data augmentation. However, most of these algorithmic aspects remain to be hand-designed.
This approach may save compute time, but has few drawbacks:

  • Human-designed components can be biased in favor of human-designed ones, which can reduce the innovation potential of AutoML. Moreover, innovation is also limited because you cannot discover what you cannot search for.
  • Secondly, constrained search spaces need to be carefully composed, thus creating a new burden on researchers, and curtailing the purported objective of saving time.

2020-03-28 12:30:07+00:00 Read the full story…
Weighted Interest Score: 3.7129, Raw Interest Score: 1.4196,
Positive Sentiment: 0.2704, Negative Sentiment 0.1352

Uber details Fiber, a framework for distributed AI model training

A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members.
Fiber — which was developed to power large-scale parallel scientific computation projects like POET — is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines.
2020-03-26 00:00:00 Read the full story…
Weighted Interest Score: 3.5706, Raw Interest Score: 1.6176,
Positive Sentiment: 0.4044, Negative Sentiment 0.1348

Dremio Receives $70 Million in Latest Funding Round

Dremio, the data lake engine company, is closing on $70 million in Series C funding, enabling the company to fuel its growth and expand its products.
“Data is an integral part of every business, and in today’s market, cost-efficient approaches to data analytics are critical,” said Billy Bosworth, CEO, Dremio. “Dremio’s Data Lake Engine makes analytics directly on data lake storage fast, efficient, and secure, which drives down cloud infrastructure costs while giving data consumers what they need, when they need it.”
The round was led by new investor Insight Partners, with participation from existing investors Cisco Investments, Lightspeed Venture Partners, Norwest Venture Partners and Redpoint Ventures. Teddie Wardi, managing director, Insight Partners will also join the Dremio Board of Directors.
Dremio has grown annual recurring revenue (ARR) over 3.5x over the past year and partnered with many of the world’s leading Global 2000 companies, to power their cloud and hybrid data lakes.
2020-03-26 00:00:00 Read the full story…
Weighted Interest Score: 3.3852, Raw Interest Score: 2.1666,
Positive Sentiment: 0.2708, Negative Sentiment 0.2031

The Incredibly Important Role Of Big Data In Academia

The role of big data in academia cannot be underestimated. Big data can make a major difference in how academia operates. Here’s what to know.
One of the most important elements in the evolution of the education system is the ability to make informed conclusions about the need to change approaches that are used and the actions that are taken. According to a2015 whitepaper published in Science Direct, big data is one of the most disruptive technologies influencing the field of academia.
The educational system continuously creates and accumulates a significant amount of data, and the question of the systematic work with these data by a wide range of subjects of education today can be called one of the most important. Big Data can be a powerful tool for transforming learning, rethinking approaches, narrowing longstanding gaps, and tailoring experience to increase the effectiveness of the educational system itself. Now it has become so popular that you can even get data structure assignment help from professionals. In the article, you will find a number of areas where Big Data in education can be applied.
2020-03-24 22:29:00+00:00 Read the full story…
Weighted Interest Score: 3.3411, Raw Interest Score: 1.7620,
Positive Sentiment: 0.0499, Negative Sentiment 0.1995

Saama Makes State-of-the-Art Clinical Analytics Platform Available to Integrate Data from all Organizations Investigating COVID-19 Treatments

Saama Technologies, Inc. (“Saama”), the #1 AI clinical analytics platform company, announced today that it will contribute its AI-powered Life Science Analytics Cloud (LSAC) technology platform to establish the EndPandemic National Data Consortium. The single goal is to integrate data from all ongoing and future clinical studies to dramatically accelerate analysis on COVID-19 and SARS-CoV-2 research in order to reduce the time to find a cure by up to 50%. Saama’s unique platform will allow researchers to dynamically visualize, analyze, and interrogate data across all available programs.
2020-03-30 00:00:00 Read the full story…
Weighted Interest Score: 2.7965, Raw Interest Score: 1.4474,
Positive Sentiment: 0.4181, Negative Sentiment 0.0965

Yellowbrick Data Collaborates with Digital Outcomes Now on IoT Data

Yellowbrick Data is partnering with Digital Outcomes Now to help the Global Telecommunications Industry convert their massive data volumes into positive outcomes.
“Yellowbrick Data has created a breakthrough in processing analytic datasets that is extremely well positioned to deliver on this coming 5G opportunity. In 1/20th the rack space of traditional Data Analytics platforms, Yellowbrick Data is able to process complex data queries at over 100x the performance of traditional systems.” said John Gillespie, president and CEO of Digital Outcomes Now. “This unique architecture is perfect for distributed analytics at the network edge, in the network core or in a hybrid cloud environment.”

