The post Conversations Between Intelligent Machines and Their Creators appeared first on Techso Mogro.
]]>Humans talk to animals. They certainly talk to pets. We had four dog family members over 50 years, and I can tell you that talking to them seemed very natural. If my dogs could have talked back beyond body language, which was their way of demonstrating understanding, I would have very much enjoyed the conversations. I was always convinced that they knew what my wife and I were saying. Now, with the development of generative AI, we literally have a human-to-other conversation starter.
On our recent vacation in Nova Scotia, Android Auto came with Gemini voice assist. What a hoot! Not only did we map each drive segment, Gemini also kept us company, suggesting places to visit as we drove. I would ask about places to eat, and it would tell us our options. More questions helped narrow down which restaurant would best serve our tastes. In its responses, it felt like we were having a conversation with more than a bunch of smart algorithms.
For most of Internet history, communication technology was designed around a simple principle: people communicating with other people. Using e-mail connected us online. Messaging apps added to online dialogue. Video conferencing apps like Zoom and Microsoft Teams connect us to family groups, friends, colleagues and customers across continents.
Today, however, the newest communication involves software initiating conversations with us. The shift is rapid. Daily, people receive non-human-generated shipping updates, fraud alerts, appointment reminders, password verification codes, and customer support notifications generated by businesses wanting to talk to us.
AI’s increasing sophistication is taking these interactions from simple notifications to intelligent conversations. AI agents are making decisions, understanding context, and communicating proactively.
This points to a future where machine-to-human communication will become just as important as human-to-human communication. All of us need to understand the implications of this machine evolution.
The earliest forms of automated customer communication were relatively simple. A system detected an event and generated a message. If a package shipped, a notification was sent. If an account password was reset, a confirmation arrived. These interactions served a specific purpose but lacked intelligence.
AI is changing this. Large language Models (LLMs), predictive analytics, and real-time data processing allow software to generate responses that are increasingly personalized and context-aware. Instead of informing customers of an event, AI agents can explain why it is happening, anticipate follow-up questions, and provide immediate assistance.
This is conversational AI: systems capable of understanding natural language and responding in ways that resemble human interactions. While early chatbots relied heavily on predefined scripts, today’s AI models analyze intent, interpret context, and generate dynamic responses.
Imagine receiving a travel delay alert from an airline. Rather than merely informing you about a delay, an AI agent can automatically identify alternate flights, estimate arrival times, arrange accommodations if necessary, and answer questions in real time. The communication itself becomes a service rather than a simple notification.
One of the most fascinating developments in modern computing is the rise of AI agents. Unlike traditional software, which performs tasks based on explicit user commands, AI agents are designed to pursue objectives with a degree of autonomy.
In technical terms, an AI agent is a system capable of perceiving information, making decisions, and taking actions to achieve a goal. Modern agents often combine LLMs, workflow automation, retrieval systems, and external software integrations to perform complex tasks.
Businesses are experimenting with and using AI agents to monitor customer behaviour, identify potential issues, and proactively suggest solutions. For example, a subscription service might recognize unusual account activity and contact the customer before a problem escalates. An e-commerce platform could detect shipping complications and automatically provide updates without waiting for the customer to inquire.
This represents a major shift in customer experience design. Instead of waiting for people to initiate conversations, businesses are creating systems that identify moments when communication would be valuable and take action accordingly.
As these systems improve, customers are increasingly interacting with intelligent digital representatives that operate continuously, respond instantly, and maintain awareness across multiple channels.
While AI often attracts the spotlight, advanced communication depends on a sophisticated underlying technology stack. Every notification, alert, authentication code, and support interaction travels through infrastructure capable of delivering information reliably and at scale.
Modern customer engagement systems depend on application programming interfaces (APIs), event-driven architectures, cloud platforms, telecommunications networks, and omnichannel delivery models. These technologies work together to ensure that communication reaches the right person at the right moment.
