AI & Machine Learning News. 05, October 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?
Analytics Vidhya Data Science Blogathon
What if you could turn your machine learning knowledge into a superpower?
Imagine you’re given the opposrunity to put all your machine learning knowledge to the test by displaying your skillset in the form of the written word? And that you would stand a chance to showcase your work to a community of 500,000+ data scientists? That sounds too good to be true, right? Well, that’s the Data Science Blogathon for you!
CloudQuant Thoughts : There were many more ML and AI articles this week than usual, most of which were summaries and top 10 lists. These appear to be as a result of a competition over at AnalyticsVidhya.com. The competition is already live and finishes on October 11th at 11.59pm. Prizes are available. The winners for all 3 categories will be declared on October 20, 2020.
Try out Teachable Machine
CloudQuant Nomintated for Benzinga Award
CloudQuant have been nominated for a Benzinga Award for their industry leading tech, specifically the CloudQuant Liberator API, which helps Funds, Quants and Trading firms get from Raw Alternative Data to Profits faster than ever before. Head over to this page to find out more, or this page to vote for us!
Top 10 Deep Learning Researchers Who Are Re-defining Its Application Areas
Most of the recently trending technologies such as BERT, GPT-3, Transformers, LSTM, GANs and others have deep learning at the core. These deep learning-based applications are transforming many industries such as self-driving, language translation, fraud detection and more. The researchers in the field of deep learning are contributing immensely to bring some fantastic applications in the field. In this article, we list ten deep learning researchers, in no particular order, who are re-defining the application areas of deep learning.
- Geoffrey Hinton
- Ian Goodfellow
- Ruslan Salakhutdinov
- Yann Lecun
- Yoshua Bengio
- Jurgen Schmidhuber
- Sepp Hochreiter
- Michael Jordan
- Ilya Sutskever
- Andrej Karpathy
2020-10-05 08:30:17+00:00 Read the full story…
Weighted Interest Score: 3.6488, Raw Interest Score: 2.5247,
Positive Sentiment: 0.1235, Negative Sentiment 0.0823
CloudQuant Thoughts : Knowing the thought leaders in any industry and following what they are up to is the key to keeping up to date!
I created a complete overview of machine learning concepts seen in 27 data science and machine learning interviews
During my last interview cycle, I did 27 machine learning and data science interviews at a bunch of companies (from Google to a ~8-person YC-backed computer vision startup). Afterwards, I wrote an overview of all the concepts that showed up, presented as a series of tutorials along with practice questions at the end of each section. I hope you find it helpful! ML Primer
CloudQuant Thoughts : This handy PDF is from the Machine Learning subreddit on Reddit.com, I noted that it used Confetti.ai to carry out its tests. Those tests are part of Confetti’s own pretty neat step by step guide… MACHINE LEARNING ENGINEER GUIDE – A curated set of exercises for becoming a machine learning engineer.
Most Data Science Projects Fail, But Yours Doesn’t Have To
In an effort to remain competitive in today’s increasingly challenging economic times, companies are moving forward with digital transformations — powered by data science and machine learning — at an unprecedented rate. According to PwC ‘s global study, AI will provide up to 26% boost in GDP for local economies by 2030. Yet, for many companies, implementing data science into various aspects of their businesses can prove difficult if not daunting.
According to Gartner analyst Nick Heudecker, over 85% of data science projects fail. A report from Dimensional Research indicated that only 4% of companies have succeeded in deploying ML models to production environment.
Even more critical, the economic downturn caused by the COVID-19 pandemic has placed increased pressure on data science and BI teams to deliver more with less. In this down market, organizations are reassessing which AI/ML models they should develop, how to optimize resources and how to best use valuable budget dollars for maximum impact. In this type of environment, AI/ML project failure is simply not acceptable.
2020-10-01 00:00:00 Read the full story…
Weighted Interest Score: 3.3551, Raw Interest Score: 1.8652,
Positive Sentiment: 0.3847, Negative Sentiment 0.4780
CloudQuant Thoughts : The fact that 85% of Data Science projects fail and only 4% of companies are succeeding in getting ML models to production should give us all pause! However, to quote the original Watson, Thomas J, founder of IBM – “You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that’s where you’ll find success – on the far side of failure.”.
NVIDIA Partners with VMware to Bring AI to Every Enterprise
At VMworld 2020 , VMware and NVIDIA announced a partnership to deliver both an end-to-end enterprise platform for AI and a new architecture for data center, cloud, and the edge that uses NVIDIA DPUs (data processing units) to support existing and next-generation applications.
