AI & Machine Learning News. 10, February 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?
YouTuber uses neural networks to upscale 1896 short film to 4K 60 fps
Upscaled and resounded version of a classic B&W movie: Arrival of a Train at La Ciotat, The Lumière Brothers, 1896
Source used to upscale: https://youtu.be/MT-70ni4Ddo
To upscale to 4k – Gigapixel AI – Topaz Labs https://vc.ru/76580
To add FPS – Dain, https://sites.google.com/view/wenbobao/dain
There are many times when we see ‘future technology’ that just feels so out of place. We call it a marketing gimmick and move on. However, a YouTuber has shown us that not everything is unreal. There are some technological advancements that require using the adage “so good to be true.”
A YouTuber, Denis Shiryaev, has used the technological advancements available and turned a black and white movie from the year 1896 into a movie with 4k crystal clarity, running at 60 frames per second.
We have all been so numbed to the technological buzzword of Artificial Intelligence, claimed to be used by every tech firm out there. Artificial intelligence has had a long history with humans and software development.
The idea kicked off with the objective of understanding and imitating how a human brain learns. Coupled with this are the concepts of machine learning, deep learning, neural networking, etc. While they all probably sound like a gimmick, just words without meaning, it is not so. There is actual scientific technology behind this.
2020-02-06 00:00:00+00:00 Read the full story…
CloudQuant Thoughts : The future looks rosy, All our old media, restored and enhanced by AI. Video Games up-rendered by Nvidia’s DLSS (Deep Learning Super Scaler), OK that one did not work out well, but in the future, who knows!?
Alternative Data, It’s Not Just For Hedge Funds Any More • Integrity Research
lternative data has become ubiquitous on Wall Street, especially among quantitative hedge funds. However, in the past few years many new client segments have started to include the use of alternative data as a key part of their business decision making processes.
The Changing Face of the Alternative Data Industry. In the past, most investment professionals built models and did their research, using conventional, widely available data sets, like financial statement data and historical stock prices. The advent of big data tools and enhanced computing power, have allowed larger and less structured, unconventional alternative data sets to be studied, in search of leading edge insights and advantages in the investment process. Enterprising alternative data providers are searching for new markets to sell their data and are always on the lookout for new data sets that may be capable of improving alpha. As with any product, opening new markets to alternative data increases the potential revenue that can be generated.
2020-02-10 02:30:04+00:00 Read the full story…
Weighted Interest Score: 7.3444, Raw Interest Score: 2.3708,
Positive Sentiment: 0.3003, Negative Sentiment 0.0474
CloudQuant Thoughts : Check out our Wednesday blogpost which is more focussed on AltData. Knowing that AltData is trending is one thing, knowing how to use it and how to find great quality AltData without all the leg work can be extremely difficult. Head over to our Data Catalog to see what we are doing to make it easier for you!
The “Rise of Alternative Data:” So, What the Heck Is It?
Big data adoption is fairly widespread, with 53% of companies using it in 2017. But it’s just the tip of the iceberg. Now, an increasing number of businesses are recognizing the value of alternative data that may not have been on their radar a few years ago. In fact, total buy-side spend jumped from $232 million in 2016 to $1.08 billion in 2019, and it’s predicted to reach over $1.7 billion by 2020. Companies that know how to properly harness alternative data can greatly improve their decision-making, and gain a significant competitive advantage.
What is alternative data? “Alternative data draws from non-traditional data sources so that when you apply analytics to the data, they yield additional insights that complement the information you receive from traditional sources,” explains Krishna Nathan, CIO of S&P Global.
2020-02-07 11:00:00+00:00 Read the full story…
Weighted Interest Score: 5.7850, Raw Interest Score: 1.9083,
Positive Sentiment: 0.2726, Negative Sentiment 0.0341
CloudQuant Thoughts : First two stories relate to Alternative Data! Alternative Data and your own Exhaust Data are going to be huge drivers for change in the markets ahead. If you have Exhaust Data that you think may be useful for Investors and Traders please get in touch. CloudQuant can help you make those connections, provide proof of the value of the data and help you to present the data in a format that the market expects.
How To Fool AI With Adversarial Attacks
Research in adversarial attacks has been the latest trend in technology, where developers, experts, and scientists are trying to trick AI bots by making subtle changes. Undoubtedly, ML models perform miserably if they are evaluated in a completely different environment as we are yet to develop an AI that can generalise and deliver superior results in new situations. But what has drawn interest from experts is that the outputs of these AI-based solutions can be swayed even with the smallest of changes. Such flaws depict that we are still a long way away from achieving an AI that we all dream of. In this article, we will show you how some researchers have deceived AI bots.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a tool called TextFooler to trick AI bots. The tool forces Alexa and Siri to predict wrong with adversarial attacks, where inputs were deliberately created to fool the ML algorithms.
