Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attack types and privacy-protected ML techniques.

Luis Ceze of OctoML discusses Apache TVM, an open source machine learning model compiler for a variety of different hardware architectures with host Akshay Manchale. Luis talks about the challenges in deploying models on specialized hardware and how TVM.

Ram Sriharsha of Pinecone discusses the role of vectors in machine learning, a technique that lies at the heart of many of the machine learning applications we use every day. Host Philip Winston spoke with Sriharsha about the basics of vectors, vector...

José Valim, creator of the Elixir programming language, Chief Adoption Officer at Dashbit, and author of three programming books, speaks with SE Radio host Gavin Henry about what Elixir is today, what Livebook is, the five spearheads of the new machine learning ecosystem for Elixir, and how they all fit together. Valim describes why he created Elixir, what “the beam” is, and how he pitches it to new users. This episode examines things you can do with Livebook and how it is well-aligned with machine learning, as well as why immutability is important and how it works. They take a detailed look at a range of topics, including tensors with Nx, traditional machine learning with Scholar, data munging with Explorer, deep learning and neural networks with Axon, Bumblebee and Huggingface, and model creation basics. Brought to you by IEEE Computer Society and IEEE Software magazine.

Catherine Nelson, author of the new O’Reilly book, Software Engineering for Data Scientists, discusses the collaboration between data scientists and software engineers -- an increasingly common pairing on machine learning and AI projects. Host Philip Winston speaks with Nelson about the role of a data scientist, the difference between running experiments in notebooks and building an automated pipeline for production, machine learning vs. AI, the typical pipeline steps for machine learning, and the role of software engineering in data science. Brought to you by IEEE Computer Society and IEEE Software magazine.

Groundbreaker Podcast associate producer Javed Mohammed [@JavedMohammed] first encountered Dr. Hulme in January 2020 at Oracle OpenWorld Middle East in Dubai, where Dr. Hulme, a featured speaker, delivered a session on “AI and the Future of Business” as part of the Transformational Technologies track. ”I was so impressed with his vision and his unconventional thinking,” Javed says. This program, which features Javed’s conversation with Dr. Hulme, grew out of their meeting in Dubai.

In this conversation Oracle Community Manager Javed Mohammed speaks with three AI-ML experts.

Autonomous technologies such as artificial intelligence (AI) and machine learning (ML) are on the tip of every tongue in tech. But what is the difference between AI and ML? What are interesting use cases? What is “under the hood” of AI/ML and the algorithms that power ML models?

We have three Subject Matter Experts who share their insights into a fascinating and at times humorous conversation.

• Charlie Berger, Sr. Director of Product Management for Machine Learning, AI and Cognitive Analytics at Oracle.
• Heli Helskyaho, CEO Miracle Finland  Oracle ACE Director, Groundbreaker Ambassador. Author. Doctoral student, University of Helsinki. Also known as HeliFromFinland.
• Katharine Jarmul, Head of Product at Cape Privacy, she is a Privacy activist, AI dissenter, machine learning engineer. Author and teacher for O'Reilly.

Listen to learn about what makes AI and ML solutions powerful as well as the challenges we face from them. Charlie, Heli and Katharine explain some of the fundamentals about this revolutionary technology but also share personal stories which make for a memorable Podcast.

Read the complete show notes here.

Red Bull Racing Honda and Oracle Team up on a Series of Machine Learning HOLs — First Lab for Beginners on Wednesday August 11 at 8 AM PST

Jim Grisanzio and Chris Bensen from Oracle Developer Relations preview the first in a series of unique hands-on labs. Starting on August 11 at 8 AM PST developers will have the opportunity to team up with Red Bull Racing Honda and Oracle in a hands-on lab that uses race data to teach machine learning. Video. 

Register for the lab here! Same link for on demand!

Podcast Host: Jim Grisanzio, Oracle Developer Relations
https://twitter.com/jimgris
https://developer.oracle.com/team/ 

Management requirements for federated machine learning systems

Functional architecture of machine learning-based quality of service assurance for the IMT-2020 network

Framework for data handling to enable machine learning in future networks including IMT-2020

Architectural framework for machine learning model serving in future networks including IMT-2020

ITU-T Y.3800-series - Quantum key distribution networks - Applications of machine learning

FIGI - DFS - Big data machine learning consumer protection and privacy

TR.sgfdm - FHE-based data collaboration in machine learning

Technical framework for the shared machine learning system

How are AI/ML strategies evolving to meet tomorrow’s mission?

