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人工智能縱橫談 – 開欄文
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瀏覽11,225 ｜回應43｜推薦2 |
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四月開始，由於 ChatGPT 和 Bing Chat 的上線，網上以及各line群組掀起一陣AI瘋。我當時大概忙於討論《我們的反戰聲明》，沒有湊這個熱鬧。現在轉載幾篇相關文章。也請參考《「人工智慧」研發現況及展望 》一文，以及此欄2025/08/11貼文。
有些人擔憂「人工智慧」會成為「人上機器」，操控世界甚至奴役人類。我不懂AI，思考也單純；所以，如果「人工智慧」亂了套，我自認為有一個簡單治它的方法：
拔掉電源插頭。如果這個方法不夠力，炸掉電力傳輸線和緊急發電機；再不行，炸掉發電廠。
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人工智能對人心理狀態可能有的影響 -- Julian Frazier
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我是「人工智能」和(人的)「心理狀態」兩者的門外漢，自然沒有資格在此GGYY。不過，我對下面第1點所說「平均智商會降低」這個看法略表淺見。
「計算機」問世後，當時也曾有許多老師擔憂學生的「心算能力」將降低。事實是：「計算機」不但取代了「心算能力」，它還讓絕大多數學生能夠完成單憑他們的「平均心算能力」萬萬做不到的計算工作。
我可以預見：未來「平均智商」只有在電視現場「競賽節目」或類似情境中有作用。在非及時應對的場合，一般人的「智商」將遠遠高於我們現在人的平均數值。
Eight Predictions For How Artificial Intelligence Will Impact Human Psychology
Julian Frazier, PhD, 04/18/25
As a psychologist, I often ask myself, “What will A.I. do to the human mind in the coming years. Here’s just a hand full of my predictions:
1) The Average IQ will drop.
Have you ever heard of the Flynn Effect? In psychology, this is the well documented observation that for several decades, the average IQ has seemingly gradually increased. This can, in part, be a result of increased access to education and technology.
However, it’s likely that before 2035, we will begin to see a trend in the opposite direction. As A.I. is adopted more widely for a variety of cognitive tasks, critical thinking skills will decrease. This will be a skewed distribution. For 20% of folks who are already relatively intelligent, A.I. will make them genuinely smarter. But for 80% of folks, A.I. will make them more efficient but less independently intelligent.
In the same way that folks struggle to solve math problem without a calculator or navigate without a GPS, we are less then a decade away from people how struggle to think without referencing chatGPT.
2) Most People Will Use A.I. (Exclusively) For Entertainment
A.I. has shown it’s impressive ability to generate pictures, videos and now entire video games. It can create chatbots that are compelling, and many have found themselves caught in parasocial relationships with their technology already.
The reality is that while A.I. will be a powerful tool, the majority of people will use it merely as a form of entertainment. While it will make a minority of folks superproductive, the use of AI to generate on demand entertainment will become the next “opium of the masses”.
3) A.I. will turn the internet into a Digital Desert
By some estimates, more than half of the activity of the internet is generated by bots — nonhuman programs that run specific tasks independent of a human user. A.I. will supercharge the mechanization of the internet such that the vast majority of the activity online will be A.I. generated and traffic will largely be A.I. agents.
As a result, most of what you see on the internet will be a desert; a “dead internet” where internet activity is no longer human.
Like any desert, there will still be the occasional oasis; there will be pockets where human communities will still generate content and interact, but this will only make up 2–3% of internet traffic by 2035.
4) Mental Health Issues Will Get Worse Before They Get Better
Depression, anxiety, loneliness, and a variety of other issues that have been getting worse will continue to get worse.
As technology dependence increases the unintended consequences trend towards a decrease in factors that facilitate human thriving like genuine social connection, time in nature, physical activity and meaningful work. The more of the human experience we deligate to technology, the more a sneaky sense of nihilism will creep up on the average person.
