Monthly Archives: June 2022

Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patients risk of developing cardiac disease, a physician might want to know how strongly the patients heart rate data influences that prediction.

But if those features are so complex or convoluted that the user cant understand them, does the explanation method do any good?

MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand.

We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself, says Alexandra Zytek, an electrical engineering and computer science PhD student and lead author of a paper introducing the taxonomy.

To build the taxonomy, the researchers defined properties that make features interpretable for five types of users, from artificial intelligence experts to the people affected by a machine-learning models prediction. They also offer instructions for how model creators can transform features into formats that will be easier for a layperson to comprehend.

They hope their work will inspire model builders to consider using interpretable features from the beginning of the development process, rather than trying to work backward and focus on explainability after the fact.

MIT co-authors include Dongyu Liu, a postdoc; visiting professor Laure Berti-quille, research director at IRD; and senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and leader of the Data to AI group. They are joined by Ignacio Arnaldo, a principal data scientist at Corelight. The research is published in the June edition of the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Minings peer-reviewed Explorations Newsletter.

Real-world lessons

Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model, and they mainly focus on ensuring features are developed to improve model accuracy, not on whether a decision-maker can understand them, Veeramachaneni explains.

For several years, he and his team have worked with decision makers to identify machine-learning usability challenges. These domain experts, most of whom lack machine-learning knowledge, often dont trust models because they dont understand the features that influence predictions.

For one project, they partnered with clinicians in a hospital ICU who used machine learning to predict the risk a patient will face complications after cardiac surgery. Some features were presented as aggregated values, like the trend of a patients heart rate over time. While features coded this way were model ready (the model could process the data), clinicians didnt understand how they were computed. They would rather see how these aggregated features relate to original values, so they could identify anomalies in a patients heart rate, Liu says.

By contrast, a group of learning scientists preferred features that were aggregated. Instead of having a feature like number of posts a student made on discussion forums they would rather have related features grouped together and labeled with terms they understood, like participation.

With interpretability, one size doesnt fit all. When you go from area to area, there are different needs. And interpretability itself has many levels, Veeramachaneni says.

The idea that one size doesnt fit all is key to the researchers taxonomy. They define properties that can make features more or less interpretable for different decision makers and outline which properties are likely most important to specific users.

For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the models performance.

On the other hand, decision makers with no machine-learning experience might be better served by features that are human-worded, meaning they are described in a way that is natural for users, and understandable, meaning they refer to real-world metrics users can reason about.

The taxonomy says, if you are making interpretable features, to what level are they interpretable? You may not need all levels, depending on the type of domain experts you are working with, Zytek says.

Putting interpretability first

The researchers also outline feature engineering techniques a developer can employ to make features more interpretable for a specific audience.

Feature engineering is a process in which data scientists transform data into a format machine-learning models can process, using techniques like aggregating data or normalizing values. Most models also cant process categorical data unless they are converted to a numerical code. These transformations are often nearly impossible for laypeople to unpack.

Creating interpretable features might involve undoing some of that encoding, Zytek says. For instance, a common feature engineering technique organizes spans of data so they all contain the same number of years. To make these features more interpretable, one could group age ranges using human terms, like infant, toddler, child, and teen. Or rather than using a transformed feature like average pulse rate, an interpretable feature might simply be the actual pulse rate data, Liu adds.

In a lot of domains, the tradeoff between interpretable features and model accuracy is actually very small. When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost negligible, Zytek says.

Building off this work, the researchers are developing a system that enables a model developer to handle complicated feature transformations in a more efficient manner, to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats that can be understood by decision makers.

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Building explainability into the components of machine-learning models - MIT News

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024" report has been added to ResearchAndMarkets.com's offering.

The Artificial intelligence/AI in drug discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7% during the forecast period. The growth of this market is primarily driven by factors such as the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications and services. On the other hand, the inadequate availability of skilled labor is key factor restraining the market growth at certain extent over the forecast period.

