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MineOS Wins Award for Machine Learning in the 2024 AI Excellence Awards Program

BOSTON and TEL AVIV, Israel, March 20, 2024 /PRNewswire/ -- Mine, a pioneering company disrupting the data privacy market, today announced that the Business Intelligence Group has recognized the MineOS platform as a winner in the2024 Artificial Intelligence Excellence Awards in the Machine Learning category. This prestigious recognition serves as a testament toMineOS's tremendous growth in its mission to innovate and streamline data privacy and governance for the modern enterprise.

With the stack of applications and tools the average organization uses growing substantially in recent years, enterprises are struggling to locate, track, and monitor their data, especially with AI gaining more popularity. This unique challenge, coupled with new data regulations passing across the world, has placed significant demands on the need for an enterprise-grade platform with next-generation AI capabilities like MineOS.

Throughout the past year, the MineOS platform's unique AI-powered data discovery and classification has helped bring never before seen speed and visibility to privacy programs so companies can comply with an ever-evolving regulatory environment. Whereas most legacy privacy solutions require months and resource-intensive scans to compile accurate and continuous data maps, MineOS's machine learning (ML) capabilities dramatically cuts timelines for data inventory insights and accurately identifies and mitigates risks that can leave organizations vulnerable if left undiscovered.

"To be recognized by the Business Intelligence Group for all the work we've put into building our next-generation AI and ML technology to innovate and push data privacy forward is an incredible achievement," said Gal Ringel, CEO and Co-Founder of Mine. "We've always strived to adapt to customers' ever-changing needs in a world of more and more data privacy regulations, and we know this award further showcases our commitment to set a new privacy paradigm that will fit and empower both consumers and companies."

"We are truly honored to recognize Mine with this prestigious award," stated Maria Jimenez, Chief Nominations Officer for the Business Intelligence Group. "The unwavering commitment of their team to excellence and their innovative AI applications have catapulted them to this remarkable achievement. Congratulations to the entire organization!"

For more information on MineOS products and services, please visithttps://www.mineos.ai/.

About Mine

Powering the privacy programs of hundreds of organizations around the world, from the trail blazers of tomorrow to globally-recognized brands like Reddit and HelloFresh, Mine's mission is to not only give companies the best data privacy & governance tools around but to form meaningful privacy partnerships.From ensuring compliance with global regulations to providing continuous inventory discovery and classifications powered by seamless integrations, MineOS is at the forefront of making data privacy intuitive, automated, and scalable. Headquartered in Tel Aviv and spanning to Boston and Germany, Mine has secured $42.5M in funding from leading investors, including Google's AI fund, Battery Ventures, PayPal Ventures, US insurance giants MassMutual and Nationwide, and more as part of our journey to empower data rights and a more privacy-conscious internet.

About Business Intelligence Group

The Business Intelligence Group was founded with the mission of recognizing true talent and superior performance in the business world. Unlike otherindustry award programs, these programs are judged by business executives having experience and knowledge. The organization's proprietary and unique scoring system selectively measures performance across multiple business domains and then rewards those companies whose achievements stand above those of their peers.

Media Contacts

Mine

Danielle Scotto/ Michael Gallo

ICR-Lumina

[emailprotected]

Business Intelligence Group

Maria Jimenez

+1 909-529-2737

[emailprotected]

SOURCE Mine

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Mine Recognized with 2024 Artificial Intelligence Award from Business Intelligence Group - PR Newswire

What is Artificial Intelligence?

Artificial Intelligence (AI) is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action. AI includes machine learning, which is a set of techniques that can be used to train AI algorithms to improve performance of a task based on data.

AI has emerged as a transformative force. The Food and Drug Administration is responsible for protecting the public health by ensuring the safety, efficacy, and security of human and veterinary drugs, biological products, and medical devices; and by ensuring the safety of our nation's food supply, cosmetics, and products that emit radiation. This landing page serves as your gateway to a wealth of information, resources, and insights into the intersection of AI and medical products. Learn more about how the FDA is shaping the future of health care through the responsible and innovative integration of AI.

For specific information related to biological products, please visit:

For specific information related to drug products, please visit:

For specific information related to devices, please visit:

For more information on the laws, regulations, executive orders, and memoranda that drives HHS's AI efforts, please visit:

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Guidance documents, discussion papers, constituent updates, and reports.

