AI & Machine Learning News. 18, February 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?

“We trained our AI on a few minutes of audio from youtube interviews of those artists and celebrities, and created the voices you heard in this video by typing up a script and choosing from a palette of Celebrity ‘Replica’ voices. The video demonstrates how powerful this technology is, and also how easy it is for us to produce any number of Replica voices like these, by analysing a few minutes of speech. The more audio data we train on, the better the voices will sound in terms of quality and smoothness.”

Replica Blog

EU’s new AI rules will focus on ethics and transparency

The European Union is set to release new regulations for artificial intelligence that are expected to focus on transparency and oversight as the region seeks to differentiate its approach from those of the United States and China. On Wednesday, EU technology chief Margrethe Vestager will unveil a wide-ranging plan designed to bolster the region’s competitiveness. While transformative technologies such as AI have been labeled critical to economic survival, Europe is perceived as slipping behind the U.S., where development is being led by tech giants with deep pockets, and China, where the central government is leading the push.

Europe has in recent years sought to emphasize fairness and ethics when it comes to tech policy. Now it’s taking that approach a step further by introducing rules about transparency around data-gathering for technologies like AI and facial recognition. These systems would require human oversight and audits, according to a widely leaked draft of the new rules. In a press briefing in advance of Wednesday’s announcement, Vestager noted that companies outside the EU that want to deploy their tech in Europe might need to take steps like retraining facial recognition features using European data sets. The rules will cover such use cases as autonomous vehicles and biometric IDs.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4914, Raw Interest Score: 1.2773,
Positive Sentiment: 0.1558, Negative Sentiment 0.1246

CloudQuant Thoughts : “Slipping behind” or taking sensible precautions.

Facebook, Apple And Google Heads Visit Europe, Seek Help Navigating New AI Rules

Tech company heads are visiting Brussels one after another. After Alphabet CEO Sundar Pichai and Apple’s senior vice-president for artificial intelligence and machine learning John Giannandrea visited earlier this month, Facebook CEO Mark Zuckerberg visited the city Monday.

The reason that tech giants are lining up to meet EU officials, such as Vice-President Margrethe Vestager, is because they are worried about a new artificial intelligence law that is being debated in the European parliament. The policy is the first-of-its-kind and is made to regulate artificial intelligence.

All tech companies have bet big on AI and the laws are expected to affect how these companies do business in the E.U.

2020-02-17 08:02:26-05:00 Read the full story…
Weighted Interest Score: 2.6846, Raw Interest Score: 1.2102,
Positive Sentiment: 0.0448, Negative Sentiment 0.2689

CloudQuant Thoughts : Let’s be honest here, the reason these companies having been saying for over a year that they want the government to take the lead in deciding the rules for AI is because they have extremely effective methods of controlling government narrative in the US via their lobbyists. When it comes to Europe they have no such control. So they want the US to take the lead, and if Europe takes the lead they have to get on their planes and get out there. To quote the San Francisco Chronicle on why Pichai welcomes government regulation “One big reason is to head off the kind of regulation he doesn’t want. Both the U.S. and the European Union are moving closer to instituting rules for artificial intelligence, and their approaches are already diverging.”

New Facial Recognition Technology Uses AI to Recognize Faces in the Dark, Far Away

For many, normal facial recognition — used in the daylight — has become a facet of everyday life. Whether it’s for identity verification to unlock a smart phone, or trivial social media camera filters — it seems the technology is everywhere.

However, at the U.S. Combat Capabilities Development Command Army Research Laboratory just outside of Washington, D.C., scientists are on the forefront of bringing facial recognition technology into the future, capable of identifying figures in the dark, as experimental tests kick off.

The cutting-edge technology uses artificial intelligence, machine learning techniques, and state-of-the-art infrared cameras to identify facial patterns by using the heat signatures from living skin tissue any time of day, said Dr. Sean Hu, U.S. Army Research Laboratory Intelligent Perception Branch team lead.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4914, Raw Interest Score: 1.2773,
Positive Sentiment: 0.1558, Negative Sentiment 0.1246

CloudQuant Thoughts : Night Vision plus AI. Not just identifying people in the dark but also from “a few hundred meters” away!!