2020-03-27 00:00:00 Read the full story…
Weighted Interest Score: 2.7956, Raw Interest Score: 1.5725,
Positive Sentiment: 0.6989, Negative Sentiment 0.0000

24 Best (and Free) Books To Understand Machine Learning

“What we want is a machine that can learn from experience“ – Alan Turing
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. There is no doubt that Machine Learning has become one of the most popular topics nowadays. According to a study, Machine Learning Engineer was voted one of the best jobs in the U.S. in 2019. Looking at this trend, we have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field.
2020-03-24 00:00:00 Read the full story…
Weighted Interest Score: 2.7794, Raw Interest Score: 2.2373,
Positive Sentiment: 0.4195, Negative Sentiment 0.1199

Study Shows That One-Third of Financial Services Companies Lack Clear Plans to Address Privacy Risks

The report released from a survey carried out by Accenture shows a third of financial services companies lack clear plans or resources to address customer data privacy risks within the next 12 months. The lack of protection mechanism hinders both the firms and customers from benefiting from data-centric value-added services. The reason being that firms were afraid of consumer privacy risks, thus preventing them from utilizing consumer data to provide tailor-made products and services to their customers. Additionally, due to their lack of proper data protection mechanisms, firms were afraid of incurring hefty fines from breaching data protection laws such as the European Union’s General Data Protection Regulations (GDPR) and the California Consumer Privacy Act (CCPA).
2020-03-27 08:00:00+00:00 Read the full story…
Weighted Interest Score: 2.7548, Raw Interest Score: 1.5155,
Positive Sentiment: 0.2067, Negative Sentiment 0.2985

How AI Can Improve Your SEO

Marketing is popularly considered a vital tool to boost businesses since it helps in gaining the attention of consumers. Over time, marketing has witnessed the use of different tools to overcome the challenges in the domain. In recent times, artificial intelligence (AI) in particular has emerged as a crucial tool in this area.
Once associated with ideas from science fixtures such as robotics, automation and others, AI has now entered every domain and has been playing a significant role. From creating recommendations on Flipkart to automating check-outs, AI has been spearheading the digital marketing and search engine optimisation (SEO) revolution in e-commerce. AI or machine learning (ML) helps determine how a search engine assesses and ranks down web pages on the list. Furthermore, they also provide a chance to create better content.

2020-03-29 10:30:31+00:00 Read the full story…
Weighted Interest Score: 2.6163, Raw Interest Score: 1.1958,
Positive Sentiment: 0.3156, Negative Sentiment 0.0664

The Debate About Electric Vehicles (EVs) and AI Autonomous Cars

Electrical Vehicles (EVs) are talked about, they are praised, they get a lot of attention, and in some parts of the United States there is a near obsession with them (hint: California).

In spite of all the hype and press, the reality is that there are only around 1.1 million such cars in the U.S. and it represents a small fraction of the 250+ million cars in the country. That’s less than one-half of one percent of the total cars in circulation.
When I say this at various industry presentations, those with an EV are quick to yell at me as a traitor and get upset at my seemingly naysayer commentary.
2020-03-26 21:30:12+00:00 Read the full story…
Weighted Interest Score: 2.5437, Raw Interest Score: 0.8443,
Positive Sentiment: 0.0722, Negative Sentiment 0.2055

When will AI-based AML be friends with European regulators?

The scale of the problem has been demonstrated by the legislative activity of the European Parliament, which over the past five years has adopted three directives regarding anti-money laundering and terrorism financing. Complying with the directives is a particular challenge for banks, exposed to a three-fold risk in terms of money laundering activities.
First of all, there are powerful sanctions imposed by supervisory authorities for unsuccessful compliance with anti-money laundering obligations. According to experts estimations, between 2008-2018, inefficient management of AML (Anti Money Laundering) and KYC (Know Your Customer) areas have cost banks a total of $26bn.

