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A new plugin from French developer Anemond utilizes machine learning to split sounds up into individual layers that can be remixed and randomized, generating thousands of rhythmic and melodic variations of any sample or loop.

Based on the same machine learning engine as Anemond's Factorsynth, a "fully-fledged sound design studio", Factoid 2 offers a simpler interface, reduced feature set and a lighter CPU load than its bigger and more expensive brother.

Described as a "loop revamper", Factoid's machine learning engine can extract between 2 and 8 layers from any sample. Anemond explains that Factoid isn't a stem separation tool, but instead a unique kind of processor that decomposes, or "factorizes" audio into a set of "temporal and spectral components".

This is "not intended to unmix instruments," Anemond says, "but to discover and extract unpredictable but interesting sound elements with a certain degree of structure", like notes, drum hits, and rhythmic or melodic motifs.

Once these layers are extracted, Factoid can remix and manipulate them in a variety of ways to create new ideas. You're able to randomize sample slices on a quantized grid to create glitchy patterns, transform melodies into textures, and solo, mute and adjust the levels for each factorized layer. After manipulating your sample, you can drag and drop the results from Factoid into your DAW.

Factoid 2 is priced at 29 and is available as a standalone app or VST3/AU plugin for macOS and Windows.

Find out more on Anemond's website.

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Anemond's Factoid 2 is an experimental sampler plugin that uses machine learning to "decompose", remix and ... - MusicRadar

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Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials and conditions.

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"It's a tool that can be used to investigate more reactions in this field," said Shuhao Zhang, a graduate student in Carnegie Mellon University'sDepartment of Chemistry. "We can offer a full simulation of the reaction mechanisms."

Zhang is the first author on the paper that explains the creation and results of this new machine learning model, "Exploring the Frontiers of Chemistry with a General Reactive Machine Learning Potential," which was published in Nature Chemistry on March 7.

Though researchers have simulated reactions before, previous methods had multiple problems. Reactive force field models are relatively common, but they usually require training for specific reaction types. Traditional models that use quantum mechanics, where chemical reactions are simulated based on underlying physics, can be applied to any materials and molecules, but these models require supercomputers to be used.

This new general machine learning interatomic potential (ANI-1xnr) can perform simulations for arbitrary materials containing the elements carbon, hydrogen, nitrogen, and oxygen and requires significantly less computing power and time than traditional quantum mechanics models. According to Olexandr Isayev, associate professor of chemistry at Carnegie Mellon and head of the lab where the model was developed, this breakthrough is due to developments in machine learning.

"Machine learning is emerging as a powerful approach to construct various forms of transferable atomistic potentials utilizing regression algorithms. The overall goal of this project is to develop a machine learning method capable of predicting reaction energetics and rates for chemical processes with high accuracy, but with a very low computational cost," Isayev said. "We have shown that those machine learning models can be trained at high levels of quantum mechanics theory and can successfully predict energies and forces with quantum mechanics accuracy and an increase in speed of as much as 6-7 orders of magnitude. This is a new paradigm in reactive simulations."

Researchers tested ANI-1xnr on different chemical problems, including comparing biofuel additives and tracking methane combustion. They even recreated the Miller experiment, a famous chemical experiment meant to demonstrate how life originated on Earth. Using this experiment, they found that the ANI-1xnr model produced accurate results in condensed-phase systems.

Zhang said that the model could potentially be used for other areas in chemistry with further training.

"We found out it can be potentially used to simulate biochemical processes like enzymatic reactions," Zhang said. "We didn't design it to be used in such a way, but after modification it may be used for that purpose.

In the future, the team plans to refine ANI-1xnr and allow it to work with more elements and in more chemical areas, and they will try to increase the scale of the reactions it can process. This could allow it to be used in multiple fields where designing new chemical reactions could be relevant, such as drug discovery.

- This press release was originally published on the Carnegie Mellon University website

Link:
Advancing Chemistry with AI: New Model for Simulating Diverse Organic Reactions - Lab Manager Magazine

Generative artificial intelligence (AI) allows anyone to leverage machine learning (ML) capabilities using natural language, and it is extremely intuitive to use. When users are able to search, analyze, and draw conclusions in secondsfrom extensive information that exists across their organization or the internetthey can make more informed decisions at speed. This can help them answer customer queries efficiently, pinpoint significant changes to contracts, and assess risks such as fraud more accurately. Organizations can make more effective use of resources and provide better services by gaining useful insights, such as peak use patterns or the likelihood of good outcomes in different scenarios.

