Why Enterprises should Build Infrastructure for Artificial Intelligence – AI first

Why Enterprises should Build Infrastructure for Artificial Intelligence - AI first

Artificial Intelligence - AI is bringing new levels of Automation to everything from Cars and Kiosks to Utility Grids, Healthcare, Life Sciences, and Financial Networks. But it’s easy to forget that before the enterprise can automate the world, it has to Automate itself first.

As with most complicated systems, IT Infrastructure Management is ripe for Intelligent Automation. As data loads become larger and more complex and the infrastructure itself extends beyond the Datacenter into the Cloud and the edge, the speed at which new environments are provisioned, optimized, and decommissioned will soon exceed the capabilities of even an army of human operators. That means Artificial Intelligence - AI will be needed on the ground level to handle the demands of Artificial Intelligence - AI initiatives higher up the IT stack.

Artificial Intelligence - AI begins with Infrastructure

In a classic Catch-22, however, most enterprises are running into trouble deploying Artificial Intelligence - AI on their infrastructure, in large part because they lack the tools to leverage the technology in a meaningful way. A recent survey by Run: AI shows that few Artificial Intelligence - AI algorithms and models are getting into production – less than 10% at some organizations – with many data scientists still resorting to manual access to GPUs and other elements of Data Infrastructure to get projects to the finish line.

Another study by Global Surveys showed that just 17% of AI and IT practitioners report seeing high utilization of hardware resources, with 28% reporting that much of their infrastructure remains idle for large periods of time. And this is after their organizations have poured millions of dollars into new hardware, software, and Cloud Resources, in large part to leverage Artificial Intelligence - AI, Machine Learning - ML, and Deep Learning.

If the enterprise is to successfully carry out the transformation from traditional modes of operation to fully digitized ones, Artificial Intelligence - AI will have to play a prominent role. IT consultancy Aarav Solutions points out that Artificial Intelligence - AI is invaluable when it comes to automating infrastructure support, security, resource provisioning, and a host of other activities. Its secret sauce is the capability to analyze massive data sets at high speed and with far greater accuracy than manual processes, giving decision-makers granular insight into the otherwise hidden forces affecting their operations.

A deeper look into all the interrelated functions that go into Infrastructure Management on a daily basis, sparks wonder at how the enterprise has gotten this far without Artificial Intelligence - AI. XenonStack COO and CDS Jagreet Kaur Gill, recently highlighted the myriad functions that can be kicked into hyper-speed with Artificial Intelligence - AI, everything from Capacity Planning and Resource Utilization to Anomaly Detection and Real-Time Root Cause Analysis. With the ability to track and manage literally millions of events at a time, Artificial Intelligence - AI will provide the foundation that allows the enterprise to maintain the scale, reliability, and dynamism of the digital economy.

Artificial Intelligence and Edge Computing

With this kind of management stack in place, says Sandeep Singh, vice president of storage marketing at HPE, it’s not too early to start talking about Artificial Intelligence - AI and Operations (AIOps) driven frameworks and fully autonomous IT operations, particularly in greenfield deployments between the Edge and the Cloud. The Edge, after all, is where much of the storage and processing of the Internet of Things - IoT, Industrial Internet of Things - IIoT, and Internet of Medical Things - IoMT data will take place. But it is also characterized by a highly dispersed physical footprint, with small, interconnected nodes pushed as close to user devices as possible. But its very nature, then, the Edge must be Autonomous. Using AIOps, organizations will be able to build self-sufficient, Real-Time Analytics and decision-making capabilities, while at the same time ensuring maximum uptime and fail-over should anything happen to disrupt operations at a given endpoint.

Looking forward, it’s clear that Artificial Intelligence - AI empowered infrastructure will be more than just a competitive advantage, but an operational necessity. With the amount of data generated by an increasingly connected world, plus the quickly changing nature of all the digital processes and services this entails, there is simply no other way to manage these environments without AI.

Intelligence will be the driving force in enterprise operations as the decade unfolds, but just like any other technology initiative, it must be implemented from the ground up – and that process starts with infrastructure.


