AI & Machine Learning News. 13, July 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?


AutoML-Zero: Evolving Code that Learns

Machine learning (ML) has seen tremendous successes recently, which were made possible by ML algorithms like deep neural networks that were discovered through years of expert research. The difficulty involved in this research fueled AutoML, a field that aims to automate the design of ML algorithms. So far, AutoML has focused on constructing solutions by combining sophisticated hand-designed components. A typical example is that of neural architecture search, a subfield in which one builds neural networks automatically out of complex layers (e.g., convolutions, batch-norm, and dropout), and the topic of much research.

An alternative approach to using these hand-designed components in AutoML is to search for entire algorithms from scratch. This is challenging because it requires the exploration of vast and sparse search spaces, yet it has great potential benefits — it is not biased toward what we already know and potentially allows for the discovery of new and better ML architectures. By analogy, if one were building a house from scratch, there is more potential for flexibility or improvement than if one was constructing a house using only prefabricated rooms. However, the discovery of such housing designs may be more difficult because there are many more possible ways to combine the bricks and mortar than there are of combining pre-made designs of entire rooms. As such, early research into algorithm learning from scratch focused on one aspect of the algorithm, to reduce the search space and compute required, such as the learning rule, and has not been revisited much since the early 90s. Until now.

2020-07-09 Read the full story…

CloudQuant Thoughts : I particularly like the idea that they start from scratch thus dramatically reducing the chance of bias!

10 Interesting and Impressive AI projects for absolute Beginners (with Python Source Code)

Artificial Intelligence has become certainly part of our lifes now. We knowingly or unknowingly use it in our day-to-day life as in recommended films, image recognition, speech recognition, sites-recommended products etc.

That’s why you also need to start learning about it. You could start by checking out the 5 Best Artificial Intelligence Books in 2020. Yet it’s not enough to understand just the Theory. That’s why students are expected to try to complete some artificial intelligence projects. That is why, if you’re a newbie, the best thing you can do is to spend some time on some real Artificial Intelligence projects. From trying to follow the trends of artificial intelligence to doing some of your own projects. A link to the Python Source Code will be included for each!

I will show you some fun ideas for Artificial Intelligence projects that beginners can work on to test their knowledge of Python.

These projects will help you develop your skill set while also checking your existing knowledge. Artificial intelligence can be used in a number of fields. The more you look at various Artificial Intelligence projects, the more you will learn.

1. Predict Housing Price
2. Stock Price Prediction
3. Chatbot
4. Spam E-Mails Identifier
5. Handwritten Digits Recognition
6. Chrome T-rex Dino Bot
7. Next Word Predictor
8. Twitter Sentiment Analyzer
9. Cancer Detection using medical data
10. Facial Emotion Recognition and Detection

2020-07-06 Read the full story…

CloudQuant Thoughts : Neat idea to bring 10 common starter ML challenges into one article.

Elon Musk Brags That Tesla Is “Very Close” to Full, Level 5 Self Driving

“I’m extremely confident that level 5 or essentially complete autonomy will happen and I think will happen very quickly.” For years, we’ve been promised a near future in which cars drive themselves as well as a human motorist — while their occupants scroll through Twitter or browse Netflix in a cozy cabin with comfy seats.

That dream, formally known as Level 5 autonomy, is probably still many years out — but Tesla CEO Elon Musk believes it could be right around the corner. In a video message recorded for the opening of Shanghai’s annual World Artificial Intelligence Conference (WAIC), Musk said he’s confident Tesla will be able to deliver basic Level 5 autonomy in its vehicles as soon as this year.

2020-07-09  Read the full story…

CloudQuant Thoughts : So many potentially dangerous tails though!

Data Prep Still Dominates Data Scientists’ Time, Survey Finds

Data scientists spend about 45% of their time on data preparation tasks, including loading and cleaning data, according to a survey of data scientists conducted by Anaconda. The company also analyzed the gap between what data scientists learn as students, and what the enterprises demand.

Data cleansing – fixing or discarding anomalous or wrong numbers and otherwise ensuring the data is an accurate representation of the phenomenon it is meant to measure — accounts for more than a quarter of average day for data scientists, followed by 19% for data loading (the “L” in ETL), according to Anaconda’s annual survey.

Data visualization tasks occupied for about 21% of their time, while model selection, model training and scoring, and model deployment each consume 11% to 12% of the day, the survey found.

