Generative AI - gettectonic.com - Page 10
Data Quality Critical
06Sep

Data Quality Critical

Data quality has never been more critical, and it’s only set to grow in importance with each passing year. The reason? The rise of AI—particularly generative AI. Generative AI offers transformative benefits, from vastly improved efficiency to the broader application of data in decision-making. But these advllucantages hinge on the quality of data feeding the AI. For enterprises to fully capitalize on generative AI, the data driving models and applications must be accurate. If the data is flawed, so are the AI’s outputs. Generative AI models require vast amounts of data to produce accurate responses. Their outputs aren’t based on isolated data points but on aggregated data. Even if the data is high-quality, an insufficient volume could result in an incorrect output, known as an AI hallucination. With so much data needed, automating data pipelines is essential. However, with automation comes the challenge: humans can’t monitor every data point along the pipeline. That makes it imperative to ensure data quality from the outset and to implement output checks along the way, as noted by David Menninger, an analyst at ISG’s Ventana Research. Ignoring data quality when deploying generative AI can lead to not just inaccuracies but biased or even offensive outcomes. “As we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias,” Menninger warns. “You’ve got to curate your data before training the models and do some post-processing to ensure the quality of the results.” Enterprises are increasingly recognizing this, with leaders like Saurabh Abhyankar, chief product officer at MicroStrategy, and Madhukar Kumar, chief marketing officer at SingleStore, noting the heightened emphasis on data quality, not just in terms of accuracy but also security and transparency. The rise of generative AI is driving this urgency. Generative AI’s potential to lower barriers to analytics and broaden access to data has made it a game-changer. Traditional analytics tools have been difficult to master, often requiring coding skills and data literacy training. Despite efforts to simplify these tools, widespread adoption has been limited. Generative AI, however, changes the game by enabling natural language interactions, making it easier for employees to engage with data and derive insights. With AI-powered tools, the efficiency gains are undeniable. Generative AI can take on repetitive tasks, generate code, create data pipelines, and even document processes, allowing human workers to focus on higher-level tasks. Abhyankar notes that this could be as transformational for knowledge workers as the industrial revolution was for manual labor. However, this potential is only achievable with high-quality data. Without it, AI-driven decision-making at scale could lead to ethical issues, misinformed actions, and significant consequences, especially when it comes to individual-level decisions like credit approvals or healthcare outcomes. Ensuring data quality is challenging, but necessary. Organizations can use AI-powered tools to monitor data quality, detect irregularities, and alert users to potential issues. However, as advanced as AI becomes, human oversight remains critical. A hybrid approach, where technology augments human expertise, is essential for ensuring that AI models and applications deliver reliable outputs. As Kumar of SingleStore emphasizes, “Hybrid means human plus AI. There are things AI is really good at, like repetition and automation, but when it comes to quality, humans are still better because they have more context.” Ultimately, while AI offers unprecedented opportunities, it’s clear that data quality is the foundation. Without it, the risks are too great, and the potential benefits could turn into unintended consequences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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September 6, 2024in Generative AI
Co-opetition
05Sep

Co-opetition

Tech companies frequently partner for mutual benefit-Co-opetition, but in the customer service and contact center sector, the competition is heating up. Established players like Genesys, Five9, and Nice are now facing significant competition from tech giants such as AWS, Microsoft, and Google. To strengthen their positions, longtime partners Genesys and Salesforce introduced a joint platform called CX Cloud earlier this year. This platform combines Salesforce’s advanced Service Cloud and CRM with Genesys’ leading contact center as a service (CCaaS) solution- the very epitome of Co-opetition. It integrates telephony, journey management, and employee-focused workforce engagement management tools to optimize contact center operations and track agent satisfaction. While both companies compete in areas like AI, digital engagement, and generative AI, the CX Cloud partnership exemplifies their “coopetition” strategy. Salesforce runs the desktop interface, while Genesys excels in workforce management. By integrating their technologies, the two companies offer customers a flexible solution, enabling them to use the tools that best suit their needs—whether it’s managing digital channels through Salesforce or Genesys. This collaboration eliminates competition in key areas, with both Salesforce and Genesys sales teams working closely together. The partnership between the two companies is not new; their integration dates back to 2015. However, the recent deeper integration, which now covers not just voice but also digital channels, offers customers a unified view of their data. This allows users to harness customer conversation data across both platforms more effectively, giving them the flexibility to use tools from either Genesys or Salesforce. In addition to competition from one another, both Genesys and Salesforce face challenges from cloud hyperscalers like AWS, Microsoft, and Google, which also offer contact center tools. Despite this, Genesys’ and Salesforce’s CX Cloud collaboration stands out by offering a unified framework that benefits customers through combined capabilities. As an example of this complex tech landscape, AWS is both a competitor and a top partner for reselling Genesys Cloud. Both companies agree that the real focus isn’t on competing with each other, but on helping customers solve challenges around customer engagement in an efficient and cost-effective way. The joint platform also integrates with other technologies, such as Google’s Contact Center AI and AWS tools like Lambda and Polly, making it adaptable to diverse enterprise needs. Both Genesys and Salesforce emphasize the importance of an open platform with pre-built integrations, allowing customers to get more value from both platforms faster than before. CX Cloud has seen adoption across various industries and company sizes, from large enterprises to smaller, faster-moving companies. Smaller businesses, in particular, have been quick to adopt this innovation, as it allows them to access enterprise-level integrations without needing to build custom solutions. Larger enterprises, such as ADP, have also benefitted from CX Cloud by using it to deliver proactive customer experiences, addressing potential issues before they arise. Overall, the partnership between Genesys and Salesforce exemplifies Co-opetition-a collaborative approach in a highly competitive market, enabling customers to leverage the strengths of both platforms for enhanced contact center operations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 5, 2024in Generative AI
Salesforce Data Quality Challenges and AI Integration
05Sep