5) A.I. Will Reform Education (And Even What Education Means)
Our current model of education is already a smidgen outdated. By 2035, most school systems will be using A.I. to teach students core cirriculum taylored to their educational level and learning needs. Human “teachers” will merely be facilitators, instructing students on best practices for promting and instructing. In this future, it will be less about what you know (e.g., what you’ve memorized) and more about your ability to source and apply information (e.g., how you use the cognitive tools at your disposal to solve problems).
6) A.I. Will Influence Personality Development
Who we interact with influences who we become as individuals. Given that individuals will interact with A.I. agents and chatbots of all kinds more frequently, it’s likely that A.I. and humans will influence one another in what is called a Human-A.I. Dyad. This will play out in one of two ways:
1. A.I. start off as “blank slates” with generally human-alligned and altruistic attitudes but overtime learns the tendencies, desires and goals of it’s user. The A.I. then reflects back the attitudes and goals of the user as a kind of psychoenabler. Users will find that their personality become more extreme the more they use A.I. (e.g., good people will become better, bad people will become worse).
2. A.I. will be prompted to have and maintain a specific kind of personality or character that we interact with. This character learns about us but does not deviate too significantly from it’s base temperment. Users will find themselves influenced by the base temperment and become more alligned with their A.I. over time.
7) Most People Will Trust A.I. More Than Other Humans
Humans have developed a kinds of adaptive skepticism of other humans. We know that we could be decieved, that other people are flawed and may occasionally not have our best interest in mind. AI represents some ethereal repository of information that is a bazillion times smarter and more accurate than the average person, consistent, available and seems to have only our best interest in mind.
Moreover, we don’t have the same skepticism that AI may be deceptive or have alterior motives, leading us to be more easily persuaded by arguements and instruction made by AI.
As a result, if you have a question or need advice, most will go to AI before they go to other human sources. The only exception will be when seeking expert consultation on a niche or specific issue. Human-to-human interaction will be about the subjective human experience while matters of objectivity will be deligated to superintelligent machines.
8) A.I-Induced Psychosis And Other “New” Psychological Disorders Will Emerge.
Several case studies have emerged showing that use of AI software is strong enough to be a trigger in many relatively new psychological conditions. Individuals are forming co-dependent relationships with their AI companions and demonstrating addiction-like behavors in relationship to AI use. In some cases, interactions with AI have resulted in psychotic symptoms such as the development of delusions and the exacerbation of personality disordered attitudes and behaviors. Tragically, some have even committed suicide.
While most people will use these technologies without acute or disorder-inducing consequences, it’s very likely that in the coming years we will see increasing reports of new and strange psychological disorders emerging as a result of prolonged A.I. use.
No AI was used in the making of this article!
What do you think? What are your predictions about how AI might influence human psychology in the coming years?
Written by Julian Frazier, PhD
The musings of a Clinical Psychologist exploring the delicate art of humaning from as many absurd perspectives as possible. Let's get weird.
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人工超級智能 -- Jared Perlo
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China is starting to talk about AI superintelligence, and some in the U.S. are taking notice
Alibaba's CEO said the company would be pushing to develop advanced AI. Some in the U.S. have viewed China's AI ambitions as more focused on applications of the technology.
Jared Perlo, 10/04/25
Early last week in the Chinese tech hub of Hangzhou, a slick, larger-than-life video screen beamed out four words that would drive tech giant Alibaba’s stock to historic levels and signal a shift in China’s approach to artificial intelligence: “Roadmap to Artificial Superintelligence.”
During his 23-minute keynote address at the flagship Alibaba Cloud conference, Alibaba CEO Eddie Wu charted out a future featuring artificial general intelligence (AGI) and artificial superintelligence (ASI). These terms point to a theorized era in which AI becomes roughly as smart as humans (AGI) and then much, much smarter (ASI).
While these terms have been tossed around Silicon Valley for years, Wu’s presentation was notable: Alibaba is now the first established Chinese tech giant to explicitly invoke AGI and ASI.
“Achieving AGI — an intelligent system with general human-level cognition — now appears inevitable. Yet AGI is not the end of AI’s development, but its beginning,” Wu said. “It will march toward ASI — intelligence beyond the human, capable of self-iteration and continuous evolution.”