2020-03-24 00:00:00 Read the full story…
Weighted Interest Score: 2.4481, Raw Interest Score: 1.1973,
Positive Sentiment: 0.3421, Negative Sentiment 0.8552

A.I. Versus the Coronavirus

Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics.

Thomas M. Siebel, founder and chief executive of, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweepi…
2020-03-26 00:00:00 Read the full story…
Weighted Interest Score: 2.4407, Raw Interest Score: 1.3578,
Positive Sentiment: 0.1358, Negative Sentiment 0.3394

How Big Data Has Revolutionized the Gaming Industry

Big data has had a profound effect on this sector. You may see a remarkable improvement in the quality of online games. Read on to learn more …! Big data is driving a number of changes in our lives. Forbes recently wrote an article about theimpact of big data on the food and hospitality industry. However, other sectors are changing as well.
Big data phenomenon has revolutionized almost every aspect of an average citizen’s life. Information about our online activity has been accumulating for years, and now is actively used to know more about us. Online shopping, gaming, web surfing – all of this data can be collected, and more importantly, analyzed. Most businesses prefer to rely on the insights gained from the big data analysis. With the help of data mining and machine learning, it is now possible to find the connections between seemingly disparate pieces of information. Thus, new and unexpected solutions come to life and open the door for new business opportunities.

2020-03-23 15:55:07+00:00 Read the full story…
Weighted Interest Score: 2.3280, Raw Interest Score: 1.4186,
Positive Sentiment: 0.2117, Negative Sentiment 0.1906

SIGKDD Launches Community Impact Program To Fund Aspiring Data Scientists

In a bid to uplift the data science that has expanded mostly over the past few years, SIGKDD (Special Interest Group on Knowledge Discovery in Data) has launched a Community Impact Program to provide funding to aspiring data scientists with projects to ensure data science is promoted in the right direction. The funded projects will be required to show their final results or outcome at the annual KDD conference, and the project duration must be of one year. The project’s proposals will be judged by a committee consisting of Mohammed Zaki, Jure Leskovec, Jian Pei and Johannes Gehrke.
The Community Impact Program is looking to fund those projects which are capable of creating a positive impact on society and expand the outreach of data science. Some of the related topics that one may cover as listed down by the program include:

  • Enhance data science community engagement
  • Expand awareness of data science
  • Increase diversity, inclusion, and participation in data science
  • Increase the societal impact of data science
  • Influence public policy and decision making through data science
  • Support for data science schools to broaden participation
  • Support for data science hackathons and summer schools

2020-03-30 08:10:23+00:00 Read the full story…
Weighted Interest Score: 2.2617, Raw Interest Score: 1.4136,
Positive Sentiment: 0.1212, Negative Sentiment 0.0404

What’s low-code all about? An interview with Mike Heffner, Appian

Low-code is much more than a catchphrase, it’s a new way to make unique software applications.
Appian is helping our clients optimize software development to propel operational efficiency, upgrade legacy systems, and delight their clients with exceptional solutions. What we are really trying to solve for is the delta between the demand for applications and the talent shortage of software developers to match the demand.
So, we pioneered the low-code market — creating a world where you build your applications with a mouse, instead of writing code with a keyboard, line by line.
Every application you build is composed of reusable components. Every data record, interface, business rule, integration, etc. So, with every app you build, it gets faster to build the next one.

2020-03-26 06:58:46+00:00 Read the full story…
Weighted Interest Score: 2.2415, Raw Interest Score: 1.3884,
Positive Sentiment: 0.4194, Negative Sentiment 0.1519

Predicting Demand During a Crisis

In the short-term, forecasting should take the back-seat.

