AI & Machine Learning News. 06, July 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?


How AI can empower communities and strengthen democracy

Each Fourth of July for the past five years I’ve written about AI with the potential to positively impact democratic societies. I return to this question in hopes of shining a light on technology that can strengthen communities, protect privacy and freedoms, and otherwise support the public good.

This series is grounded in the principle that artificial intelligence is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes. AI literacy is also, as Microsoft CTO Kevin Scott asserted, a critical part of being an informed citizen in the 21st century.

This year, I posed the question on Twitter to gather a broader range of insights. Thank you to everyone who contributed.
2020-07-04 00:00:00 Read the full story…
Weighted Interest Score: 2.9823, Raw Interest Score: 1.2540,
Positive Sentiment: 0.1704, Negative Sentiment 0.3774

CloudQuant Thoughts : It is nice to read an article that is outside the ordinary. For such a cutting edge industry, AI/ML is FULL of repetitive articles and thoughts. This article contains a number of alternative ideas. I particularly like PO.LIS which started in Seattle and was covered quite well on an episode of BBC Click last year.

AI For All: The US Introduces New Bill For Affordable Research

Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all.

The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.

The objective of this Act is to establish a task force that develops a roadmap for a national AI research cloud.

2020-07-02 Read the full story…

CloudQuant Thoughts : Top Companies, Top Universities, US Government, all pulling in the same direction, great to see!

Top 15 AI Articles You Should Read This Month – June 2020

Usually, every month we write an article about the best and most promising AI research papers from that month. In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective…

  • NASA needs your help teaching its Curiosity rover how to drive on Mars
  • Deepfake Detection Challenge Results: An open initiative to advance AI
  • AI researchers say scientific publishers help perpetuate racist algorithms
  • Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
  • NeoML Released as TensorFlow Alternative
  • Google Meet takes on Zoom with AI-powered noise cancellation
  • Machine learning helped demystify a California earthquake swarm
  • Google’s MixIT AI isolates speakers in audio recordings
  • IBM says it is no longer working on face recognition because it’s used for racial profiling
  • Recent Advances in Google Translate
  • TensorTrade: Trade Efficiently with Reinforcement Learning
  • How Artificial Intelligence Could Help Video Gamers Create the Exact Games They Want to Play
  • Russian Voice Assistant Alice Can Paint Landscapes and Abstract Concepts on Command
  • Microsoft researchers say NLP bias studies must consider role of social hierarchies like racism
  • Grading on a Curve? Why AI Systems Test Brilliantly but Stumble in Real Life

2020-06-30 Read the full story…

CloudQuant Thoughts : This is a great collection of papers and articles. We have alrady reported on a few through the month. I particularly enjoyed the California Earthquake Storm, TensorTrade and Google’s MixIt – jump to the bottom of the article and play all three samples (though I have seen more impressive audio separation including track by track extraction for music).

Debate Over Health Data on Fitness Devices Escalates as Google-Fitbit Merger Faces Scrutiny

Countries with data protection laws generally put health data in a special category of sensitive personal information that is subject to stricter regulation. These regulations also usually apply only to the medical industry, however; things like fitness apps and wearables capture some sensitive data of this nature, but are not subject to the same level of regulation. This issue has been taken up by privacy advocates, and it is now receiving some mainstream attention in the form of a petition to block the proposed merger between Google and Fitbit. The effort is led by Privacy International, and accuses Google of having a dangerous monopoly on personal data.

Health data in the hands of Google? Google and Fitbit announced a $2.1 billion acquisition in November 2019. Fitbit is one of the world’s leading manufacturers of “smart” fitness devices such as watches, wearable trackers and scales. The company has about 28 million customers. Health data that these devices track include heart rate, number of steps taken, respiratory patterns, menstrual cycles and information about sleep quality. Google formally notified the European Commission of the proposed acquisition in mid-June of this year, a necessary step in finalizing the merger. The data protection regulator must review the proposed merger for potential harm it might cause to European Union (EU) consumers.
2020-07-03 22:00:00+00:00 Read the full story…
Weighted Interest Score: 2.1255, Raw Interest Score: 1.3327,
Positive Sentiment: 0.0337, Negative Sentiment 0.4892

CloudQuant Thoughts : Remember, this is not about Google having access to the data, it is about who Google will sell it to. The rest of the world currently has public health care systems but private health care companies and insurance companies are desperate to get a piece or even all of that pie. Health data on your customers and non-customers is incredibly invasive.