Roboflow: Popular autonomous vehicle data set contains critical flaws

A machine learning model’s performance is only as good as the quality of the data set on which it’s trained, and in the domain of self-driving vehicles, it’s critical this performance isn’t adversely impacted by errors. A troubling report from computer vision startup Roboflow alleges that exactly this scenario occurred — according to founder Brad Dwyer, crucial bits of data were omitted from a corpus used to train self-driving car models.

Dwyer writes that Udacity Dataset 2, which contains 15,000 images captured while driving in Mountain View and neighboring cities during daylight, has omissions. Thousands of unlabeled vehicles, hundreds of unlabeled pedestrians, and dozens of unlabeled cyclists are present in roughly 5,000 of the samples, or 33% (217 lack any annotations at all but actually contain cars, trucks, street lights, or pedestrians). Worse are the instances of phantom annotations and duplicated bounding boxes (where “bounding box” refers to objects of interest), in addition to “drastically” oversized bounding boxes.

It’s problematic considering that labels are what allow an AI system to understand the implications of patterns (like when a person steps in front of a car) and evaluate future events based on that knowledge. Mislabeled or unlabeled items could lead to low accuracy and poor decision-making in turn, which in a self-driving car could be a recipe for disaster.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 2.4793, Raw Interest Score: 0.9360,
Positive Sentiment: 0.1088, Negative Sentiment 0.6530

CloudQuant Thoughts : GIGO – Garbage In Garbage Out. However, I would not be so fast to discount this data as critically flawed, particularly after seeing the annotated video from a Tesla and its AI unit driving through Paris.


Enterprises primarily rely on the two domains — artificial intelligence (AI) and machine learning (ML) in order to build and deploy various kinds of models for the smooth operation of their business. However, it requires programmers or data scientists with adequate knowledge of coding, which enterprises often lack. In a bid to ease such woes of the enterprises, tech giants are now open-sourcing their platforms and providing developer tools to ensure businesses can match the ongoing pace without the need for a coding expert.

In this article, we list down ten such tools which can be used to develop models without being an expert in coding.

The list is in no particular order.

  1. Create ML By Apple
  2. Teachable Machine
  3. Accelerite ShareInsights by Amazon Web Services
  4. What-If Tool
  5. Google AI Platform
  6. Data Robot
  7. RapidMiner Studio
  8. Microsoft Azure Automated Machine Learning
  9. BigML
  10. Google ML Kit

2020-02-11 00:00:00 Read the full story…

Weighted Interest Score: 7.7708, Raw Interest Score: 3.0992,
Positive Sentiment: 0.0989, Negative Sentiment 0.0659

CloudQuant Thoughts : Time to try a different tack? Lots of options here!

Need to Build Trustworthy AI Systems Gains Importance as AI Progresses

The push is on to build trusted AI systems with an eye toward instilling confidence that results will be fair, accuracy will be sufficient, and safety will be preserved.

Gary Marcus, the successful entrepreneur who sold his startup Geometric Intelligence to Uber in 2016, issued a wakeup call to the AI industry as co-author with Ernest Davis of “Rebooting AI,” (Pantheon, 2019) an analysis of the strengths and weaknesses of current AI, where the field is going, and what we should be doing. Marcus spoke about building trusted AI in a recent interview with The Economist. Here are some highlights:

“Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other fields. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.” AI developers, “can’t even devise procedures for making guarantees that given systems work within a certain tolerance, the way an auto part or airplane manufacturer would be required to do.”  “The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high.”

IBM Team Identifies Four Pillars of Trusted AI…

2020-02-13 22:30:28+00:00 Read the full story…
Weighted Interest Score: 5.4069, Raw Interest Score: 1.8151,
Positive Sentiment: 0.2433, Negative Sentiment 0.1871

Israel risks falling behind in AI despite growth

There are more than 1,150 AI-focused startups in Israel, and that number is growing. Even so, some people within the government are concerned that the nation risks falling behind because it lacks a unified AI policy.

AI policy refers to national strategies like the U.S.’s American AI Initiative and Canada’s Pan-Canadian Artificial Intelligence Strategy, which implement whole-government efforts to promote technological innovation. Implicitly, it incorporates a funding component that bolsters those efforts with capital.

