AI & Machine Learning News. 16, November 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?
The Machine Learning Behind Hum to Search
Melodies stuck in your head, often referred to as “earworms,” are a well-known and sometimes irritating phenomenon — once that earworm is there, it can be tough to get rid of it. Research has found that engaging with the original song, whether that’s listening to or singing it, will drive the earworm away. But what if you can’t quite recall the name of the song, and can only hum the melody?
Existing methods to match a hummed melody to its original polyphonic studio recording face several challenges. With lyrics, background vocals and instruments, the audio of a musical or studio recording can be quite different from a hummed tune. By mistake or design, when someone hums their interpretation of a song, often the pitch, key, tempo or rhythm may vary slightly or even significantly. That’s why so many existing approaches to query by humming match the hummed tune against a database of pre-existing melody-only or hummed versions of a song, instead of identifying the song directly. However, this type of approach often relies on a limited database that requires manual updates.
Launched in October, Hum to Search is a new fully machine-learned system within Google Search that allows a person to find a song using only a hummed rendition of it. In contrast to existing methods, this approach produces an embedding of a melody from a spectrogram of a song without generating an intermediate representation. This enables the model to match a hummed melody directly to the original (polyphonic) recordings without the need for a hummed or MIDI version of each track or for other complex hand-engineered logic to extract the melody. This approach greatly simplifies the database for Hum to Search, allowing it to constantly be refreshed with embeddings of original recordings from across the world — even the latest releases.
2020-11-12 Read the Full Story…
CloudQuant Thoughts : How did I not already know about this? Because it is only on Google App! Musical creation is about to blow up big time. Once we have software that free’s people from having to learn ANY instrument to be creative, millions of new musical creators will be born.
What’s Missing in Data Preparation & Distribution – CloudQuant CEO to speak on CRUX Summit Panel
Crux 3 Day Virtual Summit – November 17th – 19th 2020 – Full Agenda.
CloudQuant CEO Morgan Slade will be taking part in a Panel Discussion at the CRUX Summit on November 18th 2020 at 9:00am – 9:45am Eastern Time. The Panel Discussion title is “What’s Missing in Data Preparation & Distribution”
Boosting Stop-Motion to 60 fps using AI
Scientists have found a new, neural network based approach for video frame interpolation. I was curious to see how this would work on Stop Motion movies. The network called DAIN interpolates your Brickfilm or animation in a top quality, as I present on my Apollo 11 Lego Stop Motion movie.
Link to download DAIN (for free): https://www.patreon.com/DAINAPP
Link to the source code: https://github.com/baowenbo/DAIN
Link to Two Minute Papers, a channel that presents videos in the style of this one each week: https://www.youtube.com/user/keeroyz
CloudQuant Thoughts : This opens up huge possibilities for small animation studios to go up against the big guys. Very Very impressive!
Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses
Wikipedia: “Coursera is an American massive open online course (MOOC) provider founded in 2012 by Stanford University’s computer science professors Andrew Ng and Daphne Koller that offers massive open online courses (MOOC), specializations, degrees, professional and master track courses.
Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects, such as engineering, data science, machine learning, mathematics, business, financing, computer science, digital marketing, humanities, medicine, biology, social sciences, 3000 plus a variety of courses giving students a very broad range of information & experience in different fields.”
In this article, we are going to talk about the best FREE courses at Courses, from areas like Artificial Intelligence and Computer Science.
2020-11-11 Read The Full Story…
CloudQuant Thoughts : Human knowledge and potential expanding at an exponential rate and for free!
AWS Launches Visual Data Prep Tool
AWS this week unveiled Glue DataBrew, a new visual data preparation tool for AWS Glue that’s designed to help users clean and normalize data without writing code.
Data preparation is the Achille’s Heel of advanced analytics and machine learning, as it regularly consumes upwards of 80% of data scientists and analysts’ time. However, without spending this time to clean, transform, and prepare data for analysis or for training machine learning models, the analysis or ML activity risks being flawed.
