AI & Machine Learning News. 20, April 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?


Machine learning could check if you’re social distancing properly at work

Andrew Ng’s startup Landing AI has created a new workplace monitoring tool that issues an alert when anyone is less than the desired distance from a colleague.

Six feet apart: On Thursday, the startup released a blog post with a new demo video showing off a new social distancing detector. On the left is a feed of people walking around on the street. On the right, a bird’s-eye diagram represents each one as a dot and turns them bright red when they move too close to someone else. The company says the tool is meant to be used in work settings like factory floors and was developed in response to the request of its customers (which include Foxconn). It also says the tool can easily be integrated into existing security camera systems, but that it is still exploring how to notify people when they break social distancing. One possible method is an alarm that sounds when workers pass too close to one another. A report could also be generated overnight to help managers rearrange the workspace, the company says.

2020-04-17 00:01:00 Read the full story…

CloudQuant Thoughts : The introduction of this kind of technology, like the tracking of all residents via mobile phone tracking must be accompanied by protections and legislation that protects privacy post event or we will have slipped into an Orwellian future which we the people are unable to control.

Artificial intelligence is evolving all by itself

Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.

“While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”

Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks—for instance spotting road signs—and researchers can spend months working out how to connect them so they work together seamlessly.

2020-04-13 00:01:00 Read the full story…

CloudQuant Thoughts : A simplistic article but an important topic none the less, we are on the cusp of the Technological Singlarity. Narrow AI has already demonstrated how quickly it can overtake human capability at very narrow skill sets, even extremely sophisticated Narrow skills such as playing Go can supersede human ability in a matter of months, just watch the documentary about AlphaGo. People are working on General AI and every week we see astounding progress.

Comprehensive Guide of Deep Learning Interview Questions

Are you planning to sit for deep learning interviews? Have you perhaps already taken the first step, applied, and sat through the ordeal of several rounds of interviews for a deep learning role and not made the cut? Cracking an interview, especially for a complex role like a deep learning specialist, is a daunting task for most people. Deep learning is a vast field with an ever-changing nature as new developments are rolled out on a regular basis. How can you keep up with the pace? What should you focus on? These are questions every deep learning enthusiast, fresher and even expert has asked themselves at some point. That was a key reason behind penning down this article, a comprehensive list of the popular deep learning interview questions and answers. But let me expand on that a bit more.

2020-04-20 03:42:32+00:00 Read the full story…
Weighted Interest Score: 2.0863, Raw Interest Score: 1.3583,
Positive Sentiment: 0.1297, Negative Sentiment 0.2352

CloudQuant Thoughts : Brush upon your interview skills during this downturn, be ready for when the market ups back up!

All The Deep Learning Breakthroughs In NLP

Natural Language Processing (NLP) has been around for some time now. There are many benefits of NLP as it is used in almost all fields quite immensely. But it is empty without Deep Learning, as deep learning has contributed a lot in NLP and with both of them implemented as one, they have done some marvels.

In this article, I will tell you what those implementations are and how they benefit us. In short, I will give you the best practices of Deep Learning in NLP.

2020-04-16 14:30:00+00:00 Read the full story…
Weighted Interest Score: 2.0690, Raw Interest Score: 1.7174,
Positive Sentiment: 0.1782, Negative Sentiment 0.3402

CloudQuant Thoughts : NLP is a huge part of ML and AI, read up on the best practices using this article.

Tutorial: Building your Own Big Data Infrastructure for Data Science

Working on your own data science projects are a great opportunity to learn some new skills and hone existing skills, but what if you want to use technologies that you would use in industry such as Hadoop, Spark on a distributed cluster, Hive, etc. and have them all integrated? This is where I believe the value comes from when building your own infrastructure.

You become familiar with the technologies, get to know the ins and outs about how it operates, debug and experience the different types of error messages and really get a sense of how the technology works over all instead of just interfacing with it. If you are also working with your own private data or confidential data in general, you may not want to upload it to an external service to do big data processing for privacy or security reasons. So, in this tutorial I’m going to walk through how to setup your own Big Data infrastructure on your own computer, home lab, etc. We’re going to setup a single node Hadoop & Hive instance and a “distributed” spark instance integrated with Jupyter.