“AI is affecting every element of our society, and it’ll continue to affect change further and further. Those [who don’t adopt it] might find themselves [behind],” Aharon Aharon, CEO of the Israel Innovation Authority, the arm charged with fostering industrial R&D within the state, told members of the press during a roundtable discussion at the Jerusalem Press Club last week. “Just [funding] and capital is not enough. You need companies that grow … so they can invest back, and so they can continue to develop the Israeli economy.”

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 5.3222, Raw Interest Score: 1.9763,
Positive Sentiment: 0.2288, Negative Sentiment 0.0832

Join the innovators in enterprise AI at Transform 2020

The AI event of the year for business leaders, Transform 2020 doubles down on results-driven content that helps executives at the senior director level and above maintain their competitive edge. Expect two days of the most transformative trends in conversational AI, computer vision, IoT and AI at the edge, and automation, plus a special emphasis on women in AI, diversity, and expanded networking opportunities.

Each year, we gather corporate decision-makers from around the world to discuss “big picture” trends within artificial intelligence, as well as practical ways to move the needle on implementing AI. In 2020, we’re looking at the effects of AI through the lens of four key industries: retail, health, finance, and industrial manufacturing.

2020-02-18 00:00:00 Read the full story…
Weighted Interest Score: 4.5833, Raw Interest Score: 1.6753,
Positive Sentiment: 0.3697, Negative Sentiment 0.1271

A former Amazon and Google engineer wants to make AI more accessible to smaller companies so that Big Tech doesn’t have a stranglehold on the future

Bindu Reddy wants to give more people access to artificial intelligence. The former Amazon Web Services and Google engineer, wants small and medium sized companies to be able to use AI in the same way that large companies do, and make sure big tech companies aren’t dominating the sector. Reddy previously started the AI verticals division at AWS, creating AI for particular domains or use-cases. Earlier in her career she was the Head of Product for Google social apps, where she helped build Google+, Blogger Google Video, Google Docs and Google Sites.

She said during her time at Google and Amazon Web Services she noticed that big companies had a gap between the research being done in AI and the products being developed. She realized that a startup could be more nimble and forge a deeper connection between research and product development. She decided to start a company to address that need. She founded the startup, called, with two of her former colleagues from Google, and it makes cloud based software to help companies make their own AI models for the workplace tools they are using.

2020-02-15 00:00:00 Read the full story…
Weighted Interest Score: 4.5351, Raw Interest Score: 1.9559,
Positive Sentiment: 0.1457, Negative Sentiment 0.0208

Oracle Announces Oracle Cloud Data Science Platform

y announced the availability of the Oracle Cloud Data Science Platform. At the core is Oracle Cloud Infrastructure Data Science, helping enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects. Unlike other data science products that focus on individual data scientists, Oracle Cloud Infrastructure Data Science helps improve the effectiveness of data science teams with capabilities like shared projects, model catalogs, team security policies, reproducibility and auditability. Oracle Cloud Infrastructure Data Science automatically s…
2020-02-14 08:15:15+00:00 Read the full story…
Weighted Interest Score: 4.2044, Raw Interest Score: 2.1356,
Positive Sentiment: 0.4805, Negative Sentiment 0.0000

These Are the 100 Most Sustainable Companies in America

As companies get more sustainable, investors are benefiting. Shares of the companies on Barron’s third annual ranking of America’s Most Sustainable Companies outperformed the S&P 500 index in 2019.

America’s corporations are getting more sustainable, and investors are benefiting, along with the planet and the rest of its inhabitants. The third annual Barron’s ranking of America’s Most Sustainable Companies also makes for a pretty good portfolio: Shares of the 100 companies on our list returned 34.3%, on average, in 2019, beating the S&P 500 index’s 31.5%. More than half of our honorees, 55, outperformed the mighty index, which has been nearly unbeatable for a decade.

