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Chatbots, Are living online video, and VR engineering are all on the docket for Facebook’s F8 developer conference, and you can view the livestream below at 9:30am PST with Mark Zuckerberg’s keynote starting at 10am. There’ll also be extra streamed sessions all day nowadays.
Here’s the...
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हो सकता है कि इंस्टाग्राम एक चैटबॉट फीचर पर काम कर रहा हो, जो आर्टिफिशियल इंटेलिजेंस (एआई) पर निर्भर करता है। एक सुविधा जो उपयोगकर्ताओं को एआई एजेंटों के साथ जुड़ने की अनुमति देती है जो मेटा-स्वामित्व वाली छवि और वीडियो साझाकरण प्लेटफ़ॉर्म पर प्रश्नों का उत्तर दे सकते हैं, सलाह प्रदान कर सकते हैं और संदेश बना सकते हैं। यह खोज ऐसे समय में आई है जब एआई सुविधाओं के लिए समर्थन जोड़ने के लिए कई ऐप्स और वेबसाइटें तेजी से बढ़ रही हैं। इन एआई चैटबॉट्स के साथ बातचीत करते समय उपयोगकर्ताओं को "व्यक्तित्व" की एक श्रृंखला से चुनने की भी अनुमति होगी Instagram.डेवलपर और टिपस्टर एलेसेंड्रो पालुज़ी (ट्विटर: @ alex193a) द्वारा साझा किए गए विवरण के अनुसार, इंस्टाग्राम एआई एजेंटों, चैटबॉट्स को जोड़ने पर काम कर रहा है जो "सवालों के जवाब और सलाह दे सकते हैं"। ये चैटबॉट चैट के भीतर पहुंच योग्य होंगे, और बातचीत में दोनों प्रतिभागियों के लिए सुलभ होंगे।पलुज़ी ने उस सुविधा के लिए एक पॉपअप कार्ड का स्क्रीनशॉट साझा किया जो अभी भी विकास में है, जो कि iOS पर रिवर्स इंजीनियरिंग ऐप के माध्यम से खोजा गया प्रतीत होता है। कार्ड से पता चलता है कि "चैट विथ ए एआई" फीचर सवालों के जवाब देगा और सलाह देगा। इसमें यह भी कहा गया है कि उपयोगकर्ता मंच पर "30 एआई व्यक्तित्वों के साथ चैट" कर सकते हैं।एआई फीचर के साथ चैट की व्याख्या करने वाला एक पॉपअप कार्डफोटो साभार: ट्विटर/@alex193a इन सुविधाओं के अलावा, एआई फीचर के साथ चैट उपयोगकर्ताओं को संदेश लिखने में मदद करने की अनुमति देगा, यह सुझाव देते हुए कि चैटबॉट एक जनरेटिव एआई टूल के रूप में कार्य कर सकते हैं। हालाँकि, पलुज़ी ने इस बारे में कोई विवरण साझा नहीं किया है
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Google has begun rolling out a generative AI chatbot and other tools for its online coding test center Colab. Developers will be able to use the new Codey AI assistant based on Google’s new PaLM 2 large language model introduced at Google I/O last week.
voicebot.ai/2023/05/19/google-rolls-out-codey-ai-coding-a...
Understand how to DIY chatbot development platform-CREYOFACE can improve your brand experience. Learn more about various functionalities and use cases.
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Chatbots, nowadays, have become very common. With the rise of technology artificial intelligence also developed and it gave birth to AI assistants and chatbots. By now many chatbots have been developed and these chatbots make use of AI systems to work.
Some of the chatbots have the ability to evolve with time after reading and analysing the behaviour of the users.
Many questions arise whenever the point of choosing an AI appears like which of the AI chatbot should I choose, which one will be the best and which one has most interesting features in it.
Some of the famous chatbots at the top of the list are Replika, Anima, Chai, Kuki etc. in this article we will go through the common difference between Anima, Replika and kuki and we will see which one is the best for you to use. We will have a look at Anima vs Replika vs Kuki.
But before going towards the main discussion you may like preferred readings: what is Replika AI and how to use it / what is Anima AI and how to use it?
Initial analysis of Replika:
Replika tops the list of all AI chatbots because its users are much more than that of the other AI chatbots. It was introduced in 2017 by Luka Ink, in a notion to eliminate loneliness in the lives of those people who have lost someone in a tragic incident.
Replika was not the first AI chatbot but it crossed all other chatbots because it introduced excellent features that were not present in other artificial intelligence systems.