2020-07-06 00:00:00 Read the full story…
Weighted Interest Score: 3.4173, Raw Interest Score: 2.0492,
Positive Sentiment: 0.1734, Negative Sentiment 0.2049

CloudQuant Thoughts : No matter which way you cut it, data scientists still spend too much time fiddling with data before they can use it. For financial services CloudQuant provides Symbol translation, data quality checking and organisation so that the data you need is ready to go. Head over to our Data Catalog for more info.

How Data Science Is Revolutionising Our Social Visibility

The rise of artificial intelligence has been well documented, but how exactly can it enhance your social media marketing strategies?

Artificial Intelligence has the potential to revolutionize the social visibility of brands, paving the way for more incisive approaches towards marketing.

The huge potential of AI in social media has led to Markets and Markets forecasting that the industry of deep learning, machine learning and NLP within sales marketing, customer experience management and predictive risk assessment within social platforms will grow to more than $2.1 billion in value by 2023.

The rise of AI has been well documented, but how exactly can it enhance your social media marketing strategies? Let’s take a deeper look into the role that AI is set to play in boosting our exposure on social platforms:

2020-07-06 23:24:57+00:00 Read the full story…
Weighted Interest Score: 2.8377, Raw Interest Score: 1.3043,
Positive Sentiment: 0.2531, Negative Sentiment 0.0487

CloudQuant Thoughts : A very detailed article on how the big players are using AI in social media marketing.

Nvidia overtakes Intel as most valuable U.S. chipmaker

Nvidia has for the first time overtaken Intel as the most valuable U.S. chipmaker.

In a semiconductor industry milestone, Nvidia’s shares rose 2.3% in afternoon trading on Wednesday to a record $404, putting the graphic component maker’s market capitalization at $248 billion, just above the $246 billion value of Intel, once the world’s leading chipmaker.

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7865, Raw Interest Score: 1.5288,
Positive Sentiment: 0.3597, Negative Sentiment 0.1799

CloudQuant Thoughts : With the amount of work Nvidia have put into AI we are big supporters. Congratulations on overtaking Intel!

Aspiring Toward Provably Beneficial AI Including The Case Of Autonomous Cars

As AI systems continue to be developed and fielded, one nagging and serious concern is whether the AI will achieve beneficial results. Perhaps among the plethora of AI systems are some that will be or might become eventually untoward, working in non-beneficial ways, carrying out detrimental acts that in some manner cause irreparable harm, injury, and possibly even death to humans. There is a distinct possibility that there are toxic AI systems among the ones that are aiming to help mankind.

We do not know whether it might be just a scant few that are reprehensible or whether it might be the preponderance that goes that malevolent route. One crucial twist that accompanies an AI system is that they are often devised to learn while in use, thus, there is a real chance that the original intent will be waylaid and overtaken into foul territory, doing so over time, and ultimately exceed any preset guardrails and veer into evil-doing.

2020-07-09 21:30:24+00:00 Read the full story…
Weighted Interest Score: 3.7725, Raw Interest Score: 1.0067,
Positive Sentiment: 0.3079, Negative Sentiment 0.3323

RAM Active Investments launches AI-driven sustainable fund

RAM Active Investments SA (RAM AI), a systematic asset manager based in Geneva, is launching a fund with the objective of tackling climate change and providing investors an active strategy with strong ESG standards. RAM AI’s ESG approach is the result of extensive research exploring alternative data thanks to the successful development of the RAM AI Machine Learning (ML) infrastructure.

As the climate change emergency continues to grow RAM AI believes its role as an asset manager is to provide investors with a differentiated solution to low-carbon investing. Emmanuel Hauptmann, who heads the Systematic Equity research, says: “The RAM ML team has made tremendous advances in building the strategy with the objective to provide a selection of best-in-class, low-carbon companies without compromising performance.”

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 6.1887, Raw Interest Score: 2.2447,
Positive Sentiment: 0.4115, Negative Sentiment 0.0000

dotData Launches New Platform to Meet Demand for Real-Time Prediction Capabilities

dotData, a provider of full-cycle data science automation and operationalization for the enterprise, is releasing dotData Stream, a new containerized AI/ML model that enables real-time predictive capabilities for dotData users.

