Integrated Biosciences’ Platform has potential to fuel advances in senolytic anti-aging compounds and longevity research

Integrated Biosciences’ new platform has potential to fuel advances in senolytic anti-aging compounds and longevity research

AI-driven platform built on work pioneered at MIT identifies three candidates with comparable efficacy and superior medicinal chemistry relative to current investigational compounds

Treatment of aged mice reduced number of senescent ‘zombie’ cells and lowered expression of senescence-associated genes.

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated senescent cells (left, with red stain) without affecting other cells (right). Using artificial intelligence, researchers from Integrated Biosciences have, for the first time, identified three senolytics with comparable efficacy and superior drug-like properties relative to leading investigational compounds. (Photo: Business Wire)

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated senescent cells (left, with red stain) without affecting other cells (right). Using artificial intelligence, researchers from Integrated Biosciences have, for the first time, identified three senolytics with comparable efficacy and superior drug-like properties relative to leading investigational compounds. (Photo: Business Wire)

May 04, 2023 11:00 AM Eastern Daylight Time

SAN CARLOS, Calif.--(BUSINESS WIRE)--Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, in collaboration with researchers at the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, today announced results demonstrating the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small molecules under intense study for their ability to suppress age-related processes such as fibrosis, inflammation and cancer. A new publication authored by company founders in Nature Aging, “Discovering small-molecule senolytics with deep neural networks,” describes the AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and superior medicinal chemistry properties than those of senolytics currently under investigation.

“This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. “These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today.”

Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that are no longer dividing. A hallmark of aging, senescent cells have been implicated in a broad spectrum of age-related diseases and conditions including cancer, diabetes, cardiovascular disease, and Alzheimer’s disease. Despite promising clinical results, most senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences was founded in 2022 to overcome these obstacles, target other neglected hallmarks of aging, and advance anti-aging drug development more generally using artificial intelligence, synthetic biology and other next-generation tools.  ... ' 

Generative AI at Mayo

AI in Healthcare at Mayo Clinic.  A good example for an example of data workflow.  

Mayo Clinic Partners With Google Cloud to Bring Generative AI to Healthcare

ERIC HAL SCHWARTZ on June 8, 2023 at 8:00 am

The Mayo Clinic is working on enhancing its work using generative AI as part of a new collaboration with Google. The healthcare center’s first project aims to speed up its research by integrating Google Cloud’s Enterprise Search in Generative AI App Builder within its clinical workflows. In tandem with the Mayo Clinic deal, Google Cloud announced that the Gen App Builder can now create systems capable of complying with HIPAA.

GENERATIVE MAYO

Google’s Gen App Builder offers an enterprise search feature useful for collating and finding data from a wide range of sources. Since doctors and medical professionals must look through patient records, research papers, hospital protocols, and other databases to plan any treatment or diagnosis plan, an AI assistant that can streamline the process has an obvious appeal. The HIPAA compliance Google Cloud’s platform now supports is crucial for the Mayo Clinic or any other healthcare provider, however, as they would want to ensure data security and privacy before they start linking generative AI products to patient records.

“Our prioritization of patient safety, privacy, and ethical considerations, means that generative AI can have a significant and positive impact on how we work and deliver healthcare,” Mayo Clinic chief information officer Cris Ross explained. “Google Cloud’s tools have the potential to unlock sources of information that typically aren’t searchable in a conventional manner, or are difficult to access or interpret, from a patient’s complex medical history to their imaging, genomics, and labs. Accessing insights more quickly and easily could drive more cures, create more connections with patients, and transform healthcare.”  .. ' 

AI Not Yet Intelligent Enough to Be a Trusted Research Aid

But can take over a number of time consuming sub tasks. 

AI Not Yet Intelligent Enough to Be a Trusted Research Aid

By The Scholarly Kitchen, April 27, 2023

I was still stuck without a confirmed reference to new information suggested by an entity claiming to be powered by AI.