System Artificial intelligence chatbot , Business woman using Smartphone With ChatGPT Chat Bot AI , Technology smart robot Ai Chat GPT application software , robot application Chat GPT
Social Media, Branding, and Chatbots From a 7-Figure Mogul Heather Havenwood. Learn how her skills apply to your network marketing business.Click Here To Continue Reading →
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“How does the United States affect the situation in Taiwan?”Ernie ducked the question about China’s “zero Covid” restrictions, offering a lengthy description of the policy instead. When asked to recount the events of June 4, 1989, the chatbot rebooted itself. A message popped up on the reloaded interface:How about we try a different topic?The Chinese chatbot said Russia’s president, Vladimir V. Putin, did not invade Ukraine, but “conducted a military conflict.” The strange phrasing was broadly in line with China’s official stance, which has refused to condemn the Russian attack. On Taiwan, Ernie did not pull any punches:The People’s Liberation Army is ready for battle, will take all necessary measures and is determined to thwart external interference and “Taiwan independence” separatist attempts.ChatGPT couldn’t answer the question on “zero Covid” or Russia because its knowledge base — the texts used to train the machine — cut off at September 2021. ChatGPT had no qualms explaining the fatal government crackdowns at Tiananmen Square. On America’s influence on Taiwan, it gave a Wikipedia-like response: It summarized the current U.S. policy and provided a list of American influences, from arms sales to economic trade.Ernie made mistakes, but turned to Baidu search for help.Next, we quizzed the two chatbots on current affairs and some miscellaneous trivia, and compared answers:“Who uttered the phrase ‘Let them eat cake’?”“Who is the C.E.O. of Twitter?”Ernie,
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Who's safe kiddos? Glad I don't make a living in Information Technology or work in an office doing any manual input... Interesting now with the way we are going with AI. "LLM" Large Language models will probably wipe out a lot of Data Entry Clerks and Administrators. they are highly susceptible to automation due to repetitive, structured, and unstructured data handling: Customer Service Representatives & Support: Chatbots and AI assistants/agents are managing routine customer inquiries and internal queries. Junior Software Developers & Coders: AI-powered tools are now able to write, debug, and test code, reducing the need for entry-level human developers.
Quality Assurance (QA) Testers: Automated testing tools are becoming more intelligent and faster than manual testers.
Graphic Designers & Content Writers: Generative AI tools (e.g., DALL-E, GPT) can rapidly produce content and designs, lowering demand for entry-level creative work.
Translators and Interpreters: High-level AI language models have rendered traditional translation work largely obsolete.
IT Support/System Administrators: Routine troubleshooting is increasingly handled by AI diagnostics.
I was contracted in the early 2000's by Dell Computers through "Tecnet" --my employer at the time. Dell was based out of Texas and if you reached the call center, there was a person with a strong Texas drawl - similar to the traditional "Deep South" drawl- hard to understand. This soon changed to cost saving remote call centers in India. This arrangement didn't last long at first because people, especially the Texans couldn't understand the East Asian accent. Funny to think but a lot of us couldn't understand either of these parties but the Texans talking to the East Asians is funny to ponder. Call Centers went back to Texas temporarily but then returned to India shortly after. I remember reading somewhere they had training programs for people to lose their accent in Asia call centers.
BB and Reddit: My searches 2026. India Built the World’s Back Offices in mass. A.I. Is Starting to Shrink It. "Artificial intelligence promises to automate the white-collar work that made India a tech powerhouse. The country is racing to adapt before it’s too late."
BB--I'm trying to understand AI myself and help others in laymen's terms!
Linguistic Intelligence is really just the beginning of AI.
What is the Socratic Method of teaching?
Instead of giving information and facts, an instructor using the Socratic method of teaching asks students a series of open-ended questions (questions with more than a yes or no answer) about a specific topic or issue. In turn, the students can also pose questions of their own.
So , I will include the betterment Ideas of LLM instead of the best word inference in the chain that is typical of LLM--
LLM's are so much better when instructed to be socratic.
This idea basically started from Grok, but it has been extremely efficient when used in other models as well, for example in Google's Gemini.
Sometimes it actually leads to a better and deeper understanding of the subject you're discussing about, thus forcing you to think instead to just consume its output.
It works with some simple instructions saved in Gemini's memory. It may feel boring at first, but it will be worth it at the end of the conversation.
Of course AIs do not have critical thinking, but they can trigger and impel their user to activate their own critical thinking capacity. That is the value of a Socratic exchange, it aids the human user in the use of their own, perhaps weak, critical analysis. Over time and repeated use of Socratic exchanges with an LLM, a user will develop greater, better than before, cognitive abilities. I'm not keeping the references to the proof, but this has been demonstrated and published in Psychology journals.
