AI & Machine Learning News. 04, May 2020
The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?
Consistent Video Depth Estimation
Abstract: We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video. Unlike the ad-hoc priors in classical reconstruction, we use a learning-based prior, i.e., a convolutional neural network trained for single-image depth estimation. At test time, we fine-tune this network to satisfy the geometric constraints of a particular input video, while retaining its ability to synthesize plausible depth details in parts of the video that are less constrained. We show through quantitative validation that our method achieves higher accuracy and a higher degree of geometric consistency than previous monocular reconstruction methods. Visually, our results appear more stable. Our algorithm is able to handle challenging hand-held captured input videos with a moderate degree of dynamic motion. The improved quality of the reconstruction enables several applications, such as scene reconstruction and advanced video-based visual effects.
CloudQuant Thoughts : Very impressive! Real Time?
AI driven hedge fund up circa 21.53 per cent 2020 YTD
RISE Wealth Technologies, a Munich-based AI investment technology firm – has announced a second month of strong performance for its flagship Volatile Special Opportunities Program (VSOP) strategy.
Up in April by 2.27 per cent and up by 21.83 per cent for the year, VSOP entails a systematic multi-strategy approach in the S&P 500 index volatility market with a real-money track record dating back to July 2016.
The fund is composed of a Balanced Portfolio consisting of S&P 500 futures and treasuries with a duration risk of circa five years. It also trades overlay strategies on situational patterns. While the fund currently has USD30 million AUM, a further USD500 million in capital inflows is expected to be allocated in June.
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 6.9207, Raw Interest Score: 2.1841,
Positive Sentiment: 0.2340, Negative Sentiment 0.0000
CloudQuant Thoughts : I can help but think this did well, not because of its AI, because of its ‘composition’, specifically Futures and Treasuries. One would also assume they conveniently avoided Oil as well!
The Best NLP Papers From ICLR 2020
I went through 687 papers that were accepted to ICLR 2020 virtual conference (out of 2594 submitted – up 63% since 2019!) and identified 9 papers with the potential to advance the use of deep learning NLP models in everyday use cases. Here are the papers found and why they matter.
2020-04-28 15:23:26+00:00 Read the full story…
Weighted Interest Score: 2.5446, Raw Interest Score: 1.4180,
Positive Sentiment: 0.2528, Negative Sentiment 0.0879
CloudQuant Thoughts : It is so helpful when someone takes the time to sift through large amounts of complex data so that you do not have to, sure there may have been something they missed but 9 papers vs 687!
The Ultimate NumPy Tutorial for Data Science Beginners
- NumPy is a core Python library every data science professional should be well acquainted with
- This comprehensive NumPy tutorial covers NumPy from scratch, from basic mathematical operations to how Numpy works with image data
- Plenty of Numpy concepts and Python code in this article
I am a huge fan of the NumPy library in Python. I have relied on it countless times during my data science journey to perform all sorts of tasks, from basic mathematical operations to using it for image classification!
In short – NumPy is one of the most fundamental libraries in Python and perhaps the most useful of them all. NumPy handles large datasets effectively and efficiently. I can see your eyes glinting at the prospect of mastering NumPy already. As a data scientist or as an aspiring data science professional, we need to have a solid grasp on NumPy and how it works in Python.
2020-04-27 19:38:58+00:00 Read the full story…
Weighted Interest Score: 4.7344, Raw Interest Score: 2.0785,
Positive Sentiment: 0.1155, Negative Sentiment 0.1155
CloudQuant Thoughts : A very nice introduction to Numpy
Why Alternative Data is Key to Analyzing the Consumer Sector • Integrity Research
As traditional research proves to be wanting, the use of alternative data has increased. It has evolved from market tick data, expert networks, physical inventory counting to an estimated $1 billion market. However, to put it into perspective, Bloomberg eclipsed $10 billion in revenue years ago so while alternative data grown, it is still the little engine that could.
Alternative data has been most frequently applied to the consumer sector because strong correlations can be consistently drawn between the use of point-of-sale or credit card transactions and a consumer company’s revenue and gross margin trends. However, as the digital economy expands and omni-channel buying muddies the financial ground truth, transaction data alone is no longer adequate. For consumer products it is necessary to expand deeper into online consumer purchasing patterns.
2020-04-27 05:41:00+00:00 Read the full story…
Weighted Interest Score: 4.0581, Raw Interest Score: 1.7606,
Positive Sentiment: 0.2622, Negative Sentiment 0.1873
CloudQuant Thoughts : We are firm believers in the value of Alternative Data. Head over to our Data Catalog where we have pulled together a number of valuable Alternative Data sets which we have ETL’d and tested. We provide our own White Papers together with the code and the data! Yes, reproducibility with no effort!
Career paths in Business Analytics and Data Science World
“Data Scientist: The Sexiest Job of the 21st Century” is one of the most popular Harvard Business Review (HBR) articles and has inspired tons of people to pursue their careers in the field of analytics. One of the main themes of this article published in HBR was the trend of growing jobs in the analytics industry. The exact same inference was predicted by IBM recently saying that the number of US data professionals will increase from 364,000 to 2.72 million by 2020! And that has come to pass this year.
Unanimously, across the industry, we are seeing a surge in Business Analytics job openings, but do all these jobs need the exact same skill set? I have received a number of queries focused on what are the possible career trajectories in the analytics industry. These queries usually come from people seeking a break in the analytics domain or people already working in the industry and are looking for a deeper role.