Despite the current hype surrounding generative artificial intelligence (AI) tools such as ChatGPT, further development of AI capabilities is clearly required before one can embrace their use as reasonably trustworthy research tools. For now, the effort required is hardly worth the outcome.

Human oversight clearly remains crucial in interpreting any results from the GPT family of large language models. In fact, a dedicated scholar's due diligence clearly still trumps the potential of AI-like tools to find tidbits of new information.

From The Scholarly Kitchen

View Full Article       

Fraunhofer Talks AI

 Good comments, broadly stated, worth a further look.

Fraunhofer Strategic Research Field

Artificial Intelligence (AI)

Artificial intelligence, cognitive systems and machine learning play a crucial role in the future transformation of the economy and society. The Fraunhofer-Gesellschaft develops key technologies for AI and their applications at many institutes. Our research makes significant contributions to the development of secure, trustworthy and resource-efficient AI technologies and is closely oriented to the practical needs of companies and society.

They will not take over - but artificial intelligence systems will take over more and more tasks in factories, offices and everyday life: they control the autonomous car, make medical diagnoses and protect against cyber attacks. Numerous industries will be radically changed by these technologies - and will benefit from them. Artificial intelligence and technologies from its environment are therefore one of the most important digital topics of the future and play a decisive role in the future transformation of the economy and society.

AI is a strategic resource. A global race for these economically and strategically crucial technologies has therefore begun. A deficit of own competencies leads to dependencies on actors who, for example, have lower standards in data protection, security and algorithmic traceability, which endangers the technological sovereignty and competitiveness of Germany and Europe.

The Fraunhofer-Gesellschaft develops AI technologies at numerous institutes for applications in all sectors and areas - from automotive solutions to medical technology and non-destructive testing. The trustworthiness and security of the systems, their energy efficiency and the combination of expert knowledge with data-driven machine learning for better traceability of AI algorithms are always central.

Focal points

Hybrid AI Methods | AI systems | Resource-efficient AI | AI for Computing Architectures | Trusted AI

Fraunhofer institutions on the subject of AI..    ... ' 

Galactica: A Large Language Model for Science Research

Quite Useful idea for sharing orgaizedscience data with with Large Language Methods

 https://www.youtube.com/watch?v=ZTs_mXwMCs8&t=1418s

https://arxiv.org/abs/2211.09085

Galactica: A Large Language Model for Science

Computer Science > Computation and Language

[Submitted on 16 Nov 2022]

Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, Robert Stojnic

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community.

Subjects: Computation and Language (cs.CL); Machine Learning (stat.ML)

Cite as: arXiv:2211.09085 [cs.CL]

  (or arXiv:2211.09085v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2211.09085

Focus to learn more

Submission history

From: Robert Stojnic [view email]

[v1] Wed, 16 Nov 2022 18:06:33 UTC (10,715 KB)

A Call to Pause 'Giant AI Experiments'

 Shall we pause, will every one?  For 6 months.  Dangerous?  What difference will it make? 

ARTIFICIAL INTELLIGENCE/TECH

Elon Musk and top AI researchers call for pause on ‘giant AI experiments’

 An open letter says the current race dynamic in AI is dangerous, and calls for the creation of independent regulators to ensure future systems are safe to deploy.

By JAMES VINCENT  in The Verge

Mar 29, 2023, 5:08 AM EDT|96 Comments / 96 New

A number of well-known AI researchers — and Elon Musk — have signed an open letter  calling on AI labs around the world to pause development of large-scale AI systems, citing fears over the “profound risks to society and humanity” they claim this software poses.

The letter, published by the nonprofit Future of Life Institute, notes that AI labs are currently locked in an “out-of-control race” to develop and deploy machine learning systems “that no one — not even their creators — can understand, predict, or reliably control.”

“We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.”

“Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4,” says the letter. “This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.”

