Still in its early stages, one especially promisingapplication of AI in health is its use with population health data sets to help address the growth of chronic diseases. A session on Day Two of Digital Health AI and Data will focus on how AI can meet the challengesfaced by patients with multiple long-term conditions (MLTC).

Simon Fraser, professor of public health at the School of Primary Care, Population Science and Medical Education at the University of Southampton, has spent much of his career as a public health specialist looking at the epidemiology of long-term conditions.

He will join Dr Gyucha Thomas Jun, professor of socio-technical system design at the School of Design and Creative Arts at Loughborough, Krish Nirantharakumar, professor of health data science and public health at the University of Birmingham and Professor Michael Barnes, professor of bioinformatics and director of the centre for translational bioinformatics at Queen Mary, University of London for the AI and Data session, which looks at how four UK research groups are using AI and data analytics to look at Englands 14 million people living with MLTCs.

Fraser, who heads one of the National Institute for Health and Care Researchs (NIHR) seven research consortia looking at multiple long-term condition multimorbidity, observes that understanding the development of long-term conditions to better inform prevention requires information from across the life course, but that electronic health records are recent enough that they cannot provide sufficient information on their own.

We are exploring data that have the potential to look at the whole life course, Fraser told Digital Health News, adding that the research group is using both birth cohorts groups of several thousand people born in the same week in time (for example, in 1970), for whom extensive data is collected every few years and ordinary healthcare information in electronic health records.

Although social determinants of health, from upbringing to education to income, are known to influence the likelihood that an individual will develop health problems, that level of detail is often hard for researchers to access and such data is not routinely collected in health settings.

The important thing is to fill in that gap and bring those determinants into the wider story of development of long-term conditions across the life-course, Fraser said.

Because of the complexity of those data, there is the potential for AI methods to help with clustering, sequencing and various other aspects of how people develop conditions over time.

Using technology to create data links

Technology also has the potential to learn across datasets, for example between birth cohorts and routine healthcare records. In secure data environments, linkage is also possible between electronic health records and other valuable sources, such as educational and census data.

This linkage can help researchers learn without incurring the risk of identifying individuals. Ultimately, the goal is to learn at what periods in the life course it might be possible to intervene to prevent or delay the onset of multiple long-term conditions, he said.

There are challenges related to the very different data types we are using that only have some overlapping domains, he said. The birth cohorts are very rich in these social data and wider determinants, but relatively limited on the long-term condition front. The electronic health data records are rich on long-term conditions but relatively limited on social data.

The challenge for the future, Fraser said, will be finding new ways of looking across both kinds of data sets in ways that are both reliable and secure in order to give a fuller picture of the lifecourse.

AI and Data is from the organisers of the market-leadingDigital Health RewiredandDigital Health Summer Schoolsevents and includes a wide-ranging programme of events on two stages: AI and Analytics and Data and Research.

All sessions are CPD accredited. AI and Data is free for the NHS, public sector, start-ups, charities, education and research. Commercial tickets start from 275+VAT.Register here.

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Artificial-intelligence (AI) tools are becoming increasingly common in science, and many scientists anticipate that they will soon be central to the practice of research, suggests a Nature survey of more than 1,600 researchers around the world.

Science and the new age of AI: a Nature special

When respondents were asked how useful they thought AI tools would become for their fields in the next decade, more than half expected the tools to be very important or essential. But scientists also expressed strong concerns about how AI is transforming the way that research is done.

The share of research papers that mention AI terms has risen in every field over the past decade, according to an analysis for this article by Nature.

Machine-learning statistical techniques are now well established, and the past few years have seen rapid advances in generative AI, including large language models (LLMs), that can produce fluent outputs such as text, images and code on the basis of the patterns in their training data. Scientists have been using these models to help summarize and write research papers, brainstorm ideas and write code, and some have been testing out generative AI to help produce new protein structures, improve weather forecasts and suggest medical diagnoses, among many other ideas.