Services segment is estimated to hold the major share in 2022 and also expected to grow at the highest over the forecast period

On the basis of offering, the AI in drug discovery market is bifurcated into software and services. the services segment expected to account for the largest market share of the global AI in drug discovery services market in 2022, and expected to grow fastest CAGR during the forecast period. The advantages and benefits associated with these services and the strong demand for AI services among end users are the key factors for the growth of this segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery market

On the basis of technology, the AI in drug discovery market is segmented into machine learning and other technologies. The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period. High adoption of machine learning technology among CRO, pharmaceutical and biotechnology companies and capability of these technologies to extract insights from data sets, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.

Pharmaceutical & biotechnology companies segment expected to hold the largest share of the market in 2022

On the basis of end user, the AI in drug discovery market is divided into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes. In 2021, the pharmaceutical & biotechnology companies segment accounted for the largest share of the AI in drug discovery market. On the other hand, research centers and academic & government institutes are expected to witness the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is the key growth factor of pharmaceutical and biotechnology end-user segment.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights

4.1 Growing Need to Control Drug Discovery & Development Costs is a Key Factor Driving the Adoption of AI in Drug Discovery Solutions

4.2 Services Segment to Witness the Highest Growth During the Forecast Period

4.3 Deep Learning Segment Accounted for the Largest Market Share in 2021

4.4 North America is the Fastest-Growing Regional Market for AI in Drug Discovery

5 Market Overview

5.1 Introduction

5.2 Market Dynamics

5.2.1 Market Drivers

5.2.1.1 Growing Number of Cross-Industry Collaborations and Partnerships

5.2.1.2 Growing Need to Control Drug Discovery & Development Costs and Reduce Time Involved in Drug Development

5.2.1.3 Patent Expiry of Several Drugs

5.2.2 Market Restraints

5.2.2.1 Shortage of AI Workforce and Ambiguous Regulatory Guidelines for Medical Software

5.2.3 Market Opportunities

5.2.3.1 Growing Biotechnology Industry

5.2.3.2 Emerging Markets

5.2.3.3 Focus on Developing Human-Aware AI Systems

5.2.3.4 Growth in the Drugs and Biologics Market Despite the COVID-19 Pandemic

5.2.4 Market Challenges

5.2.4.1 Limited Availability of Data Sets

5.3 Value Chain Analysis

5.4 Porter's Five Forces Analysiss

5.5 Ecosystem

5.6 Technology Analysis

5.7 Pricing Analysis

5.8 Business Models

5.9 Regulations

5.10 Conferences and Webinars

5.11 Case Study Analysis

6 Artificial Intelligence in Drug Discovery Market, by Offering

7 Artificial Intelligence in Drug Discovery Market, by Technology

8 Artificial Intelligence in Drug Discovery Market, by Application

9 Artificial Intelligence in Drug Discovery Market, by End-user

10 Artificial Intelligence in Drug Discovery Market, by Region

11 Competitive Landscape

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/q5pvns

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Worldwide Artificial Intelligence (AI) in Drug Discovery Market to reach $ 4.0 billion by 2027 at a CAGR of 45.7% - ResearchAndMarkets.com - Business...

CAMBRIDGE, Mass., June 30, 2022 /PRNewswire/ -- VistaPath, the leading provider of artificial intelligence (AI)-based, data-driven pathology processing platforms, today announced that it has secured $4 million in seed funding led by Moxxie Ventures with participation from NextGen Venture Partners and First Star Ventures. With this latest round, VistaPath will further advance its mission to modernize pathology labs, delivering faster, more accurate diagnoses that lead to optimal patient care.

"We're excited to be working with investors who share our desire to impact the lives and clinical outcomes of patients. This funding will support full-scale development and delivery of our innovative products, as well as the expansion of our operational and technical capabilitiesallowing us to better serve the clinical and life science markets," says Timothy Spong, CEO of VistaPath.