Automakers Making Deals to Speed Incorporation of AI

Automakers are making deals with technology companies to produce the next generation of cars that incorporate AI technology in new ways.

Nvidia last week reached an agreement with Mercedes-Benz to design a software-defined computing network for the car manufacturer’s entire fleet, with over-the-air updates and recurring revenue for applications, according to an account in Barron’s.

“This is the iPhone moment of the car industry,” stated Nvidia CEO Jensen Huang, who founded the company in 1993 to make a new chip to power three-dimensional video games. Gaming now represents $6.1 billion in revenue for Nvidia, which is now positioning for its next phase of growth, which will involve AI to a great extent. “People thought we were a videogame company,” stated Huang. “But we’re an accelerated computing company where videogames were our first killer app.”

2020-07-01 21:30:55+00:00 Read the full story…
Weighted Interest Score: 1.8194, Raw Interest Score: 1.2085,
Positive Sentiment: 0.1528, Negative Sentiment 0.0278

CloudQuant Thoughts : Regular readers will know how much we appreciate all that Nvidia does for AI and ML. It is definitely a leader in the industry. If it can leverage this leadership to get it’s product into a huge number of new cars it will be a just reward!

How Stitch Fix used AI to personalize its online shopping experience

Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on.

Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don’t. Stitch Fix’s version of this service is called Fixes. Customers get a personalized Style Card with an outfit inspiration. It’s algorithmically driven and helps human style experts match a garment with a particular shopper. Each Fix includes a Style Card that shows clothing options to complete outfits based on the various items in a customer’s Fix. Due to popular demand, last year the company began testing a way for shoppers to buy those related items directly from Stitch Fix through a program called Shop Your Looks.

AI is a natural fit for such services, and Stitch Fix has embraced the technology to accelerate and improve Shop Your Looks. On the tech front, this puts the company in direct competition with behemoths Facebook, Amazon, and Google, all of which are aggressively building out AI-powered clothes shopping experiences.

Stitch Fix told VentureBeat that during the Shop Your Looks beta period, “more than one-third of clients who purchased through Shop Your Looks engaged with the feature multiple times, and approximately 60% of clients who purchased through the offering bought two items or more.” It’s been successful enough that the company recently expanded to include an entire shoppable collection using the same underlying technology to personalize outfit and item recommendations as you shop.

2020-07-05 00:00:00 Read the full story…
Weighted Interest Score: 2.0257, Raw Interest Score: 0.7703,
Positive Sentiment: 0.2853, Negative Sentiment 0.1522

CloudQuant Thoughts : Always nice to see a successful execution in a difficult space that is extremely customer facing!


ML/AI Bias

We need a new field of AI to combat racial bias – TechCrunch

Since widespread protests over racial inequality began, IBM announced it would cancel its facial recognition programs to advance racial equity in law enforcement. Amazon suspended police use of its Rekognition software for one year to “put in place stronger regulations to govern the ethical use of facial recognition technology.”

But we need more than regulatory change; the entire field of artificial intelligence (AI) must mature out of the computer science lab and accept the embrace of the entire community.

We can develop amazing AI that works in the world in largely unbiased ways. But to accomplish this, AI can’t be just a subfield of computer science (CS) and computer engineering (CE), like it is right now. We must create an academic discipline of AI that takes the complexity of human behavior into account. We need to move from computer science-owned AI to computer science-enabled AI. The problems with AI don’t occur in the lab; they occur when scientists move the tech into the real world of people. Training data in the CS lab often lacks the context and complexity of the world you and I inhabit. This flaw perpetuates biases.

2020-07-03 00:00:00 Read the full story…
Weighted Interest Score: 4.7659, Raw Interest Score: 1.8028,
Positive Sentiment: 0.2121, Negative Sentiment 0.3393

MIT takes down 80 Million Tiny Images data set due to racist and offensive content

Creators of the 80 Million Tiny Images data set from MIT and NYU took the collection offline this week, apologized, and asked other researchers to refrain from using the data set and delete any existing copies. The news was shared Monday in a letter by MIT professors Bill Freeman and Antonio Torralba and NYU professor Rob Fergus published on the MIT CSAIL website.

