Google is giving its translation service an upgrade with a new machine learning-powered addition that will allow users to more easily translate text that appears in the real world, like on storefronts, menus, documents, business cards and other items. Instead of covering up the original text with the translation the new feature will instead smartly overlay the translated text on top of the image, while also rebuilding the pixels underneath with an AI-generated background to make the process of reading the translation feel more natural.

Often its that combination of the word plus the context like the background image that really brings meaning to what youre seeing, explained Cathy Edwards, VP and GM of Google Search, in a briefing ahead of todays announcement. You dont want to translate a text to cover up that important context that can come through in the images, she said.

Image Credits: Google

To make this process work, Google is using a machine learning technology known as generative adversarial networks, otherwise known as GAN models the same technology that powers the Magic Eraser feature to remove objects from photos taken on the Google Pixel smartphones. This advancement will allow Google to now blend the translated text into even very complex images, making the translation feel natural and seamless, the company says. It should seem as if youre looking at the item or object itself with translated text, not an overlay obscuring the image.

The feature is another development that seems to point to Googles plans to further invest in the creation of new AR glasses, as an ability to translate text in the real world could be a key selling point for such a device. The company noted that every month, people use Google to translate text and images over a billion times in more than 100 languages. It also this year began testing AR prototypes in public settings with a handful of employees and trusted testers, it said.

While theres obvious demand for better translation, its not clear if users will prefer to use their smartphone for translations rather than special eyewear. After all, Googles first entry into the smartglasses space, Google Glass, ultimately failed as a consumer product.

Google didnt speak to its long-term plans for the translation feature today, noting only that it would arrive sometime later this year.

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Google turns to machine learning to advance translation of text out in the real world - TechCrunch

The singularity is coming for us, day by creeping day. Artificial intelligence is starting to write about cycling. It is starting to create pictures of cycling. And now, it is starting to predict the results of races that havent even happened yet.

There are humans involved at some point there always are, before the end of everything. In this case, it is a data and analytics consultancy called Decision Inc., Australia. The humans developed the modelling, fed it to their machine learning tool, let it marinate for a bit [that may be creative license] and then, the magic happened.

Machine Learning is a form of Artificial Intelligence which uses advanced data analytics [to] solve complex issues, explained Decision Inc, Australia CEO, Aiden Heke. It uses algorithms to best imitate how humans solve problems or predict outcomes.

Since the technology has evolved so much over the past few decades, we thought: why not use it to predict the outcome of the UCI World Championships?

First up, the womens road race:

A caveatthe Machines were crunching their numbers before Annemiek van Vleuten crashed out of the mixed team time trial, putting her start at risk. Also, apparently The Machines dont rate Grace Brown as a top 10 favourite. But all that aside? Those are certainly some credible names.

To the men:

Again, some curiosities in here for me. The podium seems credible, but I think Van der Poel is a bit more of a dark horse than this is letting on. Pogaar seems low; Almeida seems high. Im also furious about the Juraj Sagan erasure, but that is a me thing, not a you thing, and certainly not an AI thing.

Decision Inc. is likening their cycling foray to Deep Blue, an early machine learning venture from the mid-1990s that famously vanquished chess grandmaster Garry Kasparov. Its why were putting it to the test, to see just how far its come, said Decision Inc. CEO Aiden Heke. Were keen for everyone who fancies themselves as a bit of an expert on cycling to see if they can win where Kasparov couldnt: against the Machine.

If you want to show that you know more about this weekends cycling than a series of computer calculations, you can head to the companys Instagram account where you could win some signed cycling goodies.

Or, you can just wade into the comments here and tell us who your pick is. Thatd be fun too.

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Machine learning has predicted the winners of the Worlds - CyclingTips

image:Example of the macro placement algorithm proposed by Google. view more

Credit: Science China Press

Electronic design automation (EDA) or computer-aided design (CAD) is a category of software tools for designing electronic systems, such as integrated circuits (ICs). By EDA tools, designers can finish the design flow of very large scale integrated (VLSI) chips with billions of transistors. EDA tools are essential to modern VLSI design due to the large scale and high complexity of electronic systems.

Recently, with the boom of artificial intelligence (AI) algorithms, the EDA community are actively exploring AI for IC techniques for the design of advanced chips. Many studies have explored machine learning (ML) based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. For example, Google published a paper in Nature in 2021 entitled A graph placement methodology for fast chip design, leveraging reinforcement learning (RL) to place macros in a chip design. The basic idea is to regard the chip layout as a Go board, while each macro as a stone. In this way, an RL agent can be pre-trained with 10,000 internal design samples and learn to place one macro at a time. By finetuning the agent on each design for around 6 hours, it can outperform the performance of conventional EDA tools on Googles TPU chips, and achieve better performance, power, and area (PPA).