In this article, we will look at the major roles available in the analytics industry. I will also propose a framework to think about your career in the space of business analytics.
2020-04-30 18:32:24+00:00 Read the full story…
Weighted Interest Score: 2.5380, Raw Interest Score: 1.4436,
Positive Sentiment: 0.1893, Negative Sentiment 0.1183
FIS sets up $150 million fintech venture fund
Financial technology company FIS has set up a venture arm with a goal of investing up to $150 million in promising fintech startups over the next three years. FIS says the new unit will invest globally in early to growth-stage fintech startups with a focus on emerging technologies such as artificial intelligence and machine learning, digital enablement and automation, data and analytics, security and privacy, distributed ledger technology, and financial inclusion.
The capital injections will be accompanied by a package of operational support and access to FIS channel partners and banking clients. “At a time when many other fintech firms are scaling back their investments, FIS is deepening its commitment to stay at the forefront of innovative technologies that can help our clients accelerate digital transformation and emerge even stronger from the current pandemic ,” says Asif Ramji, chief growth officer of FIS. “FIS Ventures is a significant new component of our investment strategy to identify and bring to market innovative new technologies that advance the way the world pays, banks and invests.”
2020-04-28 13:42:00 Read the full story…
Weighted Interest Score: 6.4133, Raw Interest Score: 2.6984,
Positive Sentiment: 0.2381, Negative Sentiment 0.0000
IBM Extends Jupyter Notebooks for AI Development
IBM has released a new open source toolkit with AI extensions to the popular Jupyter Notebooks data science development platform.
The Elyra AI Toolkit extends the industry standard JupyterLab user interface with the goal of simplifying development of AI and other data science models. IBM said this week the initial release includes a visual editor for building AI pipelines along with the ability to run interactive notebooks as batch jobs. Other features include Python script execution and a “hybrid runtime” capability based on Jupyter Notebooks’ enterprise gateway.
The gateway is designed to ease the scaling of enterprise workloads. IBM said Elyra (pronounced, el-EYE-rah) would ease workload development. Elyra “aims to help data scientists, machine learning engineers and AI developers through the model development lifecycle complexities,” the company added in a blog post announcing the open source release.
2020-05-01 00:00:00 Read the full story…
Weighted Interest Score: 5.8065, Raw Interest Score: 2.1542,
Positive Sentiment: 0.0862, Negative Sentiment 0.0862
UC Berkeley researchers open-source RAD to improve any reinforcement learning algorithm
A group of University of California, Berkeley researchers this week open-sourced Reinforcement Learning with Augmented Data (RAD). In an accompanying paper, the authors say this module can improve any existing reinforcement learning algorithm and that RAD achieves better compute and data efficiency than Google AI’s PlaNet, as well as recently released cutting-edge algorithms like DeepMind’s Dreamer and SLAC from UC Berkeley and DeepMind.
RAD achieves state-of-the-art results on common benchmarks and matches or beats every baseline in terms of performance and data efficiency across 15 DeepMind control environments, the researchers say. It does this in part by applying data augmentations for visual observations. Coauthors of the paper on RAD include Michael “Misha” Laskin, Kimin Lee, and Berkeley AI Research codirector and Covariant founder Pieter Abbeel.
2020-05-02 00:00:00 Read the full story…
Weighted Interest Score: 4.8181, Raw Interest Score: 2.2589,
Positive Sentiment: 0.5057, Negative Sentiment 0.1349
Maniyar’s ‘man and machine’ macro strategy spins out of Tudor with assets of over USD1 billion
A major new standalone operation in the quant-driven, discretionary global macro space has gone live with the spin-out this month of Maniyar Capital Advisors (MCA) from Tudor Investment Corporation. MCA, which started trading on 1 May with assets in excess of USD1 billion, will utilise the same strategy and structure that was previously run for several years within Tudor – and which is believed to have delivered strong returns for investors through a variety of market environments.
Founder, CEO and CIO Dharmesh Maniyar was a senior portfolio manager and partner at Tudor from 2013, having previously spent five years as a portfolio manager at Brevan Howard Asset Management. Prior to joining Brevan Howard, Maniyar – who has a PhD in Applied Mathematics (Machine Leaning) – worked as a post-doctoral research associate on the Managing Uncertainty in Complex Models project at Aston University in the UK. His strategy involves a “man and machine” approach to discretionary macro trading, with the investment team making extensive use of quantitative and computational techniques in reaching discretionary macro decisions.
2020-05-01 00:00:00 Read the full story…
Weighted Interest Score: 4.6617, Raw Interest Score: 2.1044,
Positive Sentiment: 0.1865, Negative Sentiment 0.0533
dotData Launches Program to Meet Demand of AI Capabilities
dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, is launchg dotData AI-FastStart, a new bundle of technology and services that includes a one year license to a fully-hosted version of dotData’s autoML 2.0 platform, along with training and support. Available exclusively to North American customers who are not existing dotData clients, the dotData FastTrack program is designed to empower business intelligence teams to quickly and efficiently add AI/ML models to their BI stacks and predictive analytics applications.
At the core of the new program is dotData’s full-cycle data science automation platform, dotData Enterprise, which accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. “We are seeing a huge demand for AI and ML capabilities in the market, but finding that many companies either do not have the internal resources to launch a data science program, or don’t know how to get one started,” said Ryohei Fujimaki, founder and CEO of dotData. “The AI-FastStart program was created as an all-inclusive bundle to help enterprises fast-track AI/ML deployments, and immediately realize value from their data.”
