Industry News

Industry News

ML/AI News : Why Data Literacy Is Not Just A Math Skill But A Life Skill : AI replaces journalists at Microsoft – 50 employees handed pink slips : Uber cuts 3,700 more jobs including entire AI Lab : A Single Line of Python Code Scraping Dataset from Webpages : HSBC Launches AI-Powered Index Family

AI & Machine Learning News. 01, June 2020

AI & Machine Learning News. 01, June 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

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?

Why Data Literacy Is Not Just A Math Skill But A Life Skill

Communicating data is an essential skill and it is not just about doing complex coding. Data literacy is about deriving value from data and Dr Kirk Borne who is a data scientist and an astrophysicist and also a leading AI influencer spoke at plugin 2020 about the importance of being data literate for the future of work. He addressed how it is important for both individuals and to organizations to be data literate.
He addressed five aspects of it — data awareness (what is it?), data relevance (why me?), data literacy (show me how), data science (where’s the science?), and the data imperative (create and do something with data). He also discussed why it is important for data scientists to lead the efforts to build data literacy in society, in schools, and in professional development activities for organizations.
2020-05-30 16:10:15+00:00 Read the full story…
Weighted Interest Score: 4.5158, Raw Interest Score: 2.2590,
Positive Sentiment: 0.0466, Negative Sentiment 0.1630

CloudQuant Thoughts : We have lead with Dr Borne’s video “Introduction to Data Literacy and Storytelling” that he made for Industry Innovation Virtual AI Conference last week. Enjoy!

AI replaces journalists at Microsoft; 50 employees handed pink slips

Microsoft is reportedly laying off at least 50 news production workers and replacing them with artificial intelligence (AI)-based algorithms to perform their editorial duties. According to a report in the Seattle Times on Saturday, the roughly 50 employees contracted through staffing agencies Aquent, IFG and MAQ Consulting have been notified “that their services would no longer be needed beyond June 30”. These news production contractors work with Microsoft News, the company’s news content arm that operates and other properties.
A Microsoft spokesperson said in a statement that like all companies, they evaluate business on a regular basis. “This can result in increased investment in some places and, from time to time, redeployment in others. These decisions are not the result of the current pandemic,” said the Microsoft spokesperson. Some employees told Seattle Times that “MSN will use AI to replace the production work they’d been doing”.
2020-05-31 07:33:00+05:30 Read the full story (at IBTimes)…
2020-06-01 00:00:00 Read the full story (at ProActiveInvestors)…
Weighted Interest Score: 2.7692, Raw Interest Score: 1.1547,
Positive Sentiment: 0.0770, Negative Sentiment 0.0000

CloudQuant Thoughts : OK, contract employees, not full time employees but still, it is a chilling decision. It would be interesting to see a side by side demonstration of the AI output vs the Human, I assume they tried that before making this decision! This plus the Uber story below and the “Are We Seeing The Data Science Bubble Burst?” further down suggests that Data Scientists are not immune to the effects of Covid-19 on the jobs market.

Uber cuts 3,700 more jobs including entire AI Lab

Uber is cutting 3,700 more jobs less than two weeks after an initial round of layoffs, CNBC confirmed Monday.
In an email to employees Monday, CEO Dara Khosrowshahi said Uber would also be shutting or consolidating 45 offices around the world and it is considering cuts to other businesses, such as freight.
Uber shares were up as much as 9% on the news, which was first reported by The Wall Street Journal. The stock ended the day up 3.5%.
2020-06-01 00:00:00 Read the full story…
CloudQuant Thoughts : The key statement for those of us interested in AI and ML was “Given the necessary cost cuts and the increased focus on core, we have decided to wind down the Incubator and AI Labs and pursue strategic alternatives for Uber Works.”

A Single Line of Python Code Scraping Dataset from Webpages

Hunting for API endpoints from webpages and downloads using Python

No matter what level of data science/analytics skills we have, you cannot do anything without datasets.

Indeed, there are many open-source datasets such as Kaggle and However, they are more suitable to be used for exercises and learning purposes, but may not satisfy our general needs.
Usually, data scientist/analysts may have more or less web scraping skills, so it will be much easier to get datasets whenever you saw on the websites. After scraping the content from the websites, a series of transforming, extracting and cleansing manipulations will help us to get the clean dataset for the next step. This is one of the typical usages of Python because there are many excellent web scraping libraries available in Python such as Scrapy and Beautiful Soup.
2020-05-31 16:58:45.631000+00:00 Read the full story…
Weighted Interest Score: 4.7720, Raw Interest Score: 1.4316,
Positive Sentiment: 0.4242, Negative Sentiment 0.1060

CloudQuant Thoughts : Quite a lot of investigative work before you can create that one line of code but it is true, it is sometimes possible to write one line of code to fetch the data you need of a webpage.