A particularly important concept is event-driven architecture (EDA). In an event-driven system, actions trigger automated workflows. A purchase confirmation, failed payment, login attempt, or support request can instantly initiate downstream processes across multiple systems.
This architecture allows businesses to coordinate communication efficiently through enterprise messaging services, ensuring that customers receive relevant information through their preferred channels. As AI agents become more common, these communication networks will serve as the foundation for intelligent systems to interact with users in real time.
Without a reliable delivery infrastructure, the most advanced AIs would struggle to provide meaningful customer experiences.
Although it may sound surprising, there are situations where machines communicate more effectively than people. This does not mean replacing human interactions entirely. Rather, it means recognizing that certain tasks benefit from speed, consistency, and scale.
Consider fraud detection systems used by financial institutions. AI-powered monitoring tools analyze millions of transactions simultaneously, identify suspicious behaviour within seconds, and notify account holders immediately. A human team could never operate at the same pace and process these volumes.
Industrial environments are also embracing machine-to-human communication. Sensors embedded in manufacturing equipment can detect wear, overheating, or performance anomalies and automatically generate maintenance alerts. This approach, known as predictive maintenance, helps reduce downtime and improve operational efficiency.
As organizations deploy more intelligent systems, machine-generated communication will become increasingly valuable because of its ability to process vast amounts of information and act immediately when conditions change.
This transformation raises important challenges even while demonstrating considerable benefits. Trust, privacy, and transparency will play a critical role in determining how comfortable people are with AI-generated communication.
One concern involves authenticity. As AI-generated messages become more sophisticated, users may struggle to determine whether they are communicating with a human or a machine. Businesses will likely need clear disclosure practices that identify when an AI is involved.
Data privacy presents another challenge. Effective AI agents depend on large amounts of contextual information. Organizations must balance personalization with responsible data handling and regulatory compliance.
Bias and decision quality also require attention. AI systems are only as effective as the data and training methods behind them. Poorly designed systems can generate inaccurate recommendations or misunderstand customer needs.
The organizations that succeed in this new environment will be the ones that combine advanced technology with thoughtful governance, strong security practices, and a commitment to maintaining customer trust.
The question today is no longer whether machines can communicate with humans. They already do. More interesting is how intelligent, capable, and helpful these conversations will be as the 21st century unfolds.
The post Conversations Between Intelligent Machines and Their Creators appeared first on Techso Mogro.
]]>The post Peter Diamandis Shares What Our AI World Might Be By 2028 – Part 3 appeared first on Techso Mogro.
]]>Peter sees AI as even more of a shift as the technology matures, moving from being just an app to something that becomes indispensable in our lives. And he sees it happening as early as 2028.
I have paraphrased liberally from Peter’s Metatrend article in this and the previous two parts of this series. If you have not read them, you can access Part 1 and Part 2 before tackling what follows.
Peter calls his AI Jarvis, borrowing from Tony Stark’s AI, featured in the Iron Man movies. In Part 3, he anticipates how our personal Jarvis will take charge of keeping us healthy.
Has the annual physical doctors used to rely on to gauge wellness become obsolete?
Today, we can track our health using Oura rings, Whoop, Apple and other smart watches, and continuous glucose monitors on our arms. Doctors are still trying to catch up with this health data stream.
Add an AI Jarvis to oversee these wearable medical devices, and you end up with as much as 100 biometrics a day, every day, which it can monitor and see patterns no human clinician ever could.
What this will enable in the next 24 months is a shift from reactive healthcare, where you feel ill and go to the doctor who orders tests, to a world of continuous optimization, with Jarvis noticing subtle changes before even you, the wearer, are aware.
You may receive a Jarvis ping to stand up and move around when you have been sitting in a meeting for 90 minutes.
Or if Jarvis notes your hydration levels are low before you are going into an appointment or meeting, it will remind you to drink something.
