AI & Machine Learning News. 06, April 2020

April 06, 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?

Unacast uses tracked phone data to give states scores on social distancing

Use of Mobile Phone Location Data to Track Coronavirus Has Positive Health Outcomes, But Raises Serious Privacy Concerns

As countries around the world struggle to control the spread of coronavirus, South Korea has been held up as a model of early success in containment. This is attributed to a number of measures that were implemented rapidly: drive-through testing sites, screenings at airports, and widespread temperature checks at building entrances among them. One added measure also appears to have been critical, but is also highly controversial: widespread tracki…
2020-04-02 22:00:00+00:00 Read the full story…
Weighted Interest Score: 1.1299, Raw Interest Score: 0.7605,
Positive Sentiment: 0.1170, Negative Sentiment 0.2535

CloudQuant Thoughts : As long as you don’t think about it too much, it’s quite neat. For more information on the map above see Unacast’s Covid19 page.

AI in the Forefront of Evolving Data Privacy Protections

Several large US banks recently tightened their third-party data sharing practices, a win for consumer privacy in an era when AI systems are helping with privacy regulation compliance.

The trend is expected to grow in 2020, according to an account in BankingDive. A recent security upgrade at PNC Financial Services Group in Pittsburgh kept data aggregators from gaining access to customer account numbers and routing numbers last fall. More recently, JP Morgan Chase announced it will ban third-party apps from accessing customer passwords. The bank plans to issue tokens for access to a limited amount of data in a secure form.

“Due to the evolving nature of privacy legislation and increasing fines for data mismanagement, the banking industry is beginning to take data privacy much more seriously,” stated Ray Walsh digital privacy expert at “This will improve privacy and security levels for consumers, which is highly positive.”

2020-04-02 21:30:07+00:00 Read the full story…
Weighted Interest Score: 3.7828, Raw Interest Score: 1.6955,
Positive Sentiment: 0.2165, Negative Sentiment 0.2345

CloudQuant Thoughts : Yes, so much so that my credit card company this month informed me that they would be selling my data. That I had nothing to worry about but if I wanted to OPT OUT I should call them or log into my account and change my settings. How many people are going to take that step?

This Startup’s Computer Chips Are Powered by Human Neurons

Biological “hybrid computer chips” could drastically lower the amount of power required to run AI systems.

Australian startup Cortical Labs is building computer chips that use biological neurons extracted from mice and humans, Fortune reports. The goal is to dramatically lower the amount of power current artificial intelligence systems need to operate by mimicking the way the human brain. According to Cortical Labs’ announcement, the company is planning to “build technology that harnesses the power of synthetic biology and the full potential of the human brain” in order to create a “new class” of AI that could solve “society’s greatest challenges.”

2020-04-02 21:30:07+00:00 Read the full story…

CloudQuant Thoughts : Just don’t use my neurons, not that I want to keep them, I just think you will be disappointed!

Supervised Learning and Unsupervised Learning for Machine Learning

This is an all too common question among beginners and newcomers in machine learning. The answer to this lies at the core of understanding the essence of machine learning algorithms. Without a clear distinction between these supervised learning and unsupervised learning, your journey simply cannot progress. This is actually among the first things you should learn when you’re embarking on your machine learning journey. We cannot simply jump into the model building phase if we don’t understand where algorithms like linear regression, logistic regression, clustering, neural networks, etc. fall under.

Supervised Vs Unsupervised : If we don’t know what the objective of the machine learning algorithm is, we will fail in our endeavor to build an accurate model. This is where the idea of supervised learning and unsupervised learning comes in. In this article, I will discuss these two concepts using examples and also answer the big question – how to decide when to use supervised learning or unsupervised learning? If you prefer learning in video form, the below video explains 10 machine learning algorithms in a very easy-to-understand manner.

