Mediaboss Marketing

SAN FRANCISCO--(BUSINESS WIRE)--Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, reached a number of important milestones in 2021. Highlights include introducing the Data Version Control (DVC) and Continuous Machine Learning (CML) open source projects followed by the addition of Experiment Versioning in DVC.

Iterative was founded in 2018 and in less than three years, its tools have had more than 8 million sessions and are rapidly growing, with more than 12,000 stars on GitHub between CML and DVC. DVC users grew by almost 95% in 2021 with over 3000 monthly users. Iterative now has more than 300 contributors across the different tools.

Iterative's tools have been critical in helping our machine learning team grow and unlock their productivity," said Benjamin Jones, head of ML at DeGould. Before using DVC, we struggled to share data and pipelines were steps written in READMEs. Since adopting DVC and CML, we've been able to easily collaborate and share data and experiments across team members to improve productivity and to show progress. Iterative has made life easier with tools that work with our existing tech stack, instead of running Bash scripts to cobble everything together. And sending progress reports in emails with spreadsheets is a thing of the past!

In 2021, Iterative saw significant company growth as the headcount increased by 150%. Joining the team include Oded Messer as director of Engineering and Ken Thom as director of Operations. Messer brings more than 10 years of experience as a software engineer where he most recently worked as platform group manager at Iguazio following five years as software engineer at Intel. Thom brings 30 years of experience in the software industry where he was most recently managing director at SOMAcentral following various product positions at companies including SocialMedia.com, Unboggle, oDesk, Vazu Inc., Inktomi, E*Trade, and Apple.

We are excited to welcome Oded and Ken to the team, said Dmitry Petrov, co-founder and CEO of Iterative. We look forward to continued expansion of our team to build the best tools for machine learning engineers.

DVC brings agility, reproducibility, and collaboration into the existing data science workflow. DVC provides users with a Git-like interface for versioning data and models, bringing version control to machine learning and solving the challenges of reproducibility. DVC is built on top of Git, creating lightweight metafiles and enabling the system to handle large files, which can't be stored in Git. It works with remote storage for large files in the cloud.

CML is an open-source library for implementing continuous integration and delivery (CI/CD) in machine learning projects. Users can automate parts of their development workflow, including model training and evaluation, comparing ML experiments across their project history, and monitoring changing datasets. CML will also auto-generate reports with metrics and plots in each Git pull request.

Together, CML and DVC provide ML engineers a number of features and benefits that support data provenance, machine learning model management and automation. DVC and CML are open-source tools available for free. Iterative also provides a commercial offering of a collaboration service DVC Studio. Iterative is building additional open-source tools to complement the ML engineering workflow, and also provides a commercial offering of a collaboration service Iterative Studio.

DVC, CML and Iterative Studio are available today to work with GitHub and GitLab. To schedule a demo, visit http://www.Iterative.ai.

About Iterative

Iterative.ai, the company behind popular open-source tools DVC and CML, enables data science teams to build models faster and collaborate better with data-centric machine learning tools. Iteratives developer-first approach to MLOps delivers model reproducibility, governance, and automation across the ML lifecycle, all integrated tightly with software development workflows. Iterative is a remote-first company, backed by True Ventures, Afore Capital, and 468 Capital. For more information, visit Iterative.ai.

See the rest here:
MLOps Company Iterative Achieves Significant Customer and Company Growth in 2021 - Business Wire

On March 15, 2022, the California Fair Employment & Housing Council released draft revisions to the states employment non-discrimination laws that would dramatically expand the liability exposure and obligations of employers and third-party vendors that use, sell, or administer employment-screening tools or services that embody artificial intelligence, machine learning, or other data-driven statistical processes to automate decision-making.

As proposed, the regulations would define an automated-decision system, or ADS, in extremely broad terms: any computational process, including one derived from machine learning, statistics, or other data processing or artificial intelligence techniques, that screens, evaluates, categorizes, recommends, or otherwise makes a decision or facilitates human decision making that impacts employees or applicants. This includes, without limitation:

The proposal goes on to specify that the use of ADS in a manner that is intentionally discriminatory, or that is facially neutral but nonetheless results in discriminatory impact, is unlawful under state law.

