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The only thing standing between you and a pay bump of almost $40,000 could be a certificate in machine learning.

U.S. workers with advanced tech skills earn about 49% more than workers who don't use tech skills in their jobs, according to newly released research from Gallup and Amazon Web Services (AWS), which surveyed more than 3,000 U.S. workers and 1,170 U.S. employers in August 2022. This translates into average individual gains of $36,552 per year.

As the development and adoption of new technologies continue at a breakneck pace, the need for digitally savvy workers is "greater than ever," the report notes.

Newer technologies including cryptocurrency, the metaverse and artificial intelligence are becoming skills requirements for jobs in several industries, including finance, manufacturing and health care, with nearly two-thirds of employers saying it's highly likely" these inventions will become a core part of their business in the near future.

Those who consider digital upskilling stand to reap major benefits from this trend: At least four in 10 U.S. workers say learning new digital skills helped them boost their pay (43%), work more efficiently (42%), or get promoted (40%).

Here are the 10 tech skills employers say are "extremely likely" to become standard parts of doing business and the most in-demand skills they are hiring for according to AWS and Gallup:

At the top of the list is 5G, or the fifth generation of wireless technology, which cellphone companies began using in 2019. 5G technology can be used to make data transmission more efficient across industries: In health care, for example, large files can be transmitted more quickly between doctors and hospitals.

Generative AI tools, in particular, have become more popular in the workplace since the launch of ChatGPT in late 2022, says Jay Shankar, vice president of global talent acquisition at Amazon Web Services.

"It's a super important skillset employers are looking for, across all industries," she adds. "AI is practically everywhere now and to me, if there's one technical skill you want to learn, that's the area to focus on."

Many of the jobs hiring for these technical skills, such as machine learning engineer and full stack developer, offer competitive salaries of $100,000 per year or higher.

The rise of generative AI tools has elicited increased demand for prompt engineers, who test prompts and build user guides to improve chatbots' responses,Business Insiderreports. Some of these jobs, which don't require an engineering or coding background, can pay as much as $335,000.

If you're looking to enhance your generative AI skills, there are several certification and training courses online, from the University of Michigan, Coursera and other e-learning platforms. For other technical skills, including machine learning and data analytics, AWS offers free online courses.

While some experts have warned that certain technologies, like AI and robotics, could replace millions of jobs in the next 10 years, Shankar says such innovations should be used to help workers be better at their jobs not take them over completely. "It's enabling us to accomplish things faster, and evolve many roles," she adds. "But I don't think AI, for example, will ever fully replace humans."

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Having one of these in-demand tech skills can help boost your pay by nearly $40,000here's how - CNBC

New version 23.05 extends HALCON's comprehensive software libraryNew Deep Counting feature for counting large quantitiesRelease on May 23, 2023

Munich, April 13, 2023 - MVTec Software GmbH (www.mvtec.com), a leading international software manufacturer for machine vision worldwide, will launch version 23.05 of the standard machine vision software HALCON on May 23, 2023. The focus of the new release is deep learning methods. The main feature here is Deep Counting, a deep-learning-based method that can robustly count large quantities of objects. In addition, improvements for the training of the deep learning technologies 3D Gripping Point Detection as well as Deep OCR have been integrated into the new HALCON version. With HALCON 23.05, it is now possible to further optimize the underlying deep learning networks, which are already pre-trained on industry-related images, for the user's own application. This allows even more robust recognition rates for Deep OCR applications as well as an even more reliable detection of suitable gripping surfaces for applications using 3D Gripping Point Detection technology. In addition, there are many other helpful improvements, such as the fact that external code can now be integrated into HALCON more easily.

Training for Deep OCRDeep OCR reads texts in a very robust way, even regardless of their orientation and font. For this purpose, the technology first detects the relevant text within the image and then reads it. With HALCON 23.05, it's now also possible to fine-tune the text detection by retraining the pretrained network with application-specific images. This provides even more robust results and opens new application possibilities. For example: the detection of text with arbitrary printing type or unseen character types as well as an improved readability in noisy, low contrast environments.

Training for 3D Gripping Point Detection3D Gripping Point Detection can be used to robustly detect surfaces on any object that is suitable for gripping with suction. In HALCON 23.05 there is now the possibility to retrain the pretrained model with own application-specific image data. The grippable surfaces are thus recognized even more robustly. The necessary labeling is done easily and efficiently via the MVTec Deep Learning Tool.

Easy Extensions InterfaceWith the help of HALCON extension packages the integration of external programming languages is possible. The advantage for customers: Functionalities that go beyond pure image processing can thus be covered by HALCON. In HALCON 23.05, the integration of external code has become much easier with the Easy Extensions Interface. This allows users to make their own functions written in .NET code usable in HDevelop and HDevEngine in just a few steps, while benefiting from the wide range of functionalities offered by the .NET framework. Even the data types and HALCON operators known from the HALCON/.NET language interface can be used. This increases both the flexibility and the application possibilities of HALCON.