It can be seen that AI for EDA is being actively explored in the design automation community. Although building ML models usually requires a large amount of data, most studies can only generate small internal datasets for validation, due to the lack of large public datasets and the difficulty in data generation. To this end, an open-source dataset dedicated to ML tasks in EDA is urgently desired.

To address this issue, the research group from Peking University has released the first open-source dataset, called CircuitNet, which is dedicated to AI for IC applications in VLSI CAD. The dataset consists of over 10K samples and 54 synthesized circuit netlists from six open-source RISC-V designs, provides holistic support for cross-stage prediction tasks, and supports tasks including routing congestion prediction, design rule check (DRC) violation prediction and IR drop prediction. The main characteristics of CircuitNet can be summarized as follows:

To evaluate the effectiveness of CircuitNet, the authors validate the dataset by experiments on three prediction tasks: congestion, DRC violations, and IR drop. Each experiment takes a method from recent studies and evaluates its result on CircuitNet with the same evaluation metrics as the original studies. Overall, the results are consistent with the original publications, which demonstrates the effectiveness of CircuitNet. A detailed tutorial about the experimental setup is available on the webpage (https://circuitnet.github.io/). In the future, the authors plan to incorporate more data samples with large-scale designs in advanced technology nodes to improve the scale and diversity of the dataset.

See the article:

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

https://doi.org/10.1007/s11432-022-3571-8

Science China Information Sciences

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Peking University released the first open-source dataset for machine learning applications in fast chip design - EurekAlert

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Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study - BMC...

Nearly 20% of pregnancies in people with lupus result in an adverse pregnancy outcome (APO). In a new study, scientists were able to improve prediction accuracy of APOs using machine learning. Machine learning refers to the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.

Using a previously developed APO prediction model utilizing data from a larger multi-center, multi-ethnic study of lupus pregnancies known as the Predictors of pRegnancy Outcome: bioMarkers In Antiphospholid Antibody Syndrome and the Systemic Lupus Erythematosus (PROMISSE) study, and statistical analysis coupled with machine learning, researchers analyzed data from 385 women in their first trimester of pregnancy. They identified lupus anticoagulant positivity, disease assessment score, diastolic blood pressure or resting heartbeat, current use of antihypertension medication, and platelet count as significant baseline predictors of APO.

Researchers suggest that the ability to identify, lupus patients at high risk of APO early in pregnancy, could enhance the capacity to manage these patients and conduct trials of new treatments to prevent pre-eclampsia and placental insufficiency.

Further studies to identify new biomarkers and risk factors for APO are still needed. The Lupus Foundation of America provided the study author, Jane Salmon, MD, with a three-year grant for her IMPACT study, the first trial of a biologic therapy to prevent adverse pregnancy outcomes in high-risk pregnancies in patients with antiphospholipid syndrome (APS) with or without systemic lupus erythematosus (SLE), which also helped support this new research. Learn more about lupus and pregnancy.

Read the study

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Machine Learning Can Be Used to Improve the Ability to Predict Adverse Pregnancy Outcomes in Women with Lupus - Lupus Foundation of America

SMITHFIELD, RI Bryant University announces the launch of four new graduate programs to empower students and working professionals with the knowledge, skills, and advanced credentials to succeed in the global, data-driven digital economy. New STEM-designated in-person Masters degree programs in Business Analytics, Data Science, and Healthcare Informatics are enrolling for Fall of 2023. The Masters in Taxation will be delivered online and is also slated for next fall.

"There is an urgent need for leaders and analysts who can see connections and innovate to develop smart, effective strategies for the way business gets done and problems get solved.

As business needs change and industry boundaries blur, Bryant is committed to developing interdisciplinary academic programs and curricula that incorporate business, analytics, artificial intelligence, machine learning, finance, andhealth sciences to meet workforce demands.

These new programs provide opportunities for undergraduates to create pathways to career-accelerating graduate degrees. Professionals at all levelsfrom early career employees to C-level executiveswill be able to uplevel their skills and advance in their careers. All programs are available to students and professionals around the world, and several programs offer 4+1 options for Bryant students.

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Through Bryants marketplace-driven approach and signature real-world experiential education, the new data-centric graduate programs are answering the call for educated and skilled professionals to perform in key roles in top industries where skilled data scientists and analysts are in high demand, including health sciences, financial services, accounting, digital marketing, cyber security, manufacturing, and energy.