Salesforce Data Quality Challenges and AI Integration

Salesforce Data Quality Challenges and AI Integration Salesforce is an incredibly powerful CRM tool, but like any system, it’s vulnerable to data quality issues if not properly managed. As organizations race to unlock the power of AI to improve sales and service experiences, they are finding that great AI requires great data. Let’s explore some of the most common Salesforce data quality challenges and how resolving them is key to succeeding in the AI era. 1. Duplicate Records Duplicate data can clutter your Salesforce system, leading to reporting inaccuracies and confusing AI-driven insights. Use Salesforce’s built-in deduplication tools or third-party apps that specialize in identifying and merging duplicate records. Implement validation rules to prevent duplicates from entering the system in the first place, ensuring cleaner data that supports accurate AI outputs. 2. Incomplete Data Incomplete data often results in missed opportunities and poor customer insights. This becomes especially problematic in AI applications, where missing data could skew results or lead to incomplete recommendations. Use Salesforce validation rules to make certain fields mandatory, ensuring critical information is captured during data entry. Regularly audit your system to identify missing data and assign tasks to fill in gaps. This ensures that both structured and unstructured data can be effectively leveraged by AI models. 3. Outdated Information Over time, data in Salesforce can become outdated, particularly customer contact details or preferences. Regularly cleanse and update your data using enrichment services that automatically refresh records with current information. For AI to deliver relevant, real-time insights, your data needs to be fresh and up to date. This is especially important when AI systems analyze both structured data (e.g., CRM entries) and unstructured data (e.g., emails or transcripts). 4. Inconsistent Data Formatting Inconsistent data formatting complicates analysis and weakens AI performance. Standardize data entry using picklists, drop-down menus, and validation rules to enforce proper formatting across all fields. A clean, consistent data set helps AI models more effectively interpret and integrate structured and unstructured data, delivering more relevant insights to both customers and employees. 5. Lack of Data Governance Without clear guidelines, it’s easy for Salesforce data quality to degrade, especially when unstructured data is added to the mix. Establish a data governance framework that includes policies for data entry, updates, and regular cleansing. Good data governance ensures that both structured and unstructured data are properly managed, making them usable by AI technologies like Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). The Role of AI in Enhancing Data Management This year, every organization is racing to understand and unlock the power of AI, especially to improve sales and service experiences. However, great AI requires great data. While traditional CRM systems deal primarily with structured data like rows and columns, every business also holds a treasure trove of unstructured data in documents, emails, transcripts, and other formats. Unstructured data offers invaluable AI-driven insights, leading to more comprehensive, customer-specific interactions. For example, when a customer contacts support, AI-powered chatbots can deliver better service by pulling data from both structured (purchase history) and unstructured sources (warranty contracts or past chats). To ensure AI-generated responses are accurate and contextual, companies must integrate both structured and unstructured data into a unified 360-degree customer view. AI Frameworks for Better Data Utilization An effective way to ensure accuracy in AI is with frameworks like Retrieval Augmented Generation (RAG). RAG enhances AI by augmenting Large Language Models with proprietary, real-time data from both structured and unstructured sources. This method allows companies to deliver contextual, trusted, and relevant AI-driven interactions with customers, boosting overall satisfaction and operational efficiency. Tectonic’s Role in Optimizing Salesforce Data for AI To truly unlock the power of AI, companies must ensure that their data is of high quality and accessible to AI systems. Experts like Tectonic provide tailored Salesforce consulting services to help businesses manage and optimize their data. By ensuring data accuracy, completeness, and governance, Tectonic can support companies in preparing their structured and unstructured data for the AI era. Conclusion: The Intersection of Data Quality and AI In the modern era, data quality isn’t just about ensuring clean CRM records; it’s also about preparing your data for advanced AI applications. Whether it’s eliminating duplicates, filling in missing information, or governing data across touchpoints, maintaining high data quality is essential for leveraging AI effectively. For organizations ready to embrace AI, the first step is understanding where all their data resides and ensuring it’s suitable for their generative AI models. With the right data strategy, businesses can unlock the full potential of AI, transforming sales, service, and customer experiences across the board. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 5, 2024in Generative AI
Autonomous AI Sans Human
04Sep