2020-04-29 00:00:00 Read the full story…
Weighted Interest Score: 4.6563, Raw Interest Score: 2.1080,
Positive Sentiment: 0.2219, Negative Sentiment 0.0370
Report: Quants restrategise after markets plummet
Quant funds have had a hectic month. Many notable quants – Millennium, DE Shaw, Two Sigma – suffered losses at the end of March, leading some commentators to question if another “quant quake” like that seen in 2007 could be on the horizon. Things have been looking slightly brighter for quants over the past two weeks, with Two Sigma and DE Shaw making slow gains in April according to Institutional Investor. But as the dust settles, some market participants begin to question a reliance on market data and purely quantitative strategies.
“Structural limitations have caused the majority of quantitative funds to perform poorly during the current market crisis,” said Daniele Grassi, CEO, Axyon AI in an email. “While patterns of varying complexity can be found in asset behaviour, black swan events like the current coronavirus crisis can see asset behaviour break down completely.” According to Grassi, quantitative models will continue to provide unreliable market predictions. “This makes it very difficult for managers to make the right moves to navigate through the crisis.”
2020-04-29 00:00:00 Read the full story…
Weighted Interest Score: 4.6490, Raw Interest Score: 2.0054,
Positive Sentiment: 0.2033, Negative Sentiment 0.2957
When It Comes To AI, Capital Markets Has Barely Scratched the Surface
The current uncertainty in the market is pushing companies to rethink their technology stack and look for opportunities that create efficiencies and save on cost, such as Artificial Intelligence. Artificial Intelligence (AI) has evolved rapidly over the past few years and has achieved ‘prime time’ in applications like Netflix, Siri and Alexa. It’s rapidly demonstrating its value in many other industries including financial services, healthcare and manufacturing.
In fact, one market research firm forecasts that AI software will create $2.9 trillion of business value in 2021, a figure that is similar in size to the UK’s annual income. The revenue generator of the AI age is brilliant software that gleans insights from the world’s fast-growing mountains of data. Humans will generate an estimated 50+ zettabytes in 2020 alone, which is remarkable given that we only entered the zettabyte era in 2010.
My area of financial services, capital markets, is no stranger to either AI or Big Data. In fact, capital markets is the most data intensive segment of the financial industry, and one of the largest spenders on AI technology. Several firms are leveraging AI to generate actionable insights out of the avalanches of data generated by a range of processes, thereby increasing efficiencies and lowering costs. For example, firms are adopting machine learning models for credit scoring and risk management while using algorithms to trade securities. But these applications may just be scratching the surface of AI’s capabilities for capital markets firms.
2020-05-01 01:52:54+00:00 Read the full story…
Weighted Interest Score: 4.6126, Raw Interest Score: 2.2555,
Positive Sentiment: 0.2794, Negative Sentiment 0.1796
Data Science And Machine Learning. With Java?
In this blog, I outline briefly:
- Common Applications of Data Science
- Definitions: Machine learning, deep learning, data engineering and data science
- Why Java for data science workflows, for both production and research.
The blogosphere is full of descriptions about how data science and “AI’ is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. loans), portfolio analysis and trading strategies, but technologies are relevant elsewhere, e.g. customer churn in telecomms, personalized treatment in healthcare, predictive maintenance for manufacturers, and demand forecasting in retail. These applications outlined are largely not new, nor are “AI” algorithms like neural networks. However, increasingly commoditized, flexible and cheaper hardware with readily available algorithms and APIs have lowered barriers to data-compute intensive approaches common to data science, making the use of “AI” algorithms much more straightforward.
2020-04-30 09:36:47 Read the full story…
Weighted Interest Score: 4.5214, Raw Interest Score: 2.0932,
Positive Sentiment: 0.2102, Negative Sentiment 0.0731
Sigma Computing Extends Sigma with Release of Dataset Warehouse Views
Sigma Computing, a provider of cloud-native analytics and business intelligence (A&BI), is extending the power of Sigma to be used throughout the cloud data analytics stack.
“The proliferation of SaaS tools has not only resulted in mountains of data but also a number of applications that you need to be able to access all that data in,” said Rob Woollen, CEO and co-founder, Sigma Computing. “With Dataset Warehouse Views, organizations can now rely on Sigma for datasets and analyses wherever they need them. IT and data teams will also no longer have to make the false choice between a portfolio of best-in-class data tools and settling for less performance in a single vendor solution to aid data management because Sigma can easily sit at the center of an organization’s cloud data ecosystem, connecting all the dots and maximizing data’s value.
With this feature, Sigma is the first to provide non-technical users with the ability to create a dataset and write it back to the cloud data warehouse (CDW) for use across the organization without needing to write code.
2020-04-28 00:00:00 Read the full story…
Weighted Interest Score: 4.4583, Raw Interest Score: 1.6425,
Positive Sentiment: 0.1955, Negative Sentiment 0.0000
NVIDIA Completes Acquisition of Mellanox
NVIDIA has completed the acquisition of Mellanox Technologies, Ltd., enabling customers to achieve higher performance, greater utilization of computing resources, and lower operating costs. The acquisition, initially announced on March 11, 2019, unites two of the world’s leading companies in high performance and data center computing. The transaction value is $7 billion.