2020-04-06 03:27:17+00:00 Read the full story…
Weighted Interest Score: 5.0631, Raw Interest Score: 2.4020,
Positive Sentiment: 0.1621, Negative Sentiment 0.3684

Infragistics Adds Predictive Analytics, ML and More to Reveal Embedded BI Tool

According to a recent press release, “Infragistics is excited to announce a major upgrade to its embedded data analytics software, Reveal. In addition to its fast, easy integration into any platform or deployment option, Reveal’s newest features address the latest trends in data analytics: predictive and advanced analytics, machine learning, R and Python scripting, big data connectors, and much more. These enhancements allow businesses to quickly analyze and gain insights from internal and external data to sharpen decision-making. Some of these advanced functions include: (1) Outliers Detection—Easily detect points in your data that are anomalies and differ from much of the data set. (2) Time Series Forecasting—Reveal will make visual predictions based on historical data and trends, useful in applications such as sales and revenue forecasting, inventory management, and others. (see attached image). (3) Linear Regression—Reveal finds the relationship between two variables and creates a line that approximates the data, letting you easily see historical or future trends.”
2020-04-06 07:10:37+00:00 Read the full story…
Weighted Interest Score: 4.2345, Raw Interest Score: 2.6115,
Positive Sentiment: 0.4897, Negative Sentiment 0.0544

Feature Scaling using Normalization and Standardization

I was recently working with a dataset that had multiple features spanning varying degrees of magnitude, range, and units. This is a significant obstacle as a few machine learning algorithms are highly sensitive to these features.

I’m sure most of you must have faced this issue in your projects or your learning journey. For example, one feature is entirely in kilograms while the other is in grams, another one is liters, and so on. How can we use these features when they vary so vastly in terms of what they’re presenting?

This is where I turned to the concept of feature scaling. It’s a crucial part of the data preprocessing stage but I’ve seen a lot of beginners overlook it (to the detriment of their machine learning model).

Here’s the curious thing about feature scaling – it improves (significantly) the performance of some machine learning algorithms and does not work at all for others. What could be the reason behind this quirk?

2020-04-03 02:09:02+00:00 Read the full story…
Weighted Interest Score: 4.1121, Raw Interest Score: 1.9792,
Positive Sentiment: 0.1192, Negative Sentiment 0.1431

AI Helping Customer Analytics Dive Deeper into Customer Experience

Corporate marketers are using AI to more deeply analyze the customer experience, and to augment analytics with new approaches and new tools. Here is a review of recent trends in the use of AI by corporate marketers:

Corporate marketers surveyed in August 2019 indicated high interest in rolling out more AI capability, according to the CMO Survey as recently reported in Forbes. The corporate marketers surveyed had increased their use of AI and machine learning in marketing toolkits by 27% over the previous six months. The surveyed marketers projected a 57% increase in use of the AI tools in the coming three years.

Companies with $1 billion or more in revenue and high rates of their sales via the internet were projected to spend more on AI, and they are able to hire needed data scientists to help engage customers. Adoption rates of AI by marketers varied by industry, with the highest projections in transportation, technology and education; the lowest in manufacturing, mining and energy.

2020-04-02 21:30:43+00:00 Read the full story…
Weighted Interest Score: 3.9791, Raw Interest Score: 1.5339,
Positive Sentiment: 0.2774, Negative Sentiment 0.0653

AI is the Most Disruptive Marketing Trend Since the Printing Press

Artificial Intelligence is shaking up the marketing industry as companies race to develop and utilize its potential power.

The market for big data and AI is surging. One recent study found that the global market for these technologieswill be worth $229 billion within the next five years. There are many benefits to industries that implement AI; healthcare, finance, communications, retailers, and even art companies are making use of the technology. And in the marketing industry, AI is revolutionizing the way corporations use data, interact with customers, and grow their firm’s reach. James Paine, the founder of West Realty Advisors, compiled a list of case studies on companies using big data and AI to get more value for their marketing campaigns. Some of these companies use AI to improve the targeting of their advertising, curate higher quality content and use machine powered marketing analytics. Let’s explore some of the use cases and companies that are using AI to boost their digital marketing efforts.

2020-03-31 19:34:49+00:00 Read the full story…
Weighted Interest Score: 3.7287, Raw Interest Score: 1.2375,
Positive Sentiment: 0.2250, Negative Sentiment 0.1607

ING Uses Natural Language Processing For Libor Transition

ING is using natural language processing developed by Eigen Technologies to speed up its communication with customers and speed up the bank’s transition away from the benchmark Libor interest rate.

Eigen is being used to review documentation for references to dealing with rate benchmarks according to an ING spokesman. The data in documentation is often unstructured but NLP can speed up the process by identifying, for example, which customers can just be informed about the replacement rate and which need new documentation.