Introduced in 2006 and containing photos scraped from internet search engines, 80 Million Tiny Images was recently found to contain a range of racist, sexist, and otherwise offensive labels, such as nearly 2,000 images labeled with the N-word, and labels like “rape suspect” and “child molester.” The data set also contained pornographic content like non-consensual photos taken up women’s skirts. Creators of the 79.3 million-image data set said it was too large and its 32 x 32 images too small, making visual inspection of the data set’s complete contents difficult. According to Google Scholar, 80 Million Tiny Images has been cited more 1,700 times.
2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 2.1841, Raw Interest Score: 1.2881,
Positive Sentiment: 0.0585, Negative Sentiment 0.3318


How to Understand Global Poverty from Outer Space

Economic livelihood is difficult to estimate. Even in today’s world, there is a lack of clear data to identify impoverished areas, which leads to insufficient resource distribution — money, food, medicine, and access to education. We produce an ample amount of resources to feed, clothe, and house up to 10 billion people, yet hundreds of millions still suffer in poverty.

One approach to help alleviate this problem is to create a model utilizing computer vision to map and predict poverty in the African country of Rwanda, one small enough to provide an abundant and diverse but not overwhelming dataset.

How do we complete this task? There are several key steps…
2020-07-06 00:03:28.300000+00:00 Read the full story…
Weighted Interest Score: 3.2350, Raw Interest Score: 1.9390,
Positive Sentiment: 0.1170, Negative Sentiment 0.1003

7 Open Source Data Science Projects

Open source data science projects add a lot of value to your resume and help you stand out in an interview.

I’m going to give you a tip I wish someone had given me when I started my data science career. When I was navigating the obstacle-filled journey through the backwaters of data science, I had quite a struggle before I landed my first role. I had all the qualifications (or so I thought) but something seemed to be off. That gap between what I brought to the table and what the interviewer expected was data science project experience.

Data science projects add a lot of value to your resume, especially if you’re a beginner. Most newcomers will have certifications but adding open source data science projects will give you a significant advantage over the competition. And trust me, there are an astonishing number of open source data science projects for you. Here, I’ve put together a list of the top 7 open-source data science projects that were created or released in June. This is part of my monthly project series where I bring out the best data science projects open-sourced on GitHub.

2020-07-07 00:00:00 Read the full story…
Weighted Interest Score: 2.4149, Raw Interest Score: 1.3981,
Positive Sentiment: 0.2078, Negative Sentiment 0.0850

JPMorgan Python Training Guide: Solid Intro to Snaky Language

If you’re interviewing for an investment banking analyst or junior trading job at JPMorgan, and you don’t know how to code in Python, you should probably fix that as soon as possible. As with most banks, JPMorgan wants to hire bankers and traders who can code, and, when necessary, it will train those who can’t.

But even if you’re not interested in financial services as a career path, you can still rely on JPMorgan’s generosity to learn Python, which is one of the most ubiquitous and fastest-growing programming languages in business. That’s because the Python training modules JPMorgan uses for its existing analysts and traders are freely accessible on Github, where they were placed by Tim Paine, a developer in the company’s New York office who’s been working on products such as an artificial intelligence engine for the fashion industry in his spare time.

2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 2.1013, Raw Interest Score: 1.3094,
Positive Sentiment: 0.2757, Negative Sentiment 0.0689

GoodData Introduces Support for Geolocation Capabilities

GoodData, a global analytics company, is introducing new geo-mapping capabilities to better meet the needs of companies seeking location data analytics to inform strategic decision making.

This new set of analytical visualizations, analytics, and modeling techniques provides support for geolocation in the analytics industry for market trends evaluation, site selection, asset tracking and monitoring, and other core business needs.

The examples of location-based business insights include COVID-19 infections, economic shifts by geography, election results, unemployment trends, and even contact tracing as efforts ramp up to battle COVID-19.

“We are quickly moving into a world where essentially all data is geo-tagged for location intelligence,” says Roman Stanek, GoodData founder, and CEO. “The rise of IoT, smartphones, Bluetooth, and other wireless technologies gives businesses completely new perspectives into their operations and risks and our new capabilities lead this trend.”
2020-06-30 00:00:00 Read the full story…
Weighted Interest Score: 2.6786, Raw Interest Score: 1.5089,
Positive Sentiment: 0.1372, Negative Sentiment 0.0686

AI Being Applied in Agriculture to Help Grow Food, Support New Methods

AI continues to have an impact in agriculture, with efforts underway to help grow food, combat disease and pests, employ drones and other robots with computer vision, and use machine learning to monitor soil nutrient levels.