Consumer demand is fundamentally different in the lockdown world: historical data and modeling infrastructure built on that data are no longer representative of the world we live in today. Certain product categories will feel the effect of this shift more than others, but we can assume that most product-store observations before February 2020 are biased. Many have asked whether other crisi…
2020-03-30 03:13:53.846000+00:00 Read the full story…
Weighted Interest Score: 2.1565, Raw Interest Score: 1.2810,
Positive Sentiment: 0.2135, Negative Sentiment 0.2402

The First Step to Success for the Chief Data Officer: Changing Your Outlook on Business Value

The modern enterprise no longer needs to worry about obtaining the necessary data to understand their customer’s habits and needs. The issue that companies grapple with today is rooted in the required scale and agility to analyze all the available data at their fingertips. Add in the costs of analytics infrastructure and the technology skills gap and it becomes clear why many companies struggle to create a big enough return on their data investments to stay afloat in an ultra-competitive market.
Enter the Chief Data Officer. Created in 2002, the CDO position was established to enable companies to manage and rationalize data across the enterprise. Unfortunately, the role and the evolution of a CDO’s critical responsibility has introduced new issues to fundamental data analytics processes.
2020-03-24 00:00:00 Read the full story…
Weighted Interest Score: 2.1280, Raw Interest Score: 1.2281,
Positive Sentiment: 0.2947, Negative Sentiment 0.2129

How ISPs are using AI to address the coronavirus-driven surge in traffic

This month, under the strain millions of people self-quarantined by COVID-19 have placed on broadband infrastructure, Facebook, Disney, Microsoft, Sony, Netflix, and YouTube agreed to temporarily reduce download speeds and video streaming quality in countries around the world. Nearly 90 out of the top 200 U.S. cities saw internet speeds decline in the past week, according to BroadbandNow. And Akamai found that global traffic on March 18 was running 67% higher than the typical daily average.
As a result of government and employer mandates to “shelter in place” and work remotely from home, internet subscribers are consuming more bandwidth than during the holidays and sporting events like the Super Bowl. At the same time, ISPs are under regulatory and consumer pressure to maintain a baseline quality of service. According to new research from Park Associates, 76% of households say it would be difficult to go without broadband. And in March, FCC chair Ajit Pai introduced the Keep Americans Connected Pledge, a telecom industry measure that asks companies to prioritize connectivity for essential services.
2020-03-27 00:00:00 Read the full story…
Weighted Interest Score: 2.1080, Raw Interest Score: 1.1675,
Positive Sentiment: 0.1177, Negative Sentiment 0.1962

How to Avoid Astronomical AI Computing Costs

Brock Ferguson is a practice-over-theory kind of guy. The Chicago-based data-science and machine-learning consultancy he co-founded in 2016, Strong Analytics, puts a major focus on productionizing AI models rather than just building out proofs of concept. “We want to minimize that gap between research in the lab and deploying to production,” he said. “We think about that a lot.”
That means thinking a lot about cost — something that’s never far from the minds of machine-learning practitioners and consultants, but which came to the forefront again thanks to a much-circulated recent Andreesen Horowitz review that emphasized the high and ongoing computing costs of building and deploying artificial intelligence models. The review “definitely rang true,” Ferguson said. So what exactly can organizations do to relieve that strain? Are high cloud-provider bills an unfortunate but necessary cost of doing ML business? Does it ever make more financial sense to shift to a hybrid system? We asked Ferguson and a few other experts for advice on how to avoid perpetual sticker shock.
2020-03-26 00:00:00 Read the full story…
Weighted Interest Score: 2.1004, Raw Interest Score: 1.1664,
Positive Sentiment: 0.2493, Negative Sentiment 0.1068

Will this crisis help set autonomous AI on the right course?

The COVID-19 pandemic accelerates an automated future that’s already on its way. It serves as a wake-up call to all AI, robotics, and driverless car startups: stop building eye-dazzling demos and talking about the future possibility of general-use AI. Instead, focus on deploying real-world solutions that can run 24 hours a day with minimum human intervention and deliver true value to users.