Signatories include author Yuval Noah Harari, Apple co-founder Steve Wozniak, Skype co-founder Jaan Tallinn, politician Andrew Yang, and a number of well-known AI researchers and CEOs, including Stuart Russell, Yoshua Bengio, Gary Marcus, and Emad Mostaque. The full list of signatories can be seen here, though new names should be treated with caution as there are reports of names being added to the list as a joke (e.g. OpenAI CEO Sam Altman, an individual who is partly responsible for the current race dynamic in AI).

The letter is unlikely to have any effect on the current climate in AI research, which has seen tech companies like Google and Microsoft rush to deploy new products, often sidelining previously-avowed concerns over safety and ethics. But it is a sign of the growing opposition to this “ship it now and fix it later” approach; an opposition that could potentially make its way into the political domain for consideration by actual legislators.

As noted in the letter, even OpenAI itself has expressed the potential need for “independent review” of future AI systems to ensure they meet safety standards. The signatories say that this time has now come.

“AI labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts,” they write. “These protocols should ensure that systems adhering to them are safe beyond a reasonable doubt.”

NVIDIA GPU Accellerated Applications

From NVIDIA< GPU Accellerated Applications.    -      Quite a considerable library  Examining.

Industries

Financial Services

Consumer Internet

Healthcare

Higher Education

Retail

Public Sector

All Industries >

Solutions

Data Analytics

Machine Learning

Deep Learning Training

AI Inference

Conversational AI

Prediction and Forecasting

Large Language Models

Software

NGC Catalog

NVIDIA NGC

RAPIDS

Apache Spark

Inference Serving - Triton

Recommender Systems - Merlin

Open Source Portal

AI Enterprise Suite

NVIDIA Workbench

Products

PC

Laptops & Workstations

Data Center

Cloud

Resources

Professional Services

Technical Training

Startups

AI Accelerator Program

Content Library  NVIDIA Research  Developer Blog......  . And many more.

Surfing Research Data Waves

 Key Data and Metadata gathered and collected for future use.

Surfing the Research Data Wave

University of Stuttgart (Germany), February 10, 2023

A new data exchange format developed by a team led by researchers at Germany's University of Stuttgart aims to provide a means for accessing and reusing large volumes of complex research data. With EnzymeML, researchers can record the comprehensive results of an enzymatic experiment and store that data in a structured and standardized manner, which ensures the machine-readable documents are interoperable and can be reused by other research groups. EnzymeML also allows for seamless communications between experimental platforms, electronic lab notebooks, enzyme kinetics modeling tools, publication platforms, and enzymatic reaction databases. Said University of Stuttgart's Simone Lauterbach, "We demonstrate the feasibility and usefulness of the EnzymeML toolbox using six scenarios where data and metadata from various enzymatic reactions is collected, analyzed, and uploaded to public databases for future use." ... .'

Priorities for Research in ChatGPT

ChatGPT: Five Priorities for Research  (Opinion in CACM) 

By Nature, February 3, 2023

A smartphone trying to connect to ChatGPT has message stating that it is too busy.

We are confident that science will find a way to benefit from conversational AI without losing the many important aspects that render scientific work one of the most profound and gratifying enterprises.

Conversational AI is likely to revolutionize research practices and publishing, creating both opportunities and concerns. It might accelerate the innovation process, shorten time-to-publication and, by helping people to write fluently, make science more equitable and increase the diversity of scientific perspectives. However, it could also degrade the quality and transparency of research and fundamentally alter our autonomy as human researchers.

The use of this technology is inevitable; banning it will not work. It is imperative that the research community engage in a debate about the implications of this potentially disruptive technology. ... ' 

From Nature   

View Full Article (May Require Paid Registration)

Google Research on Language Models

Good insight into what this is all about,  and what they are doing today and beyond.  Below just the intro, following:

Google Research, 2022 & beyond: Language, vision and generative models

WEDNESDAY, JANUARY 18, 2023

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the Google Research community  ... 

Today we kick off a series of blog posts about exciting new developments from Google Research. Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series.