Autonomous AI Sans Human

Rise of Autonomous AI: Less Human Control and Increasing Adoption A recent Salesforce study reveals that nearly half of employees in Switzerland (46%) are either using or experimenting with AI technologies. While there is a general comfort with AI when it complements human efforts, many employees still prefer human oversight for tasks like training, data security, and onboarding. Despite this, the data indicates that increased investment in education and training could enhance trust in autonomous AI systems. Switzerland’s AI Adoption Compared to Other Countries Switzerland shows a higher openness to AI compared to other nations. In Germany, only 28% of respondents use AI confidently, compared to 46% in Switzerland. The UK (17%) and Ireland (15%) show even more skepticism. Conversely, India has the highest AI confidence, with 40% of respondents showing strong support. In Switzerland, however, 24% of employees are reluctant to use AI at work, and 25% are not keen on Generative AI. Sector-Specific AI Usage Trends The study also highlights significant sector differences. In the communications industry, 69% of employees are willing to use AI tools like ChatGPT and Gemini without hesitation. This contrasts with the life sciences and biotechnology sectors, where 72% of respondents are resistant to AI adoption. In the public sector, while there is general willingness, 56% express reservations due to a lack of expertise and guidelines. Notably, 39% of public sector respondents are completely opposed to using AI tools. Generational Insights on AI Proficiency Among different generations, Millennials and Gen X exhibit the highest proficiency and comfort with AI technology. In contrast, Gen Z appears more critical of AI, with 82% of this group avoiding AI assistants like IBM Watson or Microsoft Copilot. Millennials are more engaged, with 39% actively experimenting with or fully integrating AI assistants into their work routines. Gregory Leproux, Senior Director of Solution Engineering at Salesforce Switzerland, notes, “Our study reflects our customer experience: AI is widely used in Swiss companies, but human intervention remains prevalent. To fully leverage the benefits of AI, there is a need for robust control mechanisms and policies for responsible AI use, allowing for systematic review rather than piecemeal assessment. Thoughtfully designed AI systems can merge human and machine intelligence, marking the beginning of an exciting new era.” The survey, conducted by Salesforce in partnership with YouGov, took place from March 20 to April 3, 2024, with nearly 6,000 full-time employees from various industries and countries, including Switzerland (265 participants). The online survey covered nine countries: the US, UK, Ireland, Australia, France, Germany, India, Singapore, and Switzerland. Source: www.salesforce.com Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 4, 2024in Generative AI
2024 AI Glossary
02Sep

2024 AI Glossary

Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing AI solutions. Tectonic 2024 AI Glossary Active LearningA blend of supervised and unsupervised learning, active learning allows AI models to identify patterns, determine the next step in learning, and only seek human intervention when necessary. This makes it an efficient approach to developing specialized AI models with greater speed and precision, which is ideal for businesses aiming for reliability and efficiency in AI adoption. AI AlignmentThis subfield focuses on aligning the objectives of AI systems with the goals of their designers or users. It ensures that AI achieves intended outcomes while also integrating ethical standards and values when making decisions. AI HallucinationsThese occur when an AI system generates incorrect or misleading outputs. Hallucinations often stem from biased or insufficient training data or incorrect model assumptions. AI-Powered AutomationAlso known as “intelligent automation,” this refers to the integration of AI with rules-based automation tools like robotic process automation (RPA). By incorporating AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision (CV), AI-powered automation expands the scope of tasks that can be automated, enhancing productivity and customer experience. AI Usage AuditingAn AI usage audit is a comprehensive review that ensures your AI program meets its goals, complies with legal requirements, and adheres to organizational standards. This process helps confirm the ethical and accurate performance of AI systems. Artificial General Intelligence (AGI)AGI refers to a theoretical AI system that matches human cognitive abilities and adaptability. While it remains a future concept, experts predict it may take decades or even centuries to develop true AGI. Artificial Intelligence (AI)AI encompasses computer systems that can perform complex tasks traditionally requiring human intelligence, such as reasoning, decision-making, and problem-solving. BiasBias in AI refers to skewed outcomes that unfairly disadvantage certain ideas, objectives, or groups of people. This often results from insufficient or unrepresentative training data. Confidence ScoreA confidence score is a probability measure indicating how certain an AI model is that it has performed its assigned task correctly. Conversational AIA type of AI designed to simulate human conversation using techniques like NLP and generative AI. It can be further enhanced with capabilities like image recognition. Cost ControlThis is the process of monitoring project progress in real-time, tracking resource usage, analyzing performance metrics, and addressing potential budget issues before they escalate, ensuring projects stay on track. Data Annotation (Data Labeling)The process of labeling data with specific features to help AI models learn and recognize patterns during training. Deep LearningA subset of machine learning that uses multi-layered neural networks to simulate complex human decision-making processes. Enterprise AIAI technology designed specifically to meet organizational needs, including governance, compliance, and security requirements. Foundational ModelsThese models learn from large datasets and can be fine-tuned for specific tasks. Their adaptability makes them cost-effective, reducing the need for separate models for each task. Generative AIA type of AI capable of creating new content such as text, images, audio, and synthetic data. It learns from vast datasets and generates new outputs that resemble but do not replicate the original data. Generative AI Feature GovernanceA set of principles and policies ensuring the responsible use of generative AI technologies throughout an organization, aligning with company values and societal norms. Human in the Loop (HITL)A feedback process where human intervention ensures the accuracy and ethical standards of AI outputs, essential for improving AI training and decision-making. Intelligent Document Processing (IDP)IDP extracts data from a variety of document types using AI techniques like NLP and CV to automate and analyze document-based tasks. Large Language Model (LLM)An AI technology trained on massive datasets to understand and generate text. LLMs are key in language understanding and generation and utilize transformer models for processing sequential data. Machine Learning (ML)A branch of AI that allows systems to learn from data and improve accuracy over time through algorithms. Model AccuracyA measure of how often an AI model performs tasks correctly, typically evaluated using metrics such as the F1 score, which combines precision and recall. Natural Language Processing (NLP)An AI technique that enables machines to understand, interpret, and generate human language through a combination of linguistic and statistical models. Retrieval Augmented Generation (RAG)This technique enhances the reliability of generative AI by incorporating external data to improve the accuracy of generated content. Supervised LearningA machine learning approach that uses labeled datasets to train AI models to make accurate predictions. Unsupervised LearningA type of machine learning that analyzes and groups unlabeled data without human input, often used to discover hidden patterns. By understanding these terms, you can better navigate the AI implementation world and apply its transformative power to drive innovation and efficiency across your organization. Tectonic 2024 AI Glossary Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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September 2, 2024in Generative AI
Einstein Knowledge Edits
02Sep

