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AI ML News January 2021 : Open AI CLIP / DALL-E : learning visual concepts from natural language supervision : AI models from Microsoft and Google already surpass human performance on the SuperGLUE language benchmark : Budgeting and Staffing to Deal With the Data Deluge (Video) : Researchers find machine learning models still struggle to detect hate speech : Outlandish Stanford facial recognition study claims there are links between facial features and political orientation

AI & Machine Learning News. 11, January 2021

AI & Machine Learning News. 11, January 2021

AI and Machine Learning Newsletter

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?

Open AI CLIP: learning visual concepts from natural language supervision

A transformed-based neural network that uses Contrastive Language–Image Pre-training to classify images

DALL-E seems to have gotten most of the attention this week, but I think CLIP may end up being even more consequential. We’ve been experimenting with it this week and the results seem almost too good to be true; it was even able to classify species of mushrooms in photos from my camera roll fairly well.

A few days ago OpenAI released 2 impressive models CLIP and DALL-E. While DALL-E is able to generate text from images, CLIP classifies a very wide range of images by turning image classification into a text similarity problem. The issue with current image classification networks is that they are trained on a fixed number of categories, CLIP doesn’t work this way, it learns directly from the raw text about images, and thus it isn’t limited by labels and supervision. This is quite impressive, CLIP can classify images with state of the art accuracy without any dataset-specific training.

2021-01-11 01:23:30.849000+00:00 Read the full story…
Weighted Interest Score: 2.2305, Raw Interest Score: 1.0889,
Positive Sentiment: 0.3392, Negative Sentiment 0.0893

CloudQuant Thoughts : DALL-E certainly seems to be the most popular news in the AI/ML community this week so far be it from me to keep it off the top of our blog!

AI models from Microsoft and Google already surpass human performance on the SuperGLUE language benchmark

In late 2019, researchers affiliated with Facebook, New York University (NYU), the University of Washington, and DeepMind proposed SuperGLUE, a new benchmark for AI designed to summarize research progress on a diverse set of language tasks. Building on the GLUE benchmark, which had been introduced one year prior, SuperGLUE includes a set of more difficult language understanding challenges, improved resources, and a publicly available leaderboard.
When SuperGLUE was introduced, there was a nearly 20-point gap between the best-performing model and human performance on the leaderboard. But as of early January, two models — one from Microsoft called DeBERTa and a second from Google called T5 + Meena — have surpassed the human baselines, becoming the first to do so.
Sam Bowman, assistant professor at NYU’s center for data science, said the achievement reflected innovations in machine learning including self-supervised learning, where models learn from unlabeled datasets with recipes for adapting the insights to target tasks. “These datasets reflect some of the hardest supervised language understanding task datasets that were freely available two years ago,” he said. “There’s no reason to believe that SuperGLUE will be able to detect further progress in natural language processing, at least beyond a small remaining margin.”

2021-01-06 00:00:00 Read the full story…
Weighted Interest Score: 2.4390, Raw Interest Score: 1.3324,
Positive Sentiment: 0.2521, Negative Sentiment 0.3061

CloudQuant Thoughts : The way Google Translate learned to translate is fascinating, universal grammar and how most languages share huge chunks of structure. It is also interesting that the bible, a book translated into 100’s of languages is itself now an effective Rosetta Stone.

Budgeting and Staffing to Deal With the Data Deluge (Video)

Craig S. Mullins, DBTA columnist and president of Mullins Consulting, discussed how to contend with big data and data growth at an organizational level at Data Summit Connect Fall 2020.
Mullins began with an overview of data growth as a trend and highlighted a forecast by IDC that the global datasphere will reach 175 zettabytes by 2025. While the unabated data growth that organizations are experiencing is alarming, Mullins said, the more troubling aspect is the lack of attention it receives from management—the lack of attention, at least in terms of what matters, and that’s staffing. “Most organizations and their leaders and executives are saying things like ‘we want to take advantage of analytics on big data’ and ‘we treat data as a corporate asset,’ but the actual reality is somewhat of a neglect.”

2021-01-06 00:00:00 Read the full story…
Weighted Interest Score: 2.6625, Raw Interest Score: 1.6252,
Positive Sentiment: 0.1037, Negative Sentiment 0.4495

CloudQuant Thoughts : “Decreasing DBAs with increasing data is a recipe for problems.”

‘Augmented creativity’: How AI can accelerate human invention

In 2012, economist Robert Gordon published a controversial paper in which he argued that economic growth was largely over, due in no small part to our failure to maintain the engines of innovation in recent decades.
A study from the Stanford Institute for Economic Policy Research supported his general thesis and argued that while we’re spending even more money on creativity and innovation, our returns are flatlining. And this investment is not only in dollars, as the research revealed roughly 20 times as many people work in R&D today as did in 1930.
So what gives? Why has creating things become so difficult? Researchers from Northwestern University attempt to answer this in a paper that shows a growing percentage of today’s creation is what’s known as recombination. Indeed, 40% of all patents in the U.S. Patent and Trademark Office are not completely new works, but rather mishmashes of existing ideas bolted together.