In Leones, Argentina, a drone with a special camera flies low over 150 acres of wheat checking each stock, one-by-one, looking for the beginnings of a fungal infection that could threaten this year’s crop.

The flying robot is powered by a computer vision system incorporating AI supplied by Taranis, a company founded in 2015 in Tel Aviv, Israel by a team of agronomists and AI experts. The company is focused on bringing precision and control to the agriculture industry through a system it refers to as an “agriculture intelligence platform.”
2020-07-01 21:30:49+00:00 Read the full story…
Weighted Interest Score: 2.2352, Raw Interest Score: 1.0959,
Positive Sentiment: 0.0751, Negative Sentiment 0.1351

Mozilla Common Voice updates will help train the ‘Hey Firefox’ wakeword for voice-based web browsing

Mozilla today released the latest version of Common Voice, its open source collection of transcribed voice data for startups, researchers, and hobbyists to build voice-enabled apps, services, and devices. Common Voice now contains over 7,226 total hours of contributed voice data in 54 different languages, up from 1,400 hours across 18 languages in February 2019.

Common Voice consists not only of voice snippets, but of voluntarily contributed metadata useful for training speech engines, like speakers’ ages, sex, and accents. It’s designed to be integrated with DeepSpeech, a suite of open source speech-to-text, text-to-speech engines, and trained models maintained by Mozilla’s Machine Learning Group.

Collecting the over 5.5 million clips in Common Voice required a lot of legwork, namely because the prompts on the Common Voice website had to be translated into each language. Still, 5,591 of the 7,226 hours have been confirmed valid by the project’s contributors so far. And according to Mozilla, five languages in Common Voice — English, German, French, Italian, and Spanish — now have over 5,000 unique speakers, while seven languages — English, German, French, Kabyle, Catalan, Spanish, and Kinyarwandan — have over 500 recorded hours.
2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 1.7568, Raw Interest Score: 0.9134,
Positive Sentiment: 0.1015, Negative Sentiment 0.0338

Pair Finance lands €2 million in new funding

Berlin-based digital debt collection outfit Pair Finance has raised €2 million from existing investors after crossing the profitability threshold in 2019.

Currently, more than 250 companies work with Pair Finance, which offers digital debt collection services based on artificial intelligence, enabling companies to collect outstanding receivables more efficiently compared to traditional methods. Customers include Klarna, Zalando, Sixt, Grover or the Jochen Schweizer mydays Group.
2020-07-06 09:11:00 Read the full story…
Weighted Interest Score: 5.3846, Raw Interest Score: 2.3932,
Positive Sentiment: 0.3419, Negative Sentiment 0.0000

Indonesia’s Amar Bank taps Google Cloud for launch of smart phone bank

Indonesia’s Amar Bank has launched an app-only banking offshoot housed entirely in Google Cloud.

Using technology from the bank’s fintech subsidiary Tunaiku, with support from FIS Cloud and Infofabrica, the My Smile app currently offers a savings account backed up by personal financial management and account aggregation tools.

Amar bank already uses Google Cloud for Big Data architecture, AI and analytics and intends to gradually bulk up the …
2020-07-03 09:46:00 Read the full story…
Weighted Interest Score: 5.1745, Raw Interest Score: 2.7677,
Positive Sentiment: 0.1203, Negative Sentiment 0.1203

dotData and Teradata Collaborate to Enable Organizations to Derive More Value from AI

dotData, a provider of full-cycle data science automation and operationalization for the enterprise, is partnering with Teradata, a cloud data and analytics company, allowing dotData to leverage Teradata’s Vantage platform with dotData’s autoML 2.0 platform.

The collaboration will streamline and simplify the movement of data between Teradata and dotData to help the companies’ joint customers derive more value from their AI and machine learning initiatives.
2020-06-30 00:00:00 Read the full story…
Weighted Interest Score: 4.3656, Raw Interest Score: 2.2885,
Positive Sentiment: 0.2452, Negative Sentiment 0.0000

Has AI arrived for financial services?

