Mediaboss Marketing

Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage.

The bullish outlook is evident in the U.S. Bureau of Labor Statistics predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations.

The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.

Vanguards within the most data-centric industries, including pharmaceuticals, finance, insurance, aerospace and others, are investing heavily. They are assembling formidable teams of data scientists with varied backgrounds and expertise to develop and place ML models at the core of as many business processes as possible.

More often than not, they are running headlong into the challenges of executing data science projects across the regional, organizational, and technological divisions that abound in every organization. Data is worthless without the tools and infrastructure to use it, and both are fragmented across regions and business units, as well as in cloud and on-premises environments.

Even when analysts and data scientists overcome the hurdle of getting access to data in other parts of the business, they quickly find that they lack effective tools and hardware to leverage the data. At best, this results in low productivity, weeks of delays, and significantly higher costs due to suboptimal hardware, expensive data storage, and unnecessary data transfers. At worst, it results in project failure, or not being able to initiate the project to begin with.

Successful enterprises are learning to overcome these challenges by embracing hybrid-cloud strategies. Hybrid cloud the integrated use of on-premises and cloud environments also encompasses multicloud, the use of cloud offerings from multiple cloud providers. A hybrid-cloud approach enables companies to leverage the best of all worlds.

They can take advantage of the flexibility of cloud environments, the cost benefits of on-premises infrastructure, and the ability to select best-of-breed tools and services from any cloud vendor and machine learning operations tooling. More importantly for data science, hybrid cloud enables teams to leverage the end-to-end set of tools and infrastructure necessary to unlock data-driven value everywhere their data resides.

It allows them to arbitrage the inherent advantages of different environments while preserving data sovereignty and providing the flexibility to evolve as business and organizational conditions change.

While many organizations try to cope with disconnected platforms spread across different on-premises and cloud environments, today the most successful organizations understand that their data science operations must be hybrid cloud by design. That is, to implement end-to-end ML platforms that support hybrid cloud natively and provide integrated capabilities that work seamlessly and consistently across environments.

In a recent Forrester survey of AI infrastructure decision-makers, 71% of IT decision-makers say hybrid cloud support by their AI platform is important for executing their AI strategy, and 29% say its already critical. Further, 91% said they will be investing in hybrid cloud within two years, and 66% said they already had invested in hybrid support for AI workloads.

In addition to the overarching benefit of a hybrid-cloud strategy for data science the ability to execute data science projects and implement ML solutions anywhere in your business there are three key drivers that are accelerating the trend:

Data sovereignty: Regulatory requirements like GDPR are forcing companies to process data locally with the threat of heavy fines in more and more parts of the world. The EU Artificial Intelligence Act, which triages AI applications across three risk categories and calls for outright bans on applications deemed to be the riskiest, will go a step further than fines. Gartner predicts that 65% of the worlds population will soon be covered by similar regulations.

Cost optimization: The size of ML workloads grows as companies scale data science because of the increasing number of use cases, larger volumes of data and the use of computationally intensive, deep learning models. Hybrid-cloud platforms enable companies to direct workloads to the most cost-effective infrastructure; e.g., optimize utilization of an on-premise GPU cluster, and mitigate rising cloud costs.

Flexibility: Taking a hybrid-cloud approach allows for future-proofing to address the inevitable changes in business operations and IT strategy, such as a merger or acquisition involving a company that has a different tech stack, expansion to a new geography where your default cloud vendor does not operate or even a cloud vendor becoming a significant competitor.

Implementing a hybrid-cloud strategy for ML is easier said than done. For example, no public cloud vendor offers more than token support for on-premises workloads, let alone support for a competitors cloud, and the range of tools and infrastructure your data science teams need scales as you grow your data science rosters and undertake more ML projects. Here are the three essential capabilities for which every business must provide hybrid-cloud support in order to scale data science across the organization:

Full data science life cycle coverage: From model development to deployment to monitoring, enterprises need data science tooling and operations to manage every aspect of data science at scale.