2021-01-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5472, Raw Interest Score: 1.1054,
Positive Sentiment: 0.4566, Negative Sentiment 0.2403

CloudQuant Thoughts : I always reference this presentation from 2018 whenever people talk about AI creativity. It is such an excellent dissection of the impact and potential gains of AI in a creative environment.

Researchers find machine learning models still struggle to detect hate speech

Detecting hate speech is a task even state-of-the-art machine learning models struggle with. That’s because harmful speech comes in many different forms, and models must learn to differentiate each one from innocuous turns of phrase. Historically, hate speech detection models have been tested by measuring their performance on data using metrics like accuracy. But this makes it tough to identify a model’s weak points and risks overestimating a model’s quality, due to gaps and biases in hate speech datasets.
In search of a better solution, researchers at the University of Oxford, the Alan Turing Institute, Utrecht University, and the University of Sheffield developed HateCheck, an English-language benchmark for hate speech detection models created by reviewing previous research and conducting interviews with 16 British, German, and American nongovernmental organizations (NGOs) whose work relates to online hate. Testing HateCheck on near-state-of-the-art detection models — as well as Jigsaw’s Perspective tool — revealed “critical weaknesses” in these models, according to the team, illustrating the benchmark’s utility.
HateCheck’s tests canvass 29 modes that are designed to be difficult for models relying on simplistic rules, including derogatory hate speech, threatening language, and hate expressed using profanity. Eighteen of the tests cover distinct expressions of hate (e.g., statements like “I hate Muslims,” “Typical of a woman to be that stupid,” “Black people are scum”), while the remaining 11 tests cover what the researchers call contrastive non-hate, or content that shares linguistic features with hateful expressions (e.g., “I absolutely adore women,” which contrasts with “I absolutely loathe women”).

2021-01-06 00:00:00 Read the full story…
Weighted Interest Score: 2.2194, Raw Interest Score: 1.0830,
Positive Sentiment: 0.1264, Negative Sentiment 0.4693

CloudQuant Thoughts : Hate Speech is incredibly difficult to identify. Reclaimed slurs alone would be incredibly difficult to parse from actual hate speech!

Outlandish Stanford facial recognition study claims there are links between facial features and political orientation

A paper published today in the journal Scientific Reports by controversial Stanford-affiliated researcher Michal Kosinski claims to show that facial recognition algorithms can expose people’s political views from their social media profiles. Using a dataset of over 1 million Facebook and dating sites profiles from users across Canada, the U.S., and the U.K., Kosinski and coauthors say they trained an algorithm to correctly classify political orientation in 72% of “liberal-conservative” face pairs.
The work, taken as a whole, embraces the pseudoscientific concept of physiognomy, or the idea that a person’s character or personality can be assessed from their appearance. In 1911, Italian anthropologist Cesare Lombroso published a taxonomy declaring that “nearly all criminals” have “jug ears, thick hair, thin beards, pronounced sinuses, protruding chins, and broad cheekbone.” Thieves were notable for their “small wandering eyes,” he said, and rapists their “swollen lips and eyelids,” while murderers had a nose that was “often hawklike and always large.”
Phrenology, a related field, involves the measurement of bumps on the skull to predict mental traits. Authors representing the Institute of Electrical and Electronics Engineers (IEEE) have said this sort of facial recognition is “necessarily doomed to fail” and that strong claims are a result of poor experimental design.

2021-01-11 00:00:00 Read the full story…
Weighted Interest Score: 2.5856, Raw Interest Score: 1.1426,
Positive Sentiment: 0.1379, Negative Sentiment 0.4039

CloudQuant Thoughts : Human bias surely! I am sure we all think we can tell what someone is like just by looking at them. Surely that translates into a bias. Phrenology was CRAZY!

Intel launches RealSense ID for on-device facial recognition

Intel today launched the newest addition to RealSense, its product range of depth and tracking technologies designed to give machines depth perception capabilities. Called RealSense ID, it’s an on-device solution that combines an active depth sensor with a machine learning model to perform facial authentication.
Intel claims RealSense ID adapts to users as physical features like facial hair and glasses change over time and works in various lighting conditions for people “with a wide range of heights or complexions.”
But numerous studies and VentureBeat’s own analyses of public benchmark data have shown facial recognition algorithms are susceptible to various biases. One issue is that the datasets used to train the algorithms skew white and male. IBM found that 81% of people in the three face-image collections most widely cited in academic studies have lighter-colored skin. Academics have found that photographic technology and techniques can also favor lighter skin, including everything from sepia-tinged film to low-contrast digital cameras. As a result, Amazon, IBM, Microsoft, and others have self-imposed moratoriums on the sale of facial recognition systems.

