AI & Machine Learning News. 07, December 2020
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?
Introducing ArtLine, Create Amazing Line art portraits.
The main aim of the project is to create amazing line art portraits.
The amazing results that the model has produced has a secret sauce to it. The initial model couldn’t create the sort of output I was expecting, it mostly struggled with recognizing facial features.
Even though APDrawingGAN produced great results it had limitations like (frontal face photo similar to ID photo, preferably with clear face features, no glasses and no long fringe.)
I wanted to break-in and produce results that could recognize any pose. Achieving proper lines around the face, eyes, lips and nose depends on the data you give the model. APDrawing dataset alone was not enough so I had to combine selected photos from Anime sketch colorization pair dataset. The combined dataset helped the model to learn the lines better.
2020-12-05 Read the Full Story…
3 Stocks Profiting From Unstoppable Trends That Could Make You Rich
Making money by investing in stocks isn’t all that complicated. There are basically three steps involved. First, identify major trends that will drive new markets. Second, find the leading companies linked to those trends. Third, buy the stocks and hold them over a long period. The last part of that third step is usually the Achilles’ heel for many investors. Holding stocks for a long time can be more difficult than it seems. It can be tempting to sell when a stock sinks or to lock in profits when a stock soars. However, the first two steps aren’t really difficult. Here are three stocks profiting from unstoppable trends that could make you rich.
1. NVIDIA (artificial intelligence) : Artificial intelligence (AI) is here to stay. Just ask Alexa or Siri. Billionaire Mark Cuban even predicts that world’s first trillionaire will make his or her fortune in AI. While there are plenty of companies that are likely to be big winners in AI, NVIDIA (NASDAQ:NVDA) looks like a sure-fire top pick. NVIDIA originally developed its graphics chips to power video games. Along the way, though, the company found that those same chips were also ideally suited for use in datacenter servers that performed AI processing. The data center market over time became a huge business for NVIDIA.
2020-12-07 00:00:00 Read the full story…
Weighted Interest Score: 2.5170, Raw Interest Score: 1.2302,
Positive Sentiment: 0.2417, Negative Sentiment 0.0879
CloudQuant Thoughts : A slow AI week if you are not Google but Nvidia comes up tops again. Motley Fool are a very well trusted trading advice site!
TensorFlow 2 on Raspberry Pi.
TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the cost.
With the new Raspberry Pi 400 shipping worldwide, you might be wondering: can this little powerhouse board be used for Machine Learning?
The answer is, yes! TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the dollar and power cost. You can achieve real-time performance with state-of-the-art neural network architectures like MobileNetV2 by adding a Coral Edge TPU USB Accelerator.
This performance boost unlocks interesting offline TensorFlow applications, like detecting and tracking a moving object.
2020-12-03 00:19:24.450000+00:00 Read the full story…
Weighted Interest Score: 4.8138, Raw Interest Score: 1.5094,
Positive Sentiment: 0.1091, Negative Sentiment 0.0546
CloudQuant Thoughts : Running Tensorflow on a full programmable computer that costs as little as $35 is extremely impressive!
Google at NeurIPS 2020
This week marks the beginning of the 34th annual Conference on Neural Information Processing Systems (NeurIPS 2020), the biggest machine learning conference of the year. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. As a Platinum Sponsor of NeurIPS 2020, Google will have a strong presence with more than 180 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops and tutorials.
2020-12-07 Read the Full Story…
CloudQuant Thoughts : I bet Google thought this would be their top AI story of the day!
Google AI Researcher Fired?
Google AI Researcher Says She Was Fired for Critical Views
A prominent Google artificial-intelligence researcher said she was fired over an email she authored expressing dismay with management and the way it handled a review of her research.
In a tweet, Timnit Gebru, 37, who is Black, claimed she was fired from Alphabet-owned Google for refusing to retract a research paper that said AI discriminates against darker-skinned people, and complained about the company in an email to colleagues. She also criticized Google over its approach to hiring minorities and not doing enough to stamp out biases in AI systems. Gebru, a renowned scientist and one of the few Black women in the field of artificial intelligence, had been co-head of the team at Google examining the ethical ramifications of AI.