Generative AI models are trained on a large volume of datasets, which gives them the ability to generate answers to a range of questions and summarize findings in a meaningful way for the user. Common use cases in public sector could be determining the best way to reduce Friday afternoon congestion, or how to manage building utilities more efficiently.

To suggest answers, generative AI systems can combine and cross-analyse a diverse range of data in milliseconds to produce a spoken, graphical, or easy-to-understand written summary.

Generative AI models are as reliable as the data theyre trained on and can access. There is a risk of hallucination, which is when the models make something up that may sound plausible and factual but which may not be correct. Anyone who bases decisions and actions on the results of an AI-based query needs to be able to stand by that choice and articulate how it was reached, to avoid unfair targeting or other forms of bias, resource waste, or other questionable decisions.

Any organizations or teams that use generative AI to make decisions or prioritize actions, must build responsible AI systems that are fair, explainable, robust, secure, transparent, and that safeguard privacy. Good governance is fundamental for responsible systems. Its important to be able to justify how these process-support systems arrived at choices.

Organizations need to design and use a proven, well-architected AI framework and operating model to provide for continuous monitoring of the system in use. There has to be full awareness of potential issues and whats needed to mitigate them. Those issues could involve limitations with the data (its quality, level of standardization, currency, and completeness) and any risk of bias, data-protection breaches, or other regulatory or legal infringement.

Systems must be transparent: if someone challenges a decision supported by the AI system, they can track the reasoning behind it. Examples of this could be citing specific sources used in summarisation or tracking the customer data that was used in any ML models.

For a deeper dive, watch our four-part AWS Institute Masterclass series on AI/ML:

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Generative AI: Understand the challenges to realize the opportunities | Amazon Web Services - AWS Blog

Artificial intelligence (AI) is revolutionizing the way we work and live. From helping us solve social challenges and improving business outcomes to boosting our productivity and enhancing our creativity, the possibilities and potential of AI seem endless.

But while recent innovations have brought AI into the spotlight, the artificial intelligence field is not new, says Dr. Suman Kumar, Associate Professor and Computer Science Department Chair at Troy University.

It has been evolving for decades, but now, with memory advances and graphic processing units (GPUs), we can use AI far more effectively as compared to 30 or 40 years ago, Dr. Kumar explains. Now, almost every industry and field uses deep learning. It is one of the most profound shifts. AI now touches almost every aspect of human life.

These advancements have created a heightened demand for computer scientists, machine learning engineers, data scientists, software engineers, research scientists and more. So how can you become part of this promising field? By learning how to specialize in artificial intelligence.

Artificial Intelligence Job Opportunities Abound

AI is in the news today because of advances in large language models like ChatGPT and Google Bard, both powerful AI systems that can be used to produce content, conduct research and more. Stronger machine-learning capabilities have also greatly expanded what we can do with AI, from improving security measures with facial recognition to helping us improve our health with wearable devices that track our heart rates and activity levels.

AI, Dr. Kumar says, is here to stay.

All kinds of organizations, whether its big box retailers or health care, are becoming more and more data-driven. Algorithmic trading in the stock market is also data-driven. AI can help us make sense of that data. Government organizations, such as the IRS, can use AI to help detect fraud. Video game designers use it in game design. Cities can use it to predict traffic patterns. Ive even seen research about its applications in dentistry. Whether someone is using it predictively to get ahead of a problem or generativity to help them create something, AI is applicable to almost any discipline.

That means, now more than ever, professionals who can design, develop, train and improve AI are in high demand. Take computer and information research scientists, for example.

According to the U.S. Bureau of Labor Statistics (BLS), jobs within this field will grow by 23% through 2032, which is much faster than average. The average annual salary is $136,620, the BLS reports.

There are a lot of misconceptions about AI, Dr. Kumar shares. One of those misconceptions is that AI can never fail. AI does fail, and the field needs more humans behind it because it doesnt have human intelligence. We design it to operate like human intelligence, but it will never truly be human.

So, what kinds of other careers in AI are out there?

Many artificial intelligence job opportunities will be in health care, shares Dr. Kumar. Health care increasingly employs artificial intelligence to help understand patient data and prevent disease. Bioinformatics, a field in which you analyze biological data, also holds promise for those interested in AI.