TextFooler attacks natural language processing (NLP) systems such as Alexa and Siri. The framework takes the input as text and then determines the word that will be vital for NLP-based systems to make predictions. Post that, the TextFooler replaces the word with a contextual synonym while ensuring that the grammar and original meaning has not been altered. For one, instead of using the input ‘The characters, cast in impossibly contrived situations, are estranged from reality,’ TextFooler replaced it with “The characters, cast in impossibly engineered circumstances, are fully estranged from reality’ to get different outputs. TextFooler was even used with some of the most popular open-source NLP model, BERT. Researchers were successfully able to bring down the 90 plus accuracy of BERT to under 20% by only changing 10% of the input words.
2020-02-10 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.8062, Raw Interest Score: 1.6984,
Positive Sentiment: 0.2071, Negative Sentiment 0.4557
MIT CSAIL’s TextFooler generates adversarial text to strengthen natural language models
AI and machine learning algorithms are vulnerable to adversarial samples that have alterations from the originals. That’s especially problematic as natural language models become capable of generating humanlike text, because of their attractiveness to malicious actors who would use them to produce misleading media. In pursuit of a technique that illustrates the extent to which adversarial text can affect model predictio…
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5924, Raw Interest Score: 1.5155,
Positive Sentiment: 0.2312, Negative Sentiment 0.4367
CloudQuant Thoughts : As mentioned last week, Adversarial Attacks run the risk of affecting your AI model. Defending against such attacks must be part of your planning process.
How To Watermark Your Dataset With Radioactive Data Technique
Large scale machine learning projects require vast amounts of data, i.e. large datasets. Training models on these datasets is tedious and therefore poses a danger of running into redundancy. It makes no sense to train a model on some data if it has already been trained? A responsible developer would always like to know this information in order to track biases in models.
But how would one know if the data has already been used for training? To answer these questions, Facebook’s AI team, in collaboration with INRIA, proposed a new technique called Radioactive Data, in their paper titled the same. “Our objective in this paper is to enable the traceability for datasets.” Their technique, believed the authors, is robust to data augmentation and offers a higher signal to noise ratio than data poisoning methods.
2020-02-10 07:00:00+00:00 Read the full story…
Weighted Interest Score: 4.6498, Raw Interest Score: 1.8491,
Positive Sentiment: 0.1491, Negative Sentiment 0.3281
CloudQuant Thoughts : Curve fitting and use of Out of Sample data for training are difficult things to track. As demand for data scientists ramps up even more, more rookie mistakes will be made. Anything that can help prevent this will be a huge benefit. But then again.. it’s FaceBook.
Fintech workforce to expand 19% by 2030 thanks to AI, Cambridge University predicts
n a recent report, the Cambridge Centre for Alternative Finance (CCAF) and the World Economic Forum (WEF) found that rather than observing AI as a single instrument for blanket application across the industry, AI can be viewed as a toolkit that is being used to tinker and build services in an abundance of ways to achieve a variety of objectives. Using data collected in a global survey during 2019, the report analysed a sample of 151 fintechs and incumbents across 33 countries to paint a rich picture of how artificial technology is being developed and deployed within the financial services sector.
While 77% of respondents noted that they expect AI to become an essential business driver across the financial services industry in the near term, the report found that the way incumbents and fintechs are leveraging AI technologies differ in a number of ways. A higher share of fintechs tend to be creating AI-based products and services, employing autonomous decision-making systems, and relying on cloud-based systems. Whereas incumbents appear to focus on “harnessing AI to improve existing products. This might explain why AI appears to have a higher positive impact on fintechs’ profitability.” 30% of the fintechs surveyed indicated a substantial increase in profit as a result of AI, while only 7% of incumbents indicated such profitability.
2020-02-07 12:00:00 Read the full story…
Weighted Interest Score: 5.3806, Raw Interest Score: 2.1761,
Positive Sentiment: 0.4002, Negative Sentiment 0.1001
What’s Ahead in Data for 2020—And the Coming Decade
We stand at the start of a new year and on the precipice of a new decade—the 2020s. For data managers, these will likely be the “Roaring ’20s” with data at the heart of every key business initiative, accented by a growing sophistication in technologies and methodologies focused on increasing the intelligence of the enterprise.