03/20/2024

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Artificial Intelligence and Medical Products - FDA.gov

Giving artificial intelligence (AI) systems an "inner monologue" makes them considerably better at reasoning, new research shows.

The method trains AI systems to think before they respond to prompts, just as many people consider what we should say next before we speak. This is different from the way scientists have trained mainstay AI chatbots, like ChatGPT, which don't "think" about what they write or anticipate different possibilities for the next steps in a conversation.

Dubbed "Quiet-STaR," the new method instructs an AI system to generate many inner rationales in parallel before responding to a conversational prompt. When the AI answers prompts, it generates a mixture of these predictions with and without a rationale, printing the best answer which can be verified by a human participant depending on the nature of the question.

Finally, it learns by discarding rationales that proved incorrect. In effect, the training method gives AI agents the capacity to anticipate future conversations and learn from ongoing ones.

Related: AI singularity may come in 2027 with artificial 'super intelligence' sooner than we think, says top scientist

The researchers applied the Quiet-STaR algorithm to Mistral 7B, an open-source large language model (LLM), and posted the results March 14 to the pre-print database arXiv. (The paper has not yet been peer-reviewed.)

The Quiet-STaR-trained version of Mistral 7B scored 47.2% on a reasoning test versus 36.3% before any training. It still flunked a school math test, earning a score of 10.9%. But that was nearly double the starting score of 5.9% in the vanilla version.

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Models like ChatGPT and Gemini are built from neural networks collections of machine learning algorithms arranged in a way that mimics the structure and learning patterns of the human brain. However, systems built using this architecture are abysmal at common sense reasoning or contextualization and AI chatbots do not have genuine "understanding."

Past attempts to improve the reasoning capabilities of LLMs have been highly domain-specific and could not be applied to different types of AI models.

The self-taught reasoner (STaR) algorithm, which the researchers used as a basis for their work, is one example of such a training algorithm but is held back by these limitations.

The scientists who developed Quiet-STaR named it that because the principles of STaR can be applied quietly in the background and generally over several different types of LLM, independent of the original training data. Now they want to investigate how techniques like theirs can reduce the gap between neural network-based AI systems and human-like reasoning capabilities.

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Researchers gave AI an 'inner monologue' and it massively improved its performance - Livescience.com

March 19, 2024 - Mobile Industrial Robots A/S (MiR) announced the launch of the MiR1200 Pallet Jack autonomous mobile robot (AMR). With advanced artificial intelligence (AI) pallet detection, powered by NVIDIA Jetson AGX Orin, MiR1200 Pallet Jack uses 3D vision to identify pallets and do pick up and pallet delivery with unprecedented precision. The MiR1200 Pallet Jack is our latest effort to push the boundaries in autonomous material handling, said Mads Paulin, vice president of R&D at MiR, We believe that the built-in AI detection system is a significant improvement over older detection technologies. Our approach will reduce pick-and-place cycle times, deliver best-in-class pick accuracy and allow us to continuously deliver advanced, AI-based functionality and value to our customers.

Designed to integrate seamlessly into existing MiR AMR fleets and interoperate with MiRs deck load AMRs, the MiR1200 Pallet Jack is a perfect match for large-scale enterprise customers who typically operate more complex workflows with larger fleets and multiple sites. These customers can now turn to MiR as a one-stop shop for material handling. As the latest addition to MiRs wide range of AMRs, the MiR1200 Pallet Jack adds a new application area to our existing solutions for transportation scenarios from small to heavy loads, pallet transportation, and more, said Jean-Pierre Hathout, president of MiR. All robots can be seamlessly managed and integrated via our industry leading fleet management tool, MiR Fleet, and monitored and optimized using MiR Insights.

Thanks to its 3D vision capabilities, the MiR1200 Pallet Jack addresses the challenges many enterprises face with resource-intensive material handling, especially in complex environments that make automation difficult and where automation is needed due to increasing labor shortage. The MiR1200 Pallet Jack can dynamically modify its route to avoid obstacles such as loose objects on the floor or overhead obstacles. It processes a large number of cameras and LiDAR data in real time by accelerating the full stack on the GPU and several other processors built into the NVIDIA Jetson AGX Orin module. The MiR1200 Pallet Jacks ability to navigate effortlessly in tight spaces with minimal changes to the existing infrastructure makes it the perfect fit for optimizing logistics efficiency and ensuring timely delivery of pallets. With MiR1200 Pallet Jack, we have developed a rugged AMR pallet jack that will work in existing customer sites that present unique environmental challenges for automation, Hathout added. In the design of this robot, we have leveraged the accumulated expertise in software from MiR, and high-payload AMRs that are the result of MiRs 2022 merger with AutoGuide, another Teradyne-owned company. In addition, the MiR1200 Pallet Jacks robust tricycle drive system is developed through a partnership with Logitrans, which has 80+ years of experience with material handling.