Einstein Knowledge Edits

Get Quick Revisions on Knowledge Articles with Einstein Knowledge Edits (Beta) Enhance your Knowledge articles quickly using Einstein generative AI with predefined revision styles. These styles can help improve grammar, conciseness, and readability. You can also customize these styles using the Prompt Builder to tailor the revisions to your business needs. This allows you to specify what information Einstein includes, how the content is formatted, and adjust the voice and tone. Where: This feature is available in Unlimited and Enterprise editions with the Einstein for Service add-on in Lightning Experience. Important: Einstein Knowledge Edits is currently in beta and is subject to Salesforce’s Beta Services Terms or a written Unified Pilot Agreement if executed by the Customer. Participation in this beta service is at the Customer’s discretion. Who: To access Knowledge Edits, you must have the following enabled: Agents also need the Prompt Template User and Einstein Knowledge Creation permission sets. How: To revise a Knowledge article: Quickly and effectively refine your Knowledge articles to meet your business standards with Einstein Knowledge Edits! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 2, 2024in Generative AI
AI and Big Data
31Aug

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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August 31, 2024in Generative AI
Detecting the Hot Chatbot
30Aug

Detecting the Hot Chatbot

All the tech giants are eager to prove their chatbot is the hottest in the market. Like wild stallions fighting over the mares, Google, Meta, Microsoft, and OpenAI are competing to show that their AI models have the most momentum. Companies with built-in AI like Salesforce occupy a broader sector. Detecting the Hot Chatbot is the challenge for the consumer. Why Detecting the Hot Chatbot Matters These companies have poured immense resources—both talent and money—into developing their models and adding new features. Now, they’re keen to showcase that these investments are yielding results. What’s Happening In the past few dayss, several major players have released new usage statistics: The Big Picture Generative AI is still in its early stages, and the entire industry faces the challenge of proving that these products deliver real value—whether by capturing market share from the lucrative search industry or by helping companies save money through increased productivity. How are you Detecting the Hot Chatbot. In the short term, however, everyone is eager to show they’re leading the pack. TV commercials for generative AI are now common, with Meta, Google, and Microsoft all airing spots, although the effectiveness of these ads varies. Some companies even boast that their commercials were created using AI—not necessarily the most convincing selling point. Between the Lines The competition isn’t just about consumer popularity; it’s also spilling over into the battle to secure business customers. On Wednesday’s earnings call, Salesforce CEO Marc Benioff made a point of distinguishing Salesforce’s new Agentforce AI sales assistant from Microsoft’s Copilot offerings. “This is not Copilot,” Benioff said. “So many customers are disappointed with what they bought from Microsoft Copilot because they’re not getting the accuracy and response they want. Microsoft has let down many customers with AI.” Microsoft quickly responded in a comment to CNBC. “We are hearing something quite different from our Copilot for Microsoft 365 customers,” said corporate VP Jared Spataro. “When I talk to CIOs directly, and if you look at recent third-party data, organizations are betting on Microsoft for their AI transformation.” The Bottom Line The competition is heating up as tech giants vie to prove they have the upper hand in the AI race and the Hot Chatbot. Customers will ultimately decide. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 30, 2024in Generative AI, Salesforce Financial Services Solutions, Salesforce for Nonprofits, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Public Sector Solutions
Salesforce Einstein SDR and Sales Coach Agents
29Aug

Salesforce Einstein SDR and Sales Coach Agents

Salesforce Introduces Autonomous AI Sales Agents: Einstein SDR Agent and Einstein Sales Coach Agent Salesforce, the leading CRM for sales, has announced two new fully autonomous AI sales agents: Einstein Sales Development Rep (SDR) Agent and Einstein Sales Coach Agent. These groundbreaking agents, set to be generally available in October, are designed to help sales teams accelerate growth by handling key sales functions autonomously. Built on the Einstein 1 Agentforce Platform, these agents are poised to transform how sales teams operate, allowing them to focus on more complex deals while automating routine tasks. Einstein SDR Agent: Automating Pipeline 24/7 The Einstein SDR Agent autonomously engages with inbound leads, nurturing pipelines around the clock. Unlike traditional chatbots, which can only respond to pre-programmed queries, the Einstein SDR Agent uses advanced AI to make decisions, prioritize actions, and handle various lead interactions. Whether it’s answering product questions, managing objections, or booking meetings, the SDR Agent ensures that every response is trusted, accurate, and personalized, grounded in your company’s CRM and external data. Key features of the Einstein SDR Agent include: Einstein Sales Coach Agent: Enhancing Seller Performance Through AI-Driven Role-Play Einstein Sales Coach Agent takes sales enablement to the next level by autonomously engaging in role-plays with sellers. Whether simulating a buyer during discovery, pitch, or negotiation calls, the Sales Coach Agent uses generative AI to convert text into speech, providing a realistic training environment. This agent helps sellers refine their skills by offering personalized feedback based on real deal contexts. Key features of the Einstein Sales Coach Agent include: Accenture’s Collaboration with Salesforce Accenture, a global leader in business consulting, will leverage these new AI agents to enhance deal team effectiveness, scale support for more deals, and allow their sales teams to concentrate on the most complex transactions. According to Sara Porter, Global Sales Excellence Lead at Accenture, these AI-driven tools will empower their sales practitioners with advanced technology and processes to drive more intelligent customer conversations and accelerate revenue. Salesforce’s Vision for AI in Sales Salesforce sees these autonomous AI agents as a key part of the future of sales. By integrating AI that can generate high-quality pipeline and provide personalized coaching, sales teams can focus on higher-value deals and better prepare for them. Ketan Karkhanis, EVP and GM of Sales Cloud, emphasizes that every AI conversation must translate into ROI, and these new agents are designed to do just that by augmenting human sales teams to accelerate growth. Availability Both Einstein SDR Agent and Einstein Sales Coach Agent will be generally available in October, with additional functionalities expected to be rolled out throughout the year. Learn More: Note: Any unreleased services or features mentioned here are not currently available and may be subject to changes. Customers should base their purchasing decisions from Salesforce on currently available features. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 29, 2024in Agentforce Platform, Generative AI, Salesforce Einstein
Key Insights on Navigating AI Compliance
29Aug