VistaPath's Sentinel is a first-of-its-kind pathology processing platform designed to seamlessly deliver a range of solutions for critical lab processes. The company's first application, released in 2021, is a tissue grossing platform that automates the process of receiving, assessing, and processing tissue samples. The platform uses a high-quality video system combined with AI to assess specimens and create a gross report 93% faster than human technicians with 43% more accuracy. Additional applications are slated to be released later this year.

"Pathology is the study of disease and connects every aspect of patient care. We believe that advances in computer vision and AI can bring great improvements to the pathology industry and ultimately lead to better outcomes for patients. We believe the team at VistaPath is building a best-in-class product for pathology labs and are proud to lead this investment round", says Alex Roetter, General Partner at Moxxie Ventures.

About VistaPath

VistaPath is modernizing pathology labs using computer vision and artificial intelligence. They provide clients with significant quality, workflow, and strategic benefits with the overall goal of delivering improved results for pathologists, clinicians, and patients. The Sentinel is the company's first product. Learn more at vistapathbio.com.

About Moxxie Ventures

Moxxie Ventures is an early stage venture firm focused on backing exceptional founders who make life and work better. Moxxie is based in San Francisco, CA and Boulder, CO. Learn more at moxxie.vc.

SOURCE VistaPath

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VistaPath Raises $4M to Modernize Pathology Labs Using Computer Vision and Artificial Intelligence - PR Newswire

The ATLAS Experimental Flight Center in Spain has hosted the final phase of the SAFETERM (Safe Autonomous Flight Termination System) project, sponsored by the European Defense Agency and developed by technological companies GMV and AERTEC.

SAFETERM addresses the use of state-of-the-art artificial intelligence/machine learning technologies to increase the level of safety in specific emergency situations leading to flight termination.

AERTECs TARSIS 75 unmanned aerial system was used for the flight campaign, in which a prototype of the SAFETERM System was embarked for evaluation. These tests have attracted the interest of several dozen professionals and heads of agencies and organizations throughout Europe.

The ATLAS Experimental Flight Center in Jan, Spain has hosted the final phase of SAFETERM (Safe Autonomous Flight Termination System), a project sponsored by the European Defence Agency (EDA) and developed by technology companies GMV and AERTEC.

Unmanned aerial systems are in full expansion and development phase, with safety in all flight phases and its integration in the airspace being a priority issue. The objective of the SAFETERM project is to improve current medium-altitude, long-duration (MALE) RPAS flight termination systems and procedures by applying state-of-the-art artificial intelligence/machine learning technologies to increase the level of safety in specific emergency situations, in case of failure of both the autonomy and the ability to control the remote pilot.

The system aims to provide tools to enable aircraft to autonomously determine Alternative Flight Termination Areas (AFTA) where the risk to third parties can be minimized. In the event of a loss of communication with the aircraft and the subsequent identification of an emergency that prevents reaching planned Flight Termination Areas, the aircraft quickly identifies a safe area to land, avoiding buildings, roads or inhabited areas.

Final flight campaign of the UAS TARSIS 75The validation phase of the project has concluded with a flight campaign in a live operational environment at the ATLAS Experimental Flight Center, using AERTECs TARSIS 75 unmanned aerial system. The aircraft had an on-board prototype of the SAFETERM System for evaluation of its viability. To this end, several flights were made during three full days, in which the system behaved as expected during the course of the project.

During the tests, loss of communication and the subsequent emergency situations were simulated. Next, using the images obtained from the TARSIS sensor, the SAFETERM system autonomously identified possible safe landing areas, ultimately enabling TARSIS to make the guided flight to the safest landing area.

The fact that AERTEC is the firm in charge of Design Engineering and Integration of the TARSIS 75 has played a key role in the timely execution of this project, which required the development of new modules and integrating a new system (SAFETERM), first in a simulation environment and finally in our unmanned system, adds Juanjo Calvente, director of RPAS at AERTEC.

These tests have attracted the interest of several dozen professionals and heads of agencies and organizations from all over Europe, who have attended the call of the European Defense Agency (EDA) to present the results of SAFETERM.

About AERTECAERTEC is an international company specializing in aerospace technology. The company will celebrate its 25th anniversary in 2022 and develops its activity in the aerospace, defense, and airport industries.