The email itself, which was reviewed by The Wall Street Journal, began with “Hi friends,” and then proceeded to criticize her superiors, alleging among other things that Google executives quashed her research and ignored her feedback on issues like the proportion of female employees in the company.
2020-12-04 12:00:16+00:00 Read the full story…
Weighted Interest Score: 3.6727, Raw Interest Score: 1.8363,
Positive Sentiment: 0.0399, Negative Sentiment 0.8782
Google AI Ethics Co-Head Reportedly Sacked for Critical Views
A prominent Google (GOOGL) – Get Report artificial-intelligence researcher says she was fired over an email she authored expressing disappointment with management and the way a review of her research was handled internally. Timnit Gebru, 37, who is Black, claimed in a tweet she was fired from Alphabet-owned Google for refusing to retract a research paper that said AI discriminates against darker-skinned people – and for complaining about the company in an email to colleagues.
The email itself, which was reviewed by The Wall Street Journal, began with “Hi friends,” and then proceeded to criticize her superiors, alleging among other things that Google executives quashed her research and ignored her feedback on issues like the proportion of female employees in the company. Gebru also criticized Google over its approach to hiring minorities and not doing enough to stamp out biases in AI systems. Gebru, a renowned scientist and one of the few Black women in the field of artificial intelligence, had been co-head of the team at Google examining the ethical ramifications of AI.
2020-12-04 16:16:29+00:00 Read the full story…
Weighted Interest Score: 3.3480, Raw Interest Score: 1.8502,
Positive Sentiment: 0.0000, Negative Sentiment 0.8811
Google AI ethics co-lead Timnit Gebru says she was fired over an email
Timnit Gebru, one of the best-known AI researchers today and co-lead of an AI ethics team at Google, no longer works at the company. She was featured in Google promotional material as recently as May. According to Gebru, she was fired Wednesday for sending an email to “non-management employees that is inconsistent with the expectations of a Google manager.” She said Google AI employees who report to her were emailed and told that she accepted her resignation when she did not offer her resignation. VentureBeat reached out to Gebru and Google AI chief Jeff Dean for comment. This story will be updated if we hear back.
2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 3.3661, Raw Interest Score: 1.3897,
Positive Sentiment: 0.1469, Negative Sentiment 0.4519
AI Weekly: In firing Timnit Gebru, Google puts commercial interests ahead of ethics
This week, leading AI researcher Timnit Gebru was fired from her position on an AI ethics team at Google in what she claims was retaliation for sending colleagues an email critical of the company’s managerial practices. The flashpoint was reportedly a paper Gebru coauthored that questioned the wisdom of building large language models and examined who benefits from them and who is disadvantaged.
Google AI lead Jeff Dean wrote in an email to employees following Gebru’s departure that the paper didn’t meet Google’s criteria for publication because it lacked reference to recent research. But from all appearances, Gebru’s work simply spotlighted well-understood problems with models like those deployed by Google, OpenAI, Facebook, Microsoft, and others. A draft obtained by VentureBeat discusses risks associated with deploying large language models, including the impact of their carbon footprint on marginalized communities and their tendency to perpetuate abusive language, hate speech, microaggressions, stereotypes, and other dehumanizing language aimed at specific groups of people.
2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 3.2784, Raw Interest Score: 1.9025,
Positive Sentiment: 0.1268, Negative Sentiment 0.5255
Thousands petition Google for more answers on departed researcher
- More than 2,000 Google employees and industry supporters are petitioning Google for answers on its firing of renown researcher Timnit Gebru.
- Gebru, who claims she was terminated for conditional disagreements regarding one of her research papers, has been vocally critical of the company’s treatment of research, diversity and inclusion efforts, and treatment of black employees.
- Employees promptly took to Twitter, saying her managers’ explanations didn’t line up with their experiences.
Employees from Google and other organizations are asking Google for answers about how it handled the departure of renown researcher Timnit Gebru in an online petition that had more than 2,000 signatures as of Friday afternoon.