Agnostic support for data science tooling: Given the variety of ML and AI projects and the differing skills and backgrounds of the data scientists across your distributed enterprise, your strategy needs to provide hybrid cloud support for the major open-source data science languages and frameworks and likely a few proprietary tools not to mention the extensibility to support the host of new tools and methods that are constantly being developed.

Scalable compute infrastructure: More data, more use cases and more advanced methods require the ability to scale up and scale out with distributed compute and GPU support, but this also requires an ability to support multiple distributed compute frameworks since no single framework is optimal for all workloads. Spark may work perfectly for data engineering, but you should expect that youll need a data-science-focused framework like Ray or Dask (or even OpenMPI) for your ML model training at scale.

Embedding ML models throughout your core business functions lies in the heart of AI-based digital transformation. Organizations must adopt a hybrid-cloud or equivalent multicloud strategy to expand beyond initial successes and deploy impactful ML solutions everywhere.

Data science teams need end-to-end, extensible and scalable hybrid-cloud ML platforms to access the tools, infrastructure and data they need to develop and deploy ML solutions across the business. Organizations need these platforms for the regulatory, cost and flexibility benefits they provide.

The Forrester survey notes that organizations that adopt hybrid cloud approaches to AI development are already seeing the benefits across the entire AI/ML life cycle, experiencing 48% fewer challenges in deploying and scaling their models than companies relying on a single cloud strategy. All evidence suggests that the vanguard of companies who have already invested in their data science teams and platforms are pulling even further ahead using hybrid cloud.

See original here:
Unlock the Next Wave of Machine Learning with the Hybrid Cloud - The New Stack

With chatbots like ChatGPT making a splash, machine learning is playing an increasingly prominent role in our lives. For many of us, its been a mixed bag. We rejoice when our Spotify For You playlist finds us a new jam, but groan as we scroll through a slew of targeted ads on our Instagram feeds.

Machine learning is also changing many fields that may seem surprising. One example is my discipline, ornithology the study of birds. It isnt just solving some of the biggest challenges associated with studying bird migration; more broadly, machine learning is expanding the ways in which people engage with birds. As spring migration picks up, heres a look at how machine learning is influencing ways to research birds and, ultimately, to protect them.

Most birds in the Western Hemisphere migrate twice a year, flying over entire continents between their breeding and nonbreeding grounds. While these journeys are awe-inspiring, they expose birds to many hazards en route, including extreme weather, food shortages and light pollution that can attract birds and cause them to collide with buildings.

Our ability to protect migratory birds is only as good as the science that tells us where they go. And that science has come a long way.

In 1920, the U.S. Geological Survey launched the Bird Banding Laboratory, spearheading an effort to put bands with unique markers on birds, then recapture the birds in new places to figure out where they traveled. Today researchers can deploy a variety of lightweight tracking tags on birds to discover their migration routes. These tools have uncovered the spatial patterns of where and when birds of many species migrate.

However, tracking birds has limitations. For one thing, over 4 billion birds migrate across the continent every year. Even with increasingly affordable equipment, the number of birds that we track is a drop in the bucket. And even within a species, migratory behavior may vary across sexes or populations.

Story continues

Further, tracking data tells us where birds have been, but it doesnt necessarily tell us where theyre going. Migration is dynamic, and the climates and landscapes that birds fly through are constantly changing. That means its crucial to be able to predict their movements.

This is where machine learning comes in. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn tasks or associations without explicitly being programmed. We use it to train algorithms that tackle various tasks, from forecasting weather to predicting March Madness upsets.

But applying machine learning requires data and the more data the better. Luckily, scientists have inadvertently compiled decades of data on migrating birds through the Next Generation Weather Radar system. This network, known as NEXRAD, is used to measure weather dynamics and help predict future weather events, but it also picks up signals from birds as they fly through the atmosphere.