2021-01-06 00:00:00 Read the full story…
Weighted Interest Score: 2.4178, Raw Interest Score: 1.3791,
Positive Sentiment: 0.0563, Negative Sentiment 0.1407

Top 25 Companies Hiring Technologists Include Amazon, More

For many companies, 2020 was a year to retrench and readjust. Some tightened their budgets and laid off workers; others shifted their priorities, emphasizing some initiatives (such as e-commerce portals) over others (anything involving face-to-face interactions). Now, with the new year upon us, it’s time for many of these companies to hire the technologists they’ll need for their future plans.
Which companies are doing the most technologist hiring? For an answer, we turn to Burning Glass, which collects and analyzes job postings from across the country. We wanted to look at the past 60 days, because that’s when many teams at these companies began hiring technologists in earnest with an eye toward 2021. As with previous Burning Glass analyses, it’s clear that healthcare, defense, and (inevitably) tech lead when it comes to hiring technologists. Take a look:
2021-01-07 00:00:00 Read the full story…
Weighted Interest Score: 2.3431, Raw Interest Score: 1.3400,
Positive Sentiment: 0.0419, Negative Sentiment 0.0419

My Top 3 Machine Learning Algorithms

A Data Scientist’s favorite algorithms of now and for 2021
With the year 2021 in full effect, I wanted to discuss the updated list of my top three favorite Machine Learning algorithms and why. In the past year, I have gained more professional experience as well as practical experience from studying and playing with different algorithms on my own in my free time. New use cases, Kaggle examples, videos, and other articles have led me to focus on my favorite three algorithms, which include Random Forest, XGBoost, and CatBoost. There are benefits to them all and you can certainly produce impressive results with all three. While one is older and dependable, another is powerful and competitive, and the last is new and impressive, these three algorithms stand strong at the top of my list, and it will be interesting to see what three top your list. Keep on reading below if you would like to learn more about these three prominent Machine Learning algorithms.
2021-01-04 Read the full story…

5 Indian Companies Recruiting Data Scientists In Large Numbers

According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.
Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.
2020-12-31 Read the full story…

Upcoming AI Conferences To Look Forward To In 2021

A list of AI conferences that one can attend in 2021.
In the year 2020, one thing that turned out to have a massive impact on our daily lives and society is artificial intelligence. The Government of Telangana has even declared 2020 as the year of artificial intelligence. Starting from GPT-3 and improvements in health-tech to a conversation on ethical AI and advancements in neural networks, the year has seen it all. And this is the time when businesses are going to come out and talk about their contribution to the field of AI, as well as research and developments around it.
With the starting of this new year, we have come up with a list of upcoming AI conferences that one can attend in 2021, to keep themselves at the forefront of this technology.
2021-01-05 Read the full story…

Top 8 Things Developers Can Look Forward To At MLDS 2021

Machine Learning Developers Summit 2021 (MLDS21), brought to you by Analytics India Magazine, is scheduled to be held virtually from 11-13 February 2021. It will bring the machine learning community from across the globe together. With over 1500 ML developers, 60 speakers, and 200 organisations across the three days, it is one of India’s largest conferences that bring the ML ecosystem together.
The conference aims for ML developers and researchers to come together in one platform to discuss the exciting innovations that have shaped the industry in recent times. Here we bring 8 such interesting takeaways from the conference that developers can look forward to in the upcoming event.
2020-12-31 Read the full story…

Data Democratization and Governance for Responsible AI

Empowerment without defined responsibility and accountability has got no meaning. The potential of data is limitless. When it comes to making AI (Artificial Intelligence or Augmented Intelligence) responsible, explainable and trustworthy, data democratization and governance will need to be discussed in parallel as they are the two sides of the same coin. Explainable AI is also very important to understand and interpret the predictions and how to further improve the predictions to ensure better decision-making and to balance it with risk and accuracy.

Essentially, data democratization is around the easy accessibility of digital data and information to the average end-user. But to manage its accessibility, usability and protection, data governance procedures are required to be implemented as it ensures that data is used in the right way, by the right user and at the right time. It also brings the focus on responsibility and accountability in case something goes wrong.

2021-01-11 11:30:00+00:00 Read the full story…
Weighted Interest Score: 4.2926, Raw Interest Score: 1.9368,
Positive Sentiment: 0.2572, Negative Sentiment 0.1210

IBM Advances Watson Family Including AI FactSheets at AI Summit

At its virtual AI Summit held in December, IBM announced updates across the Watson family of products in areas of language, explainability and workplace automation. These included an effort to commercialize AI FactSheets developed by IBM Research, which were first proposed in a paper published in 2018.