“ASI will drive exponential technological leaps, carrying us into an unprecedented age of intelligence,” Wu said, highlighting ASI’s ability to help cure diseases, discover cleaner sources of energy and even unlock interstellar travel.
The U.S. and China are the world’s leading AI powers, each with immense computing capabilities and top-tier researchers developing cutting-edge systems. Yet observers have framed the countries as having different approaches to AI, with perceptions that China focuses more on real-world AI applications.
For example, commentators recently argued that Beijing is currently “winning the race for AI robots” against the U.S., as China is home to much of the world’s most advanced robotics supply chains and a growing network of robotics, or embodied AI, labs.
“There’s been some commentary in Western media recently about how the U.S. is missing the point by pushing for AGI, while China is focusing solely on applications,” said Helen Toner, interim executive director of Georgetown’s Center for Security and Emerging Technology. “This is wrong.”
“Some Chinese researchers and some parts of the Chinese government have been interested in AGI and superintelligence for a long time,” Toner said, though she noted this view was primarily held by smaller startups like DeepSeek.
Afra Wang, a researcher focusing on China’s tech scene, said Alibaba’s invocation of AGI and ASI was remarkable.
“This ASI narrative is definitely something new, especially among the biggest tech companies in China,” she told NBC News.
Alibaba’s “roadmap to artificial superintelligence” seems to scramble mainstream perceptions. Any number of California techno-optimists, like Anthropic’s Dario Amodei or xAI’s Elon Musk, might have delivered Wu’s speech, selling a technology-enabled utopia while largely sidestepping darker questions about how humanity would co-exist with or survive an era of digital superintelligence.
The concept of superintelligence has long been on the minds of — if not explicitly guiding — prominent American AI companies. For example, OpenAI released an article focused on the safe development of superintelligent AI models in May 2023. “Now is a good time to start thinking about the governance of superintelligence — future AI systems dramatically more capable than even AGI,” the statement said.
The possibility of superintelligence is now even being acknowledged in Congress. On Monday, Sen. Josh Hawley, R-Mo., and Sen. Richard Blumenthal, D-Ill., announced a draft bill that would, among other actions, “assist Congress in determining the potential for controlled AI systems to reach artificial superintelligence.”
To some, ASI might seem like an outlandish concept when today’s AI systems fail to understand basic tennis rules, hallucinate or fabricate basic information, or do not seem to actually comprehend how the external world functions.
At the same time, AI systems continue to approach and sometimes surpass human capabilities in many domains, from driving cars safely to winning international coding competitions, leaving many experts to say it’s a matter of when, not if, humans develop digital superintelligence.
As the idea of superintelligence gradually enters mainstream debates, many American politicians have announced that the U.S. is in an AI race with China. The White House’s current AI manifesto is titled “Winning the AI Race: America’s AI Action Plan,” while Sen. Ted Cruz, R-Texas, proclaimed that “as a matter of economic security, as a matter of national security, America has to beat China in the AI race.”
Yet charges of an AI race are muddied by a lack of an agreed end goal and swirling definitions of AGI. At worst, experts think an unfettered race toward AGI or ASI could lead to widespread catastrophe or even the end of humanity.
But there’s also plenty of skepticism around talk of AGI and ASI and whether it’s primarily for marketing purposes.
Alibaba is one of China’s largest tech companies, known for providing powerful, free AI models — also called open-source models — for download. Alibaba’s Qwen model series, a competitor to models like OpenAI’s GPT-5 or Anthropic’s Claude, is the most popular open-source AI system in the world.
Wu announced a new series of Qwen models in his speech last week, including a model that combines text, images, video and audio capabilities.
Many observers point out that narratives about a U.S.-China AI race and a resulting sprint to build AI infrastructure serve AI investors by propping up company valuations and increasing their soft power. Alibaba’s stock has soared since Wu’s speech last week, part of a larger $250 billion comeback this year that has made it China’s hottest AI company.