BirdCast is a collaborative project of Colorado State University, the Cornell Lab of Ornithology and the University of Massachusetts that seeks to leverage that data to quantify bird migration. Machine learning is central to its operations. Researchers have known since the 1940s that birds show up on weather radar, but to make that data useful, we need to remove nonavian clutter and identify which scans contain bird movement.

This process would be painstaking by hand but by training algorithms to identify bird activity, we have automated this process and unlocked decades of migration data. And machine learning allows the BirdCast team to take things further: By training an algorithm to learn what atmospheric conditions are associated with migration, we can use predicted conditions to produce forecasts of migration across the continental U.S.

BirdCast began broadcasting these forecasts in 2018 and has become a popular tool in the birding community. Many users may recognize that radar data helps produce these forecasts, but fewer realize that its a product of machine learning.

Currently these forecasts cant tell us what species are in the air, but that could be changing. Last year, researchers at the Cornell Lab of Ornithology published an automated system that uses machine learning to detect and identify nocturnal flight calls. These are species-specific calls that birds make while migrating. Integrating this approach with BirdCast could give us a more complete picture of migration.

These advancements exemplify how effective machine learning can be when guided by expertise in the field where it is being applied. As a doctoral student, I joined Colorado State Universitys Aeroecology Lab with a strong ornithology background but no machine learning experience. Conversely, Ali Khalighifar, a postdoctoral researcher in our lab, has a background in machine learning but has never taken an ornithology class.

Together, we are working to enhance the models that make BirdCast run, often leaning on each others insights to move the project forward. Our collaboration typifies the convergence that allows us to use machine learning effectively.

Machine learning is also helping scientists engage the public in conservation. For example, forecasts produced by the BirdCast team are often used to inform Lights Out campaigns.

These initiatives seek to reduce artificial light from cities, which attracts migrating birds and increases their chances of colliding with human-built structures, such as buildings and communication towers. Lights Out campaigns can mobilize people to help protect birds at the flip of a switch.

As another example, the Merlin bird identification app seeks to create technology that makes birding easier for everyone. In 2021, the Merlin staff released a feature that automates song and call identification, allowing users to identify what theyre hearing in real time, like an ornithological version of Shazam.

This feature has opened the door for millions of people to engage with their natural spaces in a new way. Machine learning is a big part of what made it possible.

Sound ID is our biggest success in terms of replicating the magical experience of going birding with a skilled naturalist, Grant Van Horn, a staff researcher at the Cornell Lab of Ornithology who helped develop the algorithm behind this feature, told me.

Opportunities for applying machine learning in ornithology will only increase. As billions of birds migrate over North America to their breeding grounds this spring, people will engage with these flights in new ways, thanks to projects like BirdCast and Merlin. But that engagement is reciprocal: The data that birders collect will open new opportunities for applying machine learning.

Computers cant do this work themselves. Any successful machine learning project has a huge human component to it. That is the reason these projects are succeeding, Van Horn said to me.

This article is republished from The Conversation, an independent nonprofit news site dedicated to sharing ideas from academic experts. Like this article? Subscribe to our weekly newsletter.

It was written by: Miguel Jimenez, Colorado State University.

Read more:

Miguel Jimenez receives funding from the National Aeronautics and Space Administration.

Visit link:
Scientists are using machine learning to forecast bird migration and identify birds in flight by their calls - Yahoo News

AUSTIN, Texas, March 23, 2023 /PRNewswire/ -- Striveworks, a pioneer in responsible MLOps, today announceda partnership with Carahsoft Technology Corp., The Trusted Government IT Solutions Provider.Under the agreement, Carahsoft will serve as Striveworks' public sector distributor, making the company's Chariot platform and other software solutions available to government agencies through Carahsoft's reseller partners, NASA Solutions for Enterprise-Wide Procurement (SEWP) V, Information Technology Enterprise Solutions Software 2 (ITES-SW2), OMNIA Partners, and National Cooperative Purchasing Alliance (NCPA) contracts.