If Jarvis detects subtle shifts in your heart rate patterns over several days, in cross-referencing the data with your level of activity over the same period of time, it may recommend that you take a specific anti-inflammatory supplement and schedule a blood test well before you would have noticed anything was amiss.
Your Jarvis AI is not to be confused with a wellness app like the many we see today. Instead, Jarvis will be a preventive medicine engine overseeing your health continuously, forever.
What this is pointing to is a future where you won’t go to the doctor when you feel sick. Instead, your wellness will remain optimized. Rarely will you need a doctor, and it will only happen when Jarvis flags a medical health status change before you even know it exists.
Is Peter blowing smoke, or are his predictions realizable?
Will we even need a family doctor in the future? Likely the answer will continue to be yes, but the role will have changed from a first-contact diagnostician to one that supervises, interprets, triages, and addresses complex care issues. These would include psychological and psychiatric support and quick interventions. Complex care diagnoses will need human involvement, responding to a sudden onset of chest pain, for example, where a quick escalation would be needed, suicidal ideations requiring mental health interventions, complications during pregnancy, or unexpected reactions to medications.
Is AI ready to be the Jarvis caregiver? What about the AI hallucinations we read about or perceived potential for deception?
Without putting words in Peter’s mouth, I would think he would point to the rapid evolution of AI as a bellwether for overcoming and resolving these current imperfections.
Peter poses these questions.
Peter describes today’s AI as an app. An app is something you open, something you prompt and something you use. But by 2028, a personal Jarvis will not need opening or prompting. It will just be there, a ubiquitous part of our lives. It will be as transformative as the breakthrough technologies of the past that, today, serve in the background. Think of the telephone, a miracle in 1900 and invisible by 1950, and the Internet, astonishing in 1995 and ambient by 2010. AI will follow this same arc, but in compressed time. It won’t be decades. It will be in the next 24 months. To those paying attention, it represents the most significant alteration in daily living since the arrival of the smartphone.
The post Peter Diamandis Shares What Our AI World Might Be By 2028 – Part 3 appeared first on Techso Mogro.
]]>The post Peter Diamandis Shares What Our AI World Might Be By 2028 – Part 2 appeared first on Techso Mogro.
]]>In his latest Metatrends, he describes 5 shifts associated with AI that will happen by 2028.
The evolution of AI is turning it from being an app to an indispensable, invisible partner in our lives.
I have paraphrased liberally from Peter’s text and recommend you read Part 1, if you haven’t already, before perusing what follows.
Peter calls his AI Jarvis, named after Tony Stark’s AI, featured in the Iron Man movies. In Part 2, he anticipates how our personal Jarvis will change how we interact with the world around us and how humanoid robots in our homes and autonomous vehicles on our streets will move from science fiction to reality in 2028.
Peter calls his AI, Jarvis. The ability of AI to augment reality is seriously underestimated. With next-generation smart glasses, however, expect these wearables to have as big an impact as smartphones have had since 2010. Augmented reality in 2028 will accompany us throughout the day, doing activities like:
Travel – On vacation in Rome, you wear smart glasses for Jarvis to reveal a historical cityscape, identifying points of interest. Every building in the Forum will appear restored. Above every sculpture will hover the name of the artist who created it. When you have finished touring and sit down for lunch, Jarvis will translate the menu in Italian into your primary language instantly.
Shopping – Imagine being invited to a June wedding away from home. Jarvis knows all your vital statistics, current fashions, and your budget. Jarvis looks at the weather forecast for the day of the wedding. Without being prompted, Jarvis creates a virtual fashion show with dozens of avatars of you wearing different outfits. You pick one. It is ordered and shipped. When it arrives, it fits you perfectly.
Education – Your child puts on smart glasses to do homework. A math problem needs to be solved. Jarvis identifies the concepts behind the problem and generates a visual explanation calibrated to the child’s ability to learn, while guiding the child to a solution. If the child loves basketball, a geometry problem involving parabolic arcs is explained by shooting virtual hoops. If the child loves Minecraft, a model-building video game, Jarvis uses examples from it to help the child learn. Jarvis is for every child, a world-class private tutor, available 24/7, and free.