In the year ahead, organizations will intensify their efforts to manage and process the data that underpins many of today’s cutting-edge initiatives such as AI and machine learning. “The competitive edge goes to the organizations that understand and treat data analytics as one discipline,” said Gavin Day, SVP of technology at SAS. “Organizations are forgetting the critical nature that timely and fit-for-purpose data plays within training, model development, and model deployment. The use of AI within data management technologies will change the role and job function of our data workers—including data scientists. The days of data workers spending time configuring data quality and data integration jobs is behind us. Data analytics platforms use AI to do the routine, heavy lifting so we can focus on what we’re good at—creativity and solving analytical challenges that move our business forward.”
In the process, AI and analytics teams will merge into one as the new foundation of the data organization. “As the importance of data grows, a multitude of ways to get insights has emerged,” said Haoyuan Li, founder and CTO of Alluxio. “Yesterday’s Hadoop platform teams are today’s AI teams. It’s no longer just about managing your data lake.” AI takes a new approach to deriving value from structured and unstructured data, said Li. “What used to be statistical models has converged with computer science, becoming AI and ML.”
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 5.1348, Raw Interest Score: 2.3518,
Positive Sentiment: 0.1611, Negative Sentiment 0.0644
Man and machine need each other – Systematica CEO
Self-driving cars may have already begun to replace human drivers but the end goal of AI in asset management should not be fully autonomous investing, according to Leda Braga, chief executive of $8.2 billion quant hedge fund Systematica Investments. “Autonomous investing is not the target. The target is the powerful association of machine learning and human investment management skills,” she said, speaking at the Cayman Alternative Investment Summit on February 6. Braga described a case study
2020-02-10 09:51:36+00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 4.7904, Raw Interest Score: 2.8056,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
60 Interview Questions On Machine Learning
We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of machine learning is needed can be tricky. Here are 60 most commonly asked interview questions for data scientists, broken into linear regression, logistic regression and clustering.
2020-02-09 08:39:52+00:00 Read the full story…
Weighted Interest Score: 3.7911, Raw Interest Score: 1.6377,
Positive Sentiment: 0.1136, Negative Sentiment 0.2741
15 Python Libraries for Data Science You Should Know – Dataquest
Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. It’s possible to work with data in vanilla Python, but there are quite a few open-source libraries that make Python data tasks much, much easier.
You’ve certainly heard of some of these, but is there a helpful library you might be missing? Here’s a line-up of the most important Python libraries for data science tasks, covering areas such as data processing, modeling, and visualization.
2020-02-05 16:14:09+00:00 Read the full story…
Weighted Interest Score: 4.0674, Raw Interest Score: 2.1138,
Positive Sentiment: 0.3325, Negative Sentiment 0.0356
New Books and Resources for Data Science Central Members
We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books.
- Statistics: New Foundations, Toolbox, and Machine Learning Recipes
- Deep Learning and Computer Vision with CNNs
- Getting Started with TensorFlow 2.0
- Book: Classification and Regression In a Weekend
- Online Encyclopedia of Statistical Science
- Book: Azure Machine Learning in a Weekend
- Book: Enterprise AI – An Application Perspective
- Book: Applied Stochastic Processes
2020-02-04 16:04:32+00:00 Read the full story…
Weighted Interest Score: 3.8875, Raw Interest Score: 1.5997,
Positive Sentiment: 0.0849, Negative Sentiment 0.0425
Finmechanics and MathLabs to provide advanced AI to corporate banks to improve processes and conduct risk in capital markets.
Finmechanics Pte. Ltd. (Finmechanics) and Math Labs Research Ltd. (MathLabs) have announced a technology partnership to provide corporate banks with enhanced client experience, as well as automated detection of trade errors in Capital Markets, using advanced AI.
“Finmechanics is executing projects providing corporate banks with digital tools to trade and negotiate a broad variety of financial instruments. We bring cutting edge AI algorithms to banks, to help direct their clients to the products they need, and prioritise those clients accordingly”, says Anindya Sarkar, CEO of Finmechanics.
“Partnering with a fast growing fintech such as Finmechanics is a key accelerator in our journey to provide the financial industry with AI-Optimal strategies.” says Prodipto Ghosh, Chief AI Scientist at MathLabs. “Finmechanics products are ideal carriers of our algorithms into banks’ architecture; they also allow for rapid development of a portfolio of intelligent tools”, he adds.
