AI & Machine Learning News. 29, June 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?
Nvidia Synthesizing High Resolution Images with StyleGAN2
This new project called StyleGAN2, developed by NVIDIA Research, and presented at CVPR 2020, uses transfer learning to produce seemingly infinite numbers of portraits in an infinite variety of painting styles. The work builds on the team’s previously published StyleGAN project.
GANs have captured the world’s imagination. Their ability to dream up realistic images of landscapes, cars, cats, people, and even video games, represents a significant step in artificial intelligence. Over the years, NVIDIA researchers have contributed several breakthroughs to GANs.
2020-07-14 Read the full story…
CloudQuant Thoughts : My daughter is an artist, this AI amazed us both. The possibilities are endless. We all knew AI was capable of something like this but the execution by Nvidia is flawless.
AI Weekly: A deep learning pioneer’s teachable moment on AI bias
I’ve lost track of the number of times I’ve heard someone say Timnit Gebru is saving the world recently. Her co-lead of AI ethics at Google, Margaret Mitchell, said it a few days ago when Gebru led events around race at Google. Gebru’s work with Joy Buolamwini demonstrating race and gender bias in facial recognition is one of the reasons lawmakers in Congress want to prohibit federal government use of the technology. That landmark work also played a major role in Amazon, IBM, and Microsoft agreeing to halt or end facial recognition sales to police.
Earlier this week, organizers of the Computer Vision and Pattern Recognition (CVPR) conference, one of the biggest AI research events in the world, took the unusual step of calling Gebru’s CVPR tutorial illustrating how bias in AI goes far beyond data “required viewing for us all.”
2020-06-26 00:00:00 Read the full story…
Weighted Interest Score: 2.6915, Raw Interest Score: 1.0650,
Positive Sentiment: 0.0592, Negative Sentiment 0.4142
CloudQuant Thoughts : This is a great summary of AI news over the last week in the form of an article, I really enjoy it when people put in this kind of effort. However, I was a little surprised that, whilst they led off with Timnit Gebru’s must watch presentation, they led the article with an image of “a couple of white dudes”.
AI Being Applied to Improve Health, Better Predict Life of Batteries
AI techniques are being applied by researchers aiming to extend the life and monitor the health of batteries, with the aim of powering the next generation of electric vehicles and consumer electronics.
Researchers at Cambridge and Newcastle Universities have designed a machine learning method that can predict battery health with ten times the accuracy of the current industry standard, according to an account in ScienceDaily. The promise is to develop safer and more reliable batteries.
In a new way to monitor batteries, the researchers sent electrical pulses into them and monitored the response. The measurements were then processed by a machine learning algorithm to enable a prediction of the battery’s health and useful life. The method is non-invasive and can be added on to any battery system.
2020-06-25 21:30:28+00:00 Read the full story…
Weighted Interest Score: 2.6520, Raw Interest Score: 1.2970,
Positive Sentiment: 0.1513, Negative Sentiment 0.2162
CloudQuant Thoughts : This is incredibly useful Research as we move away from finite energy resources and towards storing the power of the sun in more and more batteries.
This AI translates code from a programming language to another | Facebook TransCoder Explained
CloudQuant Thoughts : Does we really need ML to do this? Probably not for small scale translation, but being able to algo-rhythmically translate entire code bases will be very useful! Note that Facebook has not release any code, at the moment there is just a white paper on a preprint website.
This is our section on ESG data in the news. Don’t forget that CloudQuant also supply alternative datasets including an ESG data set. For more information head over to our Data Catalog.
RepRisk partners With Battlefin to offer ESG datasets for alternative data community
RepRisk, a specialist in ESG data science combining machine learning and human intelligence, has formed a strategic partnership with alternative data platform and marketplace BattleFin that will significantly expand alternative data buyer access to ESG risk data.
RepRisk’s daily-updated dataset on nearly 150,000 companies linked to ESG and business conduct risks will be available through BattleFin’s global alternative data marketplace, …
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 9.6320, Raw Interest Score: 3.0835,
Positive Sentiment: 0.4253, Negative Sentiment 0.0000
BNY Mellon Launches New ESG Data Analytics
The Bank of New York Mellon Corporation announced the launch of three new Data and Analytics Solutions offerings designed to help investment managers better manage their data, improve the success of U.S.-listed fund launches and support the customization of investment portfolios to preferred Environmental, Social, and Governance (ESG) factors.