2020-03-30 16:51:49+00:00 Read the full story…
Weighted Interest Score: 3.5523, Raw Interest Score: 1.7651,
Positive Sentiment: 0.2187, Negative Sentiment 0.2187

The Double Descent Hypothesis: How Bigger Models and More Data Can Hurt Performance

Bigger is better certainly applies to modern deep learning paradigms. Large neural networks with millions of parameters have regularly outperformed collections of smaller networks specialized on a given task. Some of the most famous models of the last few years such as Google BERT, Microsoft T-NLG or OpenAI GPT-2 are so large that their computational cost results prohibited for most organizations. However, the performance of a model does not increase linearly with its size. Double descent is a phenomenon where, as we increase model size, performance first gets worse and then gets better. Recently, OpenAI researchers studied how many modern deep learning models are vulnerable to the double-descent phenomenon.

The relationship between the performance of a model and its size have certainly puzzled deep learning researchers for years. In traditional statistical learning, the bias-variance trade off states that models of higher complexity have lower bias but higher variance. According to this theory, once model complexity passes a certain threshold, models “overfit” with the variance term dominating the test error, and hence from this point onward, increasing model complexity will only decrease performance. From that perspective, statistical learning tells us that “larger models are worse”. However, modern deep learning model have challenged this conventional wisdom.
2020-04-06 13:31:56.299000+00:00 Read the full story…
Weighted Interest Score: 3.1494, Raw Interest Score: 1.8134,
Positive Sentiment: 0.2647, Negative Sentiment 0.3177

Can you Lie to your Deep Learning Model?

Can you fool your deep learning model? What does lying to your deep learning model even entail? This question we’re sure most of you haven’t even considered in your learning or professional journey. But as we’ll see in this article, it’s an important question to answer.

But before we jump into our final installment in this series, let’s quickly recap we’ve learned thus far.

What We’ve Covered in this Series : In part 1, we injected noise into the CIFAR-10 dataset, trained models on that polluted data, and ran a pair of experiments. It shouldn’t come as a surprise that poor data produced poor model performance but what was far more interesting was that certain classes were much more impacted than others. Images of frogs and trucks were easy for our model to learn and the “lies” we told our model didn’t drastically impair its accuracy while noisy labels in cat data were significantly more detrimental.

Having learned that pollution affects classes rather differently in part 1, what we learned next in part 2 was that class sensitivity was not model specific. In other words, the same classes were consistently affected in consistent ways across different models, supporting the hypothesis that class sensitivity isn’t model-dependent but data-dependent. In essence: bad cat data affected each model more drastically than bad frog and truck data across the board.

In this article, we’re going to build upon those lessons. We’re going to start by comparing the impact of data noise and data volume.

2020-03-29 19:13:02+00:00 Read the full story…
Weighted Interest Score: 3.1417, Raw Interest Score: 1.8405,
Positive Sentiment: 0.2219, Negative Sentiment 0.3133

AI Based Financial Modeling Firm Daloopa Partners with Analyst Hub • Integrity Research

Analyst Hub, the New York-based “research infrastructure as a service” platform, recently announced that it has partnered with Daloopa, an AI-based provider of financial modeling tools, to provide compliance and marketing services to buy-side analysts and portfolio managers.

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.

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

AI Community of Experts Making Contributions to Coronavirus Fight

Since the White House issued a “call to action” to AI researchers to help fight the coronavirus spread, researchers have stepped up in multiple ways. Here is an update:

Lots of data is available. The Covid-19 Open Research Dataset (CORD-19) is a collection of research studies published in both peer-reviewed journals and non-peer-reviewed pre-print websites such as bioRxiv and medRxiv. Currently, it consists of over 13,000 full-text papers and abstracts for another 16,000 papers and is expected to be updated with new research as it becomes available, according to an account in Forbes. The account was written by Kashyap Kompella, the CEO of the technology industry analyst firm RPA2AI Research.

He summarized the key scientific questions about Covid-19 that need answers based on available literature.

2020-04-02 21:30:03+00:00 Read the full story…
Weighted Interest Score: 3.0071, Raw Interest Score: 1.3747,
Positive Sentiment: 0.1922, Negative Sentiment 0.2513

6 Open Source Data Science Projects to Make you Industry Ready!

The ideal time to work on your data science portfolio with these open source projects. From datasets on COVID-19 to a collection of AutoML libraries by Google Brain, there’s a lot of data science projects to learn from

Introduction : We are living in the midst of an unprecedented lockdown as governments around the world scramble to get a grip on the prevalent situation. But it’s not all doom and gloom – especially if you’re looking to upskill your data science portfolio and emerge with a solid and industry-relevant resume after the crisis abates! This is an opportunity to really dig in and work on data science projects. A lot of folks suddenly have time on their hands which they did not see coming. Why not utilize that and work on grooming yourself for your dream data science role?