The draft regulations provide that liability extends to third parties that act on behalf of an employer by providing services relating to various facets of employment, including recruiting, applicant screening, hiring, payroll, benefit administration, etc., if they adversely affect the terms or conditions of employment. These third parties would be considered agents of the employer (and thereby, also an employer of the aggrieved party) and would thus be directly liable for claims of discrimination. The regulations likewise expand the definition of employment agency to include any person who provides ADS or ADS-related servicesessentially making the vendors and administrators of employment-screening tools subject to the non-discrimination law. The proposed regulation would also create aiding and abetting liability for anyone engaged in the advertisement, sale, provision, or use of an ADS if the end use of that ADS results in unlawful discrimination.

Finally, the regulations would expand recordkeeping requirements under current law from two years to four years, and would require the retention, by the employer and all other covered third-party entities, of all data used in the process of developing or applying machine-learning algorithms that are utilized as part of an ADS. This would include datasets used to train the algorithm; data provided by individual applicants or employees; data about individual applicants and employees that have been analyzed by the algorithm; and data produced from the application of an ADS operation. The revisions would also require all third parties engaged in the advertisement, sale, provision, or use of ADS tools to preserve the assessment criteria used by the [ADS] for each such employer or covered entity to whom the [ADS] is provided.

The Council is slated to discuss these proposed regulations in a public (virtual) meeting scheduled for 3:00 p.m. (PDT) on Friday, March 25, 2022. If approved, they will be open for public comment. Ultimately, the Council may approve the draft as proposed, or presumably make modifications to the proposal based on comments received. What is clear, however, is that the Golden State is poised to regulate the use of artificial intelligence and machine learning in employment decision-making aggressively, and to extend liability to vendors and those who provide products or services to assist employers in doing so.

View post:
California Fair Employment & Housing Council Proposes Sweeping Regulation of Automated Decision-making and Artificial Intelligence in Employment -...

Machine learning and animal/human intimacy bonds are the topics for two hybrid Oxford Science Cafes scheduled for Mar. 22 and 24 by faculty researchers from the University of Mississippi and University of Texas.

Both programs will be conducted in-person at Heartbreak Coffee, 265 North Lamar Ave., Suite G, and hosted on Zoom beginning at 6 p.m.

Dawn Wilkins, UM chair and professor of computer and information science, will discuss Machine Learning Applications to Science: Dos and Donts on March 22. Steven Phelps, professor of integrative biology and director of the Center for Brain, Behavior and Evolution at the University of Texas, will discuss A natural history of intimacy on March 24.

Machine learning is a way to add intelligence to an application without explicitly programming it with knowledge, Wilkins said. Instead, machine learning uses examples data as experience and builds a model of the implicit knowledge.

The advantage of this approach is the speed at which an application can be developed and deployed.

Questions to be addressed during Wilkins 45-minute talk include what machine learning is, how it is used, and some of the pitfalls and ethical concerns.

Machine learning models reduce human bias in making decisions and are not limited to problems with scope manageable by humans, Wilkins said. On the other hand, there can be issues with the application of machine learning, including obtaining enough data, implicit biases, and difficulty in the interpretability and generalizability of the models.

Phelps will discuss close social relationships common in the animal world.

These relationships are essential aspects of the human experience, he said. They promote collaboration and engender conflict.

This talk draws from animal behavior, neuroscience and evolutionary biology to explore how and why bonds form in species as diverse as prairie voles, poison frogs and humans.

To view either of the presentations online, visit:https://olemiss.zoom.us/j/99989536748. A link to the recorded talk will be posted athttps://www.phy.olemiss.edu/oxfordsciencecafe/.

For more information about the Department of Physics and Astronomy, which organizes the Oxford Science Cafe, visithttps://physics.olemiss.edu/.

Continue reading here:
Machines, intimacy topics for two hybrid Oxford Science Cafes - The Oxford Eagle - Oxford Eagle

Driven by a proprietary set of pattern-recognition algorithms that provide the user with choice and control over the content they view

VIENNA, Va., March 15, 2022 /PRNewswire/ --Seekr,aninternet technology company, launched its searchbeta version today, streamlining access to reliable information. The company provides an alternative to existing search engines and offers objective results combined with advanced information analysis to assist users in judging the quality of content. The site will initially offer the Seekr Score, which rates each news article's quality, and a Political Lean Indicator, which classifies political news as right, center, or left. Over time, the scoring will be extended beyond the news.