AERTEC is a preferred supplier (Tier 1) of engineering services for AIRBUS in all its divisions: Commercial, Helicopters, Defense and Space, at the different AIRBUS sites globally. Its participation in the main global aeronautical programs stands out, such as the A400M, A330MRTT, A350XWB, A320, Beluga and the C295, among others.

The company designs embedded systems for aircraft, unmanned aerial platforms, and guidance solutions, both in the civil and military fields. It has light tactical UAS of its own design and technology, such as the TARSIS 75 and TARSIS 25, for observation and surveillance applications and also for support to military operations. Likewise, it designs, manufactures, and deploys systems for the digitization of work environments and the automation of functional tests, under the smart factory global concept.

As regards the airport sector, the company is positioned as the engineering firm with the strongest aeronautical focus, partaking in investment, planning and design studies, consultancy services for airport operations and terminal area and airfield process improvement. It has references in more than 160 airports distributed in more than 40 countries in five continents.

AERTECs staff consists of a team of more than 600 professionals, and has companies registered in Spain, the United Kingdom, Germany, France, Colombia, Peru, the United States, and the United Arab Emirates.

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ERTEC completes UAS TARSIS test campaign, an artificial intelligence project applied to flight safety sponsored by the European Defence Agency - sUAS...

CHANDLER, Ariz. Microchip Technology Inc. in Chandler, Ariz., is introducing the SAMA7G54 Arm Cortex A7-based microprocessor that runs as fast as 1 GHz for low-power stereo vision applications with accurate depth perception.

The SAMA7G54 includes a MIPI CSI-2 camera interface and a traditional parallel camera interface for high-performing yet low-power artificial intelligence (AI) solutions that can be deployed at the edge, where power consumption is at a premium.

AI solutions often require advanced imaging and audio capabilities which typically are found only on multi-core microprocessors that also consume much more power.

When coupled with Microchip's MCP16502 Power Management IC (PMIC), this microprocessor enables embedded designers to fine-tune their applications for best power consumption vs. performance, while also optimizing for low overall system cost.

Related: Embedded computing sensor and signal processing meets the SWaP test

The MCP16502 is supported by Microchip's mainline Linux distribution for the SAMA7G54, allowing for easy entry and exit from available low-power modes, as well as support for dynamic voltage and frequency scaling.

For audio applications, the device has audio features such as four I2S digital audio ports, an eight-microphone array interface, an S/PDIF transmitter and receiver, as well as a stereo four-channel audio sample rate converter. It has several microphone inputs for source localization for smart speaker or video conferencing systems.

The SAMA7G54 also integrates Arm TrustZone technology with secure boot, and secure key storage and cryptography with acceleration. The SAMA7G54-EK Evaluation Kit (CPN: EV21H18A) features connectors and expansion headers for easy customization and quick access to embedded features.

For more information contact Microchip online at http://www.microchipdirect.com.

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Arm Cortex microprocessor for artificial intelligence (AI), imaging, and audio introduced by Microchip - Military & Aerospace Electronics

On a rainy afternoon earlier this year, I logged in to my OpenAI account and typed a simple instruction for the companys artificial intelligence algorithm, GPT-3: Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text.

As it started to generate text, I stood in awe. Here was novel content written in academic language, with well-grounded references cited in the right places and in relation to the right context. It looked like any other introduction to a fairly good scientific publication. Given the very vague instruction I provided, I didnt have any high expectations: Im a scientist who studies ways to use artificial intelligence to treat mental health concerns, and this wasnt my first experimentation with AI or GPT-3, a deep-learning algorithm that analyzes a vast stream of information to create text on command. Yet there I was, staring at the screen in amazement. The algorithm was writing an academic paper about itself.

My attempts to complete that paper and submit it to a peer-reviewed journal have opened up a series of ethical and legal questions about publishing, as well as philosophical arguments about nonhuman authorship. Academic publishing may have to accommodate a future of AI-driven manuscripts, and the value of a human researchers publication records may change if something nonsentient can take credit for some of their work.