2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 4.4301, Raw Interest Score: 1.8952,
Positive Sentiment: 0.4032, Negative Sentiment 0.1613
GNY Launches ML As A Service Tool In AWS Marketplace
Studies have shown becoming artificial intelligence (AI) ready by 2022 is a major priority for the US companies, many of them fearing that they will lose market share to their competition if they don’t adapt. However, many of them lack the expertise and resources to get there. AI feeds on data, and many companies need to determine exactly what data they should be collecting.
GNY, a decentralised machine learning (ML) platform, has announced the launch of a new software-as-a-service (SaaS) tool designed to allow businesses to check the AI-readiness of their data.
The GNY Data Diagnostic analyses a company’s datasets and detects if the historical data is strong enough for an effective ML, or if weak or inconsistent datasets are skewing the predictions and weakening the business. GNY’s Data Diagnostic team offers everything a business would need to become AI-ready, from education about AI basics and how predictive analytics works, to learning about the company’s data collection practices and offering a tailored solution. The result includes a detailed analysis of the historical data — what needs to be done to support ML, as well as an analysis of the digital architecture’s ability to support the company’s business goals.
2020-02-06 10:27:19+00:00 Read the full story…
Weighted Interest Score: 4.3812, Raw Interest Score: 1.9623,
Positive Sentiment: 0.3078, Negative Sentiment 0.1154
Unlocking Data Silos to Reach the Promised Land of Smart Data Analytics
With mountains of market data, historical prices, and transactions data stored in disparate systems, securities and investment firms are shifting from a focus on collecting data to extracting value from it. A December 2019 paper by capital markets consultancy GreySpark Partners examined the potential for buy-side and sell-side firms to transform large quantities of big data into actionable intelligence – producing what is known as ‘smart data – through specialized analytics.
The move comes as electronic trading has generated massive data sets across equities, fixed income and currencies. Firms are hiring data scientists and coding analytics to mine this data for trading opportunities or to identify patterns that help lower transaction costs. In the report, titled “Smart Data Analytics Set to Play Key Role in Reducing Buy Side and Sell Side Trading Costs,” GreySpark predicts that smart data inputs and data analytics will become more significant in the next three-to-five years in terms of client performance analytics, competitive differentiation, and value creation.
2020-02-03 03:22:56+00:00 Read the full story…
Weighted Interest Score: 4.2167, Raw Interest Score: 2.2246,
Positive Sentiment: 0.1055, Negative Sentiment 0.1247
Benchmark Analysis of Popular Image Classification Models
6 Popular Image classification models on Keras were benchmarked for inference under adversarial attacks
Image classification models have been the torchbearers of the machine learning revolution over the past couple of decades. From medical diagnosis to self-driving cars to smartphone photography, the field of computer vision has its hold on a wide variety of applications. The advent of customized hardware for machine learning applications has propelled more research into image recognition techniques. Conventional deep learning models were tweaked and better architectures were developed. Today there are tens of good image classification models that have demonstrated state of the art results and we wanted to know how these models perform under adversarial attacks.
In this work, we use pre-trained Keras models trained on the ImageNet dataset to benchmark them for adversarial attacks. We test the accuracy of these models with and without noise using random images that are not part of the ImageNet dataset. An adversarial attack on an image can be something as simple as a blur. Keras has become popular with developers ever since the introduction because of its lightweight, written in Python and offers high-level APIs to run models with great ease. For this very reason; i.e. the ease of execution, we have used pre-trained models offered by Keras.
2020-02-04 11:01:24+00:00 Read the full story…
Weighted Interest Score: 3.9389, Raw Interest Score: 2.2694,
Positive Sentiment: 0.2960, Negative Sentiment 0.1973
ECB runs AI coding marathon to get new insights into supervisory data
The European Central Bank (ECB) has teamed up with digital innovation outfit Reply to run a 48-hour coding marathon focused on the application of AI and machine learning. Taking place in the last days of February at the ECB in Frankfurt, the supervisory data hackathon will gather more than 80 participants from the ECB, Reply and other firms.
Participants will use AI and machine learning to try to gain deeper and faster insights into the swathes of supervisory data gathered by the ECB from financial institutions through regular reporting for risk analysis. Participants will submit projects in the areas of data quality, interlinkages in supervisory reporting and risk indicators ahead of the event. The most promising submissions will be worked on for 48 hours by the multidisciplinary teams.
2020-02-06 00:01:00 Read the full story…
Weighted Interest Score: 3.9360, Raw Interest Score: 1.8541,
Positive Sentiment: 0.2472, Negative Sentiment 0.0000
Make Your Own AI
Artificial intelligence is slated to have a profound impact on the future of business. We’re seeing evidence of that every day. But the manner in which AI will change business is not always straightforward. H2O.ai CEO Sri Ambati, who has given the matter some thought, recently filled us in on the radical ramifications of the looming AI transformation.