“The expanding use of AI and data science is reshaping computing and data center architectures,” said Jensen Huang, founder and CEO of NVIDIA. “With Mellanox, the new NVIDIA has end-to-end technologies from AI computing to networking, full-stack offerings from processors to software, and significant scale to advance next-generation data centers. Our combined expertise, supported by a rich ecosystem of partners, will meet the challenge of surging global demand for consumer internet services, and the application of AI and accelerated data science from cloud to edge to robotics.”
2020-04-27 00:00:00 Read the full story…
Weighted Interest Score: 4.2197, Raw Interest Score: 2.0850,
Positive Sentiment: 0.3208, Negative Sentiment 0.0802
ATLAS INFRASTRUCTURE LIVE WITH INDATA ARCHITECT AI OMS
INDATA, a leading industry provider of software, technology and managed services for buy-side firms, today announced that ATLAS Infrastructure is live with INDATA’s Architect AI OMS and Portfolio Management solution.
ATLAS Infrastructure is a specialist listed infrastructure manager with offices in London and Sydney. The firm was launched in 2017 with the backing of Global Infrastructure Partners (GIP). ATLAS is a globally oriented firm which brings together a team with deep expertise in a broad range of sectors and geographies. This breadth serves to reduce portfolio “home bias” and “familiarity bias.”
Architect AI leverages the latest technologies including the cloud, the web, APIs, big data analytics and AI to deliver a fully modern OMS and portfolio management solution that is streamlined in terms of operation, yet comprehensive in terms of functionality, with broad-based appeal for firms looking to upgrade their front/middle/back office operations, improve productivity and reduce costs.
2020-04-28 00:00:00 Read the full story…
Weighted Interest Score: 4.1138, Raw Interest Score: 1.8307,
Positive Sentiment: 0.2670, Negative Sentiment 0.1144
Schroders Adds Sentieo To Start-Up Programme
Schroders has named the latest firm to join Cobalt, its global in-residence start-up programme with the addition of Sentieo, a financial and corporate research platform which harnesses market-leading technology to support the investment research process. This entails, for example, using natural language processing driven document search to uncover unique insights and machine learning to automatically discover key insights from financial documents.
Schroders’ Cobalt programme was launched in 2018* to help fintechs collaborate with the firm to assist their development and tackle today’s investment industry challenges.
Charlotte Wood, Head of Innovation and Fintech Alliances, Schroders, commented: “Schroders’ Cobalt programme continues to demonstrate that we are a natural home for fintech start-ups, giving us direct access to a pipeline of innovators in investment management. We are very excited about Sentieo joining Cobalt.”
2020-05-01 09:40:54+00:00 Read the full story…
Weighted Interest Score: 4.1057, Raw Interest Score: 2.0589,
Positive Sentiment: 0.6035, Negative Sentiment 0.2130
This Is What Google TensorFlow Is Giving Away For Free Now
After Quantization Aware Training (QAT) and Model Maker, tech giant Google has now open-sourced TensorFlow Runtime (TFRT), a new runtime that will replace the existing TensorFlow runtime. This new runtime will be responsible for various performance such as efficient execution of kernels, low-level device-specific primitives on targeted hardware and other such.
Machine learning is a complex domain as building or deploying these models keep changing with the dynamic needs due to the increasing investment in the ML ecosystem. While the researchers at TensorFlow have been inventing new algorithms that require more compute, application developers are enhancing their products with new techniques across edge and server.
However, the increase in computation needs and rise of computing costs has sparked a proliferation of new hardware aimed at specific ML use cases. According to the developers, the TensorFlow RunTime aims to provide a unified, extensible infrastructure layer with performance across a wide variety of domain-specific hardware.
2020-05-01 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5619, Raw Interest Score: 1.6258,
Positive Sentiment: 0.4009, Negative Sentiment 0.0668
Franz AllegroGraph 7 Provides Distributed Semantic Knowledge Graph Solution with Federated-Sharding
Franz, a provider of semantic graph database technology for knowledge graph solutions, has introduced AllegroGraph 7. With this release, the patented distributed knowledge graph solution adds innovations that address the fact that large enterprises have knowledge graphs that are so large that no amount of vertical scaling will work. The solution allows infinite data integration by unifying all data and siloed knowledge into an entity-event knowle…
2020-04-29 00:00:00 Read the full story…
Weighted Interest Score: 3.4323, Raw Interest Score: 2.0139,
Positive Sentiment: 0.2582, Negative Sentiment 0.1033
Google Enters Data Catalog Business, Updates BigQuery
Google today rolled out a data catalog that will eventually give customers visibility into all of their data assets in the Google Cloud and beyond. It also bolstered BigQuery with support for materialized views and a new method for exporting SQL-based machine learning models in the TensorFlow format.
As the big data wave continues to crash into enterprises, data catalogs have become must-have tools for making sense of the digital sprawl. In addition to giving data analysts and data scientists visibility into a wide variety of available data, they can also provide governance and security controls to help prevent sensitive data from being accessed.
As the foremost search authority, it shouldn’t be surprising that Google Cloud’s new Data Catalog has a search engine at the core. The new offering, which is generally available, uses Google’s powerful search technology to surface available data residing in BigQuery and Pub/Sub, which are the first data repositories that Google is supporting with the catalog.
