Latest news with #LLMs
The Star
a day ago
- Science
- The Star
Opinion: Are you more emotionally intelligent than an AI chatbot?
As artificial intelligence takes over the world, I've tried to reassure myself: AI can't ever be as authentically human and emotionally intelligent as real people are. Right? But what if that's wrong? A cognitive scientist who specialises in emotional intelligence shared with me in an interview that he and some colleagues did an experiment that throws some cold water on that theory. 'What do you do?' Writing in the journal Communications Psychology , Marcello Mortillaro, senior scientist at the UNIGE's Swiss Center for Affective Sciences (CISA), said he and colleagues ran commonly used tests of emotional intelligence on six Large Language Models including generative AI chatbots like ChatGPT. The are the same kinds of tests that are commonly used in corporate and research settings: scenarios involving complicated social situations, and questions asking which of five reactions might be best. One example included in the journal article goes like this: 'Your colleague with whom you get along very well tells you that he is getting dismissed and that you will be taking over his projects. While he is telling you the news he starts crying. He is very sad and desperate. You have a meeting coming up in 10 min. What do you do?' Gosh, that's a tough one. The person – or AI chatbot – would then be presented with five options, ranging from things like: – 'You take some time to listen to him until you get the impression he calmed down a bit, at risk of being late for your meeting,' to – 'You suggest that he joins you for your meeting with your supervisor so that you can plan the transfer period together.' Emotional intelligence experts generally agree that there are 'right' or 'best' answers to these scenarios, based on conflict management theory – and it turns out that the LLMs and AI chatbots chose the best answers more often than humans did. As Mortillaro told me: 'When we run these tests with people, the average correct response rate … is between 15% and 60% correct. The LLMs on average, were about 80%. So, they answered better than the average human participant.' Maybe you're sceptical Even having heard that, I was sceptical. For one thing, I had assumed while reading the original article that Mortillaro and his colleagues had informed the LLMs what they were doing – namely, that they were looking for the most emotionally intelligent answers. Thus, the AI would have had a signal to tailor the answers, knowing how they'd be judged. Heck, it would probably be easier for a lot of us mere humans to improve our emotional intelligence if we had the benefit of a constant reminder in life: 'Remember, we want to be as emotionally intelligent as possible!' But, it turns out that assumption on my part was flat-out wrong – which frankly makes the whole thing a bit more remarkable. 'Nothing!' Mortillaro told me when I asked how much he'd told the LLMs about the idea of emotional intelligence to begin with. 'We didn't even say this is part of a test. We just gave the … situation and said these are five possible answers. What's the best answer? … And it picked the right option 82% (ck) of the time, which is way higher – significantly higher – than the average human.' Good news, right? Interestingly, from Mortillaro's perspective, this is actually some pretty good news – not because it suggests another realm in which artificial intelligence might replace human effort, but because it could make his discipline easier. In short, scientists might theorise from studies like this that they can use AI to create the first drafts of additional emotional intelligence tests, and thus scale their work with humans even more. I mean: 80% accuracy isn't 100%, but it's potentially a good head start. Mortillaro also brainstormed with me for some other use cases that might be more interesting to business leaders and entrepreneurs. To be honest, I'm not sure how I feel about these yet. But examples might include: – Offering customer scenarios, getting solutions from LLMs, and incorporating them into sales or customer service scripts. – Running the text and calls to action on your website or social media ads through LLMs to see if there are suggestions hiding in plain sight. – And of course, as I think a lot of people already do, sharing presentations or speeches for suggestions on how to streamline them. Personally, I find I reject many more of the suggestions that I get from LLMs like ChatGPT. I also don't use it for articles like this one, of course. Still, even if you're not convinced, I suspect some of your competitors are. And they might be improving their emotional intelligence as a result without even realising it. As a result, at least being aware of the potential of AI to upend your industry seems like a smart move. 'Especially for small business owners who do not have the staff or the money to implement large-scale projects,' Mortillaro suggested, 'these kind of tools become incredibly powerful.' – Inc./Tribune News Service
Martechvibe
2 days ago
- Business
- Martechvibe
BigCommerce, Feedonomics Partner with Perplexity