Many individuals and software vendors have attempted to reduce the time spent on data prep by automating the process. They have been met with mixed success, however, and ETL remains an entrenched part of the process.
2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5273, Raw Interest Score: 1.6120,
Positive Sentiment: 0.0864, Negative Sentiment 0.2015
CloudQuant Thoughts : Very nice, and every little helps. But when it comes to Alternative Data, why do all the legwork? CloudQuant provides Alternative Data, easy to query (Python just one line of code!) and the data is returned, tickerized. in an easy to handle standardized format. If you are utilizing the data for investment purposes, CloudQuant has already carried out extensive research into the strengths and weaknesses of the data for many of its top datasets. This research includes results and code used (which you can run on our publicly available Back Tester CloudQuant Mariner).
AI Weekly: Tech, power, and building the Biden administration
After U.S. President Donald Trump was defeated in the recent election, President-elect Joe Biden and running mate Kamala Harris moved quickly from celebratory speeches to conversations about transition team members and key administration appointments.
Among the first names to emerge were people with tech backgrounds, like former Google CEO Eric Schmidt, who may be tapped to lead a tech industry panel in the White House. Since leaving Google, Schmidt has extended his services to the Pentagon, with a focus on machine learning. He has also led the Defense Innovation Board at the Pentagon and the National Security Commission on AI, which advised Congress that the U.S. needs to allocate more federal spending on AI to compete with China. NSCAI commissioners have also recommended steps like the creation of a government-run AI university and an increase in public-private partnerships in the semiconductor industry.
Schmidt and others have also raised questions about how close the administration will get with Big Tech companies that are increasingly viewed as the next Big Tobacco. Sentiment has shifted since 2009, when Biden first entered the White House, with industry experts warning that Big Tech’s concentration of power accelerates inequality.
2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 2.8599, Raw Interest Score: 1.4308,
Positive Sentiment: 0.1590, Negative Sentiment 0.3498
CloudQuant Thoughts : Tech is treading a fine line between being blamed for censorship to being blamed for the spreading of false truths. It would be nice to see some of the smartest people in the country re-engaging with government.
The changing landscape of asset management in China
Harvest Fund Management believes its local knowledge and approach to research gives it an edge over international asset managers on critical market issues such as the development of ESG investing.
As a leading asset manager, Harvest Fund Management is committed to the welfare and sustainability of domestic financial markets. With ESG considerations increasingly impacting upon Chinese companies and their stock prices the firm believes fiduciary managers must incorporate these considerations into their investment research and decision-making.
ESG is crucial to the sustainable development of Chinese financial markets. As wealth increases, people are also increasingly demanding improvements in quality of life in areas such as air and water quality, product safety, and cybersecurity and privacy.
2020-11-09 09:46:23.903000 Read the full story…
Weighted Interest Score: 3.5390, Raw Interest Score: 1.8758,
Positive Sentiment: 0.3288, Negative Sentiment 0.1160
The AI-Powered Cybersecurity Arms Race and its Perils
The advancement in the field of artificial intelligence (AI) is still one of the most important technological achievements in recent history. The prominence and prevalence of machine learning and deep learning algorithms of all types, being able to unearth and infer valuable conclusions about the world surrounding us without being explicitly programmed to do so, has sparked both the imagination and primordial fears of the general public.
The cybersecurity industry is no exception. It seems that wherever you go, you can’t find a cybersecurity vendor that doesn’t rely, to some extent, on Natural Language Processing (NLP), computer vision, neural networks, or other technology strains of what could be broadly categorised or branded as ‘AI’.
2020-11-12 12:00:00 Read the full story…
Weighted Interest Score: 4.5247, Raw Interest Score: 1.6106,
Positive Sentiment: 0.1683, Negative Sentiment 0.7091
Webinar: Managing data today – real time, real AI, real application
Join FinTech Futures and SmartStream’s team of innovation specialists and learn how real artificial intelligence can overcome the challenges organisations face when it comes to managing data integrity and validation processes.
In real time, you will see how artificial intelligence (AI) can be applied to no less than three complex data reconciliation activities with immediate results.