Additionally, as part of its digital strategy to collaborate with external partners, BNY Mellon has expanded its relationship with Microsoft to create data, technology and content solutions for investment managers.”Our clients want and need more flexibility in their cloud-based data solutions so they can remain agile to evolving market, client and regulatory changes,” said Roman Regelman, Senior Executive Vice President and Head of Asset Servicing and Digital, BNY Mellon. “Data Vault, Distribution Analytics and ESG Data Analytics were developed to help investment managers better manage and unlock value from their data. Further, our expanded relationship with Microsoft underscores our open culture of partnering with leading technology providers to collaborate on data solutions that address client investment needs.”
2020-06-26 10:38:45+00:00 Read the full story (marketsmedia)…
2020-06-29 07:30:25+00:00 Read the full story (fintechfutures)…
Weighted Interest Score: 4.2777, Raw Interest Score: 2.3515,
Positive Sentiment: 0.7116, Negative Sentiment 0.0000
How Data Intelligence is Shaping the Future of the U.S. Renewables Sector
Data Intelligence — Still a Buried Treasure for an Efficient Green Recovery
Regardless of whether the attraction of private capital to the renewable sector is met via a “green” stimulus, relevant stakeholders would do well to make better use of the resources already at their disposal, namely the wealth of data generated by installed IoT assets in the renewables sector.
This driving imperative to innovate is behind the recent surge in investment from venture capital, utilities, and private equity players into digital technologies like artificial intelligence (AI) and machine learning to reap fewer risks and get better returns on renewable energy.
2020-06-26 07:35:19+00:00 Read the full story…
Weighted Interest Score: 3.1108, Raw Interest Score: 1.8082,
Positive Sentiment: 0.3113, Negative Sentiment 0.3473
Apple Parts Ways With Intel, Yann LeCun’s Tweetstorm, And More In This Week’s Top AI News
This week the AI community has witnessed a couple of ground shifting controversies, a couple of software releases and more. This week also marked the launch of Apple’s 31st edition of its flagship WWDC event. Here is all the top AI news that you wouldn’t want to miss.
- Amazon Makes A Billion Dollar Entry Into Self-Driving Market
- Deep Learning Paper Taken Down
- Apple Concludes Its Biggest WWDC Event Ever
- A Tweet Stirs Up ML Bias Controversy
- Self-Driving Tech Company Waymo Joins Hands With Volvo
- Deloitte Sets Up AI Institute
- AWS Releases Honeycode To Develop Apps Without Writing Code
2020-06-27 12:30:00+00:00 Read the full story…
Weighted Interest Score: 2.7043, Raw Interest Score: 1.3016,
Positive Sentiment: 0.1795, Negative Sentiment 0.1795
Nvidia Destroys TPCx-BB Benchmark with GPUs
Traditionally, vendors have used CPU-based systems for the TPCx-BB benchmark, which simulates a Hadoop-style workload that mixes SQL and machine learning jobs on structured and unstructured data. So when Nvidia ran the benchmark on its new Ampere class of GPUs system, the results were predictably grim – for CPU systems, that is.
Nvidia today reported unofficial results for two TPCx-BB tests, including the SF1K and the SF10K. For the SF1K test, which simulated a series of queries against a 1TB dataset, the company rolled out a dual DGX A100 systems, comprising a total of 16 A100 GPUs and a Mellanox interconnect. For the SF10K test, which used a 10TB data set, it used a Mellanox interconnect to hook together 16 DGX A100 systems running a total of 128 A100 GPUs.
2020-06-22 00:00:00 Read the full story…
Weighted Interest Score: 2.4146, Raw Interest Score: 1.4601,
Positive Sentiment: 0.1825, Negative Sentiment 0.0456
QuantHouse to provide TSL machine learning capabilities as part of the QuantFactory cloud backtesting suite
tHouse, a provider of end-to-end systematic trading solutions including market data services, algo trading platform and infrastructure products and part of Iress, has added Trading System Lab’s (TSL) machine learning capabilities to its QuantFactory cloud backtesting suite.
The suite provides a fully configurable environment in which clients can develop, backtest, optimise and implement quantitative trading strategies that can later be executed in a standalone, live-trading environment. Machine learning outputs from TSL are integrated into the QuantDeveloper module of QuantFactory.
