AI & Machine Learning News. 13, January 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?


Google Research: Looking Back at 2019, and Forward to 2020 and Beyond

The goal of Google Research is to work on long-term, ambitious problems, with an emphasis on solving ones that will dramatically help people throughout their daily lives. In pursuit of that goal in 2019, we made advances in a broad set of fundamental research areas, applied our research to new and emerging areas such as healthcare and robotics, open sourced a wide variety of code and continued collaborations with Google product teams to build tools and services that are dramatically more helpful for our users.

As we start 2020, it’s useful to take a step back and assess the research work we’ve done over the past year, and also to look forward to what sorts of problems we want to tackle in the upcoming years. In that spirit, this blog post is a survey of some of the research-focused work done by Google researchers and engineers during 2019 (in the spirit of similar reviews for 2018, and more narrowly focused reviews of some work in 2017 and 2016). For a more comprehensive look, please see our research publications in 2019.

2020-01-09 00:00:00 Read the full story…

CloudQuant Thoughts : Google is at the forefront of AI and ML and this summary of their work over the last year is outstanding. If you read one article this week it should be this one.

AWS announces AutoGluon, an open-source library for writing AI models

Amazon Web Services Inc. today launched a new open-source library to help developers write, with just a few lines of code, machine learning-based applications that use image, text or tabular data sets.

Building machine learning apps that rely on such data isn’t an easy task. For example, developers need to know how to tune the “hyperparameters” that represent the choices made when constructing an AI model. They also need to grapple with issues such as neural architecture search, which enables them to find the best architecture design for their machine learning models.

AutoGluon automates many of these complicated tasks and can create a new machine earning model with as little as three lines of code by automatically tuning choices within default ranges that are known to perform well for a given task. All the developer has to do is specify how quickly they want their model to be trained, and AutoGluon will come up with the strongest model in the given timeframe.

Amazon said AutoGluon can identify models for tasks including image and text classification, object detection and tabular prediction. It also features an application programming interface for more experienced developers to fiddle with so they can improve a model’s predictive performance.
2020-01-10 03:41:43-04:00 Read the full story…
Weighted Interest Score: 2.9752, Raw Interest Score: 2.0033,
Positive Sentiment: 0.5008, Negative Sentiment 0.0000

CloudQuant Thoughts : The future looks golden as major firms make AI and ML more approachable and easy to use. There is nothing better than showing someone a few lines of code to do “Hot Dog” “No Hot Dog”.

‘Smile with your eyes’: How to beat South Korea’s AI hiring bots and land a job

In cram-school-obsessed South Korea, students fork out for classes in everything from K-pop auditions to real estate deals. Now, top Korean firms are rolling out artificial intelligence in hiring – and jobseekers want to learn how to beat the bots.

From his basement office in downtown Gangnam, careers consultant Park Seong-jung is among those in a growing business of offering lessons in handling recruitment screening by computers, not people. Video interviews using facial recognition technology to analyse character are key, according to Park.

“Don’t force a smile with your lips,” he told students looking for work in a recent session, one of many he said he has conducted for hundreds of people. “Smile with your eyes.”

“The AI won’t be naturally asking personal questions,” said Yoo Wan-jae, a 26-year-old looking for work in the hospitality industry. “That will make it a bit uncomfortable … I’ll need to sign up for cram schools for the AI interview,” said Yoo, speaking in Seoul’s Noryangjin district, known as “Exam Village”, packed with cram schools and study rooms.

2020-01-13 09:49:08+00:00 Read the full story…
Weighted Interest Score: 2.6282, Raw Interest Score: 1.0045,
Positive Sentiment: 0.1069, Negative Sentiment 0.1923

CloudQuant Thoughts : If the UK is a barometer for where most Western Manufacturing is heading, South Korea is the barometer for tech. China pushes the boundaries with its government’s encouragement of monitoring its citizens but South Korea is a western country, it is incredibly high tech yet its youth unemployment rate is sky rocketing. If South Korea has AI conducting interviews it will not be long before US companies start doing the same!