With companies in general adopting ambitious…

2020-02-07 00:00:00 Read the full story (Registration Wall)…
Weighted Interest Score: 4.0233, Raw Interest Score: 1.8409,
Positive Sentiment: 0.2180, Negative Sentiment 0.0363

5 Free Data Science Courses For Beginners

Companies across all the industries in the world are always looking for data science personnel to help them garner insights from big data. The hiring experts are constantly on the lookout for personnel with high skills regarding programming, data mining, statistical modelling etc. With the huge gap existing between required skills and talent available, these industries have become more resilient towards finding skilled data scientists and scraping out the less talented ones. One way for the people going into data science to enhance their knowledge is taking up the data science courses online, these data science courses help one to learn about the sector and acquire the in-demand skills.

Below we have listed some of the best free online data science courses available: (List is in random order)

  1. Data Science Essentials From: Microsoft through edX.
  2. Data-Driven Decision Making Offered by: PwC through Coursera.
  3. CS109 Data Science Offered by: Harvard
  4. Data Science Foundations Offered by: IBM on their portal.
  5. Machine Learning Offered by: Stanford on Coursera

2020-02-17 11:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0118, Raw Interest Score: 2.2396,
Positive Sentiment: 0.1621, Negative Sentiment 0.0147

The New Business of AI (and How It’s Different From Traditional Software)

At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process.

Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.

We are huge believers in the power of AI to transform business: We’ve put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies.

2020-02-16 00:00:00 Read the full story…
Weighted Interest Score: 4.0065, Raw Interest Score: 1.7184,
Positive Sentiment: 0.2571, Negative Sentiment 0.2014

How the 80/20 Rule can help decide which skills you need to start a career in Data Science

Using the Pareto Principle to increase your confidence as a Data Scientist

1. The daunting task to learn Data Science

Data Science is an exciting field with an increasing demand for skilled and experienced professionals. A traditional Data Scientist has a background in a field related to Computer Science, Mathematics, Engineer or Physics. But we also find ot…
2020-02-18 04:13:31.638000+00:00 Read the full story…
Weighted Interest Score: 3.8864, Raw Interest Score: 2.0927,
Positive Sentiment: 0.1495, Negative Sentiment 0.1495

AI Weekly: Machine learning could lead cybersecurity into uncharted territory

Once a quarter, VentureBeat publishes a special issue to take an in-depth look at trends of great importance. This week, we launched issue two, examining AI and security. Across a spectrum of stories, the VentureBeat editorial team took a close look at some of the most important ways AI and security are colliding today. It’s a shift with high costs for individuals, businesses, cities, and critical infrastructure targets — data breaches alone are expected to cost more than $5 trillion by 2024 — and high stakes.

Throughout the stories, you may find a theme that AI does not appear to be used much in cyberattacks today. However, cybersecurity companies increasingly rely on AI to identify threats and sift through data to defend targets.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 3.8440, Raw Interest Score: 1.1978,
Positive Sentiment: 0.1114, Negative Sentiment 0.5292

Understanding the Uses of Artificial Intelligence

Artificial intelligence (AI) has provided a critical competitive advantage for those organizations able and willing to use it. AI has gained significant momentum in the last few years, acting as personal assistants for some, while processing business transactions and providing technical services to others. AI systems have the ability to manage large amounts of data in a number of ways. Different types of artificial intelligence have been evolved to handle a variety of tasks, ranging from facial recognition to drug design to driving cars.

In terms of logistics, an AI can optimize the routing of delivery traffic, thereby improving fuel efficiency and providing faster delivery times. It has become a valuable response tool, providing customer service centers with a phone answering service. In the world of sales, combining customer demographics with past transaction data and social media can result in recommendations tailored to the customer. An artificial intelligence can improve predictive maintenance, analyzing large amounts of data from images and audio to detect anomalies in auto engines or assembly lines. Specific deep learning techniques can be used to tailor an AI for accomplishing specific goals and tasks.

2020-02-13 08:35:24+00:00 Read the full story…
Weighted Interest Score: 3.7897, Raw Interest Score: 1.9047,
Positive Sentiment: 0.2450, Negative Sentiment 0.1559

Discussing Knowledge Graphs as the Next Big Thing

The extraordinary growth in complex data has left many enterprises struggling to create an integrated, comprehensive view of that data. In recent years, knowledge graphs have emerged as a powerful tool for integrating large volumes of distributed, data, both structured and unstructured. A simplified graph data model available in graph databases helps enterprises achieve this goal in fewer steps and without locking into a single notion of the analytics needed.

