PHI - gettectonic.com - Page 6
Should AI Bug Us?
09Sep

Should AI Bug Us?

Today marks the 77th anniversary of the first computer bug, which occurred when a moth became lodged in the 25-ton Harvard Mark II. The incident led programmer Grace Hopper to file what is now recognized as the first bug report. Wait, you weren’t even alive yet? Which begs the question. Should AI Bug Us? If asked what the most popular topic on the internet is today, one might confidently answer: AI. This year has seen a variety of perspectives on the subject. Data scientist Stephanie Kirmer reminded readers that generative AI still hasn’t become profitable. Margaret Efron highlighted words that give away AI-generated content (such as the overuse of “robust”). Meanwhile, Jim the AI Whisperer addressed a quirky tendency of ChatGPT to overuse the word “delve” due to its reliance on British English in its training data. Beyond these discussions, a deeper conversation is emerging about what AI means for humanity on an existential level. Writers are increasingly considering how AI impacts our perception of ourselves. Paul Siemers, PhD, who focuses on the philosophy of technology, explores this topic in his essay The Ontological Shock of AI. Ontology, the study of existence, traces how humans have categorized the world over millennia. Siemers notes that over the last two centuries, humanity has split existence into living and non-living categories. However, AI is starting to blur those lines. He argues that humanity needs to reconsider this dualistic view and accept new forms of existence. As unsettling as this may seem, it could explain part of society’s current discomfort with AI. Katharine Esty, PhD, who celebrated her 90th birthday this summer, published a guide for navigating life in your 80s. Her reflections on life and reinvention offer inspiration to readers of all ages. Practical Wisdom for Your Day: Live Life in Semesters A useful approach to structuring life is to think in “semesters”—15 to 17 weeks of focused work. This timeframe is long enough to accomplish something significant, but short enough to avoid burnout. 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

Read More
September 9, 2024in Generative AI
Army of AI Bots
09Sep

Army of AI Bots

Salesforce Inc. has announced a significant upgrade with the launch of Industries AI, a new automation platform designed to handle a wide range of time-consuming tasks, enhancing productivity across various sectors. We are NOT advocating that the next war will be fought with AI Bots. We aren’t even suggesting there is anything negative about these bots. However, if the next war were to be information and data based, who knows. Industries AI will be integrated into all 15 of Salesforce’s cloud platforms, including Sales Cloud, Data Cloud, Service Cloud, Commerce Cloud, and Marketing Cloud. This expansive solution is capable of managing over 100 common tasks, from matching patients with clinical trials and providing maintenance alerts for vehicles and machinery, to streamlining recruitment processes and enhancing government services. The launch of Industries AI responds to findings from Salesforce’s Trends in AI for CRM Report, which indicated that over 75% of business leaders are concerned about missing out on AI advancements if they do not adopt the technology soon. With a 700% increase in urgency to implement AI over the past six months, many organizations struggle with the resources and expertise needed to develop and train AI models. Salesforce aims to address this by offering a ready-made framework for creating AI agents tailored to industry-specific needs, utilizing each customer’s proprietary data within the Salesforce platform. Industries AI will provide a foundation for quickly deploying autonomous agents, with setup times estimated at just a few minutes. To assist customers in leveraging AI automation, Salesforce has created use case libraries for each of its cloud platforms, featuring over 100 capabilities at launch. These capabilities span multiple industries: Salesforce will begin rolling out Industries AI capabilities in October 2024, with some features available by February 2025. The company plans to regularly update Industries AI with new capabilities as part of its annual Salesforce releases. Jeff Amann, executive vice president and general manager of Salesforce Industries, emphasized that this innovation aims to make powerful AI accessible to all enterprises, regardless of size or budget. “Organizations can now easily start with AI solutions tailored to their specific challenges, enhancing efficiency and productivity across various functions,” he said. 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

Read More
September 9, 2024in Generative AI
AI-Powered Field Service
09Sep

AI-Powered Field Service

Salesforce has introduced new AI-powered field service capabilities designed to streamline operations for dispatchers, technicians, and field service leaders. Leveraging the Salesforce platform and Data Cloud, these innovations aim to expedite time-consuming processes and enhance customer satisfaction by making field service operations more proactive and efficient. Why it matters: Field service teams currently spend only 32% of their time interacting with customers, with the remaining 68% consumed by administrative tasks like manually entering case notes. With 78% of field service workers in AI-enabled organizations reporting that AI helps save time, Salesforce’s new tools address these inefficiencies head-on. Key AI-driven innovations for Field Service: Availability: Paul Whitelam, GM & SVP of Salesforce Field Service, notes, “The future of field service lies in the seamless integration of AI, data, and human expertise. Our new capabilities set new standards for efficiency and service delivery.” Rudi Khoury, Chief Digital Officer at Fisher & Paykel, adds, “With Salesforce Field Service, we’re not just embracing AI and data-driven insights — we’re advancing into the future of field service, achieving unprecedented efficiency and exceptional service.” 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