SOURCE: VentureBeat | Author: Arthur Cole

URL: https://venturebeat.com/2021/11/22/why-enterprises-should-build-ai-infrastructure-first/

Microsoft Ignite 2021 | Book of News

Microsoft Ignite 2021 | Book of News
Microsoft Ignite 2021 | Book of News

Microsoft Ignite 2021 | Book of News

For the latest Technology and Business Innovations announced by Microsoft, we recommend that you visit the Microsoft Ignite Book of News, which is "Online" and located at: https://news.microsoft.com/ignite-november-2021-book-of-news/

Introduction:

Welcome everyone to Microsoft Ignite, and once again we have a book’s worth of news about Microsoft 365, Azure, Dynamics 365, Security, Power Platform, AI and much more.

Our goal with the Book of News is to provide you with a guide to all the announcements we are making, with all the detail you need. Our standing goal remains as it has always been – to make it as easy as possible for you to navigate all the latest information and provide key details on the topics you are most interested in.

Microsoft Ignite is a seminal moment for our company. We will welcome more than 100,000 global attendees across a variety of industries to experience our latest and greatest technologies while also getting a sneak peek at new products and services that will be coming in the future.

The backdrop for our news at Ignite is the Microsoft Cloud. The Microsoft Cloud powers an organization’s digital capability, while providing the safeguards necessary to keep data confidential and secure. There is no question that the past year and a half has been a catalyst for structural change in every industry, from the adoption of telehealth in healthcare, to digital wallets in financial services, to curbside pick-up and contact-less shopping in retail.

  • Digital technology will be more necessary than ever, for every organization, in every sector. The implications for IT are profound.
  • Fundamentally, we are moving into an era in which people expect their digital data to be available anywhere, at any time and on any device.
  • We have a great lineup of news and some really exciting moments planned for this year’s Ignite. I hope that you can join us.

As always, send us your feedback! We want to know how we can do better. Are you getting the information and context you need? What can we do to make the experience ever better next time?

Foreword by Frank X. Shaw


What is the Book of News?

The Microsoft Ignite Book of News is your guide to key news items that we are announcing at Microsoft Ignite. The interactive Table of Contents gives you the option to select the items you are interested in, and the translation capabilities make the Book of News more accessible globally. (Just click the Translate button above the Table of Contents to enable translations.)

We also pulled together a folder of imagery related to a few of the news items. Please take a look at the imagery here.

We hope the Book of News provides all the information, executive insight and context you need. If you have any questions or feedback regarding content in the Book of News, please email eventcom@microsoft.com.


For the latest Technology and Business Innovations announced by Microsoft, we recommend that you visit the Microsoft Ignite Book of News, which is "Online" and located at: https://news.microsoft.com/ignite-november-2021-book-of-news/

Using Machine Learning – ML to Predict High-Impact Research

Using Machine Learning - ML to Predict High-Impact Research

DELPHI, an artificial intelligence framework, can give an “early-alert” signal for future key technologies by learning from patterns gleaned from previous scientific publications.
MIT Media Lab
Publication Date:
Using machine learning to predict high-impact research

Using machine learning to predict high-impact research

An artificial intelligence framework built by MIT researchers can give an “early-alert” signal for future high-impact technologies, by learning from patterns gleaned from previous scientific publications.

In a retrospective test of its capabilities, DELPHI, short for Dynamic Early-warning by Learning to Predict High Impact, was able to identify all pioneering papers on an experts’ list of key foundational biotechnologies, sometimes as early as the first year after their publication.

James W. Weis, a research affiliate of the MIT Media Lab, and Joseph Jacobson, a professor of media arts and sciences and head of the Media Lab’s Molecular Machines research group, also used DELPHI to highlight 50 recent scientific papers that they predict will be high impact by 2023. Topics covered by the papers include DNA nanorobots used for cancer treatment, high-energy density lithium-oxygen batteries, and chemical synthesis using deep neural networks, among others.

The researchers see DELPHI as a tool that can help humans better leverage funding for scientific research, identifying “diamond in the rough” technologies that might otherwise languish and offering a way for governments, philanthropies, and venture capital firms to more efficiently and productively support science.

“In essence, our algorithm functions by learning patterns from the history of science, and then pattern-matching on new publications to find early signals of high impact,” says Weis. “By tracking the early spread of ideas, we can predict how likely they are to go viral or spread to the broader academic community in a meaningful way.”