When will artificial intelligence really have ‘arrived’? For a long time, this was a question for philosophers and computer scientists, pondering over whether passing the Turing test truly indicates intelligence, or debating about how broad our definition of artificial intelligence should be. Over the last several years, however, this question has changed considerably: with the advent of consumer AI tools such as virtual assistants and the increasing availability of off-the-shelf solutions offering to bring the power of AI to business operations, the issue has become less philosophical, and much more pragmatic. Now, for business leaders, it is often a matter not of whether to respond to the arrival of AI, but of how to respond to the arrival of AI.

The promise is, of course, huge. It’s hard to think of an area of the economy which won’t be changed, and it’s hard to think of a short summary which properly encapsulates what those changes will be. It’s probable that for every business, there is at least one thing which will be done faster, or made available to far more people, or be significantly more accurate, or will simply be possible for the first time, through the use of AI. Its importance has been compared, convincingly, to electricity itself.

Nowhere is this more true than in financial services.

2020-07-02 10:16:06 Read the full story…
Weighted Interest Score: 3.7532, Raw Interest Score: 1.6609,
Positive Sentiment: 0.1444, Negative Sentiment 0.2347

Batch Normalization in practice: an example with Keras and TensorFlow 2.0

In this article, we will focus on adding and customizing batch normalization in our machine learning model and look at an example of how we do this in practice with Keras and TensorFlow 2.0.

“In the rise of deep learning, one of the most important ideas has been an algorithm called batch normalization (also known as batch norm).
Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks.” – Jason Brownlee

Batch normalization can be implemented during training by calculating the mean and standard deviation of each input variable to a layer per mini-batch and using these statistics to perform the standardization.

2020-07-05 21:59:57.445000+00:00 Read the full story…
Weighted Interest Score: 3.7512, Raw Interest Score: 1.5239,
Positive Sentiment: 0.0743, Negative Sentiment 0.0743

Guided Labeling Episode 1: An Introduction to Active Learning

One of the key challenges of utilizing supervised machine learning for real-world use cases is that most algorithms and models require lots of data with quite a few specific requirements.

First of all, you need to have a sample of data that is large enough to represent the actual reality your model needs to learn. Nowadays, there are lots of thoughts regarding the harm generated by biased models. Such models are often trained with biased data. Usually, a rough rule of thumb is that the more data you have, the less biased your data might be. The size of the sample not only impacts the righteousness of your model but, of course, its performance too. This is especially significant if you are dealing with deep learning, which requires more data than other machine learning algorithms.

Assuming you have access to all of these pieces of data, you now need to make sure they are labeled. These labels, also called the ground truth class, will be used as the target variable in the training of your predictive model.
2020-07-02 07:30:52+00:00 Read the full story…
Weighted Interest Score: 3.5709, Raw Interest Score: 1.7043,
Positive Sentiment: 0.2093, Negative Sentiment 0.0448

Executive Interview: Steven Babitch, Head of AI Portfolio, GSA’s TTS

Primary Mission is to Accelerate AI Investment, Help Agencies Achieve Goals

Steven Babitch is Head of the Artificial Intelligence Portfolio for the GSA’s Technology Transformation Services (TTS), where he is charged to help the US federal government use AI to achieve its mission. He describes four areas of focus for that effort. He brings public policy and private industry perspectives to the task, as a former White House Presidential Innovation Fellow, and as the head of Babitch Design Group. He recently took some time to talk to AI Trends Editor John P. Desmond about his work.
2020-07-01 21:30:20+00:00 Read the full story…
Weighted Interest Score: 2.7896, Raw Interest Score: 1.2404,
Positive Sentiment: 0.2464, Negative Sentiment 0.0935

Data Scientist vs Data Analyst Interview. Here’s the Difference.

An interviewing guide – Introduction Data Scientist Interview – Data Analyst Interview – Similarities and Differences

Many of the readers here on Medium are looking to be a data scientist or data analyst, and are therefore interested in the interview process for each position. In my experience, I have interviewed with several companies for both roles. Below, I will detail the process for both roles and highlight where the…
2020-07-06 03:03:25.205000+00:00 Read the full story…
Weighted Interest Score: 2.7731, Raw Interest Score: 1.4717,
Positive Sentiment: 0.1214, Negative Sentiment 0.1062

AI Strategy: Using the 715 Framework to Build High Value Big Data

What is the Solution? 7:15 Framework®
We’ve come across organizations that want their data cleaned, and they want a culture that is able to drive growth and revenue. Yet, there is no set guide or framework currently available in the market that provides a roadmap for how to implement this within their organization. They need a plan that has the ability to identify all of the elements that need to be taken into account for a data-driven project to provide high value.