Read More
September 9, 2024in Einstein Insights, Salesforce Development Services, Salesforce Manufacturing Automotive Energy, Salesforce Service Cloud
Salesforce AI Agents Explained
09Sep

Salesforce AI Agents Explained

Salesforce’s AI Agents: Revolutionizing Enterprise Sales and Service for the Future In the rapidly evolving landscape of artificial intelligence (AI), Salesforce continues to lead the charge, transforming enterprise operations with cutting-edge AI agents. With the introduction of Agentforce, Salesforce is not just enhancing sales and service departments but reshaping business processes across sectors. This comprehensive exploration highlights how Salesforce’s AI agents are changing the game, offering enterprise-level executives insights into their revolutionary potential. Salesforce AI Agents Explained. AI Agents: Beyond Autonomous Vehicles A fitting analogy to grasp the progression of AI agents is the evolution of autonomous vehicles. Just as self-driving cars advance from basic driver assistance to full autonomy, AI agents evolve from simple automation to more complex decision-making. Salesforce’s Chief Product Officer, David Schmaier, draws this comparison: “In the autonomous driving world, we have levels of autonomy, from level zero to level five. AI agents for enterprises follow a similar path.” At the core of this evolution is what Salesforce defines as the “agentic” phase of AI. Unlike generative AI that follows instructions to create content, agentic AI autonomously determines and takes actions based on broader goals. Schmaier notes, “We’re at the point where AI not only creates content but takes strategic actions. It’s like having an infinite pool of interns handling mundane tasks so human employees can focus on higher-value activities.” Agentforce: Salesforce’s Next-Generation AI Platform Agentforce is the latest addition to Salesforce’s AI arsenal, unveiled during their Q2 ’25 earnings call and now positioned as a significant milestone in AI development. With Agentforce, organizations can build and manage autonomous agents for tasks across various business functions—not just customer service. This versatility is highlighted by Marc Benioff, Salesforce’s CEO, who described the energy around Agentforce during a recent briefing as “palpable.” Agentforce builds on Salesforce’s data management, security, and customization expertise, uniting these capabilities into an AI framework. Schmaier explains, “It’s about creating trusted, enterprise-ready agents, not just deploying a large language model. We’ve developed over 100 out-of-the-box use cases, from sales account summaries to service reply recommendations, all customizable and easy to deploy.” Agentforce “In Every App” A key announcement is the integration of Agentforce in every app across Salesforce’s product suite, including Sales, Service, Marketing, and Commerce Agents. The Atlas reasoning engine, Agent Builder, and a partner network were also introduced to further enhance its capabilities. The Atlas Reasoning Engine acts as the “brain” behind Agentforce, autonomously generating plans and refining them based on actions it needs to perform, such as running business processes or engaging customers through preferred channels. What Makes an AI Agent? Salesforce AI Agents Explained Building an AI agent with Agentforce requires five key elements: These components leverage existing Salesforce infrastructure, making it easier for businesses to deploy agents through Agent Builder, which is part of the new Agentforce Studio. Agents vs. Chatbots Unlike traditional chatbots, which provide pre-programmed responses, Salesforce’s AI agents use large language models (LLMs) and generative AI to interpret and autonomously execute customer requests based on CRM data. This distinction allows AI agents to perform tasks that go beyond simple queries, driving efficiency in customer service, sales, and other business areas. Practical Applications: Sales, Service, and Marketing Salesforce’s AI agents offer tangible business benefits. For instance, Sales Agent, available as both a Sales Development Representative (SDR) and Sales Coach, automates lead nurturing and inquiry management. It utilizes CRM data to deliver personalized pitches, handle objections, and even suggest meeting times—freeing sales teams to focus on more strategic tasks. In customer service, AI agents manage routine inquiries, allowing human representatives to address more complex customer needs. In marketing, AI agents generate data-driven insights to personalize campaigns, improving customer engagement and conversion rates. The Security and Trust Foundation Security and trust remain core to Salesforce’s approach to AI. The Einstein Trust Layer ensures that data protection, privacy, and ethical guidelines are maintained throughout AI interactions. Schmaier emphasizes, “Our platform defines what data agents can access and how they use it, adhering to strict data integrity standards.” The Trust Layer also prevents AI from training on customer data without consent, ensuring transparency and security. A Partnership Between Humans and AI-Salesforce AI Agents Explained Salesforce’s vision emphasizes the synergy between human employees and AI agents. As Schmaier points out, “AI agents handle routine tasks and deliver insights, allowing employees to focus on more creative and strategic work.” This human-AI partnership boosts productivity and innovation, ultimately improving business outcomes. The Future of AI in Business As AI technology advances, Salesforce is already working on next-generation capabilities for Agentforce, including predictive analytics and more sophisticated autonomous agents. Schmaier forecasts, “These agents will handle a wider range of tasks and provide deeper insights and recommendations.” With Agentforce launching in October 2024, businesses can expect significant returns on investment, thanks to its cost-efficient model starting at $2 per conversation. In summary, Salesforce’s Agentforce is a game-changing innovation, blending AI and human intelligence to transform sales, service, and marketing. As more details unfold, it’s clear that Agentforce will redefine the future of business operations—driving efficiency, personalization, and strategic success. 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