Machine learning delivers…
2020-06-29 00:00:00 Read the full story…
Weighted Interest Score: 6.7901, Raw Interest Score: 2.3957,
Positive Sentiment: 0.3549, Negative Sentiment 0.0444
SQream Raises Funds to Expand its Analytics Push
SQream Technologies, the GPU database vendor, has attracted new investors while adding several financial backers to its board of directors. The Tel Aviv-based database vendor said Wednesday (June 24) it raised $39.4 million in a Series B+ funding round led by Mangrove Capitol Partners and Shusterman Family Investments. Existing investors include Alibaba Group, Blumberg Capital, Hanaco Venture Capital, Silvertech Ventures, Sistema.vc and World Trade Center Ventures.
The company also said Charlie Federman of Silvertech Ventures and Roy Saar of Mangrove Capital Partners will join its board. The new investments will be used for recruitment as SQream attempts to accelerate development of its GPU-accelerated cloud analytics platform.
Investors have been steadily pouring funds into the data analytics sector as customers seek to get their arms around ever-expanding data stores fueled by a flood of unstructured data. For example, Dremio, the cloud data lake engine vendor, announced a $70 million funding round in March.
2020-06-24 00:00:00 Read the full story (Datanami)…
2020-06-24 00:00:00 Read the full story (dbta)…
Weighted Interest Score: 4.6210, Raw Interest Score: 2.4403,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
Search for COVID-19 Treatment Accelerating Use of AI in Healthcare
AI was already having an impact in healthcare before COVID-19 came along. Now the impact of AI in healthcare is accelerating.
A harbinger of the impact of AI on the spread of COVID-19 came on New Year’s Eve for 2020, when the AI platform Blue Dot registered a clutter of unusual cases in Wuhan, China. The Toronto-based company uses natural language processing and machine learning to track, locate and report on infectious disease spread. It sends alerts to its clients, which include entities in health care, government, business and public health. It had spotted what turned out to be COVID-19, nine days before the World Health Organization released an alert on the emergence of a novel coronavirus, noted a recent account in Wired.
Since then AI has been used for prediction, screening, contact alerts, faster diagnosis, automated deliveries, and laboratory drug discovery in the fight against the coronavirus. One example is an AI-powered diagnostic system that purports to detect new coronavirus cases with an accuracy of 96% compared to computerized tomography scans, according to an account in Nikkei Asian Review.
2020-06-25 21:30:00+00:00 Read the full story…
Weighted Interest Score: 4.5090, Raw Interest Score: 1.6455,
Positive Sentiment: 0.1208, Negative Sentiment 0.1208
Quant Insight launches quant analytics platform on OpenFin
Quant Insight (Qi), a macro data analytics firm that applies quantitative techniques to financial markets, has launched its desktop application on the OpenFin operating system.
Qi provides quantitative macro analytics across multiple asset classes to a wide array of investors from discretionary to systematic, from equity long/short to absolute return. Qi brings a single, comprehensive and robust solution to its clients with actionable signals.
The collaboration enables the seamless deployment of Qi’s quantitative macro analytics on OpenFin’s OS, giving end-users a simple, comprehensive macro solution to aid their strategy, via an integrated API and an enhanced user experience dashboard. This will allow users to identify investment opportunities and manage risk, whether they are bottom-up or top-down in approach.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 4.0373, Raw Interest Score: 1.8634,
Positive Sentiment: 0.5324, Negative Sentiment 0.0444
Data Science on the Buy Side
With Gary Collier, CTO of Man Group Alpha Technology, and Hinesh Kalian, Director of Data Science, Man Group
What are the main data challenges / pain points for the buy side?
A big challenge is obtaining and retaining data science talent. It is apparent that there is a growing demand, and therefore competition, for data science talent across all industries, not just in financial services. Another challenge relates to the ability to ingest and curate structured and unstructured data rapidly and in a variety of raw formats. The growth in new data providers has led to a wide variance in the quality of data offered by data providers; some providers are well-established and have appropriate data science and technology teams, whereas others can be as limited as two employees in a start-up.
For data to be useful it needs to be clean, consistent and sourced and processed appropriately. Often data is provided after some processing steps are done, which limits awareness of the raw data and can lead to the risk of false representation and predictability.
2020-06-24 11:54:29+00:00 Read the full story…
Weighted Interest Score: 3.7405, Raw Interest Score: 1.8998,
Positive Sentiment: 0.1981, Negative Sentiment 0.0932
Liquidity risk COVID-19: big vs small data
Using the right technologies to track, trace, and manage liquidity data and reporting will help financial institutions smoothly navigate the balance sheet and financial liquidity issues arising from the pandemic.