Through this collaboration, the AI software available on the NVIDIA NGCTM hub will be integrated into VMware vSphere, VMware Cloud Foundation and VMware Tanzu. This will help accelerate AI adoption, enabling enterprises to extend existing infrastructure for AI, manage all applications with a single set of operations, and deploy AI-ready infrastructure where the data resides, across the data center, cloud and edge.
2020-09-29 00:00:00 Read the full story…
Weighted Interest Score: 3.3906, Raw Interest Score: 1.5385,
Positive Sentiment: 0.2941, Negative Sentiment 0.0452
CloudQuant Thoughts : Pay attention… Nvidia are on the cutting edge of everything at all levels in ML and AI.
Springer Released 65+ free Computer Science, Machine Learning, Data Science, Web Development Books
Amazing books that you can download for free…
Well here is the good news for Computer Science, Data Science, and Machine Learning Enthusiasts because Springer has released more than 70 books in Computer Science, Data Science, and Machine Learning domain and that too for free. Personally, I found the book’s collection very impressive.
Who can read these books? Computer Science students, Web Developers, Mathematicians, Data Science and Machine Learning Enthusiasts/beginners, Software Engineers, Intermediate or Advanced level Data Science, Machine Learning, and Data Science experts etc.
Topics Covered — Right from the mathematics needed to kickstart your Machine Learning journey, Machine Learning basics, useful libraries, hands-on code, real-world examples, Programming in R and python, Deep Learning basics, robotics, and programming languages, etc. all are covered in these books.
2020-10-05 12:24:23.383000+00:00 Read the full story…
Weighted Interest Score: 2.7754, Raw Interest Score: 2.2979,
Positive Sentiment: 0.1702, Negative Sentiment 0.0000
CloudQuant Thoughts: This is pretty cool!
10 Data Science Libraries that make Data Science a Cakewalk in Python!
As the data science community grows, Python is seen dominating the front and center for both development and research. With an active community to back it up and easy open-source packages like Pandas, Tensorflow and Keras, Python has rightfully attracted developers across the globe and established itself as The Language for Data Science.
But, what most beginners miss out on are the lesser-known libraries, their methods, and functions in Python which can make our lives so much easier and our codes so much more efficient.
So here are 10 Data Science libraries that can help you get an edge:
2020-10-05 10:27:50+00:00 Read the full story…
Weighted Interest Score: 2.9914, Raw Interest Score: 1.7951,
Positive Sentiment: 0.3675, Negative Sentiment 0.2544
As AI chips improve, is TOPS the best way to measure their power?
Once in a while, a young company will claim it has more experience than would be logical — a just-opened law firm might tout 60 years of legal experience, but actually consist of three people who have each practiced law for 20 years. The number “60” catches your eye and summarizes something, yet might leave you wondering whether to prefer one lawyer with 60 years of experience. There’s actually no universally correct answer; your choice should be based on the type of services you’re looking for. A single lawyer might be superb at certain tasks and not great at others, while three lawyers with solid experience could canvas a wider collection of subjects.
If you understand that example, you also understand the challenge of evaluating AI chip performance using “TOPS,” a metric that means trillions of operations per second, or “tera operations per second.” Over the past few years, mobile and laptop chips have grown to include dedicated AI processors, typically measured by TOPS as an abstract measure of capability. Apple’s A14 Bionic brings 11 TOPS of “machine learning performance” to the new iPad Air tablet, while Qualcomm’s smartphone-ready Snapdragon 865 claims a faster AI processing speed of 15 TOPS.
But whether you’re an executive considering the purchase of new AI-capable computers for an enterprise or an end user hoping to understand just how much power your next phone will have, you’re probably wondering what these TOPS numbers really mean. To demystify the concept and put it in some perspective, let’s take a high-level look at the concept of TOPS, as well as some examples of how companies are marketing chips using this metric.
2020-09-30 00:00:00 Read the full story…
Weighted Interest Score: 2.9565, Raw Interest Score: 1.2591,
Positive Sentiment: 0.1252, Negative Sentiment 0.1031
Learn Machine Learning Concepts Interactively
Five freely available tools that intuitively break down the complicated machine learning concepts
How Machine Learning Algorithms work under the hood is an aspect not understood by many. What does a layer of CNN see? How backpropagation works,? How exactly are the weights updated in a layer? These are some of the questions that pop in our minds time and again. These concepts can be particularly overwhelming for the beginners who want to have a hard time in aligning mathematical equations with the theory. The good news is that some people understand this pain and want to provide alternative forms of learning. This article is a compilation of five such tools that go beyond the theory and instead present intuitively explanations of the standard machine learning concepts.