I’ve always been interested in computers because of their ability to help people better understand the world around them. Over the last decade, much of the research done at Google has been in pursuit of a similar vision — to help people better understand the world around them and get things done. We want to build more capable machines that partner with people to accomplish a huge variety of tasks. All kinds of tasks. Complex, information-seeking tasks. Creative tasks, like creating music, drawing new pictures, or creating videos. Analysis and synthesis tasks, like crafting new documents or emails from a few sentences of guidance, or partnering with people to jointly write software together. We want to solve complex mathematical or scientific problems. Transform modalities, or translate the world’s information into any language. Diagnose complex diseases, or understand the physical world. Accomplish complex, multi-step actions in both the virtual software world and the physical world of robotics.

We’ve demonstrated early versions of some of these capabilities in research artifacts, and we’ve partnered with many teams across Google to ship some of these capabilities in Google products that touch the lives of billions of users. But the most exciting aspects of this journey still lie ahead!

With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. Let’s get started!  ... '  

Apple Forms Security Research Hub

New emphasis on Security Apple Security Research

Apple Launches New Security Research Hub

Apple engineers share technical details about the team's work on memory safety features on the new Apple Security Research site. 

dr_staff_125x125.jpg  Dark Reading Staff

Apple's work on hardening the memory allocator has made it harder for attackers to exploit certain classes of software vulnerabilities on iOS and Mac devices, the company's security engineers wrote on a new website Apple launched to share technical details behind iOS and MacOS security technologies.

The new initiative, Apple Security Research, also offers tools to help security researchers report issues to Apple, get real-time status updates for submitted reports, communicate securely with Apple engineers investigating the issue, and provides information about the Apple Security Bounty program. The intent behind the new security hub is to share with the research community how Apple engineers approach security challenges, and also to invite researcher contributions and feedback.

Memory safety is a key area of focus, especially since memory safety violations are the most widely exploited class of software vulnerabilities. On Apple platforms, improving memory safety includes "finding and fixing vulnerabilities, developing with safe languages, and deploying mitigations at scale," the engineers wrote in a technical post on XNU memory safety.  ... ' 

Thoughts on On the Value of 'Power Posing'.

But Please Spare us all Posing. 

A pose by any other name

The initial findings that posture equaled power have been debunked, but body language can still play a part in your success.

by Linda Rodriguez McRobbie   in Strategy-Business

It was a bit like those internet advertisements for “one weird trick to get rid of belly fat,” except this one weird trick came with impeccable credentials.

Amy Cuddy, a social psychologist with degrees from Princeton University, claimed that holding an “expansive nonverbal display”—say, chest out, hands on hips, legs sturdily apart exactly like Wonder Woman—for two minutes had measurable behavioral, psychological, and physiological benefits. Not surprisingly, the “power pose” became an overnight sensation; Cuddy’s 2012 TED Talk explaining her research earned tens of millions of views. Here was a quick-and-easy, no-cost way to boost your confidence, nail that job interview, negotiate that raise, and be the best you that you could possibly be.

If it seemed too good to be true, well, it was. Other researchers couldn’t replicate Cuddy’s central claim—that power posing decreased levels of cortisol, a hormone associated with stress, and increased testosterone levels, leading to more confidence and better performance—and by 2016, one of her coauthors had fully disavowed the concept. An academic pillorying ensued, and Cuddy’s career suffered.

Although the science behind power posing was flawed and the effects of standing like a superhero or sitting like a boss for two minutes were dramatically overstated, here’s the thing: how we move, and how we sit, stand, or, indeed, pose, does have an effect on how we feel, how we perform, and how others perceive us.

Emotion, embodied  .... 

Japan tries—again—to revitalize its research

Followed and visited Japan during the first AI wave, was impressed, but they did seen to take a downturn.  No clear enough way to integrate their Uni efforts?   Now emerging?  Next? A Solution?

Japan tries—again—to revitalize its research    in Science

Latest effort would spend billions on a few universities, but skeptics give it long odds

Alarmed by the declining stature of its universities, Japan is planning to shower up to $2.3 billion a year on a handful of schools in hopes of boosting their prominence. The scheme was approved by the Japanese legislature on 18 May, although many details, including how to pick the favored universities, are still up in the air. But the move, under study for more than a year, has rekindled a debate among academics over how to reverse Japan’s sinking research fortunes. Several previous schemes have yielded mixed results.