2020-04-30 00:00:00 Read the full story…
Weighted Interest Score: 3.4136, Raw Interest Score: 1.8580,
Positive Sentiment: 0.1215, Negative Sentiment 0.0868
Using Distributed Machine Learning to Model Big Data Efficiently
As distributed computing has become an increasingly popular skill for data scientists to have, running Apache Spark on AWS EMR clusters have gradually become a common way in the industry when dealing with big data. The PySpark API allows us to write Spark in Python easily while having Spark doing the parallel computing of the data in the background. In this article, we will use the 4-gigabytes San Francisco bike-share dataset from Kaggle to model shared bike availability in real-time.
This post will cover:
- Spark initialization on AWS EMR or local environment
- Exploratory Data Analysis with Spark and Plotly
- Using Spark SQL to preprocess the data
- Modeling: Using the Random Fores Regressor in Spark ML
- Configure your AWS EMR Clusters for optimal runtime
2020-05-04 04:25:45.445000+00:00 Read the full story…
Weighted Interest Score: 3.3681, Raw Interest Score: 1.8529,
Positive Sentiment: 0.0986, Negative Sentiment 0.0591
Should You Love Or Be Scared Of Maths Required For Data science?
Data science is the future, everyone wants to learn this budding technology. Is everyone able to learn? The answer is “No”. Do you know the reason, it is none other than “mathematics”. What….did I say mathematics? Yes, Mathematics or simply math. While reading this, people who know what is data science or has worked in this field would be confused and would be asking how maths is responsible. Let me, rephrase my answer, “ Fear to Mathematics”.
Starting from our elementary education to our higher education we see students scared of mathematics or we can say students have a math phobia. Not sure who has created this buzz that data science requires a long list of math topics as a prerequisite.
It is not completely correct, elementary math is required but, as a beginner, you don’t need that much math for data science. Also, there is another side to data science and that is the practical side. For practical data science, a great deal of math is not required. Practical data science only requires skills to select the right tools. Being said that let’s understand how theoretical and practical data science differs.
2020-05-04 08:30:00+00:00 Read the full story…
Weighted Interest Score: 3.3565, Raw Interest Score: 1.7388,
Positive Sentiment: 0.1352, Negative Sentiment 0.1546
Death, Taxes, and the ‘AI Economist’
Economists, fond of their models, may have another AI-based tool for designing new tax policies that address growing economic inequality while attempting to boost the productivity that would give new meaning to the aphorism, “A rising tide lifts all boats.”
In a paper published this week by the research arm of enterprise software giant Salesforce (NYSE: CRM) and Harvard University, researchers used reinforcement learning (RL) techniques to design a tax policy that addresses income inequality and its relationship to productivity. RL varies from supervised machine learning, in which algorithms are retrained to maintain accuracy, by instead employing a feedback loop of learning “agents” built directly into the process.
The “AI Economist” is based on a RL framework that combines an agent with tax policy to learn using “observable data alone” rather than modeling assumptions. The platform is touted as able to learn “dynamic tax policies” that boost equality without sacrificing productivity in a simulated economy.
2020-04-30 00:00:00 Read the full story…
Weighted Interest Score: 3.3276, Raw Interest Score: 1.7020,
Positive Sentiment: 0.4515, Negative Sentiment 0.2431
Now is the time for an economic stimulus in artificial intelligence — or the US could fall behind
AI technology has already proven its ability to assist in the COVID-19 response by utilizing supercomputers to accelerate the research of treatments to COVID-19, as well as enabling grocery stores to better predict food and supply chain shortages. AI has remarkably helped ensure some semblance of normalcy in a drastic situation.
AI will also play a critical role in reopening the economy by speeding up testing diagnostics and restarting supply lines. But the gradual return to society will require some social distancing to remain in place, and people may not want to leave their homes and engage in public activities to the same degree as before.
With this in mind, and since consumer spending accounts for roughly 70% of US GDP, strong government action will be needed to re-energize the economy through targeted investments in key industries of the future like AI. Digital engineers in academia and industry are eager to tackle the predominant questions about AI and can create economic growth opportunities in the process if they have focused resources.
2020-04-28 00:00:00 Read the full story…
Weighted Interest Score: 3.3073, Raw Interest Score: 1.4205,
Positive Sentiment: 0.1561, Negative Sentiment 0.2810
Introduction to Normal Distribution
The normal distribution is a core concept in statistics, the backbone of data science. While performing exploratory data analysis, we first explore the data and aim to find its probability distribution, right? And guess what – the most common probability distribution is Normal Distribution.
Check out three very common examples of the normal distribution: the Birth weight, the IQ Score, and stock price return often form a bell-shaped curve. Similarly, there are many other social and natural datasets that follow Normal Distribution.
One more reason why Normal Distribution becomes essential for data scientists is the Central Limit Theorem. This theorem explains the magic of mathematics and is the foundation for hypothesis testing techniques.
In this article, we will be understanding the significance and different properties of Normal Distribution and how we can use those properties to check the Normality of our data.
2020-04-28 20:10:47+00:00 Read the full story…
Weighted Interest Score: 3.2813, Raw Interest Score: 2.0108,
Positive Sentiment: 0.0911, Negative Sentiment 0.1745
Record Demand For Data Due to Covid-19
Volatility caused by the Covid-19 pandemic has led to record data usage according to provider Refinitiv with a 50% increase in mobile usage as staff are forced to work remotely.