Other artificial intelligence jobs include machine learning specialist, AI researcher, data scientist, video game designer, research engineer, AI systems engineer and AI specialist. But the AI career thats right for you depends on how you want to contribute to the field.

As an AI researcher, you can contribute to the development of new AI systems, says Dr. Kumar. As a machine learning specialist, youll help train those systems. If you enjoy working with data, you can become a data scientist and use data to make predictions or decisions. When you have a background in AI, you have a lot of opportunities.

How To Start an Artificial Intelligence Career

If youre wondering how to start an artificial intelligence career, the first step is to learn AI. You may qualify for some roles with a bachelors degree, AI courses or related career experience. However, according to the BLS, a graduate degree in computer science or a related field is needed for most computer and information research jobs, including artificial intelligence job opportunities.

If youre already working in an entry-level role, a masters degree can help you advance because youll develop the mathematical knowledge, programming skills and research experience you need to excel in AI jobs.

The artificial intelligence field and, more broadly, computer science grew out of mathematics, notes Dr. Kumar. To be successful in AI jobs, you have to be skilled in math. Much of machine learning is math, so the better you are at math, the better youll perform. A masters in computer science program provides you with a strong foundation in math, programming and data science to prepare you for careers in AI.

Working on AI-related projects and taking graduate courses in machine learning and data science can give you an advantage in the job market. Youll want to choose a graduate program led by faculty who are knowledgeable about trends in the field. You may also want to choose an area of specialization, such as bioinformatics, to expand your career opportunities.

How To Specialize in Artificial Intelligence

While there are many paths to an artificial intelligence specialization, earning TROYs masters in computer science is one of the most effective ways to enter the AI field. TROY offers an M.S. in computer science with a specialization in artificial intelligence to help you develop technical knowledge and skills for AI jobs.

Artificial intelligence is a branch of computer science which is interdisciplinary, says Dr. Kumar. An artificial intelligence specialization will give you interdisciplinary study in computer science along with experience in designing and implementing AI systems. These skills are among the most needed in the AI industry.

Choosing an M.S. in computer science with a specialization in artificial intelligence will ensure you have those skills, along with machine learning, programming and data analysis. TROYs masters in computer science program, with a thesis and non-thesis track, will also help you gain research experience and build your portfolio. TROYs program emphasizes project-based learning to give you further advantage.

Our program is very hands-on, shares Dr. Kumar. In our courses, we try to come up with real-life problems for our students to solve. Then, students practice creating solutions to those real-world problems, learning advanced research skills, machine learning and other skills along the way.

For example, students in the artificial intelligence specialization at TROY have worked on using AI to prevent cybersecurity attacks. Theyve also completed projects in transportation and routing. One student even used AI to predict winning baseball teams, which could have applications in TROYs sports management program.

All of our projects are practical and applicable in real life. What the students find in their projects is fascinating, but more importantly, useful, says Dr. Kumar.

Learn More About TROYs Graduate Program Interested in preparing for AI jobs? Learn more about how TROYs M.S. in computer science and artificial intelligence specialization can help you launch or advance your career.

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How To Specialize in Artificial Intelligence - Troy Today - Troy University

Amazon Bedrock is the best place to build and scale generative AI applications with large language models (LLM) and other foundation models (FMs). It enables customers to leverage a variety of high-performing FMs, such as the Claude family of models by Anthropic, to build custom generative AI applications.Looking back to 2021, when Anthropic first started building on AWS, no one could have envisioned how transformative the Claude family of models would be. We have been making state-of-the-art generative AI models accessible and usable for businesses of all sizes through Amazon Bedrock. In just a few short months since Amazon Bedrock became generally available on September 28, 2023, more than 10K customers have been using it to deliver, and many of them are using Claude. Customers such as ADP, Broadridge, Cloudera, Dana-Farber Cancer Institute, Genesys, Genomics England, GoDaddy, Intuit, M1 Finance, Perplexity AI, Proto Hologram, Rocket Companies and more are using Anthropics Claude models on Amazon Bedrock to drive innovation in generative AI and to build transformative customer experiences. And today, we are announcing an exciting milestone with the next generation of Claude coming to Amazon Bedrock: Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku.

