Generative AI Startups in 2023 | eWEEK
Artificial Intelligence To Reshape Deep Science Learning
Artificial Intelligence, beyond the hype and hysteria in headlines today, plays a growing role in daily life and business—with uses ranging from predictive text to Netflix recommendations to the detection of bank fraud.
Much of that progress is thanks to researchers on the cutting edge of complex scientific exploration.
And there is more to come.
At UC Riverside, a team of four scientists has laid out their vision for using machine learning to maintain, improve and design some of the most sophisticated scientific equipment on Earth.
"Using AI to tackle major scientific challenges not only has the potential to advance science, but also to trickle down to solving problems in everyday life," said Vagelis Papalexakis, associate professor of Computer Science and Engineering at UC Riverside. "GPS being a great example."
A chapter on the UCR team's vision was published in April 2023 by World Scientific in the book "Artificial Intelligence for Science: A Deep Learning Revolution."
Chapter 7, "Machine Learning for Complex Instrument Design and Optimization," explores how AI can refine, improve—even revolutionize—large-scale scientific experiments. The idea is to leverage machine learning to simulate, computationally, an immense range of possibilities for operations and design—not only saving time, money and resources through efficiencies and comprehensive improvements, but also exploring counterintuitive designs and ideas.
"That sounds futuristic—and that is the hope," Papalexakis said. "We are asking, "What is the promise of AI?'"
His co-authors are Barry C. Barish, Nobel Laureate, California Institute of Technology professor of Physics emeritus, and UCR distinguished professor of Physics and Astronomy; Jonathan Richardson, UCR assistant professor of Physics and Astronomy; and Rutuja Gurav, a Ph.D. Candidate at UCR in computer science.
Their approach could enhance the design and operation of elaborate engineering, including the Laser Interferometer Gravitational-wave Observatory. LIGO, managed by Caltech, comprises two sets of two 2.5-mile-long laser beams, in Washington state and Louisiana, that detect gravitational waves from cosmic phenomena such as pairs of black holes merging that emit no light and thus can't be observed visually.
Gravitational waves help scientists understand the mysteries of space, the origins of the universe and fundamental laws of physics. LIGO itself has opened a new frontier in astronomy, with findings so groundbreaking that Barish, the former director of LIGO, shared the 2017 Nobel Prize in Physics.
"Advances in experimental physics rely on our ability to develop highly complex state-of-the-art instruments," Barish said. "Machine learning is playing a larger and larger role in the conception, design and the implementation of such advanced experimental facilities. It is fair to say that AI is becoming a full partner in making new discoveries in physics."
The new research envisioned would, for example, help scientists learn how to improve or even design end-to-end instruments in ways that improve their sensitivity and resilience to real-world sources of error, such as environmental noise.
"Instead of doing this in a lab, AI would do the heavy lifting of testing potential designs and finding one that works best" for LIGO's massive infrastructure, Papalexakis said. "It's a computational way of simulating things that will aid significantly in the design of large-scale experiments."
Such approaches would tap and adapt the technology that runs emerging public platforms such as ChatGPT and Bing AI, with large implications for scientific discovery and everyday innovation.
The scientists noted that using AI to test, model and improve large scientific systems would not displace researchers or engineers.
"Frontier experiments like LIGO are incredibly complex instruments, with dozens of interdependent control systems and thousands of data channels," Richardson said. "Our hope is that AI advances, such as those being pursued at UCR, will be able to recognize hidden associations in this sea of data that could diagnose operational problems. This, in turn, would inform new ways that we, as human physicists, can make physical changes that improve the performance of the detector."
The research grew from a student's fascination and a fortuitous meeting.
Gurav, a graduate student working in Papalexakis' computer science lab, brought a fascination with isolating gravitational waves from other noise. Then, a public lecture at UCR four years ago by gravitational-wave expert Barish led the group to meet, talk and collaborate on the project.
Gurav praised her UCR mentors and said, "It is wonderful to see our work included in such an amazingly diverse collection of ideas on applied AI for natural sciences. It marks a special milestone in my rather unconventional Ph.D. Journey as an aspiring computer scientist who is deeply interested in exploring applications of machine learning to advance the frontiers of scientific discovery."
Now that the chapter has been published, Papalexakis said, "I feel proud and a little terrified." Publicly laying out research directions for complex scientific study brings "a sense of responsibility that we don't take lightly. But I'm excited that people believe these things are worth investigating."