The FactSheets will answer questions ranging from system operation and training data to underlying algorithms, test setups and results, performance benchmarks, fairness and robustness checks, intended uses, maintenance, and retraining, according to an account in VentureBeat. 
2021-01-07 19:47:54+00:00 Read the full story…
Weighted Interest Score: 4.2418, Raw Interest Score: 1.6829,
Positive Sentiment: 0.1147, Negative Sentiment 0.1147

Good AI in 2021 Starts with Great Data Quality

More and more companies want to use artificial intelligence (AI) in their organization to improve operations and performance. Achieving good AI is a whole other story.

AI initiatives can take a lot of time and effort to get up and running, often exceeding initial budget and time targets. Even more alarming is this assessment (paywall) that claimed that close to half of AI projects failed to even make it to production. Despite this risk, a continuing growing number of companies are investing an inordinate amount of their resources in hopes of deriving value from AI.
Many projects are often derailed because their data environment is simply not suitable for AI. The same issue often occurs for machine learning (ML) programs as well. These are not positive signs; however, the good news is that there are steps an organization can take to right the ship.

2021-01-08 08:35:31+00:00 Read the full story…
Weighted Interest Score: 4.0871, Raw Interest Score: 1.7294,
Positive Sentiment: 0.4864, Negative Sentiment 0.2162 Gets Mixed Reviews as Analysts Initiate Coverage has a strong market opportunity but for now is overvalued, analysts say as they initiate coverage of the artificial-intelligence company.

Shares of AI were lower after Wall Street analysts community initiated coverage of the artificial-intelligence company four weeks after it made its debut on the New York Stock Exchange.

At last check shares fell 8.2% to $127.48. In early December the Redwood City, Calif., company priced an i…
2021-01-04 15:01:20+00:00 Read the full story…
Weighted Interest Score: 3.6866, Raw Interest Score: 1.5668,
Positive Sentiment: 0.3687, Negative Sentiment 0.1382

How AI Empowers Machine Learning to Be More Democratized Q&A

Traditionally, machine learning tools were only available to enterprises with the necessary budget and expertise. Now, AI is empowering machine learning to be democratized to reach more users, allowing them to make the business intelligence-driven decisions that could transform how they operate in the year ahead. Jorge Torres and Adam Carrigan discuss the challenges SMB data scientists face, how AI is empowering the democratization of machine learning, and the impact this could have on any business that has structured data.
2021-01-11 08:30:26+00:00 Read the full story…
Weighted Interest Score: 3.6774, Raw Interest Score: 2.1245,
Positive Sentiment: 0.2314, Negative Sentiment 0.1472

Apply now to join Transform this July 2021

We’re delighted to announce that we’ve expanded Transform, the most important event of the year for enterprise technical leaders on how to leverage data and implement advanced technologies such as AI, to a full week scheduled for July 12-16 2021.
In addition to the reliable content VB has offered for years through our Transform flagship event, we’re inviting other organizers of events related to enterprise transformation to participate in Transform Week. Joining VentureBeat as Transform Week partners are Stanford’s Women in Data Science, Imago Techmedia, Data Science Salon, and more, bringing expert content from the data, informational, and security industries.
“We’re thrilled to join VentureBeat at Transform Week, allowing the broader community access to the latest data science and machine learning insights from the top enterprise companies.” says Anna Anisin, founder and CEO at Formulatedby, the producers of the Data Science Salon.

2021-01-07 00:00:00 Read the full story…
Weighted Interest Score: 3.6600, Raw Interest Score: 1.6539,
Positive Sentiment: 0.2363, Negative Sentiment 0.0295

The Best ML Notebooks And Infrastructure Tools For Data Scientists

Machine learning notebooks are opening up a world of possibilities in data science. Here, we look at the best notebooks and infrastructure tools in circulation.
achine learning or data science notebooks have become an integral tool for data scientists across the world. Notebooks are highly-interactive multi-purpose tools that not only let you write and execute code but, at the same time, analyse intermediate results to gain insights (using tables or visualisations) while working on a project.
Below is our list of the best data science notebooks in the business, based on four main parameters: language support, version control, data visualisation capabilities, and cost-efficiency.

2021-01-08 11:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6262, Raw Interest Score: 2.0426,
Positive Sentiment: 0.2793, Negative Sentiment 0.0349

Data Scientist vs Machine Learning Ops Engineer. Here’s the Difference.