“We are seeing an increasing demand for real-time prediction capability, which has become an essential necessity for many enterprise companies. dotData Stream allows our customers to leverage AI/ML capability in a real-time environment,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData.

dotData Stream was developed to meet the growing market demand for real-time prediction capabilities for use cases such as fraud detection, automated underwriting, dynamic pricing, industrial IoT, and more.
2020-07-07 00:00:00 Read the full story…
Weighted Interest Score: 5.3214, Raw Interest Score: 2.7317,
Positive Sentiment: 0.1383, Negative Sentiment 0.0346

Emerging Job Roles for Successful AI Teams

Many job descriptions across organizations will require at least some use of AI in the coming years, creating opportunities for the savvy to learn about AI and advance their careers regardless of discipline.

New job titles have and will emerge to help the organization execute on AI strategy. Machine learning engineers have cemented a leading role on the AI team, for example, taking first place on best jobs listed on Indeed last year, according to a recent rapport in CIO. And AI specialists were the top job in LinkedIn’s 2020 Emerging Jobs report, with 74% annual growth in the last four years. This was followed by robot engineer and data scientist.

The number of AI-related jobs could increase globally by up to 16%, stated Ritu Jyoti, Program VP, AI Research with IDC IT consultants. With AI generating productivity returns during the pandemic, interest is growing. “IDC believes that AI spending and employment will increase among healthcare providers, education, insurance, pharmaceutical companies and federal governments,” she stated.

2020-07-09 21:30:28+00:00 Read the full story…
Weighted Interest Score: 5.2458, Raw Interest Score: 1.8605,
Positive Sentiment: 0.2608, Negative Sentiment 0.1565

Taking an Active Approach to Data Governance – A Look at How Riot Games “League of Legends” Implemented Non-Invasive Data Governance (Video)

Riot Games created and runs “League of Legends,” the world’s most-played PC game and most viewed eSport — and is now transforming to become a multi-title publisher. To keep pace with this transformation and support a growing player base of millions, Riot Games is taking a page from Bob Seiner’s book, “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” and leveraging the Alation Data Catalog to help guide accurate, well-governed analysis.

Bob Seiner will join Riot Games’ Chris Kudelka, Technical Product Manager, and Michael Leslie, Senior Data Governance Architect, and Alation’s John Wills, VP of Professional Service, for an inside look at Data Governance at one of the world’s leading gaming companies.

  • How Riot Games is implementing Non-Invasive Data Governance
  • How this new approach to Data Governance helps to drive the business
  • How the Alation Data Catalog helps Riot Games create the foundation for guiding accurate, well-governed data use

2020-07-09 21:00:59+00:00 Read the full story…
Weighted Interest Score: 2.7760, Raw Interest Score: 1.8414,
Positive Sentiment: 0.1561, Negative Sentiment 0.0000

DeepMind researchers propose rebuilding the AI industry on a base of anticolonialism

Researchers from Google’s DeepMind and the University of Oxford recommend that AI practitioners draw on decolonial theory to reform the industry, put ethical principles into practice, and avoid further algorithmic exploitation or oppression.

The researchers detailed how to build AI systems while critically examining colonialism and colonial forms of AI already in use in a preprint paper released Thursday. The paper was coauthored by DeepMind research scientists William Isaac and Shakir Mohammed and Marie-Therese Png, an Oxford doctoral student and DeepMind Ethics and Society intern who previously provided tech advice to the United Nations Secretary General’s High-level Panel on Digital Cooperation.

2020-07-11 00:00:00 Read the full story…
Weighted Interest Score: 5.0584, Raw Interest Score: 1.7916,
Positive Sentiment: 0.1558, Negative Sentiment 0.3311

Why Traditional Data Preparation Approaches Fail

At Data Summit Connect 2020, Thomas Cook, director of sales, Cambridge Semantics, described the laborious process of manually discovering, sorting, cleaning, and conforming silo’d data that consumes the lion’s share of data scientists’ time, and how new approaches are improving the process. The dirty secret of data science is that 70 to 80% of the time is spent on data preparation and feature engineering, Cook explained. While confirming this with data scientists, they typically chuckle and say, “It’s more like 90 to 95%,” Cook said.

“The aspect of discovering the data, finding the data that’s suitable, cleaning, conforming, and creating features is also the least enjoyable part of their job,” Cook said. So how can organizations make this easier and make the job more enjoyable, reducing the cost of developing the models and applications, and reducing burnout?

