AI & Machine Learning News. 26, May 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?


Microsoft Build 2020 – New ML Focus and partnership with Musk’s OpenAI

Microsoft builds OpenAI’s ‘dream system,’ an Azure supercomputer that ranks among top 5 in the world

Microsoft says it has created one of the world’s top supercomputers for the exclusive use of OpenAI, the San Francisco-based artificial intelligence company pursuing breakthroughs in artificial general intelligence, or AGI, new forms of autonomous technology that would match or surpass human abilities.

The companies say the Azure supercomputer will be used by OpenAI to train powerful new AI models, the process of wiring up the virtual brain of an autonomous system. Microsoft says the capabilities of the system allow it to process large amounts of data across many different areas, resulting in sophisticated models that go beyond traditional machine learning approaches focused on individual domains of knowledge.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now,” says Kevin Scott, Microsoft’s chief technical officer, in a blog post from the company.

2020-05-19 15:00:00+00:00 Read the full story…
Weighted Interest Score: 2.4031, Raw Interest Score: 1.3699,
Positive Sentiment: 0.2549, Negative Sentiment 0.0956

Build 2020 Showed That ML Developers Are The Focus For Microsoft

In the past few years, we have seen the explosion of large scale machine learning models and rapid advancements in artificial intelligence. Contribution of developers has been behind the innovation that we’ve seen in the last few decades. Tools are also as useful as a developer for AI to achieve its full potential. Therefore, Microsoft released a wide range of ML-based solutions at Build 2020.

Microsoft is working to democratise the solutions by making it available for everyone to read and build on top of their ML platform. Microsoft has contributed to this progress by advancing the state-of-the-art in areas like Azure cognitive services, speech recognition, computer vision and natural language understanding. Here are some of the announcements at Build 2020, which will impact AI/ML developers.

Microsoft updated its open-source library DeepSpeed for PyTorch to enable everyone to train AI models ten times bigger and five times faster on the same infrastructure. According to the announcement, the library’s optimiser improves memory consumption during training, which promises DeepSpeed users scale and speed improvements by order of magnitude during deep learning.

2020-05-26 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.7432, Raw Interest Score: 2.2627,
Positive Sentiment: 0.3381, Negative Sentiment 0.1040

Microsoft Launches New Tools For Building More Responsible & Fairer AI Systems

Microsoft, during its Build developer conference, has put a strong emphasis on machine learning, along with plenty of new tools and features that the company is going to work on for building more responsible and fairer AI systems — both in the Azure cloud and Microsoft’s open-source toolkits.

The company stated that these new tools would be utilised for differential privacy and for creating a system that would ensure that models are working well across different groups of people. Further, these new tools would enable businesses to make the best use of their data while still following strict regulatory requirements.

During the announcement, Microsoft stated that, as developers are increasingly tasked to learn how to build artificial intelligence models, the developers regularly end up asking about the system explainability and its compliance with non-discrimination and privacy regulations. And for that, developers would require tools that can help them better interpret their models’ results.

2020-05-20 13:13:11+00:00 Read the full story…
Weighted Interest Score: 1.8779, Raw Interest Score: 1.4159,
Positive Sentiment: 0.2360, Negative Sentiment 0.0000

Open AI and Microsoft Can Generate Python Code

Build 2020: Open AI language model was trained on thousands of GitHub repositories using the same unsupervised learning as the GPT models.

CloudQuant Thoughts : A lot of AI and ML news out of Microsoft’s virtual developer conference.

Top 10 Best FREE Artificial Intelligence Courses from Harvard, MIT and Stanford

Most of the Machine Learning, Deep Learning, Computer Vision, NLP job positions, or in general every Artificial Intelligence (AI) job position requires you to have at least a bachelor’s degree in Computer Science, Electrical Engineering, or some similar field. If your degree comes from some of the world’s best universities than your chances might be higher in beating the competition on your job interview.

But looking realistically, not most of the people can afford to go to the top universities in the world simply because not most of us are geniuses and don’t have thousands of dollars, or come from some poor country (like we do). No with the high demand of skilled professionals from these fields, there are exceptions being made, so we can see that people who don’t come from these fields, are learning and adjusting themselves in order to get that paycheck.

In this article we are going to list some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where they live.
2020-05-24 Read the full story…

CloudQuant Thoughts : These are some truly excellent FREE courses!