Anthropic is unveiling its next generation of Claude with three advanced models optimized for different use cases. Haiku is the fastest and most cost-effective model on the market. It is a fast compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at intelligent tasks demanding rapid responses, like knowledge retrieval or sales automation. And it strikes the ideal balance between intelligence and speed qualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art FM with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks. It can navigate open-ended prompts, and novel scenarios with remarkable fluency, including task automation, hypothesis generation, and analysis of charts, graphs, and forecasts. And Sonnet is first available on Amazon Bedrock today. Current evaluations from Anthropic suggest that the Claude 3 model family outperformscomparable models in math word problem solving (MATH) and multilingual math (MGSM) benchmarks, critical benchmarks used today for LLMs.

Specifically, Opus outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits high levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.

Through Amazon Bedrock, customers will get easy access to build with Anthropics newest models. This includes not only natural language models but also their expanded range of multimodal AI models capable of advanced reasoning across text, images, charts, and more. Our collaboration has already helped customers accelerate generative AI adoption and delivered business value to them. Here are a few ways customers have been using Anthropics Claude models on Amazon Bedrock:

We are developing a generative AI solution on AWS to help customers plan epic trips and create life-changing experiences with personalized travel itineraries. By building with Claude on Amazon Bedrock, we reduced itinerary generation costs by nearly 80% percent when we quickly created a scalable, secure AI platform that can organize our book content in minutes to deliver cohesive, highly accurate travel recommendations. Now we can repackage and personalize our content in various ways on our digital platforms, based on customer preference, all while highlighting trusted local voicesjust like Lonely Planet has done for 50 years.

Chris Whyde, Senior VP of Engineering and Data Science, Lonely Planet

We are working with AWS and Anthropic to host our custom, fine-tuned Anthropic Claude model on Amazon Bedrock to support our strategy of rapidly delivering generative AI solutions at scale and with cutting-edge encryption, data privacy, and safe AI technology embedded in everything we do. Our new Lexis+ AI platform technology features conversational search, insightful summarization, and intelligent legal drafting capabilities, which enable lawyers to increase their efficiency, effectiveness, and productivity.

Jeff Reihl, Executive VP and CTO, LexisNexis Legal & Professional

At Broadridge, we have been working to automate the understanding of regulatory reporting requirements to create greater transparency and increase efficiency for our customers operating in domestic and global financial markets. With use of Claude on Amazon Bedrock, were thrilled to get even higher accuracy in our experiments with processing and summarizing capabilities. With Amazon Bedrock, we have choice in our use of LLMs, and we value the performance and integration capabilities it offers.

Saumin Patel, VP Engineering generative AI, Broadridge

The Claude 3 model family caters to various needs, allowing customers to choose the model best suited for their specific use case, which is key to developing a successful prototype and later production systems that can deliver real impactwhether for a new product, feature or process that boosts the bottom line. Keeping customer needs top of mind, Anthropic and AWS are delivering where it matters most to organizations of all sizes:

And AWS and Anthropic are continuously reaffirming our commitment to advancing generative AI in a responsible manner. By constantly improving model capabilities committing to frameworks like Constitutional AI or the White House voluntary commitments on AI, we can accelerate the safe, ethical development, and deployment of this transformative technology.

Looking ahead, customers will build entirely new categories of generative AI-powered applications and experiences with the latest generation of models. Weve only begun to tap generative AIs potential to automate complex processes, augment human expertise, and reshape digital experiences. We expect to see unprecedented levels of innovation as customers choose Anthropics models augmented with multimodal skills leveraging all the tools they need to build and scale generative AI applications on Amazon Bedrock. Imagine sophisticated conversational assistants providing fast and highly-contextual responses, picture personalized recommendation engines that seamlessly blend in relevant images, diagrams and associated knowledge to intuitively guide decisions. Envision scientific research turbocharged by generative AI able to read experiments, synthesize hypotheses, and even propose novel areas for exploration. There are so many possibilities that will be realized by taking full advantage of all generative AI has to offer through Amazon Bedrock. Our collaboration ensures enterprises and innovators worldwide will have the tools to reach the next frontier of generative AI-powered innovation responsibly, and for the benefit of all.

Its still early days for generative AI, but strong collaboration and a focus on innovation are ushering in a new era of generative AI on AWS. We cant wait to see what customers build next.

Check out the following resources to learn more about this announcement:

Swami Sivasubramanian is Vice President of Data and Machine Learning at AWS. In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His teams mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict.

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together | Amazon Web Services - AWS Blog