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7511, Raw Interest Score: 1.6674,
Positive Sentiment: 0.1667, Negative Sentiment 0.1667

Is Zero Closer to Eight or to One?

Is zero closer to eight or to one? Is this a three or a five? This was the type of question we were pondering a few weeks ago when we examined the results of an image classification application.

Yes, indeed, a zero is closer to an eight than to a one and a two is closer to a five than to a three — of course, from an image recognition point of view rather than in a strictly mathematical sense. In the last data science example that we were preparing, we trained a machine learning model to recognize images of hand-written digits. In the end, while checking the results, we realized how sloppy people’s handwriting is and how hard it is sometimes to distinguish an eight from a zero, a two from a five, a one from a seven, a zero from a three, and other, sometimes unexpected, similar digits.

2020-07-08 00:00:00 Read the full story…
Weighted Interest Score: 2.9291, Raw Interest Score: 1.1319,
Positive Sentiment: 0.0888, Negative Sentiment 0.0666

FLASH FRIDAY: It’s All in the Data

Data data data.

If a trader does not have it, he’s lost. If he has it – and more of it, presumably he can make a better decision, execute his order flow more efficiently and generate more alpha. While it is most definitely true for equities and fixed income, it’s also becoming moreso for exchange-traded funds, which have seen an uptick in trading activity lately too.

Dan Royal, Head of Global Equity Trading at Janus Henderson Investors, recent explained the need for good solid data to Traders Magazine that the informational content of the data, coming from the SIPs, needs to be improved and the geographical latency concerns need to be addressed for the consolidated feeds to be competitive and for all to trade better.

“Reducing some of the information asymmetries between participants is a step towards a more level playing field of data consumption,” Royal said.

2020-07-10 09:18:44+00:00 Read the full story…
Weighted Interest Score: 4.9465, Raw Interest Score: 2.0594,
Positive Sentiment: 0.1720, Negative Sentiment 0.2015

Diversity continues to grow at Transform 2020

As our writers here at VentureBeat have emphasized, diversity is a long game. We recognize there’s no instant fix to the long-standing barriers people of color as well as women face, which include important challenges to those working in tech. At the same time, change will only happen with a committed focus on these issues, which is why at VentureBeat we continue to ensure they’re a prominent pillar of our events.

At Transform 2020, we’re building on the gains we made last year, and are thrilled again to offer several opportunities to dive into these issues, give ample opportunity for provocative discussion, as well as celebrate some outstanding successes.

Here’s a snapshot of our diversity program:

2020-07-09 00:00:00 Read the full story…
Weighted Interest Score: 4.7982, Raw Interest Score: 1.4540,
Positive Sentiment: 0.3923, Negative Sentiment 0.1616

White House advisory council’s AI guidance conflicts with Trump’s talent pool sabotage

Earlier this week, the President’s Council of Advisors on Science and Technology (PCAST) released a report outlining what it believes must happen for the U.S. to advance “industries of the future.” Several of the committee’s suggestions touched on how the field of AI relates to federal, state, and private-sector partnerships, as well as departmental budgetary considerations. In particular, the report recommends that the U.S. grow nondefense federal investments in AI by 10 times over the next 10 years and for the federal government to create national AI “testbeds,” expanding the National Science Foundation’s (NSF) AI Institutes with at least one AI Institute in each state and creating “National AI Consortia” to share capabilities, data, and resources.

Loosely, PCAST — which lives in the Office of Science and Technology — provides advice to the president on science and technology policy. (Its 12 members from academia and private industry met for the third time this week under the Trump Administration.) In the report, the committee argues the U.S. will need to boost AI R&D investments from $1 billion a year in 2020 to $10 billion a year by 2030 in order to remain competitive. PCAST asserts this would enable the NSF — which requested $487 million for AI in 2020 — to make at least 1,000 awards to individual investigators “without any loss of quality.”

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 4.6580, Raw Interest Score: 1.6579,
Positive Sentiment: 0.1719, Negative Sentiment 0.1596

Announcing the second annual VentureBeat AI Innovation Awards at Transform 2020

The past year has seen remarkable change. As innovation in the field of AI and real-world applications of its constituent technologies such as machine learning, natural language processing, and computer vision continue to grow, so has an understanding of their social impacts.

