AI & Machine Learning News. 11, 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?
13 ‘must-read’ papers from AI experts
After the ‘top AI books’ reading list was so well received, we reached out to some of our community to find out which papers they believe everyone should have read!
All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio.
- Learning to Reinforcement Learn (2016) – Jane X Wang et al
- Gradient-based Hyperparameter Optimization through Reversible Learning (2015) – Dougal Maclaurin, David Duvenaud, and Ryan P. Adams.
- Long Short-Term Memory (1997) – Sepp Hochreiter and Jürgen Schmidhuber
- Efficient Incremental Learning for Mobile Object Detection (2019) – Dawei Li et al
- Emergent Tool Use From Multi-Agent Autocurricula (2019) – Bowen Baker et al
- Open-endedness: The last grand challenge you’ve never heard of (2017) – Kenneth Stanley et al
- Attention Is All You Need (2017) – Ashish Vaswani et al
- Modeling yield response to crop management using convolutional neural networks (2020) – Andre Barbosa et al.
- A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis (2019) – Xiaoxuan Liu et al
- The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020) – Gary Marcus
- On the Measure of Intelligence (2019) – François Chollet
- Tackling climate change with Machine Learning (2019) – David Rolnick, Priya L Donti, Yoshua Bengio et al.
- The Netflix Recommender System: Algorithms, Business Value, and Innovation (2015) – Carlos Gomez-Uribe & Neil Hunt.
2020-05-05 Read the full story…
CloudQuant Thoughts : A really well put together post by Luke Kenworthy, Follow through to the article to find the links.
Elon Musk: Neuralink Will Do Human Brain Implant in “Less Than a Year” – “We are already a cyborg to some degree.”
For the second time in two years, entrepreneur and billionaire Elon Musk sat down with podcaster Joe Rogan to chat about the future of AI and its role in the symbiosis of man and machine.
In their conversation, Musk revealed that the secretive brain stimulation link startup Neuralink, which he co-founded, is close to starting testing in actual humans.
“We’re not testing people yet, but I think it won’t be too long,” Musk told Rogan. “We may be able to implant a neural link in less than a year in a person I think.”
2020-05-07 11:30:00+00:00 Read the full story…
CloudQuant Thoughts : What can we say about Elon Musk, no matter what your opinions of him are, he is extremely smart and is a key driver in this environment.
Top 10 Free Resources To Learn Reinforcement Learning
Reinforcement learning is one of the most popular machine learning techniques among organisations to develop solutions like recommendation systems, healthcare, robotics, transportations, among others. This learning technique follows the “trial and error” method and interacts with the environment to learn an optimal policy for gaining maximum rewards by making the right decisions.
In this article, we list down the top 10 free resources to learn reinforcement learning (in no particular order).
- Reinforcement Learning Explained : Source: edX
- Reinforcement Learning : Source: Udacity
- Advanced Deep Learning & Reinforcement Learning : Source: Youtube
- Deep Reinforcement Learning : Source: UC Berkeley Blog
- An Introduction to Reinforcement Learning : Source: Blog
- An Introduction to Reinforcement Learning : Source: freeCodeCamp
- Deep Reinforcement Learning and Control : Source: GitHub Blog
- Reinforcement Learning Specialisation : Source: Coursera
- Reinforcement Learning : Source: Online NPTEL Courses
- Reinforcement Learning Winter 2020 : Source: Stanford Education
2020-05-08 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9825, Raw Interest Score: 2.0913,
Positive Sentiment: 0.1711, Negative Sentiment 0.1521
CloudQuant Thoughts : As a parent with an offspring about to start at college (or not!) free learning is very appealing.
MemSQL raises $50 million to advance its database tech
Database tech developer MemSQL today announced it signed a debt facility that provides up to $50 million of new capital. Co-CEO Raj Verma says it will chiefly be used to deliver new and existing products and services and to “accelerate growth” in the months to come.
AI and machine learning models require fast databases like MemSQL’s in order to perform at their peak. Organizations that lack the right technical components in their production pipelines run the risk of failure — according to IDC, 25% of brands already using machine learning report a 50% failure rate. MemSQL ostensibly prevents this with a platform that serves as the backend for fraud detection, portfolio risk tracking, and even facial recognition apps in industries ranging from financial services, energy, and government and public sector to retail and ecommerce.
MemSQL — which can be deployed on-premises, as-a-service, or a hybrid of both — works like most relational databases, which is to say it accepts requests (e.g., for a user, image, video, document, or internet of things event) in the form of queries for data contained within the database. It processes these queries and returns the results in milliseconds, after which it assigns them a score that indicates their overall quality.