Gebru, a well-known artificial intelligence researcher, technical co-lead of Google’s “Ethical AI” team and vocal critic of tech companies’ treatment of Black workers, tweeted Wednesday night that her corporate account had been abruptly shut off after she discussed potentially resigning over a disagreement about a research paper that scrutinized bias in artificial intelligence, which the company asked her to retract.
2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 2.7477, Raw Interest Score: 1.6220,
Positive Sentiment: 0.2478, Negative Sentiment 0.5632
Google staff hit out at effort to ‘silence’ AI ethics leader
Hundreds of Google staff have accused the US firm of “unprecedented research censorship” in an open letter supporting Timnit Gebru, a high-profile artificial intelligence researcher who said she was fired by the organisation this week.
More than 400 members of Google’s workforce and 521 academic, industry, and civil society supporters signed a letter demanding transparency over why Dr Gebru was terminated from her post.
Earlier this week, the renowned researcher posted on Twitter that she had been abruptly fired from her role at Google, as a co-leader of the eth…
2020-12-04 00:00:00 Read the full story…
Weighted Interest Score: 2.4827, Raw Interest Score: 1.6661,
Positive Sentiment: 0.0629, Negative Sentiment 1.1317
Renowned AI researcher says Google abruptly fired her, spurring industrywide criticism of the company
- Timnit Gebru, an artificial intelligence researcher at Google, said on Thursday that she has been abruptly fired by Jeff Dean, Google’s head of AI.
- Gebru, who was the technical co-lead of the Ethical AI Team at Google, shared on Twitter what she claims is a dismissal email.
- Gebru said she had made a number of requests and threatened to leave if they weren’t met but didn’t expect immediate termination.
Renowned AI researcher says Google abruptly fired her, spurring industrywide criticism
Timnit Gebru, a well-known artificial intelligence researcher at Google, tweeted on Wednesday that the company abruptly fired her, drawing widespread statements of support from other Google employees and tech workers throughout the industry.
2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 2.5641, Raw Interest Score: 1.5202,
Positive Sentiment: 0.0950, Negative Sentiment 0.4751
ServiceNow Acquires AI Pioneer Element AI For Smarter AI Capabilities
Popular cloud-based platform, ServiceNow recently acquired leading artificial intelligence (AI) company with deep AI capabilities, Element AI. According to sources, Element AI will significantly enhance ServiceNow’s commitment to building the world’s most intelligent workflow platform, enabling employees to work smarter and faster, streamline business decisions, and unlock new levels of productivity.
Element AI will allow ServiceNow to offer purpose-built AI for our customers’ enterprise-specific use cases. With the acquisition of Element AI, ServiceNow will create an AI Innovation Hub in Canada to accelerate customer-focused AI innovation in the Now Platform.
2020-12-01 08:42:49+00:00 Read the full story…
Weighted Interest Score: 5.8421, Raw Interest Score: 1.9178,
Positive Sentiment: 0.6849, Negative Sentiment 0.0457
Making it Real: Effective Data Governance in the Age of AI
Customer trust is not only gained with delightful service offerings but also by ensuring that their data is safe. This is one of the key factors why organizations across the globe are now considering data security, compliance, and governance as a key business objective.
Data governance means laying down set of consistent rules and processes to ensure the quality and integrity of data throughout the business lifecycle. A data governance framework is a pre-requisite for any organization to convert data into assets and meet their strategic goals.
2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 4.5833, Raw Interest Score: 2.3716,
Positive Sentiment: 0.5026, Negative Sentiment 0.0942
President Trump signs an executive order guiding how federal agencies use AI tech
BOT or NOT? This special series explores the evolving relationship between humans and machines, examining the ways that robots, artificial intelligence and automation are impacting our work and lives.
President Donald Trump today signed an executive order that puts the White House Office of Management and Budget in charge of drawing up a roadmap for how federal agencies use artificial intelligence software.
The roadmap, due for publication in 180 days, will cover AI applications used by the federal government for purposes other than defense or national security. The Department of Defense and the U.S. intelligence community already have drawn up a different set of rules for using AI.