Andrea Remyn Stone, chief customer proposition officer at Refinitiv, told Markets Media: “We have seen record data usage during the pandemic, with some interesting trends in the ‘data on the data’.” She continued that, for example, there has been an eightfold increase in demand for mortgage data. There has also been more demand for debt data such as leveraged loans, corporate bonds and credit profiles. “There has been a 20% increase in web usage and 50% on mobile usage,” Remyn Stone added. “Daily messages across our platform have grown to 186 billion a day, compared to 80 billion after the Brexit vote, and between 40 to 50 billion on a normal day and we have not had any outages.”
As the volumes of data usage usage has risen, customers have needed help in making sense of the information deluge. For example, Refinitiv has overlaid economic data with news, markets data and physical data, such as shipping, in a Covid app. Users can drill down by country, sectors and companies to find opportunities, as well as assess risks.
2020-04-27 13:59:22+00:00 Read the full story…
Weighted Interest Score: 3.2654, Raw Interest Score: 1.7544,
Positive Sentiment: 0.1132, Negative Sentiment 0.1321
Google releases AI tool for processing Paycheck Protection Program loans
In an effort to help lenders expedite the processing of applications for the U.S. Small Business Administration’s (SBA) Paycheck Protection Program, which aims to keep workers employed during the coronavirus pandemic, Google developed an AI solution called PPP Lending AI that integrates with existing document ingestion tools. It’s available to eligible lending institutions through June 30.
As Google explains in a whitepaper, AI can automate the handling of volumes of loan applications by identifying patterns that would take a human worker longer to spot. Specifically, PPP Lending AI can classify and extract data in critical paperwork before readying documents for submission to the SBA.
2020-05-01 00:00:00 Read the full story…
Weighted Interest Score: 3.2323, Raw Interest Score: 1.4343,
Positive Sentiment: 0.0574, Negative Sentiment 0.1147
AI in COVID-19 Fight: Pope Issues Ethical Challenge; Voice Studied to Help in Detection
The worldwide fight against COVID-19 continues to challenge AI experts. The Pope issued a challenge for AI experts to develop an “ethical algorithm” that would ensure fairness; Some new AI research shows how people are feeling about the virus. Other researchers are experimenting with the use of sound to detect the virus. Shortly before the Vatican closed due to the virus, members of the Pontifical Academy for Life, which researches bioethics and Catholic moral theology, worked on getting a commitment from AI developers to write an “ethical” algorithm in each AI system, according to an account in SSPX.news, the communication agency of the Society of St. Pius, based in Paris.
“Following the example of electricity, AI is not necessary to perform a specific action, it is rather intended to change the way, the mode with which we carry out our daily actions,” stated Fr. Paolo Benanti, a professor of moral theology and bioethics at the Pontifical Gregorian University in Rome. He spoke at the conference held Feb. 26 and 27, 2020, on what he sees at stake in the digital revolution represented by AI. As AI learns, ingests more data, it becomes more powerful and poses a bigger moral problem. The moral problem only becomes bigger: “When the machine replaces man in decision-making, what kind of certainty would we have to let the machine choose who should be treated or not, and how? On what basis should we allow a machine to designate which of us is trustworthy and who is not?” Fr. Benanti stated.
2020-04-30 21:30:20+00:00 Read the full story…
Weighted Interest Score: 3.1514, Raw Interest Score: 1.2792,
Positive Sentiment: 0.1261, Negative Sentiment 0.1982
How Video Game Developers Can Use AI
Gamers now and then have been frustrated with how good the bots or NPCs (non-playable characters) are at playing games. While some of them present no challenge, NPCs in strategy games are often the biggest reasons for a joystick to be thrown out the window. Not only the challenges but also the visual effects and how realistic the games look in today’s era is astounding. All thanks to the hours spent by game developers on going through every detail and hard-coding them into the overall game design. However, with some of the features that AI has been providing these developers, giving attention to detail and making them as much immersive and interactive has become easier. With AI in the gaming industry, it is poised to further grow and enhance the user experience.
Below are some of the ways that game developers can use/have been using AI to reduce their burden and improve player experience.
2020-05-04 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1481, Raw Interest Score: 1.0825,
Positive Sentiment: 0.2624, Negative Sentiment 0.1476
New Tools from Verizon Help Developers Tackle COVID-19 Data
Verizon has joined the battle against COVID-19. This week, Verizon announced three new tools to help developers and data analysts leverage the deluge of data the pandemic is producing. The tools – a dataset, an API, and a dashboard – utilize Verizon Media’s Yahoo Knowledge Graph, which Verizon touts as “one of the largest organized collections of information.”
The Yahoo Knowledge COVID-19 data repository includes a wide range of variables (such as cases, deaths, and recoveries) broken down at a county-by-county level and meticulously sourced. “We created this dataset by carefully combining and normalizing raw data provided entirely by government and public health authorities,” wrote Amit Nagpal, senior director of software development engineering at Verizon Media. “We provide website level provenance for every single statistic in our dataset, so our community has the confidence it needs to use it scientifically and report with transparency.”