During World War II, leaders of the free world called on industries to get creative and focus on critical warcraft needs. A technology boom ensued. The Turing Machine – with which the allies cracked the Enigma code – was one of many tech miracles of the era that turned the course of the war. Effectively the first risk engine, the machine enabled users to analyse (decrypt) critical data, understand (enemy) positions, and thus mitigate risks.
In today’s war against coronavirus (COVID-19), industries are also answering the call to invent, retool, and ramp up to deliver vital medicines, specialised equipment, and supplies. To mitigate and beat the coronavirus, there has already been an explosion of new capabilities that capture, map, and enable users to understand COVID-19 infection data. Just as before, we can expect this global crisis to continue to precipitate technology changes into the next decade.
2020-06-23 11:30:35+00:00 Read the full story…
Weighted Interest Score: 3.6950, Raw Interest Score: 1.6509,
Positive Sentiment: 0.2830, Negative Sentiment 0.3145
MLflow Joins the Linux Foundation, Broadening Adoption of the Platform
The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced that MLflow, an open source machine learning (ML) platform created by Databricks, is joining the Linux Foundation.
The Linux Foundation provides a vendor neutral home with an open governance model to broaden adoption and contributions to the MLflow project even further.
“The steady increase in community engagement shows the commitment data teams have to building the machine learning platform of the future. The rate of adoption demonstrates the need for an open source approach to standardizing the machine learning lifecycle,” said Michael Dolan, VP of strategic programs at the Linux Foundation. “Our experience in working with the largest open source projects in the world shows that an open governance model allows for faster innovation and adoption through broad industry contribution and consensus building.”
2020-06-26 00:00:00 Read the full story…
Weighted Interest Score: 3.5960, Raw Interest Score: 2.1220,
Positive Sentiment: 0.2653, Negative Sentiment 0.1592
Databricks’ ML Platform – MLflow Joins The Linux Foundation
The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced that MLflow, an open source machine learning (ML) platform created by Databricks, will join the Linux Foundation.
Since its introduction at Spark + AI Summit two years ago, MLflow has experienced impressive community engagement from over 200 contributors and is downloaded more than 2 million times per month, with a 4x annual growth rate in downloads. The Linux Foundation provides a vendor-neutral home with an open governance model to broaden adoption and contributions to the MLflow project even further.
2020-06-26 07:17:11+00:00 Read the full story…
Weighted Interest Score: 3.3417, Raw Interest Score: 2.0253,
Positive Sentiment: 0.2848, Negative Sentiment 0.0949
A closer look at SageMaker Studio, AWS’ machine learning IDE
Back in December, when AWS launched its new machine learning IDE, SageMaker Studio, we wrote up a “hot-off-the-presses” review. At the time, we felt the platform fell short, but we promised to publish an update after working with AWS to get more familiar with the new capabilities. This is that update.
When Amazon launched SageMaker Studio, they made clear the pain points they were aiming to solve: “The machine learning development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling.” The machine learning workflow — from data ingestion, feature engineering, and model selection to debugging, deployment, monitoring, and maintenance, along with all the steps in between — can be like trying to tame a wild animal.
2020-06-27 00:00:00 Read the full story…
Weighted Interest Score: 3.2954, Raw Interest Score: 2.0045,
Positive Sentiment: 0.1706, Negative Sentiment 0.1493
Databricks Cranks Delta Lake Performance, Nabs Redash for SQL Viz
Today at its Spark + AI Summit, Databricks unveiled Delta Engine, a new layer in its Delta Lake cloud offering that uses several techniques to significantly accelerate the performance of SQL queries. The company also announced the acquisition of Redash, which develops a visualization tool that will be integrated with Databricks’ Lakehouse.
Delta Engine is a new layer that sits atop Delta Lake, the structured transactional data storage layer that Databricks launched three years ago to address a variety of data ingestion and quality issues that customers were facing with the emergence of data lakes running atop cloud object stores, such as Amazon S3.
2020-06-24 00:00:00 Read the full story…
Weighted Interest Score: 3.1955, Raw Interest Score: 1.5774,
Positive Sentiment: 0.2214, Negative Sentiment 0.1245
Why our decision-making during COVID has been so bad
No one was prepared for COVID-19. No one but a handful of epidemiologists and visionaries — including, most famously, Bill Gates in his now-viral TED talk — had an inkling that the next global pandemic was a matter of when and not if.