Join this webinar on 19 November to understand how effortless it is to transform your operations and take data processes to the next level. You will gain insight into:
- How to compare complex data sets, in huge variety of non-standard formats and structures
- How real time AI and observational learning transforms processes that would usually be measured in weeks and months, to just seconds
- How to significantly increase match rates
- SmartStream Air – the most advanced AI solution in the market for reconciliations
2020-11-10 13:43:05+00:00 Read the full story…
Weighted Interest Score: 4.3478, Raw Interest Score: 2.0871,
Positive Sentiment: 0.3630, Negative Sentiment 0.1815
Charles Schwab launches cross-channel algorithm to boost user experience
It was built by Schwab’s internal Digital Services organization, which develops innovative tools for the client journey. Last year, Schwab acquired fellow broker TD Ameritrade for $26 billion, bringing its total client assets to more than $5 trillion at the time. The cross-channel analytics tool will provide a more seamless client experience and drive Schwab’s operational efficiencies. The algorithm processes billions of pieces of client data, and is able to discern what clients want or are trying to do in real time, which allows the broker to offer more personalized services much faster.
For example, the algorithm can directly connect a call-in client to the relevant representative based on what they have recently been researching on Schwab’s platforms, avoiding drawn out waiting times or being frustratingly shuffled around multiple departments to find the right customer rep. In addition, the new tool skims across Schwab’s webpages and searches to identify the areas that are driving highest call volumes, enabling the broker to direct resources more efficiently and focus its efforts on improving the most common client roadblocks.
2020-11-16 00:00:00 Read the full story…
Weighted Interest Score: 4.3199, Raw Interest Score: 1.6355,
Positive Sentiment: 0.4381, Negative Sentiment 0.0584
Google Releases New Dataset For Advanced 3D Object Understanding
Machine learning models for computer vision tasks have been largely trained on photos. However, there lies a large possibility for scaling up to a wider range of applications such as augmented reality, autonomy, robotics, and image retrieval tasks if we train these models on 3D objects. To achieve this has been an uphill task since there is a dearth of large real-world datasets of objects in 3D, as compared to 2D datasets such as ImageNet, COCO, and Open Images.
Now, Google has released the Objectron dataset, which is a collection of short, object-centric video clips that capture a large set of common objects from various angles. Along with the dataset, the research also details a new 3D object detection solution.
2020-11-14 04:30:21+00:00 Read the full story…
Weighted Interest Score: 4.0131, Raw Interest Score: 1.3779,
Positive Sentiment: 0.0934, Negative Sentiment 0.0234
C3.ai Files to Go Public
C3.ai, the predictive analytics firm founded by CRM giant Tom Siebel, today announced plans for an initial public offering (IPO) of stock. It intends to trade shares on the New York Stock Exchange under the ticker symbol “AI.”
While the rest of the big data world was focused on using open source software like Spark and Hadoop to build giant clusters, Siebel was quietly assembling his own cloud-based application for collecting and analyzing huge amounts of data at scale.
Founded in 2009 as C3 IoT, the company successfully attracted several large public utilities to its platform. It eventually added a host of larger customers, including banks, healthcare companies, manufacturers, and oil and gas companies to its customer roll.
2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 3.8145, Raw Interest Score: 1.7241,
Positive Sentiment: 0.1999, Negative Sentiment 0.0250
How Compute Divide Leads To Discrimination In AI Research
Science doesn’t discriminate, but probably technology does, at least in terms of accessibility. New research has found that the unequal distribution of compute power in academia is promoting inequality in the era of deep learning. The study conducted jointly by AI researchers from Virginia Tech and Western University found that this de-democratisation of AI has pushed people to leave academia and opt for high-paying industry jobs.
The study found that the amount of compute power at elite universities, ranked among top 50 as per QS World University Rankings, is much more than at mid-to-low tier institutions. For the research, authors analysed over 170,000 papers presented across 60 prestigious computer science conferences such as ACL, ICML, and NeurIPS in categories like computer vision, data mining, NLP, and machine learning.