The way Ambati sees it, we are on the cusp of a new era, one that’s born largely out of the power of data and software. With limitations in storage and compute quickly melting away thanks in large part to the cloud, the key differentiator becomes how companies use AI to transform data into competitive advantage.
“Algorithms plus data has a lot of value,” Ambati tells Datnami. “If you can inspire a small band of technologists in a company, you can transform the overall company into a powerhouse of innovation.”
There are a multitude of ways that companies can adopt AI to improve aspects of their business. They can tackle smaller pieces, such as increasing the clickthrough rate of an email campaign or reducing customer churn, which can provide an iterative boost to the bottom line. But those are table stakes compared to the big, macro-level changes and entirely new business models that can be unlocked with data and AI. The former might help pay the bills, but Ambati has his sights set on the latter.
2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 3.9354, Raw Interest Score: 1.4250,
Positive Sentiment: 0.2791, Negative Sentiment 0.0588
GSA Unit Launches AI Community of Practice to Boost Agency Adoption
By AI Trends Staff
The General Services Administration’s Technology Transformation Services (TTS) unit has launched an AI community of practice (AI CoP) to capture advances in AI and accelerate adoption across the federal government. The founding was announced in November via a blog post written by Steve Babitch, head of the AI portfolio for TTS.
The action is a follow-up to an Executive Order signed by President Trump in February on Maintainin…
2020-02-06 22:30:25+00:00 Read the full story…
Weighted Interest Score: 3.7838, Raw Interest Score: 1.6487,
Positive Sentiment: 0.2274, Negative Sentiment 0.0000
Flashback 2019: Top 6 Tech Talks From The Rising
‘The Rising’ has been the biggest meeting of women data science leaders from across the domain and women professionals from the industry as well as academia. This year, the conference is going to be held in Hotel Radisson Blu, on March 20, 2020, which will serve as a forum for exchanging ideas to build a better environment for women participating in STEM. The conference will also highlight the achievements and career interests of women in data science.
In this article, we list down top tech talks from the last year, The Rising 2019.
The list is in no particular order.
- ‘Driving towards a cleaner future through data’ by Deepika Sandeep
- ‘Fighting prejudice in artificial intelligence’ by Smitha Ganesh
- ‘Unlock the power of intelligent enterprise with augmented analytics’ by Dharani Karthikeyan
- ‘Enabling early-stage breast cancer detection using AI’ by Geetha Manjunath
- ‘Applying ML at scale for new user experiences’ by Vikram Vij
- ‘My experiences with data’ by Mathangi Sri
2020-02-10 06:09:24+00:00 Read the full story…
Weighted Interest Score: 3.4515, Raw Interest Score: 1.6805,
Positive Sentiment: 0.1657, Negative Sentiment 0.2367
Explainable AI: But Explainable to Whom?
As the power of AI and machine learning have become widely recognized, and as people see the value that these approaches can bring to an increasingly data-heavy world, a new need has arisen: the need for explainable AI. How will people know the nature of the automated decisions that are made by machine learning models? How will they make use of the insights provided by AI-driven systems if they do not understand and trust the automated decisions that underlie them?
The biggest challenge to the next level of adoption of AI and machine learning is not the development of new algorithms, although of course that continues to be done. The biggest challenge is building confidence and trust in intelligent machine learning systems. Some call this need for confidence and trust a barrier for AI – and in a way it is – but I prefer to think of it as a very reasonable requirement of AI and machine learning. Explainable or interpretable AI involves the ability to present explanations for model-based decisions to humans. So explainable AI is of critical importance for the success of AI and ML systems. But explainable to whom?
2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 3.4474, Raw Interest Score: 1.6013,
Positive Sentiment: 0.1601, Negative Sentiment 0.1068
Why You Should Enhance Your Email Campaigns With AI
You can enhance your email campaigns with the help of AI, or artificial intelligence. Here’s how artificial intelligence can make your emails better.
Artificial intelligence is slowly working its way into everything. One of the newest iterations is AI-driven email marketing. Many companies are already using this and you may not have noticed. That’s because these programs are undetectable unless you know what to look for and they are highly relevant to your interests. Using AI allows you to better segment your market and it can help signal when someone is prepared to sign.