- Explained Visually
- Seeing Theory
- R2D3: Statistics and Data Visualization
- CNN Explainer
2020-10-04 15:39:02.100000+00:00 Read the full story…
Weighted Interest Score: 8.0831, Raw Interest Score: 2.5404,
Positive Sentiment: 0.2309, Negative Sentiment 0.0000
AI Weekly: Palantir, Twitter, and building public trust into the AI design process
The news cycle this week seemed to grab people by the collar and shake them violently. On Wednesday, Palantir went public. The secretive company with ties to the military, spy agencies, and ICE is reliant on government contracts and intent on racking up more sensitive data and contracts in the U.S. and overseas.
Following a surveillance-as-a-service blitz last week, Amazon introduced Amazon One, which allows touchless biometric scans of people’s palms for Amazon or third-party customers. The company claims palm scans are less invasive than other forms of biometric identifiers like facial recognition.
On Thursday afternoon, in the short break between an out-of-control presidential debate and the revelation that the president and his wife had contracted COVID-19, Twitter shared more details about how it created AI that appears to prefer white faces over black faces. In a blog post, Twitter chief technology officer Parag Agrawal and chief design officer Dantley Davis called failure to publish the bias analysis at the same time as the rollout of the algorithm years ago “an oversight.” The Twitter executives shared additional details about a bias assessment that took place in 2017, and Twitter says it’s working on moving away from the use of saliency algorithms. When the problem initially received attention, Davis said Twitter would consider getting rid of image cropping altogether.
2020-10-02 00:00:00 Read the full story…
Weighted Interest Score: 4.0426, Raw Interest Score: 1.0002,
Positive Sentiment: 0.0638, Negative Sentiment 0.2979
RBC launches ethical AI hub for Canadian firms
Royal Bank of Canada’s artificial intelligence research unit has launched a programme to help promote “ethical AI”.
While most Canadian businesses think that it is important to implement AI in an ethical and responsible way, the vast majority say they face barriers – such as cost, time and lack of understanding – to doing so.
RBC’s Borealis AI unit is hoping to tackle this with its Respect AI online hub, which brings together open source research code, tutorials, academic research and lectures that firms can use.
2020-10-05 00:01:00 Read the full story…
Weighted Interest Score: 5.6982, Raw Interest Score: 2.0063,
Positive Sentiment: 0.1254, Negative Sentiment 0.3762
Amsterdam And Helsinki Launch Open AI Registers
Amsterdam and Helsinki both launched an Open AI Register at the Next Generation Internet Summit. According to sources, these two cities are the first in the world that are aiming to be open and transparent about the use of algorithms and AI in the cities.
Currently, in the beta version, Algorithm Register is an overview of the artificial intelligence systems and algorithms used by the City of Amsterdam. The register is an effort to show where the cities are currently making use of AI and how the algorithms work.
Jan Vapaavuori, Mayor of Helsinki stated, “Helsinki aims to be the city in the world that best capitalises on digitalisation. Digitalisation is strongly associated with the utilisation of artificial intelligence. With the help of artificial intelligence, we can give people in the city better services available anywhere and at any time. In the front rank with the City of Amsterdam, we are proud to tell everyone openly what we use Artificial Intelligence for.”
2020-09-30 06:26:57+00:00 Read the full story…
Weighted Interest Score: 5.6549, Raw Interest Score: 1.5820,
Positive Sentiment: 0.2082, Negative Sentiment 0.0000
Complete Guide to Using AutoSklearn – Tool For Faster Machine Learning Implementations
Automated machine learning algorithms can be a huge time saver especially if the data is huge or the algorithm to be used is a simple classification or regression type problem. One such open-source automation in AutoML was the development of AutoSklearn. We know that the popular sklearn library is very rampantly used for building machine learning models. But with sklearn, it is up to the user to decide the algorithm that has to be used and do the hyperparameter tuning. With autosklearn, all the processes are automated for the benefit of the user. The benefit of this is that along with data preparation and model building, it also learns from models that have been used on similar datasets and can create automatic ensemble models for better accuracy.
In this article, we will see how to make use of autosklearn for classification and regression problems.
2020-10-03 04:30:01+00:00 Read the full story…
Weighted Interest Score: 5.2457, Raw Interest Score: 1.5870,
Positive Sentiment: 0.1570, Negative Sentiment 0.1918
Data Architecture and Artificial Intelligence: How Do They Work Together?
