Unlocking New Real-world Generative AI Use Cases on the Mobile CPU <\/p>\n

Top 5 use cases for small language models<\/h1>\n<\/p>\n

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In production, our evaluation approach focuses on quantitatively evaluating the real-world usage of our application with the expectations of live users. In production, we will find scenarios that are not covered in our building scenarios. The goal of the evaluation in this phase is to discover those scenarios and gather feedback from live users to improve the application further. Evaluating those different types of setups gives us different insights that we can use in the development process of generative AI applications. My team has built the entAIngine platform that is, in that sense, quite unique in that it enables low-code generation of applications with generative AI tasks that are not necessarily a chatbot application. Or, if you want to build your own testbed functionality, feel free to get inspiration from the concept below.<\/p>\n<\/p>\n

Afterward, they need to send the email to the customers and wait for a reply, and they iterate until the request is solved. Generative AI in manufacturing is gaining traction for its ability to innovate in design and production. By generating new ideas and solutions, generative AI is a valuable tool for manufacturers aiming to enhance their processes and products. Ultimately, the utilization of AI in factories lower costs, increase overall operational efficiency, and boost productivity by building data-driven, adaptive manufacturing ecosystems that adjust quickly to changing circumstances.<\/p>\n<\/p>\n

Big pharma and digital health companies that in the past were far from becoming allies are now joining forces to make healthcare delivery better. This shift sees pharmaceutical companies increasingly integrating digital solutions into their operations. In fact, many generative AI workloads, like image generation and text summarization, that are now a common part of the modern smartphone experience are being processed at the edge \u2013 on the device.<\/p>\n<\/p>\n

A state study from mid-2023 reports that 95%<\/p>\n

of ElliQ users agree it reduces feelings of isolation and acts as a mood booster. One of the most powerful capabilities of generative AI in healthcare is offering tailored recommendations and individual support. These relate both to offering psychological and physical care assistance, like drug use instructions. Generative AI cannot fully replace humans because it lacks the insight, oversight, and judgment that people provide.<\/p>\n<\/p>\n

Supercharging Product Design<\/h2>\n<\/p>\n

These leaders agree that generative AI can give companies a competitive edge, according to IBM Institute for Business Value research. CFOs tell IBM embedding gen AI throughout the enterprise will be \u201ca top enabler of competitive advantage over the next three years. Virtual assistants for external applications as a use case is prioritized by Leaders in the finance industry due to the need for seamless transactions and quick solutions for customers wherever they are. One key example is health and beauty retailer, and pharmacy chain Boots UK partnered with IBM to transfer their legacy programs over to IBM Cloud\u00ae.<\/p>\n<\/p>\n

To do so, the virtual agent can leverage insights from various knowledge sources, such as knowledge bases, CRMs, and web links. All the agent needs to do is ensure the response is relevant, and accurate before clicking \u201csend\u201d. Moreover, it can collect complementary information from CRM systems and knowledge base articles, ensuring agents have everything they need to address an issue quickly.<\/p>\n<\/p>\n

Supervisors can leverage such an assistant to gain improved team performance insights and better manage their resources, as evident in some of the following use cases. Traditionally, contact centers have had problems with live agents manually entering the codes, as they may select the wrong code or skip past the problem. While intelligent routing tools increase the chances of a customer being connected to the right agent straight away, there are still times when a conversation might need to be transferred or escalated to another professional. Virtual assistants can collect information about a customer in the call queue, summarize it, and hand it over to an agent before they begin a call.<\/p>\n<\/p>\n

Call and chat analysis can help to ensure compliance with internal and external guidelines. Although its use in research and development is still mostly experimental, Livingston said GenAI has already shown promise in helping organizations jumpstart R&D activities. The technology can find promising opportunities to explore, identify which opportunities have the most potential and then iterate through different options very quickly. The report from Enterprise Strategy Group found increased productivity as the No. 1 benefit from GenAI, with 60% of respondents stating GenAI delivered value on that front.<\/p>\n<\/p>\n

Products<\/h2>\n<\/p>\n

Developing more energy and resource-efficient AI and data centers is also an area where siloing solutions could be detrimental to the industry\u2019s growth. Conversations about how to harness AI\u2019s power both from a growth and sustainability perspective were also abound at Climate Week NYC in the fall. Representatives from tech heavyweights including Meta, Hewlett-Packard and Nvidia preached the role of collaboration in tackling the energy need of artificial intelligence. Unlike traditional search engines that rely on keyword searches, GenAI enables researchers to analyze large data sets at scale, quickly identify relevant precedents and summarize key points.<\/p>\n<\/p>\n

It has also actively encouraged the use of AI for social welfare (link resides outside of IBM.com)12, specifically in diseases detection and agricultural improvements. Get weekly insights, research and expert views on AI, security, cloud and more in the Think Newsletter. Every government can embrace trustworthy AI, which provides guidance on the safe and secure deployment and use of AI systems.<\/p>\n<\/p>\n