2020-02-05 14:15:49+00:00 Read the full story…
Weighted Interest Score: 3.2002, Raw Interest Score: 1.0836,
Positive Sentiment: 0.4575, Negative Sentiment 0.0482
Kaskada Accelerates ML Workflow with Its Feature Store
There’s a lot of surface area in the typical data science workflow for the purveyors of automation to attack. What moves the needle for the folks at the startup Kaskada is the feature engineering and deployment stage, which it’s seeking to streamline with a new automated feature store.
The typical data science workflow is fraught with inefficiency, according to Kaskada CEO and co-founder Davor Bonaci, who previously was a senior engineer at Google who …
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.1397, Raw Interest Score: 1.9706,
Positive Sentiment: 0.1471, Negative Sentiment 0.0735
Facebook Releases Open-Source Library For 3D Deep Learning: PyTorch3D
Rendering a simple shape into a proper object with geometry, texture, and other material properties is a painstakingly long process; however, with AI, researchers can now do this rendering ten times faster than the real-time.
A machine learning model is trained on images that are closer to the target. When it is presented with a shape and matching properties, it would recommend a photorealistic image. This opened a whole new field altogether — differentiable programming. Traditional rendering engines are not differentiable, so they can’t be incorporated into deep learning pipelines. Projects, such as OpenDR, Neural Mesh Renderer, Soft Rasterizer, and redner, have showcased how to build differentiable renderers that can be cleanly integrated with deep learning.
In a significant boost to 3D deep learning research, Facebook AI has released PyTorch3D, a highly modular and optimised library with unique capabilities to make 3D deep learning easier with PyTorch. PyTorch3d provides efficient, reusable components for 3D Computer Vision research with PyTorch.
2020-02-10 10:52:20+00:00 Read the full story…
Weighted Interest Score: 3.0925, Raw Interest Score: 1.8260,
Positive Sentiment: 0.3297, Negative Sentiment 0.1522
Data Scientists Key to Winning Deals at Blackstone
Investment banks and hedge funds aren’t alone in incorporating data science into their business models. Private equity funds are also turning to data science, both to win deals in the first place and to help them manage portfolio companies after a purchase.
Speaking at the recent Alternative Investments Conference in London, Lionel Assant, head of European private equity at Blackstone, said the fund now has 14 analytics professionals, “up from zero five years ago.”
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 3.0402, Raw Interest Score: 1.5797,
Positive Sentiment: 0.2385, Negative Sentiment 0.0000
dotData Achieves AWS Machine Learning Competency Status
dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status, reaching the milestone after 8 months of joining the AWS Partner Network.
“From its foundation, dotData’s vision has been to make AI and ML accessible to as many people in the enterprise as possible,” said Ryohei Fujimaki, founder and CEO of dotData. “Achieving AWS ML Competency status in just eight months recognizes our ability to deliver an outstanding product that dram…
2020-02-04 00:00:00 Read the full story…
Weighted Interest Score: 2.9525, Raw Interest Score: 1.6710,
Positive Sentiment: 0.4284, Negative Sentiment 0.0428
The Advent And Scope Of AI Marketing In 2020 And Beyond
When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.
That said, b…
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691
Yseop Launches Augmented Analyst, the Next-Generation AI NLG Platform
A recent press release reports, “Yseop, the world-leading AI software company and pioneer in Natural Language Generation (NLG), today announced the launch of Augmented Analyst, a new enterprise-wide NLG automated report generation platform. Augmented Analyst is designed to help financial companies accelerate their digital transformation. Stemming from the imperative to move away from high-cost individual point solutions, Yseop leveraged over 10 y…
2020-02-10 08:05:15+00:00 Read the full story…
Weighted Interest Score: 2.8571, Raw Interest Score: 2.0752,
Positive Sentiment: 0.1683, Negative Sentiment 0.0561
Four factors businesses need to consider when it comes to automation and decisioning
As we enter a new decade, many financial providers plan to harness the power of automation and invest in advanced analytics such as Machine Learning and Artificial Intelligence to help transform their processes.
With this transformation, many are currently investing in major projects to bring together disparate data sources together into one coherent framework. And although a central, accessible data source is essential, organisations need to consider several factors if they want to take full advantage of the myriad of ways data can improve …
2020-02-10 10:34:19 Read the full story…
Weighted Interest Score: 2.8053, Raw Interest Score: 1.7808,
Positive Sentiment: 0.4452, Negative Sentiment 0.1370
The big questions businesses must ask before AI gets bigger
Meanwhile the court of public opinion has never been so vocal. Politicians and regulators are keen to be seen as standing up for the average person in the street, so companies may have to answer the question ‘how does the algorithm used in your business meet community expectations?’ This will be particularly challenging as those expectations continue to evolve.