2020-11-16 10:30:37+00:00 Read the full story…
Weighted Interest Score: 3.5442, Raw Interest Score: 1.6701,
Positive Sentiment: 0.1237, Negative Sentiment 0.1649
Univariate and Multivariate Gaussian Distribution: Clear Understanding with Visuals
Guassian Distribution in details and its relationship with mean, standard deviation, and variance
Gaussian distribution is the most important probability distribution in statistics and it is also important in machine learning. Because a lot of natural phenomena such as the height of a population, blood pressure, shoe size, education measures like exam performances, and many more important aspects of nature tend to follow a Gaussian distribution.
I am sure, you heard this term and also know it to some extent. If not, do not worry. This article will explain it clearly. I found some amazing visuals in Professor Andrew N…
2020-11-15 23:52:17.955000+00:00 Read the full story…
Weighted Interest Score: 3.5293, Raw Interest Score: 1.8378,
Positive Sentiment: 0.1575, Negative Sentiment 0.1470
Must-Have Elements of a Modern Data Approach
The current global situation has highlighted the importance of digitalization for organizations of every kind—from businesses to hospitals and schools. But data-driven organizations must be able to access all the relevant data, store it cost efficiently, ensure it is of the highest quality, and make its insights available in real time to all users. Now more than ever, a strong data strategy is essential to every enterprise’s success.
According to the “2017 Gartner Chief Data Officer” survey, 86% of data and analytics leaders said defining such a strategy was a top responsibility, up 64% from 2016. As many leaders have realized, a large part of this responsibility requires them to implement new strategies that empower citizen and specialist users with self-service capabilities. To make this possible and accelerate digital transformation, enterprises need to adopt a modern data platform and approach.
2020-11-18 00:00:00 Read the full story…
Weighted Interest Score: 3.3110, Raw Interest Score: 1.8358,
Positive Sentiment: 0.3750, Negative Sentiment 0.0197
Data science… without any data?!
Why it’s important to hire data engineers early. “What challenges are you tackling at the moment?” I asked. “Well,” the ex-academic said, “It looks like I’ve been hired as Chief Data Scientist… at a company that has no data.” I don’t know whether to laugh or to cry. You’d think it would be obvious, but data science doesn’t make any sense without data. Alas, this is not an isolated incident. So, let me go ahead and say what so many ambitious data scientists (and their would-be employers) really seem to need to hear.
What is data engineering? If data science is the discipline of making data useful, then you can think of data engineering as the discipline of making data usable. Data engineers are the heroes who provide behind-the-scenes infrastructure support that makes machine logs and colossal data stores compatible with data science toolkits. Unlike data scientists, data engineers tend not to spend much time looking at data. Instead, they look at and work with the infrastructure that holds the data. Data scientists are the data-wranglers, while data engineers are the data-pipeline-wranglers.
2020-11-13 14:56:17.278000+00:00 Read the full story…
Weighted Interest Score: 3.2845, Raw Interest Score: 1.8466,
Positive Sentiment: 0.1692, Negative Sentiment 0.1551
How to turn Text into Features
A comprehensive guide into using NLP for Machine Learning
Simple question: How to turn text into features? Imagine you’ve been tasked with the activity of building a Sentiment Analysis tool for your company product reviews. As a seasoned Data Scientist, you built many insights about future sale predictions and was even able to classify customers based on their purchase behavior.
But now, you’re intrigued: you have this bunch of text entries and have to turn them into features for a Machine Learning model. How can that be done? That’s a common question when Data Scientists meet text for the first time.
As simple as it may look for experienced NLP Data Scientists, turning text into features is not that trivial for newcomers in the area. The purpose of this article is to provide a guide into turning Text to Features, as a continuation to the NLP Series that I’ve been building for the last months (and its been a while since the last article, I know).