Artificial intelligence (AI) is rapidly gaining ground as core business competency. Today’s machine learning (ML) or deep learning (DL) algorithms promise to revolutionize business models and processes, restructure workforces, and transform data infrastructures to enhance process efficiency and improve decision-making throughout the enterprise. Gone are the days of data silos and manual algorithms.
However, widespread belief by stating that AI’s growth was stunted in the past mainly due to the unavailability of large data sets. Big data changed all that – enabling businesses to take advantage of high-volume and high-velocity data to train AI algorithms for business-process improvements and enhanced decision making.
2020-09-29 07:35:33+00:00 Read the full story…
Weighted Interest Score: 5.0634, Raw Interest Score: 2.4184,
Positive Sentiment: 0.3857, Negative Sentiment 0.1459
Build a fully production ready machine learning app with Python Django, React, and Docker
A complete, step by step guide to building a production-grade machine learning app with Django, PostgreSQL, React, Redux and Docker.
We are going to create a simple machine learning application with Django REST framework, which predicts the species of a sample flower based on measurements of its features i.e. the sepal and petal dimensions — length and width. We have already covered this is in great detail in a previous article. Please familiarize your self with that article. We would use the same Django application here and make some modifications as required. In the previous article, the Django application was connected with a SQLite database. For this article, however, we would use Postgres as our database, as Postgres is better suited for production builds. Django comes packaged with a great admin dashboard. With the admin dashboard, we can register users to our application, who can then interact with our machine learning application to make predictions. Our Django application thus would serve the purpose of our backend and admin tasks.
2020-09-13 00:00:00 Read the full story…
Weighted Interest Score: 4.5161, Raw Interest Score: 1.9355,
Positive Sentiment: 0.6452, Negative Sentiment 0.0000
LinkedIn Open-Sources GDMix, An AI Framework That Trains Efficient Personalisation Models
Recently, developers at LinkedIn open-sourced a deep learning framework known as GDMix. GDMix or Generalised Deep Mixed model is a deep ranking framework to train non-linear fixed effect and random effect models. According to the developers, this type of models is widely used in the personalisation of search as well as recommender systems.
With more than 700 million members, billions of feed updates, and more than thousands of courses to choose from, the professional networking platform is heavily dependent on AI and machine learning techniques. Personalised ranking for search and recommender systems is one of the key technologies to achieve the goal of the best experience possible for the members in LinkedIn.
A fully personalised ranking algorithm includes features like request features, document features, context features and interactive features including a large number of categorical ID features. However, it is most often difficult to train models of this size efficiently.
2020-10-05 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.4564, Raw Interest Score: 2.6934,
Positive Sentiment: 0.2801, Negative Sentiment 0.0862
BlackLine buys Rimilia to add AI-powered accounts receivable automation to platform
Accounting automation software provider BlackLine has acquired AI-powered accounts receivable outfit Rimilia for $150 million in cash.
UK-based Rimilia provides accounts receivable automation technology that helps firms control cash flow and cash collection in real time. Using AI and machine learning, the SaaS platform simplifies the order-to-cash process by automating both the collection and allocation of customer cash.
American outfit BlackLine says Rimilia strengthens its position with the Office of the Controller by driving end-to-end automation of the cash lifecycle and ensuring greater data integrity.
Marc Huffman, president and COO, BlackLine, says: “With most companies using legacy, repetitive and manual processes to manage their order-to-cash, our customers and partners have long been asking for a solution that will enable better cash and liquidity management. This is especially critical now in these difficult economic times.
2020-10-05 00:01:00 Read the full story…
Weighted Interest Score: 4.3165, Raw Interest Score: 2.9064,
Positive Sentiment: 0.4541, Negative Sentiment 0.1817
Sandbagging AI Might Feint Being Dimwitted, Including For Autonomous Cars
Could AI become smart enough to pretend to be dimwitted, doing so to lull hapless humans into complacency while meanwhile, the AI is plotting to overtake humanity?
Sounds like a farfetched science fiction movie.
To be clear, AI is not yet akin to human intelligence and the odds are that we are a long way distant from the promise of such vaunted capabilities. Those touting the use of Machine Learning (ML) and Deep Learning (DL) are hoping that the advent of ML/DL might be a path toward full AI, though right now ML/DL is mainly a stew of computationally impressive pattern matching and we don’t know if it will scale-up to anything approaching an equivalent of the human brain.
The struggle and earnestness toward achieving full AI is nonetheless still a constant drumbeat of those steeped in AI and the belief is that we will eventually craft or invent a machine-based artificial intelligence made entirely out of software and hardware.