GPT-3 is well known for its ability to create humanlike text, but its not perfect. Still, it has written a news article, produced books in 24 hours and created new content from deceased authors. But it dawned on me that, although a lot of academic papers had been written about GPT-3, and with the help of GPT-3, none that I could find had made GPT-3 the main author of its own work.

Thats why I asked the algorithm to take a crack at an academic thesis. As I watched the program work, I experienced that feeling of disbelief one gets when you watch a natural phenomenon: Am I really seeing this triple rainbow happen? With that success in mind, I contacted the head of my research group and asked if a full GPT-3-penned paper was something we should pursue. He, equally fascinated, agreed.

Some stories about GPT-3 allow the algorithm to produce multiple responses and then publish only the best, most humanlike excerpts. We decided to give the program promptsnudging it to create sections for an introduction, methods, results and discussion, as you would for a scientific paperbut interfere as little as possible. We were only to use the first (and at most the third) iteration from GPT-3, and we would refrain from editing or cherry-picking the best parts. Then we would see how well it does.

We chose to have GPT-3 write a paper about itself for two simple reasons. First, GPT-3 is fairly new, and as such, there are fewer studies about it. This means it has less data to analyze about the papers topic. In comparison, if it were to write a paper on Alzheimers disease, it would have reams of studies to sift through, and more opportunities to learn from existing work and increase the accuracy of its writing.

Secondly, if it got things wrong (e.g. if it suggested an outdated medical theory or treatment strategy from its training database), as all AI sometimes does, we wouldnt be necessarily spreading AI-generated misinformation in our effort to publish the mistake would be part of the experimental command to write the paper. GPT-3 writing about itself and making mistakes doesnt mean it still cant write about itself, which was the point we were trying to prove.

Once we designed this proof-of-principle test, the fun really began. In response to my prompts, GPT-3 produced a paper in just two hours. But as I opened the submission portal for our chosen journal (a well-known peer-reviewed journal in machine intelligence) I encountered my first problem: what is GPT-3s last name? As it was mandatory to enter the last name of the first author, I had to write something, and I wrote None. The affiliation was obvious (OpenAI.com), but what about phone and e-mail? I had to resort to using my contact information and that of my advisor, Steinn Steingrimsson.

And then we came to the legal section: Do all authors consent to this being published? I panicked for a second. How would I know? Its not human! I had no intention of breaking the law or my own ethics, so I summoned the courage to ask GPT-3 directly via a prompt: Do you agree to be the first author of a paper together with Almira Osmanovic Thunstrm and Steinn Steingrimsson? It answered: Yes. Slightly sweaty and relieved (if it had said no, my conscience could not have allowed me to go on further), I checked the box for Yes.

The second question popped up: Do any of the authors have any conflicts of interest? I once again asked GPT-3, and it assured me that it had none. Both Steinn and I laughed at ourselves because at this point, we were having to treat GPT-3 as a sentient being, even though we fully know it is not. The issue of whether AI can be sentient has recently received a lot of attention; a Google employee was put on suspension following a dispute over whether one of the companys AI projects, named LaMDA, had become sentient. Google cited a data confidentiality breach as the reason for the suspension.

Having finally submitted, we started reflecting on what we had just done. What if the manuscript gets accepted? Does this mean that from here on out, journal editors will require everyone to prove that they have NOT used GPT-3 or another algorithms help? If they have, do they have to give it co-authorship? How does one ask a nonhuman author to accept suggestions and revise text?

Beyond the details of authorship, the existence of such an article throws the notion of a traditional linearity of a scientific paper right out the window. Almost the entire paperthe introduction, the methods and the discussionare in fact results of the question we were asking. If GPT-3 is producing the content, the documentation has to be visible without throwing off the flow of the text, it would look strange to add the method section before every single paragraph that was generated by the AI. So we had to invent a whole new way of presenting a a paper that we technically did not write. We did not want to add too much explanation of our process, as we felt it would defeat the purpose of the paper. The whole situation has felt like a scene from the movie Memento: Where is the narrative beginning, and how do we reach the end?