HSBC Launches AI-Powered Index Family

HSBC today announced the launch of the AI Powered US Equity Index (AiPEX) family, the market’s first to use artificial intelligence (AI) as a method for equity investing. The AiPEX family of indices was developed by EquBot and leverages the AI capabilities of EquBot and IBM Watson™ to turn Big Data into investment insight.
AiPEX harnesses the power of IBM Watson and EquBot’s AI to ingest and learn from the vast amounts of publicly available and continuously generated data points. Data points could include a company announcement, a tweet, a satellite image of a store parking lot, or even the tone of language a CEO uses during an earnings presentation.
Applying what has been learned through Big Data and AI, AiPEX uses a rules-based process to objectively evaluate each of the 1,000 largest U.S. publicly traded companies and selects those whose stock prices are poised for growth, according to the AI. AiPEX rebalances its portfolio monthly, and to manage short-term volatility, AiPEX reallocates among chosen equity and cash on a daily basis. AiPEX selects companies with stock prices that may be poised for growth according to an objective selection process that is similar to a fundamental equity research approach, only thousands of times faster and broader in scope.

2020-06-01 10:28:24+00:00 Read the full story (at MarketsMedia)…
2020-06-01 00:01:00 Read the full story (at FinExtra)…
Weighted Interest Score: 4.6740, Raw Interest Score: 2.2406,
Positive Sentiment: 0.1965, Negative Sentiment 0.0000

CloudQuant Thoughts : Brave (or foolish?) to launch an AI based bot ETF in this market!

Tencent pledges $70 billion investment in high-tech areas as Beijing pushes digital infrastructure

Chinese technology giant Tencent will invest 500 billion yuan ($69.9 billion) over the next five years in areas from cloud computing to artificial intelligence, a move boosted by Beijing’s calls to push digital infrastructure.

The announcement comes after the company, known for operating popular messaging service WeChat, said on Monday it would issue up to $20 billion of new bonds to professional investors to raise capital. Already, $12 billion of bonds under this program are outstanding.
2020-05-27 00:00:00 Read the full story…
Weighted Interest Score: 2.7647, Raw Interest Score: 1.5882,
Positive Sentiment: 0.2353, Negative Sentiment 0.0000

Scale AI Launches PandaSet To Promote Urban Driving Situations

Recently, the data platform for AI, Scale AI launched one of the popular large scale datasets for autonomous driving, PandaSet. According to the Scale AI team, this dataset is the first open-source dataset made available for both academic and commercial use.
Amid the pandemic, the collaboration in AI and research communities have witnessed a spike in solving the pressing issues. However, due to the lockdown, some of the industries like autonomous vehicle (AV) are witnessing difficulties in developing new technologies at scale as testing on roads is suspended for the time being to ensure the safety of those involved.
According to the Scale team, various AV organisations have turned to complementary techniques and simulated data to continue their work, but there is often no substitute for high-quality data that captures the complex and often messy reality of driving in the real world. This particular condition inspired the Scale AI team to release the PandaSet amid the crisis for training machine learning models for autonomous driving.
2020-06-01 07:30:00+00:00 Read the full story…
Weighted Interest Score: 4.8052, Raw Interest Score: 1.6985,
Positive Sentiment: 0.2972, Negative Sentiment 0.1486

New Requirements Spur Data Quality and Data Integration

A combination of factors is heightening the need for high-quality, well-governed data. These include the need for trustworthy data to support AI and machine learning initiatives, new data privacy and data management regulations, and the appreciation of good data as the fuel for better decision making.

Multiple Data Sources, Governance, and High Volume Are Top Data Quality Challenges’

  1. The top 3 challenges companies face when ensuring high quality data are multiple sources of data (70%), applying data governance processes (50%), and volume of data (48%).
  2. About three-quarters (78%) of companies have challenges profiling or applying data quality to large datasets.
  3. 29% say they have a partial understanding of the data that exists across their organization, while 48% say they have a good understanding.

2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 4.8480, Raw Interest Score: 2.7050,
Positive Sentiment: 0.3165, Negative Sentiment 0.2878

Why Is Data Strategy Important To Drive Data Science And AI Initiatives

The importance of data in today’s world is well known. While it is extremely crucial to strategise data to be used for AI, ML or other applications, there are still many businesses that do not realise its importance. Every enterprise generates a huge amount of data which oftentimes is not leveraged to derive the best result out of it. Sateesh Rai, head of analytics at Orient Electric takes us through the importance of data strategy and why storing data and planning to use it in an efficient manner can bring about tremendous business transformation. It is an important framework for companies to derive useful insights.
2020-05-29 14:58:11+00:00 Read the full story…
Weighted Interest Score: 4.5033, Raw Interest Score: 2.1015,
Positive Sentiment: 0.2729, Negative Sentiment 0.4367