2020-10-01 22:18:18+00:00 Read the full story…
Weighted Interest Score: 4.2757, Raw Interest Score: 1.1443,
Positive Sentiment: 0.1021, Negative Sentiment 0.1988
Air Street Capital: AI industry remains strong despite academic brain drain, tech nationalization
London-based venture capital firm Air Street Capital today published the State of AI Report 2020, its third annual survey canvassing research, talent, industrial, and political trends in the field of AI. Coauthored by University College London visiting professor Ian Hogarth and AI investor Nathan Benaich, the report aims to highlight technological breakthroughs and areas of commercial application for AI as well as the regulation of AI, its economic implications, and emerging geopolitical issues.
Among other findings, this year’s report implies AI remains mostly closed source, harming accountability and reproducibility, while corporate-driven academic “brain drain” appears to be impacting entrepreneurship. Self-driving cars are in the Precambrian stages. And political leaders are beginning to question whether acquisitions of AI startups should be scrutinized or outright blocked.
2020-10-01 00:00:00 Read the full story…
Weighted Interest Score: 4.1808, Raw Interest Score: 1.7461,
Positive Sentiment: 0.2034, Negative Sentiment 0.3051
PyTorch Upgrades to Cloud TPUs, Links to R
A version of the PyTorch machine learning framework that incorporates a deep learning compiler to connect the Python package to cloud Tensor processors (TPUs) is now available on Google Cloud, the public cloud vendor and PyTorch co-developer Facebook announced.
The general availability on PyTorch/XLA means users can access cloud TPU accelerators via a stable integration, the companies said Tuesday (Sept. 29).
Separately, promoters of the programming language R released a package that allows developers to use “PyTorch functionality natively from R.” The new tool, dubbed “Torch for R,” requires no Python installation.
Meanwhile, Facebook and Google said PyTorch/XLA combines the machine learning library’s APIs with XLA’s linear algebra compiler that targets CPUs, GPUS and, now, cloud TPUs. While running on most standard Python programs, PyTorch/XLA defaults to CPUs for operations not yet supported on Tensor processors.
2020-09-29 00:00:00 Read the full story…
Weighted Interest Score: 3.9629, Raw Interest Score: 2.5381,
Positive Sentiment: 0.2538, Negative Sentiment 0.0846
Build The Next Best Code Curator With MachineHack’s New Hackathon
“Can you come up with an algorithm that can predict the bugs, features, and questions based on GitHub titles?”
n average smartphone OS contains more than 10 million lines of code. A million lines of code take 18000 pages to print which is equal to Tolstoy’s War and Peace put together 14 times! There is always a simpler, shorter version of the code along with a longer more exhaustive version.
The number of tools, languages, techniques, and applications that the machine learning ecosystem has nurtured can be overwhelming to a developer. What can be even more daunting is saving the code from going stale. The hidden technical debts within a pipeline can make the product dysfunctional. So, what if there is a tool that does this job for us; to serve us with clean code and answer all your queries?
If you are one of those ML fanatics who think that this can be done and should be done then you should definitely check out this new hackathon brought to you by MachineHack in association with Embold.
2020-09-28 04:30:36+00:00 Read the full story…
Weighted Interest Score: 3.7050, Raw Interest Score: 1.8202,
Positive Sentiment: 0.2184, Negative Sentiment 0.3640
A Simple Explanation of K-Means Clustering and its Adavantages
K-means clustering is a very famous and powerful unsupervised machine learning algorithm. It is used to solve many complex unsupervised machine learning problems. Before we start let’s take a look at the points which we are going to understand.
Let us understand the K-means clustering algorithm with its simple definition. A K-means clustering algorithm tries to group similar items in the form of clusters. The number of groups is represented by K.
Let’s take an example. Suppose you went to a vegetable shop to buy some vegetables. There you will see different kinds of vegetables. The one thing you will notice there that the vegetables will be arranged in a group of their types. Like all the carrots will be kept in one place, potatoes will be kept with their kinds and so on. If you will notice here then you will find that they are forming a group or cluster, where each of the vegetables is kept within their kind of group forming the clusters.
2020-10-04 09:26:59+00:00 Read the full story…
Weighted Interest Score: 3.3501, Raw Interest Score: 1.1275,
Positive Sentiment: 0.1524, Negative Sentiment 0.1067
Would you trust Amazon to be your personal AI stylist?
Fashion technology start-ups mix human stylists with AI to come up with style suggestions for their customers
The concept of paying for the privilege of buying clothes which are likely to have been selected for you by an algorithm working with a human stylist can seem strange. But a large part of the appeal comes from the lack of access which many people have to any kind of style advice.