See Supplementary information for full methodology.

With so much excitement about the expanding abilities of AI systems, Nature polled researchers about their views on the rise of AI in science, including both machine-learning and generative AI tools.

Focusing first on machine-learning, researchers picked out many ways that AI tools help them in their work. From a list of possible advantages, two-thirds noted that AI provides faster ways to process data, 58% said that it speeds up computations that were not previously feasible, and 55% mentioned that it saves scientists time and money.

AI has enabled me to make progress in answering biological questions where progress was previously infeasible, said Irene Kaplow, a computational biologist at Duke University in Durham, North Carolina.

The survey results also revealed widespread concerns about the impacts of AI on science. From a list of possible negative impacts, 69% of the researchers said that AI tools can lead to more reliance on pattern recognition without understanding, 58% said that results can entrench bias or discrimination in data, 55% thought that the tools could make fraud easier and 53% noted that ill-considered use can lead to irreproducible research.

The main problem is that AI is challenging our existing standards for proof and truth, said Jeffrey Chuang, who studies image analysis of cancer at the Jackson Laboratory in Farmington, Connecticut.

To assess the views of active researchers, Nature e-mailed more than 40,000 scientists who had published papers in the last 4 months of 2022, as well as inviting readers of the Nature Briefing to take the survey. Because researchers interested in AI were much more likely to respond to the invitation, the results arent representative of all scientists. However, the respondents fell into 3 groups: 48% who directly developed or studied AI themselves, 30% who had used AI for their research, and the remaining 22% who did not use AI in their science. (These categories were more useful for probing different responses than were respondents research fields, genders or geographical regions; see Supplementary information for full methodology).

Among those who used AI in their research, more than one-quarter felt that AI tools would become essential to their field in the next decade, compared with 4% who thought the tools essential now, and another 47% felt AI would be very useful. (Those whose research field was already AI were not asked this question.) Researchers who dont use AI were, unsurprisingly, less excited. Even so, 9% felt these techniques would become essential in the next decade, and another 34% said they would be very useful.

The chatbot ChatGPT and its LLM cousins were the tools that researchers mentioned most often when asked to type in the most impressive or useful example of AI tools in science (closely followed by protein-folding AI tools, such as AlphaFold, that create 3D models of proteins from amino-acid sequences). But ChatGPT also topped researchers choice of the most concerning uses of AI in science. When asked to select from a list of possible negative impacts of generative AI, 68% of researchers worried about proliferating misinformation, another 68% thought that it would make plagiarism easier and detection harder, and 66% were worried about bringing mistakes or inaccuracies into research papers.

Respondents added that they were worried about faked studies, false information and perpetuating bias if AI tools for medical diagnostics were trained on historically biased data. Scientists have seen evidence of this: a team in the United States reported, for instance, that when they asked the LLM GPT-4 to suggest diagnoses and treatments for a series of clinical case studies, the answers varied depending on the patients race or gender (T. Zack et al. Preprint at medRxiv https://doi.org/ktdz; 2023) probably reflecting the text that the chatbot was trained on.

There is clearly misuse of large language models, inaccuracy and hollow but professional-sounding results that lack creativity, said Isabella Degen, a software engineer and former entrepreneur who is now studying for a PhD in using AI in medicine at the University of Bristol, UK. In my opinion, we dont understand well where the border between good use and misuse is.

The clearest benefit, researchers thought, was that LLMs aided researchers whose first language is not English, by helping to improve the grammar and style of their research papers, or to summarize or translate other work. A small number of malicious players notwithstanding, the academic community can demonstrate how to use these tools for good, said Kedar Hippalgaonkar, a materials scientist at the National University of Singapore.

Researchers who regularly use LLMs at work are still in a minority, even among the interested group who took Natures survey. Some 28% of those who studied AI said they used generative AI products such as LLMs every day or more than once a week, 13% of those who only use AI said they did, and just 1% among others, although many had at least tried the tools.