In retrospect, our unpreparedness is embarrassing and almost inexcusable. All the more so considering everything we know about HIV, SARS, MERS, and, most recently, the Ebola and Zika epidemics.
We’ve all been talking about AI’s superpower for the last decade. But AI hasn’t been able to save us so far. It cannot replace human decision-making — yet. However, the technology can provide us with tools that complement our own cognitive processes. That could make us better prepared to respond to future epidemics, crises, and black swan events.
2020-06-28 00:00:00 Read the full story…
Weighted Interest Score: 3.1946, Raw Interest Score: 1.2905,
Positive Sentiment: 0.1844, Negative Sentiment 0.4916
Sino-US tech race triggers fears of expanding bubble
“Ten years ago, you could fit all of China’s semiconductor investors around two tables,” said Alan Peng, founder of chip start-up NextVPU. “Nowadays there are hundreds of people crowding around those same two tables.”
Until recently, China’s semiconductor sector was heavily reliant on the government. The National Integrated Circuit Industry Investment Fund, popularly known as “the big fund”, put up 139 billion yuan for chip projects in 2014. It …
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 3.1652, Raw Interest Score: 1.6899,
Positive Sentiment: 0.0536, Negative Sentiment 0.1341
Machine Learning in Data Science Interviews
Machine learning questions are often the toughest parts of data science interviews, and for good reason. This post will highlight several example problems, general comments on machine learning, and what topics to study on the theory and application side. The problems discussed are featured from https://datascienceprep.com/ which covers interview questions from top tech companies.
Machine learning is not applicable to every data science role since data science is a broad field, but for relevant roles, it is an important area of study that has both depth and breadth. However, regardless of role, I think it is useful for any data scientist or aspiring data scientist to study machine learning for three main reasons:
There are two main areas of focus relevant to machine learning: theory and application. Theory entails all of the mathematical underpinnings behind models and why and how they work the way they do, whereas application entails all of the real-world use cases whereby technology at scale can leverage such models. Both are equally important to study and become well-versed in.
2020-06-29 04:41:21.547000+00:00 Read the full story…
Weighted Interest Score: 3.0948, Raw Interest Score: 1.8292,
Positive Sentiment: 0.1829, Negative Sentiment 0.3292
ING taps Expert System to boost back office automation
ING has extended a back-office automation agreement with artificial intelligence (AI) firm, Expert System.
The Dutch bank is deploying Expert System’s natural language understanding (NLU) services. This will improve its application of robotic process automation (RPA).
According to Expert System, RPA can sometimes fall short in automating tasks that require accurate comprehension, categorisation, and correlation of data.
2020-06-29 08:00:18+00:00 Read the full story…
Weighted Interest Score: 3.0656, Raw Interest Score: 2.0594,
Positive Sentiment: 0.4240, Negative Sentiment 0.0000
Synergy Between AI, 5G and IoT Yields Intelligent Connectivity
The major US mobile operators are all deploying their 5G networks in 2020, and each one claims that AI and machine learning will help them proactively manage the costs of deploying and maintaining new 5G networks. AT&T recently outlined the company’s blueprint for leveraging artificial intelligence and machine learning (ML) to maximize the return on its 5G network investment. AT&T’s Mazin Gilbert sees a “perfect marriage” of AI, ML and software defined networking (SDN) to help enable the speeds and low latency of 5G.
AT&T is using AI and ML to map its existing cell towers, fiber lines, and other transmitters that today, to build its 5G infrastructure and to pinpoint the best location for 5G build outs in the future. AT&T has more than 75,000 macro cells in its network and is using AI to guide plans for deploying hundreds of thousands of additional small cells and picocells. If AI detects a cell site isn’t functioning properly, it will signal another tower to pick up the slack.
2020-06-25 21:30:29+00:00 Read the full story…
Weighted Interest Score: 3.0001, Raw Interest Score: 1.4664,
Positive Sentiment: 0.3026, Negative Sentiment 0.2095
Eric and Wendy Schmidt back Cambridge University effort to equip researchers with A.I. skills
- The program is designed to equip young researchers with machine learning and artificial intelligence skills that have the potential to accelerate their research.
- PhD physicists, biologists, chemists and other scientists will all receive training.
- Artificial intelligence and machine learning have the potential to speed up the pace of discovery across a range of disciplines.