The Best book to Start your Data Science Journey – Data Science from scratch: First Principles with Python, 2nd edition

Data Science. It’s the sexiest job of the 21st century and everyone is talking about it. Companies are eager to hire the best talents and people are enthusiastic to jump in the data science boat. With data growing exponentially and our technology advancing at such a rapid pace, it’s no surprise that Data Science would become a fundamental job in businesses worldwide.
With data as the new oil; an immensely, untapped valuable asset, it will play an imperative role in our society. The workplace today is dominated by various specializations of Data Science positions and it has influenced business models in fundamental ways. Algorithms and formulas that run on data will replace human intuition, and data science will be the decision-maker of data-driven companies.

2020-01-13 04:25:55.208000+00:00 Read the full story…
Weighted Interest Score: 3.7650, Raw Interest Score: 1.8825,
Positive Sentiment: 0.3993, Negative Sentiment 0.1141

CloudQuant Thoughts : $30 on Amazon… a very worthwhile investment.

White House Releases 10 AI Principles for Agencies to Follow

The White House’s Office of Science and Technology Policy (OSTP) this week released what it has described as a “first of its kind” set of principles that agencies must meet when drafting AI regulations. The principles were met with less than universal approval, with some experts suggesting they represent a “hands-off” approach at a time when some regulation may be needed.

The announcement supplements efforts within the federal government over the past year to define ethical AI use. The defense and national security communities have mapped out their own ethical considerations for AI, according to an account in the Federal News Network. The US last spring signed off on a common set of international AI principles with more than 40 other countries.

  1. Public trust in AI
  2. Public participation
  3. Scientific integrity and information quality
  4. Risk assessment and management
  5. Benefits and costs
  6. Flexibility
  7. Fairness and non-discrimination
  8. Disclosure and transparency
  9. Safety and security
  10. Interagency coordination

2020-01-09 22:30:00+00:00 Read the full story…
Weighted Interest Score: 3.8051, Raw Interest Score: 1.3117,
Positive Sentiment: 0.3008, Negative Sentiment 0.1685

The World Has a Plan to Rein in AI—but the US Doesn’t Like It

US officials worry the proposal could unnecessarily slow development of artificial intelligence at American companies.

In December 2018, Canada and France announced plans for a new international body to study and steer the effects of artificial intelligence on the world’s people and economies.

Canadian prime minister Justin Trudeau said the International Panel on Artificial Intelligence would be established by the Group of Seven leading western economies and play a role in “addressing some of the ethical concerns we will face in this area.” It was to be modeled on the UN’s Intergovernmental Panel on Climate Change, which helped establish consensus on the world’s climate crisis and recommends possible responses.

Just over a year later, the IPAI has been renamed the Global Partnership on AI, but it still hasn’t quite gotten off the ground. Six of the G7 are on board—with the United States the lone holdout.

Proponents of the idea say it will help governments get up to speed on AI developments and could build international consensus on limiting certain uses of the technology, such as AI projects designed to control citizens or infringe human rights. The White House says the body is unnecessary bureaucracy that threatens to dampen AI development by being overly cautious.

2020-01-06 00:00:00 Read the full story…

Free AI Resources

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources.

2020-01-06 00:00:00 Read the full story…

Allocation models that know their unknowns

Quant firms are buzzing with ideas about algorithms that can ‘learn’ how best to adjust the mix of assets and strategies they trade. But another technology – probabilistic programming – may yet prove equally valuable in answering that question. The approach allows firms in their portfolio allocation to build models incorporating the kind of market savvy that traders take for granted and also to construct a probabilistic view of future returns, according to Thomas Wiecki, head of research at hedge fund Quantopian. Both are areas where voguish machine learning models now being employed in allocation can struggle.

Portfolio allocation decisions hinge on how markets are primed to move and how linkages between strategies or assets are likely to evolve. Allocation calls often shape fund performance more than security-level choices, particularly for a fund such as Quantopian that crowdsources strategies, including from amateur quants.
2020-01-13 04:30:00+00:00 Read the full story…
Weighted Interest Score: 5.5140, Raw Interest Score: 2.2472,
Positive Sentiment: 0.2185, Negative Sentiment 0.1873

Which Comes First, the AI or the Business Strategy?

Companies need to align the AI strategy with the business strategy. First the company needs a business strategy. Can the AI help with that?