Contribute photos to help developers build AI models with new Microsoft Garage project Trove

Every day, developers and researchers are finding creative ways to leverage AI to augment human intelligence and solve tough problems. Whether they’re training a computer vision model that can spot endangered snow leopards or help us do our business expenses more easily when we scan pictures of receipts, they need a lot of quality pictures to do it. Developers usually crowd source these large batches of pictures by enlisting the help of gig workers to submit photos, but often, these calls for photos feel like a black box. Participants have little insight into why they’re submitting a photo and can feel like their time was lost when their submissions are rejected without explanation. At the same time, developers can find that these sourcing projects take a long time to complete due to lower quality and less diverse inputs.
We’re excited to announce that Trove, a Microsoft Garage project, is exploring a solution that can enhance the experience and agency for both parties. Trove is a marketplace app that allows people to contribute photos to AI projects that developers can then use to train machine learning models. Interested parties can request an invite to join the experiment as a contributor or developer. Trove is currently accepting a small number of participants in the United States on both Android and iOS.

2020-05-27 15:53:43+00:00 Read the full story…
Weighted Interest Score: 2.1151, Raw Interest Score: 1.0821,
Positive Sentiment: 0.3978, Negative Sentiment 0.1591

This AI Can Judge Personality Based on Selfies Alone

Could this neural network really be better at predicting personality traits than humans?
A team of researchers from the Higher School of Economics University and Open University in Moscow, Russia claim they have demonstrated that an artificial intelligence can make accurate personality judgments based on selfies alone — more accurately than some humans. The researchers suggest the technology could be used to help match people up in online dating services or help companies sell products that are tailored to individual personalities.
That’s apropos, because two co-authors listed on a paper about the research published today in Scientific Reports — a journal run by Nature — are affiliated with a Russian AI psychological profiling company called BestFitMe, which helps companies hire the right employees. As detailed in the paper, the team asked 12,000 volunteers to complete a questionnaire that they used to build a database of personality traits. To go along with that data, the volunteers also uploaded a total of 31,000 selfies.
2020-05-22 Read the full story…

Assessing the Risks and Challenges on the Road to Owning Training Data

Artificial intelligence (AI) applications have an insatiable appetite for consuming data. Today’s AI models for business applications are built to ingest massive amounts of complex data sets. The cost of collecting and curating data for training AI models, however, can be staggering. In the context of the Internet of Things (IoT), for example, the costs of deploying sensors and other machinery in a network at a big data scale can be expensive.
But what if the training data that trains your AI products is accessible online to your users? Bad actors mimicking legitimate users can siphon off large amounts of the data you collected and then inexpensively build competing AI products using this training data. Losing data to competitors can translate to lost market share. In China, for example, a company invested heavily in attaching a network of sensors on buses to collect real-time bus location data. The company built a popular AI-powered app that predicted future bus times with high accuracy. The AI-powered app was trained using the real-time bus location data collected using the sensors. A competitor coded a bot that scraped the real-time bus location data to improve the accuracy of its competing AI-powered app. While this ultimately went to court, the company still suffered economic loss and damage to its brand as a direct result of losing its real-time bus location data to its competitor.

2020-05-29 07:35:37+00:00 Read the full story…
Weighted Interest Score: 4.3240, Raw Interest Score: 2.0348,
Positive Sentiment: 0.1584, Negative Sentiment 0.4874

Artificial Intelligence Essentials for Business Leaders

AI has become the need of the hour and all the industries are now integrating analytics and AI to drive the decision-making process. Bhagirath Kumar Lader, who is the Chief Manager (Business Information System) at GAIL led us through a session briefing Artificial Intelligence essentials for business leaders in today’s age. Lader is one of the key members of the digital transformation team at GAIL and carries huge knowledge about how AI, ML and DL are crucial to businesses. He gave us a quick overview of the motivation for AI, AI essentials, AI hype vs reality while taking us through use cases.

2020-05-29 10:37:01+00:00 Read the full story…
Weighted Interest Score: 4.1811, Raw Interest Score: 1.9428,
Positive Sentiment: 0.1737, Negative Sentiment 0.1895

Apple buys machine learning start-up Inductiv to improve Siri

Apple has bought a machine-learning start-up to bolster the abilities of its Siri voice recognition system. The iPhone-maker acquired US-based artificial intelligence (AI) business Inductiv for an undisclosed fee, Bloomberg reported. The deal adds to more than a dozen similar agreements struck by Apple in the past few years.
In a statement, the Cupertino giant said that it “buys smaller technology companies from time to time and we generally do not discuss our purpose or plans”. Inductiv, an Ontario-based start-up, develops AI that can identify and correct errors in large data sets. Inductiv’s technology may be used to clean data collated from users, which can improve Apple’s machine learning capabilities and deliver better voice recognition through Siri.
2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 4.1638, Raw Interest Score: 2.1233,
Positive Sentiment: 0.2055, Negative Sentiment 0.1370

