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AI Isn’t Taking Your Job – It’s Taking Everyone’s
“Going forward, as the rug of new tool after tool is pulled out from under us, and the flow of profound new capabilities continues to pick up speed, it will reach a point where humans have no choice but to surrender. Where our ability to uniquely track, learn and use any given tool better than anyone else will be irrelevant, as new tools with new capabilities will shortly solve for and reproduce the effect of whatever it was you thought you brought to the equation in the first place. That’s in the design plan. It will learn and replace the unique value of your contribution and make that available to everyone else.”
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“When you’re doing something for the first time, you don’t know it’s going to work. You spend seven or eight years working on something, and then it’s copied. I have to be honest: the first thing I can think, all those weekends that I could have at home with my family but didn’t. I think it’s theft, and it’s lazy.”
— Jonathan Ive
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AU10TIX: FaaS Drives 100% Surge in Cyber Fraud Attacks
“2024 has been a game-changer in fraud prevention. FaaS has taken cybercrime to a new level, enabling coordinated mega attacks that now average over 8,000 incidents each. “By embracing smarter fraud prevention strategies and layered defences, businesses can get ahead of these threats and build stronger trust with their users,” says Dan Yerushalmi, CEO of AU10TIX.
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“Gates announced on Thursday that he now plans to distribute “virtually all” of his wealth — around $200 billion, he estimates — within the next 20 years, before shuttering the foundation on December 31, 2045.”
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Math teachers protest against calculator use 1966.
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Teach AI to Work Like a Member of Your Team
“And here’s where the real opportunity emerged: The work graph—which included two months of activity that was vetted and context-rich—could then be used to train the AI tool. Because it captured everything the team deemed important, it provided the AI tool with real-time, human-validated context, which enabled the tool begin working in a way that aligned with how the team actually worked. With this input, the AI tool was able to produce a significantly more complete first draft, reducing iterations and accelerating the path to a final, usable contract. This approach cut the team’s manual effort in drafting each contract by more than half. While they still reviewed and verified the AI-generated output, they required far fewer iterations and much less rework. As a result, the team’s overall throughput in generating contracts increased by nearly 30%.”
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Ai Content Disclosure Reasons
People are often encouraged—or even required—to state that an article, statement, or artwork was made by AI for reasons of transparency, ethics, and trust. Here are the main reasons:
- Transparency and Honesty: It helps readers or viewers understand who—or what—created the content. This prevents misleading audiences into thinking a human wrote or created something when it was generated by a machine.
- Accountability: When AI is involved, it’s important to clarify who is responsible for the output. This is especially true in areas like journalism, academic writing, or legal and medical information, where credibility matters.
- Informed Interpretation: Knowing that a piece was generated by AI can change how someone interprets it. For example, an AI-written poem may not carry the same emotional or autobiographical weight as one by a human.
- Ethical Considerations: Many industries are working to prevent misuse of AI-generated content (e.g., deepfakes, fake news). Disclosing AI authorship helps fight misinformation and supports ethical use of technology.
- Attribution and Intellectual Property: Clarifying the role of AI in a work can help with determining authorship rights and how a work can legally be used or sold.
By ChatGPT
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Steve Jobs talk at the 1983 International Design Conference in Aspen
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“Be nice to nerds. Chances are you’ll end up working for one.”
— Bill Gates
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“Thoughts on Flash” is an open letter published by Steve Jobs, co-founder and then-chief executive officer of Apple Inc., on April 29, 2010.
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How Ai works
AI, or artificial intelligence, works by using computer systems to simulate human-like thinking. Here’s a simple breakdown:
1. Learning from Data (Machine Learning)
AI learns patterns from large amounts of data. For example:
- If you feed it lots of pictures of cats and dogs, it learns what features make a cat or a dog.
- This process is called training a model.
2. Neural Networks
A common type of AI uses neural networks, inspired by the human brain.
- It has layers of artificial “neurons” that process information.
- Each layer extracts more complex features from the input (like shapes, colors, or sounds).
3. Decision Making
After training, the AI can:
- Recognize images (e.g. face recognition)
- Understand speech (e.g. virtual assistants)
- Predict outcomes (e.g. stock price trends)
- Generate content (like writing, art, or music)
4. Feedback & Improvement
AI can improve with more data and feedback—this is called reinforcement learning or fine-tuning.
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“What I told our company was that we were just going to invest our way through the downturn,” he said. “That we weren’t going to lay off people, that we’d taken a tremendous amount of effort to get them into Apple in the first place—the last thing we were going to do is lay them off.”