Schmidt Futures, the philanthropic foundation set up by billionaires Eric (former Google CEO) and Wendy Schmidt, is funding a new program at the University of Cambridge that’s designed to equip young researchers with machine learning and artificial intelligence skills that have the potential to accelerate their research.
The initiative — known as the Accelerate Program for Scientific Discovery — will initially be aimed at researchers in science, technology, engineering, mathematics and medicine. However, it will eventually be available for those studying arts, humanities and social science.
Some 32 PhD students will receive machine-learning training through the program in the first year, the university said, adding that the number will rise to 160 over five years. The aim is to build a network of machine-learning experts across the university.
2020-06-29 00:00:00 Read the full story…
Weighted Interest Score: 2.9545, Raw Interest Score: 1.7328,
Positive Sentiment: 0.0912, Negative Sentiment 0.0456
22 Widely Used Data Science and Machine Learning Tools
- There are a plethora of data science tools out there – which one should you pick up?
- Here’s a list of over 20 data science tools catering to different stages of the data science lifecycle
What are the best tools for performing data science tasks? And which tool should you pick up as a newcomer in data science?
I’m sure you’ve asked (or searched for) these questions at some point in your own data science journey. These are valid questions! There is no shortage of data science tools in the industry. Picking one for your journey and career can be a tricky decision.
Let’s face it – data science is a vast spectrum and each of its domains requires handling of data in a unique way that leads many analysts/data scientists into confusion. And if you’re a business leader, you would come across crucial questions regarding the tools you and your company choose as it might have a long term impact.
So again, the question is which data science tool should you choose?
In this article, I will be attempting to clear this confusion by listing down widely used tools used in the data science space broken down by their usage and strong points. So let us get started!
2020-06-22 00:00:00 Read the full story…
Weighted Interest Score: 2.8669, Raw Interest Score: 1.7328,
Positive Sentiment: 0.2542, Negative Sentiment 0.1338
Spark 3.0 Brings Big SQL Speed-Up, Better Python Hooks
Apache Spark 3.0 is now here, and it’s bringing a host of enhancements across its diverse range of capabilities. The headliner is an big bump in performance for the SQL engine and better coverage of ANSI specs, while enhancements to the Python API will bring joy to data scientists everywhere.
In 10 short years, Spark has become the dominant data processing framework for parallel big data analytics. It started out as a replacement for MapReduce, but it’s still going strong even as excitement for Hadoop has faded. Today, it’s the Swiss Army knife of processing, providing capabilities spanning ETL and data engineering, machine learning, stream processing, and advanced SQL analytics.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.7924, Raw Interest Score: 1.7595,
Positive Sentiment: 0.4353, Negative Sentiment 0.1088
Would You Rather be a Data Analyst or Data Scientist?
How does it feel to be in one of these roles? Find out here.
After working as both a professional data analyst and data scientist, I thought it would be insightful to highlight the experience of each position along with some key differences in how they feel day-to-day. Ultimately, I hope my article can help you decide which role fits best for you. If you are already in one of these positions, perhaps you would like to switch to the other one. Some people start as data analysts then move on to becoming a data scientist, whereas, as a less popular route but still somewhat prominent, is going from a non-senior level data scientist position to a senior data analyst. For each position, there are several concepts and overall experiences that are important to know as you make your next big career move.
Below, I will highlight how it feels to be a data analyst as well as a data scientist. I will raise common questions about each role and answer them accordingly from what I have experienced — in addition to some close peers in each field.
2020-06-29 05:05:25.125000+00:00 Read the full story…
Weighted Interest Score: 2.7890, Raw Interest Score: 1.5320,
Positive Sentiment: 0.1129, Negative Sentiment 0.0484
Analytics Best Practices for Transforming Data into a Business Asset
Data has three main functions that provide value to the business: To help in business operations, to help the company stay in compliance and mitigate risk, and to make informed decisions using analytics.
“Data can have an impact on your top line as well as your bottom line,” said Dr. Prashanth Southekal, CEO of DBP-Institute in a recent interview with DATAVERSITY®.
“Just capturing, storing, and processing data will not transform your data into a business asset. Appropriate strategy and the positioning of the data is also required,” he said. Southekal shared best practices for analytics and ways to transform data into an asset for the business.