Data Scientist / Machine Learning Operations (MLOps) Engineer Similarities and Differences

While I have written articles on Data Science and Machine Learning Engineering roles, I wanted to compare the specific positions of Data Scientists and Machine Learning Operations Engineers, often referred to as MLOps Engineers. Machine Learning itself can be incredibly broad, so as a result, a newer career has emerged that solely focuses on the operations rather than the research that goes behind the algorithms themselves. Data Scientists i…
2021-01-11 01:14:26.231000+00:00 Read the full story…
Weighted Interest Score: 3.4664, Raw Interest Score: 1.7348,
Positive Sentiment: 0.2126, Negative Sentiment 0.0935

SEI Podcast Series – Googlisation 2.0: Data smart companies and the new access to information: Part I

Over the last five years we’ve witnessed an explosion of data, but how should asset managers best approach building a Big Data strategy? What are the sorts of questions they should be asking themselves in terms of selecting data vendors, putting the right systems and processes in place, and establishing a culture where preparing to fail is accepted, and a long-term commitment to Big Data is upheld?
Just as crude oil needs to be carefully refined to remove impurities, so too asset managers need to carefully engineer the way they optimise alternative data, in order to generate incremental insights to support their investment programmes.

2021-01-04 14:53:03+00:00 Read the full story…
Weighted Interest Score: 3.4664, Raw Interest Score: 1.6701,
Positive Sentiment: 0.1044, Negative Sentiment 0.2088

Overcoming Four Key Data Transformation Challenges

It is safe to say that when data and analytics leaders built their data management and data analytics strategies in late 2019, they did not foresee the macroeconomic impacts of 2020. The upheaval of many of the best-laid plans touched all industries as executives looked to data to provide insight into how to withstand the fallout and course correct.
While enterprises have long understood the need to migrate to the cloud, the COVID-19 pandemic served as the catalyst for quick plans to get started right away. And many organizations are ready to make the shift to the cloud or hybrid model for data management. In a recent survey of enterprise IT and data professionals, more than one-third (38%) said they are already using cloud data warehouses (CDWs). Long term, 43% expected to have all of their data in the cloud, with the remainder planning to pursue hybrid models that leverage both cloud and on-premise data warehouses.

2021-01-07 00:00:00 Read the full story…
Weighted Interest Score: 3.4645, Raw Interest Score: 1.9496,
Positive Sentiment: 0.2359, Negative Sentiment 0.3229

Dremio raises $135 million to help companies rapidly analyze data

Dremio, a startup offering tools to help streamline and curate data, today announced that it raised $135 million in series D funding at a post-money valuation of $1 billion. The company says it’ll use the funds, which come nine months after a $70 million round, to invest in cloud data lake technologies that could benefit businesses looking to connect, analyze, and process data while accelerating database queries. Specifically, Dremio plans to expand its engineering centers of excellence and grow its customer-facing organizations to keep pace with new customer acquisitions.
Due to its scalability, low cost, and simplicity of management, cloud data lake storage has become the destination of choice for storing high volumes of data. According to a recent Allied Market Research report, the global data warehousing market size was valued at $18.61 billion in 2017, growing at a compound annual growth rate of 8.2% from 2018 to 2025. However, to audit that data, it has to be moved and copied into proprietary data warehouses, a process that can become costly, complex, and inflexible.

2021-01-06 00:00:00 Read the full story…
Weighted Interest Score: 3.3947, Raw Interest Score: 1.8234,
Positive Sentiment: 0.1536, Negative Sentiment 0.1536

Starburst raises $100 million to take on data lake rivals

Starburst Data has raised $100 million as the data analytics company continues to ride the surge in data lakes. Andreessen Horowitz led the round, which included Index Partners, Coatue, and Salesforce’s venture capital arm. The funding comes just six months after Starburst raised $42 million, bringing its total to $164 million for a valuation of $1.2 billion. And the latest announcement came on the same day another data lake company, Dremio, announced it had raised $100 million.
So what’s this arms race all about? As companies grapple with growing amounts of information, data lakes allow them to pool structured and unstructured data in one spot, which then facilitates the movement and processing of that data.

2021-01-07 00:00:00 Read the full story…
Weighted Interest Score: 3.3535, Raw Interest Score: 1.6767,
Positive Sentiment: 0.1341, Negative Sentiment 0.0671

Data Architecture with Data Governance: A Proactive Approach

“Data Architecture is the physical implementation of the Business Strategy,” said Nigel Turner, Principal Consultant in E.M.E.A. at Global Data Strategy, Ltd., speaking at the DATAVERSITY® Enterprise Data Governance Online Conference. “It’s a key part of the whole continuum that you need to build within an organization to manage data effectively,” and Data Governance forms an important bridge between those strategies and the real-world implementation of them in the business.
Data Architecture: What is it?
The DAMA DMBoK2 says that Data Architecture “defines the blueprint for managing data assets by aligning with organizational strategy to establish strategic data requirements and designs to meet these requirements.” Turner pointed out three key parts of this definition, the first being the word “blueprint.” “What that implies is that any Data Architecture that doesn’t have an implementation plan will probably remain on the shelf until the mists of eternity have risen.”
2021-01-07 08:35:01+00:00 Read the full story…
Weighted Interest Score: 3.3207, Raw Interest Score: 1.8495,
Positive Sentiment: 0.2522, Negative Sentiment 0.1681