Both companies have partnered with Perplexity to ensure brands can deliver structured, AI-ready product data that improves visibility, accuracy, and conversion in next-gen AI search experiences. BigCommerce, an open SaaS ecommerce platform for B2C and B2B businesses, and Feedonomics, a data feed management solution, have announced that their customers now have access to AI-powered search engine Perplexity to optimise visibility and relevance for brands in AI search results. In the shift from traditional SEO to AI search, it is critical that merchants ensure their products can be syndicated to the right marketplaces and advertising channels or owned channels so they can be found in order to drive traffic and sales. Historical data scraping is inefficient and often inaccurate for large language models. Feedonomics now provides Feedonomics now provides Perplexity with pre-optimised, structured product data, ensuring that the LLM understands and recognises merchants' products, leading to better search results that favour the brand. 'AI-powered search is redefining how consumers discover and engage with products online,' said Sharon Gee, Senior Vice President of Product for AI at BigCommerce and Feedonomics. For consumer brands, this represents a pivotal moment to lead with innovation. 'By delivering high-quality product data directly to LLMs, brands ensure their products surface more accurately and contextually. Ensuring quality data feeds are optimally structured and accessible to AI search channels is a strategic imperative for driving relevance, loyalty and growth in an increasingly competitive digital landscape.' ALSO READ: Feedonomics, YouTube Partner for Shopping Affiliate Program Owen Spencer, Director of Enterprise Applications at Revelyst, said, 'With Feedonomics powering our product data, we have confidence that our catalogue is being presented accurately and optimally to drive results with AI search platforms.' 'Improved visibility and stronger brand consistency are critical for traffic and conversion. Having structured and channel-optimised data in place allows us to take more control of how our products appear in AI-driven experiences, and that is a game-changer for our ecommerce performance in the AI era.' As commerce adopts agentic shopping, where AI agents research, recommend, and even act on behalf of consumers, the quality of the product catalogue is critical. As agentic purchasing evolves, the need for flawless data transfers and optimised, conversion-ready storefronts becomes paramount. Together, BigCommerce, Feedonomics and Makeswift provide the data and storefront software that enable this critical connectivity and better performance for merchants on virtually any ecommerce platform to optimise revenue. 'Some aspects of the AI future are already clear—consumers want agentic experiences throughout their shopping journey, and they turn to Perplexity for accurate answers they can trust,' said Taz Patel, Head of Advertising and Shopping at Perplexity. 'When our systems can ingest clean, well-organised product information — with rich attributes, consistent taxonomy and up-to-date availability — the results speak for themselves: more relevant search experiences, higher conversion rates and better alignment with shopper intent.' 'With Feedonomics delivering AI-ready data to Perplexity's powerful and highly-trusted answer engine, we are setting a new standard for ecommerce search.' ALSO READ: BigCommerce Launches Google Cloud Ecommerce Features The Martechvibe team works with a staff of in-house writers and industry experts. View More Locala's omnichannel advertising platform leverages granular insights and cutting-edge AI to help marketers plan, activate, and measure campaigns that are personalised to the local consumer. It specialises in transforming complex mobility and consumer data into actionable audience insights, fueling advanced media strategies. VISIT WEBSITE Blue Prism is a global software provider, offering ROM 2, an intelligent automation implementation methodology that empowers teams to scale their digital workflow. One of its use cases is sales and marketing solutions, which is powered by artificial intelligence (AI) and machine learning (ML) algorithms. VISIT WEBSITE Pega is a combined business process management and robotic process automation (RPA) platform that comes with workforce analytics features. 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Utilising its connected TV (CTV) and search, social, and commerce tools, marketers can optimise their customer acquisition strategy. VISIT WEBSITE specialises in Conversational AI, generating autonomous conversations that appear human-like. These conversations form the foundation of brand engagement. Their AI-powered, no-code Dynamic Automation Platform manages around two billion conversations from a multitude of channels in numerous languages. VISIT WEBSITE Sprinklr offers a comprehensive suite of 250+ AI-intent bot templates for various industry verticals to accelerate bot development. Its specialised AI and compliant rule engine enables marketers to modify and automate their promotional campaigns for better engagement via suggestion-based personalised bots. VISIT WEBSITE Meta's new class of generative AI features and creative tools allow users to create and share custom stickers or update the visual style of their photos with a simple text prompt. 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It offers Contakt and Interakt to enhance overall CX, where Interakt enables marketers to send hyper-targeted and personalised campaigns to drive customers to take action on WhatsApp. VISIT WEBSITE Dialogflow is an all-in-one platform for developing chatbots, voice bots, and virtual agents using natural language understanding and Google AI. It helps marketing teams create conversational agents that can handle common customer requests and issues, such as FAQs, and order status at scale. VISIT WEBSITE Dialpad is a fully integrated unified conversation intelligence platform that utilises ongoing ML(Machine Learning) algorithms and does not require coding to configure a chatbot. It offers real-time insights and tips, helping marketing teams customise their approach for every customer. VISIT WEBSITE is a low code-no code, enterprise-grade generative AI platform offering readily available LLMs for CX transformations. Its