2020-11-13 10:45:18.180000+00:00 Read the full story…
Weighted Interest Score: 3.1118, Raw Interest Score: 1.7099,
Positive Sentiment: 0.0919, Negative Sentiment 0.0735
Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond
The Forrester report “Top Trends And Emerging Technologies, Q3 2020” highlights important trends and organizes emerging technologies into seven key domains that will play a big role in accelerating this shift: artificial intelligence; business automation and robotics; enterprise risk management; human experience and productivity; new compute architectures; next-generation communications; and Zero Trust security. Key trends include:
- Rising demand for ethical AI.
- Recasting of automation roadmaps.
- Moving toward hyperlocal business operations.
- Driving innovation everywhere using cloud-native technologies.
- Shifting cloud strategies toward the edge.
2021-11-16 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775
Splice Machine Launches Platform for Industrial IoT
Splice Machine, a scale-out SQL database with built-in machine learning, is releasing Livewire, its new open source Operational AI platform for industrial IoT use cases. The Splice Machine Livewire platform enables teams of data engineers, operators, and data scientists to work together with unprecedented speed and agility. By using an integrated platform these teams can deploy machine learning models 100x faster with half the staff.
Livewire is built upon the Splice Machine SQL RDBMS with built-in machine learning. The system is elastic and deployable anywhere. If the plant has already leveraged cloud computing, then the Splice Machine Livewire cloud service may be the perfect solution. Companies can provision a Livewire solution in minutes and easily manage and operate it with few people.
2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 2.9308, Raw Interest Score: 1.6626,
Positive Sentiment: 0.1635, Negative Sentiment 0.1363
Tech Employers, Jobs Cause for Optimism: Q3 Dice Tech Job Report
Although revenue and profits for many technology companies have remained relatively robust throughout 2020, the technology industry as a whole hasn’t been immune from the economic impacts of the pandemic. While third quarter tech hiring showed the continuing effects of COVID-19, as revealed by Dice’s latest Tech Job Report, the data was tempered by encouraging results in terms of posting volumes for top employers and trending occupations.
Overall, the unemployment rate for technologists remains lower (3.5 percent) than the national average. That’s cause for optimism. Furthermore, some large organizations have increased hiring—of the top 50 employers in the third quarter, 68 percent created more job postings than the second quarter, while only 32 percent created an equal or lesser number of job postings. In this article, we’ll dig more deeply into the data for top employers, as well as looking at the occupations seeing the highest growth for the quarter.
2020-11-11 00:00:00 Read the full story…
Weighted Interest Score: 2.8202, Raw Interest Score: 2.0928,
Positive Sentiment: 0.1079, Negative Sentiment 0.1726
Snap To Acquire Israel’s Voca.ai — A Maker Of AI-Based Voice Agents
Snap, the parent company of Snapchat — a messaging app for millennials, has announced the news of acquiring an Israel-based AI-based voice assistants company — Voca.ai for an estimated $70 million. According to the news, Voca.ai, so far, has raised a mere $6 million from investors like American Express Ventures, lool Ventures, Group 11, and Flint Capital. And, post this acquisition, the company will be integrated into Snap along with its 35 employed people.
According to Voca — seven out of 10 customers still prefer speaking with a human agent; however, Voca offers an AI-based agent natural, human-like conversations that leave the customers wondering if they have spoken to a virtual or human agent. The platform serves as a kind of triage system, which addresses simple inbound queries and then hands over to human agents seamlessly for more complex issues. Voca.ai was founded in 2017 by Dr Alan Bekker and Einav Itamar.
2020-11-12 07:41:37+00:00 Read the full story…
Weighted Interest Score: 2.7344, Raw Interest Score: 1.0817,
Positive Sentiment: 0.1502, Negative Sentiment 0.0601
Deep Unsupervised Learning in Energy Sector – Autoencoders in Action
In this short article, I will talk about unsupervised learning especially in the energy domain. The blog would mainly focus on the application of Deep Learning in real-time than emphasizing the underlying concepts. But first, let us see what an Unsupervised Machine Learning mean? It is a branch of machine learning which deals with identifying hidden patterns from the datasets and does not depend on the necessity of the target variable in the data to be labeled. So here the algorithms are used to discover the underlying structure of the data like the presence of data clusters, odd data detection, etc.