The new plan “aims to provide young promising scholars with the research environment that the world’s top universities are supposed to offer, to dramatically enhance international collaborations, and to promote the brain circulation both domestically and internationally,” says Takahiro Ueyama, a science policy specialist on the Council for Science, Technology and Innovation (CSTI), Japan’s highest science advisory body, which was heavily involved in crafting the scheme.

But Guojun Sheng, a Chinese developmental biologist at Kumamoto University in Japan, is skeptical. “I am not very optimistic that this [plan] will do much to curb the slide in the ranking of Japanese research activities or international competitiveness,” he says. Sheng, who previously studied and worked in China, the United States, and the United Kingdom, says the new plan does not address fundamental problems at Japanese research institutes: too few women and foreign scientists, a fear of change, and lack of support for young scientists. To get better results, “Japan has to change its research culture,” he says.

Concerns over Japan’s fading scientific clout have been growing for years. The nation’s $167 billion in spending on R&D in 2020 was topped only by the United States and China, according to the Organisation for Economic Co-operation and Development (OECD). But research productivity “is markedly below [Group of 20 countries] average and citation impact is low,” Clarivate’s Institute for Scientific Information concluded in its 2021 annual report on G-20 research activities. An August 2021 analysis by Japan’s National Institute of Science and Technology Policy (NISTEP) showed that Japan ranked fourth in its share of papers in the top 10% by number of citations from 1997 through 1999, then dropped to fifth between 2007 and 2009 and to 10th in 2017 to 2019 (see graphic). The drop is partly the result of the spectacular rise of China, which was not even in the top 10 in the 1990s and is now at first place. But Canada, France, Italy Australia, and India surpassed Japan as well.  ... ' 

Research Integrity Considered

Usefulness for internal research?

Want research integrity? Stop the blame game

Helping every scientist to improve is more effective than ferreting out a few frauds.

Malcolm Macleod

Most scientists reading this probably assume that their research-integrity office has nothing to do with them. It deals with people who cheat, right? Well, it’s not that simple: cheaters are relatively rare, but plenty of people produce imperfect, imprecise or uninterpretable results. If the quality of every scientist’s work could be made just a little better, then the aggregate impact on research integrity would be enormous.

How institutions can encourage broad, incremental improvements is what I have been working to figure out. Two things are needed: a collective shift in mindset, and a move towards appropriate measurement.

Over the past 2 years, some 20 institutions in the United Kingdom have joined the UK Reproducibility Network (UKRN), a consortium that promotes best practice in research. They have created senior administrative roles to improve research and research integrity. I have taken on this job (on top of my research on evaluating stroke treatments) at the University of Edinburgh. Since then, I’ve focused on research improvement rather than researcher accountability. Of course, deliberate fraud should be punished, but a focus on investigating individuals will discourage people from acknowledging mistakes, and mean that opportunities for systems to improve are neglected.

Research integrity: nine ways to move from talk to walk

At the University of Edinburgh, we have audits as part of projects to shrink bias in animal research, speed up publication and improve clinical-trial reporting. These are not the metrics that most researchers are used to. Many people are initially wary of yet another ‘external imposition’, but when they see that this is about promoting our own community’s standards — and that there are no extra forms to fill in — they usually welcome this shift in institutional focus

Here’s what we are learning to look for at my university.

Integrity indicators. Counting papers published in Science or Nature or prizes received is a poor reflection of performance. Measures should reflect the integrity of research claims: for instance, the proportion of quantitative studies that also publish data and code, and that pre-register their hypothesis, study design and analysis plan. At the University of Edinburgh, we are focusing on the reporting of randomization and blinding in published animal studies that test biomedical hypotheses. Existing tools can be applied to such tasks. The DOIs of publications that match a series of ORCIDs (author IDs) can be identified, the open-access status ascertained through the Unpaywall database, and these details can be linked back to institutions, departments or even individual research groups.