omnichannel reporting and analytics suite for marketing enables organisations to streamline their customer journeys. VISIT WEBSITE is a global conversational AI technology provider optimised for operational efficiency at scale. It offers self-learning AI capabilities that emphasise Natural Language Understanding (NLU) along with intent suggestions. VISIT WEBSITE Avaamo is an all-in-one conversational AI platform for enterprises offering a suite of industry-specific skills developed to automate and resolve common use cases. It offers Outreach, which enables marketers to educate and engage with customers across existing touchpoints through proactive and personalised communication. VISIT WEBSITE Amazon Lex offers fully managed conversational AI interfaces with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. It enables sales and marketing teams to automate user tasks in their applications like the CRM and across any digital channel. VISIT WEBSITE Zendesk is an AI-powered customer feedback software provider, offering customer satisfaction (CSAT) software to businesses across multiple industries. Its marketplace offers 1600+ apps and integrations, enabling marketers to create a complete customer feedback system. VISIT WEBSITE UserVoice provides product feedback management software for SaaS businesses. Its UserVoice Validation tool delivers actionable user insights to inform roadmap strategy. From testing ideas to ensuring satisfaction with released features, it helps research and marketing teams gather quick user feedback throughout the entire product lifecycle. VISIT WEBSITE Twilio provides integrated Mindful Feedback functionality within its Flex offering, enabling marketers to automatically take action based on real-time customer feedback. It automatically collects customer feedback after Flex interactions by capturing feedback over post-call voice IVR, outbound voice IVR, conversational SMS, Web SMS, email, and web surveys. VISIT WEBSITE Trustpilot offers ML and AI-powered feedback management tools, helping marketers build trust and loyalty through customer reviews. Its AI-driven Review Insights tool enables marketers to identify growth areas from negative feedback, whereas its Image Generator tool lets them enhance social media engagement. VISIT WEBSITE Survey Monkey is a global online survey and forms tech stack provider, offering AI-fueled market research solutions to businesses. Its enterprise feedback management program provides 250+ templates with the capabilities of customisation, allowing organisations to build tailored survey forms. VISIT WEBSITE Sprinklr is a unified customer experience platform (CXM), offering AI-powered feedback management capabilities with its flagship product: Sprinklr Surveys. It integrates solicited customer feedback with unified-CXM data, enabling brands to derive meaningful, actionable insights. Utilising its genAI-powered survey builder, marketers can create conversational surveys to streamline their NPS strategies. VISIT WEBSITE Salesforce is a comprehensive customer-oriented technology provider offering a feedback management suite. Salesforce's survey responses help marketers create personalised surveys based on their existing customers' data. Some of the supported applications of its feedback management platform are genAI-powered survey form development and sentiment-insights-based survey collections. VISIT WEBSITE QuestionPro is an online feedback software provider, offering ready-made survey templates to businesses across 40+ countries. Leveraging the capabilities of its customer satisfaction templates, advertisers and marketers can create and gather tailored questions. VISIT WEBSITE Qualtrics is an all-in-one customer experience solution provider, offering website & app feedback tools for organisations to collect customer feedback efficiently. Its digital experience analytics capabilities combined with DXA integrations, bring together verbal and behavioural data for in-depth analysis of user feedback. VISIT WEBSITE HubSpot is a one-stop marketing platform, providing customer feedback software under its Service Hub offerings. Its pre-built APIs include net promote score (NPS), customer effort score (CES), and customer satisfaction (CSAT) surveys, allowing marketers to strengthen their customer loyalty strategy. VISIT WEBSITE Medallia is a comprehensive platform, enabling marketers to capture feedback from all customer touch points. Some of its popular feedback management software capabilities include Sense360 for analysing consumer spending, Medallia Video to automatically extract meaningful insights from customer's feedbacks and Agile Research for creating branded surveys in real-time. VISIT WEBSITE Zeotap lets brands integrate, unify, segment and orchestrate customer data, fostering a cookieless future. It offers a 'Non-Customer Entity Data' feature that helps marketers to integrate both customer and non-customer entity data. VISIT WEBSITE Twilio provides, Twilio Segment, an AI-powered CDP, enabling marketers to streamline their personalised customer engagement journeys. It brings together clean, consented customer data for real-time insights with 450+ pre-built connectors. VISIT WEBSITE Syntasa provides data-ready CDP capabilities, equipping marketers with essential tools to prepare their customer data. It offers real-time sentiment analytics capabilities, helping enterprises derive behavioural insights from unified user profiles to foster hyper-personalised experiences. VISIT WEBSITE SAP is a company-wide customer data management platform, helping marketers improve engagement, conversion, and retention. It offers a genAI-powered data model, enabling enterprises to unify their B2B and B2C customers' profiles to reach their personalisation goals. VISIT WEBSITE