Twitter billionaire Jack Dorsey: Automation will even put tech jobs in jeopardy

The rise of artificial intelligence will make even software engineers less sought after.

That’s because artificial intelligence will soon write its own software, according to Jack Dorsey, the tech billionaire boss of Twitter and Square. And that’s going to put some beginning-level software engineers in a tough spot.

“We talk a lot about the self-driving trucks in and whatnot” when discussing how automation will replace jobs held by humans, Dorsey told former Democratic presidential hopeful Andrew Yang on an episode of the “Yang Speaks” podcast published Thursday.

But A.I. “is even coming for programming” jobs, Dorsey said.

2020-05-22 00:00:00 Read the full story…
Weighted Interest Score: 1.7943, Raw Interest Score: 2.2837,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

CloudQuant Thoughts : Note the clip above at Microsoft Build 2020 where Open AI has learned how to code. Machines will find it easier to program directly in Machine Code making faster and more efficient code. They could even develop their own Chips for faster execution.

Tech Students’ and Developers’ Favorite Learning Methods

What methods do students and technologists rely on to learn new skills? That’s a key question, one that a new HackerEarth report attempted to answer by surveying more than 16,655 developers.

As you can see from the following visualization, both students and professionals rely on a variety of online methods to gain new skills (the totals add up to more than 100 percent because respondents could choose more than one method). For both groups, though, online competitive coding platforms and YouTube tutorials are absolutely key, while coding bootcamps and “old school” textbooks aren’t significant factors:

2020-05-20 00:00:00 Read the full story…
Weighted Interest Score: 2.0862, Raw Interest Score: 1.3445,
Positive Sentiment: 0.1391, Negative Sentiment 0.0464

Cloudquant Thoughts : Interesting to see the difference between Students and Professionals and the breakdown by age!

Google refuses to build AI to extract oil and distances itself from the industry (Registration Wall)

Pledge comes as Greenpeace highlighted technology companies like Google, Microsoft and Amazon’s contracts with the oil and gas industry

Google has pledged not to build bespoke artificial intelligence algorithms for oil and gas companies to extract oil, the first of the major cloud computing providers to do so.

The technology giant has several energy customers that use Google Cloud to host and process their data so they can run their IT systems out of their own datacentres, but it will not build custom machine learning algorithms to help the companies find and extract oil. It will, however, offer customised artificial intelligence to renewable energy companies. Shell, BP, Chevron, ExxonMobil and several others have turned to cloud technology to power AI to find and extract more oil and gas and reduce production costs. The industry is expected to spend $1.3bn (£1.1bn) on cloud services in 2020, according to HG Insights data.

2020-05-19 00:00:00 Read the full story…
Weighted Interest Score: 4.5030, Raw Interest Score: 2.2165,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

Beware of these futuristic background checks

Tons of people are looking for work. AI-powered background checks could stand in the way.

Unemployment in May reached its highest levels since the Great Depression, but companies like Postmates and Uber have continued to hire new workers during the pandemic. If you’re interested in this kind of gig, however, there’s a good chance you’ll need to pass an AI-powered background check from a company like Checkr. This might not be as easy as it sounds.

Checkr is on the forefront of a new and potentially problematic kind of hiring, one that’s powered by still-emerging technology. Those hoping to quickly get extra work complain that Checkr and others using AI to do background checks aren’t addressing errors and mistakes on their criminal records reports. In these cases, a glitch in the system can cost someone a job.

But this isn’t exactly a new problem. In recent years, Checkr has faced a slew of lawsuits for making mistakes that have cost people much-desired opportunities to work, according to legal records. One complaint from a man hoping to drive for Uber alleged that he was wrongly linked to a murder conviction that actually belonged to someone with a similar name. Another person hoping to work for the ride-share giant complained that he was erroneously reported to have committed several misdemeanors — including the possession of a controlled substance — crimes that belonged to another person with the same name.

2020-05-11 Read the full story…

Stanford uses AI scans of satellite images to track poverty levels over time

The system searches for indicators of economic developments

A new AI tool can track poverty levels in African villages over time by scanning satellite images for signs of economic well-being.

The tool searches the images for indicators of development, such as roads, agriculture, housing, and lights turned on at night. Deep learning algorithms find patterns in this data to measure the villages’ wealth.

Researchers from Stanford University tested the tool on about 20,000 villages across 23 countries in Africa that had existing wealth data. They say that it successfully estimated the poverty levels of the villages over time.