2020-04-19 15:16:33.061000+00:00 Read the full story…
Weighted Interest Score: 1.6459, Raw Interest Score: 1.2950,
Positive Sentiment: 0.0617, Negative Sentiment 0.0806

CloudQuant Thoughts : What did you do during the lockdown? I built my own Big Data Infrastructure!

AIOps is Marching into the Mainstream, Replacing IT Ops

Artificial intelligence for IT operations, AIOps, refers to the application of machine learning and data science to IT operations. AIOps systems monitor huge volumes of log and performance data typically generated in a large enterprise, to gain visibility into dependencies and solve problems.

An AIOps platform should include these three capabilities, suggests a recent report in TechTarget:

  1. Automate routine practices. These include user requests and non-critical IT system alerts. For example, a help desk system can process and fulfill a user request to provision a resource automatically. The system is also able to evaluate alerts and determine which ones require action, and which are based on metrics and supporting data within normal parameters.
  2. Recognize serious issues faster and with greater accuracy than humans. The system should be able to detect behavior out of the norm, especially on critical servers, by processing volumes of data not possible for humans to monitor on their own.
  3. Streamline the interactions between data center groups and teams. AIOps provides each functional IT group with relevant data and perspectives. The AIOps system learns what analysis and monitoring data from the large pool of resource metrics to show each group or team.

2020-04-16 21:30:29+00:00 Read the full story…
Weighted Interest Score: 2.1381, Raw Interest Score: 1.2498,
Positive Sentiment: 0.1994, Negative Sentiment 0.1463

CloudQuant Thoughts : Ops is no more, Long Live AIOps!

Google Launches Tool To Adapt ML Models With Transfer Learning

The TensorFlow team at Google recently introduced a new tool for TensorFlow Lite (TFLite) known as Model Maker. The TFLite Model Maker simplifies the process of adapting and converting a TensorFlow Neural Network model to particular input data when deploying this model for on-device ML applications.

Developed by researchers and engineers from the Google Brain team, TensorFlow is one of the most sought after deep learning frameworks of all time. Last year, TensorFlow Lite was open-sourced by the TensorFlow team for mobile devices, and two development boards – Sparkfun and Coral – to perform machine learning tasks on handheld devices like smartphones.

2020-04-17 05:07:02+00:00 Read the full story…
Weighted Interest Score: 5.2191, Raw Interest Score: 2.4594,
Positive Sentiment: 0.1671, Negative Sentiment 0.0716

deeplearning.ai’s AI-Based Medicine Specialisation Courses on Coursera

deeplearning.ai has introduced artificial intelligence-based courses for medicine specialisation on Coursera.

deeplearning.ai has introduced artificial intelligence-based courses for medicine specialisation on Coursera.

In a recent LinkedIn post, Andrew Ng has confirmed the news by stating — “One of the fastest-growing AI applications is medicine. So I’m excited to announce that Courses 1 and 2 of deeplearning.ai‘s new AI For Medicine Specialization are now available on Coursera! You’ll learn to diagnose diseases from X-rays and build your prognostic models.”
2020-04-16 06:52:38+00:00 Read the full story…
Weighted Interest Score: 4.3726, Raw Interest Score: 1.7933,
Positive Sentiment: 0.1416, Negative Sentiment 0.0472

Big tech still spending on AI, as Appen affirms guidance

Artificial intelligence data services company Appen is proving to be resilient to the economic impacts of COVID-19, with the big tech giants so far still willing to open their wallets for AI projects.

The business provides the world’s largest technology companies with crowd-sourced data that’s needed to train the AI algorithms that power everything from search engines to voice assistants and driverless cars.

2020-04-15 00:00:00 Read the full story…
Weighted Interest Score: 4.1131, Raw Interest Score: 1.6379,
Positive Sentiment: 0.0862, Negative Sentiment 0.0000

How to Prepare for the Future of Data Warehousing (PDF – Registration Wall)

Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
2020-04-16 00:00:00 Read the full story…
Weighted Interest Score: 3.9216, Raw Interest Score: 2.1882,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

CaixaBank steps up quantum computing trials

CaixaBank is stepping up its experimental application of quantum computing in financial services, developing a machine learning algorithm to classify customers according to their credit risk.

The Spanish bank last year reported on its tests of IBM’s Framework Opensource Qiskit, to implement a quantum algorithm to assess the financial risk of a mortgage portfolio and treasury bills portfolio specifically created for the project using real data.