At our AI-focused Transform 2020 event, taking place July 15-17 entirely online, VentureBeat will recognize and award emergent, compelling, and influential work in AI through our second annual VB AI Innovation Awards.

Drawn both from our daily editorial coverage and the expertise, knowledge, and experience of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.

2020-07-11 00:00:00 Read the full story…
Weighted Interest Score: 4.2488, Raw Interest Score: 2.0122,
Positive Sentiment: 0.3744, Negative Sentiment 0.0234

Visit the cutting edge in AI: Transform 2020 Expo (July 15-17)

To say that the speed of technological change in AI is fast-firing is an understatement. As engineers and data scientists unlock more of the potential of AI and machine learning, ever-more innovative solutions continue to advance the goals of business leaders.

At Transform 2020 next week, you’ll have a chance to see those solutions for yourself. Transform 2020 Expo (July 15-17) will showcase some of the most cutting edge AI companies, from large tech giants like Intel and Dell to some of the most innovative growth companies and startups like Dataiku, Cloudera, and Modzy.

This means you’ll be able to get an up-close look at some of the most advanced solutions spanning AI security, automation, conversational AI, explainable AI, training data, as well as solutions for specialized areas, such as customer experience, and specific industries, such as wealth management.

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 4.1876, Raw Interest Score: 1.5092,
Positive Sentiment: 0.3354, Negative Sentiment 0.1677

Red Swan Risk and Asset Control team up to provide integrated security master and model risk service

Red Swan Risk is enhancing its multi-asset class portfolio risk management solution, RiskON, through Asset Control’s managed service offering, PaSSPort, to provide a ‘seamless data experience’ to its clients.

The collaboration between the two companies will provide asset managers with a managed service which delivers a high-quality, data mastering, data analytics and model risk solution.

Hedge funds, pension funds, funds of funds, asset managers and banks need to manage model risk with increasingly more control and transparency. This has to be done across data inputs, model configuration, statistics, stress scenarios, reporting and analysis. Front office risk management, as well as back office oversight, require these capabilities.

Red Swan’s RiskON platform provides seamless aggregation of portfolio holdings data with security level risk and reference data using a unified data model. This gives users the transparency and control over risk modelling decisions needed to monitor, analyse and manage model and portfolio risk through a web-based UI and API hosted on AWS.

2020-07-08 00:00:00 Read the full story…
Weighted Interest Score: 4.0784, Raw Interest Score: 2.6557,
Positive Sentiment: 0.4426, Negative Sentiment 0.1897

Google Cloud’s Dataproc Gets a GPU-Powered Spark Boost

Google Cloud’s Dataproc – its big data platform that allows users to run Apache Hadoop and Spark jobs – is getting a boost. Apache Spark 3 and Hadoop 3 have launched general availability, enhancing users’ data analytics capabilities with a series of new features – and naturally, those features are now available on Google Cloud’s Dataproc image version 2.0.

In a blog post, Christopher Crosbie (product manager for Google Cloud) and Igor Dvorzhak (a software engineer at Google) highlighted the new features offered in the Apache Spark 3 implementation.

  • Adaptive queries: Spark can now optimize a query plan while execution is occuring. This will be a big gain for data lake queries that often lack proper statistics in advance of the query processing.
  • Dynamic partition pruning: Avoiding unnecessary data scans are critical in queries that resemble data warehouse queries, which use a single fact table and many dimension tables. Spark 3 brings this data pruning technique to Spark.
  • GPU acceleration: NVIDIA has been collaborating with the open source community to bring GPUs into Spark’s native processing. This allows Spark to hand off processing to GPUs where appropriate.

2020-07-07 00:00:00 Read the full story…
Weighted Interest Score: 3.9969, Raw Interest Score: 2.8328,
Positive Sentiment: 0.3104, Negative Sentiment 0.1164

Said to Be Faster, More Accurate

Anomaly detection is work to identify rare events or observations that differ in a big way from the majority of surrounding data, thus raising questions as to why it is the case.

Anomaly detection, synonymous with outlier detection, is used in many fields including statistics, finance, manufacturing, networking and data mining. It can be useful for intrusion detection, fraud detection, system health monitoring and event detection in sensor networks. It is used in preprocessing to remove irregular data from the dataset, which can substantially increase accuracy.

