February 28, 2025 by Tula Masterman Leave a Comment The rapid pace of artificial intelligence innovation shows no signs of slowing down, and February 2025 has already delivered major breakthroughs that underscore this shift. From AI systems redefining scientific discovery to quantum breakthroughs that could supercharge machine learning and materials discovery, this month’s developments highlight the evolving relationship between AI, human expertise, and cutting-edge computing power. Here are 4 key AI updates from February that highlight how artificial intelligence is evolving—not just in power, but in accessibility and impact. 1. Google’s AI Co-Scientist: Revolutionizing research collaboration Google has introduced AI Co-Scientist, a multi-agent system designed to expedite scientific research. This AI-driven tool collaborates seamlessly with researchers, assisting in hypothesis generation, experimental design, and data analysis to uncover novel scientific insights. By embedding AI into the research workflow, Google aims to enhance efficiency and foster breakthroughs across scientific domains. Why AI Co-Scientist matters The AI Co-Scientist redefines the role of AI in research. Rather than merely summarizing existing research or performing literature reviews and “deep research” tasks independently, the AI Co-Scientist partners with scientists through every phase of the scientific method. It’s able to help generate innovative hypotheses, refine experimental designs, and even uncover new and original knowledge. This highlights the growing shift towards AI systems that partner with humans on not only simple tasks, but also novel and creative challenges. 2. xAI’s Grok-3: A new contender in advanced language models Elon Musk’s AI venture, xAI, has unveiled Grok-3, a cutting-edge language model designed to rival industry leading models from OpenAI, Anthropic, and Google. Grok-3 exhibits advanced reasoning capabilities, allowing it to perform tasks involving logical problem-solving and creative content generation. Notably, Grok-3 was trained on the Colossus supercluster with 10x the compute power compared to previous state-of-the-art models. Why Grok-3 matters The expertise to train large language models is highly concentrated among a few individuals across the top AI research labs today. One of the most significant achievements about Grok-3 is that xAI built a highly competitive model less than a year after the company’s founding, highlighting the rapid pace of innovation in an increasingly competitive market. 3. Anthropic’s Claude 3.7 Sonnet: Pioneering hybrid reasoning models Anthropic launched Claude 3.7 Sonnet, its first “hybrid reasoning model” that seamlessly merges rapid responses capabilities with detailed, step-by-step problem-solving. A standout feature of Claude 3.7 Sonnet is its user-adjustable token budget, which lets users control how long the model “thinks” on a task—thereby tailoring the reasoning depth to match specific requirements. Why Claude 3.7 Sonnet matters: This launch underscores Anthropic’s commitment to enhancing the user experience by unifying fast and deliberate thinking within a single model. Moreover, Anthropic shifted their focus from optimizing for problems that are well-captured in industry benchmarks to optimizing for real-world tasks. This is significant because most benchmarks are not representative of business problems and the value of benchmarks is hotly debated. This will likely be a continued trend as GenAI adoption continues across all industries. 4. Microsoft’s Majorana 1 Quantum Chip: A leap forward in quantum computing Microsoft has unveiled Majorana 1, a compact quantum chip utilizing innovative design materials to improve reliability and scalability in quantum computing. This development marks a significant milestone toward practical quantum computers capable of addressing complex problems beyond the capabilities of classical systems. Why Majorana 1 matters The Majorana 1 chip represents a breakthrough in quantum hardware, potentially accelerating the evolution of quantum computing applications. For AI, this advancement could lead to more efficient training of large models and more effective solutions to optimization problems. The enhanced computational power offered by quantum chips like Majorana 1 will likely unlock new possibilities in AI research and implementation in every industry. Looking Ahead These developments underscore the rapid progression of AI and related technologies. As AI systems become increasingly integrated into various sectors, ethical considerations and responsible deployment remain critical. The convergence of AI with quantum computing, exemplified by Microsoft’s Majorana 1, suggests a future where AI capabilities are significantly amplified, leading to innovations once considered beyond reach.
January 23, 2025 by Jason Miles Leave a Comment In today’s fast-paced business landscape, effective data integration is crucial. Organizations generate vast amounts of data from various sources. But without a streamlined way to unify, analyze, and govern it, valuable insights are lost. Microsoft’s data integrations solutions—Microsoft Fabric, Purview, and Power Platform—addresses this challenge by offering a seamless ecosystem for managing, securing, and utilizing data. Microsoft Fabric offers an end-to-end analytics solution that consolidates key data services under one SaaS platform. Microsoft Purview ensures data governance, security, and compliance across hybrid and multi-cloud environments. Meanwhile, Microsoft Power Platform democratizes low-code application development and self-service analytics, empowering business users to create solutions with minimal IT dependency. But what happens when you combine all three? Together, these tools help businesses unlock insights, ensure compliance, and innovate faster, making them essential for staying competitive in a data-driven world. What is Microsoft Fabric? Microsoft Fabric is designed to simplify analytics by uniting services like Data Factory, Synapse, SQL Server, and Power BI into one integrated platform. Its standout features include OneLake, a centralized data lake that acts as a single source of truth, and integrated pipelines that streamline data ingestion and transformation. The collaborative workspace enhances team productivity by allowing users to share tools and insights seamlessly. Some of the most recent releases focus on making real-time data a first-class member of the platform, enabling analytics respond faster than ever before. Fabric’s end-to-end approach ensures that every stage of the data lifecycle—from ingestion to reporting—is optimized for speed and reliability. What is Microsoft Purview? Microsoft Purview is a unified data governance platform designed to help organizations discover, govern, and protect their data across on-premises, multi-cloud, and SaaS environments. Its primary focus is on metadata management and cataloging, providing businesses with a centralized solution to ensure their data assets are organized, accessible, and secure. By delivering visibility into data sources and maintaining governance at scale, Purview empowers organizations to manage their data confidently. Key capabilities Purview’s core governance tools simplify data management across complex systems. Its data cataloging capability automatically scans and registers assets, reducing manual effort and ensuring accuracy. With data lineage, businesses can track the flow and transformation of data, fostering transparency and accountability. Access control enforces consistent security measures and ensures compliance with regulations like GDPR and HIPAA. Additionally, workflow and approval processes streamline how data usage requests are handled, supporting efficient collaboration. Latest enhancements Recent updates to Purview have enhanced its functionality, particularly through deeper integration with Microsoft Fabric. This seamless connection enables end-to-end governance across analytics workflows. Advanced features, such as automated data classification, lineage tracking, and comprehensive data mapping, now extend Purview’s capabilities to hybrid environments, offering organizations greater control and transparency. Essential for data-driven organizations In a data-driven organization, Purview is indispensable. It ensures data accuracy, consistency, and security, which are critical for informed decision-making. By simplifying regulatory compliance, Purview reduces the risks associated with data governance. Most importantly, it builds a foundation of trust, encouraging wider adoption of data analytics and fostering a culture where data-driven insights lead to better outcomes. What is Microsoft Power Platform? Microsoft Power Platform is a unified low-code development ecosystem designed to accelerate innovation and empower business users to solve problems quickly. It includes Power Apps for building custom applications, Power Automate for streamlining workflows, Power BI for self-service analytics, Copilot Studio for AI-assisted app development, and Power Pages for creating dynamic web solutions. Together, these tools enable rapid application development, workflow automation, and actionable insights without requiring extensive coding expertise. Recent innovations Recent innovations have made Microsoft Power Platform even more powerful and accessible. AI-powered features like Copilot in Power Apps simplify the app-building process, allowing users to create solutions through natural language inputs. In addition, expanded connectors to Microsoft Fabric provide real-time data access, enhancing the platform’s analytics capabilities. Additionally, new governance and lifecycle management tools ensure compliance, making it easier to scale solutions securely across organizations. Power Platform Use Cases Microsoft Power Platform supports a wide range of business needs. For example, custom business apps can be created to address specific internal or customer-facing requirements. Process automation through Power Automate reduces repetitive tasks and minimizes errors, boosting productivity. With Power BI, employees can build real-time dashboards and analytics that provide actionable insights. The platform also supports conversational solutions, such as chatbots and virtual assistants, which improve customer service and enhance employee interactions. By combining low-code development, robust automation, and advanced analytics, Microsoft Power Platform empowers organizations to innovate faster and improve efficiency across all business functions. How Fabric, Purview, and Power Platform work together A single source of truth The integration of Microsoft Fabric, Purview, and Power Platform creates a cohesive ecosystem where data is unified, governed, and readily actionable. At the core of this integration is OneLake in Microsoft Fabric, a centralized data lake that acts as a “single source of truth.” It consolidates data assets, ensuring consistency and reducing silos. Purview’s governance capabilities maintain metadata consistency and applies robust security layers, making OneLake a reliable foundation for any data-driven initiative. Additionally, OneLake integrates seamlessly with DataVerse in Power Platform, enabling low-code solutions to tap into governed, centralized data. Governance-first approach A governance-first approach ensures that data is secure and compliant from ingestion to consumption. With Purview, organizations can track data lineage across every step, offering full transparency on how data flows and transforms. Governance policies are automatically enforced in Fabric, allowing only authorized users in Power Platform to access and utilize data. This automated compliance fosters trust and accelerates adoption of data-driven tools across the organization. Seamless integration for improved utility Both