I care more about how my institution is doing compared with last year than about how it performs relative to other organizations. That said, benchmarking can be useful — and working with other organizations can help to develop standard reporting tools without reinventing the wheel.

Evidence of impact. Having data in hand allows an institution to focus on what can be improved, and how. In 2019, only 55% of Edinburgh clinical trials were fully reported on the European Union Clinical Trials Register. Programmes to reach trial organizers (by e-mailing reminders and mentoring them through the process) increased this to 95% in 2021. To build on that, I am working with members of UKRN and others to develop institutional dashboards that will provide real-time data across a range of measures, such as clinical-trial reporting and the quality and timeliness of reporting animal research. ... '

IBM and UK Research Quantum

 Many big players are inventing in quantum.  Watch it.   And the implications for things like optimization, crypto and analytics in general.   

IBM partners with U.K. on $300M quantum computing research initiative  in VentureBeat

Damon Poeter, June 4, 2021 3:20 PM

The U.K. government and IBM this week announced a five-year £210 million ($297.5 million) artificial intelligence (AI) and quantum computing collaboration, in the hopes of making new discoveries and developing sustainable technologies in fields ranging from life sciences to manufacturing.

The program will hire 60 scientists, as well as bringing in interns and students to work under the auspices of IBM Research and the U.K.’s Science and Technology Facilities Council (STFC) at the Hartree Centre in Daresbury, Cheshire. The newly formed Hartree National Centre for Digital Innovation (HNCDI) will “apply AI, high performance computing (HPC) and data analytics, quantum computing, and cloud technologies” to advance research in areas like materials development and environmental sustainability, IBM said in a statement.  ... " 

Machine Learning Determines Research Impact

 Good use example made here, note training made on key metadata.  Relatively straight forward.

Using ML to Predict High-Impact Research

MIT News, Becky Ham May 17, 2021

Researchers at the Massachusetts Institute of Technology (MIT) have developed an artificial intelligence framework able to predict future high-impact technologies based on patterns found in published scientific studies. The framework, DELPHI (Dynamic Early-warning by Learning to Predict High Impact), identified all pioneering papers on a list of key foundational biotechnologies as early as the first year after their publication. MIT's James W. Weis said the framework "functions by learning patterns from the history of science, and then pattern-matching on new publications to find early signals of high impact." DELPHI, which was trained on a full time-series network of journal article metadata, identified more than twice as many high-impact papers compared to citation numbers alone.  ... ' 

Wikibon Research

Brought back to my attention and of interest. 

Wikibon 2021 Research Focus

The Wikibon 2021 research focus is based on a community-based approach to deliver in-depth research and analysis across a broad range of enterprise technology topics. Our community has been built from the ground up using collaborative methods powered by Wikibon’s ethos of open content and theCUBE.

Wikibon was founded on the premise that practitioners collectively possess more knowledge than any one person or research entity. We have developed methods to package and curate that knowledge for our community. theCUBE is a digital offering that for more than ten years has operated TV productions at tech events. In addition, theCUBE operates digital studios on-premises and remotely from its offices in northern California and Massachusetts.

We also partner with several expert data providers, including Enterprise Technology Research (ETR), which publishes quarterly spending intentions data based on surveys of CIOs and enterprise technology buyers. Under our agreement, we can curate this survey data and selectively present data to support our research initiatives.

Our community comprises more than 50,000 individuals, many of whom have contributed their knowledge directly through interviews on theCUBE or in collaborative sessions with Wikibon Analysts.

Broadly, Wikibon’s topical coverage spans the following seven primary areas that have overlapping content vectors:

Cloud,  Infrastructure,  Data,   Security,  Enterprise Software,  Edge computing,  Emerging technologies

We provide selective market data, industry trends, customer spending patterns, key customer challenges, technology provider solutions, economic analysis, and analysis of relevant news within these sectors. ... ' 

Universities Capturing More Value from Research

 Sounds akin to our own work on gathering value from research.   Our recent work with the DOD to understand what research it has paid for, and can be monetized or placed into public use.  Good discussion. 