2020-04-17 00:01:00 Read the full story…
Weighted Interest Score: 3.8202, Raw Interest Score: 1.6504,
Positive Sentiment: 0.0000, Negative Sentiment 0.0750

AI researchers propose ‘bias bounties’ to put ethics principles into practice

Researchers from Google Brain, Intel, OpenAI, and top research labs in the U.S. and Europe joined forces this week to release what the group calls a toolbox for turning AI ethics principles into practice. The kit for organizations creating AI models includes the idea of paying developers for finding bias in AI, akin to the bug bounties offered in security software.

This recommendation and other ideas for ensuring AI is made with public trust and societal well-being in mind were detailed in a preprint paper published this week. The bug bounty hunting community might be too small to create strong assurances, but developers could still unearth more bias than is revealed by measures in place today, the authors say.

2020-04-17 00:00:00 Read the full story…
Weighted Interest Score: 3.6031, Raw Interest Score: 1.4183,
Positive Sentiment: 0.1538, Negative Sentiment 0.4272

Data, Cloud Jobs Top Hiring for Banks During COVID-19 Lockdown

If scientists at Harvard University are right, neither COVID-19 nor its associated lockdowns will pass soon: In a recent published paper, they predicted that the disease will become a seasonal winter illness that resurges through 2024, and that social distancing will be necessary until 2022. If they’re right, COVID-19 will become something we need to live with, rather than hold our collective breath until it passes… and in some form, business will need to continue as usual.

There are already signs that this might be happening. Even in the depths of this lockdown, banks in London and New York are releasing new jobs. Hiring can only be put on hold for so long. Humans are innately adaptable, and it may only be a matter of time before video interviewing and meeting new colleagues over Zoom could seem inherently normal.

If you’re contemplating switching jobs, therefore, there is no reason not to put your head above the parapet. Some jobs may be easier to find than others: Based on the roles released by leading banks in the past week, there are several trends emerging in terms of virus hiring.
2020-04-16 00:00:00 Read the full story…
Weighted Interest Score: 3.5692, Raw Interest Score: 1.9527,
Positive Sentiment: 0.1588, Negative Sentiment 0.1588

New Course: Introduction to Machine Learning in R – Dataquest

Machine learning can be a powerful tool in the toolkit of any data professional. Whether you’re aiming to become a data scientist or simply hoping to get more out of an interesting data set, learning to do machine learning with R can help you unlock a whole new world of insights.

That’s why we’re pleased to announce we’re launching yet another course for our Data Analyst in R career path: Introduction to Machine Learning in R.

This course follows another recent release, Linear Modeling in R, in our R course path. It includes five new missions and concludes with a new guided project
2020-04-17 15:38:50+00:00 Read the full story…
Weighted Interest Score: 3.5234, Raw Interest Score: 2.0658,
Positive Sentiment: 0.0646, Negative Sentiment 0.0646

Alibaba Cloud To Invest Extra 200 Billion Yuan In Next Three Years To Boost Cloud Business After Pandemic

The investment will focus on technologies including operating systems, servers, chips and networks, according to Alibaba Cloud

Alibaba Cloud, the data intelligence backbone of Chinese e-commerce giant Alibaba Group, will invest an additional 200 billion yuan (US$28.2 billion) in the next three years on its cloud infrastructure to help speed up the digital transformation of businesses in China following the Covid-19 pandemic.

Aimed at next-generation data centres, the investment will focus on technologies including operating systems, servers, chips and networks, according to a company statement on Monday.

“The Covid-19 pandemic has posed additional stress on the overall economy across sectors, but it also steers us to put more focus on the digital economy,” said Jeff Zhang, president of Alibaba Cloud Intelligence. “By increasing our investment on cloud infrastructure and fundamental technologies, we hope to continue providing world-class, trusted computing resources to help businesses speed up the recovery process.”