Read More
September 9, 2024in Agentforce Platform, Generative AI, Technology
guide to RAG
30Aug

Tectonic Guide to Rag Part 2

The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances, Retrieval-Augmented Generation (RAG) may no longer be necessary for many tasks, as all required data can be accommodated within the expanded context window. Several financial and analytical projects have already demonstrated that tasks can be solved without needing a vector database as intermediate storage. This trend of reducing token costs and increasing context window sizes is likely to continue, potentially decreasing the need for external mechanisms in LLMs, although they are still relevant for the time being. If the context window remains insufficient, methods for summarization and context compression have been introduced. LangChain, for example, offers a class called ConversationSummaryMemory to address this challenge. pythonCopy codellm = OpenAI(temperature=0) conversation_with_summary = ConversationChain( llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True ) conversation_with_summary.predict(input=”Hi, what’s up?”) Knowledge Graphs As the volume of data continues to grow, navigating through it efficiently becomes increasingly critical. In certain cases, understanding the structure and attributes of data is essential for effective use. For example, if the data source is a company’s wiki, an LLM might not recognize a phone number unless the structure or metadata indicates that it’s the company’s contact information. Humans can infer meaning from conventions, such as the subdirectory “Company Information,” but standard RAG may miss such connections. This challenge can be addressed by Knowledge Graphs, also known as Knowledge Maps, which provide both raw data and metadata that illustrates how different entities are interconnected. This method is referred to as Graph Retrieval-Augmented Generation (GraphRAG). Graphs are excellent for representing and managing structured, interconnected information. Unlike vector databases, they excel at capturing complex relationships and attributes among diverse data types. Creating a Knowledge Graph The process of creating a knowledge graph typically involves collecting and structuring data, which requires expertise in both the subject matter and graph modeling. However, LLMs can automate a significant portion of this process by analyzing textual data, identifying entities, and recognizing their relationships, which can then be represented in a graph structure. In many cases, an ensemble of vector databases and knowledge graphs can improve accuracy, as discussed previously. For example, search functionality might combine keyword search through a regular database (e.g., Elasticsearch) and graph-based queries. LangChain can also assist in extracting structured data from entities, as demonstrated in this code example: pythonCopy codedocuments = parse_and_load_data_from_wiki_including_metadata() graph_store = NebulaGraphStore( space_name=”Company Wiki”, tags=[“entity”] ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, space_name=space_name, tags=[“entity”] ) query_engine = index.as_query_engine() response = query_engine.query(“Tell me more about our Company”) Here, searching is conducted based on attributes and related entities, instead of similar vectors. If set up correctly, metadata from the company’s wiki, such as its phone number, would be accessible through the graph. Access Control One challenge with this system is that data access may not be uniform. For instance, in a wiki, access could depend on roles and permissions. Similar issues exist in vector databases, leading to the need for access management mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC). These access control methods function by evaluating paths between users and resources within graphs, such as in systems like Active Directory. To ensure the integrity of data during the ingestion phase, metadata related to permissions must be preserved in both the knowledge graph and vector database. Some commercial vector databases already have this functionality built in. Ingestion and Parsing Data needs to be ingested into both graphs and vector databases, but for graphs, formatting is especially critical since it reflects the data’s structure and serves as metadata. One particular challenge is handling complex formats like PDFs, which can contain diverse elements like tables, images, and text. Extracting structured data from such formats can be difficult, and while frameworks like LLama Parse exist, they are not always foolproof. In some cases, Optical Character Recognition (OCR) may be more effective than parsing. Enhancing Answer Quality Several new approaches are emerging to improve the quality of LLM-generated answers: While these advancements in knowledge graphs, access control, and retrieval mechanisms are promising, challenges remain, particularly around data formatting and parsing. However, these methods continue to evolve, enhancing LLM capabilities and efficiency. 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