2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 2.3900, Raw Interest Score: 1.6289,
Positive Sentiment: 0.0255, Negative Sentiment 0.3054
CloudQuant Thoughts : Big Data, SQL and AI are a great combination. A well thought out SQL query combined with knowledge of the data can be the difference between a quick data fetch and hours of waiting. So as well as helping us to code, it makes absolute sense for AI to step in and help up create more efficient SQL queries.
ESG data management under spotlight as investments grow
With reports of Environmental, Social and Governance (ESG) investing on the rise amid a drastic drop in oil prices, ESG data is facing scrutiny both from investors and regulators. Market participants are therefore looking to artificial intelligence (AI) to assist in data management.
“As investor demand for more clarity on ESG grows, an increasing number of companies are providing detailed information on their ESG policies, data and actions. Most ESG data, however, is self-reported and often lacks transparency and comparability,” said John Cushing, CEO, mnAI, an AI-powered M&A deal-flow search engine, in an email.
“Many businesses still use ESG factors in a box-ticking way or offer up data only on metrics where they perform well.”
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 3.7679, Raw Interest Score: 1.9058,
Positive Sentiment: 0.1713, Negative Sentiment 0.2998
CloudQuant Thoughts : Head over to our Data Catalog for information on the ESG DataSet from G&S Quotient.
Hands-On Guide To Market Basket Analysis With Python Codes
Machine learning is helping the retail industry in many ways. From forecasting the sales performance to identifying the prospective buyers, there are a lot of applications of machine learning in the retail industry. Market basket analysis is one of the key applications of machine learning in retail. By analysing the past buying behaviour of customers, one can find out which are the products that are bought together by the customers. For example, bread and butter are sold together, baby diapers and baby massage oil are sold together, etc. That means one can analyze the association among products. If the retails management can find this association, while placing the products in the shop, these associated products can be put together. Or, when seeing that a customer is buying a product, the salesman can offer the associated product to the customer.
This process of analyzing the association is called the Association Rule Learning and analyzing the products bought together by the customers is called the Market Basket Analysis. In this article, we will discuss the association rule learning method with a practical implementation of market basket analysis in python. We will use the Apriori algorithm as an association rule method for market basket analysis.
2020-05-11 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6358, Raw Interest Score: 1.1838,
Positive Sentiment: 0.0296, Negative Sentiment 0.0148
Why Google Wants Journalists To Learn Machine Learning
Artificial Intelligence has impacted every industry in the world. If we look at media, companies have deployed different AI and machine learning techniques to automatically produce news stories at scale. Here, AI/ML can be used to grow an audience, aggregate build loyalty, have better data insights, readership engagement.
Let’s look at a few examples. There is Bloomberg’s Cyborg which automatically extracts key data points from earning reports for thousands of companies. There is Yle News Lab at the Finnish Public Broadcasting Company with their smart news assistant Voitto for its personalised news. Wall Street Journal uses an ML-based dynamic paywall for personalised subscription prices based on reading habits. Reuters developed News Tracer and Lynx Insight. Both tools use machine learning and artificial intelligence technologies to support Reuters journalists in the newsgathering process.
While AI is serving great value for news media houses, we know that AI is very complex technology. It encompasses various techniques that can be leveraged to build models and this is where the challenge presents to media professionals. So, should journalists learn the techniques? According to Google, the answer is yes. Google recently introduced a course on machine learning as part of Google News Initiative in collaboration with JournalismAI and VRT News.
2020-05-09 05:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5448, Raw Interest Score: 2.1680,
Positive Sentiment: 0.2918, Negative Sentiment 0.1459
Google Rules AI, with TensorFlow at Foundation, Leadership in Core Products
The way Google came from nowhere with the launch of Android in 2007 to today dominating the smartphone operating system market, is what the company is doing now with AI, some market observers suggest.
Google now has an 80 percent share of the worldwide smartphone OS market, and it has seeded the AI market by making its TensorFlow software library open source, putting it at the foundation of many AI applications, suggests a recent account in Analytics Insight.
Some 50 Google products use TensorFlow to build deep learning applications to help differentiate companions in Photos to refinements in the core search engine. Google has become a machine learning organization.
The authors state, “Google has gone through the most recent three years constructing a gigantic platform for artificial intelligence and now they’re unleashing it on the world.”
2020-05-07 21:30:35+00:00 Read the full story…
Weighted Interest Score: 3.4818, Raw Interest Score: 1.9466,
Positive Sentiment: 0.1593, Negative Sentiment 0.0708
Nasdaq Leverages the Cloud for Data Delivery
When it comes to market data provision, there appears to be no better way to deliver the lifeblood of the markets than the Cloud. The Cloud is fundamentally reshaping the distribution, consumption, management and analysis of market data, which has become a bigger part of serving the markets than trading services. The convergence of big data, cloud capabilities and rise of mobile platforms has created the opportunity to meet investors or firms where they are. This means that all manner of financial firms, from small fintech firms and entrepreneurs to the larger, more traditional players, can be served seamlessly. This has pushed Cloud adoption to advance at a rapid pace.