"We are excited to partner with Carahsoft and its reseller partners to leverage their public sector expertise and expand access to our products and solutions," said Quay Barnett, Executive Vice President at Striveworks. "Striveworks' inclusion on Carahsoft's contracts enables U.S. Federal, State, and Local Governments to make better models, faster."

Decision making in near-peer and contested environments requires end-to-end dynamic data capabilities that are rapidly deployed. Current solutions remain isolated, not scalable, and not integrated from enterprise to edge. The Striveworks and Carahsoft partnership helps simplify the procurement of Striveworks' AI and machine learning solutions.

Striveworks' Chariot provides a no-code/low-code solution that supports all phases of mission-relevant analytics including: developing, deploying, monitoring, and remediating models. Also available through the partnership is Ark, Striveworks' edge model deployment software for the rapid and custom integration of computer vision, sensors, and telemetry data collection.

"We are pleased to add Striveworks' solutions to our AI and machine learning portfolio," said Michael Adams, Director of Carahsoft's AI/ML Solutions Portfolio. "Striveworks' data science solutions and products allow government agencies to simplify their machine learning operations. We look forward to working with Striveworks and our reseller partners to help the public sector drive better outcomes in operationally relevant timelines."

Striveworks' offerings are available through Carahsoft's SEWP V contracts NNG15SC03B and NNG15SC27B, ITES-SW2 contract W52P1J-20-D-0042, NCPA contract NCPA001-86, and OMNIA Partners contract R191902. For more information contact Carahsoft at (888) 606-2770 or [emailprotected].

About Striveworks

Striveworks is a pioneer in responsible MLOpsfor national security and other highly regulated spaces. Striveworks' MLOps platform, Chariot, enables organizations to deploy AI/ML models at scale while maintaining full audit and remediation capabilities. Founded in 2018, Striveworks was highlighted as an exemplar in the National Security Commission for AI 2020 Final Report. For more information visit http://www.striveworks.com.

About Carahsoft

Carahsoft Technology Corp. is The Trusted Government IT Solutions Provider, supporting Public Sector organizations across Federal, State and Local Government agencies and Education and Healthcare markets. As the Master Government Aggregator for our vendor partners, we deliver solutions for Artificial Intelligence & Machine Learning, Cybersecurity, MultiCloud, DevSecOps, Big Data, Open Source, Customer Experience and more. Working with resellers, systems integrators and consultants, our sales and marketing teams provide industry leading IT products, services and training through hundreds of contract vehicles. Visit us at http://www.carahsoft.com.

Media ContactMary Lange(703) 230-7434[emailprotected]

SOURCE Striveworks, Inc.

Read this article:
Striveworks Partners With Carahsoft to Provide AI and Machine ... - PR Newswire

MOUNTAIN VIEW, Calif., March 21, 2023 /PRNewswire/ -- Applied Intuition, Inc., a simulation and software provider for autonomous vehicle (AV) development, has acquired SceneBox, a data management and operations platform built specifically for machine learning (ML). The core team of Caliber Data Labs, Inc., the creator of SceneBox, will join the Applied team.

The SceneBox platform enables engineers to train better, more accurate ML models with a data-centric approach. To successfully train production-grade ML models, teams rely heavily on high-quality datasets. When working with enormous unstructured data, finding the right datasets can be difficult, time-consuming, and costly. SceneBox lets engineers explore, curate, and compare datasets rapidly, diagnose problems, and orchestrate complex data operations. The platform offers a rich web interface, extensive APIs, and advanced features such as embedding-based search.

"We are thrilled to welcome Yaser and the SceneBox team to Applied," said Qasar Younis, Co-Founder and CEO of Applied Intuition. "When we learned of Yaser's vision and our complementary product strategies, we immediately wanted to join forces. The SceneBox team brings a wealth of knowledge and experience in ML and data ops that will help strengthen our offerings. We look forward to working together and better serving our customers."

"We are proud to be a part of the Applied team and the company's mission to accelerate the world's adoption of safe and intelligent machines," said Yaser Khalighi, Founder and CEO of Caliber Data Labs. "Autonomy is a data problem. I am confident that our joint expertise will allow customers to spend less time wrangling data and more time building better ML models."

DLA Piper LLP (U.S.) served as legal counsel to Applied Intuition. Fasken served as legal counsel to Caliber Data Labs.

About Applied IntuitionApplied Intuition's mission is to accelerate the world's adoption of safe and intelligent machines. The company's suite of simulation, validation, and data management software makes it faster, safer, and easier to bring autonomous systems to market. Autonomy programs across industries and 17 of the top 20 global automotive OEMs rely on Applied's solutions to develop, test, and deploy autonomous systems at scale. Learn more at https://applied.co.

About SceneBoxSceneBox is a Software 2.0 data engine for computer vision engineers. The Caliber Data Labs team built SceneBox as a modular and scalable platform that enables engineers to quickly search, curate, orchestrate, visualize, and debug massive perception datasets (e.g., camera and lidar images, videos, etc.). Teams can measure the performance of their ML models and fix problems using the right data. By helping engineers spend more time building ML models and less time wrangling data, SceneBox aims to fundamentally change the way perception data is managed at a global scale.

Photo - https://mma.prnewswire.com/media/2030895/Scenebox_Header.jpg

SOURCE Applied Intuition

The rest is here:
Applied Intuition Acquires the SceneBox Platform to Strengthen ... - PR Newswire

Mike DeCesaris and Sachin Sancheti detail how expert witnesses are incorporating artificial intelligence and machine learning into their testimony in a variety of civil cases.

Artificial intelligence has long been present in our everyday activities, from a simple Google search to keeping your car centered in its lane on the highway. The public unveiling of ChatGPT in late 2022, however, brought the power of AI closer to home, making it accessible to anyone with a web browser. And in the legal industry, we are seeing the use of AI and machine learning ramp up in litigation, especially when it comes to expert witness preparation and testimony.

The support of expert witnesses has always required leading-edge analytical tools and data science techniques, and AI and machine learning are increasingly important tools in experts arsenals. The concept of technology being able to think and make decisions, accomplishing tasks more quickly and with better results than humans, conjures thoughts of a Jetsons-like world run by robots. However, unlike the old Jetsons cartoons of the 1960s, where flying cars were the de facto mode of transport and robot attendants addressed every need, the futuristic ideas around the impact of AI were not that far off from a rapidly approaching reality. In fact, as older, rules-based AI has evolved into machine learning (ML) where computers are programmed to accurately predict outcomes by learning from patterns found in massive data sets, the legal industry has found that AI can do far more than many imagined.

In the world of litigation, the power of AI and ML have been understood for years by law firms and economic and financial consulting firms. AI is ideally suited to support, qualify, and substantiate expert work in litigation matters, which formerly relied on a heavily manual process to improve the efficiency or quality of the data presented in testimony. Moreover, over the last several years, AI and ML have been used directly in expert testimony by both plaintiff and defense side experts.

Somewhat ironically, humans are at least partially responsible for driving the increased use of AI and ML in expert work as we produce ever-growing volumes of user-generated content. Consumer reviews and social media posts, for example, are becoming increasingly relevant in regulatory and litigation matters, including consumer fraud and product liability cases. The volume of this content can be overwhelming, so one familiar approach involves leveraging keywords to identify a more manageable subset of data for review. This is limiting, however, as it often produces results that are irrelevant to the case while omitting relevant results containing novel language. By contrast, ML-based approaches can consider the entire text, using context and syntax to identify the linguistic elements that most accurately indicate relevance.

To see this approach in action, consider litigation involving alleged marketing misrepresentations or defamatory statements, which require an examination of the at-issue content. The most robust analyses are systematic and objective, making them ideal for outsourcing to the noncontroversial training data and impartial models that are hallmarks of state-of-the-art AI and ML approaches.

AI and ML have also proven to be valuable tools for experts across a broad spectrum of consumer fraud and product liability matters. While some scenarios may be obvious, humans possess the creativity to adapt a solution to other use cases. Here, these novel uses include:

Domain-specific sentiment analysis Publicly available sentiment models perform well on many problems but often fail on tasks that feature domain-specific linguistic structures. Such failure might arise when tasked with measuring the sentiment surrounding an entity in an industry whose discussion features novel or counterintuitive language. Consider a defamation suit filed by a fitness influencer. Terms like confusion, resistance, and to failure generally have negative connotations, but in the fitness space, are often used to describe a successful workout. Likewise, slang terms like guns and shredded mean something entirely different in the fitness context than in conventional use. In these cases, a general-purpose sentiment model may mischaracterize or overlook such language, while training a domain-specific sentiment model will provide a more accurate assessment of the sentiment contained in allegedly defamatory statements. This training process could involve gathering hundreds of thousands of user-generated reviews for industry products, and then directing a context-aware language model to predict the review score from the text. This custom model will quantify the polarity of the discussion surrounding the influencer, which can then be tracked through time and around certain critical events.

Assessing marketing influence on social media To assess allegations that a company steered an online discussion through social media marketing, AI and ML can compare the companys posts to those generated by unaffiliated users (earned media). This can be done using language models and text similarity metrics that quantitatively and objectively assess whether earned media immediately following the companys posts were more like the companys posts than either earned media preceding the posts or selected at random.

Image object detection To assess the incidences of client logos and products appearing across images posted to social media, a custom object detection model can be trained and applied to a random sample of millions of social media images.

Public press topic modeling To quantify the extent and timing of the public awareness of a marketing claim at issue, AI and ML can be applied to articles published in media outlets. This approach helps isolate the at-issue topic from other closely related but distinct topics. Such distinctions can then facilitate an analysis that is more narrowly focused on the claim at hand.

Multimedia characterization Where there are allegations of product misrepresentation or improper marketing, AI and ML can characterize the nature of a companys social media presence. A model trained on text and image content from unaffiliated but topically relevant brands can learn to distinguish content along the lines of broad brand identities (e.g., healthy vs. unhealthy, eco-friendly vs. climate-damaging). Applying such a model to at-issue social media content can quantify whether it conveys each of these brand features.

The nature of allegedly defamatory statements Even in the presence of clearly negative statements, defamation is notoriously difficult to prove. Defendants may claim that statements were expressed not as fact but as opinion, possibility, entertainment or satire. By leveraging datasets and models that identify the degree of certainty present in natural language examples, experts can objectively measure the degree to which reasonable consumers may interpret the information as fact.

Product liability One growing area of research concerns the quantification and isolation of specific entities referenced in a broader text. Product liability cases, for instance, may examine user-generated product reviews to identify the importance and sentiment surrounding at-issue product features. Rather than assess the review as a whole, aspect-based sentiment analysis focuses on at-issue features only, allowing for the extraction of strong indicators from nuanced or mixed reviews.

Class certification A successful class certification challenge will demonstrate that the circumstances of putative class members were sufficiently varied to require individual treatment. Any of the methods discussed above can be taken together to quantify the heterogeneity of the at-issue materials. For example, a case concerning marketing misrepresentations may train a classifier to distinguish at-issue marketing content from content not at issue, model the topics targeted throughout multiple distinct marketing campaigns, and summarize images to demonstrate differing appeal to different consumers.

For centuries, the ability of humans to mold available resources to serve their needs has separated them from less-evolved species. We see it in all walks of life, and the above examples demonstrate it in our small corner of the world. And we will continue to see it as the availability of voluminous social media and other user-generated data continues to expand and become more complex. In its simplest terms, AI and ML are critical in helping us efficiently search through the haystack to find the needle. Those who try to find the needle by hand will inevitably be left behind.

This article was originally published byLaw.com in March 2023.

The views expressed herein do not necessarily represent the views of Cornerstone Research.

Originally posted here:
How AI and Machine Learning Are Impacting the Litigation Landscape - Cornerstone Research