Read More
August 30, 2024in Data

When to use Flow

When and Why Should You Use a Flow in Salesforce? Flow is Salesforce’s premier tool for creating configurable automation and guided user experiences. If you need to build a process that doesn’t require the complexity of Apex code, Flow should be your go-to solution. It’s versatile, user-friendly, and equipped to handle a wide range of business automation needs. Legacy tools like Process Builder and Workflow Rules are being phased out, with support ending in December 2025. While you may choose to edit existing automations in these tools temporarily, migrating to Flow should be a top priority for future-proofing your Salesforce org. Capabilities of FlowFlows allow you to: When Should You Avoid Using a Flow?Although Flow is powerful, it’s not the right choice in every scenario. Here are situations where it may not be suitable: Creating a Flow in Salesforce Pro Tips for Flow Building Flow vs. Apex: Which to Choose?Flows are simpler, faster to deploy, and accessible to admins without coding expertise. Apex, on the other hand, is suited for complex use cases requiring advanced logic or integrations. Here’s when Apex should be used instead: Why Flows Are the FutureSalesforce has positioned Flow as the central automation tool by deprecating Workflow Rules and Process Builder. With every release, Flow’s capabilities expand, making it easier to replace tasks traditionally requiring Apex. For instance: Final ThoughtsSalesforce admins should prioritize building and migrating automation to Flow. It’s a scalable and admin-friendly tool that ensures your org stays up-to-date with Salesforce’s evolving ecosystem. Whether you’re automating basic processes or tackling complex workflows, Flow provides the flexibility to meet your needs. 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

Read More
August 28, 2024in Data, Salesforce Flow
AI as a Service
25Aug

AI as a Service

The latest research study from HTF MI, titled Global Artificial Intelligence (AI) As a Service Market Size, Player Analysis & Segment Growth 2020-2032, offers an in-depth evaluation of market risks, opportunities, and strategic decision-making support. The report delves into trends, growth drivers, technological advancements, and the evolving investment landscape within the Global AI As a Service market. Key players featured in the study include Google, Amazon Web Services, IBM, Microsoft, SAP, Salesforce, Intel, Baidu, FICO, SAS, and BigML. Market Overview: The study provides an extensive view of the AI As a Service market, with segmentation across industries such as banking, financial services, insurance, healthcare, retail, telecommunications, government and defense, manufacturing, and energy. Covering 18+ countries globally, it also highlights both emerging and established players. The report offers tailored analysis based on specific business objectives or geographic requirements. AI As a Service Market: Demand Analysis & Opportunity Outlook 2030 This research defines the market size across various segments and countries by analyzing historical data and forecasting future values through 2030. It combines qualitative and quantitative insights, including market share, value, and volume forecasts from 2019 to 2023, with projections extending to 2030. Key elements such as growth drivers, restraining factors, and critical statistics shape the market’s outlook. Market Segmentation: The report categorizes the AI As a Service market into the following: Key Players: The study profiles major industry players such as Google, Amazon Web Services, IBM, Microsoft, SAP, Salesforce, Intel, Baidu, FICO, SAS, and BigML, analyzing their market strategies and positioning. Geographic Scope: The global report covers multiple regions, including: Key Questions Addressed: Report Chapters Overview: For more information, request a sample report or inquire about the full research study through the provided links. 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