Ex-DeepMind engineers picked Seattle to launch new AI startup Phaidra

Phaidra aims to help industrial companies develop in-house AI solutions. The startup is still building out the beta version of its product. “We started Phaidra to broaden access to the technology by enabling industry practitioners to directly develop their own AI solutions,” Gao explained. “While many tools already exist for data scientists or software engineers, our experience suggests that domain expertise is the primary driver of AI performance. AI would be significantly more impactful and useful if the people who understand their domains best were the ones applying AI.”
The company also wants to help its customers maintain ownership of their data and intellectual property. Gao said companies usually partner with large tech companies to develop AI products — a “double-edged sword” because those companies can use the data themselves. “The root of the problem is that the world’s AI talent is concentrated in a few large companies,” Gao said. “If you could make AI more accessible to non-experts, you can solve this problem.”
2020-05-28 14:00:00+00:00 Read the full story…
Weighted Interest Score: 4.1608, Raw Interest Score: 1.5740,
Positive Sentiment: 0.1399, Negative Sentiment 0.1049

Building Better Prices — How AI is Improving Liquidity in Corporate Credit Markets

How many traders, desk analysts and quants does it take to price a corporate bond? If you were to answer that question even a few months ago, the number could be as high as a half-dozen. Parties on both sides of the trade would be tasked with checking whether the bond traded recently, analyzing current credit and business conditions, digging into individual bond attributes and taking the pulse of the marketplace to see if the other side of the trade agrees with the price. For a complex trade involving a large portfolio of corporate credits, the process could have taken days.
Today, a single trader can do all of that in seconds thanks to advances in machine learning technology which have made it possible to calculate reference pricing in seconds based on dynamic bond market data. And that is a huge step forward for liquidity in the $9.2 trillion U.S. corporate bond market.
2020-05-28 12:35:00+00:00 Read the full story…
Weighted Interest Score: 3.8998, Raw Interest Score: 1.9928,
Positive Sentiment: 0.1610, Negative Sentiment 0.1812

Data Scientist Salary: Starting, Average, and Which States Pay Most

What’s the average data scientist salary? As you might expect, those with the right combination of data-science skills and experience can earn quite a bit—especially if they’re in a position to advise a company’s senior management on strategy. Let’s break it all down, but before we do, let’s take a moment to trace out what a data scientist actually does.
Data scientists play a vital strategic role at the companies that employ them. They’re often tasked with mining their firm’s data for strategic insights that CEOs, CTOs, and other executives can use to plot a longer-term roadmap. No wonder it’s a notably fast-growing profession. Although the term ‘data scientist’ is often used interchangeably with ‘data analyst,’ it’s important to note that those roles technically aren’t the same; data analysts often focus on much more tactical problems than data scientists.
2020-05-26 00:00:00 Read the full story…
Weighted Interest Score: 3.8719, Raw Interest Score: 2.2624,
Positive Sentiment: 0.0543, Negative Sentiment 0.1086

Why Crypto needs robo-advisors?

Robotization coupled with artificial intelligence (AI) are reshaping every aspect of our lives. When we hear the term “robo-advisor”, our imagination is filled with images from science-fiction movies. But when we refer to robo-advisors, we mean things like bots, virtual robots or algorithms that automate different tasks. Robo-advisors grew out of the ashes of the 2008 financial crisis. Robo-advisors gained traction when people lost faith in trad…
2020-06-01 00:00:00 Read the full story…
Weighted Interest Score: 3.8158, Raw Interest Score: 1.3827,
Positive Sentiment: 0.1796, Negative Sentiment 0.1437

Seattle startup DefinedCrowd lands $50M to help Mastercard, BMW, others improve their AI services

Artificial intelligence services require mountains of reliable data to reach their full potential. Seattle startup DefinedCrowd is filling that need for Fortune 500 companies such as Mastercard and BMW — and investors like what they see so far. The company announced a $50.5 million Series B round Tuesday to fuel growth of its AI training data technology platform as it aims to be “the world’s best data company for AI.”
Founded in 2015, DefinedCrowd uses a combination of machine learning with a crowdsourced community of 290,000 human contributors to train AI systems in 50 languages across 195 countries. DefinedCrowd specializes in speech technology, natural language processing and computer vision, working with clients across industries such as automotive, energy, fintech, retail, media, and healthcare. Use cases include building voice assistants, improving facial recognition apps, automating utilities inspection, and more.