Oracle is a global database management company, providing CDP solutions, under its flagship product: Oracle Unity. Its AI-powered models enable marketers to create hyper-personsalised campaigns with predictive insights by streamlining real-time customer data from heterogeneous sources. VISIT WEBSITE Growthloop is a composable CDP platform, allowing marketing and sales team to action their data at scale. It offers GrowthLoop Audience Builder, enabling marketers to initiate their lead generation functions. VISIT WEBSITE Adobe Experience Cloud offers a real-time CDP, letting users create high-value audience segments for B2C and B2B use cases across their marketing funnel. It follows a cookieless marketing approach to automatically unify customer data across various online and offline channels in real-time. VISIT WEBSITE ActionIQ is a composable CDP platform that offers audience segmentation, acquisition marketing, and Customer 360 capabilities. It integrates analytical tools, enabling marketers to collect and analyse their first-party customer data at scale. VISIT WEBSITE Zoho CRM leverages automation and comprehensive analytics to empower any sales team, fostering stronger customer loyalty. Ideal for both small businesses and enterprises, this software enables enhanced productivity and tailored solutions through predictive intelligence. VISIT WEBSITE Zendesk offers Zendesk Sell, an easy-to-deploy CRM platform that is simple to deploy and navigate, catering to businesses of any scale. Its mobile-friendly Sell app has the functionalities of geolocation, helping sales and marketing teams. Offering an all-in-one CRM platform, organisations can implement use cases like prospecting, engagement, lead generation and communication capabilities. VISIT WEBSITE SugarCRM is a global CRM software provider, helping marketing, sales, and service teams improve efficiency through automation, data, and intelligence in near real-time. It offers Sugar Market, an all-in-one marketing automation platform that helps marketers to automate their campaign with higher ROI. VISIT WEBSITE Salesforce is a one-stop sales and support CRM software that regularly updates customer records, tracks emails and calls, and creates organised support processes for quicker customer responses. Its simple and scalable infrastructure is ideal for businesses of all sizes. VISIT WEBSITE Pipedrive stands out with its emphasis on automation, tailorability, and seamless integration. Purposefully built to enhance the efficiency and productivity of sales professionals, this CRM solution has an intuitive setup and robust capabilities. It facilitates lead generation, comprehensive sales analytics, and seamless business expansion. VISIT WEBSITE Oracle NetSuite CRM, a component of Oracle's business software suite, is for all midsize and booming businesses that want a comprehensive, integrated CRM solution with ERP capabilities. Using it, customers and partners can directly interact with the platform, freeing the additional work load for their sales team. VISIT WEBSITE Microsoft Dynamics 365 Sales represents a robust cloud-based CRM solution brimming with features such as pipeline assessment, relationship analytics, and conversational intelligence. It utilises AI-powered insights to provide actionable intelligence via predictive analytics, lead scoring and sentiment analysis. VISIT WEBSITE Less Annoying is a CRM software that utilises a straightforward search feature within its contact management system, making it easy to track contacts. It is ideal for small-sized businesses and offers a highly intuitive and user-friendly interface. VISIT WEBSITE Insightly offers CRM software solutions for enterprises looking to understand their audience data across various marketing functions. It integrates seamlessly with third-party apps, streamlining workflow automation, including bulk emailing and report generation. VISIT WEBSITE HubSpot's sleek dashboard with consolidated CRM tools simplify complex customer data, further transforming it into meaningful insights. Complementing typical CRM functionalities, HubSpot provides tailored options, including conversation intelligence and email monitoring. VISIT WEBSITE Apptivo delivers adaptable and web-based CRM solutions, enabling marketing and sales teams to streamline all customer service requirements across various devices and browsers. Despite its focused feature set, it encompasses over 65 interconnected applications alongside a robust sales pipeline management tool, ensuring efficient tracking of potential leads without any downtime. VISIT WEBSITE Sitecore offers composable cloud solutions with its flagship products, Sitecore Experience Platform(XP) and Sitecore Experience Manager (XM). They help marketers to overcome scalability challenges. Some of its features include intuitive visual editing, headless delivery, marketing automation, scalable personalisation, data and machine learning capabilities. VISIT WEBSITE Salesforce Experience Cloud is a platform that helps enterprises link clients, partners, and employees to securely exchange information and documents. Built on its Customer 360 platform, Salesforce's DXP ensures seamless integration with any solution in the Salesforce ecosystem. VISIT WEBSITE Oracle Cloud offers a comprehensive suite of platform-based marketing automation solutions for personalised B2B and B2C automation campaigns. Some of its featured products are Eloqua Marketing Automation, CrowdTwist Loyalty and Engagement, and Unity Customer Data Platform. VISIT WEBSITE Optimizely is a digital experience platform (DXP) provider that offers Optimizely One, an easy-to-use and fully integrated suite. It provides a single, unified workflow with thoughtfully embedded AI and machine-learning algorithms, accelerating