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While gen AI models have traditionally excelled at retrieving and summarizing information, organizations are now using the technology for predictive analytics. In early 2024, NVIDIA announced its AI-driven Clara computing platform targeting the healthcare industry and its BioNeMo, a gen AI platform for drug discovery. Gen AI can significantly cut down the time it takes to bring new drugs to market, he says. Gen AI can conduct market analysis based on product reviews, and it can predict customer problems even before they recognize the issues, others say. In another example, Deutsche Telekom has used gen AI to improve its Frag Magenta AI assistant, and the company anticipates the chat assistant will be able to handle 38 million customer interactions each year.<\/p>\n<\/p>\n

Let\u2019s explore the various dimensions of generative AI for healthcare, including its wide-ranging applications, benefits, and real-world use cases. For over two decades CMSWire, produced by Simpler Media Group, has been the world’s leading community of digital customer experience professionals. Generative AI has made considerable strides in the recent past, marking its position as one of the most prominent technologies in the AI landscape. From amplifying creative capabilities to facilitating superior product and service offerings, generative AI promises a wealth of opportunities. According to Accenture, 90% of business leaders use AI to tackle different parts of their businesses.<\/p>\n<\/p>\n

3 marketing use cases for generative AI that aren\u2019t copywriting – MarTech<\/h3>\n

3 marketing use cases for generative AI that aren\u2019t copywriting.<\/p>\n

Posted: Fri, 18 Oct 2024 07:00:00 GMT [source<\/a>]<\/p>\n<\/p>\n

Use cases include content generation, proposal writing, planning, detection and data visualization. For example, the GenAI-powered tool BlueDot alerts public bodies to outbreaks or potential threats from new or known pathogens, such as influenza and dengue. GenAI extracts location-specific data on disease events, connects various data sets on the back end and translates epidemiological data into natural language for users. For talent coaches, the engine customizes employee career paths based on stored data, tracks their optimal career trajectory and matches staff to appropriate learning programs. GenAI tools make reports more comprehensive for all stakeholders, and users can query the bots for clarification when needed. Fashion designers utilize GenAI’s huge troves of historical and current fashion data to generate unique and avant-garde designs.<\/p>\n<\/p>\n

Finding new ways to differentiate and to generate new revenue<\/h2>\n<\/p>\n

Through tools such as ChatGPT and MidJourney, GenAI enables users to create spectacular images, new content and professional-quality videos for free. It has also revolutionized art; for instance, beatboxer Harry Yeff (aka Reeps One) synchronized his voice with AI to generate a new form of percussive sound. Working with the Leipzig Ballet, Yeff used GenAI to generate innovative dance movements against an AI-generated background. The key is identifying where generative AI creates the most value for your career and workflows. Start with practical applications that streamline routine tasks, then build on these successes.<\/p>\n<\/p>\n

A. AI is helping the manufacturing industry by improving efficiency, reducing costs, enhancing product quality, optimizing inventory management, and predicting maintenance needs. The technology also assists enterprises with data-driven decision-making, driving innovation and productivity across the entire manufacturing lifecycle. For example, AI applications in manufacturing include real-time quality control systems that automatically detect and address defects during the production process. AI for manufacturing plays a critical role in optimizing assembly lines, enabling improved accuracy, greater efficiency, and enhanced flexibility in production processes. By analyzing past performance metrics and real-time sensor data, machine learning algorithms improve workflow, reduce downtime, and enable predictive maintenance.<\/p>\n<\/p>\n

The Predix platform helps GE reduce downtime and boost factory efficiency through data analysis and machine learning. By leveraging the power of AI in manufacturing, companies are revolutionizing their approach to quality control, ensuring higher accuracy and consistency. With AI, manufacturers can employ computer vision algorithms to analyze images or videos of products and components.<\/p>\n<\/p>\n

Exploring Real-World Generative AI in Healthcare Examples<\/h2>\n<\/p>\n

AI-driven analytics streamline stakeholder interviews and requirements gathering, while automated tools improve system architectures and the design of user interfaces. Generative AI Insights provides a venue for technology leaders\u2014including vendors and other outside contributors\u2014to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld\u2019s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Implement robust validation and verification processes to assess the reliability and safety of AI-generated recommendations. Provide healthcare professionals with tools for evaluating the confidence and accuracy of AI outputs, such as probabilistic models or uncertainty estimates.<\/p>\n<\/p>\n