2020-05-22 Read the full story…

Google : Open-Sourcing BiT : Exploring Large-Scale Pre-training for Computer Vision

A common refrain for computer vision researchers is that modern deep neural networks are always hungry for more labeled data — current state-of-the-art CNNs need to be trained on datasets such as OpenImages or Places, which consist of over 1M labelled images. However, for many applications, collecting this amount of labeled data can be prohibitive to the average practitioner.

A common approach to mitigate the lack of labeled data for computer vision tasks is to use models that have been pre-trained on generic data (e.g., ImageNet). The idea is that visual features learned on the generic data can be re-used for the task of interest. Even though this pre-training works reasonably well in practice, it still falls short of the ability to both quickly grasp new concepts and understand them in different contexts. In a similar spirit to how BERT and T5 have shown advances in the language domain, we believe that large-scale pre-training can advance the performance of computer vision models.

2020-05-21 Read the full story…

Global Artificial Intelligence Conference – 2020 Sep 16th – 18th – Seattle – WA

Global Big Data Conference’s vendor agnostic Global Artificial Intelligence Conference is held on Sep 16th – 18th 2020 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc.. ). It will be the largest vendor agnostic conference in AI space. The Conference allows thought leaders & practitioners to discuss AI through effective use of various techniques.

You get to meet technical experts, Senior, VC and C-level executives from leading innovators in the AI space (Executives from startups to large corporations will be at our conference.)
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 4.4335, Raw Interest Score: 2.1565,
Positive Sentiment: 0.3697, Negative Sentiment 0.0000

Weka Furthers Weka AI by Integrating with Valohai

WekaIO™ (Weka), an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN) and an innovation leader in high-performance and scalable file storage, is pleased to announce its integration with the deep learning pipeline management solution from Valohai, a Weka Innovation Network™ (WIN) partner. The announcement underpins Weka’s commitment to empower Data Scientists and Chief Data Officers to manage and prioritize data science pipelines. The tools from Valohai are supported in an Amazon Virtual Private Cloud (Amazon VPC), and available in AWS Marketplace.

“New workloads are driving the need for modern foundational architectures, and the recently launched Weka AI offers a transformative solution framework for Accelerated DataOps,” said Shailesh Manjrekar, head of AI and strategic alliances at Weka. “Our partnership with Valohai and our integration with its Deep Learning Pipeline Management tools expand Weka AI’s capabilities to offer Explainable AI (XAI). This is a critical factor for use cases with a social impact, including autonomous driving, healthcare, and genomics.”
2020-05-25 07:10:33+00:00 Read the full story…
Weighted Interest Score: 4.1921, Raw Interest Score: 1.9055,
Positive Sentiment: 0.6098, Negative Sentiment 0.0000

Visualization Startup Brytlyt Combines AI, GPUs, PostgresSQL

The world may seem like it has slowed down in the midst of a pandemic, but technologies like real-time data analytics keep accelerating as one startup after another comes up with new ways to crunch numbers and leverage the results.

Among them is U.K. analytics and visualization startup Brytlyt, which announced a $4 million funding round this week. Investors include Amadeus Capital Partners and Finch Capital, bringing Brytlyt’s total funding to a modest $6 million.

The visualization company specializes in helping telecommunications carriers and others sift through huge data sets—up to 1 terabyte—generated by millions of handsets. Once organized, those data are used to create maps, charts and graphs for predictive analytics applications.

The London-based startup claims its platform is among the first AI- and GPU-based analytics platforms built on PostgresSQL. That combination is said to yield sub-second responses to multiple tables joining billions of records. Brytlyt further claims benchmark testing showed its SQL database runs 30 times faster than other GPU-based platforms, 300 times quicker than in-memory databases and 1,000 times faster than legacy systems.

2020-05-21 00:00:00 Read the full story…
Weighted Interest Score: 3.9044, Raw Interest Score: 2.0868,
Positive Sentiment: 0.2020, Negative Sentiment 0.1683

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: 3.8679, Raw Interest Score: 2.2474,
Positive Sentiment: 0.0391, Negative Sentiment 0.0977

Is Remote Work Leading to a Paradigm Shift on the Trading Desk?

This evolution of financial technology may be amplified by the current crisis, ushering in a new era of further innovation. With people working from home or from different remote offices, many minds are thinking about longer-term solutions exacerbated by COVD-19.