Fabric and Purview’s seamless integration with Power Platform enhances its utility for business users. Power BI, natively integrated into Fabric, enables advanced analytics and visualization directly from the centralized data lake. Power Apps can leverage datasets curated and governed by Purview, empowering teams to build low-code applications confidently. Meanwhile, Power Automate orchestrates workflows and triggers, connecting Fabric pipelines with broader business processes to streamline operations. AI and machine learning scenarios For advanced scenarios, Microsoft’s data integration solutions support AI and machine learning workflows. Microsoft Fabric’s machine learning (ML) capabilities enable businesses to build predictive models that drive deeper insights. Microsoft Purview governs these ML pipelines, ensuring compliance and tracking data lineage throughout the modeling process. These insights can then be operationalized in Power Apps or visualized in Power BI dashboards, creating actionable strategies grounded in AI-driven predictions. By integrating analytics, governance, and low-code solutions, Microsoft’s data integration solutions offer an unparalleled data ecosystem. Organizations gain a seamless, secure way to unify data, govern its use, and transform it into actionable outcomes that fuel innovation and efficiency. Real-world scenarios The combined capabilities of Microsoft Fabric, Purview, and Power Platform address diverse business challenges, enabling organizations to unlock actionable insights and drive efficiency across critical areas. Below are three real-world scenarios: Supply chain optimization Consider supply chain optimization. Microsoft Fabric ingests supplier data and real-time IoT feeds from warehouses, providing a comprehensive view of the supply chain. With Purview’s governance, organizations can standardize data across global regions, ensuring compliance with local regulations. Power Apps empowers field managers to update inventory tasks in real-time, streamlining workflows and reducing delays. These updates are visualized in Power BI dashboards, giving stakeholders the insights needed to manage inventory more effectively and respond to disruptions proactively. Personalized customer experience For businesses focused on delivering personalized customer experiences, Microsoft’s data integration solutions offers a robust offering. Microsoft Fabric unifies customer data from CRM systems, website analytics, and support channels into a single, cohesive view. Microsoft applies classification rules and access controls to protect sensitive personal information (PII), ensuring compliance with privacy regulations. Power Automate enables dynamic workflows, such as triggering personalized marketing campaigns based on customer interactions. Additionally, chatbots built with Power Virtual Agents enhance customer engagement by handling inquiries in real-time, improving satisfaction and retention. Financial reporting and compliance In financial reporting and compliance, Microsoft Fabric simplifies the consolidation of data from multiple financial systems, providing a unified source for reporting. Microsoft Mi’s data lineage features allow auditors to trace any data point back to its origin, ensuring transparency and accuracy. CFOs and stakeholders can rely on Power BI dashboards for real-time reporting, enabling faster decision-making and compliance with regulatory standards. Four best practices for implementation To fully leverage Microsoft Fabric, Purview, and Power Platform, organizations must take a strategic and structured approach to implementation. Below are four best practices to ensure a smooth rollout and long-term success. 1. Plan your data strategy first: Begin by aligning your data strategy with key business outcomes. Identify the data assets that are most critical to achieving these goals and map them to Microsoft Purview for governance from the outset. Early integration with Purview helps establish a foundation of trust, security, and compliance for all subsequent data activities. 2. Adopt a phased roll-out: Start by targeting critical workflows or business functions that will benefit the most from the trifecta’s capabilities. For example, begin with use cases that require robust analytics or compliance, then gradually expand to more complex datasets and advanced analytics scenarios as organizational maturity increases. This phased approach minimizes risks and allows teams to build confidence with the tools. 3. Establish data governance policies and training. Clearly define data ownership, classification, and access policies to ensure consistent governance across your organization. Train your staff to use Purview’s auditing and governance features effectively, fostering a culture of accountability and data literacy. Well-trained teams are better equipped to maintain compliance and maximize the value of your data assets. 4. Leverage Power Platform Centers of Excellence. Encourage a low-code culture by empowering employees to use Microsoft Power Platform for app development and process automation. However, maintain centralized oversight to avoid tool sprawl and ensure consistency. Establish a governance model for app lifecycle management, including compliance checks and performance monitoring, to ensure that low-code innovation aligns with organizational standards. Looking ahead AI integration continues Continued AI integration will further elevate the capabilities of Fabric and Power Platform. Expect more advanced AI and machine learning features embedded directly into Fabric and Power Platform tools, enabling businesses to derive insights faster and more intuitively. Features like auto-generated insights and natural language data exploration will make data analytics more accessible, empowering users at all levels to uncover actionable information without requiring technical expertise. Government standards evolve Evolving governance standards will remain a priority, particularly as global data privacy and residency regulations become more complex. Microsoft Purview will continue to lead in this area, adapting to new requirements and ensuring compliance across diverse environments. Additionally, as organizations adopt AI at scale, the importance of ethical AI and responsible data practices will grow. Purview’s ability to enforce policies and track data lineage will be critical in ensuring transparency and accountability. Deeper cross-platform integrations Deeper cross-platform integrations are on the horizon, enhancing connectivity across Microsoft 365, Dynamics 365, and external applications. These integrations will enable businesses to streamline operations and foster collaboration across departments. Improvements to developer workflows, such as integrated DevOps pipelines with GitHub, will also boost productivity and accelerate innovation, making it easier to manage complex data and app development projects. Next steps Microsoft Fabric, Purview, and Power Platform transform how organizations leverage data. To start leveraging this powerful ecosystem, explore Microsoft’s detailed documentation and trial programs to understand the tools in action. Collaborate with internal stakeholders to identify a proof-of-concept project that aligns with your business goals. Join Microsoft’s community forums and user groups to learn from others’ experiences and share best practices. Finally, connect with Neudesic for expert guidance on implementing these solutions efficiently and effectively, ensuring your organization realizes the full value of Microsoft’s data capabilities. Contact us to learn more at https://www.neudesic.com/about/contact/
December 19, 2024 by Tula Masterman Leave a Comment Addressing the Risks of Modern Generative AI Systems AI systems are changing the way we work, interact, and innovate – but they aren’t without risk. Whether it’s a chatbot giving unsafe medical advice, generating violent or inappropriate content, or even inadvertently recommending a competitor’s product, these failures can erode trust, damage reputations, and, in some cases, put people at risk. Two core challenges to building AI systems include identifying content safety violations and prompt injection attacks. Content safety involves identifying violent, hateful, or otherwise inappropriate inputs and preventing outputs that are harmful or potentially dangerous and could pose risks to users, violate ethical or business guidelines, or are inappropriate given the context. Prompt injections are deliberate attempts to manipulate AI systems into behaving in ways outside of their intended use. Addressing these risks is critical to ensuring the development of properly safeguarded AI systems. A New Approach to AI Safety Classification At Neudesic, we believe existing approaches for safety classification are not good enough. Many existing approaches are focused on identifying clear-cut hate speech or violence, but often miss language that is inappropriate for a business context. Other approaches are great at identifying nefarious prompt injection attacks, but often miss prompt injections that violate business rules or the intended use of the system. Effectively solving these challenges requires a much better approach, one that is highly accurate, scalable, and adaptable to real-world AI systems. That’s why Mason Sawtell, Tula Masterman, Sandi Besen, and Jim Brown authored the paper Lightweight Safety Classification Using Pruned Language Models, introducing Layer Enhanced Classification (LEC) – a method combining the computational efficiency of simple machine learning classifiers with the robust language understanding of Language Models. They worked alongside Erin Sanders, our Responsible AI lead at Neudesic, to overcome challenges related to content safety and prompt injection classification. LEC effectively identifies content safety issues and prompt injection attacks with greater accuracy than existing solutions and without requiring massive training datasets. In fact, with as few as 15 examples, LEC outperforms leading models like GPT-4o and Meta’s Llama Guard 3 (1B and 8B) on content safety identification tasks while running at a fraction of the cost. For identifying prompt injections, LEC models outperformed GPT-4o using 55 training examples and special purpose model deBERTa v3 Prompt Injection v2 using as few as 5 examples. Whether you’re working with closed-source models like OpenAI’s GPT series or open-source models like IBM’s Granite series, LEC can be adapted to meet your needs by either integrating directly into the inference pipeline of open-source models or working alongside closed-source models to provide safety checks before and after the model generates a response. The implications of LEC go beyond AI safety. While the initial research was focused on responsible AI based tasks, the approach can be modified to take on other types of text-classification like sentiment analysis, intent detection, and product categorization. Figure 1: Illustration of training approach How can Businesses Use Layer Enhanced Classification (LEC)? We believe that LEC has many promising applications for enabling the safe and responsible deployment of Generative AI solutions for both new implementations and existing systems. The LEC process can be applied to multiple stages of Language Model based workflows including before, during, and after a model generates an output as well as across multiple stages of agent-based applications. Working with Closed-Source Models For use cases leveraging closed-source models like OpenAI’s GPT-4o or o1, where the model architecture is not publicly available, and modifying the model’s inference pipeline is not feasible, a lightweight task-specific classification model trained using LEC can be used as a safeguard before sending the user input to the LLM. This can prevent any unsafe content