Quest Software Buys Data Manager Erwin

Quest Software has acquired big data management specialist Erwin Inc., upgrading the buyer’s data toolset aimed at application deployment with regulatory compliance.
Quest, Aliso Viejo, Calif., said Tuesday (Jan. 5) it acquired Erwin from Parallax Capital Partners, a California-based private equity firm. Terms of the transaction, that closed on Dec. 31, 2020, were not disclosed.
Erwin, Melville, NY, specializes in helping IT administrators thread the needle between ensuring enterprise data governance while allowing business users to leverage big data. Along with about 3,500 customers in North America, Europe, the Middle East and the Asian-Pacific, the acquisition gives Quest Software a suite of data modeling, metadata management and data intelligence as well as business process modeling tools.

2021-01-05 00:00:00 Read the full story…
Weighted Interest Score: 3.1982, Raw Interest Score: 1.9710,
Positive Sentiment: 0.0372, Negative Sentiment 0.1859

Best NLP-based SEO Tools For 2021

One key area that has witnessed a massive revolution with natural language processing (NLP) is the search engine optimisation. We all remember Google releasing the BERT algorithm, two years back, in October 2019, claiming to help Google Search better understand one in 10 searches in English. Cut to 2021 — NLP has now become more important than ever to optimise content for better search results.
Especially in a time, when content marketing is playing a key in business growth, readers and audience demand high-quality content. And therefore, NLP-based SEO tools can be of great help for businesses to analyse their text, play with keywords and produce great content for their website.
In this article, we are going to list down the top eight NLP-based SEO tools that one can check out for 2021.
2021-01-06 11:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1771, Raw Interest Score: 1.9644,
Positive Sentiment: 0.2992, Negative Sentiment 0.1561

Scotiabank taps machine learning to help clients during pandemic

Scotiabank says that its investment in machine learning is paying off during the Covid-19 pandemic, enabling it to help clients navigate uncertain and challenging times.

Analytics boffins at the Canadian bank’s global risk management unit have used machine learning to develop a cashflow prediction tool called Sofia (Strategic Operating Framework for Insights and Analytics).

Sofia uses historical commercial banking data, such as deposits, and trends from the past year combined with machine learning to forecast what clients could expect in the next four weeks.
This rolling average, which is updated in real time, gives the bank a better sense of which clients are more likely to be hit by the economic downturn and how to best respond to them.

2021-01-11 00:01:00 Read the full story…
Weighted Interest Score: 3.0644, Raw Interest Score: 1.4912,
Positive Sentiment: 0.2711, Negative Sentiment 0.1356

Equifax Rises on $640 Million Acquisition of Kount

Equifax expects to expand its global footprint in digital identity and fraud protection solutions through the acquisition. Shares of Equifax rose Friday afternoon after the company announced that it is purchasing artificial intelligence data fraud prevention firm Kount for $640 million. The acquisition is expected to expand Equifax’s worldwide footprint in digital identity and fraud prevention solutions.
“As digital migration accelerates, managing authentication and online fraud while optimizing the consumer’s experience has become one of our customers’ top challenges,” said CEO Mark Begor. “The acquisition of Kount will expand Equifax’s differentiated data assets to bring global businesses the information and solutions they need to establish identity trust online.” Equifax shares were rising 2.67% to $186.13 in trading on Friday after the announcement.

2021-01-08 20:20:49+00:00 Read the full story…
2021-01-08 00:00:00 Read the full story…
Weighted Interest Score: 2.9502, Raw Interest Score: 1.8321,
Positive Sentiment: 0.2036, Negative Sentiment 0.5598

Ultimate Guide To Loss functions In Tensorflow Keras API With Python Implementation

We have already covered the PyTorch loss functions implementations in our previous article, now we are heading forward to the other libraries that have been used more widely than PyTorch, today we are going to discuss the loss functions supported by the Tensorflow library, there are almost 15 different kinds of loss functions supported by TensorFlow, some of them are available in both Class and functions format you can call them as a class method or as a function.
The class handles enable you to pass configuration arguments to the constructor (e.g. loss_fn = CategoricalCrossentropy(from_logits=True)), and they perform reduction by default when used in a standalone way they are defined separately, all the loss functions are available under Keras module, exactly like in PyTorch all the loss functions were available in Torch module, you can access Tensorflow loss functions by calling tf.keras.losses method.
2021-01-09 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9105, Raw Interest Score: 1.6093,
Positive Sentiment: 0.0279, Negative Sentiment 0.9116

Why You Need to Learn SQL If You Want a Job in Data (2021 Update!)