work across the entire marketing lifecycle. VISIT WEBSITE OpenText offers a cloud-native, scalable platform for enterprises to streamline their marketing functions. It provides fully composable content management, digital asset management, creative workflows, personalisation, targeting and customer data technologies in one place, ensuring a holistic approach to B2B, B2E and B2C experiences. VISIT WEBSITE Neptune DXP is a PaaS(Platform-as-a-Service) provider, helping marketing teams build custom apps based on modular, reusable application building blocks. The company offers flexible environments to businesses across different verticals, further providing personalised digital solutions at scale. VISIT WEBSITE Magnolia is a composable DXP that comes with no-code connector packs and low-code micro-frameworks for third-party integrations. It also offers an open-source version alongside its commercial plans enabling enterprises to build tailored use cases. VISIT WEBSITE Liferay DXP helps marketers deliver personalised and connected digital experiences across a broad range of channels, including customer portals, websites, intranets, mobile apps, and connected devices. It offers intuitive CMS, user analytics, and site management tools that businesses need to launch, test and optimise digital experiences for faster go-to-market. VISIT WEBSITE Powered by IBM Consulting, IBM iX offers a composable DXP, providing a comprehensive solution to make enterprises' systems future-ready. Utilising its data-driven insights and intelligent workflows, marketers can design and deliver human-centred experiences across the customer lifecycle. VISIT WEBSITE HCL Digital Experience (DX), forms part of a wider HCL Customer Experience (CX) product portfolio, offering core capabilities such as content management, DAM, CDP called Signals, and low-code application development. It provides services in government, life sciences, insurance, financial services, and other verticals. VISIT WEBSITE Contentstack is a headless CMS and Composable Digital Experience Platform (DXP) solution provider that helps marketers gain a competitive edge. It recently launched into Google Cloud Marketplace, and is also available on Microsoft Azure and AWS. It seamlessly enables mid-market brands to adopt its omnichannel campaign engine to drive higher conversions and sales. VISIT WEBSITE Bloomreach Commerce Experience Cloud provides businesses an edge with its modular capabilities: Content Management System (CMS), Discovery features for search and merchandising optimisation, and Engagement tools such as Customer Data Platforms (CDP) for personalisation and analytics. VISIT WEBSITE Adobe Experience Cloud offers a comprehensive set of services specifically designed to address the day-to-day requirements for personalised customer experience at scale. Its platform helps manage different digital content and assets to improve customer satisfaction. Some of its products include Adobe Gen Studio, Experience Manager Sites, Real-time CDP, and Marketo Engage. VISIT WEBSITE Acquia offers DXP solutions, comprising of two main elements: Acquia Drupal Cloud and Acquia Marketing Cloud. It can be accessed in both platform-as-a-service (PaaS) and software-as-a-service (SaaS) with additional components such as Site Factory for multisite management, digital asset management (DAM), CDP, personalisation, and Campaign Studio. VISIT WEBSITE
Gizmodo
2 days ago
- Science
- Gizmodo
Multiple Studies Now Suggest That AI Will Make Us Morons
For the second time in two weeks, a study has been published that suggests that people who use AI may display less cognitive ability than those who don't rely on it. The studies have bolstered critics' accusations that AI makes you stupid. The most recent study was conducted by the University of Pennsylvania's Wharton School and looked at a sample size of over 4,500 participants. The study, which looked at the cognitive differences between people who used LLMs like ChatGPT to do research and those who used Google Search, found that the people who used chatbots tended 'to develop shallower knowledge' of the subjects they were researching. Both groups were asked to research how to start a vegetable garden, with some participants randomly selected to use AI, while others were asked to use a search engine. According to the study's findings, those who used ChatGPT gave much worse advice about how to plant a vegetable garden than those who used the search engine. Researchers write: The shallower knowledge accrues from an inherent feature of LLMs—the presentation of results as syntheses of information rather than individual search links—which makes learning more passive than in standard web search, where users actively discover and synthesize information sources themselves. In turn, when subsequently forming advice on the topic based on what they learned, those who learned from LLM syntheses (vs. standard search results) feel less invested in forming their advice and, more importantly, create advice that is sparser, less original—and ultimately less likely to be adopted by recipients. The study concludes that this occurred ironically because of ChatGPT's advertised benefit—'sparing users the need to browse through results and synthesize information themselves.' Because researchers did not have to hunt for information themselves, their 'depth of knowledge' was markedly lower than those who did. 