2020-05-26 14:00:00+00:00 Read the full story (at GeekWire)…
2020-05-27 00:00:00 Read the full story (at DBTA)…
Weighted Interest Score: 3.6961, Raw Interest Score: 1.6054,
Positive Sentiment: 0.1751, Negative Sentiment 0.1168

Determining the ROI of AI Projects A Key to Success

The best practices around determining whether your AI project will achieve a return for the business center around determining at the outset how the return on investment will be measured.
The evidence shows it will be time well spent. An estimated 87% of data science projects never make it to the production stage, and 56% of global CEOs expect it to take three to five years to see any real ROI on AI investments, according to a recent account in Forbes.
Like any other technology investment, business leaders need to define the specific goals of the AI projects, and commit to tracking it with benchmarks and key performance indicators, suggested author Mark Minevich, Advisor to Boston Consulting Group, venture capitalist and cognitive strategist. The company needs to think about the types of business problems that can be addressed with AI, so as not to set unrealistic expectations and not set the AI off in search of a business problem to solve.

2020-05-28 21:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6784, Raw Interest Score: 1.2115,
Positive Sentiment: 0.2643, Negative Sentiment 0.4846

Are We Seeing The Data Science Bubble Burst?

COVID-19 has led to shifting priorities, and companies are re-assessing strategies across their business as resources are constrained. This has led to companies coming to terms with the reality of business value with data science.
The mass layoffs in the technology industry, including many data scientists, have many saying the talent bubble has finally popped. The pandemic gave a good reason for organisations to decrease the compensations for data scientists, which was earlier soaring high due to the rising demand. It is true that the number of candidates had been increasing exponentially in data science over the past five years.
It is expected that companies will now run for cost-cutting, business process optimisation and automation due to the pandemic, and this will ultimately decrease demand for data scientists in the coming future.

2020-05-30 12:19:25+00:00 Read the full story…
Weighted Interest Score: 3.2954, Raw Interest Score: 1.8308,
Positive Sentiment: 0.1831, Negative Sentiment 0.1831

Government presses ahead with Cummings’ data science revolution

A British artificial intelligence firm involved in the Vote Leave campaign has been handed a £400,000 contract to tap data from places such as social media sites to help steer the Government’s response to Covid-19.
Official documents from the Government show Faculty Science was awarded the contract by the Ministry of Housing, Communities and Local Government (MHCLG) in April to provide data scientists who could set up “alternative data sources (e.g. social media, utility providers and telecom bills, credit rating agencies, etc.)”.
They would, the contract said, apply data science and machine learning to the data, which could help identify trends, and then develop “interactive dashboards” to inform policymakers.

2020-06-01 00:00:00 Read the full story…
Weighted Interest Score: 3.1690, Raw Interest Score: 1.6681,
Positive Sentiment: 0.0000, Negative Sentiment 0.0878

Statistical pitfalls in data science – How stereotypical results can alter data distributions in people’s minds

There are plenty of ways to infer a large and varied amount of results from a given dataset, but there are infinitely many ways to incorrectly reason from it as well. Fallacies can be defined as the products of inaccurate or faulty reasoning which usually leads to one obtaining incorrect results from the data given.
The good thing is that since numerous people have made these mistakes for so long and the results have been documented throughout history in a variety of fields, it is easier to identify and explain many of these statistical fallacies. Here are some statistical traps that data scientists should avoid falling into.
2020-06-01 04:32:32.278000+00:00 Read the full story…
Weighted Interest Score: 3.1058, Raw Interest Score: 1.3763,
Positive Sentiment: 0.1564, Negative Sentiment 0.3649

OnMobile Invests In AI-Based Firm rob0 To Acquire 25% Stake

OnMobile Global Limited, a Bengaluru-based mobile entertainment company, has announced the investment of ₹5.4 crores (approx) in rob0. With this, OnMobile will hold a 25 per cent stake in the AI-based analytics organisation rob0.
“We couldn’t have hoped for a better partner than OnMobile to help rob0 embody its vision and become an essential solution for game developers. We are thrilled to bring our expertise and participate in the success of OnMobile’s new gaming offer,” said Richard Rispoli, co-founder and CEO of Technologies rob0.
rob0 offers SDK to allow video game developers to gain insights on how the users are interacting with the game. This will help developers to understand the behaviours of the gamers, thereby assisting them in optimising the games with a clear goal in mind. rob0 utilises cutting-edge technologies such as machine learning to deliver insights into the data extracted from the games while users play. It reduces the time taken by traditional methods, where game developers used to check hours of footage to evaluate the gamers behaviours.

2020-05-25 12:29:00+00:00 Read the full story…
Weighted Interest Score: 3.0045, Raw Interest Score: 1.5539,
Positive Sentiment: 0.3008, Negative Sentiment 0.0000

Facebook AI Research applies Transformer architecture to streamline object detection models

Six members of Facebook AI Research (FAIR) tapped the popular Transformer neural network architecture to create end-to-end object detection AI, an approach they claim streamlines the creation of object detection models and reduces the need for handcrafted components. Named Detection Transformer (DETR), the model can recognize objects in an image in a single pass all at once.
DETR is the first object detection framework to successfully integrate the Transformer architecture as a central building block in the detection pipeline, FAIR said in a blog post. The authors added that Transformers could revolutionize computer vision as they did natural language processing in recent years, or bridge gaps between NLP and computer vision.