Moreover, the most popular use among all groups was for creative fun unrelated to research (one respondent used ChatGPT to suggest recipes); a smaller share used the tools to write code, brainstorm research ideas and to help write research papers.

Some scientists were unimpressed by the output of LLMs. It feels ChatGPT has copied all the bad writing habits of humans: using a lot of words to say very little, one researcher who uses the LLM to help copy-edit papers wrote. Although some were excited by the potential of LLMs for summarizing data into narratives, others had a negative reaction. If we use AI to read and write articles, science will soon move from for humans by humans to for machines by machines, wrote Johannes Niskanen, a physicist at the University of Turku in Finland.

Around half of the scientists in the survey said that there were barriers preventing them from developing or using AI as much as they would like but the obstacles seem to be different for different groups. The researchers who directly studied AI were most concerned about a lack of computing resources, funding for their work and high-quality data to run AI on. Those who work in other fields but use AI in their research tended to be more worried by a lack of skilled scientists and training resources, and they also mentioned security and privacy considerations. Researchers who didnt use AI generally said that they didnt need it or find it useful, or that they lacked experience or time to investigate it.

Another theme that emerged from the survey was that commercial firms dominate computing resources for AI and ownership of AI tools and this was a concern for some respondents. Of the scientists in the survey who studied AI, 23% said they collaborated with or worked at firms developing these tools (with Google and Microsoft the most often named), whereas 7% of those who used AI did so. Overall, slightly more than half of those surveyed felt it was very or somewhat important that researchers using AI collaborate with scientists at such firms.

The principles of LLMs can be usefully applied to build similar models in bioinformatics and cheminformatics, says Garrett Morris, a chemist at the University of Oxford, UK, who works on software for drug discovery, but its clear that the models must be extremely large. Only a very small number of entities on the planet have the capabilities to train the very large models which require large numbers of GPUs [graphics processing units], the ability to run them for months, and to pay the electricity bill. That constraint is limiting sciences ability to make these kinds of discoveries, he says.

Researchers have repeatedly warned that the naive use of AI tools in science can lead to mistakes, false positives and irreproducible findings potentially wasting time and effort. And in the survey, some scientists said they were concerned about poor-quality research in papers that used AI. Machine learning can sometimes be useful, but AI is causing more damage than it helps. It leads to false discoveries due to scientists using AI without knowing what they are doing, said Lior Shamir, a computer scientist at Kansas State University in Manhattan.

When asked if journal editors and peer reviewers could adequately review papers that used AI, respondents were split. Among the scientists who used AI for their work but didnt directly develop it, around half said they didnt know, one-quarter thought reviews were adequate, and one-quarter thought they were not. Those who developed AI directly tended to have a more positive opinion of the editorial and review processes.

Reviewers seem to lack the required skills and I see many papers that make basic mistakes in methodology, or lack even basic information to be able to reproduce the results, says Duncan Watson-Parris, an atmospheric physicist who uses machine learning at the Scripps Institution of Oceanography in San Diego, California. The key, he says, is whether journal editors are able to find referees with enough expertise to review the studies.

That can be difficult to do, according to one Japanese respondent who worked in earth sciences but didnt want to be named. As an editor, its very hard to find reviewers who are familiar both with machine-learning (ML) methods and with the science that ML is applied to, he wrote.

Nature also asked respondents how concerned they were by seven potential impacts of AI on society which have been widely discussed in the news. The potential for AI to be used to spread misinformation was the most worrying prospect for the researchers, with two-thirds saying they were extremely or very concerned by it. Automated AI weapons and AI-assisted surveillance were also high up on the list. The least concerning impact was the idea that AI might be an existential threat to humanity although almost one-fifth of respondents still said they were extremely or very concerned by this prospect.

Many researchers, however, said AI and LLMs were here to stay. AI is transformative, wrote Yury Popov, a specialist in liver disease at the Beth Israel Deaconess Medical Center in Boston, Massachusetts. We have to focus now on how to make sure it brings more benefit than issues.