2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 2.7650, Raw Interest Score: 0.8801,
Positive Sentiment: 0.1048, Negative Sentiment 0.3772
Top Tech Skills of 2020 Include Swift, Kafka
“Which skills are most valuable to me?” That’s a question that some technologists constantly ask, and it’s a good one: Knowing the right skills can keep you employed, and give you the leverage to negotiate for better salary and benefits from your employer. But as with so many things in tech, the “right” skills constantly shift, and 2020 is no different, according to the latest Dice Salary Report.
For example, there are certain, often-popular skills—such as Python, or how to best utilize various tools for back-end development—that never go out of style. However, those skills also evolve at a pretty rapid clip, and you risk falling behind if you don’t constantly keep your knowledge sharply honed. It’s an exhausting treadmill for any technologist to step onto, but it’s worth it if you can truly stand out from the pack because you’ve mastered the latest updates.
With new technologies, especially complicated ones such as machine learning or artificial intelligence (A.I.), a relatively small pool of experts can command high salaries and lots of benefits from hungry employers. For example, top-tier A.I. researchers, such as the autonomous-driving experts at Google, have managed to rack up millions of dollars in compensation over the past few years.
Very old technologies, such as mainframes that were first brought online in the 1960s and 1970s, can also draw high salaries, if only because there are relatively few people left who’ve mastered them.
2020-02-06 00:00:00 Read the full story…
Weighted Interest Score: 2.6894, Raw Interest Score: 1.8823,
Positive Sentiment: 0.1390, Negative Sentiment 0.0505
Automatic Speech Transcription And Speaker Recognition Simultaneously Using Apple AI
Last year, Apple witnessed several controversies regarding its speech recognition technology. To provide quality control in the company’s voice assistant Siri, Apple asked its contractors to regularly hear the confidential voice recordings in the name of the “Siri Grading Program”. However, to this matter, the company later apologised and published a statement where it announced the changes in the Siri grading program.
This year, the tech giant has been gearing up a number of researchers regarding speech recognition technology to upgrade its voice assistant. Recently, the researchers at Apple developed an AI model which can perform automatic speech transcription and speaker.
2020-02-08 08:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6497, Raw Interest Score: 1.4020,
Positive Sentiment: 0.1020, Negative Sentiment 0.1275
How enterprises combine Natural language generation (NLG) and BI
It has never been easier to measure and monitor business operations — the amount of data available to organizations is staggering. Access to insight provides businesses with a clear competitive advantage, but many enterprises struggle to make sense of the seemingly endless reams of data at their disposal.
To overcome hurdles with data literacy, smart businesses have embraced various business intelligence (BI) solutions to collect, aggregate, tra…
2020-02-07 16:34:42+00:00 Read the full story…
Weighted Interest Score: 2.6275, Raw Interest Score: 1.8753,
Positive Sentiment: 0.3464, Negative Sentiment 0.1553
What is the Data Architecture We Need?
In the new era of Big Data and Data Sciences, it is vitally important for an enterprise to have a centralized data architecture aligned with business processes, which scales with business growth and evolves with technological advancements. A successful data architecture provides clarity about every aspect of the data, which enables data scientists to work with trustable data efficiently and to solve complex business proble…
2020-02-09 05:23:37.326000+00:00 Read the full story…
Weighted Interest Score: 2.5904, Raw Interest Score: 1.4458,
Positive Sentiment: 0.2238, Negative Sentiment 0.1549
How to make the most hated task at work suck less
A recent study showing that data entry is one of the most redundant and hated workplace tasks raises questions about why, in the age of artificial intelligence, data mining, and smart technologies, this task is still being done manually.
Is there any way it could be less despised? My ongoing fieldwork in a data-driven startup, referred to as Sage (a real company, but not its real name due to confidentiality requirements), suggests that technological solutions are not nearly as sophisticated as many assume—and are not going…
2020-02-09 08:00:14 Read the full story…
Weighted Interest Score: 2.5656, Raw Interest Score: 1.5175,
Positive Sentiment: 0.0973, Negative Sentiment 0.1167
Top Databases Used In Machine Learning Projects
One of the most critical components in machine learning projects is the database management system. With the help of this system, a large number of data can be sorted and one can gain meaningful insights from them. According to the Stack Overflow Survey report 2019, Redis is the most loved database, whereas MongoDB is the most wanted database.
In this article, we list down 10 top databases used in machine learning projects.