2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 2.8227, Raw Interest Score: 1.4187,
Positive Sentiment: 0.3153, Negative Sentiment 0.2522

The In-Memory Computing Landscape in 2020

As companies have evolved toward digital business models and undertaken digital transformation initiatives, they have increasingly faced two challenges. First, the data they need to drive their real-time business processes is typically spread across multiple, siloed datastores. Second, their existing applications often cannot scale to address the increase in end-user demands for real-time engagement.
Thanks to the relatively low cost of RAM today and the availability of open source solutions, in-memory computing technologies have progressed dramatically over the last few years, becoming a foundation for accelerating and scaling real-time business processes in support of the range of digital transformation and big data/fast data initiatives. As we move through 2020, in-memory computing will be particularly important in enabling data centers to accelerate the use of the following new strategies for supporting real-time business processes and analytics:
2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 2.8222, Raw Interest Score: 1.8516,
Positive Sentiment: 0.1941, Negative Sentiment 0.1941

Spending Surge Predicted for Factory Data Management

As big data goes, the industrial sector is among the largest producers, with sensors collecting data along assembly lines on everything from the status of manufacturing equipment to product inspection cameras.
Industrial Internet of Things deployments are therefore expected to boost manufacturers’ already hefty investments in data management and analytics tools as producers seek to up their game from merely collecting to organizing and gleaning insights from industrial data.
That trend is seen pushing industrial spending to new heights. For example, ABI Research last week forecast that manufacturers and industrial firms will spend $19.8 billion in 2026 on data management, data analytics and related digital services. Those investments will target operations ranging from predictive equipment maintenance to production line optimization.
2020-05-26 00:00:00 Read the full story…
Weighted Interest Score: 2.7405, Raw Interest Score: 1.6341,
Positive Sentiment: 0.0875, Negative Sentiment 0.1167

Top Cloud Data Warehouses for the Enterprise

Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users.

But which solution is the right one for you and your business? Download the eBook to see a side-by-side comparison of the leading cloud data warehouse vendors and explore:

The top cloud data warehouses at a glance – Amazon Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and Snowflake Cloud …
2020-05-27 00:00:00 Read the full story…
Weighted Interest Score: 2.7219, Raw Interest Score: 1.6568,
Positive Sentiment: 0.4734, Negative Sentiment 0.0000

The 37 Major Machine-Learning Tools For 2020

Enterprises need more artificial intelligence and machine-learning (ML) solutions to drive value, transform their businesses, and outperform the competition. But firms find it challenging to navigate the lifecycle of developing, deploying, and maintaining their ML models and AI solutions. A key problem? They don’t have the right PAML (predictive analytics and machine learning) solutions that make it possible to scale AI — in a rapid, reliable, repeatable, and governable fashion — across the organization.

Thankfully, there is a growing landscape of vendors offering PAML solutions designed to help enterprises rapidly develop custom AI and ML solutions and push them beyond proof-of-concept (PoC) purgatory to full-scale production. In our recently published report, “Now Tech: Predictive Analytics And Machine Learning, Q2 2020,” we’ve identified and researched the 37 major PAML vendors and categorized them into three segments based on their capabilities…
2020-05-27 17:16:14-04:00 Read the full story…
Weighted Interest Score: 2.6451, Raw Interest Score: 1.5195,
Positive Sentiment: 0.2669, Negative Sentiment 0.1027

Reproducibility in Data Analytics Under Fire in Stanford Report

Armed with the same data and told to test the same hypotheses, dozens of independent researchers instead came to widely different conclusions using a variety of analytics techniques, according to a new report from Stanford University that pushes the reproducibility crises in science into a new realm.
The study involved 70 independent research teams from around the world, who were all presented with the same data: functional magnetic resonance imaging (fMRI) scans of volunteers’ brains while they performed a monetary decision-making task.

2020-05-27 00:00:00 Read the full story…
Weighted Interest Score: 2.5253, Raw Interest Score: 1.3072,
Positive Sentiment: 0.0297, Negative Sentiment 0.1783