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AI and science: what 1,600 researchers think - Nature.com

New research focused on brook trout is using artificial intelligence to identify individual fish, with the goal of building population models that track trout health and habitat changes.

This groundbreaking use of AI, a collaboration between data scientists at the University of Virginia and the U.S. Geological Survey, will create a more efficient and accurate way to track trout by using fish-ial recognition software.

Researchers are classifying fish in both controlled and natural environments in West Virginia and Massachusetts, building a unique database that has the potential to save the taxpayer millions of dollars and advance protective measures for trout and streams. They hope to engage anglers as boots-on-the-ground citizen scientists to assist with the project, creating an interactive application where fishermen can upload images of fish and participate in protecting the health of brook trout and preservingtheir natural environment.

Fish biologists have been studying climate change and conservation for decades, and tracking fish is not new. Previously, however, scientists have had to use markers or injections to identify individual fish, methods that are invasive, require minor surgery, and do not work on small fish. The new frontier is individual recognition using AI technology, said Nathaniel Hitt, a research fish biologist with the U.S. Geological Survey.

The project originated during work at Shenandoah National Park by researchers from the U.S. Geological Surveys Ecological Science Center in West Virginia. We were using video sampling in stream pools to estimate the abundance of brook trout. We would take underwater video and have human observers count fish, said Hitt. We actually crowdsourced this to schools across the nation.

The success of the crowdsourcing got the fish biologists thinking about how they could automate the process. With the rise of AI and computer science applications like facial recognition software, they thought, why not apply it to fish? Brook trout have unique identifying markings, making them the perfect fish species to test this theory.

Brook trout are unique in that they are the only native trout of Appalachia and have been around for millions of years. Anglers for generations have come to love the fish and are invested in protecting its future. Brook trout have ecological importance as well, according to Hitt: Theyre the canary in the coal mine for climate change.

Ben Letcher, a research ecologist at the Conte Research Laboratory in Turners Falls, Mass., who is partnering on the project, explains: Each state in New England has cold-water criteria, and some states use the presence of a brook trout to identify a cold-water stream. Cold-water streams get special protections, so knowing where the trout are now and where they will be in the future is important for land protection and conservation.

To build a database of images large enough to be useful for prediction models, the researchers are capturing fish images in both controlled fisheries in West Virginia and in the wild streams of western Massachusetts, using different methods while working toward the same goal.

In Massachusetts, the team uses an electrofisher backpack to collect fish. They then place the caught fish in a bucket, anesthetize a few at a time, and then take measurements and photographs before releasing them back into the stream from which they came. In West Virginia researchers have used GoPro cameras to collect images of fish while they swim in tanks. The team then uses anesthetics to capture measurements and take additional photographs.

All of those images are then shared with data scientists at the University of Virginia who feed them into an image processing pipeline that identifies individual fish features. The team, led by Sheng Li, an assistant professor of data science at UVA, then trains the model to improve image recognition.

Its quite challenging, said Li. You see a large variation in fish appearance such as body size and other changes over time. We have had to developmultiple AI methods to improve the recognition of each individual fish.

The data scientists rely heavily on the images provided by the on-the-ground fish biologists and ecologists wading into streams, catching fish, and carefully and categorically photographing them.

Everyone on the project credits the projects success to interdisciplinary collaboration. Fish experts like Hitt and Letcher work with computer and data scientists like Li and others to use new techniques to solve old, persistent problems.

Hitt believes the tools they are developing using AI could have applications toward research on any animal with spots. We envision this transforming fish biology globally.

But the challenge of amassing a large enough and current database of images remains, which is why the team hopes to appeal to citizen scientists and anglers to be active participants.

By using their phone to capture and upload photos of fish caught, a future interactive database has the potential to identify a specific fish, trace its tracking history, and feed up-to-date information in real time. The U.S. Geological Survey is working with fishing expedition companies to test out this new method of collecting data.