Today anomaly detection is also used in cyber security for spam filters, credit card fraud detection, network security and social media content moderation.

2020-07-09 21:30:25+00:00 Read the full story…
Weighted Interest Score: 3.8127, Raw Interest Score: 1.6941,
Positive Sentiment: 0.1583, Negative Sentiment 0.8233

CaixaBank: Innovation Drives Digital Transformation (Podcast 35m)

At no time has the importance of innovation and the deployment of advanced technologies in banking been more evident than today. With more consumers embracing digital banking, banks are being challenged to replicate the level of engagement of bigtech and new tech organizations such as Amazon, Google, Netflix, Zoom and Alibaba.

One of the perennial global leaders in technological innovation for banking is CaixaBank, Spain’s leading digital financial services provider. As a pioneer in digital transformation, the application of artificial intelligence, innovative product design, and hybrid cloud utilization, CaixaBank continues to be at the forefront of what banking can become.

2020-07-07 06:00:17+00:00 Read the full story…
Weighted Interest Score: 3.6601, Raw Interest Score: 2.1456,
Positive Sentiment: 0.3366, Negative Sentiment 0.1262

On-Ramp to AI: The Path to Democratize AI Starts with One Class (Course Advert)

On-Ramp to AI: The Path to Democratize AI Starts with One Class

Artificial Intelligence (AI) is like a superhighway, it’s moving fast, evolving, and growing quickly. Like most things in life, data scientists are not born with AI and Machine Learning (ML) knowledge. They learn it. Learning is a journey.

At H2O.ai, we are on a mission to democratize AI. To help every company become an AI company. Companies are also on an AI transformation journey. AI is being used to improve decision making by leveraging data to better find patterns, predict behavior, mitigate risk, understand customers, and optimize supply chains. Every day there is a growing number of AI use cases across all industries around the world. But how do you get started and join this AI transformation? You need an on-ramp, an on-ramp to the AI superhighway.

2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 6.6347, Raw Interest Score: 2.2073,
Positive Sentiment: 0.1679, Negative Sentiment 0.0240

8 Key Considerations for AI in the Enterprise – H2o.ai

If you’re developing or thinking of developing an AI strategy to transform your business, there’s a lot to consider, let us help. We’re the creator of the leading open source machine learning and artificial intelligence platform and our vision is to democratize AI for all and empower every company to be an AI company. This is a fundamental concept for the future of every business and organization. AI empowers companies to augment their human intelligence and to gain more value, and most importantly achieve a competitive edge in their markets.

2020-07-06 00:00:00 Read the full story…
Weighted Interest Score: 6.1415, Raw Interest Score: 2.4129,
Positive Sentiment: 0.6702, Negative Sentiment 0.0000

MLOps Vendor dotData Boosts Automation with Containers

As data science platforms expand across enterprise applications like predictive analytics, automated machine learning vendors are steadily integrating AI models with emerging infrastructure to ease deployment and orchestration.

For example, data science automation specialist dotData this week released a container-based machine learning model aimed at real-time prediction. Applications include automated loan processing, dynamic pricing, fraud detection and industrial Internet of Things deployments such as a smart manufacturing partnership also announced this week.

The Stream platform is designed to deliver real-time prediction using dotData’s AI and machine learning models. Those models are downloaded from the company’s flagship platform via a one-click process akin to launching a Docker application container.

2020-07-07 00:00:00 Read the full story…
Weighted Interest Score: 3.5827, Raw Interest Score: 2.2152,
Positive Sentiment: 0.0396, Negative Sentiment 0.1187

Why Tesla Invented A New Neural Network

Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints.

According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations and a constraint satisfaction solver to classify valid configurations for the particular platform, where the neural network model will run efficiently.

The Reason Behind the Patent – Neural network models are increasingly relied upon for different problems due to the ease at which they can label or classify the input data. Different neural networks are trained with different hyperparameters, and then they are used to analyse the same validation training set. A particular neural network is selected for future-use based on the desired performance as well as the accuracy goals of specific applications.
2020-07-13 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.4520, Raw Interest Score: 2.4368,
Positive Sentiment: 0.1819, Negative Sentiment 0.0909

BNP Paribas Securities Services Automates Processing of Asset Servicing Docs

BNP Paribas Securities Services, a leading global custodian with USD 10.6 trillion in assets under custody[1], has automated the processing of key asset servicing documentation, a major milestone in the bank’s digital transformation programme.