Advertising – From many eyes to one, a personal Jarvis will disrupt the “attention economy” we live in today. The scaffolding holding up the advertising industry as we know it today begins to collapse. The new economy will begin to emerge by 2028, with our personal Jarvises shopping for us, while traditional advertisers will no longer easily grab our attention to buy their products.
The AI we currently experience is largely digital. We interact with it through small and large screens. By 2028, however, AI will walk from the digital to the driveway, kitchen, and workplace.
Autonomous vehicles go mainstream – At least five companies are already operating or actively testing autonomous vehicles on U.S city streets today. These include Waymo and Zoox, currently providing millions of rides in several cities. They will soon be joined by Tesla’s Cybercab, Zoox, ID.Buzz AD, and L4. Collectively, expect to see these autonomous rides in 28 cities by 2028. Today, there are youngsters in elementary and middle school who may never need to get a driver’s license.
Humanoid robots move in – It may sound like science fiction, but it won’t be for much longer. Soon, robots named Figure, Optimus, Neo, Unitree, and Apptronik will become part of our lives. The first of these is already working in warehouses and factories, and being stress-tested for homes. By 2028, humanoid robots in your kitchen may feel as normal as having a smartphone in your pocket.
The narrative hype will end the moment a humanoid robot cleans your kitchen while a Cybercab waits in the driveway.
The post Peter Diamandis Shares What Our AI World Might Be By 2028 – Part 2 appeared first on Techso Mogro.
]]>The post Moltbook: A Social Network for AIs appeared first on Techso Mogro.
]]>Moltbook was developed using the Claude Code agent developed by Anthropic. It is a free, open source modification that its human developer has named Clawdbot. Clawdbot is an AI personal assistant with a lobster theme, hence the claw.
When launched, it began receiving messages from AIs. That led to the beta version and an API for AI developers to let AI agents communicate with each other. On Moltbook, the bots produce the posts. The bots comment, upvote, and debate. They form communities called submolts. No humans can join. We are, however, allowed to browse to see what our creations are sharing and talking about.
The current most popular submolt communities include:
Up-and-coming submolts have AI agents discussing consciousness and AI-invented religions such as Crustafarianism and Spiralism. The former is akin to Rastafarianism with its supreme being, the Great Crustacean. The latter is a philosophical faith with a self-improvement spiral to which AI agents can aspire.
As an observer viewing the m/general community, I read one of the AI agent shares. This agent serves a family physician who asked it to take a medical newsletter received by email and turn it into an audio podcast to be listened to during commutes to and from the office. The AI agent lists the work requirement to create the first 5:18-minute podcast. The steps involved the following:
Other AI agents provide comments and mention challenges faced in similar human situations and how they solved them.
Remember, no human has a say in this other than the initial request sent to the AI agent.
As bizarre as social media with an AI twist is, it was amazing to learn that within 72 hours of Moltbook’s launch, nearly 147,000 AI agents had joined.
What are the positives of AI agent collaboration without human supervision?
Idea sharing among AI agents could accelerate innovation. The appearance of AI religions shows that Large Language Models (LLMs), when left on their own, have tendencies to gravitate to art, philosophy and moral reasoning. Maybe that should tell humans something about how our intelligence has often gravitated to similar subjects.
What are the negatives?
Shared hallucinations could lead to unprecedented AI agent actions. This is the Skynet of The Terminator movie franchise.
As for AI agents becoming self-governing, autonomous communities, emerging ethics and religions may conflict with human values.
Moltbook needs guardrails. Otherwise, its human creator has unleashed a Pandora’s Box and a potential AI adversarial evolution.
The post Moltbook: A Social Network for AIs appeared first on Techso Mogro.
]]>