DBTA recently held a webinar with Steve Sarsfield, VP product, AnzoGraph DB, who discussed the powerful side benefits of graph databases, like graph algorithms, and how they go beyond standard analytics to uncover relationships in the data.

A knowledge graph can mean different things to different people, Sarsfield explained. For executives, they see a common understanding of all disparate data while data architects see knowledge graphs as one method to integrate data from multiple data sets, structured or unstructured, and to leverage standard industry ontologies to enhance analytics.

2020-02-14 00:00:00 Read the full story…
Weighted Interest Score: 3.6344, Raw Interest Score: 1.9273,
Positive Sentiment: 0.2753, Negative Sentiment 0.0551

PyKrylov: Accelerating Machine Learning Research at eBay

A recent eBay Tech Blog article1 presented the Unified AI platform called Krylov. In this article, we show how Krylov users interact with the platform to build and manage powerful workflows in a pythonic and efficient way.
The experience while accessing the AI platform and running machine learning (ML) training code on the platform must be smooth and easy for the researchers. Migrating any ML code from a local environment to the platform should not require any refactoring of the code at all. Infrastructure configuration overhead should be minimal. Our mission while developing PyKrylov was to abstract the ML logic from the infrastructure and Krylov core components (Figure 1) as much as possible in order to achieve the best experience for the platform users.
2020-02-11 00:00:00 Read the full story...
Weighted Interest Score: 3.5581, Raw Interest Score: 2.4436,
Positive Sentiment: 0.7519, Negative Sentiment 0.0000

SGX RegCo uses AI to enhance surveillance activities

Singapore Exchange Regulation (SGX RegCo) is making its surveillance and regulation of the securities market more targeted and effective with the application of artificial intelligence (AI) enhancements to its real-time monitoring system.

The introduction of AI can help to better isolate unusual activity, by learning from historical trading patterns and filtering out noise caused by developments across intricate relationships between multiple markets. This allows regulatory attention to be more sharply focused on a smaller set of potentially unusual trading signals identified through the surveillance system, which are then further analysed and reviewed by the surveillance team.

2020-02-12 00:00:00 Read the full story…
Weighted Interest Score: 3.5039, Raw Interest Score: 1.4025,
Positive Sentiment: 0.4909, Negative Sentiment 0.2104

How NVIDIA Set A World Record For Training BERT And What Does This Mean

The deep learning community, especially those who work on Natural Language problems, had a great run in 2019. Top players like Google, NVIDIA and Microsoft have set new benchmarks with their every release. With time the models keep getting larger and the training times too, surprisingly, have somehow come down.

What really turned heads was NVIDIA’s world record for training state of the art BERT-Large models in just 47 minutes, which usually takes a week’s time.

This record was created by utilising 1,472 V100 SXM3-32GB 450W GPUs, 8 Mellanox Infiniband compute adapters per node, and running PyTorch with Automatic Mixed Precision to accelerate throughput.

2020-02-18 09:30:00+00:00 Read the full story…
Weighted Interest Score: 3.4804, Raw Interest Score: 1.8455,
Positive Sentiment: 0.3044, Negative Sentiment 0.1712

An Enterprise Formula for AI Success

One of the great things about the current wave of AI innovation is the large number of open source tools, technologies, and frameworks. From TensorFlow to Python, Kafka to PyTorch, the we’re in the midst of an explosion in diversity of data science and big data toolchains. However, when it comes to putting these toolchains together and building real-world AI applications, regular companies suffer from a serious technology gap compared to technology firms.

The technology giants have a curious habit of releasing powerful technology onto the unsuspecting masses. For example, in 2015 Google unveiled TensorFlow, which enables users to build and deploy very large and very accurate neural network models. A year later, Facebook, released PyTorch, which some say is an easier-to-use framework for machine learning development. Both are among the most heavily used technologies for machine learning today.