Key Insights on Navigating AI Compliance

Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI strategies, along with guidance on evaluating AI solution providers, including those offering generative AI. While the webinar avoided deep legal analysis and did not serve as legal advice, Moran and Luk spotlighted key regulations emerging from both the U.S. and European Union (EU), highlighting the rapid response of regulatory bodies to AI’s growth. Overview of AI Regulations The AI regulatory landscape is changing quickly. A May 2024 report from law firm Davis & Gilbert noted that nearly 200 AI-related laws have been proposed across various U.S. states. Grammarly’s presentation emphasized the need for organizations to stay updated, as both U.S. and EU regulations are shaping the future of AI governance. The EU AI Act: A New Regulatory Framework The EU AI Act, which took effect on August 2, 2024, applies to AI system providers, importers, distributors, and others connected to the EU market, regardless of where they are based. As Moran pointed out, the Act is designed to ensure AI systems are deployed safely. Unsafe systems may be removed from the market, establishing a regulatory baseline that individual EU countries can strengthen with more stringent measures. However, the Act does not fully define “safety.” Legal experts Hadrien Pouget and Ranj Zuhdi noted that while safety requirements are crucial to the Act, they are currently broad, allowing room for further development of standards. The Act prohibits certain AI practices, such as manipulative systems, those exploiting personal vulnerabilities, and AI used to assess or predict criminal risk. AI systems are categorized into four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems—such as those in critical infrastructure or public services—face stricter regulation, while minimal-risk systems like spam filters have fewer requirements. Full enforcement of the Act will begin in 2025. U.S. AI Regulations Unlike the EU, the U.S. focuses more on national security than consumer safety in its AI regulation. The U.S. Executive Order on Safe, Secure, and Trustworthy AI addresses these concerns. At the state level, Moran highlighted trends such as requiring clear disclosure when interacting with AI and giving individuals the right to opt out of having their data used for AI model training. States like California and Utah are leading the way with specific laws (SB-1047 and SB-149, respectively) addressing accountability and disclosure in AI use. Key Considerations When Selecting AI Vendors Moran stressed the importance of thoroughly vetting AI vendors. Organizations should ensure vendors meet cybersecurity standards, such as SOC 2, and clearly define how their data will be used, particularly in training large language models (LLMs). “Eyes off” agreements, which prevent vendor employees from accessing customer data, should also be considered. Martha Buyer, a frequent contributor to No Jitter, emphasized verifying the originality of AI-generated content from providers like Grammarly or Microsoft Copilot. She urged caution in ensuring the ownership and authenticity of AI-assisted outputs. The Importance of Strong Third-Party Agreements Luk highlighted Grammarly’s commitment to data privacy, noting that the company neither sells customer data nor uses it to train models. Additionally, Grammarly enforces agreements preventing its third-party LLM providers from doing so. These contractual protections are crucial for safeguarding customer data. Organizations should also ensure third-party vendors adhere to strict guidelines, including securing customer data, encrypting it, and preventing unauthorized access. Vendors should maintain updated security certifications and manage risks like bias, which, while not entirely avoidable, must be actively addressed. Staying Ahead in a Changing Regulatory Environment Both Moran and Luk stressed the importance of ongoing monitoring. Organizations need to regularly reassess whether their vendors comply with their data-sharing policies and meet evolving regulatory standards. As AI technology and regulations continue to evolve, staying informed and agile will be critical for compliance and risk mitigation. In conclusion, organizations adopting AI-powered solutions must navigate a dynamic regulatory environment. As AI advances and regulations become more comprehensive, remaining vigilant and asking the right questions will be key to ensuring compliance and reducing risks. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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August 29, 2024in Generative AI
Uplimit AI-Powered ELP
27Aug