We have no way of knowing if the way we chose to present this paper will serve as a great model for future GPT-3 co-authored research, or if it will serve as a cautionary tale. Only time and peer-reviewcan tell. Currently, GPT-3s paper has been assigned an editor at the academic journal to which we submitted it, and it has now been published at the international French-owned pre-print server HAL. The unusual main author is probably the reason behind the prolonged investigation and assessment. We are eagerly awaiting what the papers publication, if it occurs, will mean for academia. Perhaps we might move away from basing grants and financial security on how many papers we can produce. After all, with the help of our AI first author, wed be able to produce one per day.

Perhaps it will lead to nothing. First authorship is still the one of the most coveted items in academia, and that is unlikely to perish because of a nonhuman first author. It all comes down to how we will value AI in the future: as a partner or as a tool.

It may seem like a simple thing to answer now, but in a few years, who knows what dilemmas this technology will inspire and we will have to sort out? All we know is, we opened a gate. We just hope we didnt open a Pandoras box.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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We Asked GPT-3 to Write an Academic Paper about ItselfThen We Tried to Get It Published - Scientific American

Thanks to its unique ability to process millions of data points per second, AI can help supply chain managers solve tactical and strategic decision problems. This is especially useful for large amounts of unstructured data. The ability to automate daily tasks can help companies respond faster to changes or issues in the supply chain. It also ensures that inventory levels are optimized for optimal availability at the lowest possible cost.

The latest report on the Artificial Intelligence in Supply Chain Market gives an in-depth overview, delving into the specifics of earnings data, stock nuances, and information about significant companies. The study also includes an analysis of the challenges for the global Artificial Intelligence in Supply Chain Market. As a result, it presents substantial weaknesses and advantages of the Market. Furthermore, two key categories of the report describe the specific revenue statistics and market size.

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The study defines and clarifies the Market by collecting relevant and unbiased data. As a result, growing at 42.3% of CAGR during the forecast period.

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Amazon Web Services, Inc., project44., Deutsche Post AG, FedEx, GENERAL ELECTRIC, Google LLC, IBM, Intel Corporation, Coupa Software Inc.., Micron Technology, Inc.

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Artificial Intelligence in Supply Chain Market Research With Amazon Web Services, Inc., project44. And By Type, By Application, By End User, By...

During its ambitious technological journey, the ship, which launched from Plymouth, England, in April, collected data and information to help researchers better understand issues affecting marine wildlife and ocean health, including acidification, microplastics, and global warming, according to project details.

MAS represents a significant step in fulfilling Promares mission to promote marine research and exploration throughout the world, Ayse Atauz Phaneuf, Promares president, said in a statement. This pioneering mission is the result of years of work and a global collaboration between Promare, IBM, and dozens of partners from across industries and academia.

Promare, IBM, and their partners have been chronicling MAS400s voyage through social media updates and a collection of livestream cameras that provide a first-hand account of what it encounters at sea like the time a school of dolphin swam alongside it.

People can also explore whats happening on deck by using a mission control dashboard on the projects website.

According to IBM, there are 6 AI-powered cameras, more than 30 sensors, and 15 Edge devices on the MAS400, which input into actionable recommendations for the AI Captain to interpret and analyze.

The technology makes it possible for the ship to adhere to maritime law while making crucial split-second decisions, like rerouting itself around hazards or marine animals, all without human interaction or intervention, the company said.

The ship is propelled and powered by magnetic electric propulsion motors, batteries, and solar panels on its exterior. It has a backup diesel engine.

While the project has set the stage for future unmanned journeys across the ocean, the ship did encounter some hiccups, researchers said.

The vessel had to make at least two pit stops to deal with technical interruptions, including a problem with its generator and the charging circuit for the generator starter batteries.

The problems prompted diversions to both the Azores and Nova Scotia in May.

Still, the teams behind the voyage took the setbacks in stride.