or prompt injections from ever reaching the model. Similarly, once the LLM generates an output, this response can be sent back through the appropriate classifiers to make sure no unsafe content is present in the reply. Augmenting Open-Source Models For use cases leveraging open-source models where the underlying model architecture is accessible, the LEC approach can be directly applied to that model. This means the same model can be used to create the features needed to identify content safety and prompt injections as well as generate the final response for the user. Safeguarding AI Agent Systems In agent-based scenarios, LEC offers several layers of protection. First, it can be used to determine whether the agent should work on a given request. Next, it can be used to validate suggested tool calls or intermediate responses adhere to content safety requirements. If an agent retrieves additional information from a tool call, search engine, or internal data source, LEC can help determine if the retrieved information contains violations before the agent uses it. The application of LEC in multi-agent setups depends on the models used for each agent. For instance, an agent using a closed-source model would rely on separate classifiers to run safety checks before and after generating its response. By contrast, an agent using an open-source model could be adapted to integrate the LEC process directly into its workflow, simultaneously generating outputs and performing safety checks within the same inference. Figure 2: Illustration of generating the predictions for a given input Conclusion Neudesic’s Layer Enhanced Classification (LEC) provides a breakthrough approach for two pressing Generative AI safety challenges: content safety violations and prompt injection attacks. With its focus on efficiency, accuracy, and adaptability, LEC provides businesses with a practical solution to safeguard their Generative AI systems. In future articles we will dive into the practical applications of LEC, including demonstrating the ability of this approach to effectively enforce an AI system’s intended use and avoid business inappropriate language, going beyond the tools available in the marketplace. We will also cover how this approach could extend to other types of classification problems like groundedness detection. Dive into the research in the full paper now available on ArXiv: https://arxiv.org/abs/2412.13435
September 24, 2024 by Hannah Leonhard Curious about the latest AI news? This blog covers recent breakthroughs like OpenAI’s new o1 reasoning model, Microsoft’s new AI features, and Apple’s global AI rollout. Discover how these developments are shaping various industries. Key Takeaways AI technologies are advancing rapidly, enhancing industries from healthcare to entertainment while raising concerns about job market shifts and misinformation. Effective regulation and ethical oversight for AI are becoming increasingly crucial to address risks, protect individual rights, and enhance trust in AI systems. The Rapid Advancement of AI Technologies Artificial intelligence has made remarkable strides, pushing the boundaries of what machines can achieve across various industries. From optimizing processes in healthcare and finance to enhancing education, entertainment, and even art, AI's influence is growing rapidly. The global market for AI is projected to reach $1.35 trillion by 2030, with an impressive annual growth rate of 36.8% from 2023 to 2030. This explosive growth is driven by continuous innovations, from groundbreaking models to novel applications, expanding the horizons of AI's potential and transforming the way we live and work. Innovations in AI Technology Next, we explore exciting AI developments: OpenAI’s o1 model, Microsoft’s Copilot agents, Apple’s global rollout of Apple Intelligence, and robotics with human-like muscles. Each of these innovations represents a significant leap forward, showcasing the diverse potential of AI to transform various aspects of our lives. 1. OpenAI's "o1" Model Achieves Human-Level IQ OpenAI has unveiled its latest artificial intelligence models, o1-preview and o1-mini, which has been designed with advanced machine learning techniques to enhance its reasoning skills. This model has shown remarkable skills in understanding and solving mathematical problems, scoring 83 percent on the International Mathematics Olympiad qualifying exam. With an estimated IQ of 120 in reasoning capabilities, the o1 model demonstrates proficiency comparable to human performance. The development of the o1 model is part of OpenAI’s broader initiative to improve AI’s comprehension and problem-solving abilities across various domains. This achievement not only highlights the potential of AI to perform complex intellectual tasks but also underscores the rapid advancements being made in the field of artificial intelligence. 2. Microsoft Unveils Copilot Agents for Microsoft 365 Microsoft has introduced Copilot agents for Microsoft 365, a revolutionary feature that integrates AI assistance into productivity apps. Designed to automate tasks and enhance collaboration, Copilot agents let users create tailored AI chatbot agents without coding. By simply @ mentioning a Copilot agent in a chat, users can interact with it to perform various tasks, thereby boosting productivity. The Copilot agents offer specialized insights and workflows for various business functions, including Sales, Service, and Finance, ensuring that proprietary data remains confidential and compliant with user data security standards. This feature automates business processes and enhances functionality and insights, making it a valuable tool for streamlining operations. 3. Apple's Generative AI Goes Global by 2025 Apple’s generative AI suite, Apple Intelligence, is set to expand globally by 2025. This suite includes highly-capable models specialized for everyday tasks, such as text generation, notification summarization, and creating playful images. The rollout will begin with localized English support in 2024 for regions like Australia, Canada, and South Africa, followed by support for additional languages in 2025, including German, Italian, Korean, Chinese, Vietnamese, Spanish, Japanese, French and more. Apple Intelligence leverages both on-device models with ~3 billion parameters and larger cloud-based models, offering a seamless and efficient user experience across devices and tasks. Apple’s expansion of its Intelligence AI reflects its commitment to broader accessibility and enhancing the global reach of Apple’s AI capabilities. This strategy will bolster Apple’s AI market position and provide advanced AI functionalities to users worldwide. 4. Advancements in Robotics with Human-Like Muscles Researchers at ETH Zurich and the Max Planck Institute for Intelligent Systems have developed a muscle-powered robotic leg that mimics the human musculoskeletal system. This robotic leg utilizes artificial electro-hydraulic muscles to automatically adjust to uneven surfaces, making it more energy-efficient than conventional motor-powered legs. Their animal-inspired design uses electro-hydraulic actuators, known as HASELs, which mimic the flexor and extensor muscles in living organisms. This innovative approach allows the robotic leg to perform high jumps and fast movements without producing excess heat, unlike traditional motors. This robotic leg development signifies a major advancement in robotics, potentially enhancing mobility and adaptability in various applications. Future enhancements aim to enable free movement, further increasing the leg’s versatility and utility. This breakthrough underscores the potential of AI and robotics to transform industries and improve human lives through deeper partnership with emerging technology. 5. Google's Initiative to Flag AI-Generated Images Google has announced an initiative to flag AI-generated content in its search results, aiming to improve transparency and help users make informed decisions about the content they view online. Users will be able to utilize the ‘About this image’ new feature to check if images contain gen AI or altered content, enhancing their understanding of the information they encounter. This initiative will include metadata from the Coalition for Content Provenance and Authenticity (C2PA) to indicate AI involvement, extending to Google’s advertising systems for compliance with policies. Similar disclosures may also appear on other Google properties, like YouTube, in the future, further enhancing transparency across Google’s platforms. 6. California's Legislation on AI in Entertainment California has taken significant steps to address the ethical concerns surrounding AI in the entertainment industry. California Governor Gavin Newsom signed two bills into law on September 17th that restrict the use of artificial intelligence digital replicas in film and television projects. These new laws mandate that actors must give their consent before their digital likenesses can be used in media projects, ensuring that they retain control over their digital representations. This legislation is a direct response to growing concerns about the unauthorized use of actors’ digital images in AI-generated content. Requiring consent, these laws aim to protect actors from potential exploitation and misuse of their digital likenesses. This legal framework is a crucial step in addressing the ethical implications of AI in the entertainment industry, ensuring that technological advancements do not come at the cost of individual rights. Regulatory Changes and Ethical Considerations As AI technologies continue to advance, the demand for regulatory measures is intensifying. Professionals are expressing concerns over the accuracy and trust in AI outputs, prompting governments and organizations to take action. Effective regulation is crucial to ensuring that the benefits of AI are maximized while minimizing potential risks. A collaborative approach between governments and tech companies is essential to create a framework that promotes innovation while safeguarding against misuse. Such a balanced approach will address ethical considerations and build trust in AI systems, paving the way for responsible development and deployment. Summary The landscape of artificial intelligence is marked by rapid advancements and significant investments, reflecting its transformative potential. Each of these innovations highlights the diverse applications of AI and its ability to revolutionize various industries. However, with great power comes great responsibility. The intersection of technological progress and societal impact necessitates careful regulation and ethical considerations. As we continue to navigate this rapidly evolving landscape, staying informed and balanced in our approach will be key to unlocking the full potential of AI while ensuring its responsible use. Looking Ahead The future of artificial intelligence holds immense possibilities. Expected trends include more sophisticated AI models, broader applications across various sectors, and continued advancements in AI-driven robotics. Keeping informed about these developments is crucial, as they can significantly impact daily life and the global economy. As new features and innovations emerge, understanding and adapting to these changes is critical. The true potential of AI lies not just in its capabilities but in how it empowers people to achieve more together than either could alone. By combining human creativity, intuition, and ethical reasoning with AI’s computational power and efficiency, we can tackle complex intellectual challenges and enhance physical tasks in ways previously unimaginable. A future where people and AI collaborate promises transformative outcomes—accelerating innovation, solving global problems, and creating a more efficient and equitable world.