Uplimit AI-Powered ELP

Uplimit Secures $11M in Series A Funding to Enhance AI-Powered Enterprise Learning SAN FRANCISCO, July 24, 2024 /PRNewswire/ — Uplimit, a leading provider of AI-powered enterprise learning solutions, has announced the successful completion of an $11M Series A funding round. This funding, led by Salesforce Ventures with participation from existing investors GSV Ventures, Greylock Partners, and Cowboy Ventures, as well as new investors Translink Capital, Workday Ventures, and Conviction, underscores the growing importance of effective employee upskilling in response to the rapid advancements in Generative AI technology. Uplimit AI-Powered ELP. “Helping employees stay ahead of technological advancements is now a critical priority for the organizations we serve,” said Claudine Emeott, Partner at Salesforce Ventures and Head of the Salesforce Ventures Impact Fund. “AI has the potential to equip both companies and individuals with the necessary skills to thrive, and Uplimit is at the forefront of integrating AI into education and training. We are excited to support their continued growth and look forward to seeing the significant impact they will have in the coming years.” “AI has the potential to equip both companies and individuals with the necessary skills to thrive, and Uplimit is at the forefront of integrating AI into education and training. We are excited to support their continued growth and look forward to seeing the significant impact they will have in the coming years.” Claudine Emeott, Partner at Salesforce Ventures and Head of the Salesforce Ventures Impact Fund Uplimit AI-Powered ELP With this new funding, Uplimit plans to expand its enterprise platform offerings, aiming to provide comprehensive upskilling solutions to more organizations and employees. Traditional education systems often require extensive resources for content creation, personalized feedback, and support, which can hinder scalability. While some scalable solutions exist, they often compromise on quality and outcomes. Uplimit is addressing this challenge with an innovative approach that combines scale and effectiveness. Their AI-driven platform enhances cohort management, learner support, and course authoring, enabling companies to deliver personalized learning experiences at scale. Uplimit’s recent introduction of AI-enabled role-play scenarios provides learners with immediate feedback, revolutionizing training and development for roles such as managers, support teams, and sales professionals. “Quality education has historically been a scarce resource, but AI is changing that,” said Julia Stiglitz, CEO and Co-founder of Uplimit. “AI allows us to create and update educational content rapidly, ensuring that learners receive personalized experiences even in large-scale courses. This is crucial as the demand for new skills, driven by the rapid evolution of AI technologies, continues to grow. Uplimit provides the tools needed for employees to quickly grasp new skills, tailored to their current knowledge and needs.” Uplimit has collaborated with a diverse range of companies, from Fortune 500 giants like GE Healthcare and Kraft Heinz to innovative startups such as Procore. Databricks, a leader in AI-powered data intelligence, was an early adopter of Uplimit’s platform for customer education. “We needed a learning platform that could scale to hundreds of thousands of learners while maintaining high levels of engagement and completion,” said Rochana Golani, VP of Learning and Enablement at Databricks. “Uplimit’s platform offers the perfect blend of real-time human instruction and personalized AI support, along with valuable peer interaction. This approach is set to be transformative for many of our customers.” The new funding will enable Uplimit to further enhance its enterprise and customer education offerings, expanding its AI capabilities to include advanced cohort management tools, rapid course feedback integration, interactive practice and assessment modules, and AI-powered course authoring. Join us on August 14th for our launch event, where we will explore how this funding will accelerate our mission and demonstrate the impact our platform is having on enterprise learning. About Uplimit Uplimit is a comprehensive AI-driven learning platform designed to equip companies with the tools needed to train employees and customers in emerging skills. The platform leverages AI to scale learning programs effectively, offering features such as AI-powered learner support, generative AI for content creation, and live cohort management tools. This approach ensures high engagement and completion rates, significantly surpassing traditional online courses. Uplimit also offers a marketplace of advanced courses in AI, technology, and leadership, taught by industry experts. Founded by Julia Stiglitz, Sourabh Bajaj, and Jake Samuelson, Uplimit is backed by Salesforce Ventures, Greylock Partners, Cowboy Ventures, GSV Ventures, Conviction, Workday Ventures, and Translink Capital, with contributions from the co-founders of OpenAI and DeepMind. Notable customers include GE Healthcare, Kraft Heinz, and Databricks. Uplimit has been featured in leading industry publications such as ATD, Josh Bersin, and Fast Company. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 27, 2024in Generative AI
Pulse for Salesforce
24Aug

Pulse for Salesforce

Salesforce Unveils Pulse for Salesforce: Integrating Tableau Analytics with CRM to Revolutionize Data-Driven Decision-Making In today’s data heavy business world, where data-driven decision-making is essential for success, the fusion of advanced analytics with customer relationship management (CRM) systems is more crucial than ever. Addressing this need, Salesforce has introduced Pulse for Salesforce, a groundbreaking tool that integrates Tableau’s powerful analytics directly into the Salesforce CRM environment. Meeting the Demand for Actionable Insights This launch aligns with a broader trend in the business intelligence (BI) market, where companies strive to make data analytics more accessible and actionable for non-technical users. Recent studies indicate that while 80% of business leaders view data as critical to decision-making, nearly one-third feel overwhelmed by the sheer volume of information available. Moreover, 91% of these leaders believe their organizations would significantly benefit from generative AI (Gen AI) technologies. Pulse for Salesforce marks a significant milestone in Salesforce’s ongoing strategy following its $15.7 billion acquisition of Tableau in 2019. Tableau, a leader in data visualization and BI since its founding in 2003, has been central to Salesforce’s mission of enhancing customer data management and analysis. The integration of Tableau’s capabilities within Salesforce’s CRM platform represents a major step forward in providing a comprehensive, data-driven solution. Ryan Aytay, President and CEO of Tableau, on the New Integration “Historically, sales leaders and teams have lacked personalized, accessible data insights in their daily flow of work, and analysts often spend considerable time on ad hoc requests and repetitive queries, slowing down decision-making and business growth,” says Ryan Aytay, CEO of Tableau. “By integrating Tableau Pulse’s AI-driven insights into Salesforce, we’re addressing these needs and enhancing data-driven decision-making to help businesses accelerate growth.” Boosting CRM Productivity with Salesforce’s AI Platform Pulse for Salesforce is built on Salesforce’s Einstein 1 AI Platform and leverages Gen AI to provide contextual metrics and insights directly within the Salesforce interface. This seamless integration streamlines decision-making for sales teams by reducing the need for manual data searches or reliance on analysts for ad-hoc queries. Key Features of Pulse for Salesforce Practical Applications and Data Security A practical application of Pulse for Salesforce is performance monitoring. Sales leaders can track team win rate trends directly from their homepage, quickly identifying areas or individuals needing additional support. Similarly, individual sales representatives can monitor their conversion rates and use natural language queries to analyze data by industry, potentially leading to more targeted sales efforts. The integration also addresses data security concerns, a critical issue in the age of AI-powered analytics. Pulse for Salesforce employs the Einstein Trust Layer, a secure AI architecture built into the Einstein 1 Platform, ensuring that customer data remains protected while benefiting from the advanced capabilities of generative AI. Collaboration Salesforce partnered with key industry players and partners to bring this innovative solution to market. With Pulse for Salesforce, organizations can now fully harness the power of integrated analytics and CRM to drive informed decision-making, enhance productivity, and ultimately accelerate business growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 24, 2024in Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce Development Services, Salesforce Einstein, Salesforce Optimization, Salesforce Tableau
GPT 4o and GPT 4
23Aug

