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Federated, distributed, and few-shot learning can make consumers direct participants in Artificial Intelligence processes

NEW YORK, May 25, 2022 /PRNewswire/ -- Artificial Intelligence (AI) is all around us, but the processes of inference and learning that form the backbone of AI typically take place in big servers, far removed from consumers. New models are changing all that, according to ABI Research, a global technology intelligence firm, as the more recent frameworks of Federated Learning, Distributed Learning, and Few-shot Learning can be deployed directly on consumers' devices that have lower compute and smaller power budget, bringing AI to end users.

"This is the direction the market has increasingly been moving to, though it will take some time before the full benefits of these approaches become a reality, especially in the case of Few-Shot Learning, where a single individual smartphone would be able to learn from the data that it is itself collecting. This might well prove to be an attractive proposition for many, as it does not involve uploading data onto a cloud server, making for more secure and private data. In addition, devices can be highly personalized and localized as they can possess high situational awareness and better understanding of the local environments," explains David Lobina, Research Analyst at ABI Research.

ABI Research believes that it will take up to 10 years for such on-device learning and inference to be operative, and these will require adopting new technologies, such as neuromorphic chips. The shift will take place in more powerful consumer devices, such as autonomous vehicles and robots, before making its way into the likes of smartphones, wearables, and smart home devices. Big players such as Intel, NVIDIA, and Qualcomm have been working on these models in recent years, which in addition to neuromorphic chipset players such as BrainChip and GrAI Matter Labs, have provided chips that offer improved performance on a variety of training and inference tasks. The take-up is still small, but it can potentially disrupt the market.

"Indeed, these learning models have the potential to revolutionize a variety of sectors, most probably the fields of autonomous driving and the deployment of robots in public spaces, both of which are currently difficult to pull off, particularly in co-existence with other users," Lobina concludes. "Federated Learning, Distributed Learning, and Few-shot Learning reduces the reliance on cloud infrastructure, allowing AI implementers to create low latency, localized, and privacy preserving AI that can deliver much better user experience for end users."

These findings are from ABI Research's Federated, Distributed and Few-Shot Learning: From Servers to Devicesapplication analysis report.This report is part of the company'sAI and Machine Learningresearch service, which includes research, data, and ABI Insights. Application Analysisreports present in-depth analysis on key market trends and factors for a specific technology.

About ABI ResearchABI Research is a global technology intelligence firm delivering actionable research and strategic guidance to technology leaders, innovators, and decision makers around the world. Our research focuses on the transformative technologies that are dramatically reshaping industries, economies, and workforces today.

ABI Research

For more information about ABI Research's services, contact us at +1.516.624.2500 in the Americas, +44.203.326.0140 in Europe, +65.6592.0290 in Asia-Pacific, or visitwww.abiresearch.com.

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Global Deborah PetraraTel: +1.516.624.2558[emailprotected]

SOURCE ABI Research

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More Than 2 Billion Shipments of Devices with Machine Learning will Bring On-Device Learning and Inference Directly to Consumers by 2027 - PR Newswire

In this free webinar, learn how deep learning artificial intelligence (AI) can be used in histology or pathology image analysis. Attendees will learn how AI augments preclinical investigation workflows. The featured speaker will discuss case studies of CROs, pharma and biotech and the benefits they experienced from using AI software. The speaker will also discuss how to create AI models without the need for coding for any image analysis task in histology or pathology.

TORONTO (PRWEB) May 25, 2022

The preclinical phase of drug discovery commonly includes going through numerous histopathological samples. This contributes to the drug development process being time-consuming and labour-intensive for pharmaceutical and contract research organizations. Difficulties also stem from having to detect very subtle changes with high precision and accuracy. Manual quantification of small changes, or specific cell counting, is not only cumbersome but also, often involves high costs.

The digitization of glass slides has paved the way for even more advanced technology like artificial intelligence (AI), to further advance image analysis in a variety of medical fields. AI-based methods have the potential to standardize slide review by reducing bias while increasing the speed and accuracy of analysis.