AI Cloud Developments Offer Remarkable Improvements in IT security

IT security is becoming more important as data breaches become more common. Fortunately, AI tools and cloud resources are offering new solutions. Cloud technology is creating a number of amazing changes in the way we live. Many of these trends are predicated on new advances in artificial intelligence.
One of the biggest ways that AI-driven cloud developments are important is with greater IT security. Cloud AI technology is going to be more important in stopping the growing number of data breaches that we have witnessed in recent years. Data breaches cost businesses an average of $3.92 million. If your business is attacked, you not only risk losing profits, but it could potentially ruin your reputation. You will have a hard time convincing your clients that they can trust you with their data. To prevent that, you need to be proactive and put in place necessary security measures. Cloud computing and AI have both proved to be a very effective ways for businesses to tackle security issues. It is not in vain that many companies choose to work with Amazon AWS. They understand the benefits that AI cloud tools have for the security of their data. Here are the ways cloud AI applications can improve your IT security.
2020-05-28 19:28:46+00:00 Read the full story…
Weighted Interest Score: 2.4622, Raw Interest Score: 1.1541,
Positive Sentiment: 0.4360, Negative Sentiment 0.3591

KgBase Aims to Close the Knowledge Gap

Organizations are discovering the power of knowledge graphs to extract useful information from unstructured data. But full-fledged graph databases can require specialized skills to interact with, while online spreadsheets can leave the user wanting more. Now a company called KgBase is hoping to split the gap between these two extremes with an affordable knowledge graph tool that doesn’t require programming.
KgBase was developed by ThinkNum Alternative Data, a company that provides alternative data, such as store locations, job listings, product pricing, and lists of active social media users. It was originally intended to be used as a mapping tool based on the open source Gremlin query language that allowed ThinkNum’s customer to interact with its alternative datasets in new and exciting ways.
2020-05-29 00:00:00 Read the full story…
Weighted Interest Score: 2.4085, Raw Interest Score: 1.2968,
Positive Sentiment: 0.2081, Negative Sentiment 0.0640

CFOs are driving the digital conversation in legal

The pathway to become digital involves a commitment to fundamentally evolve the company’s legal operating model. It entails a transition from manual to digitized processes to harness the power of data and fully embrace artificial intelligence (AI). In the context of the law department, many c-suite executives recognise this as digital legal transformation (DLX).
DLX enables law departments to change their operating models, transitioning certain costs from fixed to variable, with immediate up-front and ongoing cost savings. In leveraging digital, Legal gains insight across the spectrum of legal work to enable faster, better-informed business decisions, improved legal risk management capabilities, and enriched business processes that create expansive value across the enterprise.

2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 2.3849, Raw Interest Score: 1.3690,
Positive Sentiment: 0.3526, Negative Sentiment 0.0622

Jeff Bezos is buying a stake in UK digital supply chain startup Beacon

The Amazon CEO and world’s richest man is taking part in a Series A fundraising round worth $15 million for the British startup.
Freight forwarding is a trillion dollar industry, and Beacon aims to act as the booking agents between importers and exporters while facilitating trade logistics and finance through the use of AI, search optimization, data science, cloud and automation technologies, its website says.
Based in London and founded in 2018, Beacon’s investors already include executives from Uber, Google and Amazon, according to its site. Its chief technology officer, Pierre Martin, was formerly head of software engineering for Amazon’s package and freight transport technology.
2020-05-31 00:00:00 Read the full story…
Weighted Interest Score: 2.3099, Raw Interest Score: 1.5572,
Positive Sentiment: 0.1460, Negative Sentiment 0.1460

Big Data Is Offering Awesome Homework Solutions For Students

Big data is changing the way students learn by offering amazing homework solutions. Here’s what to know about what big data offers.
The education technology market is growing at a remarkable pace. One study found that it was worth $55 billion in 2019. Growth in the education technology market is largely attributed to advances in big data.

In our progressive world, Big Data is a social and economic phenomenon that is associated with the rapid development of new technological capabilities that aid in the analysis of huge amounts of data. This data is processed so that people can get particular results for further application. Let’s find out how Big Data is used in education.
2020-05-29 13:01:11+00:00 Read the full story…
Weighted Interest Score: 2.3050, Raw Interest Score: 1.3787,
Positive Sentiment: 0.1939, Negative Sentiment 0.1508

New AI technique speeds up language models on edge devices

Researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and MIT-IBM Watson AI Lab recently proposed Hardware-Aware Transformers (HAT), an AI model training technique that incorporates Google’s Transformer architecture. They claim that HAT can achieve a 3 times inferencing speedup on devices like the Raspberry Pi 4 while reducing model size by 3.7 times compared with a baseline.
Google’s Transformer is widely used in natural language processing (and even some computer vision) tasks because of its cutting-edge performance. Nevertheless, Transformers remain challenging to deploy on edge devices because of their computation cost; on a Raspberry Pi, translating a sentence with only 30 words requires 13 gigaflops (1 billion floating-point operations per second) and takes 20 seconds. This obviously limits the architecture’s usefulness for developers and companies integrating language AI with mobile apps and services.
2020-05-29 00:00:00 Read the full story…
Weighted Interest Score: 2.2618, Raw Interest Score: 1.3986,
Positive Sentiment: 0.2576, Negative Sentiment 0.1104