Letcher predicts a phone application could be created where an angler takes a photo of caught fish; uploads it to an open, shared database; and learns its exact identification and history. This could be very valuable for collecting scientific information but also to engage anglers in new ways, he said.

Using images, we can create individual fish ID and could monitor population trajectories, said Hitt, but this also changes the relationship between anglers and these natural resources. It fosters a deeper sense of stewardship and connection to the streams and rivers.

When speaking of brook trout, Letcher and his colleagues become almost reverent. Youre taking something so ancient, so deeply rooted in the evolution of our planet, and developing a new appreciation and respect for it.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Fish-ial recognition software aims to protect trout - EurekAlert

Duality leverages modern PETs and privacy-preserving AI to deliver faster and more secure data collaboration for healthcare, financial services, government, and more.

HOBOKEN, N.J., Sept. 28, 2023 /PRNewswire/ -- Duality Technologies, the leader in secure data collaboration for enterprises and government agencies, today announced it has joined the Amazon Web Services (AWS) Partner Network (APN) and launched its secure data collaboration platformin AWS Marketplace, a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on AWS.

Duality protects the intellectual property of AI/ML models and the security and privacy of the data used for training.

The APN is a global community of AWS Partners that leverage programs, expertise, and resources to build, market, and sell customer offerings in diverse global markets. Duality Technologies underwent the comprehensive AWS Foundational Technical Review (FTR) to certify the enterprise readiness of its platform. As an APN member, Duality allows AWS users to securely collaborate on data without requiring direct access to the raw data, supporting privacy regulations and unlocking additional data sources not previously permitted. Duality is also being used to train models while protecting the intellectual property (IP) of the artificial intelligence and machine learning (AI/ML) models and maintaining the security and privacy of the protected health or personally identifiable information (PII/PHI) used for training and model personalization.

"Duality's inclusion in the APN and its availability in AWS Marketplace means AWS customers can more easily collaborate on data science projects utilizing sensitive and regulated data across their business ecosystem from a single location within AWS," said VP of Product, Adi Hirschstein, Duality Technologies. "This adds privacy and security guardrails required by various regulated industries and organizations to leverage AWS services like AWS Nitro Enclaves and Amazon SageMaker. Not only that, but AWS customers will find that by making it easier to work with sensitive, these integrations will accelerate data-driven innovations and growth strategies."

Duality's enterprise-ready secure data collaboration platform operationalizes PrivacyEnhancing Technologies (PETs) to empower users to unleash the full value of collaborative data science and AI while minimizing risk. Organizations can securely share, analyze, and enrich sensitive data to gain business value while raw data remains encrypted throughout the entire data science lifecycle, minimizing the risk of exposure and ensuring compliance with data protection and industry regulations. Duality's uniquely secure solution is made possible vialeading-edge cryptographic and security technologies.

Duality has integrated with both Amazon SageMaker and AWS Nitro Enclaves to enable seamless integration with AWS services. The integration with AWS Nitro Enclaves expands the privacy-enhancing capabilities of Duality's platform, allowing organizations to collaborate on any data type with any type of model. The integration with Amazon SageMaker now allows companies to benefit from AWS model outputs using data that would otherwise be off-limits due to IP/PII/PHI in the data set.

"As an unequivocal global leader in making privacy technology real and practical, we're thrilled to bring the power of secure data collaboration to AWS. Combining Duality and AWS allows data-first organizations to securely apply data science and machine learning on sensitive data, further breaking down silos that exist within and between organizations," said Prof.Kurt Rohloff, chief technical officer and co-founder of Duality.

AWS customers can utilize Duality's secure collaboration solution today through AWS Marketplace.

As an APN member, Duality joins a global network of 100,000 AWS Partners from more than 150 countries working with AWS to provide innovative solutions, solve technical challenges, and deliver value to mutual customers.