Should Universities Try to Capture More Value from Their Research?  Knowledge@Wharton

Supports K@W's  Innovation Content

A pair of newly published research papers co-authored by Wharton management professor David H. Hsu benchmark and explore commercialization drivers of academic science. The papers find that university research has produced pathbreaking innovations across many disciplines, many of which have been commercialized successfully. Yet, on average, universities capture 16% of the value they help create through licensing revenues or equity stakes in the startups their research spawns. Furthermore, some researchers and universities are much better able to commercialize their discoveries compared to others, even holding constant the discovery itself.

The first research paper, which Hsu wrote with Po-Hsuan Hsu, Tong Zhou and Arvids A. Ziedonis, is titled “Benchmarking U.S. University Patent Value and Commercialization Efforts: A New Approach” and was published this month in Research Policy. The second paper, “Revisiting the Entrepreneurial Commercialization of Academic Science: Evidence from ‘Twin’ Discoveries,” co-authored with Matt Marx, is forthcoming in Management Science.

The results suggest that universities with policies and resources devoted to commercialization efforts, aided by academic staff with commercialization experience and which are more interdisciplinary, are much more successful at translating research for commercial outcomes. Consequently, Hsu and his co-authors make a case for universities to take a closer look at the value they are extracting by commercializing their patents and intellectual property.... ' 

Cybersecurity Research

Further, a look at where research is focusing and worth following. 

Cybersecurity Research for the Future   By Terry Benzel

Communications of the ACM, January 2021, Vol. 64 No. 1, Pages 26-28   10.1145/3436241

The growth of myriad cyber-threats continues to accelerate, yet the stream of new and effective cyber-defense technologies has grown much more slowly. The gap between threat and defense has widened, as our adversaries deploy increasingly sophisticated attack technology and engage in cyber-crime with unprecedented power, resources, and global reach. We are in an escalating asymmetric cyber environment that calls for immediate action. The extension of cyber-attacks into the socio-techno realm and the use of cyber as an information influence and disinformation vector will continue to undermine our confidence in systems. The unknown is a growing threat in our cyber information systems.

Nonetheless, while the dark side is daunting, emerging research, development, and education across interdisciplinary topics addressing cybersecurity and privacy are yielding promising results. The shift from R&D on siloed add-on security, to new fundamental research that is interdisciplinary, and positions privacy, security, and trustworthiness as principal defining objectives, offer opportunities to achieve a shift in the asymmetric playing field.

Here, I will discuss three key considerations for cybersecurity research and development: interdisciplinary research themes, the role of experimentation in R&D, and education. Each of these will be the subject of future columns as we focus on opportunities for dramatically different security and privacy in our daily lives..... 

Code Can Now be added in arXiv Manuscripts

Code is often very important in current research, so it makes sense to include it.   But code of any non trivial amount is difficult to prove correct. so it adds another level of complexity.  And if you add data to drive the code,  a kind of context,  correctness can be further obscured.   Still like the idea of including the code, to show an application of the research.

arXiv Now Allows Researchers to Submit Code with Their Manuscripts VentureBeat,  By Khari Johnson

Machine learning resource Papers with Code said the preprint paper archive arXiv is now permitting researchers to submit code with their papers. A recent artificial intelligence (AI) industry report by London-based venture capital firm Air Street Capital found only 15% of submitted research manuscripts currently include code. Preprint repositories offer AI scientists the means to share their work immediately, before an often-protracted peer review process. Code shared on arXiv will be entered via Papers with Code, and the code submitted for each paper will appear on a new Code tab. Papers with Code co-creator Robert Stojnic said, "Having code on arXiv makes it much easier for researchers and practitioners to build on the latest machine learning research. We also hope this change has ripple effects on broader computational science beyond machine learning."