2020-04-20 01:33:16-04:00 Read the full story…
Weighted Interest Score: 3.3833, Raw Interest Score: 1.6792,
Positive Sentiment: 0.1033, Negative Sentiment 0.1033

Intel Joins Georgia Tech in DARPA Program to Mitigate ML Deception Attacks

A recent press release states, “Intel and the Georgia Institute of Technology (Georgia Tech) announced today that they have been selected to lead a Guaranteeing Artificial Intelligence (AI) Robustness against Deception (GARD) program team for the Defense Advanced Research Projects Agency (DARPA). Intel is the prime contractor in this four-year, multimillion-dollar joint effort to improve cybersecurity defenses against deception attacks on machine learning (ML) models… Why It Matters: While rare, adversarial attacks attempt to deceive, alter or corrupt the ML algorithm interpretation of data. As AI and ML models are increasingly incorporated into semi-autonomous and autonomous systems, it is critical to continuously improve the stability, safety and security of unexpected or deceptive interactions. For example, AI misclassifications and misinterpretations at the pixel level could lead to image misinterpretation and mislabeling scenarios, or subtle modifications to real-world objects could confuse AI perception systems. GARD will help AI and ML technologies become better equipped to defend against potential future attacks.”
2020-04-20 07:05:04+00:00 Read the full story…
Weighted Interest Score: 3.3062, Raw Interest Score: 1.4658,
Positive Sentiment: 0.3257, Negative Sentiment 1.1401

Thematic ETF Growth Slowed by COVID-19

The Covid-19 related sell-off shrunk the asset base on thematic ETFs, but not equally. Thematic ETFs provide investors the opportunity to own a broad group of publicly traded companies positioned to benefit from a medium- or long-term investment thesis, driven by forces such as disruptive technologies or changing demographics and consumer behavior.

At the end of the first quarter of 2020, there were 125 thematic ETFs, as classified by Global X Management, an ETF provider that tracks this type of fund, up from 121 three months earlier. Franklin Templeton launched three thematic ETFs in February. Yet, due largely to weakness in the underlying equities inside, the asset base shrunk by 9.9% to $25 billion. Most mega-theme—such as robotics and new consumer trends—had much smaller asset bases three months later, hurt by the impact of Covid-19, even as connectivity- and digital content-focused ETFs benefited from renewed investor interest.
2020-04-14 16:35:53+00:00 Read the full story…
Weighted Interest Score: 3.2746, Raw Interest Score: 1.7288,
Positive Sentiment: 0.2701, Negative Sentiment 0.1801

Intel & Udacity To Launch New Edge AI Program To Train Developers

Intel, in collaboration with Udacity, has announced the launch of a new edge artificial intelligence program in order to train one million developers.

The new program — Intel Edge AI for IoT Developers Nanodegree Program — has been designed to train the developer community in deep learning and computer vision, with the aim of accelerating the development and deployment of artificial intelligence-based models at the edge by leveraging the Intel Distribution of OpenVINO toolkit.

According to the company blog, the students who complete the aforementioned nano degree program, which is estimated to take about three months, will receive a Udacity graduation certificate.
2020-04-20 08:00:00+00:00 Read the full story…
Weighted Interest Score: 3.1800, Raw Interest Score: 1.8077,
Positive Sentiment: 0.1928, Negative Sentiment 0.0482

5 Reasons Qlik and Snowflake Are Better Together: Automating the Data Warehouse for Faster Time-to-Insight

Automating the Data Warehouse for Faster Time-to-Insight

Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end data integration solution that delivers rapid time-to-insight.

How does Qlik’s Data Integration Platform enable Snowflake users to speed analytics projects, achieve greater agility and reduce …
2020-04-15 00:00:00 Read the full story…
Weighted Interest Score: 3.1288, Raw Interest Score: 2.0457,
Positive Sentiment: 0.4813, Negative Sentiment 0.0000

Getting Started with Community Detection in Graphs and Networks (PDF behind registration wall)

Automating the Data Warehouse for Faster Time-to-Insight

Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end data integration solution that delivers rapid time-to-insight.

How does Qlik’s Data Integration Platform enable Snowflake users to speed analytics projects, achieve greater agility and reduce risk – all while fully realizing the advantages of Snowflake’s cloud-built data platform? Download this eBook to find out, with topics including:

  • Ingesting and delivering data from multiple sources to Snowflake in real time
  • Fully automating and dramatically accelerating the entire data warehouse lifecycle
  • Supporting your data analytics workflows at any scale
  • And more

2020-04-12 19:34:40+00:00 Read the full story…
Weighted Interest Score: 2.9561, Raw Interest Score: 1.1517,
Positive Sentiment: 0.1056, Negative Sentiment 0.0192

Deep Learning Made Easy: Part 2: Neural Networks with Gradient Descent

This is the second part of the series Deep Learning Made Easy. Check out part 1 here.