September 4, 2024 by Karun Ramesh Last revised: January 13, 2025 Microsoft Fabric and Databricks: Two SaaS offerings that host a variety of analytical workloads that help enable key use cases like data science, data engineering, and machine learning & AI. While commonly used separately, these platforms can come together to meet almost all your data demands while providing an array of experiences that can fit the need of a user at any level of the technical spectrum. In this article, we dive into analyzing these platforms, focusing on their strengths, use cases, and cost-effectiveness. If you are weighing options for big data processing, machine learning, and seamless integration, understanding how these platforms work is essential. Discover which platform aligns best with your data aspirations and constraints, or when it might be best to combine the two. Key Takeaways Microsoft Fabric serves as a user-friendly all-in-one analytics platform leveraging Azure technologies, ideal for business users, while Databricks excels in big data processing and machine learning across major cloud providers, catering to more technical data professionals. Both Fabric and Databricks provide robust capabilities for data engineering, with Fabric emphasizing ease of use and integration, and Databricks offering advanced capabilities for complex data processing tasks. Security, compliance, and flexible pricing models are integral to both platforms, with Fabric offering a pay-as-you-go plan and Databricks using a usage-dependent pricing model, ensuring businesses can align costs with their specific needs and data security standards. Depending on specific requirements, a combined model that leverages the unique strengths and capabilities of Fabric and Databricks may be practical. With Azure Databricks, enterprises can harness the power and flexibility of Databricks while leveraging the native integrations available within the Azure platform. Exploring Microsoft Fabric and Databricks Within the ever-evolving world of data analytics, two titans stand out: the recent entrant, Microsoft Fabric, and the reigning champ, Databricks. These platforms are not just tools; they are the architects of insightful data products—serving distinct yet complementary roles in the domain of big data management and analytics. Microsoft Fabric distinguishes itself as a comprehensive all-in-one analytics platform, intricately woven using Microsoft Azure core technologies. On the other hand, Databricks excels as an analytics powerhouse, renowned for its prowess in big data processing and machine learning. Together, they offer a lot of versatility - whether it is storing data in OneLake and using Databricks to process that data or using Databricks to ingest raw data and shortcutting it to Microsoft Fabric, your data strategies can become more creative and reach new heights. The Core of Microsoft Fabric At the heart of its operations, Microsoft Fabric functions as a cloud-based platform embodying simplicity and integration. As a SaaS solution, it caters to a spectrum of users by embracing a no-code/low-code approach, ensuring that from the novice to the seasoned professional, everyone can access the data they need in a single-pane of glass. When comparing Microsoft Fabric vs other platforms, its user-friendly nature sets it apart. Fabric’s mastery lies in its seamless integration with Azure technologies, including Azure Data Factory, creating a unified environment that supports open data formats like Parquet and Delta Lake. This is a meaningful change for those who seek agility and interoperability in their data solutions. Ultimately, Fabric provides the most flexibility with the least amount of administrative overhead. Databricks: The Unified Analytics Powerhouse Databricks positions itself as a cloud-agnostic platform that uses a highly optimized version of Apache Spark to offer an all-encompassing analytics ecosystem. The platform is built for collaboration that allows users to tackle complex data problems and manage data at granular level thanks to the capabilities of Unity Catalog. In contrast to Microsoft Fabric, Databricks offers integration with various analytics tools (Fivetran, Informatica, etc.) and Customer Experience ecosystem partners directly within their platform, allowing users of all personas to leverage their existing tools to bring data into the Databricks platform. Analyzing Data Engineering Capabilities In further exploring these platforms’ technical strengths, we analyze their data engineering capabilities: As a SaaS offering, Microsoft Fabric eliminates the provisioning requirements of Microsoft Data Engineering tools like Azure Data Factory and Synapse. Instead, Fabric simplifies the Data Engineering experience by tailoring it to a specific “persona”. Within the user-friendly interface, a user need only to select Data Factory or Synapse as a persona, with the former being a tailored experience for low code/no code users while the latter supports a more seasoned professional. Databricks’ core competency, in contrast, has always been focused around its data engineering capabilities. The data engineering experience on Databricks is built around its notebooks and workflows, with the biggest benefit being the granular controls Databricks offers when managing the clusters that run these notebooks. Data Ingestion and Integration A unified platform’s usefulness rests in its proficiency to ingest and incorporate data efficiently. Microsoft Fabric rises to the challenge with its data engineering tools by offering streamlined data ingestion from a multitude of sources and facilitating seamless integration with a no-code/low-code paradigm. When using the previously mentioned Data Factory persona, a user can leverage the Copy Data Activity to select a data source to ingest directly through the user interface and is provided with a host of additional options when using the same persona but ingesting data via a Data Flow. Databricks complements this with its prowess in constructing declarative data pipelines via Delta Live Tables, making it a stalwart for those who navigate the big data seas with precision and control. The platforms’ approaches to data integration not only reflect their core strengths but also cater to the diverse skill sets of data professionals. If a company has an existing data analytics tool being used for ingestion, Databricks makes it easy to integrate the tool into its Unity Catalog environment via their Partner Connect feature. Existing tools such as Fivetran and Informatica can be easily called within the Databricks UI and used to ingest data with existing processes. Data Transformation and Storage Beyond the scope of ingestion, one enters the transformative domain of data storage and manipulation. Here, Microsoft Fabric introduces its data warehouses components and OneLake storage, offering a streamlined path from data lakes to insights. On the other side, Databricks employs a serverless lakehouse architecture, a scalable and efficient approach in the data storage universe. Both platforms flex their muscles in data transformation, yet their methodologies diverge, presenting a choice between the structured world of Fabric and the fluid architecture of Databricks. Databricks Delta Live tables allow users to define streaming tables which in turn makes the data transformation experience more accessible to users who may only have experience transforming data via SQL scripts. If not using Delta Live Tables, storing data in Unity Catalog makes data easily accessible to the other language types (Scala, Python, R) for further data processing. Microsoft Fabric offers Data Transformation capabilities via Synapse notebooks and Data Flows. Fabric Data Flows offer a no code experience allows users to curate their data transformations through a set of activities that are offered through a simple dropdown. For extremely new users, the use of Microsoft Copilot in Fabric could help them develop a pipeline fairly quickly simply by defining their transformation needs in a natural language query and allowing AI to build the pipeline for them. Below is a summary of both platforms’ Data Transformation capabilities: Data Science and Machine Learning Showdown As the discussion transitions to data science and machine learning, both platforms offer similar experiences Databricks, with its robust set of collaborative tools, is a popular platform for complex data science endeavors. Its capabilities in advanced analytics and machine learning are a testament to its strength. In Microsoft Fabric, the Data Science persona allows you to easily create new ML Model objects and group those objects within an Experiment, allowing you to track the development of multiple models. Collaborative Data Science Notebooks The cooperative data science notebooks provided by Microsoft Fabric and Databricks narrate their individual tales of synergy and innovation. Fabric’s notebooks foster a collaborative environment with features that allow multiple users to co-edit and contribute simultaneously, thus democratizing the data science process. On the flip side, Databricks Notebooks streamline the development experience, offering a rich environment that seamlessly connects to the Lakehouse Platform, enabling rapid iteration and sharing of work across teams, personas, and skill levels. Model Serving and Management In terms of model serving and management, both platforms impress with scalable solutions tailored to meet the demands of contemporary businesses. Databricks, with its unified interface for deploying AI models as REST APIs, offers a sophisticated suite of tools for workflow orchestration and observable execution. Microsoft Fabric, not to be outdone, enables efficient model management through its notebook integrations and tracking capabilities, ensuring that data scientists can refine their models with precision. Business Intelligence and Reporting Insights As we shift our focus towards the realm of business intelligence and reporting, the insights derived from Microsoft Fabric and Databricks shed light on the way forward. Both platforms integrate seamlessly with a host of visualization tools, offering real-time analytics capabilities that empower users to craft reports with the latest data architecture trends in mind. The clarity and context provided by these tools are invaluable for businesses looking to make informed decisions swiftly. Real-time Analytics and Intelligence The race for real-time analytics and intelligence is a competitive field where both Microsoft Fabric and Databricks outperform. Their capabilities in data streaming and processing with minimal latency enable immediate insights and data-driven decision-making. Databricks stands out with its Serverless SQL Warehouse , which offers a powerful, scalable solution for real-time analytics. Microsoft Fabric has similar capabilities through its Real-Time Intelligence persona, where you can use an Eventhouse to rapidly load structured, unstructured and streaming data for querying or use a KQL Queryset to produce shareable tables and visuals. Seamless Integration with Office 365 Microsoft Fabric offers a seamless integration with Office 365, providing a unified analytics platform that brings together data from across the Microsoft ecosystem. The integration with Microsoft 365 data creates a cohesive environment where insights from: Teams Outlook SharePoint and other sources can be leveraged to generate comprehensive business intelligence, ensuring data reliability. Pricing Model Comparison In the sphere of data analytics, cost considerations hold equal importance to the capabilities of the platforms themselves. The pricing model Microsoft Fabric offers—pay-as-you-go hourly or monthly—provides flexibility and simplicity, allowing businesses to scale their data solutions in alignment with their usage patterns. Databricks, with its usage-dependent pricing model, presents a different approach, charging based on resource consumption and offering the potential for cost optimization based on workloads. This is a highly flexible model that allows cost to be tailored to the exact requirements of your production workloads. Microsoft Fabric's Subscription Details Peering into the subscription landscape of Microsoft Fabric reveals an enticing offer—a free trial for Power BI users, extending the platform’s reach and allowing businesses to explore its data engineering capabilities risk-free. With a pricing structure based on capacity units, Microsoft Fabric caters to a range of business sizes and needs, offering flexibility and scalability within its subscription models. Databricks' Usage-Dependent Pricing Model The economics of Databricks’ usage-dependent pricing model present a calculated approach where costs are tied to the runtime hours of virtual machines. . This model speaks to the efficiency and scalability needs of businesses, ensuring they only pay for what they use, thereby enabling a more tailored allocation of resources. Security and Compliance Standards In an environment where data security and compliance are uncompromisable, both Microsoft Fabric and Databricks maintain stringent standards and proudly hold certifications such as SOC 2 Type 2, ISO 27001, and HIPAA. These certifications are a testament to their commitment to safeguarding data and ensuring the integrity of their platforms. Encryption and Authorization Protocols The encryption and authorization protocols deployed by Microsoft Fabric and Databricks are the bedrock of their security architectures. With comprehensive data encryption and robust authentication features, both platforms ensure that data remains secure, whether at rest or in transit, while also providing granular control over access permissions. Achieving Compliance with Major Cloud Providers Navigating the compliance landscape, Microsoft Fabric stands out with a litany of certifications, showcasing its ability to meet and exceed the expectations