Seekr makes it easier to assess the reliability of information by offering ratings and filtering

Consumer rating systems exist across several industries; however, until today, no one has created a system to automatically evaluate the reliability of information at web scale. Developed over many years and packaged with long-tail search support from existing engines, the platform was built on an independent index, utilizing proprietary Lite-Web Technology to serve both news and the best of the web search results.It provides a unique scoring and filtering system that will empower users to make informed decisions on what they consume, share, and trust online. The goal is to provide both people and advertisers with a way to evaluate all web-based content. To showcase these advanced capabilities, the company has built a new user interface designed for clarity. This design approach foreshadows the next generation of a more consumer-centric search experience.

"We believe that a user-driven search experience coupled with our content rating system is a step in the right direction towards reducing the distrust of online information that continues to grow among all democracies today," says Pat Condo,SeekrFounder and Chief Executive Officer."We want users to see all sides of an argument and have every source of information available to make their own decisions rather than having other search engines draw conclusions for them."

Seekr Score and Political Lean Indicator Are the First of Many Tools

Asuite of machine-learning algorithms generates a specific score for each news article, just as FICO scores and other rating systems are used to evaluate products and services. With each query, results are evaluated with the same scrutiny that a data scientist or expert journalist would provide.The Seekr Score analyzes the quality of information and adherence to journalistic principles for each article. Principlesinclude Title Exaggeration, Personal Attack, and Subjectivity, among others.

Individual news articles containing political content are rated right, center, or left through the Political Lean Indicator. The AI technology does this by extracting and deeply analyzing the text for expressions, words, and semantics typically associated with a political position.

"We believe all machine-learning systems need to be explainable and transparent. We want our users to understand how our scoring systems work and trust them," says Rob Clark,Executive Vice President of

Development at Seekr."To achieve this confidence, ongoing automated and manual testing is employed to ensure accuracy, prevent bias or inaccurate drifts in the model."

The company plans to offer ad-supported search with user consent in the future. When ads are included, they will be placed next to content that reflects the quality and suitability of their brands.

"We are not driven by any political ideology nor by a business model that puts the consumer at a disadvantage. Our motivation is to provide you with a deeper understanding of the content you may rely on through transformative and groundbreaking technologies which can advance the state of how people use search to enhance their lives," says Condo.

Access http://www.seekr.com.

For press materials, visit http://www.seekr.com/press-center

AboutSeekr Technologies Inc.

Seekris aprivately heldinternet technology company that prioritizes transparency and empowers user choice and control by streamlining access to reliable information. Current services include an independentsearch engine powered by AI technology, which evaluates information and presents a Seekr Score and Political Lean Indicator. Seekris committed to giving everyoneaccess to technology that makes it easy to find trustworthy content in context.

Media Contact: Erika CruzHead of Communications[emailprotected]

SOURCE Seekr Technologies

Originally posted here:
Seekr Technologies launches the first search platform to rate web content by employing a fully automated machine-learning process - PR Newswire

Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it in a summarized way as:

With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance.

A machine has the ability to learn if it can improve its performance by gaining more data.

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem. The below block diagram explains the working of Machine Learning algorithm:

The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.

We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.

Following are some key points which show the importance of Machine Learning:

At a broad level, machine learning can be classified into three types:

Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output.

The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not.

The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering.

Supervised learning can be grouped further in two categories of algorithms:

Unsupervised learning is a learning method in which a machine learns without any supervision.

The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

In unsupervised learning, we don't have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms:

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance.

The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.

Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of our daily life. Machine learning is making our day to day life easy from self-driving cars to Amazon virtual assistant "Alexa". However, the idea behind machine learning is so old and has a long history. Below some milestones are given which have occurred in the history of machine learning:

Now machine learning has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc.

Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.

Before learning machine learning, you must have the basic knowledge of followings so that you can easily understand the concepts of machine learning:

Our Machine learning tutorial is designed to help beginner and professionals.

We assure you that you will not find any difficulty while learning our Machine learning tutorial. But if there is any mistake in this tutorial, kindly post the problem or error in the contact form so that we can improve it.

See the rest here:
Machine Learning Tutorial | Machine Learning with Python ...