In Part 1, I introduced you with topics like What is Neural Networks, Supervised and Unsupervised learning and Why Deep learning is becoming so popular. In this 2nd part of the series, we’ll be discussing –

What is a binary classification (0 vs 1) Logistic Regression Cost function and Loss function Gradient Descent Forward and Backward Propagation
2020-04-20 04:30:34.336000+00:00 Read the full story…
Weighted Interest Score: 2.9504, Raw Interest Score: 1.3651,
Positive Sentiment: 0.1283, Negative Sentiment 0.2917

Insurtechs Lead Insurance Industry Transformation with AI

Technology-driven insurance businesses – insurtechs – are startups helping established insurers study how to gain an advantage by employing AI.

Take the weather. It’s been unsettled recently, forming new patterns, reaching new extremes. To gain insight into changing weather patterns, insurance companies are turning to AI. A recent analysis by Deloitte cited in an account in FinTech stated, “Advanced analytics could further help companies assess historical weather records, insured property data, and assumptions regarding future climate conditions to improve risk selection and pricing.”

AI acting on data from a growing number of Internet of Things (IoT) devices is allowing companies and scientists to better track and understand global weather patterns.

2020-04-16 21:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8696, Raw Interest Score: 1.4841,
Positive Sentiment: 0.2563, Negative Sentiment 0.3373

Palantir Adds COVID-19 Deal to Growing List of U.S. Contracts

Federal health officials are reportedly using Palantir Technologies’ Gotham big data analytics platform in their efforts to mount a response to COVID-19.

Forbes.com reported late last week that a unit within the U.S. Department of Health and Human Services (HHS) awarded Palantir a $17.3 million contract in early April for “COVID-19 emergency response.” The website said the contract covers Gotham licenses for an HHS unit called the Program Support Center. The agreement was reportedly signed on April 10.

Palantir, Palo Alto, Calif., was co-founded by Peter Thiel, an early Facebook (NASDAQ: FB) backer.

The Gotham platform uses big data analysis techniques to enable customers to combine large amounts of structured and unstructured data “into a single coherent data asset,” then analyze it. The platform has previously been used for applications like spotting health care fraud.

2020-04-16 00:00:00 Read the full story…
Weighted Interest Score: 2.6575, Raw Interest Score: 1.5256,
Positive Sentiment: 0.0984, Negative Sentiment 0.1476

Rethinking Technology’s Role in the Evolving Asset Management Landscape

Investment managers undoubtedly feel the pressure of change. Costs are rising, fees are stretched, and margins are being compressed. Simultaneously, the industry is facing significant regulatory and compliance shifts.

Under such circumstances, it’s nearly impossible to drive efficiencies and adapt to the changing landscape without making some operational changes. Asset managers that want to future-proof their firms are repositioning themselves by re-evaluating and optimising their operating models.

2020-04-17 12:11:15 Read the full story…
Weighted Interest Score: 2.6304, Raw Interest Score: 1.4706,
Positive Sentiment: 0.5263, Negative Sentiment 0.1238

Data Science Platforms As a New Force Multiplier

Models matter. Companies that are able to build their businesses around meaningful models generate competitive advantage through better understanding the needs of their customers, their business model, and their ability to influence the market.

With artificial intelligence on the verge of a breakthrough, companies are heavily investing in people and technology, yet the majority of companies struggle to generate value from their data science practices.

The creation of these models creates new challenges to the modern enterprise. Model management is different from seemingly similar practices like software development. Models are developed through a research process and behave probabilistically whereas software development is deterministic. These differences mandate the use of different materials, processes and behaviors.

2020-04-14 00:00:00 Read the full story…
Weighted Interest Score: 2.6155, Raw Interest Score: 1.4585,
Positive Sentiment: 0.3008, Negative Sentiment 0.1823

How machine learning helps with combating financial fraud

Fraud is an ever-lasting problem for banks and other financial institutions, which only continues to persist. As we are moving toward ubiquitous digitalization, criminals are discovering new weak spots in financial digital applications.

Paradoxically, the technology works both ways: it helps firms to provide better customer experience and optimize operations and, at the same time, assists cybercriminals in carrying out more sophisticated illegal schemes. Moreover, fraudulent actors have learnt to collaborate, share data and techniques, making financial institutions understandably paranoid about the slightest deviations in their customers’ activities.