of major cloud providers. This dedication to compliance is essential for businesses that demand the highest standards of data security and governance. Summing it up: Choosing the Right Platform for Your Data Needs The mission to choose the appropriate data platform depends on matching specific data requirements with the unique strengths of Microsoft Fabric and Databricks. For those seeking an integrated Azure-based solution, Microsoft Fabric offers a compelling proposition, while Databricks stands as the champion of big data processing and machine learning for more complex data projects. Assessing Your Data Squad and Project Goals Understanding the expertise of your data squad and the objective of your project is paramount in choosing between Microsoft Fabric and Databricks. Fabric’s beginner-friendly ecosystem is inviting for those taking their first steps into data analytics, whereas Databricks is the playground for seasoned data scientists seeking deeper data exploration and big data analytics. Generative AI (GenAI) Capabilities Both platforms offer GenAI capabilities and choosing the best one will be entirely dependent on your use case. If you’re looking to develop chat-bot like applications for end users within your enterprise, then Databricks will be the better option. For example, with their Mosaic AI Agent Framework, you will be able to invite subject matter experts to quickly assess the quality of a GenAI application and allow you to iterate on your application to ensure that the answers your application is generating meets the standards of the enterprise. Databricks also offers a robust toolset for the development of GenAI applications supporting everything from prompt engineering and RAG to fine tuning and pretraining. Databricks integration with Hugging Face and flexible Model Serving capabilities allow you to select any model that works for your business whether proprietary or open source. Databricks has also recently introduced AI/BI Genie, which is a feature that allows teams to interact with their data using natural language. Analysts can connect a collection of Unity Catalog tables to a Genie space and ask questions and generate visuals to help users better understand operational data. Microsoft also offers similar capabilities with Azure AI Studio, but this experience (as of this writing) is not yet integrated with Microsoft Fabric and would have to be maintained separately. Fabric’s Gen AI capabilities separate itself from Databricks by catering the Microsoft Copilot experience to the different personas they offer within Fabric. For example, if you are looking to quickly develop a data pipeline, the Copilot experience through the Data Factory persona will allow you to do so. As a Data Engineer, you will be able to use natural language to generate code for you (Databricks offers something similar through their Databricks Assistant), and if you’re a Power BI user, natural language can be used to create entire reports/dashboards for you by simply using natural language. Similar to Databricks’ Genie spaces, Microsoft recently announced a Fabric feature called AI skill. This feature allows you to create a ‘skill’ which allows you to connect either a lakehouse or data warehouse to a language model and ask questions of your data. These AI skills offer several configuration options that allow users to customize the language model behavior to better suit specific use cases. In addition, if you really want to push the value of Fabric and GenAI, you can also create custom copilots with Microsoft’s low-code/no-code Copilot Studio solution. Balancing Features with Budget Constraints Striking a balance between desired features and budget limitations is a vital factor in the process of platform selection. Both Microsoft Fabric and Databricks offer specialized features that cater to advanced analytics and real-time streaming, yet their respective pricing models and integration capabilities must be weighed against the financial boundaries of the organization. When you might want to use Microsoft Fabric If you have a small, inexperienced data squad and minimal interest in managing infrastructure, then Fabric will be the choice for you. If many of your source data sits in a SQL-based data warehouse, then migrating to Fabric will be a smooth transition as Fabric has native TSQL and stored procedure compatibility through their Fabric Data Warehouse. When you might want to use Databricks If your data team consists of experienced professionals, then Databricks will be the choice for you. Databricks would also be a natural selection if you are using a host of different vendors to accomplish your data goals. Many of these vendors can be accessed within the Databricks platform and will allow you to easily write data into Unity Catalog. The auto scaling capabilities of Databricks clusters, combined with the many different cluster offerings available, makes the processing of many different big data use cases much simpler. Additionally, the Databricks platform offers a tightly integrated feature set that accelerates collaboration when multiple teams or personas need to operate out of a single data stack. Better together Combining the two platforms could allow you to reap the benefits of both while still offering the user experience flexibility that keeps all data users engaged and create a greater sense of data ownership. Fabric’s Dataflow Gen2, with all its native connectors, offers a low code alternative to connect to a larger set of data sources, which in turn reduces the ingestion time and allows a larger swath of data professionals to be involved in the process. Once ingested a more seasoned professional can then use Databricks, and all its processing capabilities, to process the data as needed before exposing the data to PowerBI users via a Databricks SQL endpoint. Databricks and Microsoft recently announced Unity Catalog Mirroring in Fabric, which allows customers to read data managed by Unity Catalog from Fabric workloads. Using Fabric to read Unity Catalog data does not require any data movement and replication, meaning any changes made in Databricks is reflected immediately in Fabric, highlighting how these 2 platforms work better together to enhance the user experience. Summary We’ve traversed the intricate landscapes of Microsoft Fabric and Databricks, weighed their capabilities, and measured their cost implications. Whether seeking seamless integration with Azure services or delving into the depths of big data analytics, the platform you choose should align with your team’s expertise, project goals, and financial considerations. Embrace the analytics adventure ahead, and let the data lead you to new discoveries and successes. Need help, we have Fabric workshops and Databricks experts to help. Frequently Asked Questions How does Microsoft Fabric's pricing model differ from Databricks'? Microsoft Fabric's pricing model differs from Databricks' by employing a pay-as-you-go model based on capacity units, offering flexibility with hourly or monthly rates, whereas Databricks' pricing is usage-dependent and scales costs according to virtual machine usage, runtime hours, and data storage. Can Databricks handle real-time analytics, and how does it compare to Microsoft Fabric? Yes, Databricks supports real-time analytics through data streaming and its SQL serverless data warehouse, making it a powerful and scalable solution. Microsoft Fabric also offers real-time analytics capabilities, making them both competitive in this arena. What security certifications do Microsoft Fabric and Databricks hold? Microsoft Fabric and Databricks hold security certifications such as SOC 2 Type 2, ISO 27001, and HIPAA, showcasing their dedication to strict security and compliance standards. Is Microsoft Fabric suitable for beginners in data analytics? Yes, Microsoft Fabric is suitable for beginners in data analytics as it is designed to be beginner-friendly with its no-code/low-code options and integrated tools. Can I integrate Microsoft Fabric with Office 365 for business intelligence purposes? Yes, you can integrate Microsoft Fabric with Office 365 to gain comprehensive business insights from Microsoft 365 sources like Teams, Outlook, and SharePoint.
August 28, 2024 by AI Innovation Group Discover the latest AI advancements. From IBM’s and Meta’s new tools to groundbreaking research and new AI laws, our recent AI news keeps you informed on all fronts. Significant AI developments in August 2024 include innovations in artificial intelligence, regulatory discussions about AI, and notable releases. Key Takeaways Major tech companies like IBM and Meta are leading AI innovation, focusing on enhancing productivity and developing new tools that transform various industries. Recent AI research breakthroughs, including Alibaba Cloud’s Qwen2-Math and Paige’s collaboration with Microsoft for cancer diagnosis, showcase AI’s potential to solve complex problems and improve healthcare. The growing influence of AI in politics prompts regulatory efforts, exemplified by states' proposed and passed legislation and the EU’s AI Act, aiming to balance innovation with public safety and trust. Major Tech Companies Unveil New AI Tools Artificial intelligence is significantly enhancing productivity across various sectors by automating routine tasks and providing insights from data analysis. Apple, IBM, and Meta are making significant strides in AI development, each with unique approaches and goals. Continue reading below for more updates. Apple Plans to Debut the First Generative AI iPhone Apple's upcoming event on September 9, 2024, is expected to unveil the iPhone 16, with a focus on enhanced artificial intelligence (AI) capabilities. The event, "It’s Glowtime," hints at significant AI integrations, including personalized features like AI-generated emojis and an advanced Siri that could potentially learn user preferences over time. Despite these advancements, analysts suggest that the iPhone 16's AI features will be part of a gradual evolution rather than a revolutionary shift, with some key functionalities not expected until later models, possibly affecting the device's market impact. The anticipated AI enhancements could alter Apple's trajectory, especially as it faces declining sales in key markets like China. The collaboration with OpenAI and the introduction of generative AI features could set the iPhone 16 apart, though the timing of these advancements will likely shape consumer and investor expectations. Pricing remains a significant question, with potential AI-driven price increases being a topic of debate among enthusiasts and analysts. Read more here. Meta's Long-term AI Strategy & Llama Release Meta is focusing on a sustainable AI strategy that prioritizes long-term development and innovation over immediate financial returns. This approach emphasizes significant advancements in both AI and metaverse technologies, aiming to create a future where AI seamlessly integrates into various aspects of digital and physical life. Meta’s release of Llama 3.1 405B marked a significant milestone for open-source AI, as it was the first frontier model that truly rivaled closed-source giants like GPT-4 and 4o. With Meta's announcement, they significantly narrowed the gap between open and closed-source models, making a strong case for the potential and competitiveness of open-source AI in the broader landscape. Read more here. IBM Introduces AI-Powered Cybersecurity Assistant for Threat Detection IBM has taken a significant leap in the cybersecurity landscape with the introduction of its generative AI-powered cybersecurity assistant, specifically designed to enhance threat detection and response services. This cutting-edge tool harnesses the power of generative AI to assist security teams in identifying, analyzing, and responding to cyber threats with unprecedented speed and accuracy. By processing vast volumes of data from various sources, the assistant can generate actionable insights that help mitigate risks before they escalate. This advancement not only improves the efficiency of threat detection but also reduces the workload on cybersecurity professionals, allowing them to focus on more strategic tasks. The new AI assistant is part of IBM's broader strategy to integrate artificial intelligence into its cybersecurity solutions, reflecting the company's commitment to staying ahead in the rapidly evolving digital threat landscape. As cyberattacks become more sophisticated, the need for advanced tools that can adapt and respond in real-time is critical. IBM's generative AI-powered assistant is expected to transform how businesses approach cybersecurity, providing a higher level of protection and resilience against an ever-growing array of cyber threats. Read more about the release here. Groundbreaking AI Research Innovations The realm of AI research is witnessing groundbreaking advancements that are pushing the boundaries of human intelligence and creativity. Researchers are developing sophisticated AI models capable of solving complex problems across various domains, from mathematics to healthcare. These innovations are not only enhancing efficiency but also opening new avenues for future developments, benefiting humans. Three remarkable breakthroughs stand out in recent AI research: Alibaba Cloud’s Qwen2-Math, Paige and Microsoft’s collaboration on cancer diagnosis, and SingularityNET’s decentralized supercomputer network for Artificial General Intelligence (AGI). Each of these developments showcases the transformative potential of AI in different fields and its ability to tackle challenges that have long eluded human intelligence. Qwen2-Math: Revolutionizing AI in Mathematics Alibaba Cloud’s Qwen2-Math is an advanced AI model specifically designed to tackle intricate mathematical problems efficiently. This model leverages sophisticated AI techniques to solve complex mathematical challenges, significantly outperforming