About Duality Technologies

Dualityis the leader in privacy enhanced secure data collaboration, empowering organizations worldwide to maximize the value of their data without compromising on privacy or regulatory compliance. Founded and led by world-renowned cryptographers and data scientists, Duality operationalizes privacy enhancing technologies (PETs) to accelerate data insights by enabling analysis and AI on sensitive data while protecting data privacy, compliance, and protecting valuable IP. A World Economic Forum (WEF) Tech Pioneer and a Gartner Cool Vendor, Duality is recognized by numerous industry awards, including Fast Company 2023 World Changing Ideas award, 2023 CyberTech 100 Most Innovative Companies list, 2022 CB Insights' AI 100, the 2022 RegTech 100 Awards, and the AIFinTech100 2022 Awards.Learn more.

CONTACT: Derek Wood[emailprotected]+1 917-310-1175[emailprotected]

SOURCE Duality Technologies, Inc.

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Theres no talking about the Summer of Data without including a significant addition about the year of artificial intelligence the two are inextricably linked and will remain so in the months to come.

Thats because the rise of AI has led to the need for incredible amounts of data, and projections indicate that data centers are to become the worlds largest energy consumers, rising from 3% of total electricity use in 2017 to 4.5% by 2025. Indeed, more companies are seeing their data needs grow on a yearly basis, leading to the characterization that every company is a data company.

With an eye on approaching this challenge, various technological advancements have rolled out in 2023, including a necessity for data storage innovation. Next-generation storage solutions are estimated to be valued at more than $150 billion by 2032, according to a recent studyfrom Global Market Insights Inc.

Its also no surprise that every vendor offering data-related solutions are striving to secure a share of whats estimated to be a total addressable market in the tens of billions of dollars when it comes to data platforms, noted Rob Strechay, lead analyst for theCollective from theCUBE, in an analysis for SiliconANGLE.

The opportunities for storage platform vendors and data platform vendors lie in integrating data platforms as-a-service into their storage offerings, Strechay wrote.

With all of these changes and demands in mind, some of the major players in data including Snowflake Inc., MongoDB Inc., VAST Data Inc. and Databricks Inc. spent the Summer of Data unveiling their strategies as data becomes even more important in support of AIs evolution.

Though all of these companies and others like them are responding to the same challenges, their solutions differ. Thats why, with the Summer of Data in the rearview, its worth a recap of what we learned so far and where these companies could be heading next.

This feature is part of SiliconANGLE Medias ongoing series with theCUBE exploring the latest developments in the data storage and AI market.

Before this years Snowflake Summit, the companys stated target of $10 billion in revenue for fiscal year 2028 left plenty of open questions about how they might get there. Over the course of this year, meanwhile, theCUBE has produced a number of in-depth analyses, laying out a mental model for the future of data platforms.

In his post-summit analysis, theCUBE analyst Dave Vellante discussed the vision outlined by Snowflake during this years Snowflake Summit, from its keynote presentations to product announcements. The companys intention was clearly to be the number one platform on which this new breed of data applications will be built, according to Vellante.

This weeks Snowflake Summit further confirmed our expectations with a strong top-line message of All Data/All Workloads, and a technical foundation that supports an expanded number of ways to access data, Vellante wrote. Squinting through the messaging and firehose of product announcements, we believe Snowflakes core differentiation is its emerging ability to be a complete platform for data applications. Just about all competitors either analyze data or manage data.

Other companies have also been weighing their strategies as the world of data storage evolves and as data and AI converge. For VAST, that looks like an evolution beyond being a storage company. In early August, VAST announced a new, global data infrastructure for AI called the VAST Data Platform, with an aim to unify data storage, database and virtualized compute engine services in a scalable system.

By bringing together structured and unstructured data in a high-performance, globally distributed namespace with real-time analysis, VAST is not only tackling fundamental DBMS challenges of data access and latency but also offering genuinely disruptive data infrastructure that provides the foundation organizations need to solve the problems they havent yet attempted, Market Strategy analyst Merv Adrian said at the time of the announcement.