More information: Alok Choudhary et al, Artificial Intelligence for Science (2023). DOI: 10.1142/13123
Citation: Artificial Intelligence to reshape deep science learning (2023, June 30) retrieved 30 June 2023 from https://techxplore.Com/news/2023-06-artificial-intelligence-reshape-deep-science.Html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
How Computers And Artificial Intelligence Evolve Together
Co-design, that is, designing software and hardware simultaneously, is one way of attempting to meet the computing-power needs of today's artificial intelligence applications. Compilers, which translate instructions from one representation to another, are a key piece of the puzzle. A group of researchers at the Chinese Academy of Sciences summarized existing compiler technologies in deep learning co-design and proposed their own framework, the Buddy Compiler.
The group's review paper was published in the journal Intelligent Computing.
Although others have summarized optimizations, hardware architectures, co-design approaches, and compilation techniques, no one has discussed deep learning systems from the perspective of compilation technologies for co-design. The researchers studied deep learning from this angle because they believe that "compilation technologies can bring more opportunities to co-design and thus can better achieve the performance and power requirements of deep learning systems."
The review covers five topics:
Since the 1950s, neural networks have gone through many rises and falls leading up to today's explosive growth of deep learning applications and researches. Co-design began in the 1990s and has since then been adopted in various fields, progressing from manual work to computer-aided design and ultimately becoming a complex process involving modeling, simulation, optimization, synthesis, and testing.
Since 2020, a network model called a transformer has seen great success: ChatGPT is a chatbot built using a "generative pre-trained transformer." Current AI applications like ChatGPT are reaching a new performance bottleneck that will require hardware-software co-design again.
Deep learning and co-design nowThe breakthrough of deep learning comes from the use of numerous layers and a huge number of parameters, which significantly increase the computational demands for training and inference. As a result, relying solely on software-level optimization, it becomes challenging to achieve reasonable execution times. To address this, both industry and academia have turned to domain-specific hardware solutions, aiming to achieve the required performance through a collaborative effort between hardware and software, known as hardware-software co-design.
Recently, a comprehensive system has emerged, comprising deep learning frameworks, high-performance libraries, domain-specific compilers, programming models, hardware toolflows, and co-design techniques. These components collectively contribute to enhancing the efficiency and effectiveness of deep learning systems.
Compilation technologies for deep learning co-designThere are two popular ecosystems that are used to build compilers for deep learning: the tensor virtual machine, known as TVM, and the multi-level intermediate representation, known as MLIR. These ecosystems employ distinct strategies, with TVM serving as an end-to-end deep learning compiler and MLIR acting as a compiler infrastructure. Meanwhile, in the realm of hardware architectures customized for deep learning workloads, there are two primary types: streaming architecture and computational engine architecture.
Hardware design toolflows associated with these architectures are also embracing new compilation techniques to drive advancements and innovations. The combination of deep learning compilers and hardware compilation techniques brings new opportunities for deep learning co-design.
Current problems and future trendsWith performance requirements increasing too fast for processor development to keep up, effective co-design is critical. The problem with co-design is that there is no single way to go about it, no unified co-design framework or abstraction. If several layers of abstraction are required, efficiency decreases. It is labor-intensive to customize compilers for specific domains. Unifying ecosystems are forming, but underlying causes of fragmentation remain. The solution to these problems would be a modular extensible unifying framework.
The Buddy CompilerThe contributors to the Buddy Compiler project are "committed to building a scalable and flexible hardware and software co-design ecosystem." The ecosystem's modules will include a compiler framework, a compiler-as-a-service platform, a benchmark framework, a domain-specific architecture framework, and a co-design module. The latter two modules are still in progress.
The authors predict continued development of compilation ecosystems that will help unify the work being done in the rapidly developing and somewhat fragmented field of deep learning.
More information: Hongbin Zhang et al, Compiler Technologies in Deep Learning Co-Design: A Survey, Intelligent Computing (2023). DOI: 10.34133/icomputing.0040
Provided by Intelligent Computing
Citation: How computers and artificial intelligence evolve together (2023, June 30) retrieved 30 June 2023 from https://techxplore.Com/news/2023-06-artificial-intelligence-evolve.Html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
Artificial Intelligence And Machine Learning In Customer Engagement
In the modern era, the utilization of Artificial Intelligence (AI) and Machine Learning (ML) has emerged as a crucial factor in contributing to the success of large-scale businesses. This article explores the manifold ways in which AI can revolutionize customer engagement strategies.
By leveraging past customer behavior data, businesses can construct robust recommendation models that empower customers in making informed decisions.