2020-06-23 07:35:28+00:00 Read the full story…
Weighted Interest Score: 2.7507, Raw Interest Score: 1.6859,
Positive Sentiment: 0.4141, Negative Sentiment 0.1923
Eventus Systems partners with VoxSmart
Global trade surveillance and risk management software platform provider Eventus Systems has partnered with VoxSmart, a specialist in communications surveillance technology.
The firms are collaborating to build custom solutions for global market participants looking to enhance their ability to surveil and manage risk across the entire order and trade lifecycle, from pre-trade communications to execution and post-trade monitoring.
Both VoxSmart and Eventus use artificial intelligence (AI) to help clients reduce time spent on false-positive alerts and flag suspicious activity to mitigate risk in their organisations.
2020-06-26 00:00:00 Read the full story…
Weighted Interest Score: 2.6439, Raw Interest Score: 1.6070,
Positive Sentiment: 0.4017, Negative Sentiment 0.0670
Banking on the cloud in the face of COVID-19 and beyond
As coronavirus (COVID-19) continues to significantly affect the world around us, financial services organisations are playing a key role in supporting societies around the globe.
In light of the pandemic, however, many financial services businesses must adapt to shifting customer and market landscapes. This new environment is putting pressure on existing systems and, as a result, the need for resilient, flexible and secure systems and applications to ensure operational resilience has become vital. Even before the pandemic, discussions between technology providers and financial firms have increasingly become focused on what business issues the cloud can solve.
How is it already aiding savvy banks and their fintech counterparts? This conversation is now more important than ever, as firms adjust to new realities, including more remote workforces and more digital interactions with customers. So, how can the cloud help financial businesses in the face of COVID-19 and beyond? And how is it already aiding savvy banks and their fintech counterparts?
2020-06-22 00:00:51+00:00 Read the full story…
Weighted Interest Score: 2.6316, Raw Interest Score: 1.3085,
Positive Sentiment: 0.1845, Negative Sentiment 0.1845
Expressive power of graph neural networks and the Weisfeiler-Lehman test
How powerful are graph neural networks?
Do you have a feeling that deep learning on graphs is a bunch of heuristics that work sometimes and nobody has a clue why? In this post, I discuss the graph isomorphism problem, the Weisfeiler-Lehman heuristic for graph isomorphism testing, and how it can be used to analyse the expressive power of graph neural networks. This is the first in the series of three posts on the expressivity of graph neural networks. In Part 2, I will discuss how to depart from the Weisfeiler-Lehman hierarchy and in Part 3, I will suggest why it may be a good idea to revisit the whole graph isomorphism framework.
2020-06-29 10:39:26.489000+00:00 Read the full story…
Weighted Interest Score: 2.6310, Raw Interest Score: 1.2477,
Positive Sentiment: 0.1248, Negative Sentiment 0.2382
SoftBank-backed Lemonade wants IPO investors to think of it as a technology company. Here’s why it really isn’t.
chnologies” more times than did Casper or WeWork, two startups that also tried to boost their offerings by pretending to be tech companies.
The company also touts its use of artificial intelligence, machine learning, and other buzzy technologies.
But it has little reason to be considered a tech firm — Nearly all of the company’s revenue comes from selling insurance; technology development is only a small portion of its expenses; and it holds no patents.
Online insurance company Lemonade is pitching itself as a tech company and hoping public investors wil…
2020-06-27 00:00:00 Read the full story…
Weighted Interest Score: 2.4936, Raw Interest Score: 1.4190,
Positive Sentiment: 0.1075, Negative Sentiment 0.0645
Adverse media screening: a key pillar of financial crimes compliance
The scale, complexity and sophistication of financial crimes has been rapidly rising over the last decade. Technology advancements have improved banks’ and financial institutions’ (FI) capabilities of prevention, detection and reporting of such crimes.
But the same advanced technology is being exploited by criminals to benefit them by proliferating more innovative financial crimes, leading to expanding typologies in fraud and money laundering among others.
Can we leverage AI to enhance the effectiveness and efficiency of adverse media screening?
Adverse media screening is a critical component of financial anti-crime measures adopted by banks and FIs, as regulators across the globe, from Financial Crimes Enforcement Network (USA) to European Commission (EU), Financial Conduct Authority (UK) and several others, are enforcing strict requirements on the same. It is mandatory during onboarding know your client (KYC) checks, scheduled reviews and ongoing monitoring as part of customer due diligence (CDD).