Through Vision 2030, we are forging a new era of growth and academic innovation at Bryant University.

Developing new graduate academic programs aligned to evolving workforce demand is part of a key Bryant University Vision 2030 Strategic Plan.

Through Vision 2030, we are forging a new era of growth and academic innovation at Bryant University, says Bryant University President and respected economistRoss Gittell, Ph.D. The value and return on investment on our innovative, highly ranked academic programs is attracting increasing attention of students, families, alumni, media, and corporate partners around the world.

The return on investment on a Bryant education is in the top 1% nationally, according to a recent survey by theGeorgetown University Center on Education and the Workforce.

Advances in technology, artificial intelligence, and machine learning are increasingly integral parts of life and business today. There is an urgent need for leaders and analysts who can see connections and innovate to develop smart, effective strategies to solve problems, says Provost and Chief Executive Officer Rupendra Paliwal Ph.D. These new graduate programs build on our historic strengths and culture of creativity and innovation to prepare our students to be leaders, disruptors, and valuable contributors anywhere in the world.

Business Analytics, Data Science, and Taxation will join other successful programs offered by the College of Business including the MBA, Professional MBA Online, and Master of Professional Accounting (MPAC). Healthcare Informatics is offered by the newly launched School of Health and Behavioral Sciences joining the Master of Science in Physician Assistant Studies (MSPAS), which launched in 2014. Additional graduate programs offered through the College of Arts and Sciences will be announced this fall.

More About the new programs

The Master of Science in Business Analytics prepares future business leaders to use advanced analytics to support organizational goals and strategies and use analytics to tell compelling stories that impact business strategy. Working with state-of-the-art business analytics tools and techniques, students learn the whole process of data analytics lifecycle from business understanding, data preparation, data exploration, model building, and data visualization and communication. The MSBA is a full-time, in-person cohort program comprising eight required business analytics courses and a three-course specialization, or a generalist track that tailors electives to individual personal and professional needs.

Building on the strengths of Bryant University in business and undergraduate data science programs, MSDS program is applied with a foundation in business and helps train the next generation of data scientists to work in various fields. The programs core courses include data ethics, statistics, machine learning, deep learning, natural language processing, large-scale data analytics and more. The MSDS program is a full-time, in-person cohort program and will run over the fall, spring, and summer sessions. Students will complete eight required data science courses and choose a three-course specialization, or a generalist track that tailors electives to individual personal and professional needs.

Healthcare Informaticsis an interdisciplinary field of study in the healthcare industry that uses information technology to organize and analyze health data and records to improve healthcare outcomes. Bryants program provides a holistic understanding of the healthcare system and emphasizes the need for collaboration to improve healthcare delivery and patient outcomes. Graduates of the program are equipped with knowledge of the healthcare industry and technology solutions and the technical skills needed to effectively analyze complex health data, manage evolving health information systems and support the increased utilization of electronic health records. The 10-course, 30-credit, in person program can be completed in 18 months or 12 months with classes over winter and summer sessions.

Bryants Master of Science in Taxation, offered online, will prepare graduates to enhance or launch their professional careers in accounting with a focus on taxation. Gaining in-depth expertise in taxation will enable graduates to understand the nuances of complex tax-related issues in terms of theory and practical application for individuals, partnerships and corporations. The MST program will incorporate data analytics and visualization with machine learning technology to ensure that graduates are well-equipped to best serve their organizations and clients. Graduates will be prepared to advise on retirement and compensation plans as well as navigate estate planning. The 10-course, 31-credit, online program will be delivered in 10-week increments with specific start times to be announced soon.

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For more information about Bryants Graduate programs, contact the Bryants Graduate Programs office at graduateprograms@bryant.edu or 401-232-6230.

About Bryant University

For 160 years, Bryant University has been at the forefront of delivering an exceptional education that anticipates the future and prepares students to be innovative leaders of character in a changing world. The University delivers a uniquely integrated academic and student life experience with nationally recognized academic programs at the intersection of business, liberal arts, and STEM fields. Located on a beautiful 428-acre campus in Smithfield, R.I., Bryantis recognized as a top 1% national leader in student education outcomes and return on investment and regularly receives high rankings fromU.S. News and World Report, Money, Bloomberg Businessweek, Wall Street Journal, College Factual and Barrons. Visitwww.bryant.edu.

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Bryant launches graduate programs in Business Analytics, Data Science, Healthcare Informatics, and Taxation - Bryant University

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