Let\u2019s now look at some real-word examples of AI in the life science sector, and how they\u2019re already benefiting researchers and patients alike. As I\u2019ve mentioned earlier, AI can \u2018see\u2019, \u2018listen\u2019, and \u2018read\u2019 data and handle it by the billion. Drug developers can turn to it to find eligible candidates who meet various criteria \u2013 from pure demographics and previous medical treatments to DNA sequencing data. This makes it easier for clinicians and drug developers to shortlist patients for each trial phase. Before AI in life sciences became a reality, as many as 86%<\/p>\n

of trials never took off because researchers couldn\u2019t put together a sufficient, eligible patient sample. Data analyses which would normally take weeks or months to complete manually can now be performed almost instantaneously with the help of AI.<\/p>\n<\/p>\n

Regardless of the GenAI approach, 65 percent of enterprises rely on some form of external support\u2014managed service providers (MSPs)\u2014to implement their GenAI initiatives. In terms of where GenAI money is being spent, ISG reports 36 percent is being spent on applications and software, including software-as-a-service. As a result, software development is emerging as a leading application for GenAI, with 70 percent of respondents report using ChatGPT for software development activities, with 33 percent using GitHub CoPilot. CRN breaks down the biggest GenAI market trends in the enterprise that every channel partner, vendor and customer needs to know about. Let\u2019s look at the top five use cases where organizations consider leveraging small language models, and the leading small language models for each use case. A. Generative AI in healthcare can significantly impact diagnostic accuracy by enhancing the interpretation of medical images, improving data synthesis for rare diseases, and aiding in the identification of subtle patterns or anomalies.<\/p>\n<\/p>\n

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To do so, the virtual assistant may help analyze previous customer interactions to suggest process improvement opportunities and automation strategies a manager might have overlooked. Indeed, as contact centers start applying automation, they can boost efficiency, enhance employee experiences, and improve customer satisfaction. Supervisors need to give agents the freedom to improve engagement and satisfaction levels, but they can\u2019t risk approving shift swaps and time off if it means that customer experiences and team morale will suffer. Negative customer sentiment isn\u2019t the only thing that contact center supervisors need to worry about. When an agent\u2019s mood suffers, perhaps as the result of a difficult conversation or high periods of stress, this can create well-being and performance issues, too. A contact center virtual assistant can simplify this process by summarizing the conversation so far and ensuring that the summary passes through to the next person talking to the customer.<\/p>\n<\/p>\n

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• However, its small dataset and the challenge of simulating real conversations still need to be improved.<\/li>\n
• Deploying AI is a transformative journey that aims for significant productivity growth, but involves addressing challenges that span technological integration, human adaptation in ways of working, and reimagined business processes.<\/li>\n
• Thriving in 2025 will require fresh strategies around emerging technologies, customer loyalty, operations, and adaptation.<\/li>\n<\/ul>\n

  The article will also uncover real-world examples of companies like PathAI and Zebra Medical Vision, showcasing the profound role of the technology in modernizing healthcare services. Generative artificial intelligence is a branch of AI that uses machine learning models to take user input and output different media formats in response to what the user gives it. It takes training data from data scientists and learns to identify patterns based on what it has learned. The artificial intelligence (AI) realm saw a significant stir towards the close of 2022, as OpenAI unleashed ChatGPT to the digital world, promptly amassing an impressive 100 million users in just a few months. Generative AI models, designed to mirror human thought processes, producing output derived from their intensive training data. GenAI also supports real estate companies in day-to-day operations like customer interactions.<\/p>\n<\/p>\n

  Decision-makers have to lead by example if they want to ensure a widespread adoption of AI. I\u2019ve mentioned that GenAI can boost relationships with customers, but the same can be said about investor relations. Not only can it help you with targeting the right investors, but also maintain a good relationship with them. On top of these, you can also indicate the tone of voice, text length, and preferred content format.<\/p>\n<\/p>\n

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  Maintaining high standards in manufacturing can be challenging, but AI-driven systems can relieve the process by spotting possible product defects instantly. Generative AI tools can be trained to distinguish defective from perfect-quality products and alert teams of possible flaws. This could lead to a decrease in product recalls and ensure output consistency, refining overall manufacturing reliability.<\/p>\n<\/p>\n

  The “Voice of SecOps 5th Edition 2024” report from cybersecurity company Deep Instinct — conducted by Sapio Research — surveyed 500 senior cybersecurity experts from companies with 1,000-plus employees in the U.S. The report found that 97% of security professionals worry their organization will fall victim to an AI-generated cybersecurity incident; 33% of respondents said they view adversarial AI as a major or critical threat to their organization. Knowing when to use agentic AI versus a process-driven approach is a key best practice. Tasks with dynamic decision-making, like proactive customer retention calls, thrive with agentic AI, while structured processes like password resets are best handled with traditional automation. Below, each participant shares emerging use cases for copilots and virtual assistants, trends for 2025, and best practices for deploying them.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

  Unlocking New Real-world Generative AI Use Cases on the Mobile CPU Top 5 use cases for small language models In production, our evaluation approach focuses on quantitatively evaluating the real-world usage of our application with the expectations of live users. In production, we will find scenarios that are not covered in our building scenarios. The
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