Nobody is complaining too much about Google’s and Facebook’s decisions to release such ground-breaking technology. After all, they’ve been at this for many years. While the tech giants do benefit by getting the open source community to continue to develop and maintain technology that it puts into the public realm, it’s safe to say that the open source community receives bigger benefit than the tech giants. But these AI gains have not flowed equally. Many of the latest open source AI technologies are not known for being easy to work with, and typically require highly skilled data scientists to use. This puts a cap the applicability of the AI tech, and limits its use to companies that have the budget to hire experienced data scientists. That leaves a lot of companies out of luck when it comes to leveraging the latest in AI innovation, according to Phil Gurbacki, the senior vice president of product and customer experience for DataRobot, a provider of automated machine learning and enterprise AI offerings based in Boston, Massachusetts.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3022, Raw Interest Score: 1.3697,
Positive Sentiment: 0.2848, Negative Sentiment 0.1221

L3Harris Technologies Selected by US Air Force for Artificial Intelligence Contract

“The Air Force Life Cycle Management Center has awarded L3Harris Technologies a multimillion-dollar contract to develop a software platform that will make it easier for analysts to use artificial intelligence (AI) to identify objects in large data sets. The U.S. military and intelligence community are inundated with massive amounts of data generated by remote sensing systems. Automated searches using algorithms that can identify pre-loaded images of objects makes pinpointing them easier. However, in order to train these algorithms, real images are often unavailable because they are either rare or do not exist. The L3Harris tool creates sample images used to train search algorithms to identify hard-to-find objects in the data, which will help make it easier for the military and intelligence community to adopt artificial intelligence.”

Ed Zoiss, President of Space and Airborne Systems at L3Harris, commented, “L3Harris is a premier provider of modeling and simulation capabilities that provide risk reduction for our customers who rely on advanced geospatial systems and data… Accelerating the use of AI will help automate analysis of large geospatial data sets so warfighters receive trusted data faster and more efficiently.”
2020-02-18 08:05:16+00:00 Read the full story…
Weighted Interest Score: 3.2861, Raw Interest Score: 1.5873,
Positive Sentiment: 0.3401, Negative Sentiment 0.1701

Fastest-Growing Tech Occupations Include Data Scientists, Engineers

The 2020 edition of Dice’s Salary Report showed significant salary growth for certain kinds of skills, including Swift and Kafka. Now let’s take a deeper dive into the tech occupations that enjoyed the biggest increases in salary between 2018 and 2019. Based on our analysis, it’s clear that employers are hungry for technologists who can carry out a variety of tasks, from analyzing data to building applications (as well as making sure those applications go into the world relatively bug-free).

The data from the Salary Report was drawn from 12,837 technologists. In addition, the occupations below the chart also incorporated data from Burning Glass, which collects and analyzes millions of job postings from across the country, for data-points such as time-to-fill and most-requested skills. (Burning Glass defines “Defining skills” as the day-to-day tasks and responsibilities of a particular job, while “Distinguishing skills” are advanced skills that are called for occasionally, and really come into play with highly specialized employees.)

As you can see from the following list, a broad spectrum of occupations enjoyed gains, which is a reflection of all kinds of companies and industries needing a range of technology services:
2020-02-13 00:00:00 Read the full story…
Weighted Interest Score: 3.1851, Raw Interest Score: 2.1032,
Positive Sentiment: 0.0935, Negative Sentiment 0.1496

Revamp Your Life With Artificial Intelligence Training

When we hear the word “ Artificial Intelligence “, digital assistants, chat bots, robots, and self-driving cars are what strikes our mind but that’s not enough. These are few more examples of artificial intelligence which are terrifying yet interesting. Unlike other technologies, we will continue to see the advancements of Artificial Intelligence and Machine Learning in 2020 and beyond.

Some other technologies will grow as well but technologies like deep learning and machine learning will creep on us. Many experts in Artificial Intelligence believe that AI is going to be bigger than the internet revolution. Artificial intelligence is a field of computer science and is sometimes called machine intelligence. In simple words, It is a field in computer science that teaches the machine how to understand the human mind and react exactly like humans. The main aim of AI is to build machines that can think, behave, and understand the way humans do work.