And there is no shortage of open source data science projects and ideas in the community. From computer vision and Natural Language Processing (NLP) projects to Python and data engineering ideas, there is a project out there for everyone. The only question is – where should you start? And that’s the question I have tried to answer in this open source data science project series. This is the 27th edition of the series and I feel this has never been more relevant than it is today. So strap in, get your coding environment ready, and start working on your data science skills!

2020-04-06 00:00:00 Read the full story…
Weighted Interest Score: 2.8641, Raw Interest Score: 1.6406,
Positive Sentiment: 0.2051, Negative Sentiment 0.1538

New dashboard launches to track ecommerce spend during COVID-19

A new platform called Covid-19 Commerce Insight (ccinsight) has launched to measure consumer expenditure online at both a global and regional level in multiple industry sectors on a daily basis.

The platform is currently powered by anonymous data and technology from the leading customer engagement platform Emarsys and leading data analytics provider GoodData. Ccinsight draws on activity from more than a billion engagements and 400 million transactions in 120 countries, providing a global and regional picture of ecommerce activity and trends — a key indicator of overall economic conditions in these unprecedented times. Key insights from the new platform include how the pandemic is affecting the number of online consumer transactions, order numbers, the average order value, types of items purchased and more — in any industry and region in the world — in context of the extraordinary measures taken by governments globally.
2020-04-03 11:34:27+11:00 Read the full story…
Weighted Interest Score: 2.8157, Raw Interest Score: 1.6718,
Positive Sentiment: 0.2200, Negative Sentiment 0.1760

How AI Is Solving Banking Challenges During The Coronavirus Pandemic

AI is solving banking challenges during the coronavirus pandemic in many ways. Here’s how artificial intelligence is making a difference during this crisis.
Artificial intelligence has been leveraged to solve countless challenges in recent years. The coronavirus crisis is putting the benefits of AI to the test. The Hill recently discussed this in its article “Enlisting AI in our war on coronavirus: Potential and pitfalls.”

One of the ways AI is helping people with the recent pandemic is by improving banking. AI is solving some pressing challenges in the banking sector, which is struggling to respond to the growing concerns about the virus.  The coronavirus pandemic has proved to be a very difficult time for businesses and households, with the impact of unemployment and loss of revenue exceeding any recession in recent history. The good news is that AI can be beneficial. The World Economic Forum has said that AI is going to be very valuable in the fight against the coronavirus. However, our inputs are going to be key. The banking sector is a prime example of this.

Here are some ways it can help.

2020-04-01 15:44:53+00:00 Read the full story…
Weighted Interest Score: 2.7512, Raw Interest Score: 1.1744,
Positive Sentiment: 0.2349, Negative Sentiment 0.3132

Reasons to Choose PyTorch for Deep Learning

Reasons to Choose PyTorch for Deep Learning

Before jumping onto the reasons why should not give PyTorch a try, below are a few of the unique and exciting Deep Learning projects and libraries PyTorch has helped give birth to:

  • CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning.
  • Horizon: A platform for applied reinforcement learning (Applied RL)
  • PYRO: Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.
  • Kaolin by NVIDIA as a PyTorch library for accelerating 3D Deep Learning
  • TorchCV for implementing computer vision to your projects
  • PyDLT as a set of tools for deep learning
  • fastai library optimizes your neural net training process
  • and a lot more.

2020-04-06 13:32:47.280000+00:00 Read the full story…
Weighted Interest Score: 2.7418, Raw Interest Score: 1.6763,
Positive Sentiment: 0.3572, Negative Sentiment 0.0824

Seattle machine learning startup OctoML raises $15M from Amplify and Madrona

OctoML is charging ahead with its machine learning deployment software and on Friday announced a $15 million investment round to help support growth.

The Seattle startup spun out of the University of Washington this past July, when it raised a $3.9 million seed round. Founded by a group of computer science experts, the company aims to help companies deploy machine learning models on various hardware configurations.

OctoML is led by the creators…
2020-04-03 15:00:59+00:00 Read the full story…
Weighted Interest Score: 2.6667, Raw Interest Score: 2.0096,
Positive Sentiment: 0.1608, Negative Sentiment 0.0536

Take Your Machine Learning Models To Production With These 5 Simple Steps

Creating a great machine learning system is an art.