The move, which took 12 months to implement, aims to enhance back office operational efficiency, increase straight-through processing rates and reduce services turn-around times for the benefit of the bank’s clients.

Using Natural Language Understanding (NLU) and machine learning, including Intelligent Document Processing (IDP), BNP Paribas Securities Services is now able to automatically capture, extract and classify data from documents such as fund prospectuses and order confirmations. The resulting structured datasets are then fed directly into the bank’s operational systems.

The bank has so far automated the processing of 500,000 documents a year.

2020-07-13 06:57:10+00:00 Read the full story…
Weighted Interest Score: 3.4356, Raw Interest Score: 2.4345,
Positive Sentiment: 0.5972, Negative Sentiment 0.0000

The top 20 most valuable venture-backed AI companies, including Palantir, UiPath, and Databricks — valued at $120 billion total

A list of the 20 most valuable venture-backed companies in artificial intelligence boasts a combined valuation of some $120 billion.

Most of the list are privately-held startups; some of them — namely Waymo and Uber Advanced Technology Group — are subsidiaries of much larger companies, but that are said to be eyeing IPOs of their own.

Investment remains robust despite an uncertain economy, a reflection of the great potential of AI innovation, analysts say.
2020-07-11 00:00:00 Read the full story…
Weighted Interest Score: 3.2864, Raw Interest Score: 1.5198,
Positive Sentiment: 0.2763, Negative Sentiment 0.2211

UBS Big Data tool tracks the risks to companies from activist investors

UBS has released a new Big Data tool to predict and quantify the probability of a company being targeted by activist investors.

The new predictive algorithm, UBS-Guard (Global Utility for Activism Risk and Defence) aims to tackle the threat posed by activist investors undermining strategic business objectives by highlighting the risks of an approach and providing built-in analytics which offer insights into the underlying caus…
2020-07-10 10:40:00 Read the full story…
Weighted Interest Score: 3.1616, Raw Interest Score: 1.4052,
Positive Sentiment: 0.1756, Negative Sentiment 0.4098

AI, Machine Learning Playing Important Role in Fighting COVID-19

AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads.

Chatbots employing AI are speeding up communication around the pandemic. One example is…
2020-07-09 21:30:25+00:00 Read the full story…
Weighted Interest Score: 3.1349, Raw Interest Score: 1.7178,
Positive Sentiment: 0.2736, Negative Sentiment 0.1976

Deutsche Bank and Google Cloud agree multi-year deal

German banking giant Deutsche Bank has agreed a multi-year partnership with Google Cloud for the provision and joint development of cloud services.

The arrangement will see the bank accelerate its plan to transition services to the cloud but also co-develop products with engineers from Google Cloud with the two parties sharing any revenue that arises.

A letter of intent has been signed and a multi-year contract will be agreed in the coming mont…
2020-07-07 09:05:00 Read the full story…
Weighted Interest Score: 2.9002, Raw Interest Score: 1.6699,
Positive Sentiment: 0.2330, Negative Sentiment 0.1165

Aligning Data Architecture and Data Strategy

Peter Aiken disagrees with the popular idea that it’s impossible to put a dollar value on Data Architecture.

“It won’t be the right number, but it will be at least a dollar value on it, and if there’s money involved, people should be paying attention to it.”Aiken is an author, an associate professor of Information Systems, a researcher, and the Founding Director of Data Blueprint. He spoke about Data Architecture and Data Strategy with attendees…
2020-07-07 07:35:30+00:00 Read the full story…
Weighted Interest Score: 2.8538, Raw Interest Score: 1.3627,
Positive Sentiment: 0.3061, Negative Sentiment 0.2567

Ardent Financial Selects SteelEye For Compliance

SteelEye, the compliance technology and data analytics firm, has been selected by Ardent Financial, a new FCA authorised Securities Dealer, to provide MiFID II and MAR compliance services.

Launched on 8th June, Ardent Financial wanted to use modern best-of-breed tools for compliance from day one. Automation was a top priority, but they also wanted a unified platform which would increase efficiency and allow them to manage, oversee, mitigate, and pre…
2020-07-09 10:00:20+00:00 Read the full story…
Weighted Interest Score: 2.8390, Raw Interest Score: 1.4805,
Positive Sentiment: 0.2961, Negative Sentiment 0.0423

Tellius Introduces On-Demand Platform Utilizing Machine Learning to Glean Insights

Tellius, the Guided Insights platform, is releasing Tellius On-Demand, an on-demand SaaS application for business users and analytics teams to quickly understand why metrics change in their data with machine learning automation.