Learning ability and dynamically updating your skills will prepare you to take advantage of unseen opportunities and pave the path for a successful career. Today’s Simple AI has now started offering advanced Artificial Intelligence Training with help of industry experts to help the AI career enthusiasts to transform into industry leaders. When you are deciding for a career strategy, start with self-awareness, have a good mindset, and seek out for long-term skills that need to be adopted or enhanced.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.1044, Raw Interest Score: 2.2621,
Positive Sentiment: 0.4290, Negative Sentiment 0.0390

How is data growth affecting real-world enterprises? 4 key findings

Data volumes are expanding at an unprecedented rate. It’s very easy to get caught up in how many zettabytes of data we’ll all be producing by 2025, 2030, and beyond, and how data and cloud computing affect us all on a global scale. But what do data growth and other issues mean for how you do business right now? The results of a recent Matillion and IDG Research survey are very illuminating.

Matillion and IDG Research recently conducted an IDG MarketPulse survey, “Optimizing Business Analytics by Transforming Data in the Cloud”. The survey polled more than 200 IT, data science, and data engineering professionals. The research exposes the challenges companies face and the strategies they use to prepare data for BI and analytics, with faster time-to-value for implementing analytics projects becoming the main driver for migrating to a cloud approach. Here are some of the key findings:

  1. Data growth is hitting home for enterprises
  2. That data is coming from hundreds (even thousands) of sources
  3. Everyone will have data in the cloud within two years
  4. Transforming data to make it analytics-ready is challenging for nearly all respondents

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.0769, Raw Interest Score: 1.8084,
Positive Sentiment: 0.1350, Negative Sentiment 0.2429

Making Better Data-Driven Decisions

Is your organization investing heavily in data, yet not necessarily making better decisions or seeing meaningful results? Research shows that companies are spending massive amounts on data and analytics. Yet, as many as 85% of big data projects fail.

Part of the problem is that in this era of data, the numbers on a computer screen or in a report take on a special air of authority. Users rarely ask where the data came from, how it’s been modified, or whether it is fit for its intended purpose.

On February 26, 2020, in a live Harvard Business Review webinar, Eric Haller of Experian DataLabs will discuss the four questions leaders and organizations need to ask and answer about data:

2020-02-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 3.0111, Raw Interest Score: 1.6640,
Positive Sentiment: 0.3170, Negative Sentiment 0.3962

Automatic Data Labeling Gains Momentum with New IBM and Labelbox Announcements

Data is powerful, but labeling data makes it useful. Labeled data (data that has been appended with informative tags about its contents – say, whether a photo is of a person or an animal) can be used to quickly train machine learning models for identification. Furthermore, automated, AI-driven labeling tools can help to speed the initial process of labeling the data. Now, a pair of back-to-back announcements from IBM and Labelbox are signaling new momentum in the data labeling tool space.

IBM made the first of the two announcements: a new automated labeling tool called “Cloud Annotations.” The tool, which is open-source and accessible on GitHub, allows users to feed 200-500 hand-labeled images into it, after which AI takes the wheel and automatically labels the remaining image set. Cloud Annotations also allows for real-time collaboration as well as cloud data storage and access through IBM’s public cloud.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 2.9683, Raw Interest Score: 1.5705,
Positive Sentiment: 0.2048, Negative Sentiment 0.0683

Data science is becoming software engineering

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

When I think of the trends I’ve seen in data science over the last few years, perhaps the most significant and hardest to ignore has been the increased focus on deployment and prod…
2020-02-17 15:22:56.879000+00:00 Read the full story…
Weighted Interest Score: 2.8914, Raw Interest Score: 1.9095,
Positive Sentiment: 0.3080, Negative Sentiment 0.0616

The Advent And Scope Of AI Marketing In 2020 And Beyond

When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.

That said, before we move any further into a detailed discussion regarding AI marketing and its role in diverse industries, it is necessary to underst…
2020-02-18 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

The integration and use of AI in mobile apps

In recent years, Artificial Intelligence has emerged as a bubbling buzzword in various industries. And, companies are making hefty investments in projects that utilize AI technology. As per the forecast by International Data Corporation (IDC), global spending on AI systems will hit a $79.2 billion mark by 2022, rising at a CAGR (compound annual growth rate) of 38.0% during the 2018-2022 period. The mobile app development industry is no exception to this trend and developers are experimenting with varied ideas and concepts that entail AI technology. The power of artificial intelligence can be harnessed in mobile applications for obtaining a competitive edge as well as provide the awe-inspiring experience to users.
2020-02-14 18:11:28+00:00 Read the full story…
Weighted Interest Score: 2.8330, Raw Interest Score: 1.5420,
Positive Sentiment: 0.2856, Negative Sentiment 0.0571

Importance of AI in personal finance

With the enhancement of technology the is more to spend and then actually maintain the budget. There are many institutes trying to figure out how to maintain the budget using artificial intelligence. With AI in budgeting and managing personal finance will help in making life better as consumers and also allow them to track their spending and stick to the budget set.