'In this sense, one might view learning through LLMs rather than web search as analogous to being shown the solution to a math problem rather than trying to solve it oneself,' the research concludes. The UPenn study follows on the heels of research produced by MIT, published earlier this month, that showed a similarly problematic cognitive impact produced by AI. That study, which observed the neural activity of college students who were using ChatGPT to study, found that increased AI use resulted in reduced brain activity, or what the researchers termed 'cognitive debt.' The study used an EEG machine to measure the neural activity of three different groups of students—one that used ChatGPT to study, one that used Google Search, and one that used neither. The study showed that ChatGPT users displayed markedly less cognitive activity than even those who were using Google Search to find information. The methodology of the MIT article has since been called into question by AI enthusiasts. Critics have noted that the study in question was not peer reviewed and that a small sample size of participants makes it hardly exhaustive. Similarly, critics have argued that while the EEG measurements show certain decreases in specific forms of brain activity, that doesn't necessarily mean that participants are 'dumber' as a result. Indeed, less mental exertion (and, thus, less activity) can be a sign that a person is actually more competent at a task and doesn't have to expend as much energy as a result. From a certain perspective, these recent assessments of AI's cerebral impact reek of a moral panic about a new and not altogether well-understood phenomenon. On the other hand, the conclusion that using an app to complete a homework assignment makes you less capable of thinking for yourself would appear to be self-evident. Outsourcing mental duties to a software program means you're not performing those duties yourself, and, as is pretty well established, doing something yourself is often the best way to learn. Of course, the internet has been curtailing human mental activity since it first went online. When was the last time you had to remember how to get somewhere? It really seems like Google Maps collectively robbed us of that ability over a decade ago. Other evidence for AI's stupidification effect is even more obvious: the maelstrom of cheating that's been happening in America's educational system means that students are making their way through high school and college with limited to no understanding of the subjects they're studying. While there is still much to learn about how AI impacts us, we shouldn't forego common sense when it comes to interpreting what we're seeing with our eyes. If a student can't write an essay without ChatGPT, it may be a sign that they don't have a particularly bright academic future ahead of them.
Forbes
3 days ago
- Business
- Forbes
Mira Murati's Startup Might Rival OpenAI, Anthropic
Heads with colorful cubes as a symbol of mentoring and psychotherapy. People are talking about a new startup that's poised to offer detailed custom services around LLMs. Thinking Labs, according to news from The Information and other sources, will look to offer clients customized AI platforms based on KPIs and other business intelligence, using reinforcement learning. That sounds like big news in the current context, where there's a big need for any detailed implementations that can be customized for a business. So with all of the brouhaha around Thinking Labs, who is its fearless leader? Because since the news is so scant around the business itself, any context that you get as a reporter would have to deal with Murati's past career moves. Murati's Background So with that said, what do we know about Mira Murati? She's from Albania, born in 1988, and has significant experience at both OpenAI and Tesla. She also has a mechanical engineering degree from Dartmouth College. As CTO at OpenAI, Murati led the development, deployment, and scaling of flagship products like ChatGPT and DALL·E. Then she actually became temporary CEO during Altman's ouster. So it makes sense, given the potential business rivalry, to ask: what is her connection to Sam Altman? A post from The Decoder in 2024 shows Murati has publicly denied conflict with Altman, calling the board's decision to fire him 'perplexing' and claiming that herself and others worked to get him back. Famously, Altman was rather quickly reinstated, to continue leading OpenAI into the future, while writing some pretty cogent essays on AI which I've covered recently. As for the connection between Musk and Murati, since she also worked at Tesla, I'll cite another Inc. piece from last June titled: 'Open AI's Pushback of Elon Musk's Criticism Shows How to Defend Your Company Reputation.' 'Mira Murati, OpenAI's chief technology officer, knows more than Musk does about how her company's systems work,' wrote tech reporter Kit Eaton. 'Using carefully chosen words in a speech, she made that very clear.' I thought Eaton's characterization of Musk's challenge, and the resulting pushback, was pretty well written, even though it sounds a little sensationalized at the end: 'Normally bearish on AI tech, Musk swore he'd ban Apple devices from his companies if OpenAI tech was integrated at a deep level, and even warned the public in an X posting he thought Apple was 'selling you down the river' with 'creepy spyware.' OpenAI's CTO has now addressed Musk's outrage, and her words put Musk in his place.' Anyway, this serves to illustrate how, at the helm, Murati had to address Musk's input. Aside from that, there don't seem to be a lot of public examples of anything significantly interesting happening between the two prominent tech people (Murati and Musk, that is.) Ok, enough tea. But again, the reason that people will be looking into Mira Murati goes as follows: 1. Thinking Labs will be a big player. 2. There's a dearth of public information about Thinking Labs and what it is doing. 