About MVTec Software GmbHMVTec is a leading manufacturer of standard software for machine vision. MVTec products are used in all demanding areas of imaging: semiconductor industry, surface inspection, automatic optical inspection systems, quality control, metrology, as well as medicine and surveillance. By providing modern technologies such as 3D vision, deep learning, and embedded vision, software by MVTec also enables new automation solutions for the Industrial Internet of Things aka Industry 4.0. With locations in Germany, the USA, and China, as well as an established network of international distributors, MVTec is represented in more than 35 countries worldwide. http://www.mvtec.com

About MVTec HALCONMVTec HALCON is the comprehensive standard software for machine vision with an integrated development environment (HDevelop) that is used worldwide. It enables cost savings and improved time to market. HALCON's flexible architecture facilitates rapid development of any kind of machine vision application. MVTec HALCON provides outstanding performance and a comprehensive support of multi-core platforms, special instruction sets like AVX2 and NEON, as well as GPU acceleration. It serves all industries, with a library used in hundreds of thousands of installations in all areas of imaging like blob analysis, morphology, matching, measuring, and identification. The software provides the latest state-of-the-art machine vision technologies, such as comprehensive 3D vision and deep learning algorithms. The software secures your investment by supporting a wide range of operating systems and providing interfaces to hundreds of industrial cameras and frame grabbers, in particular by supporting standards like GenICam, GigE Vision, and USB3 Vision. By default, MVTec HALCON runs on Arm-based embedded vision platforms. It can also be ported to various target platforms. Thus, the software is ideally suited for the use within embedded and customized systems. http://www.halcon.com, http://www.embedded-vision-software.com

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MVTec further expands HALCON functionality with new deep ... - Robotics Tomorrow

Multimodal Deep Learning and its Applications

As humans, our perception of the world is through our senses. We identify objects or anything through vision, sound, touch, and odor. Our way of processing this sensory information is multimodal. Modality refers to the way something is recognized, experienced, and recorded. Multimodal deep learning is an extensive research branch in Deep learning that works on the fusion of multimodal data.

The human brain consists of millions of neural networks that process multiple modalities from the external world. It could be recognizing a persons body movements, tone of voice, or even mimicking sounds. For AI to interpret Human Intelligence, we need a reasonable fusion of multimodal data and this is done through Multimodal Deep Learning.

Multimodal Machine Learning is developing computer algorithms that learn and predict using Multimodal datasets.

Multimodal Deep learning is a subset of the machine learning branch. With this technology, AI models are trained to identify relationships between multiple modalities such as images, videos, and texts and provide accurate predictions. From identifying the relevant link between datasets, Deep Learning models will be able to capture any place's environment and a person's emotional state.

If we say, Unimodal models that interpret only a single dataset have proven efficient in computer vision and Natural Language Processing. Unimodal models have limited capabilities; in certain tasks, these models failed to recognize humor, sarcasm, and hate speech. Whereas, Multimodal learning models can be referred to as a combination of unimodal models.

Multimodal deep learning includes modalities like visual, audio, and textual datasets. 3D visual and LiDAR data are slightly used multimodal data.

Multimodal Learning models work on the fusion of multiple unimodal neural networks.

First unimodal neural networks process the data separately and encode them, later, the encoded data is extracted and fused. Multimodal data fusion is an important process carried out using multiple fusion techniques. Finally, with the fusion of multimodal data, neural networks recognize and predict the outcome of the input key.

For example, in any video, there might be two unimodal models visual data and audio data. The perfect synchronization of both unimodal datasets provides simultaneous working of both models.

Fusing multimodal datasets improves the accuracy and robustness of Deep learning models, enhancing their performance in real-time scenarios.

Multimodal Deep learning has potential applications in computer vision algorithms. Here are some of its applications;

The research to reduce human efforts and develop machines matching with human intelligence is enormous. This requires multimodal datasets that can be combined using Machine Learning and Deep Learning models, paving the way for more advanced AI tools.

The recent surge in the popularity of AI tools has brought more additional investments in Artificial Intelligence and Machine Learning technology. This is a great time to grab job opportunities by learning and upskilling yourself in Artificial Intelligence and Machine Learning.

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Multimodal Deep Learning - A Fusion of Multiple Modalities - NASSCOM Community

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Prediction prolonged mechanical ventilation in trauma patients of ... - Nature.com

Transport for NSW (TfNSW) is installing a number of machine learning traffic counting and classifying cameras across the state.

Images of heavy vehicles are taken by the cameras, which then classify the type of vehicle in transit and the type of cargo being transported.

In a bulletin to industry TfNSW said the information collected helps to shape the future of freight, to better understand freight movements, improve road safety, and enable more efficient deliveries.

The cameras are not used for enforcement or monitoring people or private vehicles, said TfNSW.

Truckies can expect to see cameras installed at the following locations over the coming weeks:

According to thefact sheet on the cameras webpage, there is a radar sensor and camera on the unit that takes a picture of the heavy vehicles when certain criteria are met.

After the picture is taken, artificial intelligence within the unit can tell the difference between different types of heavy vehicles, for example, a container carrying heavy vehicle, B-double or semi-trailer.

The units are also able to track changes in load. If a shipping container truck entered a location carrying one container and left with two containers the platform contains a record of this change.

Aside from the above locations, TfNSw says similar cameras can also be found at:

For more information, visit the Machine Learning cameras webpagewhich includes a factsheet with details about what these units are and what they do.

If you have any questions or would like more information, you can contact the TfNSW project team at freight@transport.nsw.gov.au.

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More 'machine learning' cameras to track truckies through NSW - Big Rigs