previous models. The Qwen2-Math model includes a variant with 72 billion parameters, achieving an impressive 84% score on the MATH Benchmark for challenging mathematics problems. This breakthrough represents a significant leap in the combination of AI and mathematics, addressing problems that human researchers have struggled with for years. Paige and Microsoft's AI Models for Cancer Diagnosis Paige’s AI model, developed in collaboration with Microsoft, focuses on enhancing early cancer detection and diagnosis capabilities in pathology. This partnership aims to deploy AI models that can analyze over 40 different tissue types, improving the accuracy and reliability of cancer diagnoses. The collaboration between Paige and Microsoft represents a significant step forward in the use of AI for medical purposes. By harnessing the power of artificial intelligence, these models can provide more precise treatment options, potentially saving countless lives through early and accurate diagnosis. SingularityNET's Supercomputer Network for AGI SingularityNET is creating a global network of supercomputers designed to support the development of Artificial General Intelligence (AGI). This decentralized approach aims to facilitate collaboration and innovation in the pursuit of AGI, a goal that has long been considered the holy grail of AI research. SingularityNET’s supercomputer network pioneers a new era of AI development, showcasing the potential for artificial intelligence to push the boundaries of machine capabilities and move closer to achieving AGI. AI in Politics and Legislation Artificial intelligence is increasingly permeating political discourse and legislative actions worldwide. As AI technologies become more pervasive, lawmakers are grappling with the need to regulate their use to minimize public harms and ensure public trust. While the intersection of AI and politics is a critical area of focus, particularly regarding electoral integrity, automated decision making and bias implications for certain use cases are also top worries. Two notable developments in this arena are the European Union’s AI Act and the relatively active state AI legislative calendars in the absence of US federal regulatory progress. These efforts highlight the political, technical, and commercial realities of advancing regulatory frameworks that protect public interests without dampening innovation. EU's AI Act: Balancing Innovation and Trust The EU’s AI Act, the first horizontal AI regulatory framework, will technically go into effect August 26, 2024, though won’t be enforced until August 2, 2026. The Act seeks to promote human-centric and trustworthy AI across member states. This framework is designed to foster innovation while ensuring user safety and maintaining public trust. The AI Liability Directive is still being defined and will ensure that EU subjects harmed by AI have protection similar to other technologies. The act introduces a risk-based framework, categorizing AI systems (e.g. use cases) based on their potential risks and implementing strict compliance measures for high-risk systems. The EU AI Act aims to foster a responsible environment for AI to thrive by balancing innovation and trust. US AI Regulatory Advancement – AB2930, SB1047, CO California State Senator Scott Wiener proposed a bill (SB1047) to require large AI model makers to test and confirm their models can’t be used to attack critical infrastructure, engage in cyberattacks, terrorism, or to make weapons. It would also establish a public cloud to host and build AI tools (CalCompute) aiming to provide more equitable development and research. Finally, it would provide whistleblower protections for workers at companies making AI tools. Colorado recently passed their AI Act (SB205) which regulates AI that makes “consequential” decisions about CO consumers’ education, employment, financial, or lending services, essential government services, healthcare, housing, insurance, or legal services. The particular focus is on “high risk” AI systems and “algorithmic discrimination”, but there’s a disclosure (e.g. transparency) requirement for consumers, as well. The automated decision-making portion is similar to California’s AB2930, which also seeks to regulate automated decision making and suffers from the same vagueness, leaving “consequential” (CO) and “substantial” (CA) to likely be adjudicated in our courts. Virtually all agree that AI regulation is necessary, including AI makers like OpenAI, but how to craft legislation that protects people, and in some cases the environment, while not stifling innovation is hotly debated. The main observation is that the states will continue to make progress in the absence of federal legislation, likely leaving the industry with a patchwork of laws pushing the limits of enforcement and compliance. AI's Impact on Industries Artificial intelligence is revolutionizing various industries, enhancing productivity and creating new business opportunities. Leading tech companies are actively launching new AI tools that promise to transform sectors such as finance, healthcare, and entertainment. These advancements are not only driving efficiencies but also enabling new business models and innovations. Three key areas where AI is making a significant impact are data centers, legal services, and the video game industry. Each of these sectors is experiencing transformative changes driven by the integration of AI technologies. AI Revolution in Data Centers The integration of AI in data centers is projected to streamline operations, driving efficiencies and reducing operational costs. As AI requires substantial computational power, the efficiency of data centers becomes critical for processing large volumes of data. The market for AI in data centers is expected to reach between $2 to $4 trillion USD by 2030, reflecting its growing importance. This revolution is set to transform how data centers operate, making them more efficient and capable of handling the demands of advanced AI applications. AI in Legal Services AI can assist law firms in improving efficiency by automating document analysis and optimizing legal research, leading to reduced operational costs. Law firms adopting AI technologies can achieve significant improvements in operational efficiency and return on investment (ROI). Implementing AI technologies can lead to substantial cost savings and productivity gains for law firms, making them more competitive and effective in their operations. AI in Video Game Industry The ongoing strike by video game performers highlights concerns regarding the use of artificial intelligence in creating realistic character performances. These performers are negotiating over the potential risks AI poses to employment opportunities and job security in the industry. Video game performers are advocating for regulations surrounding the use of AI, reflecting broader concerns about technology’s impact on employment. This situation underscores the need for careful consideration of AI’s role in creative industries. AI-driven Speech Analytics in Contact Centers AI-driven speech analytics can analyze customer interactions in real-time, providing insights that improve service quality. These analytics enable companies to identify customer sentiment and adjust their service strategies accordingly. AI provides companies with actionable insights into customer interactions, enhancing understanding of customer needs and improving agent performance. New Tools for Training AI Models Explore the latest tools designed to streamline AI model training, offering enhanced automation, faster processing, and improved accuracy. These innovations empower data scientists to build more robust models with less manual intervention, driving faster insights and smarter solutions. Nous Research Unveils New Tool to Train AI Models with 10,000x Efficiency Nous Research has introduced a revolutionary tool that could dramatically reshape the landscape of AI model training. Per Venture Beat, the tool boasts a staggering 10,000x increase in efficiency, offering the potential to significantly reduce the time and computational resources required to train powerful AI models. This leap in efficiency not only accelerates the development process but also makes advanced AI more accessible to a broader range of organizations, from startups to industry giants. By streamlining the training process, Nous Research is poised to lower the barriers to entry for creating sophisticated AI systems, enabling faster innovation and more rapid deployment of AI-driven solutions. The implications of this breakthrough extend far beyond just speed and cost savings. With AI models becoming easier and more affordable to train, the democratization of AI technology could spur a new wave of creativity and problem-solving across industries. From healthcare and finance to manufacturing and education, the ability to train powerful AI models with unprecedented efficiency could lead to transformative changes, driving forward advancements that were previously constrained by time and resources. Gigabyte Lets You train AI models on Your Own Gigabyte has introduced AI TOP, a new software utility designed to enable home users to train and fine-tune advanced AI models on their local systems. Launched at Computex, AI TOP supports models with up to 236 billion parameters when used with recommended hardware and offers enhanced privacy, flexibility, and real-time adjustments. The software is optimized for Gigabyte's hardware, supports over 70 open-source LLM models from Hugging Face, and is available for free download on Linux systems from Gigabyte's official website. Summary In summary, the rapid advancements in AI are transforming various sectors, from major tech companies unveiling new tools to groundbreaking research and innovations. The influence of AI extends to politics and legislation, highlighting the need for balanced regulatory frameworks. Industries such as data centers, legal services, and the video game industry are experiencing significant changes driven by AI, while everyday life is becoming more efficient and personalized thanks to AI-driven technologies. As we look to the future, it’s clear that AI will continue to shape our world in profound ways. By embracing these advancements responsibly and ethically, we can harness the full potential of AI to enhance human experiences and drive innovation.
August 13, 2024 by Jeff Blankenburg Key Takeaways: Role of AI-driven applications: Intelligent Applications (IA) enable informed decision-making by integrating a disparate knowledge base and feedback loops, creating new predictive insights and recommending workflows to enhance both internal operations and customer-facing personalized decision-making. Future standards: IA enables trustworthy, personalized recommendations at scale. Use cases: IAs offer a wide array of use cases across industries, including retail / ecommerce, customer service, healthcare, and media. Key considerations: IT departments can build IA that deliver trustworthy recommendations by understanding the components of quality recommendations and their implications for IT planning. AI-driven decision-making is becoming mainstream in both internal operations and customer interactions. Examples include: Amazon: Utilizes context-aware user and search parameters (e.g., seasonality or historical purchases) Customer service: Supports representatives by leveraging customer insights to recommend solutions, boosting customer satisfaction Security and environmental management technology: Leverages multimodal data to drive automated decision-making or prompt the account owner Medical knowledge: Providers like IBM Watson Health provide patient-specific recommendations with clear traceability back to sources Streaming services: Prioritize media content based on prior viewing patterns With each accurate recommendation, both enterprise users and customers gain confidence that future suggestions will also be trustworthy. How does an enterprise create quality recommendations at scale to begin with? To start, we need to understand what makes a quality recommendation. In this blog, we explore the components of a quality recommendation, and how Intelligent Applications leverages cross-ecosystem predictive insights to deliver personalized recommendations, enable decision adaptability based on feedback loops, and use self-learning mechanisms to continuously improve quality of recommendations. How effective are your current enterprise recommendations? Today, many organizations adopt a web of recommendation solutions that remain siloed across applications and data sets. This fragmentation leads to missed opportunities for insights and challenges in prioritizing recommendations at decision points. These recommendations often lack personalization and adaptability to real-time user feedback. In other words, current recommendations are not adaptive to individual circumstances and fall short of users' rising expectations from user experiences. For example, consider a financial services firm facing consistent regulatory updates to their products and services across multiple consumer touchpoints. A combination of manual efforts and coordinated responses across platforms will be required to deliver consistent updates across knowledge portals, customer engagement chat, and customer service support. With IA, a cohesive response ecosystem is more seamless, enabling comprehensive knowledge updates across the organization. Comparison: Fragmented Insight Mechanisms vs. Intelligent Application Recommendations Limited Insight Recommendation Mechanisms: Siloed data sources resulting in basic or incomplete recommendation options Unclear prioritization and sequence of recommendations Low adaptability to changing user preferences or contexts; high dependency on past user behavior Poor or non-existent user feedback loop; requires manual intervention to adapt recommendations