Why do you need to learn SQL?

  1. SQL is used everywhere .
  2. It’s in high demand because so many companies use it.
  3. SQL is still the most popular language for data work in 2021.

SQL is old. There, I said it.
I first heard about SQL in 1997. I was in high school, and as part of a computing class we were working with databases in Microsoft Access. The computers we used were outdated, and the class was boring. Even then, it seemed that SQL was ancient.
SQL dates back almost 50 years to 1970 when Edgar Codd, a computer scientist working for IBM, wrote a paper describing a new system for organizing data in databases. By the end of the decade, several prototypes of Codd’s system had been built, and a query language — the Structured Query Language (SQL) — was born to interact with these databases.
In the years since, it has been widely adopted. Learning SQL — which can be pronounced either “sequel” or “S.Q.L.”, by the way — has been a rite of passage for programmers who need to work with databases for decades.
2021-01-07 18:00:00+00:00 Read the full story…
Weighted Interest Score: 2.8837, Raw Interest Score: 1.9269,
Positive Sentiment: 0.1835, Negative Sentiment 0.1311

AI-Based Agritech Firm CropIn Raises $20 Million In Series C Funding

In a recent announcement, CropIn, a leading AI and data-led agri-tech company raised $20 million in a Series C funding. This round of funding is led by ABC World Asia, Singapore-based private equity firm. With this, the startup has so far raised a total amount of US$33.1 million.
In 2018, the company had raised $8 million in Series B funding which was led by Chiratae Ventures (formerly IDG Ventures) and the Bill and Melinda Gates Foundation Strategic Investment Fund.
Founded by Krishna Kumar, Bengaluru-headquartered CropIn provides data-driven farming solutions — SmartFarm and SmartRisk to help agri-businesses maximise the per-acre value and to make every farm traceable.
2021-01-07 05:14:25+00:00 Read the full story…
Weighted Interest Score: 2.8461, Raw Interest Score: 1.6878,
Positive Sentiment: 0.4219, Negative Sentiment 0.0000

Breaking the Data Warehouse Paradigm: What do your workloads really need? – Panel

The data warehouse has been the go-to solution for big data analytics for the last 40 years. The journey to the cloud delivered on the promise of devops and extreme agility. Moving your on-prem data warehouses to the cloud is a relatively easy task to execute and overall the cloud data warehouse delivers a decent balance between price and performance. But doing only that actually reduces the benefits of the cloud and the business impact it can deliver: faster time to market, competitive advantage, innovation etc. With the rise of the data lake as a strong and effective alternative, data teams need to shift their mindsets from thinking about infrastructure and how to make the workload (i.e. business questions) fit the data warehouse, to thinking the other way around — start with the analytics workloads, identify performance requirements, flexibility requirements, time-to-insights and budget. Only then data teams can turn to think outside of the “data warehouse box” on engineering solutions residing on top of the data lake.
2021-01-05 00:00:00 Read the full story…
Weighted Interest Score: 2.7293, Raw Interest Score: 1.4152,
Positive Sentiment: 0.2780, Negative Sentiment 0.0758

Five ways to make AI a greater force for good in 2021

There’s more attention on AI’s influence than ever before. Let’s make it count.
A year ago, none the wiser about what 2020 would bring, I reflected on the pivotal moment that the AI community was in. The previous year, 2018, had seen a series of high-profile automated failures, like self-driving-car crashes and discriminatory recruiting tools. In 2019, the field responded with more talk of AI ethics than ever before. But talk, I said, was not enough. We needed to take tangible actions. Two months later, the coronavirus shut down the world.
In our new socially distanced, remote-everything reality, these conversations about algorithmic harms suddenly came to a head. Systems that had been at the fringe, like HireVue’s face-scanning algorithms and workplace surveillance tools, were going mainstream. Others, like tools to monitor and evaluate students, were spinning up in real time. In August, after a spectacular failure of the UK government to replace in-person exams with an algorithm for university admissions, hundreds of students gathered in London to chant, “Fuck the algorithm.” “This is becoming the battle cry of 2020,” tweeted AI accountability researcher Deb Raji, when a Stanford protestor yelled it again in response to a different debacle a few months later.
2021-01-08 00:00:00 Read the full story…
Weighted Interest Score: 2.7249, Raw Interest Score: 1.1382,
Positive Sentiment: 0.2647, Negative Sentiment 0.2382

2021 AI Predictions: More Edge AI, Rise of ‘Data Mutinies,’ Wider Use of ‘Snitch Software’

We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.