Maybe so. AI is being applied to the model decision-making of governments and corporations. A recent article in Forbes described the Real Time Strategy (RTS) technology involved in Google DeepMind’s gaming software that works with “imperfect information.” RTS is a deep neural network trained on past games and which evolves by playing itself to get better.

“The core of the approach is a system that uses a mash-up of supervised learning and reinforcement learning, but the key concept to absorb is that a complicated RTS game only humans could play is now being solved very comprehensively by an artificial intelligence system,” states author Dan Shapiro, PhD CTO and co-founder of AuditMap.ai, offering compliance AI for the enterprise.

2020-01-09 22:30:28+00:00 Read the full story…
Weighted Interest Score: 5.4549, Raw Interest Score: 2.2755,
Positive Sentiment: 0.1940, Negative Sentiment 0.2470

How Legal Sports Betting Can Win The Gamble With Artificial Intelligence

Those days are almost gone when one placed a bet on their favourite team hiding from authorities — sports betting is legal now in many parts of the world. While betting, whether one loves their team or a player or places a bet purely hating the opposing team, one needs to have some knowledge aka in technical terms, some data. The sports betting industry is turning to Artificial Intelligence with this data.

Legal sports betting around the world involves working with a lot of information and data collection. Various sports leagues around the globe give bookmakers this data to come up with better products to enhance legal betting. And to improve the field of legal sports betting with massive data, no technology could work better than Artificial Intelligence.

2020-01-13 10:50:36+00:00 Read the full story…
Weighted Interest Score: 4.9363, Raw Interest Score: 1.7002,
Positive Sentiment: 0.2013, Negative Sentiment 0.3132

AI in the Lead of US Emerging Jobs in 2020, says LinkedIn

Major trends shown in the LinkedIn’s third annual Emerging Jobs Report recently released by LinkedIn, the professional network company, include show a strong showing for AI, that professionals are moving to the most attractive regions, and demand for soft skills around communication, creativity and collaboration are increasing as automation becomes more widespread.

In top US job trends, data science is booming and starting to replace legacy roles. “Data science is seeing continued growth on a tremendous scale,” the report states. It also shows data scientists are taking on responsibilities that had been in the domain of statisticians.

2020-01-09 22:30:25+00:00 Read the full story…
Weighted Interest Score: 4.6225, Raw Interest Score: 2.8585,
Positive Sentiment: 0.3149, Negative Sentiment 0.0969

Between 2012 and 2013 job listings for data science skyrocketed 15,000%

The hottest new job of the century is here. It combines science with technology with business intelligence, giving people with this job the potential to hold more influence over their industries than CEOs and Founders. This job of the future is Data Scientist, and between 2012 and 2013 job listings for them skyrocketed 15,000%. Data science has been around for nearly 300 years, but it is based on ancient philosophical principles.

The main idea behind data science is that information is objective and the more information you have the more objective the conclusions you draw from it can be.

Data scientists’ job is to gather information in a way that it can be analyzed by a machine learning algorithm. This means that data fields should be standardized so that the intake of data is all done in the same manner, otherwise the data will be corrupted and too difficult to use properly. Data scientists have to develop a framework for not only how data is collected but also how it is stored in order to ensure it remains usable.

2020-01-10 20:46:24+00:00 Read the full story…
Weighted Interest Score: 4.5568, Raw Interest Score: 2.3911,
Positive Sentiment: 0.2498, Negative Sentiment 0.1428

Brazil is emerging as a world-class AI innovation hub

Brazil’s government has big plans for AI, despite having come late to the party. In Oxford Insights’ AI Readiness Index 2019, Brazil was ranked 40 out of 192 countries, a sign that the South American powerhouse is moving up in the AI world. The report looks at how ready countries are to take advantage of the AI technologies PwC forecasts will add $15 trillion to the global economy by 2030.

The 2019 report also cautions that the “Global South could be left behind by the so-called fourth industrial revolution.” But even as some of the planet’s richest nations, including Canada, China, Germany, Japan, Singapore, and the U.S, have become recognized AI innovation hubs, according to studies by Deloitte and others, South America — led by Brazil — is rapidly emerging as a leader in AI-enabled businesses.

Brazil’s future economy is banking on a big contribution from AI technologies, and the country is leading the rest of Latin America, based on a proprietary AI Global Vibrancy Tool used to compile the recent 2019 AI Index report from Stanford University. The study found that during the last four years Brazil has shifted into high gear as one of the top five countries in the world with the fastest growth in AI hiring.