Analyzing data with spreadsheets and visualization tools relies entirely on manual ‘slicing-and-dicing’ of data that takes time and produces incomplete results.

Advanced tools that can process large amounts of data require users to pay a high upfront cost to deploy dedicated resources to meet worst-case scenarios for maximum usage of computing and storage, even when they sit unused for long periods of time.

2020-07-08 00:00:00 Read the full story…
Weighted Interest Score: 2.7316, Raw Interest Score: 1.6680,
Positive Sentiment: 0.1986, Negative Sentiment 0.1986

Altair Shows Off Converged Analytics Lineup

If you’re in the market for analytics or machine learning software, you may want to keep your eyes on Altair Engineering. Best known for its product simulation and computer aided engineering software, Altair has quietly assembled an impressive big data platform that extends from data preparation and business intelligence to streaming analytics and AutoML.

Altair Engineering traces its roots back to 1985, when CEO James Scapa, George Christ, and Mark Kistner founded the Troy, Michigan company to develop CAE software. Its initial product, called HyperWorks, allowed designers to simulate manufactured products, whether it’s a car chassis or an airplane wing.

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7268, Raw Interest Score: 1.8019,
Positive Sentiment: 0.2670, Negative Sentiment 0.0267

Op-ed: Hyperwar is coming. America needs to bring AI into the fight to win — with caution

  • Hyperwar, or combat waged under the influence of AI, where human decision making is almost entirely absent from the observe-orient-decide-act (OODA) loop, already is beginning to intrude on military operations.
  • This is the central issue for 21st century armed conflict: the superpower that can master AI, data analytics, and supercomputing, will inevitably prevail in conflict.
  • The human dimension of war will be sorely tested in a hyperwar environment. It will demand the utmost of the services in recruiting, educating and training and leading the human talent able to fight and win

The United States recently sent two aircraft carrier strike groups into the South China Sea in a show of military strength. The move of multiple American warships is in reaction to China holding military exercises in international waters that are contested by Vietnam and the Philippines. The stand-off raises global tensions at a time when each superpower has developed advanced technological capabilities in terms of artificial intelligence, remote imaging, and autonomous weapons systems. It is important officials in each nation understand how emerging technologies speed up decision-making but through crisis acceleration run the risk of dangerous miscalculation.

2020-07-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5984, Raw Interest Score: 1.2009,
Positive Sentiment: 0.2528, Negative Sentiment 0.3792

With Modernization Comes Data Challenges

Modernization is driving many of today’s enterprise data strategies—and cloud stands out as the primary vehicle for attaining this modernization. However, many enterprises are struggling with data quality issues, as well as integrating cloud-based and on-premise data.

That’s the word from a recent survey of 1,840 data executives and professionals, released by Progress (“The 2020 Data Connectivity Survey Report”). The 2019 survey collected input from respondents across more than 13 distinct industries worldwide to identify patterns and insights for ongoing data management strategies.

More than eight in 10 respondents indicated they are in the midst of a modernization effort.

2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 2.5817, Raw Interest Score: 1.5987,
Positive Sentiment: 0.2108, Negative Sentiment 0.2108

Modern Data Warehousing: Enterprise Must-Haves

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

UiPath raises $225 million to automate repetitive back-office tasks

Robotic process automation (RPA) startup UiPath today announced it has closed a $225 million funding round, bringing its total raised to over $1.2 billion. While the new round is roughly half the $568 million UiPath raised last April, it catapults the New York-based company’s post-money valuation to $10.2 billion, up from $7 billion in 2019 and $3 billion in 2018.

CEO Daniel Dines says the funding will be used to scale UiPath’s platform and deepen its investments in “AI-powered innovation” as it expands its cloud software-as-a-service (SaaS) offerings. The round will also likely lay the groundwork for future strategic deals, following UiPath’s acquisition of startups StepShot and ProcessGold last October.

2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 2.5355, Raw Interest Score: 1.3822,
Positive Sentiment: 0.1141, Negative Sentiment 0.1014


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