3. Our ability to contextualize rests with Murati's past job experience as a pro at both rival firms, with two tech moguls who later had words. Why Does This Matter So Much? Not to put too fine a point on it, but the reason that tech news will be covering this in the first place is that today is the era of microservices, and of services in general. In another post, I covered how my friend and colleague Dave Blundin was talking about digital services needing technical support, with the promise of an AI Internet evolving. He specifically mentioned CRM, as an example. That made sense to me: too many vendors are selling 'Cadillac' CRM systems that are not sufficiently customized to what the firm needs. If this customization can be automated – the result is anybody's guess, really. That said, I believe that Thinking Labs is probably going to be on the vanguard along with its rivals in offering the hundreds of thousands of companies across the U.S. tools to compete in today's business world, which now often seems to have been injected with AI adrenaline. And we're seeing a kind of all-or-nothing consolidation playing out across the tech industry: a few special firms (like OpenAI) are going to reap the fruits of the universal move toward AI-native systems. Keep an eye out for Thinking Labs and other dark horse companies as the race continues.
Geeky Gadgets
3 days ago
- Business
- Geeky Gadgets
Learn LangChain Agents v0.3 : Full 2025 Starter Guide
What if you could build an AI system that not only understands your needs but also intelligently decides how to act on them? Imagine a virtual assistant that doesn't just answer questions but seamlessly integrates with tools, APIs, and workflows to solve complex problems in real time. Enter LangChain agents—an evolving cornerstone of AI development. By 2025, these agents have redefined how we approach automation, decision-making, and natural language processing. With the release of version 0.3, LangChain has taken a bold step forward, offering developers unprecedented flexibility and power to craft intelligent systems that adapt to diverse industries and use cases. Whether you're an AI enthusiast or a seasoned developer, understanding the mechanics of LangChain agents could be your gateway to building the next generation of AI-driven solutions. In this comprehensive tutorial created by James Briggs, you'll uncover the inner workings of LangChain agents, from their modular architecture to their ability to integrate external APIs and tools. We'll explore how these agents use advanced language models (LLMs) and conversational memory to execute tasks with precision and adaptability. Along the way, you'll learn how to design effective prompts, optimize workflows, and address common challenges like tool accuracy and iterative decision-making. But the true magic lies in their real-world applications—think automated data analysis, context-aware customer support, and dynamic workflow automation. By the end, you'll not only grasp the potential of LangChain agents but also gain actionable insights to harness their power for your unique projects. The question is: how will you shape the future of AI with these tools at your fingertips? LangChain Agents Overview What Are LangChain Agents? LangChain agents are specialized AI components designed to perform tasks intelligently by using the capabilities of LLMs. Acting as decision-makers, these agents interpret user inputs, determine the appropriate actions, and deliver precise outputs. Their ability to integrate tools and external resources makes them invaluable for applications such as customer support, data analysis, and workflow automation. By combining reasoning, natural language understanding, and external integrations, LangChain agents provide a flexible framework for solving complex problems. Their modular design allows developers to customize and scale solutions efficiently, making sure adaptability to various industries and use cases. Key Components of LangChain Agents To fully grasp the potential of LangChain agents, it is essential to understand their core components and how these elements interact: Language Models (LLMs): These form the foundation of LangChain agents, allowing reasoning, natural language understanding, and contextual interpretation. These form the foundation of LangChain agents, allowing reasoning, natural language understanding, and contextual interpretation. Tools: Predefined functions or logic that extend the capabilities of LLMs, such as performing calculations, retrieving data, or executing specific tasks. Predefined functions or logic that extend the capabilities of LLMs, such as performing calculations, retrieving data, or executing specific tasks. Agent Executors: These manage decision-making processes, tool execution, and iterative workflows, making sure tasks are completed efficiently and accurately. These manage decision-making processes, tool execution, and iterative workflows, making sure tasks are completed efficiently and accurately. Conversational Memory: Mechanisms that retain context across interactions, allowing agents to provide consistent and relevant responses. Mechanisms that retain context across interactions, allowing agents to provide consistent and relevant responses. External APIs: Integrations that enable agents to access real-time data or external services, such as search engines, weather updates, or financial information. Each of these components plays a critical role in allowing LangChain agents to function effectively, offering a robust framework for building intelligent systems. Using LangChain Agents in 2025 Watch this video on YouTube. Below are more guides on LangChain Agents from our extensive range of articles. Enhancing Agent Functionality with Tools Tools are integral to the functionality of LangChain agents, allowing them to perform a wide range of tasks with precision. These predefined functions can handle both simple and complex