Today’s order could well be the Trump administration’s final word on a technology marked by rapid innovation — and more than a little controversy.
2020-12-04 02:00:00+00:00 Read the full story…
Weighted Interest Score: 4.0862, Raw Interest Score: 1.9349,
Positive Sentiment: 0.3085, Negative Sentiment 0.0280
Deep Q-Learning Tutorial: minDQN
A Practical Guide to Deep Q-Networks : Reinforcement Learning is an exciting field of Machine Learning that’s attracting a lot of attention and popularity. An important reason for this popularity is due to breakthroughs in Reinforcement Learning where computer algorithms such as Alpha Go and OpenAI Five have been able to achieve human level performance on games such as Go and Dota 2. One of the core concepts in Reinforcement Learning is the Deep Q-Learning algorithm. Naturally, a lot of us want to learn more about the algorithms behind these impressive accomplishments. In this tutorial, we’ll be sharing a minimal Deep Q-Network implementation (minDQN) meant as a practical guide to help new learners code their own Deep Q-Networks.
2020-12-05 19:23:09.257000+00:00 Read the full story…
Weighted Interest Score: 4.0216, Raw Interest Score: 2.2496,
Positive Sentiment: 0.3100, Negative Sentiment 0.1590
Shortcut Learning, The Reason ML Models Often Fail in Practice
TLDR, models always take the route of least effort.
Training machine learning models is far from easy. In fact, the unaware data scientist might trip and fall in as many pitfalls as there are living AWS instances. The list is endless but divides itself nicely into two broad categories: underfitting, your model is bad, and overfitting, your model is still bad, but you think it isn’t. While overfitting can manifest itself in various ways, shortcut learning is a recurring flavor when dealing with custom datasets and novel problems. It affected me; it might be affecting you.
Informally, shortcut learning occurs whenever a model fits a problem on data not expected to be relevant or present, in general.
A practical example is a dog/cat classifier that, instead of properly recognizing dog- and cat-features, specializes in detecting leashes. Assuming leashes means dogs will likely do well most of the time, but leashes are not a general descriptor of dogness. That’s lazy work!
2020-12-01 13:21:54.182000+00:00 Read the full story…
Weighted Interest Score: 3.9326, Raw Interest Score: 1.6197,
Positive Sentiment: 0.0704, Negative Sentiment 0.3169
UOB boosts AML efforts with AI
UOB is hailing the accuracy of its new AI anti-money laundering technology in helping the Singaporean bank cut through large volumes of transactions to pinpoint suspicious activities.
The bank is using AI concurrently in two AML risk dimensions – transaction monitoring and name screening, helping it to pinpoint higher-priority cases from the more-than-5700 average monthly suspicious transaction alerts.
Once the AI system – which was built with regtech Tookitaki – flags suspicious activity, the bank’s compliance officers step in to conduct in-depth investigations and report to authorities.
2020-12-07 00:01:00 Read the full story…
Weighted Interest Score: 3.8520, Raw Interest Score: 1.8141,
Positive Sentiment: 0.0756, Negative Sentiment 0.7559
Deutsche Bank Partners With Google Cloud
- Deutsche Bank and Google Cloud to co-innovate the next generation of cloud-based financial services
- The bank’s move to the cloud will improve resilience, deliver new capabilities to market quicker and reduce cost over time
- Co-innovation use cases already being explored include new lending products, one retail bank interface and enhancements to the Autobahn platform
- Deutsche Bank and Google Cloud intend to selectively co-innovate with promising start-ups and fintechs and plan to make Deutsche Bank products available on Google Cloud Marketplace for the first time
Deutsche Bank and Google Cloud have finalised a strategic, multi-year partnership to accelerate the bank’s transition to the cloud and co-innovate new products and services. It is the first partnership of this kind for the financial services industry.
2020-12-07 05:31:04+00:00 Read the full story…
Weighted Interest Score: 3.6523, Raw Interest Score: 2.1353,
Positive Sentiment: 0.5403, Negative Sentiment 0.0515
Sber unveils cloud-based AI model training platform
Sber has developed a cloud platform for AI model training that the Russia bank says will help data scientists push ahead with their experiments.