My company has been doing research on a framework, and, during our research, we have discovered that there are seven primary objects and 15 secondary elements which create the framework. A total of 22 elements have been built into this framework to assist organizations in identifying and helping to assess if they are structurally ready to get high value from data. Here is a list of the top seven primary elements in the framework:

  • The High Value
  • Organizational Maturity
  • Internal Competence
  • Clear Objectives
  • Data Governance
  • Engagement
  • Business Transformation

2020-07-06 07:35:00+00:00 Read the full story…
Weighted Interest Score: 2.6207, Raw Interest Score: 1.3621,
Positive Sentiment: 0.2484, Negative Sentiment 0.1159

VAPAR raises $700K in seed funding to accelerate growth

VAPAR an Australian startup in the Internet of Things arena which utilises artificial intelligence (AI) and machine learning (ML) to automate condition assessments for stormwater and sewerage pipelines, has raised $AU700,000 in seed funding.

The funding round closed with a diverse range of angel investors in addition to Startmate and Australia’s premier VC, Blackbird Ventures, who are also investors in Canva, SafetyCulture, and Culture Amp.
2020-07-06 10:47:04+10:00 Read the full story…
Weighted Interest Score: 2.6141, Raw Interest Score: 1.5492,
Positive Sentiment: 0.2951, Negative Sentiment 0.1475

How Autodesk Used Data Wrangling to Accelerate Analytics by 66% (Webinar)

Due to the economic fallout from the COVID-19 pandemic, every company needs to do more with less. This has sparked a massive effort within organizations large and small to modernize processes, incorporate automation wherever they can and utilize data to increase the efficiency of their operations. However, not all operations are intuitive to automate and when it comes to cleaning, blending and structuring diverse customer data, many data teams get stuck.

In Trifacta’s upcoming webinar with Snowflake, AWS and Autodesk, Autodesk’s John Gardner will walk through the challenges his team experienced wrangling diverse sources of customer data to build a 360-degree view of their customers to identify cross-sell/upsell opportunities. John will share how his team at Autodesk established a cloud data platform combining Snowflake, AWS and Trifacta to automate traditionally siloed data preparation activities and reach new levels of efficiency with their analytics initiatives.

Join this webinar to learn how Autodesk…

2020-07-06 00:00:00 Read the full story…
Weighted Interest Score: 2.5935, Raw Interest Score: 1.5976,
Positive Sentiment: 0.3495, Negative Sentiment 0.0999

Creating a Vanilla Neural Network with Tensorflow

A beginner’s friendly guide detailed on creating a neural network using Tensorflow.

Nowadays, Tensorflow is a highly demanded skill in the market, ensuring ease of production, standardizing some crucial stages of Machine Learning.
Today you’ll learn how to make your first neural network with Tensorflow; We’re going to build a Multilayer Perceptron model, also called the “Vanilla” Neural Network. Are you ready? So let’s start!

  • Table of Contents
  • What is Tensorflow?
  • What is a Neural Network?
  • The Google Colab
  • Structure of Today’s Project
  • Keras? What is that?
  • Building our Model
  • Fit with training sets
  • Evaluation with validation sets
  • Prediction with test sets
  • Final Considerations
  • Bibliography

2020-07-06 02:32:58.973000+00:00 Read the full story…
Weighted Interest Score: 2.5000, Raw Interest Score: 1.9399,
Positive Sentiment: 0.1252, Negative Sentiment 0.1877

DBS Bank followed four design principles when building their enterprise data platform

I recently had the pleasure to talk with Siew Choo So, Group Head Consumer Banking Technology and Big Data/AI at DBS, on how the bank has set up their enterprise data platform to enable a data driven organization. That session was part of Forrester’s APAC Financial Services Webcast Week 2020, and you can find the full session for replay (as well as all other session replays) at https://forr.com/apacfsweek (free registration required).