There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project.

But do we ever think about how we will deploy our models once we have them?

I have seen a lot of ML projects, and a lot of them are doomed to fail as they don’t have a set plan for production from the onset.

2020-03-31 14:46:26+00:00 Read the full story…
Weighted Interest Score: 2.5986, Raw Interest Score: 1.5246,
Positive Sentiment: 0.3401, Negative Sentiment 0.1525

Dell EMC and Comet Announce Machine Learning Platform Collaboration

Dell EMC, a leading provider of full-stack solutions for data science teams, and Comet, the industry-leading meta machine learning experimentation platform, announced a collaboration with a reference architecture for data science teams looking to harness the power of the Dell EMC infrastructure in tandem with Comet’s meta machine learning platform.

With Dell EMC PowerEdge reference architectures, organizations can deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than it would have taken to design the correct configurations and deploy the solution. Organizations can now rely on architectures that are tested and validated by our Dell engineers and know that services are available when and where you need them.

2020-04-03 07:05:19+00:00 Read the full story…
Weighted Interest Score: 2.5219, Raw Interest Score: 1.5960,
Positive Sentiment: 0.2201, Negative Sentiment 0.1101

The importance of data visualisation – Cuemacro

In finance, we spend so much time doing analysis and working with data. What’s the most important part of the process? Clearly, sharing your findings. If you are unable to communicate what you’ve done, and your audience can’t understand it, the value of your research is reduced. One way to share your research is through tables of numbers, but these can be difficult to decipher. A key part of making these numbers and tables more accessible is through data visualisation.

In the past few weeks, we’ve seen how important data visualisation is with the unfolding coronavirus crisis, to communicate what’s happening with the public, phrases such as “flattening the curve” have become very common. One example of particularly the effective data visualisations about the coronavirus has been the work of the FT. I’ve tried to mimic some of the charts on a Jupyter notebook, with some help from Ewan Kirk who’s coded up an interactive dashboard for coronavirus data.
2020-04-04 00:00:00 Read the full story…
Weighted Interest Score: 2.3562, Raw Interest Score: 1.1322,
Positive Sentiment: 0.1836, Negative Sentiment 0.0918

Using Big Data To Create An Award Winning Giveaway Bot

Big data is changing the future of digital marketing in countless ways. One of the benefits big data offers comes in the form of chatbots and giveaway bots.

Big data is driving a number of changes in the business community. Some of the benefits of big data incredibly obvious. However, there are also a lot of other benefits big data creates that don’t get as much publicity.

One of the biggest benefits of big data is that it can create giveaway bots for online businesses. These benefits can be incredible for many ecommerce stores.
2020-04-03 13:09:11+00:00 Read the full story…
Weighted Interest Score: 2.3460, Raw Interest Score: 1.2610,
Positive Sentiment: 0.3372, Negative Sentiment 0.0880

Privitar raises $80 million to let companies use big data without compromising privacy

Privitar, a U.K. startup that helps companies embed privacy protection into their data projects, has raised $80 million in a series C round of funding led by Warburg Pincus, with participation from Accel, Partech, IQ Capital, Salesforce Ventures, and ABN AMRO Ventures.

Founded in 2014, London-based Privitar enables companies to extract value from data without compromising their customers’ privacy and confidentiality. The platform is all about allowing companies to leverage large, sensitive data sets while adhering to regulations and ethical data principles.

For example, Privitar can embed invisible watermarks into protected data so that if any of the data is distributed without authorization it can be easily tracked back to the responsible party.

2020-04-06 00:00:00 Read the full story…
Weighted Interest Score: 2.3036, Raw Interest Score: 1.4350,
Positive Sentiment: 0.1511, Negative Sentiment 0.1133

Data-Driven Guide To Growing SaaS Business Traffic Through SEO

Big data is redefining the world of marketing. A growing number of SaaS companies are looking for ways touse big data to get more value from leads and business opportunities.

One of the ways businesses can rely more on big data is with SEO. Ahrefs is a leading SEO tool provider, which has used big data to deliver greater value to its customers. A growing number of other digital marketing experts have highlighted benefits with big data in SEO.