2020-04-17 17:20:01 Read the full story…
Weighted Interest Score: 2.5729, Raw Interest Score: 1.4751,
Positive Sentiment: 0.2339, Negative Sentiment 0.7735

Object Stores Starting to Look Like Databases

Don’t look now, but object stores – those vast repositories of data sitting behind an S3 API – are beginning to resemble databases. They’re obviously still separate categories today, but as the next-generation data architecture takes shape to solve emerging real-time data processing and machine learning challenges, the lines separating things like object stores, databases, and streaming data frameworks will begin to blur.

Object stores have become the primary repository for the vast amounts of less-structured data that’s generated today. Organizations clearly are using object-based data lakes in the cloud and on premise to store unstructured data, like images and video. But they’re also using them to store many of the other types of data, like sensor and log data from mobile and IoT devices, that the world is generating.

2020-04-16 00:00:00 Read the full story…
Weighted Interest Score: 2.5602, Raw Interest Score: 1.6911,
Positive Sentiment: 0.1656, Negative Sentiment 0.0591

Evolution of Data Wrangling Users Interfaces

We’re back for session two of the data school! In this video, we travel back in time to the early days of data transformation and take a closer look at how user interfaces have evolved over the years. Even though the wider data management field has grown in leaps and bounds, it’s striking how little innovation there has been in user interfaces for data transformation since the 1980s.

In the beginning there was code. In the early 1970s we saw the first programming language designed specifically for data transformation: DATA STEP from the SAS Institute. Like most programming languages developed around that time, you had to type out the code using a keyboard connected to a mainframe. Similar functionality can be found in programming libraries for more recent languages, like Python’s “pandas” library, or R’s “dplyr” library.

Experts love to write code, and programming libraries are certainly powerful. But let’s talk about the standard UI for code: the text editor.

2020-04-20 00:00:00 Read the full story…
Weighted Interest Score: 2.3759, Raw Interest Score: 1.3622,
Positive Sentiment: 0.1603, Negative Sentiment 0.0534

Druid Developer Expands Query Options

The latest release of a real-time data analytics platform takes makes use of a new SQL feature in Apache Druid that combines data or rows from multiple tables based on common values.

Imply, the real-time analytics startup founded by the authors of the Apache Druid database, also said it has added a “query laning” feature akin to a carpool lane targeting the most urgent queries, prioritizing resources to handle critical workloads.

Druid, the column-oriented, in-memory OLAP data store, recently added support for SQL JOIN. Imply said this week its version 3.3 release incorporating JOIN would extend Druid’s performance beyond data lake and data warehouse query engines via architectural advantages such as horizontal query distribution and advanced indexing capabilities.

2020-04-17 00:00:00 Read the full story…
Weighted Interest Score: 2.3757, Raw Interest Score: 1.6813,
Positive Sentiment: 0.1827, Negative Sentiment 0.2193

Sinequa Creates Intelligent Insight Portal to Fight COVID-19

Sinequa, a provider of intelligent search software, has created a scientific research repository and portal called COVID-19 Intelligent Insight to help in the fight against COVID-19. The free and open portal, built on Sinequa’s intelligent technology and expertise, was developed to help professionals in science and medicine rapidly sift through and analyze the numerous and evolving research on COVID-19.

Sinequa created the portal in response to the White House Office of Science and Technology Policy (OSTP) and calls from other global health organizations for “AI machine-readable technology” to address the rapidly evolving Coronavirus literature.

The portal brings together scientific papers, publications, health authority guidance, and clinical trial information into a single interface, allowing researchers to identify critical insights and analyze the vast and growing information about the COVID-19 pandemic.

2020-04-16 00:00:00 Read the full story…
Weighted Interest Score: 2.2344, Raw Interest Score: 1.4912,
Positive Sentiment: 0.0000, Negative Sentiment 0.1356

SAP Makes Support Experience Even Smarter With Machine Learning and AI Enhancements