According to recent data from Greenwich Associates, 93 percent of market data professionals plan to use the Cloud to manage their data needs. Of that 93 percent, more than half said there was a “very high” probability of usage while only one percent reported a “very low” chance.
Usage of the Cloud has obvious benefits. First, data is much more accessible wherever one is located. Secondly, the Cloud does not have the size limitation that a physical server or computer memory bank does – it is theoretically boundless – hence offering virtually unlimited capacity at a fraction of the cost. And as the demand for new and more esoteric information grows, a place to store it that is easily accessible becomes essential.
2020-05-05 17:11:18+00:00 Read the full story…
Weighted Interest Score: 3.4606, Raw Interest Score: 1.4935,
Positive Sentiment: 0.1262, Negative Sentiment 0.0631
An Enterprise Guide to a Secure Data Science Pipeline
Open source is the backbone driving digital innovation (Gartner, 2019). It’s crucial to many of today’s leading-edge digital fields, including data science and machine learning. No single technology vendor can outmatch the pace of innovation the open-source data science community maintains. Thousands of open-source Python, R, and Conda packages provide data science practitioners with the building blocks they need to create models and applications using predictive analytics, natural language processing, robotics, and other cutting-edge tools.
These open-source tools are powerful, and they are essential for differentiation in a future where organizations must adopt AI to remain viable. But, there’s one thing many enterprise data science teams are missing: security protocols. In many organizations, there simply are no security protocols or governance tools for open-source software (OSS) use in data science. A lack of security protocols exposes the organization to overlooked defects and vulnerabilities, not to mention potential licensing and intellectual property issues.
2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3945, Raw Interest Score: 2.1532,
Positive Sentiment: 0.4507, Negative Sentiment 0.1753
Leading businesses reveal the power of combining human ingenuity with AI
Businesses of all sizes are experiencing exceptional disruption and change as they grapple with strategies to stabilize and return to growth. In this new environment, human ingenuity, innovation and adaptability will be critically important. As a result of COVID-19, businesses’ digital transformation is accelerating more rapidly than ever before. As Satya Nadella, Microsoft CEO, recently observed during our earnings announcement, “We’ve seen two years’ worth of digital transformation in two months.”
However, as people move to distributed working and companies move essential workloads to the cloud, what isn’t instantly apparent is the growing role artificial intelligence (AI) is playing at the heart of digital transformation. For some organizations, its use had already accelerated. Others are looking at bringing forward the benefits AI can deliver. AI is helping us discover, learn, ideate and make decisions. It’s making business operations more efficient, enhancing product and service development, and enabling new customer experiences. In industries like health care, it’s helping improve patient outcomes and save lives. Before the pandemic, most of our customers were addressing a similar challenge: How do they ensure their people have the right skills and mindset to thrive in a world where AI is driving real business impact?
2020-05-07 13:44:18+00:00 Read the full story…
Weighted Interest Score: 3.3781, Raw Interest Score: 1.1698,
Positive Sentiment: 0.6066, Negative Sentiment 0.1300
5 Concepts You Should Know About Gradient Descent and Cost Function
Why is Gradient Descent so important in Machine Learning? Learn more about this iterative optimization algorithm and how it is used to minimize a loss function.
Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function. The loss function describes how well the model will perform given the current set of parameters (weights and biases), and gradient descent is used to find the best set of parameters. We use gradient descent to update the parameters of our model. For example, parameters refer to coefficients in Linear Regression and weights in neural networks. In this article, I’ll explain 5 major concepts of gradient descent and cost function, including:
- Reason for minimising the Cost Function
- The calculation method of Gradient Descent
- The function of the learning rate
- Batch Gradient Descent (BGD)
- Stochastic gradient descent (SGD)
2020-05-05 00:00:00 Read the full story…
Weighted Interest Score: 3.3694, Raw Interest Score: 1.5022,
Positive Sentiment: 0.1018, Negative Sentiment 0.2292
How this Israel-based startup develops AI software to fix device malfunctions
Artificial Intelligence (AI) and Internet of Things (IoT) are two rapidly emerging and evolving technologies that are finding their way into myriad new information-based applications. In simple words, both AI and IoT play a vital role where connected and smart homes are concerned. Armies of smart devices participate in IoT to facilitate digital capabilities contributing vast volumes of data to AI frameworks, which then provides intelligence for greater functionality.
The rate of expansion of connected homes, where people are now using 20+ smart devices, has been witnessing exponential growth. Organisations, customer experience groups, marketing and other departments, however, cannot possibly grasp it without utilising heavy AI.
To mitigate such challenges, Israel-based startup, Veego, has been utilising AI, ML and IoT to diminish malfunctions in connected homes by autonomously discovering and resolving problems before subscribers even experience them.