Developed by the bank’s SberCloud unit, Machine Learning Space (ML Space) takes advantage of Sberbank’s supercomputer, Christofari, to ensure that resource-intensive models take hours – rather than weeks or months – to train.
2020-12-04 13:31:00 Read the full story…
Weighted Interest Score: 3.6284, Raw Interest Score: 2.0378,
Positive Sentiment: 0.1456, Negative Sentiment 0.0000
‘Biggest Leap’: Qualcomm Introduces Range Of AI Capabilities In New Snapdragon Processor
n the first day of the Snapdragon Tech Summit Digit, Qualcomm has announced the release of the Snapdragon 888, which is its latest 5G-equipped flagship smartphone processor. This, according to the company, would set the benchmark for flagship smartphones in 2021.
This new smartphone processor offers industry-leading mobile innovations in 5G for improved enterprise mobility, video telephony, console-quality cloud gaming etc. Of special interest is the artificial intelligence offered by this processor, which uses the 6th generation Qualcomm AI Engine with Hexagon 780 processor, which provides performance improvement by up to 50%.
Enhanced AI Capabilities : Qualcomm describes Snapdragon 888 as its ‘biggest leap in architecture and performance in years’. With the introduction of the new Hexagon 780 processor, Qualcomm removes the distance between the accelerators and combines them to form the fused AI accelerator architecture, as opposed to a separate scalar, vector, and tensor accelerators used for earlier models. As a matter of fact, the performance per watt on the Hexagon 780 processor is three times higher than the previous generation.
2020-12-07 04:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5084, Raw Interest Score: 1.4855,
Positive Sentiment: 0.3875, Negative Sentiment 0.0215
Microsoft unveils Azure Purview for data governance, Azure Synapse Analytics hits general availability
Microsoft today unveiled Azure Purview, a new data governance solution in public preview. Additionally, the company announced that Azure Synapse Analytics is now generally available.
Azure Purview automates the discovery of data and cataloging while minimizing compliance risk. Purview helps businesses map all their data, no matter where it resides, and provides an end-to-end view of their data estate. Azure Synapse Analytics meanwhile leverages on-demand or provisioned resources to ingest, prepare, manage, and serve data for business intelligence. Azure Synapse Analytics changes how enterprises store data and gain insight by bringing together data warehousing, big data, data integration, and AI.
Businesses are increasingly leveraging data as a strategic asset, which makes data services critical. Data needs to not only be stored and managed, but also discovered and analyzed at ever-growing volumes. Having designed services that do exactly that for itself, Microsoft is comfortable selling access to them.
2020-12-03 00:00:00 Read the full story…
Weighted Interest Score: 3.3692, Raw Interest Score: 1.7840,
Positive Sentiment: 0.1768, Negative Sentiment 0.1768
SEC Announces Office Focused on Innovation and Financial Technology
The Securities and Exchange Commission today announced that the SEC’s Strategic Hub for Innovation and Financial Technology, commonly referred to as FinHub, will become a stand-alone office. Valerie A. Szczepanik will continue to lead FinHub as its first director and will report directly to the SEC Chairman.
Established within the Division of Corporation Finance in 2018, FinHub has spearheaded agency efforts to encourage responsible innovation in the financial sector, including in evolving areas such as distributed ledger technology and digital assets, automated investment advice, digital marketplace financing, and artificial intelligence and machine learning. Through FinHub, market and technology innovators as well as domestic and international regulators have been able to engage with SEC staff on new approaches to capital formation, trading, and other financial services within the parameters of the federal securities laws.
2020-12-04 12:34:41-05:00 Read the full story…
Weighted Interest Score: 3.3555, Raw Interest Score: 1.7906,
Positive Sentiment: 0.6373, Negative Sentiment 0.0000
AI will explain it to you
Explainable artificial intelligence (XAI) removes the last barrier in entrusting transaction monitoring to algorithms: the skepticism about its results.