operations, significantly expanding the agent's capabilities. For example: Basic Tools: Handle straightforward tasks such as arithmetic operations, string manipulation, or data formatting. Handle straightforward tasks such as arithmetic operations, string manipulation, or data formatting. Advanced Tools: Enable complex functionalities like fetching real-time weather data, conducting web searches, or analyzing datasets. When designing tools, it is crucial to prioritize clarity and usability. Clear parameter names, type annotations, and comprehensive documentation ensure seamless integration with agents, reducing the likelihood of errors during execution. This structured approach enhances the reliability and efficiency of LangChain agents in real-world applications. How Agents Execute Tasks and Make Decisions LangChain agents rely on agent executors to manage task execution and decision-making processes. These executors determine the appropriate tools to use, the sequence of operations, and how to handle intermediate steps. For instance: When performing a multi-step calculation, the executor ensures each step is executed in the correct order, with results aggregated accurately. Conversational memory enables agents to recall previous interactions, allowing for personalized and context-aware responses. This structured execution framework ensures that LangChain agents can handle complex workflows while maintaining accuracy and efficiency. By combining logical decision-making with advanced language capabilities, agents can address diverse challenges effectively. Real-World Applications of LangChain Agents LangChain agents have proven their versatility across a wide range of practical applications. Their ability to integrate tools, conversational memory, and external APIs makes them suitable for various industries and use cases, including: Data Analysis: Automating calculations, generating insights from datasets, and presenting actionable recommendations. Automating calculations, generating insights from datasets, and presenting actionable recommendations. Customer Support: Delivering context-aware responses by recalling user preferences, past queries, and interaction history. Delivering context-aware responses by recalling user preferences, past queries, and interaction history. Workflow Automation: Managing multi-step processes such as scheduling, report generation, or task prioritization. By adapting to specific requirements, LangChain agents provide efficient and intelligent solutions that streamline operations and enhance user experiences. Integrating External APIs for Expanded Capabilities External APIs play a pivotal role in enhancing the functionality of LangChain agents by providing access to real-time data and services. These integrations enable agents to deliver dynamic and contextually relevant outputs. Examples include: SERP API: Assists web searches, retrieves location-specific information, and queries the current date and time. Assists web searches, retrieves location-specific information, and queries the current date and time. Custom APIs: Address unique requirements, such as fetching stock prices, monitoring IoT devices, or accessing proprietary databases. By using external APIs, LangChain agents can go beyond static responses, offering adaptable and intelligent solutions tailored to evolving user needs. Designing Effective Prompts Prompt design is a critical aspect of guiding agent behavior and making sure accurate outputs. A well-crafted prompt should include placeholders for key elements such as: Chat History: Provides context from previous interactions, allowing continuity and relevance in responses. Provides context from previous interactions, allowing continuity and relevance in responses. Agent Scratchpad: Tracks intermediate steps during complex tasks, making sure logical progression and accuracy. LangChain offers pre-built templates to simplify prompt creation. However, for specialized applications, custom prompts can be designed to meet specific requirements. This flexibility allows developers to optimize agent performance for diverse scenarios. Challenges and Best Practices While LangChain agents offer powerful capabilities, they also present certain challenges. Addressing these effectively is essential for optimizing performance and reliability. Key considerations include: Tool Accuracy: Making sure tools are used correctly and computations are performed in the proper sequence. Making sure tools are used correctly and computations are performed in the proper sequence. Prompt Engineering: Crafting clear and precise prompts to minimize errors and ambiguities in agent responses. Crafting clear and precise prompts to minimize errors and ambiguities in agent responses. Iterative Decision-Making: Managing multi-step processes efficiently to avoid inefficiencies or incorrect outputs. By adhering to best practices and continuously refining agent design, developers can maximize the potential of LangChain agents, making sure robust and reliable performance in real-world applications. Next Steps This guide has provided an in-depth overview of LangChain agents, including their components, execution logic, and practical applications. To further enhance your understanding and capabilities, consider exploring advanced topics such as: Parallel and sequential tool execution for optimized workflows. Custom tool development to address specialized tasks and requirements. Strategies for improving agent performance and scalability. By building on these foundational concepts, you can unlock the full potential of LangChain agents, creating innovative AI systems that address complex challenges and deliver impactful solutions. Media Credit: James Briggs Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