The paper has been published in Nature Biotechnology.

Searching for the “diamond in the rough”

The machine learning algorithm developed by Weis and Jacobson takes advantage of the vast amount of digital information that is now available with the exponential growth in scientific publication since the 1980s. But instead of using one-dimensional measures, such as the number of citations, to judge a publication’s impact, DELPHI was trained on a full time-series network of journal article metadata to reveal higher-dimensional patterns in their spread across the scientific ecosystem.

The result is a knowledge graph that contains the connections between nodes representing papers, authors, institutions, and other types of data. The strength and type of the complex connections between these nodes determine their properties, which are used in the framework. “These nodes and edges define a time-based graph that DELPHI uses to learn patterns that are predictive of high future impact,” explains Weis.

Together, these network features are used to predict scientific impact, with papers that fall in the top 5 percent of time-scaled node centrality five years after publication considered the “highly impactful” target set that DELPHI aims to identify. These top 5 percent of papers constitute 35 percent of the total impact in the graph. DELPHI can also use cutoffs of the top 1, 10, and 15 percent of time-scaled node centrality, the authors say.

DELPHI suggests that highly impactful papers spread almost virally outside their disciplines and smaller scientific communities. Two papers can have the same number of citations, but highly impactful papers reach a broader and deeper audience. Low-impact papers, on the other hand, “aren’t really being utilized and leveraged by an expanding group of people,” says Weis.

The framework might be useful in “incentivizing teams of people to work together, even if they don’t already know each other — perhaps by directing funding toward them to come together to work on important multidisciplinary problems,” he adds.

Compared to citation number alone, DELPHI identifies over twice the number of highly impactful papers, including 60 percent of “hidden gems,” or papers that would be missed by a citation threshold.

"Advancing fundamental research is about taking lots of shots on goal and then being able to quickly double down on the best of those ideas,” says Jacobson. “This study was about seeing whether we could do that process in a more scaled way, by using the scientific community as a whole, as embedded in the academic graph, as well as being more inclusive in identifying high-impact research directions."

The researchers were surprised at how early in some cases the “alert signal” of a highly impactful paper shows up using DELPHI. “Within one year of publication we are already identifying hidden gems that will have significant impact later on,” says Weis.

He cautions, however, that DELPHI isn’t exactly predicting the future. “We’re using machine learning to extract and quantify signals that are hidden in the dimensionality and dynamics of the data that already exist.”

Fair, efficient, and effective funding

The hope, the researchers say, is that DELPHI will offer a less-biased way to evaluate a paper’s impact, as other measures such as citations and journal impact factor number can be manipulated, as past studies have shown.

“We hope we can use this to find the most deserving research and researchers, regardless of what institutions they’re affiliated with or how connected they are,” Weis says.

As with all machine learning frameworks, however, designers and users should be alert to bias, he adds. “We need to constantly be aware of potential biases in our data and models. We want DELPHI to help find the best research in a less-biased way — so we need to be careful our models are not learning to predict future impact solely on the basis of sub-optimal metrics like h-Index, author citation count, or institutional affiliation.”

DELPHI could be a powerful tool to help scientific funding become more efficient and effective, and perhaps be used to create new classes of financial products related to science investment.

“The emerging metascience of science funding is pointing toward the need for a portfolio approach to scientific investment,” notes David Lang, executive director of the Experiment Foundation. “Weis and Jacobson have made a significant contribution to that understanding and, more importantly, its implementation with DELPHI.”

It’s something Weis has thought about a lot after his own experiences in launching venture capital funds and laboratory incubation facilities for biotechnology startups.

“I became increasingly cognizant that investors, including myself, were consistently looking for new companies in the same spots and with the same preconceptions,” he says. “There’s a giant wealth of highly-talented people and amazing technology that I started to glimpse, but that is often overlooked. I thought there must be a way to work in this space — and that machine learning could help us find and more effectively realize all this unmined potential.”


Source: Massachusetts Institute of Technology

Source URL: https://news.mit.edu/2021/using-machine-learning-predict-high-impact-research-0517?utm_campaign=Learning%20Posts&utm_content=167488607&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323