The MiR1200 Pallet Jack is not only about speed and efficiency. Safety remains a top priority. Safety is in the DNA of every MiR product, Hathout said. The MiR1200 Pallet Jack complies with the latest product safety standards, including ISO3691-4. The fusion of multiple sensor platforms and top-tier safety features provides a much safer alternative to traditional forklifts, pallet trucks and manual pallet jacks. Key features of the MiR1200 Pallet Jack include:

Go to Mobile Industrial Robots (MiR) website

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MiR1200 Pallet Jack: Artificial Intelligence to Revolutionize Pallet Handling - Automation.com

Citizen scientists and artificial intelligence have come together to discover a staggering 430,000 galaxies scattered across the universe. The massive haul includes 30,000 ring galaxies, which are considered to be the rarest of all possible galaxy shapes.

The discoveries represent the first results from the "GALAXY CRUISE" citizen science project. They were delivered by 10,000 volunteers who scoured through data collected with the Subaru Telescope.

Subaru is an 8.2-meter, optical-infrared telescope located near the summit of the extinct volcano Maunakea on the island of Hawaii. This cutting-edge telescope collects a ton of incredible data so much so that astronomers struggle to sift through it all to spot and classify the shapes of new galaxies. That is where the 10,000 citizen scientists came in, but even this vast army of volunteers needed a little assistance. Hence, they turned to AI for a helping hand.

Related: Speck of light glimpsed by Hubble is truly an enormous old galaxy, James Webb Space Telescope reveals

"Although AI classification takes less than one hour even for 700,000 galaxies, this work cannot be done without the data collected by GALAXY CRUISE over the past two years," team leader and Waseda University researcher Rhythm Shimakawa said in a statement. "We would like to thank all the citizen astronomers who participated in the project. I hope to see more collaborative outcomes in the future."

Galaxies come in a range of shapes and sizes; often, these morphologies can reveal important galactic history. For instance, our own galaxy, the Milky Way, is a neat spiral galaxy. Spiral galaxies are named as such because of their long arms that wind out from a bright and dense central concentration of stars, gas and dust. This is the most common type of galaxy in the universe, accounting for approximately 70% to 80% of all galaxies

Far less common are ring galaxies, which account for no more than 1% to 3% of galaxies in the observable universe. In fact, in some estimates of ring galaxy populations, that number can fall as low as 0.1%.

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Ring galaxies get their name because they exhibit dense cores of ancient stars surrounded by distant rings of bright blue young stars. The first ring galaxy discovered by humanity was "Hoag's Object," identified in 1950.

To enable AI to make rapid classifications of galaxies, Shimakawa and colleagues first had to introduce it to catalogs of classifications made by humans. After familiarizing the AI with 20,000 galaxies classified by humans, they let it loose on 700,000 galaxies in the Subaru data.

The AI classified 400,000 galaxies as spiral galaxies and 30,000 of the galaxies as ring galaxies. This finally offers scientists a sample size of these rare galaxies that's large enough to start making a meaningful analyses of the realms.

The team determined that ring galaxies display characteristics that make them intermediates between spiral galaxies and what are known as elliptical galaxies, which have a less defined structure.

The current thinking is that they are created when two spiral galaxies slam together and merge. This violent process is known to wipe out the prominent features of spiral galaxies, particularly their distinctive spiral arms. The theory agrees with recent findings regarding ring galaxy formation, which suggest they are created in a special kind of collision.

When one galaxy hits another head-on like a bullet, it's thought to cause pulses that radiate out from the galactic center of the target galaxy, spurring a ring structure.

The team's findings not only broaden our understanding of an extremely rare galaxy type, but also demonstrate the power of AI to sort through vast catalogs of astronomical data.

The team's research was published in January in the journal Publications of the Astronomical Society of Japan.

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Citizen scientists and AI take a cosmic cruise to discover 430000 new galaxies - Space.com