According to a new press release, “SAP SE today announced several updates, including the Schedule a Manager and Ask an Expert Peer services, to its Next-Generation Support approach focused on the customer support experience and enabling customer success. Based on artificial intelligence (AI) and machine learning technologies, SAP has further developed existing functionalities with new, automated capabilities such as the Incident Solution Matching service and automatic translation. ‘When it comes to customer support, we’ve seen great success in flipping the customer engagement model by leveraging AI and machine learning technologies across our product support functionalities and solutions,’ said Andreas Heckmann, head of Customer Solution Support and Innovation and executive vice president, SAP. ‘To simplify and enhance the customer experience through our award-winning support channels, we’re making huge steps towards our goal of meeting customer’s needs by anticipating what they may need before it even occurs’.”
2020-04-17 07:15:49+00:00 Read the full story…
Weighted Interest Score: 2.1344, Raw Interest Score: 1.4136,
Positive Sentiment: 0.4560, Negative Sentiment 0.3648

Unlearn.ai raises $12 million to accelerate clinical trials with ‘digital twins’

Unlearn.ai, a company that designs software tools for clinical research, today announced that it secured $12 million in equity financing. Unlearn’s “digital twin” approach to trials, in which digital models are used in place of real test subjects, could reduce the number of people required to run a trial without sacrificing standards of evidence.

Unlearn’s technology could also help to solve the systemic reproducibility problem in clinical research, which a pair of surveys by Bayer and Amgen recently brought into sharp relief. Bayer reported successfully replicating just 25% of published preclinical studies it analyzed, while Amgen confirmed findings in just 6 of 53 landmark cancer studies (11%).

2020-04-20 00:00:00 Read the full story…
Weighted Interest Score: 2.0792, Raw Interest Score: 1.1578,
Positive Sentiment: 0.1766, Negative Sentiment 0.2551

How We Managed to Beat the Crypto Market Using Machine Learning

Years ago, during my time as a freelancer, I was randomly contacted by an American trader who managed to single-handedly beat the market and needed some help with the infrastructure code. I was in South America traveling as a digital nomad, but this request was too tempting to be ignored, so I took a 15-hour long flight to join him in Macau, which is as close to Las Vegas as you can get in China.

We lived in 5-star hotels, worked on trading bots and gambled in local casinos for a break. It was a surreal experience that completely changed my career. Upon leaving, I was confident that I’d take on beating the market myself, but years have passed, and I haven’t got into it.

While that experience was inspiring, it was also quite demotivating. The only person I knew who managed to beat the market was clearly out of my league, both intellectually and psychologically. He had a brilliant mind and an outstanding ability to handle stress. I had severe doubts about whether I was good enough.
2020-04-20 11:10:50.655000+00:00 Read the full story…
Weighted Interest Score: 2.0187, Raw Interest Score: 1.0094,
Positive Sentiment: 0.2884, Negative Sentiment 0.2704

Pepperdata Adds Kafka Monitoring to Tune Queries

A new tool for tracking data analytics performance adds monitoring capabilities based on the Apache Kafka streaming data platform. The combination aims to provide better visibility across analytics stacks deployed in hybrid configurations.

The result is said to be improved understanding of query execution and database performance.
2020-04-15 00:00:00 Read the full story…
Weighted Interest Score: 1.9170, Raw Interest Score: 1.5165,
Positive Sentiment: 0.2289, Negative Sentiment 0.2575

Microsoft Unveils Falcon To Secure Computation of AI Models

Researchers from Microsoft, Princeton University, Technion and Algorand Foundation recently introduced a new framework known as Falcon. Falcon is an end-to-end 3-party protocol that can be used for fast and secure computations of deep learning algorithms on larger networks.

Today, a vast amount of private data and sensitive information is continuously being generated. According to the researchers, combining this data with deep learning algorithm…
2020-04-19 04:30:00+00:00 Read the full story…
Weighted Interest Score: 1.9089, Raw Interest Score: 1.0426,
Positive Sentiment: 0.3258, Negative Sentiment 0.4344

Language may help AI navigate new environments

In a new study published this week on the preprint server Arxiv.org, scientists at the University of Toronto and the Vector Institute, an independent nonprofit dedicated to advancing AI, propose BabyAI++, a platform to study whether descriptive texts help AI to generalize across dynamic environments. Both it and several baseline models will soon be available on GitHub.

One of the most powerful techniques in machine learning — reinforcement learning, which entails spurring software agents toward goals via rewards — is also one of the most flawed. It’s sample inefficient, meaning it requires a large number of compute cycles to complete, and without additional data to cover variations, it adapts poorly to environments that differ from the training environment.