In this webinar, the featured speaker discusses utilizing a cloud-based software from Aiforia Technologies, for automating image analysis tasks with AI to enhance the CRO's work by providing higher quality data and therefore confidence in this data to their clients across pharmaceutical companies. Through the software, the speaker created several AI models for assessing different markers from the central nervous system (CNS) tissue.

Join this webinar to hear case studies with large pharmaceutical and biotechnology clients, discover ways to harness artificial intelligence for higher quality data in preclinical trials and translational research and discuss how deep learning augments workflows, providing quantifiable benefits from CRO to client.

Join Tate York, Director of Digital Image and Analysis, NSA Labs, for the live webinar on Monday, June 13, 2022, at 12pm EDT (9am PDT).

For more information, or to register for this event, visit Harnessing Artificial Intelligence for Higher Quality Data in Preclinical Trials and Translational Research.

ABOUT XTALKS

Xtalks, powered by Honeycomb Worldwide Inc., is a leading provider of educational webinars to the global life science, food and medical device community. Every year, thousands of industry practitioners (from life science, food and medical device companies, private & academic research institutions, healthcare centers, etc.) turn to Xtalks for access to quality content. Xtalks helps Life Science professionals stay current with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.

To learn more about Xtalks visit http://xtalks.comFor information about hosting a webinar visit http://xtalks.com/why-host-a-webinar/

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A drone developed by AI company Qrious and MAUI63has been using artificial intelligence to recognise a Mui dolphin and follow them. Similar gear - only fixed cameras - will eventually be used on 300 fishing vessels.

"This will improve trust and accountability in the seafood sector. It will further burnish their credentials," said Oceans and Fisheries Minister David Parker.

Qrious, which is part of Spark, has been appointed to manage the rollout and supply the technology.

Qrious CEO Stephen Ponsford said the gear could detect things such as fishing dumping, nets or lines being retrieved, and sorting of fish.

"You can think of it as a human doing a new job. We simply need to train what to do and the system is fully capable of learning that," Ponsford said.

This means only pertinent footage will be kept, saving the laborious task of trawling through hundreds of hours of footage.

"This will absolutely save time in the review process," said Bubba Cook, WWF Western and Central Pacific tuna programme manager.

Cook was consulted on the plan.

"This could be groundbreaking. It's probably the most advanced system that's being proposed for electronic monitoring on fishing vessels around the world," Cook said.

The rollout has seen years of controversy and interference.

In 2016, Newshub revealed Operation Achilles, a report detailing widespread illegal fish dumping where MPI failed to prosecute.

In 2017, then-Minister Nathan Guy then promised cameras on "every boat", but it didn't happen.

In 2019, Stuart Nash said 20 boats in Mui dolphin habitat would get cameras.

In 2020, he delayed a wider rollout, with Nash stating in a recording Newshub obtained that NZ First didn't want them.

Then, new Minister David Parker said 300 vessels would get cameras by late 2024.

Finally there's momentum, with 50 of the 300 vessels - ones that work in Mui dolphin and yellow-eyed penguin habitats - to be fitted with the equipment and start transmitting data to MPI from late November.

"It's quite a sophisticated project so it's taken a while to put together," Parker said.

A project that will eventually accurately assess catch records and give detailed data about the plight of some of our most threatened species.

The cost of the rollout - which will be staggered - is estimated to be $68 million, with the industry paying back about $10 million of the total costs.

Cameras will be fitted to all vessels that use set nets and are eight metres or larger, surface longline vessels, and bottom longline boats. Trawlers of 32 metres or less are also included.

Originally posted here:
Artificial intelligence to be used in monitoring illegal fishing in New Zealand - Newshub

May 23, 2022

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On May 12, 2022, more than six months after the Equal Employment Opportunity Commission (EEOC) announced its Initiative on Artificial Intelligence and Algorithmic Fairness,[1] the agency issued its first guidance regarding employers use of Artificial Intelligence (AI).[2]

The EEOCs guidance outlines best practices and key considerations that, in the EEOCs view, help ensure that employment tools do not disadvantage applicants or employees with disabilities in violation of the Americans with Disabilities Act (ADA). Notably, the guidance came just one week after the EEOC filed a complaint against a software company alleging intentional discrimination through applicant software under the Age Discrimination in Employment Act (ADEA), potentially signaling more AI and algorithmic-based enforcement actions to come.