2020-04-01 15:53:28+00:00 Read the full story…
Weighted Interest Score: 1.7645, Raw Interest Score: 1.1701,
Positive Sentiment: 0.5201, Negative Sentiment 0.0743

Book Tour

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

‘Reprogramming the American Dream’: Microsoft CTO returns to rural roots to find the future of AI

Kevin Scott is Microsoft’s chief technology officer, its executive vice president of AI and Research, and the author, with Greg Shaw, of the new book, “Reprogramming the American Dream: From Rural America to Silicon Valley, Making AI Serve Us All.”

Scott, who joined Microsoft with its acquisition of LinkedIn, goes back to his roots in rural Virginia in the book, making the case that there is a middle ground between the extreme viewpoints about the future of artificial intelligence — one in which short-term disruption is followed by long-term benefits as technology augments and improves human endeavors.

But first, he says, we must ensure equal access to technology, starting with rural broadband, the importance of which is underscored by the rise of remote work during the current COVID-19 crisis.

2020-04-03 19:26:06+00:00 Read the full story…
Weighted Interest Score: 1.6773, Raw Interest Score: 0.8100,
Positive Sentiment: 0.4629, Negative Sentiment 0.1736

A conversation with Kevin Scott, author of “Reprogramming the American Dream”

Artificial intelligence is already changing virtually every aspect of our lives, from how we communicate with each other to how we grow our food, and technology experts believe we are just at the beginning of understanding how AI could expand people’s capabilities.

In his new book, “Reprogramming the American Dream,” Kevin Scott, Microsoft’s chief technology officer, looks at how he went from a childhood in rural Virgi…
2020-04-03 17:59:35+00:00 Read the full story…
Weighted Interest Score: 1.1362, Raw Interest Score: 0.7004,
Positive Sentiment: 0.4377, Negative Sentiment 0.1501

AWS Announces General Availability of Amazon Detective

A recent press release reports, “Today, Amazon Web Services Inc., an company, announced the general availability of Amazon Detective, a new security service that makes it easy for customers to conduct faster and more efficient investigations into security issues across their AWS workloads. Amazon Detective automatically collects log data from a customer’s resources and uses machine learning, statistical analysis, and graph theory to build interactive visualizations that help customers analyze, investigate, and quickly identify the root cause of potential security issues or suspicious activities. There are no additional charges or upfront commitments required to use Amazon Detective, and customers pay only for data ingested from AWS CloudTrail, Amazon Virtual Private Cloud (VPC) Flow Logs, and Amazon GuardDuty findings. To get started with Amazon Detective, visit”
2020-04-06 07:15:35+00:00 Read the full story…
Weighted Interest Score: 1.5138, Raw Interest Score: 0.9358,
Positive Sentiment: 0.0891, Negative Sentiment 0.4456

Finally, progress on regulating facial recognition

Amid the current need to continually focus on the COVID-19 crisis, it is understandably hard to address other important issues. But, this morning, Washington Governor Jay Inslee has signed landmark facial recognition legislation that the state legislature passed on March 12, less than three weeks, but seemingly an era, ago. Nonetheless, it’s worth taking a moment to reflect on the importance of this step. This legislation represents a significant breakthrough – the first time a state or nation has passed a new law devoted exclusively to putting guardrails in place for the use of facial recognition technology.

In 2018, we urged the tech sector and the public to avoid a commercial race to the bottom on facial recognition technology. In our view, this required a legal floor of responsibility, governed by the rule of law. Since that time, the issue has migrated around the world with a wide range of reactions, with some governments banning or putting a moratorium on the use of facial recognition. But, until today, no government has enacted specific legal controls that permit facial recognition to be used while regulating the risks inherent in the technology.

Washington state’s new law breaks through what, at times, has been a polarizing debate. When the new law comes into effect next year, Washingtonians will benefit from safeguards that ensure upfront testing, transparency and accountability for facial recognition, as well as specific measures to uphold fundamental civil liberties.
2020-03-31 00:00:00 Read the full story…
Weighted Interest Score: 1.1838, Raw Interest Score: 0.7893,
Positive Sentiment: 0.2170, Negative Sentiment 0.2664

Securing ML Services on the Web

If you’re looking to host a machine learning service over the web, then it’s usually necessary to lock down the endpoint so that calls to the service are secure and only authorized users are able to access the service. In order to make sure that sensitive information is not exposed over the web, we can use secure HTTP (HTTPS) to encrypt communication between clients and the service, and use access control to limit who has access to the endpoint. If you’re building a machine learning service in 2020, you should plan on implementing both secure HTTP and access control for your endpoints.
This post will show how to build a secure endpoint implemented with Flask to host a scikit-learn model. We’ll explore the following approaches:

  • Enabling HTTPS directly in Flask
  • Using a WSGI Server (Gunicorn)
  • Using a secure load balancer (GCP)

2020-04-06 13:26:03.241000+00:00 Read the full story…
Weighted Interest Score: 1.1630, Raw Interest Score: 0.8173,
Positive Sentiment: 0.1153, Negative Sentiment 0.0852

Google and Facebook are inadvertently funding the global COVID-19 misinformation pandemic

Advertising technology belonging to tech giants Google and Facebook is fuelling the global spread of COVID-19 misinformation, despite the efforts of both to stem the widespread fraudulent misconduct.