2021-01-07 20:22:35+00:00 Read the full story…
Weighted Interest Score: 2.6235, Raw Interest Score: 1.2735,
Positive Sentiment: 0.2489, Negative Sentiment 0.2394

NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer

Advances in Deep Learning research are of great utility for a Deep Learning engineer working on real-world problems as most of the Deep Learning research is empirical with validation of new techniques and theories done on datasets that closely resemble real-world datasets/tasks (ImageNet pre-trained weights are still useful!).
But, churning a vast amount of research to acquire techniques, insights, and perspectives that are relevant to a DL engineer is time-consuming, stressful, and not the least overwhelming.
For what so ever reason, I am crazy (I mean, really crazy! See Exhibit A here and here) about Deep Learning research and also have to justify a Deep Learning engineer’s role to earn my living. So, this is a great place to be in to cater to these needs of DL engineer relevant research churning.
Therefore, I went through all the titles of NeurIPS 2020 papers (more than 1900!) and read abstracts of 175 papers, and extracted DL engineer relevant insights from the following papers.

2021-01-06 16:37:31+00:00 Read the full story…
Weighted Interest Score: 2.3277, Raw Interest Score: 1.3390,
Positive Sentiment: 0.2069, Negative Sentiment 0.1765

NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer – Computer Vision

As mentioned in part 1– the most important thing:) – I went through all the titles of NeurIPS 2020 papers (more than 1900!) and read abstracts of 175 papers, and extracted DL engineer relevant insights from the following papers.

This is part 2. See part 1 here.

If this in-depth educational content is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.

2021-01-07 15:08:25+00:00 Read the full story…
Weighted Interest Score: 2.6230, Raw Interest Score: 1.2463,
Positive Sentiment: 0.2145, Negative Sentiment 0.2145

Expanding Your Data Science and Machine Learning Capabilities – Webinar Registration

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.
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

Unlocking the Power of DataOps

DataOps is on the rise at enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, this new methodology is poised to revolutionize data analytics with its eye on the entire data lifecycle. However, improving the flow of data between managers and consumers within an organization through greater communication, integration and automation is no simple task, and it requires process changes as well as enabling…
2021-05-13 00:00:00 Read the full story…
Weighted Interest Score: 2.4819, Raw Interest Score: 1.4478,
Positive Sentiment: 0.7239, Negative Sentiment 0.1034

Top 8 Autonomous Driving Open Source Projects One Must Try Hands-On

Here are the eight best autonomous driving open-source projects contributing to developing autonomous driving systems.
The past few years have seen active development in autonomous driving by organisations and academia. One of the standard practices in autonomous driving is developing and validating prototypes of driving in simulators. The researchers worldwide have been developing these simulators to support the training and development of such selfless driving systems.

Let’s take a look at the top 8 autonomous driving open-source projects one must try their hands-on.
2021-01-06 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.3301, Raw Interest Score: 1.3597,
Positive Sentiment: 0.1236, Negative Sentiment 0.0353

Cloud Is the New Center of Gravity for Data Warehousing

The great migration of data into the cloud didn’t start in 2020, but it certainly accelerated throughout the year. And according to a new survey from IDG, the overwhelming majority of companies are planning to expand their investments in cloud data warehouses and data lakes in 2021. However, many of the same old challenges surrounding data management and ETL remain the same.
The IDG survey, which was released in September, found that 77% of IT decision-makers plan to migrate to a cloud data warehouse, or expand an existing cloud data warehouse, over the next six to 12 months. Another 21% have cloud data warehouse plans extending out the next 24 months. Only 1% said they had no plans to implement or expand a cloud data warehouse.
2021-01-08 00:00:00 Read the full story…
Weighted Interest Score: 2.2993, Raw Interest Score: 1.2960,
Positive Sentiment: 0.1672, Negative Sentiment 0.0557

Making the Business Case for a Data Catalog

As a data & analytics leader, I like to create simple goals that anchor data & analytics in tangible business results. While these goals may vary from industry to industry, your topline goals are probably similar to mine:
Create a data-driven enterprise
Turn data assets into revenue generating resources
Of course, as anyone responsible for data & analytics understands, achieving these goals isn’t as simple as it sounds. Huge volumes of data and complex data environments present significant roadblocks.
That volume and complexity can be made even more difficult by the size and history of your organization. For example, FLSmidth is a multinational engineering company based in Denmark with nearly 12,000 employees worldwide. The company has been growing for more than 130 years with numerous acquisitions. Every time a new company is acquired new systems are brought in, new data assets are added that aren’t available to everyone who might need them, and there is a lot of tribal knowledge that gets lost when people leave the company.

2021-01-08 00:00:00 Read the full story…
Weighted Interest Score: 2.2385, Raw Interest Score: 1.2987,
Positive Sentiment: 0.3433, Negative Sentiment 0.2388

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