There is no doubt that artificial intelligence will play a role in matching trades. Machine learning can scan through vast amounts of historical transaction data or open positions on the sell-side OMS blotter to identify natural buyers and sellers of securities.

2020-05-20 10:22:56+00:00 Read the full story…
Weighted Interest Score: 3.8385, Raw Interest Score: 1.5114,
Positive Sentiment: 0.1616, Negative Sentiment 0.2852

Research Into Hardware Aims to Lower Demands and Expense of AI Software

With the energy and compute demands of AI machine learning models trending at what appears to be an unsustainable rate, researchers at Purdue University are experimenting with specialized hardware aimed at offloading some of the AI demands on software.

The approach exploits features of quantum computing, especially proton transport.

“Software is taking on most of the challenges in AI. If you could incorporate intelligence into the circuit components in addition to what is happening in software, you could do things that simply cannot be done today,” stated Shriram Ramanathan, a professor of materials engineering at Purdue University, in an account from Purdue University published on sciencesprings.

2020-05-21 20:22:56+00:00 Read the full story…
Weighted Interest Score: 3.7604, Raw Interest Score: 1.9166,
Positive Sentiment: 0.2076, Negative Sentiment 0.1437

Top 10 Papers On Transfer Learning One Must Read In 2020

Transfer Learning has recently gained attention from researchers and academia and has been successfully applied to various domains. This learning is an approach to transferring a part of the network that has already been trained on a similar task while adding one or more layers at the end, and then re-train the model.

In this article, we list down the top 10 researchers papers on transfer learning one must read in 2020. (The papers are listed according to the year of publishing)

2020-05-26 09:30:00+00:00 Read the full story…
Weighted Interest Score: 3.3122, Raw Interest Score: 2.0884,
Positive Sentiment: 0.2573, Negative Sentiment 0.1513

Exploring AI Dependence Upon ‘Artificial Stupidity’ For Autonomous Cars

The role of Artificial Stupidity needs to be included in the discussion of Artificial Intelligence for self-driving cars, to be realistic.

We all generally seem to know what it means to say that someone is intelligent. In contrast, when you label someone as “stupid,” the question arises as to what exactly that means. For example, does stupidity imply the lack of intelligence in a zero-sum fashion, or does stupidity occupy its own space and sit adjacent to intelligence as a parallel equal? Let’s do a thought experiment on this weighty matter.

2020-05-21 20:11:24+00:00 Read the full story…
Weighted Interest Score: 3.0797, Raw Interest Score: 1.2591,
Positive Sentiment: 0.0961, Negative Sentiment 0.2115

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

OnMobile Global Limited, a Bengaluru, India-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

Reversing the 80/20 Ratio in Data Analytics

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. This is unsustainable, as the pressure to deliver insights in a rapid manner is increasing. When time to answer is critical, “you can’t afford to spend hours cleaning up data, nor can you waste time worrying whether your data is good enough,” said Peter Bailis, Stanford University professor and CEO of Sisu.

The need to flip the 80/20 ratio is urgent. “Just 5 or 6 years ago, innovative companies were satisfied with one- or even multiple-day delays for insights from their data,” said Ben Newton, director of operations analytics at Sumo Logic. “That is no longer the case. Many companies have hours, or even minutes, to respond to user behavior and market trends. The companies that are winning are basing much of their competitive muscle on the ability to leverage their data effectively and quickly.”

Data teams spend “copious amounts of time finding, cleansing and organizing data,” agreed Thameem Khan, general manager of data catalog and preparation at Boomi. “This creates a number of problems that hamper business progress, especially as it relates to understanding where data is, what the data says, and if the data is actually available to be used.” As a result, business users may need to wait for weeks for data teams to deliver responses.
2020-05-21 00:00:00 Read the full story…
Weighted Interest Score: 2.9470, Raw Interest Score: 1.7893,
Positive Sentiment: 0.2982, Negative Sentiment 0.3976

Covid 19 and Future of Regtech

The use of specialized tech to help financial institutions meet heightened regulatory requirements — a.k.a. regtech — is poised to continue taking off this year. Given the uncertainty surrounding COVID-19, the rate of adaptation is, like much else, unclear. Institutions may have to scramble while focusing on the immediate fallout of the pandemic, but eventually, the tech that’s been gaining a foothold over the past couple of years will continue moving forward.

With fines posing a significant risk for institutions, harnessing artificial intelligence (AI) is becoming paramount. Automating data collection and interpreting that data as quickly as possible — and thereby heightening the odds of raising the alarm when patterns of suspicious transactions appear — are dual processes now flourishing in the finance sector.