When the Target value of the desired variable under study is unknown, the unsupervised form of Deep learning techniques is used to find the relations between the target (Desired Variable) and other variables in the data to arrive at the outcome ( That is the probable value of the Target).
2020-11-12 09:19:13+00:00 Read the full story…
Weighted Interest Score: 2.7155, Raw Interest Score: 1.4941,
Positive Sentiment: 0.0776, Negative Sentiment 0.1067
Finding Important Features using Genetic Algorithms (for Heart Failure Survival Prediction)
This data set has 12 features and you can download it from the UCI Machine Learning Repository. It is a binary classification, supervised learning problem, with “DEATH_EVENT” as the target variable, 1 meaning died and 0 meaning survived.
Here’s the question: what is the most efficient way to find the best learner (algorithm) and best feature subset? Sometimes, surprisingly small subsets of the features perform better than the complete feature set. What is the best way of finding that set?
First, let’s put the question of the best learner aside. One thing we know is that some learners are faster to train than others. If you want to test out the genetic learning algorithm for feature selection, you’ll find that using Logistic Regression is the fastest way to go, whereas something tree-based like the Random Forest or LightGBM takes a lot longer, and might not even work properly, depending on the library you are using.
2020-11-09 18:40:45.734000+00:00 Read the full story…
Weighted Interest Score: 2.7132, Raw Interest Score: 1.3130,
Positive Sentiment: 0.2820, Negative Sentiment 0.0881
How the MiFID II review and Covid-19 are reshaping the hedge fund operational landscape
With certain aspects of the EU’s ongoing MiFID II review affected by the coronavirus pandemic, Hedgeweek explores how a fresh overhaul of the framework may further impact hedge fund operations, and why the Covid-19 crisis may provide an easing of the regulatory burden. Introduced in January 2018, the European Union’s Markets in Financial Instruments Directive (MiFID) II brought sweeping changes to transparency rules and transaction reporting requirements across the financial markets spectrum.
Among the major reforms impacting hedge funds was a package of measures covering third party research. These included extra scrutiny over the ways that asset managers pay for sell-side analysis, and the unbundling of research from brokerage fees, a move aimed at curbing inducements to trade. Almost three years on, industry consensus indicates MiFID II has led to a reduction in hedge fund research spend. But anecdotal evidence also suggests portfolio managers have sought to capitalise on the reduced amount of stock analysis with targeted research budgets to help them gain an edge.
2020-11-10 00:00:00 Read the full story…
Weighted Interest Score: 2.6651, Raw Interest Score: 1.3711,
Positive Sentiment: 0.1808, Negative Sentiment 0.1959
Android gains support for hardware-accelerated PyTorch inference
Google’s Android team today unveiled a prototype feature that allows developers to use hardware-accelerated inference with Facebook’s PyTorch machine learning framework. This enables more developers to leverage the Android Neural Network API’s (NNAPI) ability to run computationally intensive AI models on-device. Google says this partnership between the Android team and Facebook will allow millions of Android users to benefit from experiences powered by real-time computer vision and audio enhancement models, like Facebook Messenger’s 360-degree virtual backgrounds.
On-device machine learning can bolster features that run locally without transferring data to a remote server. Processing the data on-device results in lower latency and can improve privacy, allowing apps to work without connectivity.
2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.6297, Raw Interest Score: 1.7191,
Positive Sentiment: 0.3292, Negative Sentiment 0.0000
Hospitals Make Massive Inroads on COVID-19 Battle with EMS Data
We have previously talked about some of the biggest ways that policy makers and the healthcare sector are using big data to fight COVID-19. Their approach has evolved in recent weeks, as they find ways to use EMS data to streamline their response.
COVID-19 has changed how hospitals and EMS services function dramatically. Due to the impact that the virus has had on their systems, the healthcare field will likely be changed forever. This sector is using data to control the pandemic and will likely use these new approaches to curb future healthcare crises in the months to come. All areas of EMS are being stretched thin; ambulances, fire departments, and even the police are playing crucial roles in today’s healthcare system.