Read More
August 25, 2024in Salesforce
Slack AI Exploit Prevented
22Aug

Slack AI Exploit Prevented

Slack AI Exploit Prevented. Slack has patched a vulnerability in its Slack AI assistant that could have been for insider phishing attacks, according to an announcement made by the company on Wednesday. This update follows a blog post by PromptArmor, which detailed how an insider attacker—someone within the same Slack workspace as the target—could manipulate Slack AI into sending phishing links to private channels that the attacker does not have access to. The vulnerability is an example of an indirect prompt injection attack. In this type of attack, the attacker embeds malicious instructions within content that the AI processes, such as an external website or an uploaded document. In this case, the attacker could plant these instructions in a public Slack channel. Slack AI, designed to use relevant information from public channels in the workspace to generate responses, could then be tricked into acting on these malicious instructions. While placing such instructions in a public channel poses a risk of detection, PromptArmor pointed out that an attacker could create a rogue public channel with only one member—themselves—potentially avoiding detection unless another user specifically searches for that channel. Salesforce, which owns Slack, did not directly reference PromptArmor in its advisory and did not confirm to SC Media that the issue it patched is the same one described by PromptArmor. However, the advisory does mention a security researcher’s blog post published on August 20, the same day as PromptArmor’s blog. “When we became aware of the report, we launched an investigation into the described scenario where, under very limited and specific circumstances, a malicious actor with an existing account in the same Slack workspace could phish users for certain data. We’ve deployed a patch to address the issue and have no evidence at this time of unauthorized access to customer data,” a Salesforce spokesperson told SC Media. How the Slack AI Exploit Could Have Extracted Secrets from Private Channels PromptArmor demonstrated two proof-of-concept exploits that would require the attacker to have access to the same workspace as the victim, such as a coworker. The attacker would create a public channel and lure the victim into clicking a link delivered by the AI. In the first exploit, the attacker aimed to extract an API key stored in a private channel that the victim is part of. The attacker could post a carefully crafted prompt in the public channel that indirectly instructs Slack AI to respond to a request for the API key with a fake error message and a URL controlled by the attacker. The AI would unknowingly insert the API key from the victim’s private channel into the URL as an HTTP parameter. If the victim clicks on the URL, the API key would be sent to the attacker’s domain. “This vulnerability shows how a flaw in the system could let unauthorized people see data they shouldn’t see. This really makes me question how safe our AI tools are,” said Akhil Mittal, Senior Manager of Cybersecurity Strategy and Solutions at Synopsys Software Integrity Group, in an email to SC Media. “It’s not just about fixing problems but making sure these tools manage our data properly. As AI becomes more common, it’s important for organizations to keep both security and ethics in mind to protect our information and keep trust.” In a second exploit, PromptArmor demonstrated how similar crafted instructions could be used to deliver a phishing link to a private channel. The attacker would tailor the instructions to the victim’s workflow, such as asking the AI to summarize messages from their manager, and include a malicious link. PromptArmor reported the issue to Slack on August 14, with Slack acknowledging the disclosure the following day. Despite some initial skepticism from Slack about the severity of the vulnerability, the company patched the issue on August 21. “Slack’s security team had prompt responses and showcased a commitment to security and attempted to understand the issue. Given how new prompt injection is and how misunderstood it has been across the industry, this is something that will take the industry time to wrap our heads around collectively,” PromptArmor wrote in their blog. New Slack AI Feature Could Pose Further Prompt Injection Risk PromptArmor concluded its testing of Slack AI before August 14, the same day Slack announced that its AI assistant could now reference files uploaded to Slack when generating search answers. PromptArmor noted that this new feature could create additional opportunities for indirect prompt injection attacks, such as hiding malicious instructions in a PDF file by setting the font color to white. However, the researchers have not yet tested this scenario and noted that workspace admins can restrict Slack AI’s ability to read files. 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

Read More
August 22, 2024in Salesforce Slack
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

Read More
August 20, 2024in Generative AI
APIs and Software Development
11Aug

APIs and Software Development

The Role of APIs in Modern Software Development APIs (Application Programming Interfaces) are central to modern software development, enabling teams to integrate external features into their products, including advanced third-party AI systems. For instance, you can use an API to allow users to generate 3D models from prompts on MatchboxXR. The Rise of AI-Powered Applications Many startups focus exclusively on AI, but often they are essentially wrappers around existing technologies like ChatGPT. These applications provide specialized user interfaces for interacting with OpenAI’s GPT models rather than developing new AI from scratch. Some branding might make it seem like they’re creating groundbreaking technology, when in reality, they’re leveraging pre-built AI solutions. Solopreneur-Driven Wrappers Large Language Models (LLMs) enable individuals and small teams to create lightweight apps and websites with AI features quickly. A quick search on Reddit reveals numerous small-scale startups offering: Such features can often be built using ChatGPT or Gemini within minutes for free. Well-Funded Ventures Larger operations invest heavily in polished platforms but may allocate significant budgets to marketing and design. This raises questions about whether these ventures are also just sophisticated wrappers. Examples include: While these products offer interesting functionalities, they often rely on APIs to interact with LLMs, which brings its own set of challenges. The Impact of AI-First, API-Second Approaches Design Considerations Looking Ahead Developer Experience: As AI technologies like LLMs become mainstream, focusing on developer experience (DevEx) will be crucial. Good DevEx involves well-structured schemas, flexible functions, up-to-date documentation, and ample testing data. Future Trends: The future of AI will likely involve more integrations. Imagine: AI is powerful, but the real innovation lies in integrating hardware, data, and interactions effectively. 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