2020-01-12 00:00:00 Read the full story…
Weighted Interest Score: 4.3043, Raw Interest Score: 1.6810,
Positive Sentiment: 0.4821, Negative Sentiment 0.1173

2020: The Year of the Citizen Data Engineer

Data sophistication has forever and profoundly changed the way in which companies do business today. Data-driven innovation has led to better business decisions, enhanced customer engagement, and improved customer retention, all of which are essential to succeeding in today’s competitive market. Data scientists and data engineers play the primary role in accelerating a company’s data sophistication, providing both the technology and domain expertise from a sea of zeros and ones into valuable data products. As we reflect on 2019 and look forward to the year and decade ahead, we will examine the evolving nature of these roles and teams.

2019 was the year of the “citizen data scientist,” a term that refers to individuals who utilize data science practices and tools but whose training and primary role is not that of a data scientist. The explosion in data science initiatives across departments and job functions has stirred an increasing need for streamlined and scalable access to data, a function typically handled by data engineers. Having a strong data engineering team is important in today’s world, and more companies are taking notice. Much of the work done by data scientists proves to be very difficult without the support of data engineers. The volume, velocity, and variety of data available to data scientists increases daily, and data engineers are fundamental to ensuring their success by creating scalable, reliable, and efficient systems for processing and delivering data. However, there are simply not enough data engineers to meet the demand. To fill this void, I predict 2020 will be the year of the “citizen data engineer.”

2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 3.7037, Raw Interest Score: 2.0812,
Positive Sentiment: 0.3807, Negative Sentiment 0.0508

Technology Trends to Keep an Eye on in 2020

“Artificial intelligence and machine learning, with an eye toward task automation.” For Senior Data Scientist James Buban at iHerb, those are just a couple of the tech trends he’ll be watching in 2020.

As companies enter a new decade, it’s important for their leaders to anticipate how the latest tech trends will evolve in order to determine how they can benefit their businesses and their customers. CEO of 20spokes Ryan Fischer said his company uses machine learning data to “provide a better user experience for our clients’ customers by leveraging data on individual user behavior.”

2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 3.6210, Raw Interest Score: 2.4694,
Positive Sentiment: 0.2934, Negative Sentiment 0.0244

11 top open-source API testing tools

How do you find the right open-source API testing tool for your needs? While most vendors are talking up the benefits of AI- and UI-based testing tools in general, AI- and machine learning-based applications that help with API testing have arrived. Before you begin API testing, however, make sure you understand test automation basics and know how to avoid the most common test automation mistakes.

2020-01-08 18:09:30-04:00 Read the full story…
Weighted Interest Score: 3.5461, Raw Interest Score: 1.9002,
Positive Sentiment: 0.0000, Negative Sentiment 0.2375

KNIME on Amazon Web Services Now Available to Productionize AI/ML

ZURICH & AUSTIN, Texas–(BUSINESS WIRE)–KNIME, a unified software platform for creating and productionizing data science, today announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials.

Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers. Further, there are many steps in the process of bringing an AI/ML solution into production that require a transfer of context and knowledge from data preparation to analysis and modeling to deployment.

KNIME on AWS is a visual data workflow editor that allows customers of all skill levels to extract and prepare their data from Amazon Simple Storage Service (Amazon S3), Amazon Redshift, or other sources; utilize AWS AI/ML services along with custom data science to build an impactful model; and deploy this solution “as a service” or to an analytics application. In each step, the solution is underpinned by the storage, compute, security and scale of AWS. This end-to-end solution from data to deployment can be realized with no coding required, and scheduling/automation can be employed in order to create a continuous stream of insights or decisions with minimal manual effort required.

2020-01-13 08:15:00+00:00 Read the full story…
Weighted Interest Score: 3.5088, Raw Interest Score: 2.0672,
Positive Sentiment: 0.1034, Negative Sentiment 0.1550

LG Unveils New Framework For Advancing AI Technology

According to a recent press release, “At CES® 2020, LG Electronics (LG) President and Chief Technology Officer Dr. I.P. Park unveiled the framework for the future of artificial intelligence (AI) development with the title of “Levels of AI Experience: the Future of AI and the Human Experience”.