In the session, Siew Choo and I talked about the situation at the starting point of the bank’s journey and how the team set the objectives and design principles for a single end-to-end platform. That platform, named “ADA — Advancing DBS with AI,” was conceptualized to provide data ingestion, data security, data storage, data governance, data visualization, and analytics model management capabilities.
2020-07-06 01:31:20-04:00 Read the full story…
Weighted Interest Score: 2.4988, Raw Interest Score: 1.4230,
Positive Sentiment: 0.2453, Negative Sentiment 0.0736

Adverse media screening: a key pillar of financial crimes compliance

It is essential to gather all details about a customer, or prospect to be onboarded as a customer, including any negative information about them so that the bank can take a risk-based approach on the relationship with such customer.

Technology has enabled us to access staggering volumes, variety and velocity of news and information from around the world. Is it then humanly possible to screen millions of customers of any bank by searching the web for adverse news, analyse every negative news item and then consider them for risk profiling of the accused customer? Can we leverage artificial intelligence (AI) instead, to enhance the effectiveness and efficiency of adverse media screening?

2020-06-29 00:00:20+00:00 Read the full story…
Weighted Interest Score: 2.4667, Raw Interest Score: 1.2978,
Positive Sentiment: 0.2329, Negative Sentiment 0.6323

AI is Making BI Obsolete, and Machine Learning is Leading the Way (Registration Wall)

BI has become a must-get for any company, and while it does offer some great value, what are you really getting from it? Although BI is great at visualizing your data and giving you digestible reports, it’s hard to make predictions and automate your insights to really optimize your operations. Building predictive models that can cut down your decision time and offer better insights is a must, but achieving them sounds impossible. So, why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data. With machine learning leading the way, data science is quickly making BI obsolete.
2020-06-29 00:00:00 Read the full story…
Weighted Interest Score: 2.4169, Raw Interest Score: 1.6667,
Positive Sentiment: 1.0606, Negative Sentiment 0.4545

The Sunny Side of Privacy Laws and Compliance Mandates

Enterprises are faced with a growing onslaught of data and increasing data privacy regulations. Those regulations include the General Data Protection Regulation (GDPR), which regulators began enforcing in May 2018. These businesses often see protecting data from misuse and abuse as a procedural chore and financial burden. Some organizations even look at data privacy regulation as a legal nightmare.

Such organizations often respond by throwing resources at the problem. That may involve appointing a chief data officer (CDO) and other professionals to enact and enforce restrictive policies, while bracing for costly non-compliance fines at the same time. But rather than seeing data privacy initiatives as a necessary evil, organizations should look at them as an opportunity for positive change. Data privacy efforts can be valuable in enabling businesses to build trusted relationships with their customers.

In a world in which customer experience is paramount but distrust and misinformation are rampant, there’s no better time for organizations to have a 360-degree view of their data.

2020-06-29 00:00:00 Read the full story…
Weighted Interest Score: 2.4041, Raw Interest Score: 1.2747,
Positive Sentiment: 0.3346, Negative Sentiment 0.2390

Learn how to accelerate your business using automation and AI technology: Transform 2020

If companies were already investing in automation and AI technologies before March 2020, they have only accelerated those investments since. No one expected the jolt the COVID-19 pandemic would bring to business. With leaders looking for ways to avoid human contact, machines, software, and new processes that avoid those humans are even more imperative.

That’s why we’ve committed a whole day of our Transform 2020 digital conference to the Technology and Automation Summit, presented by collaborative data science software maker Dataiku, on July 15. Hear from industry leaders at Dataiku, Intuit, Chase, Walmart, Goldman Sachs, and more about their journeys and learnings in implementing these technologies, how they unlocked value/ROI from them, and their thoughts about what the future holds.
2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 2.3932, Raw Interest Score: 1.4552,
Positive Sentiment: 0.2782, Negative Sentiment 0.0214

Data privacy rules stop banks from auditing algorithms for bias

“Being oblivious to race gets in the way of being fair…. Unfortunately, both company policies and government policies like GDPR prevent the collection and use of those sensitive attributes.”

Roger Taylor, chair of the Centre of Data Ethics and Innovation, said: “In the UK, GDPR and the Data Protection Act should not prevent organisations from effectively auditing their algorithms for bias, but it is clear that uncertainty on this point could pr…
2020-06-29 00:00:00 Read the full story…
Weighted Interest Score: 2.3513, Raw Interest Score: 1.0381,
Positive Sentiment: 0.2076, Negative Sentiment 0.2076

Democratizing Data: Do Your People Have the Access They Need?