GPT 4o and GPT 4

OpenAI’s GPT-4o: Advancing the Frontier of AI OpenAI’s GPT-4o builds upon the foundation of its predecessors with significant enhancements, including improved multimodal capabilities and faster performance. GPT 4o and GPT 4. Evolution of ChatGPT and Its Underlying Models Since the launch of ChatGPT in late 2022, both the chatbot interface and its underlying models have seen several major updates. GPT-4o, released in May 2024, succeeds GPT-4, which launched in March 2023, and was followed by GPT-4o mini in July 2024. GPT-4 and GPT-4o (with “o” standing for “omni”) are advanced generative AI models developed for the ChatGPT interface. Both models generate natural-sounding text in response to user prompts and can engage in interactive, back-and-forth conversations, retaining memory and context to inform future responses. TechTarget Editorial tested these models by using them within ChatGPT, reviewing OpenAI’s informational materials and technical documentation, and analyzing user reviews on Reddit, tech blogs, and the OpenAI developer forum. Differences Between the GPTs While GPT-4o and GPT-4 share similarities, including vision and audio capabilities, a 128,000-token context window, and a knowledge cutoff in late 2023, they also differ significantly in multimodal capabilities, performance, efficiency, pricing, and language support. Introduction of GPT-4o Mini On July 18, 2024, OpenAI introduced GPT-4o mini, a cost-efficient, smaller model designed to replace GPT-3.5. GPT-4o mini outperforms GPT-3.5 while being more affordable. Aimed at developers seeking to build AI applications without the compute costs of larger models, GPT-4o mini is positioned as a competitor to other small language models like Claude’s Haiku. All users on ChatGPT Free, Plus, and Team plans received access to GPT-4o mini at launch, with ChatGPT Enterprise users expected to gain access shortly afterward. The model supports text and vision, and OpenAI plans to add support for other multimodal inputs like video and audio. Multimodality Multimodal AI models process multiple data types such as text, images, and audio. Both GPT-4 and GPT-4o support multimodality in the ChatGPT interface, allowing users to create and upload images and use voice chat. However, their approaches to multimodality differ significantly. GPT-4 primarily focuses on text processing, requiring other OpenAI models like DALL-E for image generation and Whisper for speech recognition to handle non-text input. In contrast, GPT-4o was designed for multimodality from the ground up, with all inputs and outputs processed by a single neural network. This design makes GPT-4o faster for tasks involving multiple data types, such as image analysis. Controversy Over GPT-4o’s Voice Capabilities During the GPT-4o launch demo, a voice called Sky, which sounded similar to Scarlett Johansson’s AI character in the film “Her,” sparked controversy. Johansson, who had declined a previous request to use her voice, announced legal action. In response, OpenAI paused the use of Sky’s voice, highlighting ethical concerns over voice likenesses and artists’ rights in the AI era. Performance and Efficiency GPT-4o is designed to be quicker and more efficient than GPT-4. OpenAI claims GPT-4o is twice as fast as the most recent version of GPT-4. In tests, GPT-4o generally responded faster than GPT-4, although not quite at double the speed. OpenAI’s testing indicates GPT-4o outperforms GPT-4 on major benchmarks, including math, language comprehension, and vision understanding. Pricing GPT-4o’s improved efficiency translates to lower costs. For API users, GPT-4o is available at $5 per million input tokens and $15 per million output tokens, compared to GPT-4’s $30 per million input tokens and $60 per million output tokens. GPT-4o mini is even cheaper, at $0.15 per million input tokens and $0.60 per million output tokens. GPT-4o will power the free version of ChatGPT, offering multimodality and higher-quality text responses to free users. GPT-4 remains available only to paying customers on plans starting at $20 per month. Language Support GPT-4o offers better support for non-English languages compared to GPT-4, particularly for languages that don’t use Western alphabets. This improvement addresses longstanding issues in natural language processing, making GPT-4o more effective for global applications. Is GPT-4o Better Than GPT-4? In most cases, GPT-4o is superior to GPT-4, with improved speed, lower costs, and multimodal capabilities. However, some users may still prefer GPT-4 for its stability and familiarity, especially in critical applications. Transitioning to GPT-4o may involve significant changes for systems tightly integrated with GPT-4. What Does GPT-4o Mean for ChatGPT Users? GPT-4o’s introduction brings significant changes, including the availability of multimodal capabilities for all users. While these advancements may make the Plus subscription less appealing, paid plans still offer benefits like higher usage caps and faster response times. As the AI community looks forward to GPT-5, expected later this summer, the introduction of GPT-4o sets a new standard for AI capabilities, offering powerful tools for users and developers alike. Like Related Posts Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Objects and Fields Salesforce objects and fields are analogous to database tables and the table columns. Objects and fields structure your data. For Read more JSON Wrapper in Salesforce A JSON wrapper class is a custom class in Salesforce that wraps or encapsulates standard or custom objects, along with Read more AI in Sales Enablement When it comes to integrating artificial intelligence (AI) into the workplace, the question isn’t whether but when. The rapid expansion Read more