The conceptual framework aligns with the LG ThinQ brand and its ambitious vision to transform the daily experience by connecting all aspects of people’s lives with intelligent touchpoints. This far-reaching plan creates a clear roadmap for AI where the ultimate destination is a cohesive system comprising products and services that can make anywhere feel like home.

Speaking at the Mandalay Bay Convention Center in Las Vegas, Dr. Park explained that, amidst a wave of AI-related ideas and concepts, it is important to share a structured framework for the development of AI across the industry so that we may create a meaningful impact on the lives of customers we serve.”
2020-01-13 08:10:59+00:00 Read the full story…
Weighted Interest Score: 3.4624, Raw Interest Score: 1.4686,
Positive Sentiment: 0.2295, Negative Sentiment 0.0000

Security Partnership Combines ML with Net Flow Data

A framework known as network flow is emerging as a cyber-security tool based on the enterprise requirement for broader “network awareness” derived from network flow data.

A recent effort with roots in university research from the 1980s called the Open Argus Project is applying machine learning techniques to network flow data to spot threats in enterprise network traffic, including growing amounts of encrypted traffic.

The Argus Project that includes researchers from Carnegie Mellon University, Duke University and Stanford University announced its first commercial sponsor this week. Network security startup CounterFlow AI will contribute its proprietary technology to the project. The startup based in Charlottesville, Va., also becomes the first company to license and integrate Argus into its ThreatEye platform.

2020-01-09 00:00:00 Read the full story…
Weighted Interest Score: 3.4302, Raw Interest Score: 2.3249,
Positive Sentiment: 0.1860, Negative Sentiment 0.4030

Accelerating Data-Driven Innovation with DataOps

DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.
2020-01-07 00:00:00 Read the full story…
Weighted Interest Score: 3.3493, Raw Interest Score: 1.9139,
Positive Sentiment: 0.7177, Negative Sentiment 0.0000

Data Science, Automation, and Cloud Scalability (VIDEO)

At Data Summit 2019, Pythian VP Lynda Partner stressed the importance of regarding data projects as software projects, with attendant DevOps, DataOps, and ML Ops components.

“The consumers of data and data platforms aren’t just via the data warehouse. Increasingly, now, what we’re seeing is the data scientists who are showing up with their own tools that they want to use whether they’re Jupyter notebooks or R or whatever products and they want to plug in,” Partner said. “And they don’t want to plug in to the data warehouse exclusively. They want access to all that raw data that data that they need for their models. So they’re bringing their tools and hooking in. Apps are hooking in. You’re starting to see mobile development that’s using the data that’s been integrated into the data platform.”

2020-01-06 00:00:00 Read the full story…
Weighted Interest Score: 3.2364, Raw Interest Score: 1.9231,
Positive Sentiment: 0.0469, Negative Sentiment 0.1407

Obama-era tech advisors list potential challenges for the White House’s AI principles

Former Obama administration advisors say the White House regulatory AI principles announced this week are a good start in many ways, but their simplified mandate to avoid overregulation of private business use is a mistake, and the Trump administration could face an uphill battle in appealing to the rest of the world.

Though the current administration has developed a reputation for blaming the Obama administration when things go wrong and for trying to erase Obama-era policy, when it comes to artificial intelligence, the Trump administration has at times struck a remarkably similar tone to its predecessor. This was evident in the AI research and development strategy plan for federal agencies released in summer 2019. In some instances the same people are driving White House AI policy, like Dr. Lynne Parker, White House deputy CTO and assistant director of AI at the White House Office of Science and Technology Policy (OSTP), who also served in the Obama administration.

2020-01-12 00:00:00 Read the full story…
Weighted Interest Score: 3.1257, Raw Interest Score: 0.9593,
Positive Sentiment: 0.2695, Negative Sentiment 0.2263

Ten 2020 Visions for Data Modelers

2020 is a nice number and sounds comforting. And it is about vision(s). Are there any foresights of importance for data modelers? Well, here are 10 suggestions. No warranties, I suggest you use them as lighthouses, hopefully plotting your course from 2020 onwards.