The post Federal Executive Forum Artificial Intelligence & Machine Learning Strategies in Government Progress and Best Practices 2024 first appeared on Federal News Network.

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Engineering Library- QA76.9.D343M34 2016

Come one, come all! nycdevops does its first virtual meetup! All are invited!

• Speaker: Adarsh Shah
• Topic: "Continuous Delivery for Machine Learning"
• Time: Thursday, September 17, 2020, 5-630 PM
• Link: https://www.meetup.com/nycdevops/events/272914068/

Hope to see you there!

The new SAS Certified Specialist: Statistics for Machine Learning credential is designed to help you showcase your expertise and commitment to staying ahead in the industry.

Earn this SAS certification to validate your skills and training in machine learning was published on SAS Users.

Artificial intelligence (AI) is everywhere. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making your breakfast. Verification is never truly complete; it is over when you run...(read more)

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning (ML) and artificial intelligence (AI) marking significant milestones in this journey. ML, a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging the available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture, storage, and accessibility for manufacturing and process operations. This paper highlights the pivotal role of AI in converting process data into actionable knowledge to support critical functions throughout the whole product life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

Two pioneers of artificial intelligence have won the Nobel Prize in physics for discoveries and inventions that formed the building blocks of machine learning.

Finished chips coming in from the foundry are subject to a battery of tests. For those destined for critical systems in cars, those tests are particularly extensive and can add 5 to 10 percent to the cost of a chip. But do you really need to do every single test?

Engineers at NXP have developed a machine-learning algorithm that learns the patterns of test results and figures out the subset of tests that are really needed and those that they could safely do without. The NXP engineers described the process at the IEEE International Test Conference in San Diego last week.

NXP makes a wide variety of chips with complex circuitry and advanced chip-making technology, including inverters for EV motors, audio chips for consumer electronics, and key-fob transponders to secure your car. These chips are tested with different signals at different voltages and at different temperatures in a test process called continue-on-fail. In that process, chips are tested in groups and are all subjected to the complete battery, even if some parts fail some of the tests along the way.

Chips were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests.

“We have to ensure stringent quality requirements in the field, so we have to do a lot of testing,” says Mehul Shroff, an NXP Fellow who led the research. But with much of the actual production and packaging of chips outsourced to other companies, testing is one of the few knobs most chip companies can turn to control costs. “What we were trying to do here is come up with a way to reduce test cost in a way that was statistically rigorous and gave us good results without compromising field quality.”

A Test Recommender System

Shroff says the problem has certain similarities to the machine learning-based recommender systems used in e-commerce. “We took the concept from the retail world, where a data analyst can look at receipts and see what items people are buying together,” he says. “Instead of a transaction receipt, we have a unique part identifier and instead of the items that a consumer would purchase, we have a list of failing tests.”

The NXP algorithm then discovered which tests fail together. Of course, what’s at stake for whether a purchaser of bread will want to buy butter is quite different from whether a test of an automotive part at a particular temperature means other tests don’t need to be done. “We need to have 100 percent or near 100 percent certainty,” Shroff says. “We operate in a different space with respect to statistical rigor compared to the retail world, but it’s borrowing the same concept.”

As rigorous as the results are, Shroff says that they shouldn’t be relied upon on their own. You have to “make sure it makes sense from engineering perspective and that you can understand it in technical terms,” he says. “Only then, remove the test.”

Shroff and his colleagues analyzed data obtained from testing seven microcontrollers and applications processors built using advanced chipmaking processes. Depending on which chip was involved, they were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. Extending the analysis to data from other types of chips led to an even wider range of opportunities to trim testing.

The algorithm is a pilot project for now, and the NXP team is looking to expand it to a broader set of parts, reduce the computational overhead, and make it easier to use.

We recently launched a completely reimagined version of Machine Learning Crash Course.

AutMedAI, a new medlinkmachine learning/medlink (ML) model, enhances early detection of medlinkAutism Spectrum Disorder/medlink (ASD) with minimal medical and background data.

Ravi Kiran Magham highlights the transformative role of Machine Learning Feature Stores in modern data infrastructures.

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia’s DGX Quantum computing platform and Quantum Machine’s advanced quantum control hardware. We didn’t hear much about the results of this partnership for a while, but it’s now starting to bear fruit and […]

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WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert.