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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.

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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.

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SOURCE Applied Intuition

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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.

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How AI and Machine Learning Are Impacting the Litigation Landscape - Cornerstone Research

Two is better than one, isnt it? This is undoubtedly true in the case of IoT and machine learning. These two most popular and trending technologies are offering a solid growth system for companies if implemented together correctly. When combined, they help you unlock the true power of data and boost business efficiency, sales, and customer relationships.

Therefore, incorporation of IoT and machine learning in business is seen on a wide scale. We are going to discuss some of the popular areas where these technologies are used. Before that, lets see some statistics around them.

Statistics Showing Trend of IoT and MLAccording to IoT analytics, the world will have 14.4 billion IoT-connected devices by the end of 2022 which is 10% more than the previous year.

By 2025, this number will reach approximately 27 billion clearly indicating that businesses are quickly adopting it. The market of machine learning, on the other hand, is expected to cross the $200 billion mark by 2025. These figures are enough to confidently say that the market of IoT and machine learning are not going to slow down anytime, but rather will increase over time.

Now, a question pops up: what are the benefits of using IoT and machine learning in business? First things first, knowing how they work together will help you understand the true value they add to your business.

How IoT and Machine Learning Work Together?As the name suggested, the Internet of things is a network of all devices having sensors, connected through the internet. This connection gives them the ability to communicate with any other device on the network.

What after that? How will you put that data to use? Machine learning is the answer. It is a subset of AI and a process of using data to develop mathematical models or algorithms to train the computer without much human interference.

With that learning, the system can be used to anticipate the most likely plot based on the data. The prediction can be wrong or right and depending on that algorithm updates itself to deliver a better possible scenario next time.

Thus, both complement each other to give a competitive advantage to businesses over others through data accumulation and analysis so that they can decide whats better for their growth. This is true for every type of sector, be it healthcare, finance, automotive, agriculture, manufacturing, and more.

But theres more than the above-mentioned reason to use IoT and machine learning in business processes. Lets understand their role in different businesses better and what advantages they offer.

Benefits of IoT and Machine Learning for Businesses -It Automates the Business ProcessesFor any organization, whether small or large, there are a certain set of business processes. Each one should be efficient to achieve the organizations goal. However, monotonous tasks like scheduling emails or record-keeping processes can cause unnecessary delays and hamper overall productivity.

Machine learning and IoT can automate those boring and repetitive tasks to streamline the business process. Not just that, it reduces the chances of human errors, and inefficiencies, improves follow-up with the lead, scheduling of marketing campaigns, events, etc.

Adds an Extra Layer of SecurityNo place is protected from accidents, frauds, and cyber-attacks. They are common in the industry and if not addressed immediately can cause major losses to the business, its employees, and customers.

But it is hard to keep an eye on every single area or device. Using IoT and machine learning in business not only help in monitoring each aspect to identify loopholes and threats but also let you take necessary preventive measures beforehand.

Helps Identifying the Productive ResourcesWhether it's financial, human, physical, or technological resources your business has, it is essential to filter out the most productive ones and eliminate the rarely used resources. With use of IoT and machine learning in business processes, you can assist you in analysing this and prevent unnecessary expenses on those unused and non-productive resources. They can also suggest where your company needs to utilize those resources.

Helps Understanding the CustomersCustomers are an important asset of any company. Making them satisfied is thus important to be successful and increase revenue. Machine learning and IoT can help companies in delivering what their customers want without guessing it. They can learn how customers are interacting with their brand and what things they dislike or like the most.

With all the valuable insights in your hands, you can create products and services they are expecting the most. Or analyze which one is doing good in the market. This way brands can benefit in two ways- delivering better customer experience and increasing revenue by delivering the right products to the audience. For e-commerce platforms, machine learning and IoT are the go-to technologies to achieve this.

Use Cases of IoT and Machine Learning in Various Businesses -Retail Industry: Supply Chain ManagementThe supply chain industry is data-reliant which means wrong or incomplete data can cause several issues in the process. Cost inefficiency, technical downtimes, problem in determining price and transportation costs, inventory theft and loss, etc are a few such problems they face.

Implementing IoT sensors on the devices involved to extract vital data and then send them to machine-learning models can help in the following ways.

Improve the quality of products Reduce operational costs Check the status of delivery Prevent inventory theft and fraud Maintain the balance between demand and supply Improve supply chain visibility to boost customer satisfaction Boost transportation of goods across borders Increase operational efficiency and revenue opportunities Check for any defects in the product or industrial equipment

Automotive Industry: Self-Driving CarsIoT sensors are enhancing the capabilities of vehicles making them smarter and more independent. We call them smart cars or self-driving cars, where human presence is not even an option. Together with artificial intelligence and machine learning, these vehicles can evaluate the situation on the road and can make better decisions in real-time.

They now have reliable cameras to get a clear understanding of roads. Radar detectors allow autonomous vehicles to see even at night thus improving their visibility.

Healthcare Industry: Smart Healthcare SolutionsPatient monitoring has become easy with machine learning and IoT. Doctors can now get real-time data on patients health conditions from connected gadgets and suggest tailored treatments.

Remote glucose monitoring is one such use case where doctors can monitor the glucose level of patients through CGM( continuous glucose monitoring) systems. If there is any anomaly in the glucose level, a warning notification is issued so that patients can immediately connect to the doctor and get the necessary treatment.

AI-equipped Apple Watch is another best use case of machine learning and IoT. The smartwatch is very useful in monitoring the heartbeat. According to a study by Cardiogram, the Apple watch gives 97 percent accurate results on heart rate monitoring and can detect paroxysmal atrial fibrillation which is mainly caused due to irregularity in heart rhythm.

Manufacturing Industry: Condition-Based MonitoringMachines are undoubtedly not going to last forever; they continuously undergo wear and tear and ultimately reach a point where they need to be repaired or discarded. As the manufacturing industry is one of the sectors that depend heavily on machines, they need to keep an eye on machines health strictly.

CBM is one of the most important predictive maintenance strategies that work in this case. Using machine learning techniques and combined with the information gathered from the IoT sensors, conclusions regarding the status of the equipment can be monitored.

For example, mechanical misalignment, short circuits, and wear-out conditions can be detected through this technique. This helps identify the root problem and how early a machine needs maintenance.

Furthermore, this type of automated machine learning assistance decreases the human engineering effort by 50 %, reduces the maintenance budget, and boosts the availability of machines. False alarming, which is one of the main issues of condition monitoring, is also solved by 90% with the help of machine learning models in CBM.

ConclusionNo single technology can alone bring massive success to businesses. Thus, they should be flexible enough to incorporate several technologies together. The Internet of Things (IoT), and Machine Learning are two such powerful combinations that when used correctly can scale up the growth of a business.

They are reshaping almost every industry from agriculture to IT making them more efficient, scalable, and productive.

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Making an Impact: IoT and Machine Learning in Business - Finextra

As machine learning progresses at breakneck speed, its intersection with sustainability is ... [+] increasingly crucial.

Irene Unceta is a professor and director of the Esade Double Degree in Business Administration & AI For Business

As machine learning progresses at breakneck speed, its intersection with sustainability is increasingly crucial. While it is clear that machine learning models will alter our lifestyles, work environments, and interactions with the world, the question of how they will impact sustainability cannot be ignored.

To understand how machine learning can contribute to creating a better, greener, more equitable world, it is crucial to assess its impact on the three pillars of sustainability: the social, the economic, and the environmental.

The social dimension

From a social standpoint, the sustainability of machine learning depends on its potential to have a positive impact on society.

Machine learning models have shown promise in this regard, for example, by helping healthcare organizations provide more accurate medical diagnoses, conduct high-precision surgeries, or design personalized treatment plans. Similarly, systems dedicated to analyzing and predicting patterns in data can potentially transform public policy, so long as they contribute to a fairer redistribution of wealth and increased social cohesion.

However, ensuring a sustainable deployment of this technology in the social dimension requires addressing challenges related to the emergence of bias and discrimination, as well as the effects of opacity.

Machine learning models trained on biased data can perpetuate and even amplify existing inequalities, leading to unfair and discriminatory outcomes. A controversial study conducted by researchers at MIT showed, for example, that commercial facial recognition software is less accurate for people with darker skin tones, especially darker women, reinforcing historical racial and gender biases.

Moreover, large, intricate models based on complex architectures, such as those of deep learning, can be opaque and difficult to understand. This lack of transparency can have a two-fold effect. On the one hand, it can lead to mistrust and lack of adoption. On the other, it conflicts with the principle of autonomy, which refers to the basic human right to be well-informed in order to make free decisions.

To promote machine learning sustainability in the social dimension, it is essential to prioritize the development of models that can be understood and that provide insights into their decision-making process. Knowing what these systems learn, however, is only the first step. To ensure fair outcomes for all members of society, regardless of background or socioeconomic status, diverse groups must be involved in these systems design and development and their ethical principles must be made explicit. Machine learning models today might not be capable of moral thinking, as Noam Chomsky recently highlighted, but their programmers should not be exempt from this obligation.

The economic dimension

Nor should the focus be solely on the social dimension. Machine learning will only be sustainable for as long as its benefits outweigh its costs from an economic perspective, too.

Machine learning models can help reduce costs, improve efficiency, and create new business opportunities. Among other things, they can be used to optimize supply chains, automate repetitive tasks in manufacturing, and provide insights into customer behavior and market trends.

Even so, the design and deployment of machine learning can be very expensive, requiring significant investments in data, hardware, and personnel. Models require extensive resources, in terms of both hardware and manpower, to develop and maintain. This makes them less accessible to small businesses and developing economies, limiting their potential impact and perpetuating economic inequality.

Addressing these issues will require evaluating the costs and benefits carefully, considering both short- and long-term costs, and balancing the trade-offs between accuracy, scalability, and cost.

But not only that. The proliferation of this technology will also have a substantial impact on the workforce. Increasing reliance on machine learning will lead to job loss in many sectors in the coming years. Efforts must be made to create new job opportunities and to ensure that workers have the necessary skills and training to transition to these new roles.

To achieve economic sustainability in machine learning, systems should be designed to augment, rather than replace, human capabilities.

The environmental dimension

Finally, machine learning has the potential to play a significant role in mitigating the impact of human activities on the environment. Unless properly designed, however, it may turn out to be a double-edged sword.

Training and running industrial machine learning models requires significant computing resources. These include large data centers and powerful GPUs, which consume a great deal of energy, as well as the production and disposal of hardware and electronic components that contribute to greenhouse gas emissions.

In 2018, DeepMind released AlphaStar, a multi-agent reinforcement-learning-based system that produced unprecedented results playing StarCraft II. While the model itself can be run on an average desktop PC, its training required the use of 16 TPUs for each of its 600 agents, running in parallel for more than 2 weeks. This raises the question of whether and to what extent these costs are justified.

To ensure environmental sustainability we should question the pertinence of training and deploying industrial machine learning applications. Decreasing their carbon footprint will require promoting more energy-efficient hardware, such as specialized chips and low-power processors, as well as dedicating efforts to developing greener algorithms that optimize energy consumption by using less data, fewer parameters, and more efficient training methods.

Machine learning may yet contribute to building a more sustainable world, but this will require a comprehensive approach that considers the complex trade-offs of developing inclusive, equitable, cost-effective, trustworthy models that have a low technical debt and do minimal environmental harm. Promoting social, economic, and environmental sustainability in machine learning models is essential to ensure that these systems support the needs of society, while minimizing any negative consequences in the long term.

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We Need To Make Machine Learning Sustainable. Here's How - Forbes