2020-11-12 15:44:49+00:00 Read the full story…
Weighted Interest Score: 2.6252, Raw Interest Score: 1.2478,
Positive Sentiment: 0.2755, Negative Sentiment 0.2269
Expanding Your Data Science and Machine Learning Capabilities
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. As a result, 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.
To educate IT decision-makers and practitioners about new technologies and strategies for expanding data science and machine learning capabilities, DBTA is hosting a special roundtable webinar on June 24th. Reserve your seat today!
2021-06-24 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6536,
Positive Sentiment: 0.2611, Negative Sentiment 0.1741
Impact of Coronavirus on Businesses
Savanta recently conducted some research with 500 business decision makers across the US in regards to working in a pandemic environment and the impact of coronavirus.
- Impact of Coronavirus on Businesses
- Approach to work from home
- Company approach to remote working
- Impact of Covid-19 on business Increase / Positive impact Decrease / Negative impact
- Covid-19 impact over time
- How companies are dealing with coronavirus?
- Strategies to mitigate business risks
- Priorities of the organizations
- Post Covid-19 technological changes in US corporate sector
2020-11-12 18:28:45+00:00 Read the full story…
Weighted Interest Score: 2.5945, Raw Interest Score: 1.4476,
Positive Sentiment: 0.1304, Negative Sentiment 0.1304
Researchers investigate why popular AI algorithms classify objects by texture, not by shape
In a paper accepted to the 2020 NeurIPS conference, Google and Stanford researchers explore the bias exhibited by certain kinds of computer vision algorithms — convolutional neural networks (CNNs) — trained on the open source ImageNet dataset. Unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. Their work indicates that CNNs’ bias toward textures may arise not from differences in their internal workings but from differences in the data that they see.
CNNs attain state-of the-art results in computer vision tasks including image classification, object detection, and segmentation. Although their performance in several of these tasks approaches that of humans, recent findings show that CNNs differ in key ways from human vision. For example, recent work compared humans to ImageNet-trained CNNs on a dataset of images with conflicting shape and texture information (e.g. an elephant-textured knife), concluding that models tend to classify according to material (e.g. “checkered”) and humans to shape (e.g. “circle”).
2020-11-13 00:00:00 Read the full story…
Weighted Interest Score: 2.5513, Raw Interest Score: 1.4395,
Positive Sentiment: 0.1469, Negative Sentiment 0.1469
Palantir reports 52% sales growth in first earnings statement since public market debut
Palantir, the maker of software and analytics tools for the defense industry and large corporations, reported 52% revenue growth in its first earnings announcement since going public in September. The stock bounced around in extended trading, falling more than 8% before bouncing back and gaining more than 1%. It plunged 8.7% during the regular trading day. The software and analytics company went public in September, 17 years after it was co-founded by Peter Thiel, CEO Alex Karp and others. Palantir said that its “customer concentration is decreasing,” and that it now gets a smaller percentage of revenue from its top clients.
2020-11-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5346, Raw Interest Score: 1.2842,
Positive Sentiment: 0.0676, Negative Sentiment 0.2028
AI Holistic Adoption for Manufacturing and Operations: Data
For the executive leader who is taking their enterprise on a journey of Digital Transformation and AI Holistic Adoption, we started this series with the foundation of Value and then moved to the framework of the Program. Although these are the fundamental building blocks required for success, the results of any enterprise’s analytics, do, in the end, rely on the Data.
The executive leader has the responsibility to ensure that they and their team are dedicated to mastering data fluency and data excellence in the enterprise. The facets of Data Management are vast with the standard areas of focus including data discovery, collection, preparation, categorization and protection. Strategies for achieving maturity in these areas are well-established in most industries, and yet many industries still struggle. These standard areas of focus in Data Management are indeed necessary but are not sufficient for the needed AI Holistic Adoption.
2020-11-12 23:36:45+00:00 Read the full story…
Weighted Interest Score: 4.7808, Raw Interest Score: 2.2741,
Positive Sentiment: 0.3876, Negative Sentiment 0.0689
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