We all like to think we have style. Whether it’s a trademark hoodie or sharp suit, the way we dress is supposed to be a reflection of our personalities. With an explosion in online fashion retailers, however, finding the perfect outfit can prove to be a bewildering task. …
2020-09-30 00:00:00 Read the full story…
Weighted Interest Score: 3.3215, Raw Interest Score: 1.1872,
Positive Sentiment: 0.1979, Negative Sentiment 0.0791
Confusion Matrix is No More a Confusion
After we have trained our model and have predicted the outcomes, we need to evaluate the model’s performance. And here comes our Confusion Matrix. But before diving into what is a confusion matrix and how it evaluates the model’s performance, let’s have a look into the picture below.
What is the Confusion Matrix?
2020-10-05 09:35:00+00:00 Read the full story…
Weighted Interest Score: 3.2317, Raw Interest Score: 1.1935,
Positive Sentiment: 0.2387, Negative Sentiment 0.7161
Ideal Prediction adds two to advisory board
Ideal Prediction (Ideal), a trading analysis and data science company for the capital markets, has appointed Jonathan Fieldman and Geoff Jones to its Advisory Board.
Fieldman says: “Having spent the past 16 years at Broadway Technology leading the transformation of FICC electronic trading, I have witnessed the need for increased assurance and attestation to ethical behaviour. With its leadership, depth of knowledge, and proven financial analytics solutions, Ideal Prediction is perfectly positioned to lead the evolution of the supervisory function and first line of defence.”
2020-10-01 00:00:00 Read the full story…
Weighted Interest Score: 3.1935, Raw Interest Score: 1.6206,
Positive Sentiment: 0.7150, Negative Sentiment 0.0000
Ideal Prediction Expands Board with New Advisors
Executives join from Broadway Technology and RBC Capital Markets
Ideal Prediction (Ideal), the independent trading analysis and data science company for capital markets, has expanded its advisory Board with new members Jonathan Fieldman and Geoff Jones, according to a company press release. The new advisors previously held executive positions at RBC Capital Markets and Broadway Technology.
Ideal won FX Market’s e-FX Award for ‘Best Surveillance Provider’ in 2019 and 2020 and provides banks and regulators with analysis or raw transact…
2020-10-01 15:08:14+00:00 Read the full story…
Weighted Interest Score: 3.0629, Raw Interest Score: 1.5315,
Positive Sentiment: 0.6623, Negative Sentiment 0.0414
The Origin Story and Impact of Neural Networks in Data Science
Neural networks are ubiquitous right now. Organizations are splurging money on hardware and talent to ensure they can build the most complex neural networks and bring out the best deep learning solutions.
Although Deep Learning is a fairly old subset of machine learning, it didn’t get its due recognition until the early 2010s. Today, it has taken the world by storm and captured public attention in a way that very few algorithms have managed to accomplish.
In this article, I wanted to take a slightly different approach to neural networks and understand how they came to be. This is the story of the origin of neural networks!
2020-09-29 09:55:29+00:00 Read the full story…
Weighted Interest Score: 3.1924, Raw Interest Score: 1.8221,
Positive Sentiment: 0.2680, Negative Sentiment 0.0715
How Money Laundering Concerns Require New AI Monitoring Solutions
Artificial intelligence has created a number of amazing opportunities for the financial sector. The benefits of AI are endless. Financial institutions are using AI to enhance decision-making, improve customer service, project customer needs and much more.
We have talked about the benefits of using big data and AI to improve cybersecurity. But there are other processes that could be equally important for financial institutions.
AI can solve some pressing challenges that financial institutions can’t afford to overlook. This includes the growing threat of money laundering.
2020-09-27 20:43:23+00:00 Read the full story…
Weighted Interest Score: 3.1689, Raw Interest Score: 1.6142,
Positive Sentiment: 0.1932, Negative Sentiment 0.8002
Learning Graph Databases Just Got a Whole Lot Easier
Graph databases are the fastest growing database technology, representing a departure from the relational and NoSQL models – a departure that is inherently worthwhile.
Graph Databases For Dummies, Neo4j Special Edition, a new book by Dr. Jim Webber, Neo4j Chief Scientist, and Rik Van Bruggen, Neo4j Regional Vice President, is all about getting started with graph databases. This book walks readers through modeling, querying and importing graph data, all the way through to their first production system.
This article extracts some main highlights of Chapter 1 of the book – the fundamental graph database building blocks.
2020-10-05 00:00:00 Read the full story…
Weighted Interest Score: 3.1507, Raw Interest Score: 1.9313,
Positive Sentiment: 0.1704, Negative Sentiment 0.0852
Common Feature Selection Filter Based Techniques in Python!