Read More
August 11, 2024in Salesforce
Demandbase One for Sales iFrame
09Aug

Demandbase One for Sales iFrame

Understanding the Demandbase One for Sales iFrame in Salesforce The Demandbase One for Sales iFrame (formerly known as Sales Intelligence) allows sales teams to access deep, actionable insights directly within Salesforce. This feature provides account-level and people-level details, including engagement data, technographics, intent signals, and even relevant news, social media posts, and email communications. By offering this level of visibility, sales professionals can make informed decisions and take the most effective next steps on accounts. Key Points: Overview of the Demandbase One for Sales iFrame The iFrame is divided into several key sections: Account, People, Engagement, and Insights tabs. Each of these provides critical information to help you better understand and engage with the companies and people you’re researching. Account Tab People Tab Engagement Tab Final Notes: The Demandbase One for Sales iFrame is a powerful tool that provides a complete view of account activity, helping sales teams make informed decisions and drive results. 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

Read More
August 9, 2024in Data
Rold of Small Language Models
02Aug

Role of Small Language Models

The Role of Small Language Models (SLMs) in AI While much attention is often given to the capabilities of Large Language Models (LLMs), Small Language Models (SLMs) play a vital role in the AI landscape. Role of Small Language Models. Large vs. Small Language Models LLMs, like GPT-4, excel at managing complex tasks and providing sophisticated responses. However, their substantial computational and energy requirements can make them impractical for smaller organizations and devices with limited processing power. In contrast, SLMs offer a more feasible solution. Designed to be lightweight and resource-efficient, SLMs are ideal for applications operating in constrained computational environments. Their reduced resource demands make them easier and quicker to deploy, while also simplifying maintenance. What are Small Language Models? Small Language Models (SLMs) are neural networks engineered to generate natural language text. The term “small” refers not only to the model’s physical size but also to its parameter count, neural architecture, and the volume of data used during training. Parameters are numeric values that guide a model’s interpretation of inputs and output generation. Models with fewer parameters are inherently simpler, requiring less training data and computational power. Generally, models with fewer than 100 million parameters are classified as small, though some experts consider models with as few as 1 million to 10 million parameters to be small in comparison to today’s large models, which can have hundreds of billions of parameters. How Small Language Models Work SLMs achieve efficiency and effectiveness with a reduced parameter count, typically ranging from tens to hundreds of millions, as opposed to the billions seen in larger models. This design choice enhances computational efficiency and task-specific performance while maintaining strong language comprehension and generation capabilities. Techniques such as model compression, knowledge distillation, and transfer learning are critical for optimizing SLMs. These methods enable SLMs to encapsulate the broad understanding capabilities of larger models into a more concentrated, domain-specific toolset, facilitating precise and effective applications while preserving high performance. Advantages of Small Language Models Applications of Small Language Models Role of Small Language Models is lengthy. SLMs have seen increased adoption due to their ability to produce contextually coherent responses across various applications: Small Language Models vs. Large Language Models Feature LLMs SLMs Training Dataset Broad, diverse internet data Focused, domain-specific data Parameter Count Billions Tens to hundreds of millions Computational Demand High Low Cost Expensive Cost-effective Customization Limited, general-purpose High, tailored to specific needs Latency Higher Lower Security Risk of data exposure through APIs Lower risk, often not open source Maintenance Complex Easier Deployment Requires substantial infrastructure Suitable for limited hardware environments Application Broad, including complex tasks Specific, domain-focused tasks Accuracy in Specific Domains Potentially less accurate due to general training High accuracy with domain-specific training Real-time Application Less ideal due to latency Ideal due to low latency Bias and Errors Higher risk of biases and factual errors Reduced risk due to focused training Development Cycles Slower Faster Conclusion The role of Small Language Models (SLMs) is increasingly significant as they offer a practical and efficient alternative to larger models. By focusing on specific needs and operating within constrained environments, SLMs provide targeted precision, cost savings, improved security, and quick responsiveness. As industries continue to integrate AI solutions, the tailored capabilities of SLMs are set to drive innovation and efficiency across various domains. 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