At the outset of my professional career, I worked as an analyst in the anti-money laundering (AML) department of a global financial institution. I still recall boundless open spaces packed with analysts trying to determine how risky a cooperation with a particular entity actually is. Every couple of weeks we would hit another employment level… 100, 200, 300 people aboard. Back then, we were only armed with internet access to decide if a given risk can be mitigated or reported further up the chain of command.
Today, 15 years later, while I think we had a great time, virtually following our clients to Caymans, Belize or at least Wyoming, it strikes me how inefficient and fallible we were. As the current numbers show, we were also quite expensive, as the complete know your customer (KYC) process costs banks around $141,000 per FTE a year, according to LexisNexis.
2020-12-02 13:30:53+00:00 Read the full story…
Weighted Interest Score: 3.1380, Raw Interest Score: 1.3297,
Positive Sentiment: 0.1922, Negative Sentiment 0.6408
Best structure for a data team
Some thoughts for setting data science teams for success
Recently I read a post on who should Data Science report to. This is really a hot topic for established companies and startups alike, since everybody wants to have a well functioning data science team, and the reporting line of data seems to be one of the unknowns in the equation. I believe that these discussions completely miss the point, and I’ll try to explain why.
Understand the company strategy : First of all, the most important thing is to know what your company wants to achieve. Is your company expanding to newer cities? Opening new verticals? Are you focus on monetising your current user base? Once you know this, you need to understand the value are you going to add to your company from Data Science.
2020-12-07 12:44:01.230000+00:00 Read the full story…
Weighted Interest Score: 2.9976, Raw Interest Score: 1.8067,
Positive Sentiment: 0.2235, Negative Sentiment 0.1304
We can reduce gender bias in natural-language AI, but it will take a lot more work
Thanks to breakthroughs in natural language processing (NLP), machines can generate increasingly sophisticated representations of words. Every year, research groups release more and more powerful language models — like the recently announced GPT-3, M2M 100, and MT-5 — that are able to write complex essays or translate text into multiple languages with better accuracy than previous iterations. However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself.
This summer, GPT-3 researchers discovered inherent biases within the model’s results related to gender, race, and religion. Gender biases included the relationship between gender and occupation, as well as gendered descriptive words. For example, the algorithm predicted that 83% of 388 occupations were more likely to be associated with a male identifier. Descriptive words related to appearance, such as “beautiful” or “gorgeous” were more likely to be associated with women.
When gender (and many other) biases are so rampant in our language and in the language data we have accumulated over time, how do we keep machines from perpetuating them?
2020-12-06 00:00:00 Read the full story…
Weighted Interest Score: 2.9197, Raw Interest Score: 1.3693,
Positive Sentiment: 0.1920, Negative Sentiment 0.1920
Predictiv AI on track to profit from a more predictable future using artificial intelligence
- Recently rebranded to stress its commitment to artificial intelligence innovation
- Focused on profitable fundamental-growth technology
- Has launched ThermalPass fever detection system to help fight coronavirus
What Predictiv AI does: Toronto-based Predictiv AI Inc (CVE:PAI) (OTCMKTS:INOTF), formerly Internet of Things Inc, is committed to using its expertise to accelerate artificial intelligence (AI) innovation as it advances AI and machine learning solutions. The company’s AI Labs Inc subsidiary is its research and development business arm, which uses artificial intelligence sensor-based technology solutions to solve real-world problems.
Predictiv AI also owns data-science company Weather Telematics Inc, which uses a vehicle-mounted mobile Internet of Things (IoT) sensor network and artificial intelligence system to generate historical, current and forecasted weather conditions for road hazard risk alerts.