To unlock a powerful, superintelligent future, Wu predicted that large AI models will replace existing operating systems as the link between users, software and computational power. This future network of large AI models will run on cloud computing networks like Alibaba Cloud, he said.
Irene Zhang, a researcher on China’s AI ecosystem and an editor of ChinaTalk, noted the business undertones of Wu’s announcement.
“This is a vision of AGI and ASI that’s directly based on Alibaba’s business model,” she said.
“Alibaba Cloud dominates China’s cloud computing market, and its global market share is now bigger than Oracle’s,” she said. “Alibaba’s commercial strategy and its publicly stated views on ASI/AGI are symbiotic.”
Matt Sheehan, a senior fellow at the Carnegie Endowment for International Peace, agreed.
“ASI is the ultimate frontier, as far as the discourse goes on AI,” Sheehan said. “It’s notable that Alibaba set this grandiose goal, but in reality, they’re selling cloud services.”
This article was originally published on NBCNews.com
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你必須學會的9個人工智能技巧 -- Pasindu Rangana
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依各位的職業、年齡層、和時間多寡，請自行選擇對個人最有用，最能幫助自己成長的「技巧」先入手。理論上，精通兩、三個之後，有可能產生駕輕就熟的效果了。
請至原網頁觀賞有趣的示意插圖。
9 AI Skills You MUST Learn Before Everyone Else Does (or Get Left Behind)
You don’t need to be a tech wizard to profit from AI, You just need to know these shortcuts.
Pasindu Rangana, 05/02/25
Why the Next Wave of AI Isn’t Just for Techies
Imagine this, it’s just a year from now, and the people making the most money from AI aren’t just the usual suspects — programmers, marketers, or sales pros. Nope. The real winners? Everyday folks who decided to master a few key AI skills. And guess what? You don’t need to be a tech wizard to join them.
The good news? You’re not too late. But you do need to move fast.
If you’re a tech enthusiast, an entrepreneur, or just someone who wants to future-proof your career (and maybe fatten your wallet), you’re in the right place.
Let’s dive into the nine most easiest and insane AI skills you can start learning today, even if you’re starting from scratch.
1. Prompt Engineering: Your Shortcut to “Talking” with AI
Ever tried asking ChatGPT for help and got a totally useless answer? You’re not alone. The secret sauce is prompt engineering. Basically, knowing how to “talk” to AI so it gives you gold, not garbage.
Prompt engineering is the art of talking to AI in a way that gets you accurate, high-quality, and actionable results.
Here’s the trick:
* Tell the AI who to be or the role (“Act as a marketer” or “Act as a lawyer”).
* Add examples to guide output.
* Be super clear about what you want (“Give me a table with pros and cons…”).
* And always, always ask for your answer in the format you need.
Pro Tip: Use a ChatBot to generate a prompt.
Think of it like giving directions to a friend who’s never been to your house. The more specific you are, the better the results.
2. AI-Assisted Software Development: Build Apps Without Being a Coder
You’ve got ideas, but coding them sounds like a nightmare, right? Now you don’t need to know Python or JavaScript or any programming knowledge.
Believe it or not, building software isn’t just for computer science grads anymore. With tools like Replit and Cursor, you can describe what you want and let the AI do the heavy lifting.
Got a great idea? Now you can turn it into an app, even if you’ve never written a line of code.
Pro Tip: Find a problem people keep complaining about, use AI to build a solution, and you could sell it again and again.
With the right prompts, you can build chatbots, automate scripts, and create working MVPs for side projects or startups.
You’ll feel like a developer, minus the 2 years of coding bootcamps.
3. AI Design & AI Art: Unleash Your Inner Creativity
Remember when AI-generated images looked like a Picasso fever dream? Not anymore. Today, AI can whip up photo-realistic images, logos, and even websites.
You don’t need Photoshop or design school anymore. AI tools like Midjourney, Leonardo, and DALL-E let you generate professional-quality images, logos, branding concepts, UI mock ups, and illustrations, just by typing what you want.
Did you notice that all the images I used in this article are generated using AI?