So what will be the trends for the rest of 2020? Underlying them will be the continued expansion of robust investment in regtech.
2020-05-22 19:01:56 Read the full story…
Weighted Interest Score: 2.9309, Raw Interest Score: 1.6459,
Positive Sentiment: 0.1247, Negative Sentiment 0.2244

How Neural Network Can Be Trained To Play The Snake Game

At the present scenario, video games portray a crucial role when it comes to AI and ML model development and evaluation. This methodology has been around the corner for a few decades now. The custom-built Nimrod digital computer by Ferranti introduced in 1951 is the first known example of AI in gaming that used the game nim and was used to demonstrate its mathematical capabilities.

Currently, the gaming environments have been actively utilised for benchmarking AI agents due to their efficiency in the results. In one of our articles, we discussed how Japanese researchers used Mega Man 2 game to assess AI agents. Besides this, there are several popular instances where researchers used games to benchmark AI such as DeepMind’s AlphaGo to beat professional Go players, Libratus to beat pro players of Texas Hold’em Poker, among others.

In this article, let’s take a look at another simple video game called Snake and how machine learning algorithms can be implied to play this simple game.

2020-05-25 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9066, Raw Interest Score: 1.5239,
Positive Sentiment: 0.2540, Negative Sentiment 0.0462

Matillion ETL for Azure Synapse to Enable Data Transformations

Matillion, a provider of data transformation software for cloud data warehouses (CDWs), is releasing Matillion ETL for Azure Synapse to enable data transformations in complex IT environments at scale.

The release features pre-built data source components to integrate cloud and on-prem databases, files, NoSQL, and SaaS applications, incluuding Oracle, SQL Server, Excel, SharePoint, MongoDB, Salesforce, Facebook, and Bing, as well as simple-to-advanced transformation components to address even the most complex data transformations to customize output.

Empowering enterprises to achieve faster time to insights by loading, transforming, and joining together data, the release extends Matillion’s product portfolio to further serve Microsoft Azure customers.

2020-05-20 00:00:00 Read the full story…
Weighted Interest Score: 2.6886, Raw Interest Score: 1.7564,
Positive Sentiment: 0.4858, Negative Sentiment 0.0000

How To Build An Online Course On Data Science

The extension of the lockdown brought in social distancing, which not only impacted businesses but also shut down schools and colleges. This disruption has forced students, as well as working professionals, transition to online courses. The pandemic has also provided opportunities for data scientists to upskill themselves using online data science courses.

Responding to this, many ed-tech companies have come up with a variety of online courses for data science enthusiasts to use their content for free. These courses usually have experienced faculties and professors, along with the interactive live sessions, which can help students get a better understanding of the field.

These online courses in data science have left students with many options. The market is crowded, and there is more supply than demand for these online courses. And therefore, ed-tech companies need to build a comprehensive online data science course that can stand out in the market. Here is how businesses can create an all-inclusive online course for data science.

2020-05-24 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5992, Raw Interest Score: 1.4887,
Positive Sentiment: 0.1553, Negative Sentiment 0.0906

Expanding Your Data Science and Machine Learning Capabilities – Webinar

SPECIAL DBTA ROUNDTABLE WEBINAR THURSDAY, JUNE 25, 2020 – 11:00 am PT / 2:00 pm ET

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. In responses, 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.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.5744, Raw Interest Score: 1.7004,
Positive Sentiment: 0.2429, Negative Sentiment 0.0810

Modern Data Warehousing: Enterprise Must-Haves – webinar

SPECIAL DBTA ROUNDTABLE WEBINAR THURSDAY, NOVEMBER 19, 2020 – 11:00 am PT / 2:00 pm ET

To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.
2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000

Data – Exact. True. Indubitable. Transparent. AI – Not so much.

In today’s analytics-oriented environment, data is being wielded as an indispensable instrument for efficient decision making backed by concrete insights. Especially intriguing is the way internet firms are analysing voluminous consumer-centric data that is being generated at an estimated rate of 2.5 quintillion bytes per day. Businesses have moved on from the traditional approach centered around intuition and/or guesswork, to a data-driven decision-making mechanism backed by quantitative rigour. But that is just the tip of the iceberg.