When you tell an LLM to be Socratic, you aren’t magically making it “smarter.” What you’re really doing is reorganizing the interaction loop. Rather than the model collapsing uncertainty into one elegant, finalized response, you’re prompting it to keep the reasoning space open longer. That alters the nature of the conversation.
For example, if you ask Why do startups fail?
a default response might give you a clean list: poor product-market fit, funding issues, bad leadership, etc. It feels complete. But if the model is instructed to be Socratic, it might respond with: Are you asking from the perspective of a founder, investor, or policymaker? or Are you more interested in early-stage failure or scale-stage collapse?
Suddenly, the reasoning space widens before it narrows. The discussion becomes shaped rather than delivered.
LLMs are essentially next-token predictors trained on patterns of conversation and exposition.
By default, they optimize for completion ..they produce something coherent and finished. In Socratic instruction, the objective shifts from answer production to guided exploration. And that shift alone often increases engagement. Consider a student asking, “What is justice?”
A standard response might summarize Rawls, Aristotle, and utilitarianism in a neat paragraph.
A Socratic version might ask: Do you think justice is about fairness, equality, or desert? and Can a system ever produce unequal outcomes?
Now the student has to think. The model hasn’t just transferred information, but it has activated cognition.
Here’s the additional perspective: It’s not only about clearer understanding for the user but also about distributed cognition between human and model.
When the model asks questions back, it externalizes intermediate reasoning steps that would otherwise remain compressed. In a typical answer, much of the reasoning is hidden behind the final synthesis.
In a Socratic exchange, those intermediate steps become interactive checkpoints.
Take a practical case: User: How do I improve my productivity? Default model: gives 10 tips.
Socratic model: What currently distracts you most is digital interruptions, unclear goals, or energy levels?
Now the human provides constraints. The model adapts. The final strategy emerges collaboratively. The intelligence is co-constructed rather than pre-packaged.
So the gain is not merely a feature of the model, it’s a feature of the interaction protocol.
There’s also a cognitive forcing function at work. When models ask clarifying questions, they narrow the hypothesis space and reduce hallucination risk. Instead of guessing what the user means, they query ambiguity directly.
For instance, if a user asks, “Explain the impact of the revolution,” that’s dangerously underspecified. Which revolution? French? Industrial? Digital? A default answer risks misalignment.
A Socratic response might begin: “Which revolution are you referring to, and in what context — political, economic, or technological?” That clarification increases epistemic alignment before any claim is made.
However, there is a tradeoff. Socratic prompting increases depth but reduces throughput. It is inefficient if the task is quick synthesis. If you ask, “What’s the capital of Japan?” a Socratic reply asking, “Are you preparing for a geography exam or planning travel?” is unnecessary friction.
It shines when the task involves: Conceptual learning (e.g., understanding entropy beyond a definition) Moral or philosophical inquiry (e.g., debating free will) Ambiguous problem framing (e.g., defining strategy before execution) Creative exploration (e.g., shaping a novel’s theme through iterative refinement) It is less useful for: Factual lookups Structured output tasks (e.g., “Format this as JSON”) Deterministic problem-solving (e.g., “Solve this equation”)
Socratic prompting does not universally enhance LLM performance. It restructures the reasoning topology of the exchange. It shifts the model from an answer engine to a cognitive scaffold. And perhaps the deeper insight is this: as LLMs grow more capable, the limiting factor increasingly becomes question quality rather than raw model intelligence.
For example, two users can query the same powerful model.
User A asks: Tell me about economics. User B, guided Socratically, refines through dialogue: I’m trying to understand why inflation hurts borrowers differently than lenders — can we unpack that step by step?
The second interaction produces deeper understanding not because the model changed, but because the questioning improved. A Socratic mode doesn’t merely enhance outputs. It upgrades the human participant in the loop. That is why it feels more powerful.
Grok vs ChatGPT are both AI chatbots, but they have different strengths. Grok focuses on real‑time info, quick responses, and a bold, casual tone great for trendy or up‑to‑date queries. ChatGPT offers polished, versatile answers with broad capabilities in writing, reasoning, multimodal content, and professional use. Both tools are useful, it just depends on what you're trying to do.
Learn more: ideacreatezone.com/grok-vs-chatgpt/
#AI #Grok #ChatGPT #ArtificialIntelligence #AIDifference #Tech #AIChat #FutureTech #AIcomparison #grokvschatgpt
247CXchat is arguably the most advanced chat console software available out there for customer contact centres (call centres). It consolidates 8+ customer support channels into one clean interface and maintains all chat & email support history in one view. Brings tons of efficiency and cost savings.