It’s theorized that prior knowledge of tasks through structured language could be combined with reinforcement learning to mitigate its shortcomings, and BabyAI++ was designed to put this theory to the test. To this end, the platform builds upon an existing reinforcement learning framework — BabyAI — to generate various dynamic, color tile-based environments along with texts that describe their layouts in detail.
2020-04-17 00:00:00 Read the full story…
Weighted Interest Score: 1.8091, Raw Interest Score: 1.1548,
Positive Sentiment: 0.1561, Negative Sentiment 0.2497

How AI Is Helping the Supply Chain Cope With COVID-19

COVID-19 is wreaking havoc on the American supply chain as companies scramble to respond to rapidly shifting consumer demand, limited supply of some products, and new workplace rules. It’s a lousy time to embark upon a new supply chain optimization project using AI at the moment, but for organizations that already have it in place, AI is paying dividends.

For a look at how the COVID-19 pandemic is impacting $635-billion U.S. consumer goods suppl…
2020-04-13 00:00:00 Read the full story…
Weighted Interest Score: 1.7281, Raw Interest Score: 0.9292,
Positive Sentiment: 0.1287, Negative Sentiment 0.2287

Investor Mary Meeker says Covid-19 crisis is separating businesses with strong online strategies from laggards

Mary Meeker, who is known for her lengthy annual “Internet Trends” report, sent a letter to her firm’s investors detailing observations from the Covid-19 crisis.
Among them: The businesses who were already well along the offline-to-online transition are faring best.

Mary Meeker, the former tech investment banker who has spent the past decade in venture capital, is out with a new 29-page report on how the coronavirus is shaping economic activity, consumer behavior and technology. The report, which Axios published on Friday, says businesses that are doing the best in the current crisis use cloud technologies, sell products that are always needed, can easily be found online, make other businesses more efficient and have a good social media presence. Those dynamics are at work for restaurants, stores, online education, health providers and software companies.
2020-04-17 00:00:00 Read the full story…
Weighted Interest Score: 1.6795, Raw Interest Score: 1.0557,
Positive Sentiment: 0.3359, Negative Sentiment 0.1919

Big Data Career Notes: April 2020 Edition

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the big data community. Whether it’s a promotion, new company hire, or even an accolade, we’ve got the details. Check in each month for an updated list and you may even come across someone you know, or better yet, yourself!

2020-04-15 00:00:00 Read the full story…
Weighted Interest Score: 1.6667, Raw Interest Score: 1.0002,
Positive Sentiment: 0.3726, Negative Sentiment 0.0588

Confusion Matrix for Machine Learning – Not So Confusing!

Have you been in a situation where you expected your machine learning model to perform really well but it sputtered out a poor accuracy? You’ve done all the hard work – so where did the classification model go wrong? How can you correct this?

There are plenty of ways to gauge the performance of your classification model but none have stood the test of time like the confusion matrix. It helps us evaluate how our model performed, where it went wrong and offers us guidance to correct our path.

In this article, we will explore how a Confusion matrix gives a holistic view of the performance of your model. And unlike its name, you will realize that a Confusion matrix is a pretty simple yet powerful concept. So let’s unravel the mystery around the confusion matrix!

2020-04-17 00:45:49+00:00 Read the full story…
Weighted Interest Score: 1.6517, Raw Interest Score: 0.8980,
Positive Sentiment: 0.3571, Negative Sentiment 1.0408

Agrex.ai Develops AI-Enabled Thermal Cameras To Combat COVID-19

Agrex.ai has developed AI-enabled thermal cameras to aid early detection of COVID-19 and combat the spread of the virus.

As the deadly pandemic covering the world, the global healthcare sector has been under great distress. Early detection of COVID-19 symptoms can help in curbing the spread of the virus and perhaps also save lives. And therefore this a video analytics company, Agrex.ai’s developed a thermal sensor-based detection system aka thermal cameras, which is capable of scanning a large number of people from a distance up to 20 metres.

Agree.ai aims to help organisations derive operational intelligence, monitor compliance and automate visual surveillance. The advanced thermal camera comes with a ready to use plug and play system, which can be set up within 10 minutes. The camera can also scan temperature within a fraction of seconds eliminating the need to stop and scan each person individually, which in turn, enables users to examine 80-100 people in one minute, ensuring fast data collection.

2020-04-20 09:00:00+00:00 Read the full story…
Weighted Interest Score: 1.6479, Raw Interest Score: 0.9454,
Positive Sentiment: 0.2101, Negative Sentiment 0.1313


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