2020-05-09 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.3414, Raw Interest Score: 1.5838,
Positive Sentiment: 0.1540, Negative Sentiment 0.2640
Creative People Using AI to Explore New Territory
Human thought has always been central to creativity. This has been true through development of printing presses, gramophones, cameras, camcorders, typewriters, word processors, photo editing software and many other tools invented over centuries. Maybe AI changes the game, suggests a recent account in TechTalks based on a reading of “The Artist in the Machine: The World of AI-Powered Creativity,” by Arthur I. Miller. Not that the book asserts AI will replace human creativity, but that AI is bringing change to the creative arts.
Key advances include: AI-assisted art, including an application called style transfer. Well-trained neural networks map the style of one image onto another. First proposed in 2015 by Leon Gatys in a paper titled, “A Neural Algorithm of Artistic Style.” It allows for example a photograph to take on the style of a van Gogh painting. Gatys is affiliated with the University of Tuebingen, Germany. Style transfer has caught on, finding commercial applications in social media platforms. “I want to have a machine that perceives the world in a similar way as we do, then to use that machine to create something that is exciting to us,” Gatys is quoted as Miller’s book.
2020-05-07 21:30:45+00:00 Read the full story…
Weighted Interest Score: 3.3040, Raw Interest Score: 1.3595,
Positive Sentiment: 0.5562, Negative Sentiment 0.0309
Business Analytics vs. Data Science – Which Path Should you Choose?
“Business Analytics” and “Data Science” – these two terms are used interchangeably wherever I look. But there’s one indisputable fact – both industries are undergoing skyrocket growth. Today, the current market size for business analytics is $67 Billion and for data science, $38 billion. The market size in 2025 is expected to reach $100 Billion and $140 billion respectively. This means we can expect a surge in demand for these two profiles very soon.
I have come across a lot of aspiring analytics professionals who want to choose “Business Analytics” or “Data Science” as their career, but they’re not even sure about the distinction between these two roles. Before diving into your own choice, you should be clear about which path you want to take, right? It could be a career-defining choice! Here’s what I suggest. You can enroll in the free Introduction to Business Analytics course, where Kunal Jain, CEO, and founder of Analytics Vidhya, explains the difference between these two roles and also introduces a methodology to decide which path to choose (Business Analytics or Data Science) based on multiple factors like education, skills, and others.
2020-05-10 07:08:44+00:00 Read the full story…
Weighted Interest Score: 3.2733, Raw Interest Score: 1.7865,
Positive Sentiment: 0.2127, Negative Sentiment 0.3119
Google, Splunk Partner on Multi-Cloud Data
As cloud vendors seek to reduce data movement as a way of preserving network bandwidth and computing resources, they are also promoting greater cloud access to “holistic” data sets spawned by the increasing number of hybrid deployments.
That’s part of the rationale behind a cloud partnership announced this week by Google (NASDAQ: GOOGL) and data analytics platform specialist Splunk Inc. (NASDAQ: SPLK). Along with integrating Splunk’s cloud with Google Cloud, the partners also said Tuesday (May 5) they will introduce new cloud-native integrations via Anthos, Google’s on-premise runtime based on Kubernetes.
Among the goals of the cloud partnership goal is providing “real-time visibility across the enterprise,” said Google, which has been striving under CEO Thomas Kurian to differentiate its hybrid cloud offerings from cloud leaders Amazon Web Services and Microsoft Azure.
2020-05-05 00:00:00 Read the full story…
Weighted Interest Score: 3.1994, Raw Interest Score: 1.9936,
Positive Sentiment: 0.1424, Negative Sentiment 0.0712
How To Build Your Data Science Competency For A Post-Covid Future
The world collectively has been bracing for a change in the job landscape. Driven largely by the emergence of new technologies like data science and artificial intelligence (AI), these changes have already made some jobs redundant. To add to this uncertainty, the catastrophic economic impact of the Covid-19 pandemic has brought in an urgency to upskill oneself to adapt to changing scenarios.
While the prognosis does not look good, this could also create the demand for jobs in the field of business analytics. This indicates that heavily investing in data science and AI skills today could mean the difference between you being employed or not tomorrow.
By adding more skills to your arsenal today, you can build your core competencies in areas that will be relevant once these turbulent times pass over. This includes sharpening your understanding of business numbers and analysing consumer demands – two domains which businesses will heavily invest in very soon.
2020-05-08 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1822, Raw Interest Score: 1.7197,
Positive Sentiment: 0.2707, Negative Sentiment 0.0637
Detecting Consumer Signals in the 90% Economy
As COVID-19 lockdowns are lifted across the United States, consumers will venture out of their homes and begin to spend money again. But the new buying patterns in the 90% economy are likely to look dramatically different. Will machine learning-based forecasting methods still work?