2020-04-30 00:00:00 Read the full story…
Weighted Interest Score: 3.0351, Raw Interest Score: 1.2613,
Positive Sentiment: 0.1577, Negative Sentiment 0.1577
Top New AI & ML Releases For Developers: PyTorch 1.5, AppFlow & More
The life of machine learning developers gets easier with every passing week as the AI leaders such as Facebook, Google, Amazon and a few others keep on releasing their tools out to the public. Last week there has been significant releases, and we bring you all the hottest releases in this article:
- Facebook AI, AWS Collaborated To Release New PyTorch Libraries
- PyTorch 1.5 Released
- NVIDIA and King’s College London Announce MONAI
2020-04-30 10:30:50+00:00 Read the full story…
Weighted Interest Score: 2.9137, Raw Interest Score: 1.3580,
Positive Sentiment: 0.3339, Negative Sentiment 0.2004
Data exploration with the COVID-tracking Project
How to easily do exploratory data analysis (EDA) with one of the most comprehensive US databases on COVID-19.
As per their website, “The COVID Tracking Project collects and publishes the most complete testing data available for US states and territories. Understanding the evolving dynamics and the precise location of regional outbreaks requires a complete testing picture — how many people have actually been tested in each state/territory, when the tests were done, and what the results were. Indeed, the project has been cited in and used by major media companies and agencies throughout the nation.
How to verify the quality and veracity of the data? The website further adds “…our data team uses website-scrapers and trackers to alert us to changes, but the actual updates to our dataset are done manually by careful humans who double-check each change and extensively annotate changes areas of ambiguity.” Some of the visualizations in popular news outlets (e.g. NY Times, Politico, The Wall Street Journal, etc.) have been created from these data.
In this article, we will see how simple Python scripting enables you to read this dataset and create meaningful visualizations of your own for tracking and understanding the spread of COVID-19 across the U.S.
2020-05-04 04:13:07.768000+00:00 Read the full story…
Weighted Interest Score: 2.8239, Raw Interest Score: 1.2465,
Positive Sentiment: 0.1662, Negative Sentiment 0.0554
Fuzzy Anonymity Rules Could Stymie EU’s Big Data Sharing Ideas
The EU wants to see more non-personal data shared between businesses, but that could prove easier said than done. On 19 February, the European Commission presented a three-part package to boost Europe’s digital economy, including a European strategy for data. Buried beneath the headlines about artificial intelligence is a set of policy options that Internal Market Commissioner Thierry Breton believes could herald a new age of European data success.
As former CEO of Atos, Breton is well aware of the value of business data and wanting to leverage that for the European economy seems like a “no brainer.” But lurking among the proposals are some ideas that have given the tech sector cause for consideration. One of the suggestions to encourage data sharing across the bloc is to give public subsidies to a so-called “European cloud,” prompting cries of “protectionism” from outside the EU.
2020-05-01 16:00:00+00:00 Read the full story…
Weighted Interest Score: 2.7743, Raw Interest Score: 1.4955,
Positive Sentiment: 0.2162, Negative Sentiment 0.2523
Google open-sources AI that searches tables to answer natural language questions
Google today open-sourced a machine learning model that can point to answers to natural language questions (for example, “Which wrestler had the most number of reigns?”) in spreadsheets and databases. The model’s creators claim it’s even capable of finding answers spread across cells or that might require aggregating multiple cells. Much of the world’s information is stored in the form of tables, Google Research’s Thomas Müller points out in a blog post, like global financial statistics and sports results. But these tables often lack an intuitive way to sift through them — a problem Google’s AI model aims to fix.
To answer questions like “Average time as champion for top 2 wrestlers?” the model jointly encodes the question, as well as the table content row by row. It leverages a Transformer-based BERT architecture — one that’s both bidirectional (allowing it to access content from past and future directions) and unsupervised (meaning it can ingest data that’s neither classified nor labeled) — extended along with numerical representations called embeddings to encode the table structure.
2020-04-30 00:00:00 Read the full story…
Weighted Interest Score: 2.7602, Raw Interest Score: 1.6051,
Positive Sentiment: 0.1493, Negative Sentiment 0.2613
Google Launches TensorFlow Runtime For Its TensorFlow ML Framework
Google has launched TensorFlow RunTime (TFRT), which is a new runtime for its TensorFlow machine learning framework.
According to a recent blog post by Eric Johnson, TFRT Product Manager and Mingsheng Hong, TFRT Tech Lead/Manager, “TensorFlow RunTime aims to provide a unified, extensible infrastructure layer with best-in-class performance across a wide variety of domain-specific hardware. It provides efficient use of multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency.”
The company has made TFRT available on GitHub. According to the company, as part of a benchmarking study for TensorFlow Dev Summit 2020 — while comparing the performance of GPU inference over TFRT to the current runtime, we saw an improvement of 28% in average inference time. These early results are strong validation for TFRT to provide a significant boost to performance.
2020-04-30 05:55:55+00:00 Read the full story…
Weighted Interest Score: 2.6875, Raw Interest Score: 1.2508,
Positive Sentiment: 0.5316, Negative Sentiment 0.0625
Data Privacy Is on the Defensive During the Coronavirus Panic
Privacy is on the run in the race to save the world from the ravages of coronavirus. COVID-19 has given surveillance advocates the upper hand in any discussions of AI for the public good. The ongoing pandemic has provided a readymade justification for using AI-driven solutions to engage in facial fever detection and other continuously intimate monitoring of the general population.
Encroachments on privacy are deepening at a disturbing pace around the world, but that doesn’t mean that public health concerns are a carte blanche for privacy encroachment.
