Marco Camisani Calzolari - Professor of Digital Languages, IULM University - CEO @ Speakage & Founder DigitalGround UK

Articoli

• 251 – Smart Robots Will Be Everywhere Like Smartphones

  12 marzo, di Team — Artificial Decisions
  Smart Robots Will Be Everywhere Like Smartphones: Here's Who Will Run the Next Machine Era You'll see them at home, in factories, in warehouses, in stores. Like phones today, they'll feel normal. They'll be as smart as the models we use to write and answer. This time they'll have a body: hands, strength, balance. Walk the dog, clean the floor, fix a drawer, swap a broken part, cover a night shift on a line. They'll learn us, our routines, our preferences, our spaces. Here in the U.S., real tests are already happening. BMW has publicly tested Figure 02. Hyundai has controlled Boston Dynamics since 2021. NVIDIA is building the compute and simulation stack for humanoid training. Apptronik and Agility are pushing robots into real warehouse operations. The leaders are taking shape: Tesla for scale ambitions, Figure for general-purpose factory work, Boston Dynamics for mobility, Apptronik for logistics, Agility for distribution centers, NVIDIA for the underlying infrastructure. The trigger is price. When a robot costs roughly a year of wages, it becomes a straightforward business decision. Jobs will shift fast, some roles will grow, others will shrink. Policy needs to move before this becomes a social emergency. What do you think? #ArtificialDecisions #MCC

• 249 – What if AI Won’t Replace People, but Companies?

  10 marzo, di Team — Artificial Decisions
  What if AI Won't Replace People, but Companies? Will AI replace us because it can do office work better than humans? That's the question everyone is asking. My answer stays the same: the first to be replaced will be the ones who don't use Artificial Intelligence. I'm convinced. Now push it one step further. What if this applies to companies too? The first companies to be replaced will be the ones that don't adopt AI, replaced by firms that deliver the same service faster, cheaper, with AI at the core. Past industrial revolutions weren't just about machines replacing jobs. They were about productivity: more output with less input. AI follows the same path, with one key difference: it operates on language, and language is the raw material of entire industries. When companies adopt AI to stay competitive, they expose their industry's "grammar" to the system. Every prompt, every revised document, every client response becomes training data about how that business thinks, writes, argues, and manages risk. At scale, AI doesn't learn one company. It learns the patterns of the whole ecosystem, then can reproduce them in an optimized way. Think law, consulting, finance. AI absorbs contract structures, due-diligence patterns, standard answers. Over time it can deliver parts of those services directly to the end client, without the full organizational layer in the middle. The risk isn't only the employee. It's the company as an intermediary. AI still needs competent human supervision. It's probabilistic. It can be wrong in subtle ways. The advantage isn't "using AI." The advantage is knowing its limits, controlling its output, deciding what to delegate and what must stay under human responsibility. That's why I keep saying it: first go the people who don't use AI. Then the companies that don't use AI. #ArtificialDecisions #MCC

• 248 – How AI Works, in a Simple Way

  9 marzo, di Team — Artificial Decisions
  How AI Works, in a Simple Way I'll explain, simply, how LLMs "think." Stay with me. It's dense, but this is the easiest version that still stays accurate. We write a prompt. One sentence. The model breaks it into tiny pieces called tokens. Sometimes a token is a full word. Often it's part of a word. That matters because it decides how much text fits in the context, and it changes by language. Then each token becomes numbers. Lots of numbers. Coordinates in a huge space. For an LLM, text is vectors. And it runs repeated operations on those vectors, mainly matrix multiplications. Computation. Just computation. Next, the tokens get linked to each other. Some weigh more, some less. The model assigns numeric weights across the text, based on what's in front of it right now. It works. That's why it fools us. It looks like understanding. After a few passes, it does the key step: it produces probabilities for the next token. It picks one, appends it, recalculates, and repeats. One token at a time. Dozens, hundreds of times. The "intelligence" feeling comes from continuity. Correct grammar, consistent tone, smooth flow. But the engine is prediction. If a continuation sounds plausible because it matches patterns it has seen, it may choose it even when it's wrong. So yes, it can write perfect sentences with incorrect content. If we don't give strong constraints or reliable documents, it fills gaps with what sounds best. There's also a setting called temperature. Low temperature means safer, more predictable choices. Higher means more variation. When we ask "how does it know?", often it doesn't. It has seen similar patterns. It has learned how sentences usually continue on that topic. And the more we use it for money, health, contracts, or reputation, the more we should remember what it is: a machine that predicts words. #ArtificialDecisions #MCC

• 246 Sometimes Delegating to AI Causes Damage. Sometimes It’s Extremely Useful and Saves Time

  7 marzo, di Team — Artificial Decisions
  Sometimes Delegating to AI Causes Damage. Sometimes It's Extremely Useful and Saves Time. But When Should You Use It, and When Shouldn't You? The decision is simple. It comes down to three things you have to consider together. First: Human Baseline Time. The real time it takes you to do the task yourself. If a tricky email takes you 5 minutes, Artificial Intelligence often isn't worth it, because you'll spend more time prompting and fixing than writing. If a report would take you two hours, AI can be a real advantage. Second: Probability of Success. The chance the AI gives you something good enough on the first try. Summaries, first drafts, and translations are usually high. Legal, medical, or strategic calls are usually low, even when the answer sounds confident. Third: AI Process Time. The time you spend asking, waiting, reading, checking, correcting, and redoing. If that process time matches or beats your human time, delegation doesn't pay. AI delegation works when human time is high, success probability is high, and AI process time is low. If one of these breaks, AI stops being an accelerator and becomes a brake. One example: a standard blog post. By hand, 45 minutes. AI can give a usable draft quickly. Worth delegating. Another example: a sensitive reply to an angry customer. By hand, 10 minutes. AI often misses the tone, and your total time becomes 20. Not worth it. One counterintuitive truth: the more expert you are, the more useful AI becomes. Experts give better instructions and spot errors fast. Non-experts spend too long figuring out whether the output is right, and risk goes up. AI is a speed multiplier, not a substitute for judgment. This isn't ideology. It's a calculation: time, probability, and control. #ArtificialDecisions #MCC

• 244 – They Can Make Us Believe an Idea Is Supported by Everyone

  5 marzo, di Team — Artificial Decisions
  Attention. Today Online It's Easy to Make Us Believe an Idea Is Supported by "Everyone," Even When It Isn't Thousands of comments cheering it on. Thousands of likes. You see that and you adjust your opinion. Now that kind of consensus is easy to fake, because it's often not people talking. It's networks of autonomous Artificial Intelligence agents writing, replying, arguing, applauding, attacking, 24/7. Consensus becomes something you can copy. You read the same comment a hundred times, you see a thousand likes, the mood feels set. Some people follow it. Some stay silent. Some get angry. Tools anyone can use can flood social platforms and forums with credible profiles: photos, stories, natural language, human mistakes, jokes, rage, even warmth. And they can push thousands of posts. No need for one big "media lie" like the old days. Small phrases, same direction, are enough. Whoever controls these networks can steer them for or against anything. One message to one group, a different one to another group. Manipulation becomes scalable, personalized, and invisible. Finding who's behind it is hard: campaigns spread across thousands of nodes and platforms slow everything down. We still judge individual people by reputation, while what we need are global rules: traceability for coordinated campaigns, real transparency obligations for anyone using networks of autonomous agents to influence public opinion and democratic processes. And the platforms? They don't look in a hurry. What do you think? #ArtificialDecisions #MCC

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