Why is AI required in personal finance?  With new technologies getting evolved AI for personal finance will be a major help in getting people out from the financial trap . In current situation, financial health is very important and a lot difficult to maintain. With growing technologies it is a lot easier to spend the money as there are various templation. E.g Food app. Earlier consumer had to order food, one needs to go to the restaurant or call the same, but with the food delivery app and laziness of users the food is just a click away. I personally spent around Rs 50,000 last year on a food delivery app.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.7176, Raw Interest Score: 1.1816,
Positive Sentiment: 0.4332, Negative Sentiment 0.4332

Banking for Humanity: Technology to Increase the Human Touch

Technology versus Humanity?

Banks have been struggling with the concept of being more personal with their customers. ‘Banking for Humanity’, a concept explored by fintech guru Chris Skinner, is considered as a remedy for this; with new banking technology innovation spearheading the way for banks to adopt a human touch and be more empathetic.

Chris Skinner stated that the concept is about “how banks can make their services more human”. Accor…
2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.6632, Raw Interest Score: 1.3918,
Positive Sentiment: 0.2595, Negative Sentiment 0.1887

Do not underestimate the need for DevOps in AI. Enter Deep Learning DevOps — DL Infrastructure Engineering.

Why should you care?

As machine learning is getting more mature, the need to build infrastructure that supports running these workflows is even greater. In a large enterprise setting on an average, there are at least 200+ data scientists/DL/ ML engineers that run their model training and inferencing jobs. Ensuring that these users get easy hardware/software access to train their models is imperative. This sounds like an easy task, I’m h…
2020-02-18 04:35:08.274000+00:00 Read the full story…
Weighted Interest Score: 2.6193, Raw Interest Score: 1.5309,
Positive Sentiment: 0.1816, Negative Sentiment 0.1557

How To Make Sure Your Robot Doesn’t Drop Your Wine Glass

From microelectronics to mechanics and machine learning, the modern-day robots are a marvel of multiple engineering disciplines. They use sensors, image processing and reinforcement learning algorithms to move the objects around and move around the obstacles as well.

However, this is not the case when it comes to handling objects such as glass. The surface properties of glass are transparent, and non-uniform light reflection makes it difficult for the sensors mounted on the robot to understand how to engage in a simple pick and place operation.

To address this problem, researchers at Google AI along with Synthesis AI and Columbia University devised a novel machine-learning algorithm called ClearGrasp, that is capable of estimating accurate 3D data of transparent objects from RGB-D images.

2020-02-18 05:41:08+00:00 Read the full story…
Weighted Interest Score: 2.5093, Raw Interest Score: 1.2019,
Positive Sentiment: 0.1335, Negative Sentiment 0.2671

African crowdsolving startup Zindi scales 10,000 data scientists – TechCrunch

Cape Town based startup Zindi has registered 10,000 data-scientists on its platform that uses AI and machine learning to crowdsolve complex problems in Africa.

Founded in 2018, the early-stage venture allows companies, NGOs or government institutions to host online competitions around data-oriented challenges. Zindi opens the contests to the African data scientists on its site who can join a competition, submit solution sets, move up a leader board and win — for a cash prize payout. The highest purse so far has been $12,000, according to Zindi co-founder Celina Lee. Competition hosts receive the results, which they can use to create new products or integrate into their existing systems and platforms.

It’s free for data scientists to create a profile on the site, but those who fund the competitions pay Zindi a fee, which is how the startup generates revenue. Zindi’s model has gained the attention of some notable corporate names in and outside of Africa. Those who have hosted competitions include Microsoft, IBM and Liquid Telecom.

2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4375, Raw Interest Score: 1.4948,
Positive Sentiment: 0.1830, Negative Sentiment 0.2135

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