Before COVID-19, companies in the retail and consumer goods sectors were adopting machine learning at a healthy rate. That’s because AI gives them powerful tools to use data to detect what customers want and predict what they’ll buy, increasingly at the individual level. And with a better demand signal, consumer-facing businesses can better match supply to it, which helps reduce costs.
Then COVID-19 arrived, and it changed everything. We’ve seen the results play out in real time, as non-essential stores are shuttered, certain items fly off grocery-store shelves, and consumers flock to e-commerce sites like Amazon.com, which hired 80,000 additional workers to handle the surge.
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 2.9892, Raw Interest Score: 1.3000,
Positive Sentiment: 0.1169, Negative Sentiment 0.2045
Expanding Data Governance into the Future
Shortened time frames to leverage business insights and navigate data privacy and ethics call for the next generation of Data Governance (DG). This DG describes a collaborative, thoughtful, long-term framework consisting of processes managing trusted data assets across the organization. Kelle O’Neal, Founder, and CEO of First San Francisco Partners, sees a need to make firms aware of Next-Gen Data Governance, while at the same time helping companies adapt to successful Data Governance practices with other business areas.
Recognition that good Data Governance has become a must has come none too soon. Donna Burbank, Managing Director at Global Data Strategy, notes that many companies are beginning or planning to begin a Data Governance program, including a broader range of industries than before.
2020-05-05 07:35:27+00:00 Read the full story…
Weighted Interest Score: 2.9881, Raw Interest Score: 1.8451,
Positive Sentiment: 0.2590, Negative Sentiment 0.1403
Learn Python to automate regular market tasks – Cuemacro
Over the past few weeks our routines have changed somewhat. I’ve made fresh pasta, which resembled something quite unlike any other pasta I’ve tasted. Entirely coincidentally, I’ve discovered why I should not make fresh pasta, and there’s a reason why folks buy ready made pasta. Aside from delving into the world of pseudo-pasta creation, like many of you reading, I’ve been working at home. I’ve also spent time thinking, in part imagining how things will be, once we’re back to “normal”.
Of course, “normal” doesn’t necessarily mean that things won’t be different. Just thinking about my area of work analysing financial markets, there’s many things that can be improved. In particular, we all have those tasks, which are repetitive in markets. These are often necessary but can soak up a lot of time. I dread to think how much time I’ve spent in the past updating Excel spreadsheets. We can learn Python to help automate a lot of these processes. As a bit of a plug here, if you are interested in learning Python, I’ve developed a Python for finance workshop, which I can teach at your firm (via video conference given the current situation) and I also offer consulting services to help you in automating your processes with Python. Whilst Python can be used to do a lot of cool analysis (eg. natural language processing), in practice, the “low hanging” fruit is automating all those manual spreadsheet tasks. Below I’ll go through a few tasks which can be automated using Python.
2020-05-09 00:00:00 Read the full story…
Weighted Interest Score: 2.9390, Raw Interest Score: 1.3862,
Positive Sentiment: 0.1032, Negative Sentiment 0.0295
Unlocking the Power of DataOps (Webinar)
A new methodology is on the rise at insights-hungry enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation, to reporting. However, improving the flow of data between managers and consumers within an organization through greater communication, integration and automation is no simple task, and it requires cultural changes as well as enabling technologies.
2020-05-07 00:00:00 Read the full story…
Weighted Interest Score: 2.8200, Raw Interest Score: 1.6269,
Positive Sentiment: 0.7592, Negative Sentiment 0.0000
Report Finds Technology Will Enhance Finance Jobs
Technology has enhanced most American careers in finance, according to a new paper.
According to a new report entitled “The Future of Trading: the People” produced by Refinitiv in conjunction with Greenwich Associates, only “4% of Gen Xers and 7% of millennials told us that technology innovation has limited their career opportunities.” Meanwhile the report found that 80%, “of capital markets professionals believe technology has provided them new career opportunities.”
The report continued, “The vast majority of financial professionals feel that technology innovation has, in fact, enhanced their career thus far. Roughly 4 out of 5 finance professionals feel that technology innovation has presented them with new opportunities, and about half say that it has accelerated their career growth. While the positive sentiment is slightly stronger among the digital-native millennial crowd, Gen Xers and baby boomers are similarly excited about the impact of the market’s digitization on their job progression.”
2020-05-11 01:39:56+00:00 Read the full story…
Weighted Interest Score: 2.8005, Raw Interest Score: 1.7304,
Positive Sentiment: 0.5464, Negative Sentiment 0.1138
The Art of Storytelling in Analytics and Data Science
The idea of storytelling is fascinating; to take an idea or an incident, and turn it into a story. It brings the idea to life and makes it more interesting. This happens in our day to day life. Whether we narrate a funny incident or our findings, stories have always been the “go-to” to draw interest from listeners and readers alike.