The scope of Data Science, which is the larger umbrella term in this domain has expanded beyond prescriptive and predictive modelling for business purposes. It has rather ventured into areas where the possibility of ‘humanising’ machines is being explored through machine learning and its more sophisticated form – deep learning. We commonly call this developing area of technology, Artificial Intelligence (AI).

Of course, much has been achieved here, especially in the last few years with applications like voice-recognition, image-processing, semi-self-driving vehicles, etc. But without undermining the technological revolution it has brought about; it will be conservatively prudent to not over sensationalise AI.
2020-05-26 06:31:00+00:00 Read the full story…
Weighted Interest Score: 2.4996, Raw Interest Score: 1.2282,
Positive Sentiment: 0.1939, Negative Sentiment 0.2424

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

EU Analytics Effort Goes ‘Extreme Scale’

A European Union analytics initiative seeks to forge a new software architecture for what developers dub “extreme-scale” data analytics that would be applied to autonomous transportation and “smart mobility” systems. As the name suggests, the EU’s ELASTIC (Extreme-ScaLe Big-Data AnalyticS in Fog CompuTIng ECosystems) initiative seeks to develop an agile software architecture in which computing is dynamically distributed to real-time analytics.

Launched in December 2018, the three-year, €5.9 million ($6.4 million) project is being coordinated by the Barcelona Supercomputing Center. ELASTIC also seeks to address the shortfalls associated with real-time analytics running in the cloud. Program managers note that communications and data movement make real-time analytics difficult.
2020-05-22 00:00:00 Read the full story…
Weighted Interest Score: 2.1577, Raw Interest Score: 1.4509,
Positive Sentiment: 0.1488, Negative Sentiment 0.2604

Broadcom Lauches AI-Driven Network Monitoring & Analytics Solution

Broadcom announced the availability of DX NetOps powered by Broadcom Silicon, the industry’s first AI-driven, high scale operations monitoring and analytics solution. Captured at the chip level for advanced network triage and remediation, DX NetOps delivers fine-grain per packet and flow level visibility to mitigate complex network congestion.

“Today’s businesses are now hyper-connected, reliant upon complex infrastructures, multi-cloud environments connecting billions of devices through a mesh of networks. This means more congestion and less visibility to triage the delivery of today’s digital experience,” said Serge Lucio, vice president and general manager, Enterprise Software Division, Broadcom. “In this complex environment, businesses demand a new approach to network monitoring to solve the new network congestion issue, one that is AI-driven, self-healing and powered by silicon.”

2020-05-22 08:50:17+00:00 Read the full story…
Weighted Interest Score: 2.1421, Raw Interest Score: 1.3809,
Positive Sentiment: 0.2267, Negative Sentiment 0.2061

Demystifying DataOps: What We Need to Know to Leverage It

The term “DataOps” has picked up momentum and is quickly becoming the new buzz word. But we want it to be more than just a buzz word for your company, after reading this article you will have the knowledge to leverage the best of DataOps for your organization.

Let’s start by looking at where DataOps stands in the zoo of current IT methodologies. If you are familiar with ETL (extract, transform, and load) and MDM (master data management systems), think about DataOps as the next level in organizing data and processes around it. You can also think about it as a methodology that brings together DevOps and Agile in the field of Data Science in that DataOps is about changing people’s minds and the way they approach everyday challenges.
2020-05-22 00:00:00 Read the full story…
Weighted Interest Score: 2.0583, Raw Interest Score: 1.0428,
Positive Sentiment: 0.2406, Negative Sentiment 0.2540

AI researchers say they created a better way to generate 3D photos

A group of AI researchers from Facebook, Virginia Tech, and the National Tsing Hua University in Taiwan say they’ve created a novel way to generate 3D photos that’s superior to Facebook 3D Photos and other existing methods. Facebook 3D Photos launched in October 2018 for dual-camera smartphones like the iPhone X, which uses its TrueDepth camera to determine depth in photos. In the new research, the authors use a range of photos taken with an iPhone to demonstrate how their approach gets rid of the blur and discontinuity other 3D methods introduce.
2020-05-25 00:00:00 Read the full story…
Weighted Interest Score: 2.0509, Raw Interest Score: 1.1765,
Positive Sentiment: 0.1368, Negative Sentiment 0.0821

China’s trillions towards tech won’t buy dominance

Big spending numbers are being thrown around in China, once again. This time, it’s trillions of yuan of fiscal stimulus on all things tech. The plans are bold and vague: China wants to bring technology into its mainstream infrastructure build-out and, in the process, heave the economy out of a gloom due only partly to the coronavirus.