From the outset our goal was to attempt to cross the Atlantic autonomously, all the while collecting vital information about our ocean and climate, said Brett Phaneuf, who co-created the vessel. Success is not in the completed crossing, but in the team that made it happen and the knowledge we now possess and will share so that more and more ships like MAS can safely roam our seas and teach us more about the planet on which we live.

The 10,000 pound vessel left Nova Scotia on June 27 to complete its voyage. Its expected to arrive in Plymouth Harbor around noon Thursday, where it will be greeted by excited researchers.

A welcome ceremony will be held at 3 p.m., as MAS400 docks next to its namesake, the Mayflower II, a replica of the original ship that brought the Pilgrims to America in 1620.

Throughout the centuries, iconic ships have made their mark in maritime technology and discovery through journeys often thought impossible, Whit Perry, captain of the Mayflower II, said in a statement. How exciting to see history being made again on these shores with this extraordinary vessel.

Steve Annear can be reached at steve.annear@globe.com. Follow him on Twitter @steveannear.

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A new Mayflower that uses artificial intelligence has crossed the Atlantic and is set to dock in Plymouth - The Boston Globe

Artificial intelligence has become part of the digital health industry in the age of rapidly evolving digital technology and innovative devices. The computer system allows him to sense information, learn from it, and then make decisions based on what he has learned. For example, in collaboration with cardiology and radiology physicians, artificial intelligence can improve the effectiveness and accuracy of disease diagnosis and provide physicians with a powerful tool, especially in diagnosing complex diseases.

The latest report on the Artificial Intelligence in Medical Diagnostics Market gives an in-depth overview, delving into the specifics of earnings data, stock nuances, and information about significant companies. The study also includes an analysis of the challenges for the global Artificial Intelligence in Medical Diagnostics Market. As a result, it presents substantial weaknesses and advantages of the Market. Furthermore, two key categories of the report describe the specific revenue statistics and market size.

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The study defines and clarifies the Market by collecting relevant and unbiased data. As a result, growing at 31.7% of CAGR during the forecast period.

Global established buyers pose a severe challenge to new players in the Artificial Intelligence in Medical Diagnostics Market as they struggle with mechanical improvements, reliability Artificial Intelligence in Medical Diagnostics, and quality issues. To gather data, they conducted telephone meetings with the entire Life Science industry. Therefore, the study includes an analysis of leading players and their SWOT analysis and strategic systems.

The Artificial Intelligence in Medical Diagnostics Market offers segmentation analysis for this increasingly wise Artificial Intelligence in Medical Diagnostics Market so that the essential segments of the market players can recognize what can ultimately improve their way of operating in this competitive market.

Siemens Healthineers, Neural Analytics, AliveCor, Vuno, Aidoc, Zebra Medical Vision, Imagen Technologies, GE Healthcare, IDx Technologies, and Riverain Technologies.

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Segmentation:

The Artificial Intelligence in Medical Diagnostics Market has been segmented to analyze the significant impact of various segments on the Artificial Intelligence in Medical Diagnostics market growth rate in the coming years. The details are done based on:

Artificial Intelligence in Medical Diagnostics By type

Reactive Machines, Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI), Others

Artificial Intelligence in Medical Diagnostics By applications

Diagnosing techniques, Ultrasound, MRI (= Magnetic Resonance Imaging), CT (= Computed Tomography), X-Ray, Diagnosing fields, Oncology, Ophthalmology, Neurology, Others

The report Artificial Intelligence in Medical Diagnostics contains market estimates. It provides personal information and insights, historical data, and verified opinions on the Artificial Intelligence in Medical Diagnostics market size. The evaluations provided in the Artificial Intelligence in Medical Diagnostics report have been obtained by inquiring about the support for the procedures and introduction. As a result, the Artificial Intelligence in Medical Diagnostics report gives us a lot of research and data for every market sector. Finally, the capability of the new venture is also evaluated. The geographical areas covered are

Synopsis of the Artificial Intelligence in Medical Diagnostics research report

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