Scaling the Analytics Team: Developing Key Roles

In an enterprise analytics team, different roles exist to fill different needs, and those needs must be met in order to be successful. Launching an analytics program doesn’t necessarily require a massive influx of personnel before producing usable insights from data, yet it’s important that critical roles are filled, whatever the size of the team. Multiple options exist for starting small and scaling up an analytics program, according to Evan Terry, VP of Operations at CPrime and co-author of Beginning Relational Data Modeling, in his presentation titled Roles in Enterprise Analytics at the DATAVERSITY® Enterprise Analytics Online Conference.
Data scientists often explore data independently, but the reality is that an entire support team is necessary for this type of exploration, he said. Data Science operates less like a rock climber and more like a baseball team, where all nine individuals with different specialized roles are on the field at the same time working together, all necessary to compete successfully.
2020-05-26 07:35:09+00:00 Read the full story…
Weighted Interest Score: 2.2438, Raw Interest Score: 1.2350,
Positive Sentiment: 0.1791, Negative Sentiment 0.1508

It All Comes Down to the Data

Today, whether it is company leaders dealing with customer and business concerns or public health experts talking about the COVID-19 pandemic, what you hear again and again is that they are relying heavily on data. And, in this issue we look at the range of data management challenges and opportunities.
Preparing data for analysis remains a problem. While certainly not new, it is one that is becoming increasingly difficult to deal with due to the vast quantities of data being created and stored, and the variety of types and sources. In our cover story, BDQ writer Joe McKendrick looks at the challenges of data prep for integration and analysis, and shares insights from a wide range of industry executives on the topic. “Even the most ambitious data analytics initiatives tend to get buried by the 80/20 rule—with data analysts or scientists only able to devote 20% of their time to actual business analysis, while the rest is spent simply finding, cleansing, and organizing data,” McKendrick observes. “This is unsustainable, as the pressure to deliver insights in a rapid manner is increasing.”
2020-05-28 00:00:00 Read the full story…
Weighted Interest Score: 2.2067, Raw Interest Score: 1.1321,
Positive Sentiment: 0.2965, Negative Sentiment 0.4313

AI Autonomous Cars And The Problem Of Where To Drop Off Riders

Determining where to best drop-off a passenger can be a problematic issue. It seems relatively common and downright unnerving that oftentimes a ridesharing service or taxi unceremoniously opts to drop you off at a spot that is poorly chosen and raft with complications.
I remember one time, while in New York City, a cab driver was taking me to my hotel after my having arrived past midnight at the airport, and for reasons I’ll never know he opted to drop me about a block away from the hotel, doing so at a darkened corner, marked with graffiti, and looking quite like a warzone. I walked nearly a city block at nighttime, in an area that I later discovered was infamous for being dangerous, including muggings and other unsavory acts.
In one sense, when we are dropped off from a ridesharing service or its equivalent, we often tend to assume that the driver has identified a suitable place to do the drop-off. Presumably, we expect as a minimum:

  • The drop-off is near to the desired destination
  • The drop-off should be relatively easy to get out of the vehicle at the drop-off spot
  • The drop-off should be in a safe position to get out of the vehicle without harm
  • And it is a vital part of the journey and counts as much as the initial pick-up and the drive itself.

In my experience, the drop-off often seems to be a time for the driver to get rid of a passenger and in fact the driver’s mindset is often on where their next fare will be, since they’ve now exhausted the value of the existing passenger and are seeking more revenue by thinking about their next passenger.

2020-05-28 21:30:00+00:00 Read the full story…
Weighted Interest Score: 2.1833, Raw Interest Score: 0.7624,
Positive Sentiment: 0.1028, Negative Sentiment 0.3255

OpenAI debuts gigantic GPT-3 language model with 175 billion parameters

A team of more than 30 OpenAI researchers have released a paper about GPT-3, a language model capable of achieving state-of-the-art results on a set of benchmark and unique natural language processing tasks that range from language translation to generating news articles to answering SAT questions. GPT-3 has a whopping 175 billion parameters. By comparison, the largest version of GPT-2 was 1.5 billion parameters, and the largest Transformer-based language model in the world — introduced by Microsoft earlier this month — is 17 billion parameters.
OpenAI released GPT-2 last year, controversially taking a staggered release approach due to fear that the model could be used for malicious purposes. OpenAI was criticized by some for the staggered approach, while others applauded the company for demonstrating a way to carefully release an AI model with the potential for misuse. GPT-3 made its debut with a preprint arXiv paper Thursday, but no release details are provided. An OpenAI spokesperson declined to comment when VentureBeat asked if a full version of GPT-3 will be released or one of seven smaller versions ranging in size from 125 million to 13 billion parameters.
2020-05-29 00:00:00 Read the full story…
Weighted Interest Score: 2.1234, Raw Interest Score: 1.3839,
Positive Sentiment: 0.2570, Negative Sentiment 0.2570

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