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August 23, 2024in Generative AI
Collabrate With AI
20Aug

Collabrate With AI

Many artists, writers, musicians, and creators are facing fears that AI is taking over their jobs. On the surface, generative AI tools can replicate work in moments that previously took creators hours to produce—often at a fraction of the cost and with similar quality. This shift has led many businesses to adopt AI for content creation, leaving creators worried about their livelihoods. Yet, there’s another way to view this situation, one that offers hope to creators everywhere. AI, at its core, is a tool of mimicry. When provided with enough data, it can replicate a style or subject with reasonable accuracy. Most of this data has been scraped from the internet, often without explicit consent, to train AI models on a wide variety of creative outputs. If you’re a creator, it’s likely that pieces of your work have contributed to the training of these AI models. Your art, words, and ideas have helped shape what these systems now consider ‘good’ in the realms of art, music, and writing. AI can combine the styles of multiple creators to generate something new, but often these creations fall flat. Why? While image-generating AI can predict pixels, it lacks an understanding of human emotions. It knows what a smile looks like but can’t grasp the underlying feelings of joy, nervousness, or flirtation that make a smile truly meaningful. AI can only generate a superficial replica unless the creator uses extensive prompt engineering to convey the context behind that smile. Emotion is uniquely human, and it’s what makes our creations resonate with others. A single brushstroke from a human artist can convey emotions that might take thousands of words to replicate through an AI prompt. We’ve all heard the saying, “A picture is worth a thousand words.” But generating that picture with AI often takes many more words. Input a short prompt, and the AI will enhance it with more words, often leading to results that stray from your original vision. To achieve a specific outcome, you may need hours of prompt engineering, trial, and error—and even then, the result might not be quite right. Without a human artist to guide the process, these generated works will often remain unimpressive, no matter how advanced the technology becomes. That’s where you, the creator, come in. By introducing your own inputs, such as images or sketches, and using workflows like those in ComfyUI, you can exert more control over the outputs. AI becomes less of a replacement for the artist and more of a tool or collaborator. It can help speed up the creative process but still relies on the artist’s hand to guide it toward a meaningful result. Artists like Martin Nebelong have embraced this approach, treating AI as just another tool in their creative toolbox. Nebelong uses high levels of control in AI-driven workflows to create works imbued with his personal emotional touch. He shares these workflows on platforms like LinkedIn and Twitter, encouraging other creators to explore how AI can speed up their processes while retaining the unique artistry that only humans can provide. Nebelong’s philosophy is clear: “I’m pro-creativity, pro-art, and pro-AI. Our tools change, the scope of what we can do changes. I don’t think creative AI tools or models have found their best form yet; they’re flawed, raw, and difficult to control. But I’m excited for when they find that form and can act as an extension of our hands, our brush, and as an amplifier of our artistic intent.” AI can help bring an artist 80% of the way to a finished product, but it’s the final 20%—the part where human skill and emotional depth come in—that elevates the piece to something truly remarkable. Think about the notorious issues with AI-generated hands. Often, the output features too many fingers or impossible poses, a telltale sign of AI’s limitations. An artist is still needed to refine the details, correct mistakes, and bring the creation in line with reality. While using AI may be faster than organizing a full photoshoot or painting from scratch, the artist’s role has shifted from full authorship to that of a collaborator, guiding AI toward a polished result. Nebelong often starts with his own artwork and integrates AI-generated elements, using them to enhance but never fully replace his vision. He might even use AI to generate 3D models, lighting, or animations, but the result is always driven by his creativity. For him, AI is just another step in the creative journey, not a shortcut or replacement for human effort. However, AI’s ability to replicate the styles of famous artists and public figures raises ethical concerns. With platforms like CIVIT.AI making it easy to train models on any style or subject, questions arise about the legality and morality of using someone else’s likeness or work without permission. As regulations catch up, we may see a future where AI models trained on specific styles or individuals are licensed, allowing creators to retain control over their works in the same way they license their traditional creations today. The future may also see businesses licensing AI models trained on actors, artists, or styles, allowing them to produce campaigns without booking the actual talent. This would lower costs while still benefiting creators through licensing fees. Actors and artists could continue to contribute their talents long after they’ve retired, or even passed on, by licensing their digital likenesses, as seen with CGI performances in movies like Rogue One. In conclusion, AI is pushing creators to learn new skills and adapt to new tools. While this can feel daunting, it’s important to remember that AI is just that—a tool. It doesn’t understand emotion, intent, or meaning, and it never will. That’s where humans come in. By guiding AI with our creativity and emotional depth, we can produce works that resonate with others on a deeper level. For example, you can tell artificial intelligence what an image should look like but not what emotions the image should evoke. Creators, your job isn’t disappearing. It’s

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August 20, 2024in Generative AI

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