Intelligent Application Recommendations: Cross-ecosystem data integration for a robust understanding of end-user objectives and intent (consumer or employee) Clear prioritization of recommendations based on understanding user objectives and intent across user and similar-user feedback loops Real-time adaptation to user behavior, sentiment and contextual factors Validation and cross-ecosystem redeployment of feedback loop data What Makes a Quality Recommendation? Trust and quality in IA recommendations mirror the fundamentals of person-to-person trust: accuracy, authority, authenticity, and responsiveness. Each of these factors comes with unique considerations for IA planning: Accuracy: Are the Recommendations correct and useful? Recommendations should be: Relevant: Correct interpretation and application of multimodal data Objective-aligned: Responses aligned to propel workforce or customer interaction towards desired business outcomes Compliant: Cohesive ecosystem updates for regulatory and internal guideline changes Of practical use: Predictive data integration across logistics, skillsets, required assets, and practical realities Authority: Are the Recommendations consistent and evolving? Recommendations should be: Consistent: Consistent intent and guidance in recommendations across the user base and similar scenarios Flexible: Integrate feedback loops and algorithm applicability testing to ensure user profile updates are integrated into evolving recommendations Situationally aware: Maintain consistency in messaging and actions. For example, avoidance of safety close-out prompts while a vehicle is in motion. Authentic: Are the Recommendations personalized and contextually relevant? Recommendations should be: Personalized: A balanced mix of standardized yet adaptable recommendations. For example, a maintenance worker should follow a standardized set of safety protocols, but is able to guide the IA toward the optimal repair path in particular conditions Demonstrative of sentiment understanding and response: Adjust responses based on sentiment, fatigue, or risk elevation. Responsive: Are the Recommendations safe and scalable? Recommendations should be: Validated: Usability validation across data, IT ecosystem, and user base without crossing privacy boundaries Transparent, explainable and ethical: Traceable recommendations and integration of Responsible AI principles and regulations Offer user control: Correct, adjust, and/or eliminate recommendations to mitigate harmful, irrelevant, inaccurate, outdated, non-useful, or non-personalized content Support ongoing learning: Replicate best practice efficiencies across user and data interactions, including transfer of knowledge across other dimensions of decision-making With key trust factors supported across the IT ecosystem, IA can now enable enterprise decision-making with personalization at scale, system adaptability to feedback loops, and ongoing recommendation improvement mechanisms. Below are a few use cases for each type of impact. Enhanced Recommendations Use Cases A Retailer Uses Personalization at Scale: Traditional customer insights might recommend outdoor activity trackers based on similar purchases across a user group. However, they might not consider the user’s recent move to a different geographic climate, recent queries about indoor sportswear, and a 2-year cycle on outfit refreshes. By understanding and applying pattern recognition, prioritization, and evolving scenarios, IA can deliver more customized customer engagements that considers current trends, user behavior in real-time, and external factors like seasonality or cyclical purchasing. An Energy Firm Delivers System Adaptability to Real-Time Events: A traditional solution for a weather-related incident response would require employees to navigate various field applications, mobile communication channels, and internal sites to gather necessary information. The fragmented experience, delayed information gathering, and unclear prioritization of response activities creates inefficiencies in response, posing potential risks to worker safety. Alternatively, with IA, the resolution team accesses one web-based intelligent application as a central command center for response. Sensing the outages, the IA sorts the user interface autonomously—prioritizing critical outage, safety, and repair information— and recommends tasks based on the user’s location, skill sets, and available inventory in the employee's truck. A Medical Monitoring Firm Leverages Feedback for Ongoing Recommendation Improvement: Traditional machine learning (ML) may identify connections between inactivity and heart rate, providing activity reminder prompts, but fails to easily integrate feedback from a user with a strained back who seeks activities to maintain fitness while recovering. If responses don’t align to their current scenario, the user is more likely to abandon remote monitoring usage altogether. Alternatively, IA can provide recommendations based on unseen user adoption and usage patterns, fill in probable data gaps, and mitigate irrelevant messaging. These are just a few examples of IA’s potential to leverage context and feedback to provide elevated engagement and superior recommendations. Conclusion Intelligent Applications provide a pathway for superior enterprise outcomes in AI-driven recommendations for both workforce and customer engagement. The status quo of fractured and uncoordinated insights is no longer sustainable, as non-personalized experiences fail to meet advancing user expectations and business needs With a robust IA strategy for delivering confident recommendations, businesses can look forward to personalized recommendations at scale, system adaptability to user feedback, and ongoing recommendation improvement mechanisms. The process of creating quality recommendations at scale begins with understanding the technology ecosystem and its implications for accuracy, authority, authenticity and responsiveness. Establishing this foundation enables enterprises to begin on their IA journey with strong content governance—an essential factor for managing predictive insights and user feedback effectively.
August 1, 2024 by Tyler Suss Need help with Artificial Intelligence (AI) in your business? AI consulting can get you up and running and integrated. This article reviews the top AI consulting services for 2024, so you can see the benefits. Quick Insights on Artificial Intelligence from the Expert AI Consultants AI consulting services assist in identifying and implementing AI opportunities, developing strategies and maintaining competitiveness and innovation, with Statista predicting the global AI market to grow 28.46% between 2024 and 2030 and reach $826.70 billion by 2030. Top AI consulting companies are chosen based on client reviews, ethics, and technical expertise, ensuring you receive a reliable and effective AI strategy and implementation. Leading AI consulting firms like IBM Consulting, Neudesic, and Accenture offer full AI services including strategy development, implementation, training and ongoing support so you can get up and running and achieve long-term success. Top AI Consulting Services for Your Business in 2024 AI technologies can be overwhelming, but expert AI consultants excel at helping you identify relevant applications, develop implementation strategies, and avoid common pitfalls. These professionals ensure you maximize your ROI, avoid the typical mistakes and get the most out of AI for your business. The top AI consulting companies are chosen based on criteria like the volume of client reviews, ethics and technical expertise in AI. These are the key factors in determining the reliability and effectiveness of an AI consulting partner. By choosing a top-rated AI consulting company, you gain access to the best guidance and support. AI consulting services help businesses stay competitive and innovative by: Identifying AI opportunities Developing AI strategy Staying ahead of the AI curve and industry trends Leveraging experience and expertise across various use cases, industries, and technologies Overview of Artificial Intelligence Consulting Services AI consulting companies provide the expertise to help you maximize the benefits of AI. They guide you through the complexity of AI adoption, addressing challenges such as limited in-house expertise and integration hurdles. By assisting you in overcoming these obstacles, an expert AI consultant enables you to make informed decisions about your AI investments and stay ahead in your industry. The benefits of AI consulting services go beyond implementation. These experts help you stay ahead of the AI curve so you can get the growth and innovation out of AI. As the AI market continues to grow, the role of AI consulting companies will become increasingly vital. Why AI Consulting Companies AI consulting companies help you find AI opportunities, develop strategy and implement. They help you navigate the AI landscape, find relevant applications and avoid the pitfalls. By partnering with a trusted AI consulting firm you get better outcomes and avoid the mistakes. These are the companies you need to stay ahead of the curve in the fast changing AI landscape. They help you find AI opportunities and develop strategy for implementation so you can get the most out of AI tech. The top AI consulting firms are chosen based on client reviews, ethics and technical expertise in AI. These firms also ensure ethical AI practices within the organization. They help you navigate the complex ethical considerations of AI implementation so AI systems are fair, transparent, and inclusive. This is especially important as AI capabilities get more integrated into your business. What an AI Consulting Company Does AI consulting companies undertake several critical tasks to ensure successful AI adoption, including: Identifying and prioritizing significant AI opportunities for your business Guiding you through the entire strategy development process Creating tailored AI roadmaps for your organization Ensuring alignment with your organization’s goals These actions help you effectively implement AI and maximize its benefits. In addition to strategy development, AI consulting firms provide: Training and upskilling to bridge the AI talent gap and upskill the workforce Training to turn employees into AI savvy professionals Training programs offered by companies like Accenture and IBM as part of their AI services Support services are another key function of AI consulting companies. They provide ongoing support to ensure continuous delivery and maintenance post implementation. Companies like IBM and Neudesic offer a full spectrum of AI services, including implementation and support, ensuring the sustainability of your AI initiatives and delivering long-term results. Top AI Consulting Companies 2024 The top AI consulting companies in 2024 showcase the transformative power of artificial intelligence across various industries, providing tailored solutions specific to each sector. These are companies recognized for their technical expertise, ethical practices, and their ability to deliver customized AI strategies for their clients. In this section we’ll look at the leading AI consulting companies that are making significant impacts. From Neudesic’s AI transformation services to IBM Consulting’s full spectrum AI solutions, these companies are the leaders in AI. They offer a range of services, including: Strategy Implementation Training Support This ensures you can maximize the benefits of AI. Let’s get into the details of these top AI consulting firms and what sets them apart. Neudesic Neudesic is a leading global technology services company that has been a leader in AI transformation since shifting its focus to machine learning and AI in 201. Neudesic is Microsoft’s US AI Partner of the Year and is known for its breadth and depth across various industries and its ability to transform operations and client-facing applications into Intelligent Enterprises and Intelligent Applications respectively. The company is known for: Being a leader in Responsible AI, for ethical use of AI technologies AI accelerators for computer vision and Azure OpenAI Workshops and jumpstart programs to help organizations get started with their AI initiatives fast Expertise in Azure services, machine learning, computer vision and product development Being the right partner for small to medium-sized businesses looking to leverage AI for competitive advantage Nordcloud Nordcloud is a cloud based AI solutions company, helping businesses scale efficiently. As a European AI leader, Nordcloud offers: Using advanced AI to optimize business processes Unlocking the value of data Cloud based solutions to get AI up and running fast and secure. By using the latest AI technologies Nordcloud helps businesses turn data into insights, drive efficiency and growth. Their cloud based AI solutions means businesses can scale and stay ahead in the fast moving AI landscape. Binariks Binariks delivers data driven insights and custom AI models to improve business accuracy and efficiency. They offer custom AI solutions to help businesses improve customer experiences and get better business outcomes. With expertise in AI and machine learning Binariks helps businesses get more out of data to drive growth and innovation. By developing custom AI models Binariks improves business accuracy and efficiency, so businesses can stay ahead of the competition. Their services include custom AI models, business accuracy and efficiency, decision making and operations optimization. Their data driven approach means businesses can make informed decisions and optimize their operations. IBM Consulting IBM Consulting is a global full service AI company, offering strategy, implementation, training and support. Their IBM Consulting Advantage platform increases productivity and consistency by providing a library of role based Assistants