Meanwhile, the realities of modern business with challenges such as the skills shortage considered mean developers must be kept happy. That has been good news for companies such ascloud database provider MongoDB.

The company recently saw its stock soar with blowout fiscal first-quarter earnings results, which posed an interesting question to watch in advance of MongoDB .local NYC in June: Was AI contributing to the surge in stock price?

DevOps democratization has surged over the past 20 years, but AI has posed a new wrinkle. AI isnt the only thing to consider as developers seek to go next-level with their data, according to Mark Porter, chief technology officer of MongoDB, during an interview with theCUBE during MongoDB .local NYC.

It is currently the thing thats really exciting, and being able to build great apps that do great things with your core data is always going to be important, he said. But whats happening is people are enhancing their apps with AI.

With hundreds of people using MongoDB as the foundation of their AI apps, Porter pointed to the companys developer data platform as key to this arrangement. Meanwhile, Databricks recently acquired Okera Inc., a data governance platform with a focus on AI with a stated goal to expand its own governance and compliance capabilities when it comes to machine learning and large language model AIs. Customers used to control access to their data using simple data controls that only needed to address one plane, such as a database.

The rise of AI, in particular machine learning models and LLMs, is making this approach insufficient, the Databricks team, including Chief Executive Officer Ali Ghodsi, explained in the announcement.

As an industry leader, many are watching what Databricks is doing closely, including Vellante. The big question for the company this summer was surrounding how it would execute its critical strategic decisions in the future as hype and confusion continued to swirl around the world of AI.

Emerging customer data requirements and market forces are conspiring in a way that we believe will cause modern data platform players generally and Databricks specifically to make some key directional decisions and perhaps even reinvent themselves, Vellante wrote in an edition of his Breaking Analysis series.

After the Data + AI Summit, those connections began to come into better view. In a new world where data is influenced by broader trends in AI, Databricks is back in its wheelhouse, according to Doug Henschen, vice president and principal analyst at Constellation Research Inc.

I think generative AI for the last three years, theyve been building up the warehouse side of their Lakehouse and making a case, he said. All this time data science has been their wheelhouse, and their strength and their customers are here, while others are making announcements of previews thatll help eventually down the road on AI. This is where its really happening, and theyre building generative models today.

TheSummer of Data may be over, but its clear that the evolution of AI will continue to play into the strategy of major players in data for many months to come. That will lead to solutions such as the adoption of next-generation storage solutions and their valuation at over $150 billion by 2023.

Though the AI-powered hybrid-multi-super cloud comes with various demands on data, companies such as those mentioned above have laid out their plans during the Summer of Data, and the year ahead will be critical as those same companies are tasked to execute. So, too, will various data platforms continue to evolve.

Most traditional applications are built on compute, networking and storage infrastructure, but the future will see applications program the real world, George Gilbert, a contributor to theCUBE, wrote in a recent analysis.

In that world, data-driven digital twins representing real-world people, places, things and activities will be on the platform, Gilbert wrote, which explains the stakes at hand.

On balance, we believe that the distributed nature of data originating from real-world things, combined with the desire for real-time actions, will further stress existing data platforms. We expect a variety of approaches will emerge to address future data challenges, he wrote. These will come at the problem starting from a traditional data management perspective (such as Snowflake), a data science view of the world (such as Databricks) and core infrastructure prowess (cloud/infrastructure-as-a-service, compute and storage vendors).

Clearly, the challenges around data remain the same as AI continues its meteoric rise. The upcoming months will be critical as the needs of companies in this new world continue to be of paramount importance.

TheCUBEis an important partner to the industry. You guys really are a part of our events and we really appreciate you coming and I know people appreciate thecontent you create as well Andy Jassy

THANK YOU

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With the summer of data in the rear-view mirror, here are the key ... - SiliconANGLE News