The EEOCs AI Guidance

The EEOCs non-binding, technical guidance provides suggested guardrails for employers on the use of AI technologies in their hiring and workforce management systems.

Broad Scope. The EEOCs guidance encompasses a broad-range of technology that incorporates algorithmic decision-making, including automatic resume-screening software, hiring software, chatbot software for hiring and workflow, video interviewing software, analytics software, employee monitoring software, and worker management software.[3] As an example of such software that has been frequently used by employers, the EEOC identifies testing software that provides algorithmically-generated personality-based job fit or cultural fit scores for applicants or employees.

Responsibility for Vendor Technology. Even if an outside vendor designs or administers the AI technology, the EEOCs guidance suggests that employers will be held responsible under the ADA if the use of the tool results in discrimination against individuals with disabilities. Specifically, the guidance states that employers may be held responsible for the actions of their agents, which may include entities such as software vendors, if the employer has given them authority to act on the employers behalf.[4] The guidance further states that an employer may also be liable if a vendor administering the tool on the employers behalf fails to provide a required accommodation.

Common Ways AI Might Violate the ADA. The EEOCs guidance outlines the following three ways in which an employers tools may, in the EEOCs view, be found to violate the ADA, although the list is non-exhaustive and intended to be illustrative:

Tips for Avoiding Pitfalls. In addition to illustrating the agencys view of how employers may run afoul of the ADA through their use of AI and algorithmic decision-making technology, the EEOCs guidance provides several practical tips for how employers may reduce the risk of liability. For example:

Enforcement Action

As previewed above, on May 5, 2022just one week before releasing its guidancethe EEOC filed a complaint in the Eastern District of New York alleging that iTutorGroup, Inc., a software company providing online English-language tutoring to adults and children in China, violated the ADEA.[11]

The complaint alleges that a class of plaintiffs were denied employment as tutors because of their age. Specifically, the EEOC asserts that the companys application software automatically denied hundreds of older, qualified applicants by soliciting applicant birthdates and automatically rejecting female applicants age 55 or older and male applicants age 60 or older. The complaint alleges that the charging party was rejected when she used her real birthdate because she was over the age of 55 but was offered an interview when she used a more recent date of birth with an otherwise identical application. The EEOC seeks a range of damages including back wages, liquidated damages, a permanent injunction enjoining the challenged hiring practice, and the implementation of policies, practices, and programs providing equal employment opportunities for individuals 40 years of age and older. iTutorGroup has not yet filed a response to the complaint.

Takeaways

Given the EEOCs enforcement action and recent guidance, employers should evaluate their current and contemplated AI tools for potential risk. In addition to consulting with vendors who design or administer these tools to understand the traits being measured and types of information gathered, employers might also consider reviewing their accommodations processes for both applicants and employees.

___________________________

[1] EEOC, EEOC Launches Initiative on Artificial Intelligence and Algorithmic Fairness (Oct.28, 2021), available at https://www.eeoc.gov/newsroom/eeoc-launches-initiative-artificial-intelligence-and-algorithmic-fairness.

[2] EEOC, The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees (May 12, 2022), available at https://www.eeoc.gov/laws/guidance/americans-disabilities-act-and-use-software-algorithms-and-artificial-intelligence?utm_content=&utm_medium=email&utm_name=&utm_source=govdelivery&utm_term [hereinafter EEOC AI Guidance].

[3] Id.

[4] Id. at 3, 7.

[5] Id. at 11.

[6] Id. at 13.

[7] Id. at 14.

[8] For more information, please see Gibson Dunns Client Alert, New York City Enacts Law Restricting Use of Artificial Intelligence in Employment Decisions.