Read More
August 2, 2024in Salesforce
Salesforce Campaign Flows
30Jul

Salesforce Campaign Flows: A New Era of Automation

Salesforce’s Flow has long been a powerful automation tool, democratizing access to sophisticated automation for non-coders. It also drives the automation behind Marketing Cloud Growth Edition, including email dispatch. Salesforce Campaign Flows mark a significant step toward making automation more accessible for marketers. For the first time, Salesforce introduces what are termed “non-admin flows,” which offer a streamlined interface for managing Flow automation without dealing with complex nodes and elements. This marks a significant development, as no other Salesforce products currently feature this simplified approach. Marketers now have direct access to Flow’s capabilities. Introduction to Salesforce Campaign Flows Campaign Flows in Salesforce provide a user-friendly interface for setting up email content, applying actions to records triggered by events, and more. This functionality closely parallels tools like Engagement Studio in Account Engagement and Journey Builder in Marketing Cloud. However, the timeline for incorporating features such as Journey Builder’s goals and exit criteria into Campaign Flows remains undisclosed. Flow now supports shorter wait periods, a sought-after feature for orchestrating marketing journeys more effectively. Branching logic with Decision elements allows users to create “yes/no” paths based on lead or contact criteria, adding flexibility to the marketing automation process. Types of Campaign Flows Currently, there are two types of Campaign Flows: Segment-triggered Flows and Form-triggered Flows. The key differences between these Campaign Flows and traditional Salesforce Flows include: Available Elements Campaign Flows are a simplified version of Salesforce Flows, with some elements unavailable in this reduced interface. Key elements such as Wait and Decision elements are included, which are essential for marketing use cases. The following table compares available elements: Element Name Salesforce Flow Segment-triggered Flows Form-triggered Flows Action ✅ ❌ ❌ Add Prompt Instructions ✅ ❌ ❌ Apex Action ✅ ❌ ❌ Assignment ✅ ✅ ✅ Collection Filter ✅ ✅ ✅ Collection Sort ✅ ✅ ✅ Create Records ✅ ✅ ✅ Custom Error ✅ ❌ ❌ Decision ✅ ✅ ✅ Delete Records ✅ ✅ ✅ Email Alert ✅ ❌ ❌ Get Records ✅ ✅ ✅ Loop ✅ ✅ ✅ Recommendation Assignment ✅ ❌ ❌ Screen ✅ ❌ ❌ Send Email Message * ✅ ❌ Send SMS Message * ✅ ❌ Start ✅ ✅ ✅ Subflow ✅ ❌ ❌ Transform ✅ ❌ ❌ Update Records ✅ ✅ ❌ Wait ✅ ✅ ✅ Wait Until Event * ✅ ✅ *Only with Marketing Cloud Growth Wait vs. Wait Until Event Elements The “Wait” element allows for fixed pauses, such as waiting for three days. The “Wait Until Event” element, available in Marketing Cloud Growth, holds Leads/Contacts until a specified event makes them eligible to proceed. This mirrors functionality found in Engagement Studio. User Access and Capabilities In Marketing Cloud Growth, Campaign Flow sharing is set to private by default, with visibility influenced by associated records, sharing rules, and manual sharing settings. This means Campaign Flows are generally private unless additional sharing rules are established. Creating and Editing Campaign Flows Campaign Flows in Marketing Cloud Growth have a simplified user interface compared to Salesforce’s traditional flows. Summary The introduction of non-admin flows in Salesforce marks a significant step toward making automation more accessible for marketers. These simplified interfaces enable the creation of effective marketing campaigns while maintaining the option to integrate with more complex flows in Marketing Cloud Growth Edition. Future developments will likely expand the use cases and capabilities of these streamlined flows. 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

Read More
July 30, 2024in Marketing Automation
Enhance Payer Patient Education
30Jul