For instance; when we talk of how one of our friends got scolded by a teacher, we tend to narrate the incident from the beginning so that a flow is maintained.
Let’s take an example of the most common driving distractions by gender. There are two ways to tell this.
2020-05-08 03:15:39+00:00 Read the full story…
Weighted Interest Score: 2.7710, Raw Interest Score: 1.1152,
Positive Sentiment: 0.2665, Negative Sentiment 0.1473
Social Listening Tools Powered by AI Going for Deep Personalization
Social listening tools powered with AI are becoming a powerful way to measure customer sentiment and conduct audience research. These tools are good at mining unstructured text, such as in social media posts, and taking measurements. Brands use them to track, analyze and respond to conversations about them on social media.
“The combination of data analytics, A.I. and social media affords us the ability to deeply and rapidly analyze customer opinions. Trends and patterns appear and enable comprehensive market research into key consumer insights,” states Sarah Lim in an account on the blog of Remesh, which offers a platform to support products, campaigns and brands through research.
Social listening helps to discover customer insights and see what the customers value. The insights help to build the relationship with the customer, offer relevant product recommendations and increase sales. One objective is to know what the customers want before they do. Another goal is to reach a deeper level of customer engagement.
2020-05-07 21:30:13+00:00 Read the full story…
Weighted Interest Score: 2.7096, Raw Interest Score: 1.5460,
Positive Sentiment: 0.1699, Negative Sentiment 0.0340
The challenges businesses face with data analytics
Before businesses implement data analytics into their businesses they first need to understand the challenges ahead of them.
In a recently released NEC report, Taming Your Data Assets and Delivering Real Business Outcomes, it highlights a number of roadblocks companies need to identify and understand.
- The sheer volume and variety of data sources which need to be corralled;
- No coherent, scalable data infrastructure to provide a comprehensive view of the data;
- Integrating disparate sources of data;
- An inability to analyse the internal and external data for strategic decision-making;
- Poor data governance and a lack of defined policies for quality management; and
- A lack of qualified professionals with the necessary skills sets to harness big data and analytics tools effectively.
2020-05-11 00:28:07+10:00 Read the full story…
Weighted Interest Score: 2.6562, Raw Interest Score: 1.5470,
Positive Sentiment: 0.2043, Negative Sentiment 0.3211
Expanding Your Data Science and Machine Learning Capabilities (Webinar)
Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. In responses, new solutions have emerged to deliver key capabilities in areas including visualization, self-service and real-time analytics. Along with the rise of DataOps, greater collaboration and automation have been identified as key success factors.
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
Sweden’s Finansinspektionen selects Abacus Regulator for data collection
BearingPoint RegTech, a leading international provider of innovative supervisory, regulatory and risk technology solutions (SupTech, RegTech and RiskTech), has won Sweden’s Finansinspektionen (FI) as a new customer for Abacus Regulator.
The financial supervisory authority was looking for a service provider that could best help them to improve and streamline their data collection processes, validation, monitoring and analysis. Finansinspektionen will be using the Abacus Regulator software to fulfill both a comprehensive range of data collection and analysis for EBA, EIOPA, and ESMA reports as well as different national reports. In addition, BearingPoint RegTech will cover implementation, service, and support for the platform, including updates, upgrades, consulting services, as well as options and changes that come with any variation of business needs and technical requirements.
2020-05-11 10:25:00 Read the full story…
Weighted Interest Score: 2.5486, Raw Interest Score: 1.3078,
Positive Sentiment: 0.4695, Negative Sentiment 0.0000
Modern Data Warehousing: Enterprise Must-Haves
To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.
2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000
Exploring AI in wealth management
Much has been discussed and published on the use of AI, RPA and ML for the benefit of the wealth management industry. The robo advisory evolution is a pragmatic and real trend with firms deploying or delivering such services.
2020-05-05 00:00:00 Read the full story…
Weighted Interest Score: 2.5155, Raw Interest Score: 1.7072,
Positive Sentiment: 0.6238, Negative Sentiment 0.0657
Cloudera Expands Machine Learning Abilities for MLOps
Cloudera, the enterprise data cloud company, is releasing an expanded set of production machine learning capabilities for MLOps, now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML’s new MLOps features and Cloudera SDX for models.
Data scientists, machine learning engineers, and operators can collaborate in a single unified solution, drastically reducing time to value and minimizing business risk for production machine learning models. The release of Cloudera Machine Learning with new MLOps features and Cloudera SDX for models provides a fundamental set of model and lifecycle management capabilities to enable the repeatable, transparent, and governed approaches necessary for scaling model deployments and ML use cases.