Organizations have invested heavily in engineering resources to centralize data across the enterprise, often creating sophisticated environments with robust data pipelines. But even as they have successfully gathered and corralled data this way, many still struggle with effectively sharing and orchestrating the data across the enterprise.

That’s a pressing concern because, to successfully experiment, explore and activate data for the entire organization, IT, analytics and marketing teams must all have the data access they need to succeed. This notion isn’t new, but for many businesses, despite their commitment to democratizing data, that access—leveraging each group’s strengths—is insufficient or absent.
2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 2.1806, Raw Interest Score: 1.1229,
Positive Sentiment: 0.3417, Negative Sentiment 0.2116

Unilever and Alibaba announce technology partnership to enhance shopping experiences

Unilever, one of the biggest multinational consumer goods companies, is partnering with Alibaba Cloud, as part of a strategic initiative that will enable the global consumer goods business to action on next-generation digital marketing campaigns, according to the companies.

Fang Jun, VP Data and Digital, Unilever China: “Customer buying patterns are ever-changing; when and where they buy has caused marketing to become even more agile and precise in order to stay relevant and reduce marketing waste. The use of Alibaba Cloud’s cutting-edge technology will ensure that our customers enjoy even more value from their relationship with the Unilever brand, through relevant campaigns and activities based on true insights into their buying preference.”

The Unilever and Alibaba Cloud collaboration were announced at the Alibaba Cloud Global Summit, in which “China Gateway 2.0” was also launched. The programme, that Unilever is part of, hopes to help Alibaba Cloud’s partners and customers to accelerate their growth in China by capitalizing Alibaba Cloud’s local business expertise, technologies, and matured ecosystem. In the partnership, Unilever will apply Alibaba Cloud’s artificial intelligence (AI) and cloud-based technologies to its omnichannel, online and offline demand generation activities.
2020-07-03 11:23:40+10:00 Read the full story…
Weighted Interest Score: 1.9428, Raw Interest Score: 1.3611,
Positive Sentiment: 0.2500, Negative Sentiment 0.0278

Yellowbrick Data Launches Cloud Disaster Recovery Service

Yellowbrick Data, the Hybrid cloud data warehouse company, is releasing its Cloud Disaster Recovery service, as well as introducing new database replication and enhanced backup/restore features to its platform.

“We’re complementing the existing business continuity functionality inside a single Yellowbrick Data Warehouse–including support for high availability, erasure coding, and fault tolerance–with new features that provide continuity across databases and locations in a low-cost, low-effort way using the power and flexibility of hybrid cloud architecture,” said Nick Cox, Yellowbrick head of products. “That is essential for business-critical applications.”
2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 1.8671, Raw Interest Score: 1.0983,
Positive Sentiment: 0.1647, Negative Sentiment 0.3295

CCPA Enforcement Begins: Are You Ready?

The California Consumer Privacy Act (CCPA) became law six months ago, but enforcement has been delayed until today. If you haven’t yet started your CCPA remediation effort, you’ve got a lot of catching up to do.

California residents gained new data rights under CCPA, and companies are now subject to new requirements regarding that data. Residents of the state can demand to know what personal data companies are collecting about them, whether they’re selling that data, and to whom. Residents can demand access to that data, and even request that companies delete their personal data.

2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 1.8617, Raw Interest Score: 1.0517,
Positive Sentiment: 0.1813, Negative Sentiment 0.3506

These 5 Chicago Tech Companies Made $146M in June

This month, the five biggest funding rounds in Chicago’s tech scene pulled in a combined total of $146 million. This marks the city’s strongest month of tech funding since March. Topping the list was renewable energy startup LanzaTech. Continue reading below for the details on all of June’s top funding rounds in Chicago tech.

  • Ocient – Data Analytics
  • Tovala – Meal Delivery
  • Kalderos – Drug Claim Analysis
  • M1 Finance – Financial Management Tool
  • LanzaTech Inc. – Bio Aviation Fuel

2020-07-01 00:00:00 Read the full story…
Weighted Interest Score: 2.8345, Raw Interest Score: 1.7763,
Positive Sentiment: 0.0658, Negative Sentiment 0.0658


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