Enhance Payer Patient Education

Data and Technology Strategies Enhance Payer Patient Education Analytics platforms, omnichannel engagement tools, telehealth, and other technological advancements have become essential in driving successful, enhanced payer patient education. Cathy Moffitt, MD, a pediatrician with 15 years of experience in the pediatric emergency department and now the senior vice president and Aetna chief medical officer at CVS Health, understands the critical role of patient education. “Education is empowerment. It is engagement. It is very critical to making patients more equipped to handle their healthcare journey,” Moffitt said in an episode of Healthcare Strategies. “Even overseeing a large payer like Aetna, I still believe tremendously in health education.” Enhance Payer Patient Education For large payers, effective patient education begins with data analytics and a deep understanding of their member population. Through data, payers can identify key insights, including when members are most receptive to educational materials. “People are more open to hear you and to be educated and empowered when they need help right then,” Moffitt explained. Timing is crucial—offering educational resources when they’re most relevant to a member’s immediate needs increases the likelihood that the information will be absorbed and acted upon. Aetna’s Next Best Action initiative, launched in 2018, exemplifies this approach. Through this program, Aetna employees reach out to members with specific conditions, offering guidance on the next best steps for managing their health. By providing education at a time when members are most open to it, the initiative ensures that patient education is both timely and impactful. In addition to timing, payer data can shape patient education by providing insights into a member’s demographics, including race, sexual orientation, gender identity, ethnicity, and location. Tailoring educational efforts to these factors ensures that communication is accessible and resonates with members. To better connect with a diverse member base, Aetna has integrated translator services into its customer support and trained representatives on sensitivity to sexual orientation and gender identity. Additionally, updating the provider directory to reflect demographic data is crucial. When members see providers who share their language, culture, and experiences, they are more likely to engage with and retain the educational materials provided. “Understanding, in a multicultural and multifactorial way, who our members are and trying to help understand what they need…as well as understanding both acute and chronic illness from an actionability standpoint, where we can best engage to good effect as we reach out to people—that’s the cornerstone of our intent and our philosophy around how we scrub data,” Moffitt shared. With over 20 years in the healthcare industry, both as a provider and now in a payer role, Moffitt has observed key trends and identified strengths and weaknesses in patient education efforts. She noted that the most successful patient education initiatives have been in mental health and preventive care, with technology playing a crucial role in both areas. Patient education has significantly reduced the stigma around mental healthcare and highlighted the importance of mental wellness. Telemedicine has vastly improved access to care, particularly in mental health, Moffitt noted. In preventive care, more people are now aware of the benefits of cancer screenings, vaccines, wellness visits, and other preventive measures. Moffitt suggested that the increased use of home health visits and retail clinics has contributed to these improvements, particularly among Aetna’s members. Looking ahead, Moffitt predicted that customized engagement is the next frontier for patient education. Members increasingly want educational materials delivered in a personalized and streamlined manner that suits their preferences. Omnichannel engagement solutions will be vital in meeting this demand. While significant progress has been made in enabling members to receive educational materials through various channels such as email, text, and phone calls, Moffitt anticipates even more advancements in the future. “I can’t tell you exactly where we’re going to be in 10 years because I wouldn’t have been able to tell you 10 years ago where we are now, but we will continue to respond and meet the demands with the technological commitments that we’re making,” Moffitt said. 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

Read More
July 30, 2024in Life Sciences Cloud, Public Sector Solutions, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services
Cloud PBX
30Jul

Cloud PBX

The Clock is Ticking on the Big UK Traditional Telephony Switch-Off As the UK approaches the traditional telephony switch-off, millions of small businesses are prioritizing the digitization of their voice communications. The move to cloud-powered replacements – Cloud PBX – is not just about meeting the January 2027 deadline; it’s an opportunity to modernize and leverage the benefits of cloud-based communications. The switch-off represents a chance for businesses to embrace a mobile-first, omnichannel approach to communication, unifying voice, video, emails, messaging, webchat, and more. This integration empowers employees to work smarter and enhances the customer experience. For small businesses and their IT service provider partners, modernization depends on deploying feature-rich, affordable technology that simplifies complexity and delivers tangible efficiency gains. Choosing the right product and vendor is crucial. “Cloud-powered, unified communication is no longer just for larger enterprises; small businesses must also embrace transformational change to keep pace with modern work trends. What may seem like a major undertaking can be easier than they think,” says Arya Zhou, Head of Global Sales at Yeastar. Yeastar’s recently launched P520 IPPBX digitizes voice calling and seamlessly integrates it with video, messaging, and customer experience into one platform. Discover the Yeastar P520 The Yeastar P520, part of the P-Series Appliance Edition, supports up to 20 users and 10 concurrent calls. It combines a compact, lightweight hardware body with powerful software capabilities. It supports Yeastar’s Linkus UC Client for various platforms, integrates with Microsoft Teams, and provides comprehensive call analytics and graphical call reports to improve communication efficiency and productivity. The P520 offers advanced call center features, including: Additionally, it includes team chat with presence and file sharing, integrated lightweight video conferencing, PBX-native external contacts management, extension groups, and ready-made integrations with popular CRMs and helpdesks. All these features come with single-point configuration and enterprise-grade security. “The Yeastar P520 is ideal for smaller teams looking to enhance their communication infrastructure,” says Zhou. “It delivers advanced communication capabilities and improved productivity tailored for SMBs and startups, without high costs.” 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

Read More
July 30, 2024in Salesforce

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com