2020-05-06 00:00:00 Read the full story…
Weighted Interest Score: 2.3422, Raw Interest Score: 2.1277,
Positive Sentiment: 0.1606, Negative Sentiment 0.0401
Too much data, too little time
Too much data, too little time. You don’t need to process those 2 million data points with 1,000 features to get good results
We all know why it’s nice to have more data. Your results can be more reliable, you can (hopefully) conclusively prove or disprove a given hypothesis. However, there is such a thing as having too much data, or at least having so much data that it’s hard to efficiently run certain models.
2020-05-11 14:31:00.993000+00:00 Read the full story…
Weighted Interest Score: 2.3181, Raw Interest Score: 1.2363,
Positive Sentiment: 0.2259, Negative Sentiment 0.1545
Material World Data, Accounting & Sustainability Reporting
Dr Matthew Smith Chief Product Officer at Agrimetrics following 12 years as a scientist and architect at Microsoft, Mark Line a Director of Challenge Sustainability, which provides consultancy services to international companies on sustainability strategy, reporting and communications and Kristian Ronn, Co-Founder & CEO of Normative who help companies assess their social and environmental impact by analysing data inside their ERP systems using artificial intelligence sat down with Richard Peers of ResponsibleRisk to debate what is going on at the cutting edge of Sustainability data analysis.
2020-05-11 11:13:00 Read the full story…
Weighted Interest Score: 2.3140, Raw Interest Score: 1.6556,
Positive Sentiment: 0.0000, Negative Sentiment 0.1656
Decoded Data Lineage Helps Tackle Bad Data Quality
What are your outcome expectations of data lineage? No one’s just doing it for fun, after all. Generally speaking, data lineage is a major asset for: Regulatory reporting/governance; trust in decision-making; and, on-premise to cloud migrations.
Data lineage tools track business data flow from originating source through all the steps in its lifecycle to destination. Data lineage tools can also track technical data transformation logic. A visual representation provides an intuitive way to view the overall flow.
2020-05-06 07:35:37+00:00 Read the full story…
Weighted Interest Score: 2.3028, Raw Interest Score: 1.4506,
Positive Sentiment: 0.1269, Negative Sentiment 0.1088
Dealing with DateTime Features in Python and Pandas – The Complex yet Powerful World of DateTime in Data Science
I still remember coming across my first DateTime variable when I was learning Python. It was an e-commerce project where I had to figure out the supply chain pipeline – the time it takes for an order to be shipped, the number of days it takes for an order to be delivered, etc. It was quite a fascinating problem from a data science perspective. The issue – I wasn’t familiar with how to extract and play around with the date and time components in Python.
There is an added complexity to the DateTime features, an extra layer that isn’t present in numerical variables. Being able to master these DateTime features will help you go a long way towards becoming a better (and more efficient) data scientist. It’s definitely helped me a lot!
2020-05-05 19:54:41+00:00 Read the full story…
Weighted Interest Score: 2.2974, Raw Interest Score: 1.3545,
Positive Sentiment: 0.1123, Negative Sentiment 0.0973
The Next Great Frontier: Automating Data and Application Deployments
DevOps, DataOps, AI, and containers all lead to one important innovation for enterprises seeking to be more data-driven—and that is greater automation. Data-driven enterprises cannot function if data resources and applications are in any way being manually administered, deployed, remediated, or upgraded.
The ability to move fast, make decisions in real time, and respond quickly to events requires automated processes for ingesting and managing data. Organizations that fail to effectively leverage and deploy their data assets will find themselves falling behind. Data managers are turning to automation and autonomous databases and platforms, a recent survey of 217 data managers by Unisphere Research, a division of Information Today, Inc., found. According to the research, three in four DBAs feel that applications can be deployed faster with increased database management automation, and seven in 10 expect increased database automation to boost the impact of their roles (“2019 IOUG Autonomous Database Adoption Survey”).
2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 2.2919, Raw Interest Score: 1.4079,
Positive Sentiment: 0.1207, Negative Sentiment 0.2816
Training Machine Learning Models on Amazon SageMaker – Ephemeral clusters, experiments, visualization and more
It’s midnight. You’ve spent hours fine-tuning your script, and you’re racing to get it onto the server before your deadline tomorrow. You’re building Naive Bayes, Logistic Regression, XGBoost, KNN, and any model under the sun in your massive for-loop.You’…
2020-05-11 14:46:55.485000+00:00 Read the full story…
Weighted Interest Score: 2.2890, Raw Interest Score: 1.6231,
Positive Sentiment: 0.0451, Negative Sentiment 0.0000
Oracle Cloud Powers End to End Data Golden Record Provider
A recent press release states, “As a leading provider of data cleansing solutions, Naveego enables organizations to proactively manage, detect and address data accuracy issues across all enterprise data sources in real-time–regardless of structure or schema. To best support their cloud-based, distributed architecture, Naveego chose Oracle Cloud Infrastructure to advance its goal of becoming the leader in cleansing datasets used to train AI and machine learning applications. Naveego selected Oracle because they were looking for a trusted, collaborative partnership that would grow alongside their business. The cloud-native enterprise-scale business needed a reliable partner to help them compute and store agnostic data in multiple formats from multiple distributed sources. From the time Naveego selected Oracle Cloud Infrastructure to full migration was only 30 days, and the company has already realized a 60 percent cost saving compared to their previous AWS solution, driven mainly by lower compute and network charges.”