It is essential to gather all details about a customer, or prospect to be onboarded as a customer, including any negative information about them so that the bank can take a risk-based approach on the relationship with such customer.
Technology has enabled us to access staggering volumes, variety and velocity of news and information from around the world. Is it then humanly possible to screen millions of customers of any bank by searching the web for adverse news, analyse every negative news item and then consider them for risk profiling of the accused customer? Can we leverage artificial intelligence (AI) instead, to enhance the effectiveness and efficiency of adverse media screening?
2020-06-29 00:00:20+00:00 Read the full story…
Weighted Interest Score: 2.4667, Raw Interest Score: 1.2978,
Positive Sentiment: 0.2329, Negative Sentiment 0.6323
AI startup 7bridges just raised $3 million in a seed round backed by LocalGlobe and Crane Venture Partners
AI logistics startup 7bridges has raised $3.4 million in a seed funding round backed by LocalGlobe and Crane Venture Partners.
The AI logistics market is expected to be worth more than $6 billion globally by 2023, according to analysts at Infoholic Research.
Scott Sage, partner at Crane Venture Partners, said COVID-19 had created “an incredibly important opportunity for disruption”.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.4442, Raw Interest Score: 1.2226,
Positive Sentiment: 0.3373, Negative Sentiment 0.1686
$6.2 billion AI startup Databricks, which is rolling out a new strategy this week, has a stockpile of more than $500 million to ride through the recession to an IPO thanks to CEO’s ‘sky is falling’ pa
Ali Ghodsi, the CEO of artificial intelligence firm Databricks, was so paranoid about an economic downturn that the company raised two rounds of funding in under six months from the likes of Andreessen Horowitz.
Now the company has “well over half a billion dollars on its balance sheet” left to ride out the recession to an IPO, likely next year.
This week the company is unveiling a new concept called “the data lakehouse” that will allow com…
2020-06-23 00:00:00 Read the full story…
Weighted Interest Score: 2.3788, Raw Interest Score: 1.2583,
Positive Sentiment: 0.1601, Negative Sentiment 0.2288
Data Science Certifications: Are They Worth It?
With data science becoming key to many companies’ overall strategies, it’s well worth asking if data science certifications are necessary if you want to land a job as a data scientist.
In simplest terms, data scientists mine company datasets for key insights, which executives and team leaders then use to tailor strategies. Data scientists often utilize statistical and machine learning techniques to create predictive analytics and models; they also interface with data and application engineers to integrate these models into the product. It’s a job that not only requires technical skills, but also “soft skills” that allow successful interactions with people from multiple departments, including business development, sales, product management, project management, UX/UI designs, and software engineering teams.
In other words, it’s a job that demands a lot of skills (and, depending on the company and position, a lot of experience). But do companies want you to have certifications that validate some of those skills? Dice Insights spoke with Dustin Weaver, analytics lead at data analytics consulting firm Atrium, about certifications and what hiring managers want.
What are some certifications for data science professionals?
2020-06-24 00:00:00 Read the full story…
Weighted Interest Score: 2.3734, Raw Interest Score: 1.3392,
Positive Sentiment: 0.1933, Negative Sentiment 0.0690
Fixed Income EMS AxeTrader Integrates MSX Platform for Data Analytics
AxeTrader’s fixed-income EMS for the sell-side, buy-side, and agency brokers has integrated the Mosaic Smart Data’s MSX platform, according to an official statement.
The fixed-income trading market continues to rapidly evolve as a wave of change is driving the adoption of automation, algorithmic driven trading and expansion of trading models. AxeTrader seamlessly generates, collects and standardizes all the relevant data from multiple sources including OTC transactions, voice trade confirmations, and trades across 22 execution venues, 12 leading market data sources, and internal platforms.
AxeTrader provides fixed income traders with market aggregation and trading workflows in a single desktop enabling more efficient and transparent trading. The firm partnered with Mosaic Smart Data for its analytics tool, which will deliver AxeTrader users a range of new capabilities including real-time counterparty insights, TCA, hit ratios, and profitability analytics, fuelled by machine learning. Its data analytics platform, MSX, and advanced suite of machine-learning powered tools, MSX360, provide value to buy-side, sell-side, custodians, market infrastructure providers and trading venues alike.
2020-06-26 19:34:49+00:00 Read the full story…
Weighted Interest Score: 5.0566, Raw Interest Score: 2.2636,
Positive Sentiment: 0.5992, Negative Sentiment 0.0333
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