Many nations—including one-party states such as China and multiparty democracies such as Israel and South Korea–have passed emergency laws under which they’ve implemented surveillance systems for tracking COVID-19. And it’s no surprise that the right-wing Trump administration is exploring how it might gain access to the cellphone location data of all Americans in order to track the spread of the disease.
2020-04-28 00:00:00 Read the full story…
Weighted Interest Score: 2.6547, Raw Interest Score: 1.1653,
Positive Sentiment: 0.0800, Negative Sentiment 0.4227
Top Machine Learning Books Made Free due to Covid-19
Since e-learning is on the rise because of social distancing, the data science community earlier offered free online courses and now provides free e-books. While online data science courses are useful, books deliver structured as well as an in-depth understanding of the techniques. Reading books has its own advantages as it keeps you focused while eliminating distractions that your witness in online learning.
Springer Nature, popularly known for publishing books on science, business, and data science, has released numerous machine learning books for free. However, the below list only contains the most popular machine learning related books.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.5744, Raw Interest Score: 1.7004,
Positive Sentiment: 0.2429, Negative Sentiment 0.0810
Absolutdata Launches AI-Based COVID-19 Toolkit To Help Businesses Navigate Uncertain Times
In a recent development, Absolutdata, a leading analytics and data science services company announced the launch of Absolutdata COVID-19 toolkit to help businesses navigate uncertain times.
The toolkit includes three solutions:
- ASK NAVIK: It is an AI-powered virtual assistant that provides instantaneous answers to critical business questions by pulling information from dashboards, databases and documents.
- NAVIK SIGNALS: It helps answer questions on how consumers will think, feel and act after the COVID-19 crisis is over; and
- COVID-19 SWAT Team: It helps to quickly develop dashboards and custom models for the current COVID impacted environment.
While the intelligent virtual assistant doesn’t have any installation fee, the other two tools will be free for a limited period of time.
2020-05-04 05:41:15+00:00 Read the full story…
Weighted Interest Score: 2.5062, Raw Interest Score: 1.3227,
Positive Sentiment: 0.2153, Negative Sentiment 0.2768
The Ultimate Guide to Linear Regression
In this post, we are going to discuss the linear regression model used in machine learning. Modeling for this post will mean using a machine learning technique to learn — from data — the relationship between a set of features and what we hope to predict. Let’s bring in some data to make this idea more concrete.
How can we tackle the problem of predicting TARGET from LSTAT? A good place to start one’s thinking is: say we develop many models to predict our target, how would we pick the best one? Once we determine this, our goal is then to minimize/maximize that value. It is extremely useful if you can reduce your problem to a single evaluation metric because then it makes it very easy to iterate on model development. In industry, though, this can be tricky. Sometimes it isn’t extremely clear what you want your model to maximize/minimize. But that is a challenge for another post. So for this problem, I would propose the following evaluation metric: mean squared error (MSE). To understand MSE, let’s define some terminology:
2020-05-04 04:27:26.211000+00:00 Read the full story…
Weighted Interest Score: 2.4327, Raw Interest Score: 1.2241,
Positive Sentiment: 0.2010, Negative Sentiment 0.1723
How Businesses Are Using Big Data For Social Media Marketing?
The term “data” has been a staple of the internet industry ever since its inception in the ’80s. With more and more focus shifting towards the digital sphere managing data has been quite essential especially considering the amount of data that needs to be stored and analysed. Big data is the field of science that deals with data sets that are too large and complex that your traditional data processing tools cannot handle.
According to sources, Big Data consists of data from both inside and outside of your corporation that can be a great tool for ongoing analysis and strategy creation. With the amount of information that is now available on the internet, getting those data in the proper order for greater insights has become very necessary and this is where Big Data comes into play. With Big Data coming into the scene, social media marketing has taken on a whole different level. With the help of these data sets professionals are able to craft personalized marketing strategies that a regular internet user might find overwhelming. There are many websites that take advantage of big data and AI to design perfect strategy for their clients who are in the need to grow Instagram followers to boost their engagement.
If you are feeling powerless in front of the sheer tide of data that you are looking at, you have come to the right spot. With just a few tweaks and tips and you should be able to compete with the big guns on the market if you can harness the true potential of Big Data. So, let us take a deeper dive in order to better understand how businesses are using big data for social media marketing.
2020-04-30 18:13:38+00:00 Read the full story…
Weighted Interest Score: 2.4031, Raw Interest Score: 1.2425,
Positive Sentiment: 0.4064, Negative Sentiment 0.0813
This Latest Model Serving Library Helps Deploy PyTorch Models At Scale
PyTorch has become popular within organisations to develop superior deep learning products. But building, scaling, securing, and managing models in production due to lack of PyTorch’s model server was keeping companies from going all in. The robust model server allows loading one or more models and automatically generating prediction API, backed by a scalable web server. Besides, it also offers production-critical features like logging, monitoring, and security.
Until now, TensorFlow Serving and Multi-Model Server catered to the needs of developers in production, but the lack of a model server that could effectively manage the workflows with PyTorch was causing hindrance among users. Consequently, to simplify the model development process, Facebook and Amazon collaborated to bring TorchServe, a PyTorch model serving library, that assists in deploying trained PyTorch models at scale without having to write custom code.
2020-05-03 10:19:52+00:00 Read the full story…
Weighted Interest Score: 2.3836, Raw Interest Score: 1.4276,
Positive Sentiment: 0.2900, Negative Sentiment 0.2008
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