As a programmer who is engaged in the field of AI and Machine Learning related activities, it is very important for him/her to perform AI-related stuff most efficiently. The efficient way here in this context means that he/she should be well capable of getting the best predictive analysis result when feeding it to the Machine learning model and to achieve this efficiency many preprocessing steps are required before the prediction to be made.
These preprocessing steps are data handling, manipulations, creation of features, updating the features, normalizing data, etc. From all these preprocessing steps present out there one of the main steps is to do the feature selection. As we know that Machine learning is an iterative process in which the machine tries to learn based on the historical data we are feeding to it and then makes predictions based on the same.
2020-10-05 10:01:24+00:00 Read the full story…
Weighted Interest Score: 3.0521, Raw Interest Score: 1.7362,
Positive Sentiment: 0.2993, Negative Sentiment 0.2195
IBM and Fenergo join forces to speed customer onboarding
IBM (NYSE: IBM) and Fenergo, a leading provider of client lifecycle management (CLM) solutions for financial institutions, today announced the general availability of IBM Customer Lifecycle Management (CLM) with Fenergo.
The offering is designed to incorporate artificial intelligence (AI) from IBM Watson and analytics on the IBM Cloud to help financial institutions drive efficiencies in customer onboarding through improved personalization, risk assessment and regulatory compliance.
2020-10-05 13:29:00 Read the full story…
Weighted Interest Score: 2.9522, Raw Interest Score: 1.7478,
Positive Sentiment: 0.5202, Negative Sentiment 0.1665
TIBCO reveals ‘Hyperconverged Analytics’ to speed up data insights
Enterprise data company TIBCO Software has announced a “disruptive approach to analytics” with TIBCO Hyperconverged Analytics, a real-time analytics service the company says reduces the time between business events and insights.
TIBCO says its approach empowers its customers to connect, unify, and confidently predict business outcomes, solving the world’s most complex data-driven challenges.
As part of the Hyperconverged Analytics experience, the company also unveiled TIBCO Spotfire 11 and TIBCO Cloud Data Streams, which dramatically accelerate insights and actions for businesses.
2020-10-01 04:00:20+00:00 Read the full story…
Weighted Interest Score: 2.9130, Raw Interest Score: 1.7820,
Positive Sentiment: 0.1714, Negative Sentiment 0.0685
Ask HBR: Data Science and the Art of Persuasion
Despite significant investments to hire talented data scientists, many companies are disappointed with their results.
The problem, says author and data visualization expert, Scott Berinato, is that most data scientists are trained to ask smart questions, wrangle the relevant data, and uncover insights. But few data scientists are skilled at effectively communicating what those insights mean for the business. How can companies get greater value from their data science teams?
On September 29th, Berinato will join the next Ask HBR webinar and will share insights from his recent HBR article, Data Science and the Art of Persuasion. Berinato will discuss what data science teams need to do to achieve greater success and will answer questions around the kinds of talents data science teams need.
2020-09-29 04:00:00+00:00 Read the full story…
Weighted Interest Score: 2.9046, Raw Interest Score: 1.5214,
Positive Sentiment: 0.4841, Negative Sentiment 0.4841
Three Necessities for a Modern Analytics Ecosystem (Webinar)
Now, more than ever, enterprises need speed, agility, and insight to navigate today’s rapidly-changing business environments. Fast, actionable intelligence is a universal goal. However, making the right data available to the right people at the right time is an ongoing challenge. To cover the full spectrum of enterprise data — and the diverse needs of enterprise data users — traditional data warehousing and analytics systems need to be reexamined.
- A Public Cloud Strategy: Public clouds enable a new era of application and data management while freeing companies from costly infrastructure administration and resource constraints necessary for data warehouses to reach their full potential.
- An Integrated Data and Analytics Ecosystem: Modernizing the analytics ecosystem may begin with cloud data lakes or data science teams, but it is necessary to have a data warehouse within the cloud environment for integrated and defined data hubs and subject areas to draw upon.
- A Streaming Data-First Strategy: Embracing a paradigm whereby all data flows in streams resets the common denominator for all analytics applications to leverage easily and faster.
2020-10-15 00:00:00 Read the full story…
Weighted Interest Score: 2.9044, Raw Interest Score: 1.5523,
Positive Sentiment: 0.3005, Negative Sentiment 0.2003
Most Used Loss Functions To Optimize Machine Learning Algorithms
his article gives us a brief overview of the most used loss functions to optimize machine learning algorithms. We all use machine learning algorithms to solve various complex problems and select them based on the loss function value and evaluation metrics. But do we know that we are selecting the correct loss function for our algorithm? If not, then let’s find out.