Furthermore, a diverse array of machine learning techniques, such as recommendation models, similarity models, and explore-exploit models, can be effectively deployed to facilitate desired customer outcomes. This article aims to provide an in-depth analysis of the potential of AI and ML in enhancing customer engagement within the context of large enterprises.
In today's highly competitive business landscape, customer engagement plays a vital role in achieving and sustaining success. This article sheds light on the significance of AI and ML as the "secret sauce" behind the triumph of very large and prosperous businesses. It highlights the utilization of customer behavior data and the implementation of advanced ML models to improve customer decision-making and desired outcomes.
AI and Customer Engagement
2.1 Customer Behavior Data Analysis-
Utilizing AI techniques, businesses can analyze vast amounts of customer behavior data to gain valuable insights. By examining purchase history, browsing patterns, and demographic information, AI algorithms can identify patterns and trends, helping businesses understand their customers better.
2.2 Recommendation Models-
AI-driven recommendation models are an indispensable tool for enhancing customer engagement. By leveraging customer behavior data, these models can suggest personalized recommendations, whether it's products, services, or content. This not only improves the customer experience but also increases the likelihood of customer satisfaction and loyalty.
Machine Learning Strategies for Customer Engagement
3.1 Similarity Models-
Similarity models employ ML algorithms to identify customers with similar preferences or behaviors. By analyzing the behavior and preferences of customers with comparable profiles, businesses can predict and recommend products or services that are likely to resonate with individual customers.
3.2 Explore-Exploit Models:-
Explore-exploit models employ a strategic balance between exploration (introducing customers to new options) and exploitation (emphasizing known preferences). By dynamically adapting recommendations based on customer feedback and interactions, businesses can optimize customer engagement, satisfaction, and loyalty.
Implications and Benefits
The adoption of AI and ML techniques in customer engagement confers several notable benefits to large enterprises. Firstly, personalized recommendations based on customer behavior data can significantly enhance the customer experience, driving customer satisfaction and repeat business. Secondly, the ability to identify and target customer segments with similar preferences enables businesses to optimize marketing efforts and generate higher conversion rates. Lastly, the deployment of explore-exploit models ensures a balanced approach to recommendations, offering customers both familiar and novel options, thereby fostering engagement and curiosity.
We spoke to an AI professional who works in the customer engagement domain and AI to get deeper insights into how some of the largest technology companies leverage AI for winning in the customer engagement space. Arun Kumar Pillai is a software engineer and customer engagement tech professional. Arun has constantly made a name for himself in the business by using the potential of artificial intelligence (AI) and machine learning (ML), pushing the frontiers of customer engagement and delivering revolutionary innovation in the process.
Pillai, who has left indelible imprints in the sands of artificial intelligence and machine learning, has been instrumental in driving customer growth in his business operations. Recognizing the transformative potential of these technologies, he has deployed them as powerful tools for understanding customer behavior, preferences, and needs. This sophisticated approach capitalizes on AI and ML's ability to analyze complex, massive volumes of data, deriving actionable insights and enabling businesses to provide highly personalized and efficient customer experiences.
He has deployed them as powerful tools for understanding customer behavior, preferences, and needs. This sophisticated approach capitalizes on AI and ML's ability to analyze complex, massive volumes of data, deriving actionable insights and enabling businesses to provide highly personalized and efficient customer experiences. He sees machine learning and artificial intelligence playing an increasingly important role in customer engagement technologies. These technologies can help to identify patterns in communication engagement such as email open rate, click thru, subsequent action on the product as well as fatigue-driven actions such unsubscribe, and opting out of channels. Machine learning models can effectively help to select the best channel or communication method, the best product feature, and the best time to reach a customer to maximize engagement.
Challenges and Ethical Considerations
The integration of AI and ML into customer engagement strategies presents certain challenges and ethical considerations. Ensuring the privacy and security of customer data is of paramount importance, and businesses must implement robust safeguards. Additionally, biases and discriminatory outcomes within ML models must be actively identified and mitigated to ensure fair and inclusive customer engagement.
Conclusion
AI and ML have emerged as powerful tools for large enterprises to elevate customer engagement strategies. Leveraging customer behavior data and implementing advanced ML models such as recommendation, similarity, and explore-exploit models can effectively drive customer decision-making and desired outcomes. By embracing the potential of AI and ML, businesses can forge stronger relationships with customers, enhance customer satisfaction and loyalty, and ultimately achieve sustainable growth and success.
(To receive our E-paper on WhatsApp daily, please click here. To receive it on Telegram, please click here. We permit sharing of the paper's PDF on WhatsApp and other social media platforms.)
Comments
Post a Comment