And the problem again exposes the great weakness of self-regulation — the platforms themselves are among the beneficiaries of commercial malfeasance, albeit inadvertently, due to the way their algorithms optimise for engagement.

On March 11th the World Health Organisation confirmed COVID-19 to be a pandemic — “a crisis that will touch every sector”. As of 5 April, there were 1.2 million confirmed cases of coronavirus and over 64,000 deaths worldwide, although Australia has so far avoided the worst-case outcomes that are emerging in the US, UK, Italy and Spain.

More than a month before it declared a pandemic, WHO had already warned information associated with the virus would cause an “infodemic”.

Since then the amount of COVID-19 related information spreading online has eclipsed that of any other event.

“I’ve never seen anything like it,” says Sydney University Associate Professor, Dr Adam Dunn, an expert in biomedical informatics and digital health.

Dunn and his colleagues use social media data and machine learning to monitor what people are exposed to online and how the information impacts their behaviour. His recent work has focused on the effect of the online “anti-vax” movement, where groups have sought to discredit the safety and importance of vaccinations.

2020-04-06 05:53:27+10:00 Read the full story…
Weighted Interest Score: 1.1282, Raw Interest Score: 0.7757,
Positive Sentiment: 0.1675, Negative Sentiment 0.5113

Apple’s Dark Sky acquisition could be bad news for indie weather apps

When Apple acquires a popular app, it’s often bad news for the people who use it. Just look at the fate of apps such as Swell, Hopstop, and Texture, all of which shut down after being bought by Apple.

But Apple’s latest acquisition, the popular weather app Dark Sky, affects more than just the app’s users. Apple isn’t merely shutting down the Android version of the app—it’s also planning to cut off other weather apps that rely on Dark Sky’s data, both on iOS and Android. When that happens at the end of 2021, independent weather apps such as Carrot, Weather Line, and Partly Sunny will no longer have access to inexpensive, hyperlocal weather forecasts. (Dark Sky’s own iOS app will continue to work for now, and the Android version will work for existing users through July 1.)

“I think the effect of this is going to be tons of apps will have to sunset because they won’t have the time or energy to switch,” says Jonas Downey, the cocreator of Hello Weather for iOS and Android. “Or if they want to switch, [Dark Sky’s] competitors are expensive, and they won’t be able to afford it.”

2020-04-02 07:00:25 Read the full story…
Weighted Interest Score: 0.9972, Raw Interest Score: 0.6415,
Positive Sentiment: 0.1782, Negative Sentiment 0.1426

Seattle tech veterans rush to build an app to trace COVID-19 exposure only to run into Apple rejection

It turns out that two weeks of self isolation and social distancing is a good amount of time to build an app — especially if that app’s intention is to help in the fight against COVID-19.

Coronavirus Live Updates: The latest COVID-19 developments in Seattle and the world of tech
But the non-stop work of three Seattle software engineers may not see the light of day due to restrictions Apple has placed in the App Store on COVID-19-related apps that deal with private data.

COVID Trace is the creation of Dudley Carr, Wes Carr and Josh Gummersall, three tech veterans with prior experience at Moz, Google, Uber and elsewhere. The scalable, automated, contact tracing app — with a heavy emphasis on protecting user privacy — is intended to use cell phone data to warn people if they have been exposed to COVID-19. It compares user location data with locations of potential exposure.

The calls for digital tracing, already used in countries such as Singapore and South Korea, are getting louder among some in the U.S., including Trevor Bedford, an epidemiologist at Seattle’s Fred Hutchinson Cancer Research Center who is leading an effort called NextTrace.

2020-04-03 16:00:08+00:00 Read the full story…
Weighted Interest Score: 0.9710, Raw Interest Score: 0.6724,
Positive Sentiment: 0.0747, Negative Sentiment 0.2540

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