Mainly the loss functions are divided into three categories:
- Regression loss functions
- Mean Squared Error
- Mean Squared Logarithmic Error
- Mean Absolute Error
- Binary classification loss functions
- Binary Cross Entropy
- Hinge Loss
- Squared Hinge Loss
- Multi-class classification loss functions
- Multi-class cross entropy
- Sparse multi-class cross entropy
- Kullback Leibler divergence loss
2020-10-05 10:30:52+00:00 Read the full story…
Weighted Interest Score: 2.9030, Raw Interest Score: 1.6957,
Positive Sentiment: 0.1622, Negative Sentiment 1.1501
AI of Growing Importance to Gaming (Gambling) Industry
Operators of casinos and online games are incorporating AI in efforts ranging from maximizing profits to helping problem gamblers.
The gaming industry is technically savvy, having integrated automation into its operations to gain efficiencies and offer conveniences to customers. Now AI is being applied to casinos and the gambling industry, in-person and online, enabling more advances such as allowing multiple users to play the same game at the same time from different locations.
Other advantages include ability to track compliance with online gambling regulations, collection of data on gambling preferences to enable predictions and deliver customized service to customers, according to a recent account in LA Progressive.
It might be difficult for operators to enhance the customer experience without the use of AI in the future, suggested a speaker at SBC Summit Barcelona – Digital, the Global Betting & Gaming Show, usually held in Barcelona but held online recently.
2020-10-01 22:41:19+00:00 Read the full story…
Weighted Interest Score: 2.7230, Raw Interest Score: 1.2533,
Positive Sentiment: 0.3244, Negative Sentiment 0.2359
New Product Opportunities Seen by AI; Some Follow Pandemic Disruptions
AI is helping companies identify new product opportunities by searching through mountains of data quickly to find patterns that can be analyzed for new product and service opportunities; by iterating new product or service concepts through trial and error virtually, simulating consumer response in a fraction of the time and at a lower cost than real-world testing; and by predicting demand for product offerings and adaptations for local markets by analyzing search and purchase patterns in each geography.
These insights are contained in a recent account in Forbes written by Michelle Greenwald, CEO of Catalyzing Information, described as an “innovation hub.” She has worked in marketing capacities at many companies including Disney, Pepsi, Nestle, J. Walter Thompson and General Foods. She has also taught marketing courses at many colleges including Wharton, Columbia and NYU Stern.
She has identified examples of how AI is being used in product development.
2020-10-01 22:45:22+00:00 Read the full story…
Weighted Interest Score: 2.6824, Raw Interest Score: 1.3427,
Positive Sentiment: 0.3197, Negative Sentiment 0.1758
Are We a $1B Investment Away from General AI?
As most anyone following AI knows, the R&D company OpenAI recently released a paper on GPT-3, its third-generation language model which, at 175 billion parameters, has the claim to fame of being about an order of magnitude larger than any language model that came before it.
Currently available in private beta to select developers, GPT-3 has shown that it can generate everything from believable short stories, rap songs, and press releases to HTML code for creating web page layouts, all with minimal inputs or prompts. This is a very big deal.
Until now, the most advanced language models include Google’s BERT, Microsoft’s Turing Natural Language Generation, and GPT-3’s predecessor GPT-2, which can do things like complete sentences in a natural-sounding way, suggest short replies to email messages, offer answers to basic questions, and generate text that seems like it could be written by a human. While impressive, oftentimes, these models also generate clunky or absurd results, giving skeptics reason to believe that we’re still a very long way from machines being able to approximate human-level language capabilities.
2020-10-02 07:30:56+00:00 Read the full story…
Weighted Interest Score: 2.6767, Raw Interest Score: 1.5684,
Positive Sentiment: 0.1882, Negative Sentiment 0.1673
AI Is A Double-Edged Sword In Phishing
Every day, on average, 56 million phishing emails are sent, and it takes just 82 seconds for a person to be victimised by such attacks. Phishing is one of the oldest yet effective forms of a cybersecurity threat. Over time it has graduated from scamming emails from a Nigerian prince to more sophisticated and sly techniques, such as Distributed Spam Distraction, polymorphic attacks, and visual similarity attack.
Artificial intelligence has played a great role in thwarting attacks of such nature. Let us look at a few such examples.
2020-10-04 07:30:17+00:00 Read the full story…
Weighted Interest Score: 2.5779, Raw Interest Score: 1.1187,
Positive Sentiment: 0.2693, Negative Sentiment 0.5594
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