to support daily tasks. Consultants can toggle across multiple AI models and share insights fast. IBM Consulting is big on ethical AI and data security. Their platform has private instances of AI models to keep data private and secure. By using these services businesses can optimize their processes and get ahead in their industry. Infosys Infosys is big on ethical AI through its Responsible AI framework which minimizes business risks and ensures ethical AI use. By promoting Responsible AI Infosys helps businesses improve operational margins and get better business outcomes. Their AI and Automation practice connects technology to business processes in a human way. Infosys’ commitment to ethical AI sets them apart. Their focus on responsible AI means businesses can use AI technologies while minimizing risks and ensuring ethical practices. This means businesses can grow sustainably and stay ahead in the market. Deloitte Deloitte is a well known name in the AI consulting space with many successful AI implementations to their name. Their AI consulting expertise has earned them a strong reputation with both small and large businesses. Deloitte’s full service approach to AI implementation means businesses can get the most out of AI technologies. By working with Deloitte businesses can get: Their years of experience and proven track record in AI consulting Delivering tangible business outcomes through AI To achieve their strategic goals and stay ahead of the competition. QuantumBlack QuantumBlack, the AI consulting arm of McKinsey, applies AI in complex industries like mining and metallurgy. One of their notable projects is optimizing processes in copper ore mining with Freeport-McMoRan. QuantumBlack’s expertise in applying AI to complex problems means they are one of the most in demand AI consulting firms even though they are more expensive. Their ability to deliver AI solutions in tough industries sets them apart from other consulting firms. By using QuantumBlack businesses can get big efficiency and productivity gains. Boston Consulting Group (BCG) Boston Consulting Group (BCG) are the pioneers of scaling AI solutions, offering services that include machine learning and wide ranging optimisation. One of their notable AI projects is Scentmate, an AI powered platform for personalized perfumes. BCG’s focus on strategic planning and optimisation means businesses can achieve their strategic goals and get ahead. By working with BCG businesses can get: Their years of experience in scaling AI solutions Their expertise in driving global business change To get the most out of AI technologies for sustainable growth. Cambridge Consultants Cambridge Consultants are machine learning and AI experts. They offer AI strategies to fit individual client needs so businesses can achieve their strategic goals. Notable clients like the UK Ministry of Defense get big results from Cambridge Consultants’ AI projects. Their focus on bespoke AI solutions means businesses can use AI to drive growth and innovation. By working with Cambridge Consultants businesses can get better business outcomes through tailored AI. Accenture Accenture offers full AI services including strategy development, implementation, training and continuous maintenance. By using Accenture businesses can optimize their operations and grow sustainably. Accenture’s focus on delivering tangible business outcomes through AI includes helping businesses stay ahead in the fast changing AI landscape, investing in AI technologies to get better business outcomes, and driving innovation in AI. This means clients not only adopt AI technologies but also get ongoing support and advancements so they can succeed in the long term. LeewayHertz LeewayHertz is known for building custom AI models to fit individual client needs. By building AI applications that provide recommendations and detailed information LeewayHertz can improve customer experiences. One of their notable projects is with WineWizzard where they built an LLM application using ZBrain to recommend wines and provide information, so customer satisfaction can improve. Their ability to deliver personalized AI solutions means businesses can provide experiences to their customers, drive loyalty and growth. By working with LeewayHertz businesses can get the latest AI technologies to improve customer engagement and satisfaction. Deeper Insights Deeper Insights turn complex concepts into practical AI solutions, particularly in healthcare. Their expertise in machine learning (ML), natural language processing (NLP) and other AI technologies means they can build solutions that get efficiency and business outcomes. Their focus on practical AI solutions means businesses can get real results from their AI investment. By working with Deeper Insights businesses can get the latest AI technologies to drive innovation and efficiency. AI Strategic Planning and Governance Having a strategic plan for implementing AI into a business is key to understanding business needs and to anticipate the benefits of AI adoption. AI strategy consultants help organizations build AI roadmaps that fit with the company’s existing plans and strategic vision. By focusing on commercial solutions they ensure AI initiatives are practical and impactful. AI governance policies are essential for setting guidelines on data collection, privacy and mitigating risks of AI implementation. Strong AI governance frameworks mean effective AI deployment and compliance with changing regulations. AI consulting firms can help build these ethical frameworks so AI is used responsibly within the business. By having clear strategic plans and governance policies businesses can navigate the complexity of AI adoption. These mean AI initiatives are aligned to business objectives and are implemented ethically and effectively. AI Implementation and Integration AI implementation and integration involves: Outdated infrastructure which means AI systems can’t process large amounts of data quickly so investment in new technology is needed. Not enough data which means AI algorithms won’t be effective. Collecting and organizing relevant data is key to successful AI implementation. High costs which includes costs of expert collaboration, employee training and updating IT infrastructure. By addressing these challenges businesses can implement AI technology. One of the biggest challenges in AI implementation is dealing with not enough or poor quality data which can lead to biased or inaccurate results. Knowledgeable firms can help mitigate these challenges by providing pre-emptive solutions and building custom AI solutions to fit the business needs. Integrating AI into existing systems means not just technological upgrades but also training employees to use and troubleshoot the new tools. AI consultancies offer full support services to ensure a seamless AI transition, to help businesses overcome the challenges of AI implementation and get results. Generative AI and Its Impact Generative AI can boost productivity and automate work across industries. Here are the benefits of generative AI: For someone new to a subject generative AI can increase productivity by 40% Even experienced employees will see significant gains in productivity This can automate tasks that currently take up 60-70% of employees time so they can focus on more strategic work. Generative AI will: 0.1-0.6% annual productivity growth through 2040 Customer interactions Creative content for marketing and sales Code from natural language prompts By combining generative AI with other technologies businesses can get significant productivity gains and growth. This technology impacts: Customer ops Marketing and sales Software engineering R&D These areas account for 75% of total annual value from use cases. Quick Start AI Options Quick start AI options from consulting firms help businesses get started with AI fast and effectively. Neudesic’s Azure OpenAI Jumpstart Workshop accelerates the design of AI use cases, AI vision workshops help businesses get an AI ready workforce. These programs provide a structured approach to kickstart AI initiatives so businesses can get results fast. Nordcloud’s GenAI Accelerator on AWS provides: A safe way to try AI A sandbox to build AI prototypes and PoCs Experiment with AI technologies and refine solutions before full deployment This allows businesses to build enterprise scale solutions and harness the power of AI. AI readiness assessments evaluate an organization’s infrastructure, culture and processes before adoption. These assessments help businesses identify the challenges and prepare for a seamless AI integration so the AI journey is smooth and successful. Industry Specific AI Solutions Industry specific AI solutions address the unique challenges of industries like healthcare, finance and manufacturing. AI consulting firms build custom AI solutions to fit industry specific needs so businesses can achieve their strategic goals. In manufacturing Neudesic’s Document Intelligence Platform (DIP) uses AI and OCR to automate document workflows such as invoice automation, and increase efficiency. In finance AI experts implement AI algorithms for fraud detection and automated trading to increase efficiency and accuracy. Aside from DIP, BCG’s SmartBanking AI program uses AI for customer centric banking and personalized communication and predictive customer insights. By using AI powered solutions businesses in various industries can address their challenges and grow. These custom AI applications allow businesses to get the most out of their operations and achieve better business outcomes. AI Expertise and Talent Consultants have deep expertise in AI technologies, programming languages and ethical frameworks so they can build custom solutions for businesses. They are proficient in AI frameworks like TensorFlow or PyTorch and cloud services so they can build scalable and cost effective AI solutions. They invest in continuous education and training so they are up to date with the latest trends and best practices. AI consultants have years of experience working with multiple clients across industries so they can build custom solutions to meet business needs. They can monitor emerging trends and evaluate new tools and platforms so businesses can stay ahead of the curve. Expert AI consulting firms offer: Knowledge transfer to build internal AI capabilities within the organization Train existing employees or hire specialist talent to overcome the AI talent gap Successful AI adoption AI Ethics AI ethics is about applying ethical principles in the development, deployment and use of AI technologies. AI consultants help clients understand the importance of fairness, transparency and inclusivity in AI systems. A thorough ethical impact assessment can identify potential ethical risks and impacts before any AI project starts. Transparency in AI systems is key to building trust with users and stakeholders. Inclusive design means making AI systems accessible to people with different abilities, languages and cultural backgrounds. AI consultants should promote inclusive design practices to prevent discrimination and make sure AI benefits all users. Ethical leadership within the consulting firms is key to promoting ethical AI. By working with ethicists and stakeholders from different disciplines AI consultants can improve ethical decision making and responsible AI adoption. Conclusion In summary AI consulting services help businesses navigate the AI landscape and get the most out of AI. From strategy to implementation to training and support AI consultants have the expertise to drive growth and innovation. Partner with expert AI consulting firms and get AI adoption right and stay ahead of the AI curve. Choosing the right AI consulting firm is key to better business outcomes and ethical AI. With the AI market set to grow exponentially in the next few years the importance of expert guidance cannot be overstated. As businesses adopt more AI the role of AI consulting services will only become more important driving innovation and transformation across industries. FAQs What does an AI consulting firm do? Strategy development, implementation, training and support to get AI adoption right and continuous. How do AI consulting firms promote ethical AI? AI consulting firms promote ethical AI by doing ethical impact assessments, transparency and inclusivity and working with ethicists and stakeholders. This helps businesses navigate the ethical implications of AI. What are some quick start AI options? You can look into quick start AI options from consulting firms like Neudesic’s Azure OpenAI Jumpstart Workshop, Nordcloud’s GenAI Accelerator on AWS and AI readiness assessments. These can help you get ready for AI. How does generative AI boost productivity? Generative AI can boost productivity by automating tasks that take up most of employees time and that means increased efficiency and productivity growth. Why choose a top rated AI consulting firm? Choose a top rated AI consulting firm because they are selected based on client reviews, ethical standards and technical expertise so you get quality guidance, successful AI adoption and better business outcomes. So your investment in AI consulting pays off. Get Started Today Let Neudesic show you the future, today. Neudesic, an IBM Company, is a global professional services firm dedicated to advancing businesses with Microsoft technology expertise. We excel where people, technology, and business intersect, focusing on turning challenges into opportunities. Our role is to guide clients from identifying their core challenges to implementing tailored business solutions, setting them up for sustained success. For more information, or to connect with Neudesic’s expert AI consultants, visit www.neudesic.com.
June 27, 2024 by June 27, 2024 – Neudesic was recognized for demonstrating innovation and delivering exceptional customer solutions using Microsoft technologies.