2020-05-25 00:00:00 Read the full story…
Weighted Interest Score: 2.0433, Raw Interest Score: 1.0339,
Positive Sentiment: 0.2164, Negative Sentiment 0.1443

Fairness and interpretability in AI: Putting people first

At the 2005 Conference on Neural Information Processing Systems, researcher Hanna Wallach found herself in a unique position—sharing a hotel room with another woman. Actually, three other women to be exact. In the previous years she had attended, that had never been an option because she didn’t really know any other women in machine learning. The group was amazed that there were four of them, among a handful of other women, in attendance. In that moment, it became clear what needed to be done. The next year, Wallach and two other women in the group, Jennifer Wortman Vaughan and Lisa Wainer, founded the Women in Machine Learning (WiML) Workshop. The one-day technical event, which is celebrating its 15th year, provides a forum for women to present their work and seek out professional advice and mentorship opportunities. Additionally, the workshop aims to elevate the contributions of female ML researchers and encourage other women to enter the field. In its first year, the workshop brought together 100 attendees; today, it draws around a thousand.

In creating WiML, the women had tapped into something greater than connecting female ML researchers; they asked whether their machine learning community was behaving fairly in its inclusion and support of women. Wallach and Wortman Vaughan are now colleagues at Microsoft Research, and they’re channeling the same awareness and critical eye to the larger AI picture: Are the systems we’re developing and deploying behaving fairly, and are we properly supporting the people building and using them?
2020-05-19 15:02:12+00:00 Read the full story…
Weighted Interest Score: 1.9855, Raw Interest Score: 0.9879,
Positive Sentiment: 0.1394, Negative Sentiment 0.2788

Covid-19 could hasten rise of the robots as companies seek to cut expensive labour costs

Healthcare staff and bank clerks have been on the front line of the health and economic crises gripping the UK, but behind the scenes, another group of workers has been toiling away and straddling both emergencies with no fear of coronavirus: robots.

Robot process automation, or RPA, is software that automates repetitive back-office tasks. The NHS has used it during the pandemic to control demand and capacity planning in intensive care units, distribute lab results and automate its 111 calls to a Covid-19 database.
2020-05-24 00:00:00 Read the full story…
Weighted Interest Score: 1.9169, Raw Interest Score: 1.4385,
Positive Sentiment: 0.0533, Negative Sentiment 0.3729

What are Python Iterators and Generators?

Iterables are objects that are capable of returning their members one at a time. Generators are also iterators but are much more elegant.

Python is a beautiful programming language. I love the flexibility and the incredible functionality it provides. I love diving into the various nuances of Python and understand how it responds to different situations.

During my time working with Python, I have come across a few functionalities whose usage is not commensurate to the number of complexities they simplify. I like to call these “hidden gems” in Python. Not a lot of people know about them but they’re super useful for analytics and data science professionals. Python Iterators and Generators fit right into this category. Their potential is immense!

If you’ve ever struggled with handling huge amounts of data (who hasn’t?!), and your machine running out of memory, then you’ll love the concept of Iterators and generators in Python. Rather than putting all the data in the memory in one go, it would be better if we could work with it in bits, dealing with only that data that is required at that moment, right? This would reduce the load on our computer memory tremendously. And this is what iterators and generators do!

2020-05-21 19:33:51+00:00 Read the full story…
Weighted Interest Score: 1.9092, Raw Interest Score: 1.0836,
Positive Sentiment: 0.2580, Negative Sentiment 0.0000

Alibaba to invest $1.4 billion in AI system for smart speakers

SHANGHAI (Reuters) – Alibaba Group Holding Ltd will invest 10 billion yuan ($1.41 billion) into an AI (artificial intelligence) and IoT (Internet of Things) system centered around its Tmall Genie smart speaker, the company announced on Wednesday. The announcement comes as the e-commerce giant continues its push into new technologies and business sectors beyond online shopping.

The money will be used to add more content to Tmall Genie, as well as develop proprietary technology, Alibaba said. It launched the first model of Tmall Genie in 2017. Like the Amazon Echo, which is not for sale in China, the smart speaker can interact with users via a voice interface to play music, give out weather information, and perform other functions.
2020-05-20 07:28:06+00:00 Read the full story…
Weighted Interest Score: 1.6925, Raw Interest Score: 1.0578,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000


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