2020-12-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9179, Raw Interest Score: 1.6844,
Positive Sentiment: 0.2323, Negative Sentiment 0.1549
The Top Trends in Data Management for 2021 : register for Expert Panel
The Top Trends in Data Management for 2021
THURSDAY, DECEMBER 10, 2020 – 11:00 am PT / 2:00 pm ET
From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relational and non-relational databases, transactional and analytical systems, and on-premises and cloud sites.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7460, Raw Interest Score: 1.7571,
Positive Sentiment: 0.0764, Negative Sentiment 0.0764
Mindtree and Databricks Collaborate to Bring Users Advanced, Cloud-Based Data Intelligence
Mindtree, a leading digital transformation and technology services company, is partnering with Databricks, the data and AI company, to help customers implement cloud-based data platforms for advanced analytics. This service will support use of the Databricks platform from implementation throughout the entire customer journey.
“Companies are accelerating their digital transformation, boosting demand for our open, cloud-based platform,” said, Michael Hoff, SVP of business development and partners, Databricks. “This partnership with Mindtree will bring together the right skills and technologies to help organizations advance their digital adoption journey and drive far-reaching business impact for our customers.”
2020-12-01 00:00:00 Read the full story…
Weighted Interest Score: 2.7165, Raw Interest Score: 1.7584,
Positive Sentiment: 0.5672, Negative Sentiment 0.0000
How Self-Supervised Text Annotation Works In TagTog
TagTog is an AI startup company making NLP modelling easier with its text analytics, visualization and annotation system democratized by subject matter experts bringing in domain-specific insights. It can annotate text, pdf, source code, or web URLs manually, using semi-supervised learning, and automation. It was launched in October 2017. Founders Jorge Campos Prieto and Dr Juan Miguel Cejuela during their PhD research in text mining applied biomedical in the University of Munich. Dr Cejuela along with some colleagues had represented a paper-based on TagTog. TagTog is based in Munich (Germany) and Gdansk (Poland).
TagTog helps in generating high-quality text datasets for training NLP algorithms with moderation and customization. The platform uses ML assisted models in learning from pre-annotated data to quickly annotate new data and put through the relevant information in the text. Manually annotation services are also provided following customer’s guidelines. TagTog specializes in text classification and annotation, entity extraction, entity normalisation, concept search ( Discover patterns in unstructured text, identify problems, realize solutions), Big Texts, annotated corpus, semantic search, text mining, business intelligence, and CRM data enrichment. Its automatic review annotations help in saving costs and time.
2020-12-07 11:30:23+00:00 Read the full story…
Weighted Interest Score: 2.6373, Raw Interest Score: 1.5355,
Positive Sentiment: 0.0649, Negative Sentiment 0.1081
Is Data Science for you? Read these books to find out.
How do you know if Data Science is right for you? Here are the best Data Science books for beginners. No coding involved.
Following my article on Switching Career to Data Science in your 30s, many readers asked me for more details. Especially on how to get started. Despite the sea of information, it can be daunting to navigate through hundreds of videos, courses, and articles. As a result, you feel stuck and might lose your motivation. Data science, machine learning and artificial intelligence are exciting topics, but we need to walk before we can run.
Finding someone who understands where you are in your journey is not easy. The majority of authors and YouTubers assume that all you want is something as straightforward as a Python tutorial. However, as beginners, I believe most of us have two kinds of concerns when learning Data Science:
2020-12-07 12:46:53.989000+00:00 Read the full story…
Weighted Interest Score: 2.4828, Raw Interest Score: 1.3994,
Positive Sentiment: 0.3137, Negative Sentiment 0.3016
Tune your models on Kubernetes the correct way
Let Katib handle the mundane work of HP optimization for you.
In Machine Learning, a hyperparameter is a user-defined value that is kept fixed during training. Examples of hyperparameters are the value of k in k-means clustering, the learning rate, the batch size, or the number of hidden nodes in neural networks.
To deploy a Machine Learning system in a production setup, we have to support the needs of both Data Scientists and DevOps Engineers. This isn’t easy because these two users of the environment rarely speak the same language. Data Scientists are interested in developing the most accurate ML model possible; thus, to tune the hyperparameters of a model, they work as follows:
2020-12-07 12:44:57.009000+00:00 Read the full story…
Weighted Interest Score: 2.4347, Raw Interest Score: 1.1355,
Positive Sentiment: 0.1323, Negative Sentiment 0.0992
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