2020-01-13 08:35:04+00:00 Read the full story…
Weighted Interest Score: 3.1148, Raw Interest Score: 1.6734,
Positive Sentiment: 0.2391, Negative Sentiment 0.0580

Enterprise Must-Haves for Modern Data Warehousing

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.

DBTA recently held a webinar with Clive Bearman, director of product marketing, Qlik; Keith Lambert, VP, marketing and business development, Kore Technologies; and Brian Bulkowski, CTO, Yellowbrick, who discussed the must-have capabilities for modern data warehousing today—how they work and how best to use them.

2020-01-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0063, Raw Interest Score: 1.8182,
Positive Sentiment: 0.1983, Negative Sentiment 0.0000

risQ and Intercontinental Exchange Announce Collaboration to Bring Climate Risk Analytics to the Municipal Bond Ecosystem

risQ and Intercontinental Exchange Announce Collaboration to Bring Climate Risk Analytics to the Municipal Bond Ecosystem

BOSTON –(BUSINESS WIRE)– risQ, Inc., a Boston headquartered start-up focused on best-in-class climate risk analytics, today announced a relationship with Intercontinental Exchange (NYSE:ICE) to help enable the municipal bond ecosystem to incorporate climate risk into project and investment decisions.

Combining cutting-edge…
2020-01-08 00:00:00 Read the full story…
Weighted Interest Score: 2.9069, Raw Interest Score: 1.6905,
Positive Sentiment: 0.2266, Negative Sentiment 0.1046

New liquidity providers to drive out FX last look

New liquidity providers (LPs) are putting pressure on the market to move away from holding periods in the last look window on electronic spot FX trades, according to the head of futures and FX at OCBC Securities.

“Indirectly these [new players] are trying to differentiate their service, differentiate their pricing, and they are indirectly pushing for a reform in this market,” says OCBC’s Keeve Tan.

“Many LPs are going in the direction that if t…
2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7931, Raw Interest Score: 1.5863,
Positive Sentiment: 0.1525, Negative Sentiment 0.0915

The End of the Bonus Culture Is Coming to Wall Street

Chris Purves has been at the cutting edge of markets for more than a decade – from algorithmic trading to machine learning. Now the head of UBS Group AG’s Strategic Development Lab is turning his focus to the human survivors of the tech invasion, persuading them to understand things will never be the same. They’re going to have to — in the jargon of Silicon Valley’s missionaries — “unlearn” how they’ve always operated.

It’s not just that their software may know their next move before they do. The extinction of an entire way of life is looming, as Purves sees it: the end of the bonus culture. Compensation will be a last frontier in the onslaught of technology on finance. He’s moving traders toward electronic systems as the era of legendary gamblers stalking the markets in search of the $100 million payday becomes a distant memory. What’s coming is increased bureaucratization, an evolution that renders individuals’ judgment less important — and with it the need to reward them as they might have once expected.

2020-01-13 08:00:00+00:00 Read the full story…
Weighted Interest Score: 2.7759, Raw Interest Score: 1.5235,
Positive Sentiment: 0.1325, Negative Sentiment 0.1546

Cardiologs raises $15 million for AI that helps spot heart conditions

Cardiologs, a French medical technology startup that’s leveraging artificial intelligence (AI) to help detect heart conditions, has raised $15 million in series A funding. The round was led by Paris-based venture capital (VC) firm Alven, which touts its credentials for helping French-founded startups expand into the U.S. Other participants include Bpifrance, Idinvest Partners, Kurma Diagnostics, ISAI, and Paris Saclay Seed Fund.

2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7141, Raw Interest Score: 1.4488,
Positive Sentiment: 0.2113, Negative Sentiment 0.1811

The reason you aren’t getting a pay rise

What’s changed? The stagflation of the early 1980s appears to have given way to the secular stagnation of the 2020s.

An American economist, Alvin Hansen, warned of secular stagnation in the late 1930s, drawing on Keynes’ theory of an underemployment equilibrium caused by insufficient consumer and investor spending.

Former US Treasury Secretary Larry Summers has revived and modernised the 1930s theory of secular stagnation, suggesting it is rele…
2020-01-13 00:00:00 Read the full story…
Weighted Interest Score: 2.6932, Raw Interest Score: 1.5044,
Positive Sentiment: 0.0442, Negative Sentiment 0.3097

Financial advisers believe robo-advisers will replace jobs

A recent survey of financial advisers found that 43% believe their population will shrink in the next five years.