2020-05-11 07:05:54+00:00 Read the full story…
Weighted Interest Score: 2.2764, Raw Interest Score: 1.3022,
Positive Sentiment: 0.4341, Negative Sentiment 0.0000
IonQ CEO Peter Chapman on how quantum computing will change the future of AI
Businesses eager to embrace cutting-edge technology are exploring quantum computing, which depends on qubits to perform computations that would be much more difficult, or simply not feasible, on classical computers. The ultimate goals are quantum advantage, the inflection point when quantum computers begin to solve useful problems. While that is a long way off (if it can even be achieved), the potential is massive. Applications include everything from cryptography and optimization to machine learning and materials science.
As quantum computing startup IonQ has described it, quantum computing is a marathon, not a sprint. We had the pleasure of interviewing IonQ CEO Peter Chapman last month to discuss a variety of topics. Among other questions, we asked Chapman about quantum computing’s future impact on AI and ML.
Strong AI – The conversation quickly turned to Strong AI, or Artificial General Intelligence (AGI), which does not yet exist. Strong AI is the idea that a machine could one day understand or learn any intellectual task that a human can.
2020-05-09 00:00:00 Read the full story…
Weighted Interest Score: 2.2637, Raw Interest Score: 1.1718,
Positive Sentiment: 0.3780, Negative Sentiment 0.1890
Artificial intelligence in classrooms: How is taking over?
rtificial intelligence is known widely by its initials ‘AI’, is human intelligence programmed into machines giving them the capacity to think and act logically. Computers can store, process and retrieve huge amounts of data in a very short time. Coupled with intelligence, machines do an effective job of finding patterns in variables and predicting and modeling functions accurately. Hence AI has found great application in problem-solving and learning. The technology is taking over industries such as transport through the advancement of self-driving cars, security through speech and facial recognition and now education through tutoring. AI is taking over the classroom at an alarming rate, let us explore the many different ways it is doing so.
The use of AI tutors – AI tutoring systems already exist and they are improving so fast. They have two great advantages over human teachers; they do not get tired and they can be accessed from anywhere. As long as the student is set, all they need to do is turn on their computers and start learning. Artificial intelligence tutors function by providing accurate answers to questions and help students learn to speak languages through chatbot such as Duolingo.
2020-05-04 06:28:00+00:00 Read the full story…
Weighted Interest Score: 2.2293, Raw Interest Score: 1.0851,
Positive Sentiment: 0.4432, Negative Sentiment 0.2598
Key Takeaways from ICLR 2020
I had the pleasure of volunteering for ICLR 2020 last week. ICLR, short for International Conference on Learning Representations, is one of the most notable conferences in the research community for Machine Learning and Deep Learning.
ICLR 2020 was originally planned to be in Addis Ababa, Ethiopia. But due to the recent COVID-19 induced lockdowns around the world, the conference was shifted to a fully virtual format. While this took away the networking aspect of the event, the fully online conference allowed budding researchers and machine learning enthusiasts to join in as well.
In this article, I will share my key takeaways from ICLR 2020. I will also share a data-based survey (that I personally undertook) to find the preferred tools among the research community, with an emphasis on tools for performing cutting-edge Deep Learning research, aka PyTorch and TensorFlow.
2020-05-04 10:12:32+00:00 Read the full story…
Weighted Interest Score: 2.1812, Raw Interest Score: 1.2711,
Positive Sentiment: 0.2195, Negative Sentiment 0.0613
Charting Your Course to Cloud Analytics Success (Webinar – Registration Reqd)
The cloud is increasingly becoming the go-to destination for data analytics at enterprises today. Looking to capitalize on the promise of reduced costs and greater scalability and flexibility, more and more organizations are adopting hybrid and multicloud strategies to break down data silos, increase collaboration and equip decision-makers with faster access to actionable business insights. However, success means identifying the right approach to meeting the needs of all…
2020-05-21 00:00:00 Read the full story…
Weighted Interest Score: 2.1352, Raw Interest Score: 1.0676,
Positive Sentiment: 0.5338, Negative Sentiment 0.0890
Data Preparation: Don’t Try to Be Data-Driven Without It (PDF Register to dowload)
Data visualization, dashboards and predictive data science are only as good as the data you start with.
2020-05-08 00:00:00 Read the full story…
Weighted Interest Score: 10.6796, Raw Interest Score: 4.8544,
Positive Sentiment: 0.9709, Negative Sentiment 0.0000
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