2020-02-07 12:09:07+00:00 Read the full story…
Weighted Interest Score: 2.5216, Raw Interest Score: 1.7218,
Positive Sentiment: 0.2609, Negative Sentiment 0.1565
Startup Stellus Claims New Data Platform Drives Massive Throughput
It’s rare that a new file system of any type becomes available to the enterprise IT market. This is because so many enterprises have long been standardized on older, well-trampled file systems—even if they long predate the servers, storage and networking they handle.
However, the time has come for a new-gen file system engineered specifically for unstructured file and object-based data, wherever it resides in a system. A new player in the busine…
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5154, Raw Interest Score: 1.5511,
Positive Sentiment: 0.2341, Negative Sentiment 0.1171
Is the DBA dead… or alive and preparing for the future?
Is the DBA dead… or alive and preparing for the future?
Sure, there’s some doom and gloom out there about the future of the DBA. But in this session, you’ll learn how to adapt, evolve, survive and even thrive in a changing database world. You’ll get to see real-world statistics on the evolving role of the DBA, common DBA concerns and valuable insights for career success. You’ll learn how the trends of DevOps, cloud, NoSQL, big data and more will shape the future
2020-02-07 00:00:00 Read the full story…
Weighted Interest Score: 2.4331, Raw Interest Score: 1.2165,
Positive Sentiment: 0.4866, Negative Sentiment 0.2433
If You Need Somebody — Not Just Anybody — Data Literacy Help Is Here
Some organizations need a little help with data literacy just to get their feet on the ground. Maybe they can’t seem to move the bar in terms of using data to make decisions. Or they sense that their employees struggle to understand and aren’t so self-assured when it comes to data. Others lack the resources and talent needed to deliver timely insights or scale existing internal efforts. The report “Data Literacy Matters: The Writing’s On The Wall…
2020-02-05 09:37:14-05:00 Read the full story…
Weighted Interest Score: 2.4272, Raw Interest Score: 1.2684,
Positive Sentiment: 0.2819, Negative Sentiment 0.1253
Incorta 4.6 Platform Release Makes Cloud Data Lakes Analytics-ready
According to a recent press release, “Incorta, the industry’s only Unified Data Analytics Platform powered by Direct Data Mapping, today announced the latest version of its platform, Incorta 4.6. With this release, Incorta introduces new features, capabilities, and performance enhancements that double down on its commitment to bringing data engineers, data scientists, and data analysts together on a single platform. Specifically, Incorta 4.6—the “cloud data lake release”—enables seaml…
2020-02-07 08:15:31+00:00 Read the full story…
Weighted Interest Score: 2.4207, Raw Interest Score: 1.4524,
Positive Sentiment: 0.2152, Negative Sentiment 0.0538
Crumbling Infrastructure and AI Autonomous Cars
I should sue! That’s what my friends told me to do.
They were looking at the damage done to the right fender and front right tire of my car. I had been driving innocently down a street in downtown Los Angeles and encountered a whopper of a pothole. I was doing the legal speed limit and was not driving recklessly.
When I turned a corner, an unexpected pothole loomed just after making the turn, and the right side of my car was doomed to enter into the gaping asphalt gash.
2020-02-06 22:30:27+00:00 Read the full story…
Weighted Interest Score: 2.3958, Raw Interest Score: 0.7268,
Positive Sentiment: 0.1284, Negative Sentiment 0.2787
The Benefits of Building Predictive Analytics on Unified Customer Data
Predictive customer lifetime value (CLV) is a key element in modern marketing analytics, allowing marketers to prioritize customers that have the highest predicted business value. The most popular data science approach to predicting CLV is the extended Pareto/NBD model (EP/NBD) generative model which leverages a few summary statistics about customer transactions: the frequency of repeat purchases, the total customer age, most recent purchase, and the historical average order value. Despite using only a few signals and being over fifteen years old, the EP/NBD models has maintained strong relative performance according to a recent comparison of several CLV prediction approaches.
There have been many attempts to substantially improve CLV prediction via more sophisticated modeling techniques (SVMs, boosted decision trees, and neural networks), but these models also assume the time-series of past customer transactions as the primary data signal. Further improvements to CLV prediction, and predictive analytics generally, are more likely to come from exploiting new sources of customer data rather than modeling techniques or feature engineering. To quote Rule #41 from Google’s Rule of Machine Learning: “When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals.”
2020-02-05 00:00:00 Read the full story…
Weighted Interest Score: 2.3690, Raw Interest Score: 1.4326,
Positive Sentiment: 0.2956, Negative Sentiment 0.1251
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