[9] EEOC AI Guidance at 14.

[10] Id.

[11] EEOC v. iTutorGroup, Inc., No. 1:22-cv-02565 (E.D.N.Y. May 5, 2022).

The following Gibson Dunn attorneys assisted in preparing this client update: Harris Mufson, Danielle Moss, Megan Cooney, and Emily Maxim Lamm.

Gibson Dunns lawyers are available to assist in addressing any questions you may have regarding these developments. To learn more about these issues, please contact the Gibson Dunn lawyer with whom you usually work, any member of the firmsLabor and Employmentpractice group, or the following:

Harris M. Mufson New York (+1 212-351-3805, hmufson@gibsondunn.com)

Danielle J. Moss New York (+1 212-351-6338, dmoss@gibsondunn.com)

Megan Cooney Orange County (+1 949-451-4087, mcooney@gibsondunn.com)

Jason C. Schwartz Co-Chair, Labor & Employment Group, Washington, D.C.(+1 202-955-8242, jschwartz@gibsondunn.com)

Katherine V.A. Smith Co-Chair, Labor & Employment Group, Los Angeles(+1 213-229-7107, ksmith@gibsondunn.com)

2022 Gibson, Dunn & Crutcher LLP

Attorney Advertising: The enclosed materials have been prepared for general informational purposes only and are not intended as legal advice.

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Keeping Up with the EEOC: Artificial Intelligence Guidance and Enforcement Action - Gibson Dunn

In financial services, it is important to gain any competitive advantage. Your competition has access to most of the same data you do, as historical data is available to everyone in your industry. Your advantage comes with the ability to exploit that data better, faster, and more accurately than your competitors. With a rapidly fluctuating market, the ability to process data faster gives you the opportunity to respond quicker than ever before. This is where AI-first intelligence can give you the leg up.

To implement AI infrastructure there are some key considerations to maximize your return on investment (ROI).

When designing for high utilization workloads like AI for financial analytics, it is best practice to keep systems on premise. On premise computing is more cost effective than cloud-based computing when highly utilized. Cloud service costs can add up quickly and any cloud outages inevitably leads to downtime.

You can leverage a range of networking options, but we typically recommend high speed fabrics like 100 gig Ethernet or 200 gig HDR InfiniBand.

You should also consider that the size of your data set is just as important as the quality of your model. So, you will want to allow for a modern AI focused storage design. This will allow you to scale as needed to maximize your ROI

It is also important to keep primary storage close to on premise computing resources to maximize network bandwidth while limiting latency. Keeping storage on premise also keeps your sensitive data safe. Let us look at how storage should be set up to maximize efficiency.

Traditional storage, like NAS (Network Attached Storage), cannot keep up. Bandwidth is limited to around 10 gigabits per second, and it is not scalable enough for AI workloads. Fast local storage does not work for modern parallel problems because it results in constantly copying data in and out of nodes which clogs the network.

AI optimized storage should be parallel and support a single namespace data lake. This enables the storage to deliver large data sets to compute nodes for model training.

Your AI optimized storage must also support high bandwidth fabrics. A good storage solution should enable object storage tiering to remain cost effective, and to serve as an affordable long term scale storage option for regulatory retention requirements.

With AI and machine learning, you can significantly reduce the number of false positives, leading to higher customer satisfaction. Automating minor insurance claims can often now be done by AI, allowing employees to focus on larger and more complex issues.

AI can also be used to review claims or flag cases for more thorough, in-depth analysis by detecting potential fraud or human error. Regular tasks prone to human error can either be reviewed, or in many cases performed entirely by applications with AI, often increasing both efficiency and accuracy.

The chat bot today is different from years past. They are more advanced and can now often replace menial tasks or requests and assist customers looking for self-service, thereby reducing both call volume and length.

AI provides a new future to financial analytics, increasing your ROI and allowing your employees to use their time more efficiently.

Learn more in this webinar.

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Leveraging Artificial Intelligence in the Financial Service Industry - HPCwire