Thousands of Americans have started to work from home amidst the current pandemic. Retailers have struggled with supply while nervous consumers are hoarding everything from toilet paper to hand soap. Across the globe, Chinese e-commerce giant JD began testing a level 4 autonomous delivery robot in Wuhan and running its automated warehouses 24 hours a day to cope with a surge in demand.
2020-03-26 00:00:00 Read the full story…
Weighted Interest Score: 2.0885, Raw Interest Score: 1.2076,
Positive Sentiment: 0.1533, Negative Sentiment 0.2492

Top Questions To Detect Unskilled Data Scientists In Job Interviews

With data science subsumed into critical systems across a wide range of industries, it demands that greater care is taken when recruiting for these positions. Moreover, in some cases, an erroneous evaluation can not only affect a company’s profit margins, but also potentially put lives at risk. For instance, with data science integrated into the AI engines of self-driving companies or medical products and services, there is far more at stake.
Looking for ideal candidates who are stress-resistant and adept at vital technologies is challenging enough, but the volley of ‘fake’ data scientists masquerading as skilled professionals makes it even harder.
With data scientists hailed as one of the ‘sexiest jobs of the 21st century’, there is an emerging trend of more and more people branding themselves as such, even if they remotely happen to work with data, or have a few related tech skills.
2020-03-30 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.0747, Raw Interest Score: 0.9572,
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Clinical Data Sharing for AI: Proposed Framework Could Rouse Debate

A group of doctors from Stanford University has proposed a framework for sharing clinical data for artificial intelligence (AI) that could set off a firestorm of debate about who truly owns medical data, ethical obligations to share it, and how to properly police researchers who use it. On the other hand, the envisioned approach has parallels to the open science tactics currently being uniformly deployed to battle the COVID-19 pandemic.
The framework’s central premise is that clinical data should be treated as a public good when it is used for secondary purposes such as research or the development of AI algorithms, as detailed in a special report (doi: 10.1148/radiol.2020192536) published recently in Radiology. That means broadening access to aggregated, de-identified clinical data, forbidding its sale and holding everyone who interacts with it accountable for protecting patient privacy, explains study lead author David B. Larson, M.D., M.B.A., vice chair of clinical operations for the radiology department at Stanford University School of Medicine.
2020-03-26 21:30:27+00:00 Read the full story…
Weighted Interest Score: 2.0714, Raw Interest Score: 1.0794,
Positive Sentiment: 0.1789, Negative Sentiment 0.2655

Three Data Science Technologies to Explore while you Self-Isolate: What are Docker, Airflow and Elasticsearch?

Mandatory Two Week Stay-in!

Like in many other states (and even countries), Minnesotans were issued orders to stay inside to help flatten the COVID-19 infection rate curve. Besides giving my dog lots of walks, to pass the time as I stay home for the next few weeks I am prepared with several streaming services, Lego, puzzles, video games, and a ton of new tech to learn. At the top of my To-Learn tech list sits a few technologies I haven’t used in…
2020-03-30 04:02:41.780000+00:00 Read the full story…
Weighted Interest Score: 2.0000, Raw Interest Score: 1.3645,
Positive Sentiment: 0.2729, Negative Sentiment 0.0606

Massive Ways AI Is Improving The Quality Of Exams

Although exams are an essential part of the academic structure, conducting examinations requires a serious amount of energy, money, infrastructure, and manpower. It is a stressful time for students and teachers alike and involves a lot of steps- from creating and printing the question papers to correcting answer scripts for the results to be published. Once a phase of the examination is complete, the institution needs to start preparing for anoth…
2020-03-27 16:16:57+00:00 Read the full story…
Weighted Interest Score: 1.9902, Raw Interest Score: 1.0319,
Positive Sentiment: 0.3283, Negative Sentiment 0.6332

Weather data and AI are improving the efficiency of solar batteries

Geo-specific weather data and artificial intelligence from IBM and The Weather Company are helping solar inverter manufacturer Selectronic efficiently store energy in solar batteries, increasing the value of the expensive renewable energy storage devices.

Since the early 80s, Selectronic has been designing and manufacturing solar inverters. In this case, the inverters, the brains inside the actual energy storage battery system, control the renewabl…
2020-03-30 05:00:44+11:00 Read the full story…
Weighted Interest Score: 1.9496, Raw Interest Score: 1.2131,
Positive Sentiment: 0.1903, Negative Sentiment 0.0951

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completed at 2020-03-30 14:08:23.538897