And of that group, 43% believe robo-advisers are the leading factor in eliminating financial adviser roles, according to the survey conducted by research firm Greenwich Associates.

Startup robo-advisers enjoyed growth last decade, forcing traditional wealth managers to consider how to adapt to changing customer tastes.

2020-01-11 00:00:00 Read the full story…
Weighted Interest Score: 2.6465, Raw Interest Score: 1.5581,
Positive Sentiment: 0.1619, Negative Sentiment 0.1416

First steps of building quant – Cuemacro

If you are trying to move your discretionary process towards incorporating quant tools and Python, how can you avoid the missteps? What should you focus your initial energy on this move? First, you can try taking small steps. Yes, I know machine learning is a buzzword, but get other things done first. Are focusing on training your team so they can use Python (and yes, I do run Python for finance workshops if you’re interested in hiring me!)? Are you investing in a database to store all the data you need? What data are you capturing? If you haven’t given much thought to the data, then you can’t really go any further in any analysis. Of course, data storage doesn’t sound as cool, as machine learning though!

Once you’ve sorted out all your data feeds and data storage issues and have begun to train your team, you can think about what analysis you can do. Probably the best place to start is on those tasks which are heavily manual. I have spent far too much time in the past updating Excel spreadsheets, which are well in excess of 100 MB – and I really don’t wish that upon anyone else! This might require a bit of brainstorm, to understand what manual processes you have across your team and working out which are in most need of automation. The icing on the cake is to fully automate reports which use these spreadsheets.

2020-01-11 00:00:00 Read the full story…
Weighted Interest Score: 2.6159, Raw Interest Score: 1.3080,
Positive Sentiment: 0.0793, Negative Sentiment 0.0396

2020 Predictions: Autonomous Digital Enterprise, Edge Computing, IoT, ITSM, and AIOps

We are at the start of a new year and a new decade and technology is poised for fast change. Recently, Ram Chakravarti, CTO at BMC Software offered his predictions for 2020.

Autonomous Digital Enterprise: “Businesses will make progress moving toward an autonomous digital enterprise through technology innovations in AIOps, edge computing, the convergence of IT service management and IT operations management, SecOps, and DevOps. Rather than focusi…
2020-01-09 00:00:00 Read the full story…
Weighted Interest Score: 2.5430, Raw Interest Score: 1.3411,
Positive Sentiment: 0.2751, Negative Sentiment 0.2063

The Environmental Impacts of AI and IoT In Agriculture

By Caleb Danziger, Science Writer

As the world’s citizenry continues to grow, with estimates putting 2100’s population at 11.2 billion, the agriculture industry will need to compensate for increased demand. Within the next 80 years, an additional 3.6 billion people will need food; production needs to improve beyond what’s possible today. At the same time, land dedicated to agricultural projects may become limited, calling for enhanced efficiency…
2020-01-09 22:30:05+00:00 Read the full story…
Weighted Interest Score: 2.4816, Raw Interest Score: 1.2938,
Positive Sentiment: 0.3310, Negative Sentiment 0.1053

Utilities Sitting on Energy Data Goldmine

Billions of dollars have been invested over the last decade or so in smart meters intended to reduce energy usage and achieve savings. A new study finds that utilities have largely failed to tap data that might yield those savings.

A report released this week by the American Council for an Energy-Efficient Economy found that the 52 U.S. utilities surveyed “vastly underused” advanced metering infrastructure. AMI is defined as smart meters, communications networks and data management. The energy group estimates that infrastructure connects half of all smart meters in the United States.

2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4535, Raw Interest Score: 1.3021,
Positive Sentiment: 0.1116, Negative Sentiment 0.2232

Dun & Bradstreet acquires Orb Intelligence

Dun & Bradstreet, a provider of business decisioning data and analytics, has acquired Orb Intelligence, a prominent digital business identity and firmographic data provider.

“The acquisition of Orb Intelligence cements our strategy to link the digital and physical worlds in the largest global repository of B2B data, and to provide enriched firmographic data to customer profiles to help our clients more effectively execute campaigns to improve cu…
2020-01-10 00:00:00 Read the full story…
Weighted Interest Score: 2.4531, Raw Interest Score: 1.6240,
Positive Sentiment: 0.2887, Negative Sentiment 0.0722

Intel Brings Innovation to Life with Intelligent Tech Spanning the Cloud, Network, Edge & PC

A new press release states, “Breakthroughs in artificial intelligence (AI) that pave the way for autonomous driving. A new era of mobile computing innovation. The future of immersive sports and entertainment. Intel demonstrated all of these and more today at CES 2020, showcasing how the company is infusing intelligence across the cloud, network, edge and PC, and driving positive impact for people, business and society. Intel CEO Bob Swan kicked off today’s news con…
2020-01-10 08:05:38+00:00 Read the full story…
Weighted Interest Score: 2.2766, Raw Interest Score: 1.1959,
Positive Sentiment: 0.3417, Negative Sentiment 0.1139

Data for Machine Learning Gets a New Lease on Language

A recent press release reports, “Venga has released its third solution in its rapidly growing suite of products for Natural Language Processing (NLP) Data Collection. The new addition to the family is InVimage – a cloud-based solution for annotating text within images. With each annotation, we automatically capture the X and Y coordinates, OCR (Optical Character Recognition) the annotation and have the option to machine or manually translate the …
2020-01-13 08:05:59+00:00 Read the full story…
Weighted Interest Score: 2.2753, Raw Interest Score: 1.2929,
Positive Sentiment: 0.1124, Negative Sentiment 0.1686

Can We Do Deep Learning Without Multiplications?

A neural network is built around simple linear equations like Y = WX + B, which contain something called as weights W. These weights get multiplied with the input X and thus plays a crucial in how the model predicts.

Most of the computations in deep neural networks are multiplications between float-valued weights and float-valued activations during the forward inference.

The prediction scores can even go downhill if a wrong weight gets upda…
2020-01-07 11:18:19+00:00 Read the full story…
Weighted Interest Score: 2.2501, Raw Interest Score: 1.4318,
Positive Sentiment: 0.1746, Negative Sentiment 0.1572

No, IBM is not the only relevant player in virtual agents

Last month, IBM General Manager of Data and Watson AI, Rob Thomas, told VentureBeat that IBM was the only major enterprise provider in the red-hot area of virtual agents.

Virtual agents are software that can chat with customers through text, voice, or web chat. “There really are no big players, except for us,” Thomas said at the time. He called the rest of the virtual agent providers “fireflies,” because they are small and there are so many of t…
2020-01-11 00:00:00 Read the full story…
Weighted Interest Score: 2.1999, Raw Interest Score: 1.1670,
Positive Sentiment: 0.2280, Negative Sentiment 0.1073

Cloud Looms Large for Big Data in 2020

If you’re involved with big data in 2020, then it will be hard to avoid the cloud, which has become the de-facto standard platform for storing and processing vast amounts of data. The cloud will change quickly this year, as cloud giants battle for supremacy. Successfully navigating these dynamics in the cloud could mean the difference between celebrating a big data victory and cleaning up a digital mess.

2020-01-07 00:00:00 Read the full story…
Weighted Interest Score: 2.1444, Raw Interest Score: 1.1154,
Positive Sentiment: 0.2121, Negative Sentiment 0.1807

DARPA Floats an ‘Ocean of Things’

The Pentagon’s top research agency is pushing the notion of a data lake to new depths with an oceanic network of floating sensors that would provide the raw data for analytics, including everything from sea state and weather to maritime traffic.

The Defense Advanced Research Agency’s “Ocean of Things” initiative would then transmit data collected by commercial sensors and crunched by new analytics tools via satellite to a cloud network. There it would be stored and prepped for real-time analysis, program officials said.

According to a recent DARPA contract solicitation, the data analytics portion of the Ocean of Things effort requires development of cloud-based software and data analytics tools to crunch data collected by low-cost floating sensor platforms. Among the data points to be gathered are dynamic displays of a float location, sensor status along with the processing of environmental data such as ocean temperature and sea state.

2020-01-07 00:00:00 Read the full story…
Weighted Interest Score: 2.1156, Raw Interest Score: 1.1628,
Positive Sentiment: 0.0298, Negative Sentiment 0.0596


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