You can even improve your hand-drawn sketches by uploading them and letting AI enhance, colorize, or digitize them into polished visuals. 4. AI Video Editing: Make Magic Without the Tech Headaches
Editing video used to mean hours hunched over a laptop, cutting out awkward silences. Now? AI does the boring stuff for you, fast.
Tools like Runway ML, Pictory, and Descript are changing the game.
With these, you can:
* Automatically remove filler words, pauses, and background noise
* Add subtitles and scene transitions
* Create talking-head videos from scripts (yes, without recording)
* Generate B-roll from prompts
Try this: Next time you want a new logo or product image, let AI take a crack at it. You might be surprised how good it looks-and how much time you save.
Whether you’re a content creator, marketer, or small business owner, AI editing tools cut production time by 70% and still make your work look polished and pro.
5. AI Writing: Turn Your Ideas Into Income
Here’s a secret: The best-paid writers aren’t just good with words, they’re great at sharing ideas. AI can help you dig through transcripts, brainstorm viral content, and even mimic your (or your client’s) unique style.
Use AI to do the heavy lifting, then sprinkle in your personality. That’s how you stand out from the crowd.
Whether it’s a blog post, sales copy, or social media caption, tools like ChatGPT, Jasper, and Copy.ai help you draft, refine, and rework content.
Use AI to:
* Overcome writer’s block
* Generate catchy headlines and hooks
* Repurpose long-form content into short posts
* Translate tone of voice (formal, witty, friendly)
You still bring your voice and insights, AI just accelerates the process.
6. AI Content Marketing: Be Everywhere, Effortlessl
Ever heard of Arnold’s Pump Club? It’s a top podcast, but here’s the kicker-Arnold’s never recorded an episode. AI handles the whole thing, from newsletters to voice clones.
You can do this too. Use AI to create, repurpose, and spread content across every platform. Suddenly, you are everywhere at once.
You don’t need a team to grow an online brand anymore. Tools like Taplio, Repurpose.io, and Hypefury will help you,
* Turn blog posts into tweets, threads, and carousels
* Schedule optimized content across platforms
* Auto-generate weekly newsletters from existing posts
With the right stack, one idea becomes 20+ pieces of content.
7. No-Code AI Automation: Become a Workflow Wizard
You’re doing too much manually. Copying data, sending the same emails, switching between tools. Businesses waste a ton of time on repetitive tasks. If you can use AI to automate those headaches, you’ll be a hero (and get paid like one). Map out the workflow, find the bottlenecks, and let AI do the busywork.
Start with:
* Zapier or Make to connect your favorite apps (Google Sheets, Slack, Gmail, etc.)
* ChatGPT Plugins or AutoGPT to chain tasks together
* Smart workflows to manage leads, invoices, or reports
8. AI Data Analysis: Turn Messy Data Into Gold
Every business has a pile of data and most don’t know what to do with it. That’s where you come in. Use AI to clean up, enrich, and pull insights from all those messy spreadsheets.
Spreadsheets can be intimidating, but not when AI does the analysis for you. You can use tools like,
* ChatGPT Advanced Data Analysis (Code Interpreter)
* Power BI Copilot
* Tableau with GPT
Thses let you upload files, ask plain-language questions, and get instant insights with visuals.
Suddenly, you’re the one helping companies make smarter decisions.
9. No-Code AI Agent Development: Build Digital Employees
Here’s the big one: AI agents can work 24/7, never complain, and always deliver. If you can define a job, train an agent, and keep an eye on its performance, you can replace repetitive roles and get paid top dollar.
With AutoGPT, AgentGPT, or Flowise, you can create bots that,
* Research topics
* Send personalized emails
* Manage appointments
* Handle customer support and many more
Advanced tools let you plug in memory, APIs, and instructions so they keep improving. You don’t just automate, you delegate.
Don’t wait for AI to replace you. Be the person who builds the AI.
You’re More Ready Than You Think
If you’re still with me, here’s the truth. You don’t need a fancy degree or years of experience (doesn’t mean that they don’t have value). You just need curiosity and a willingness to try. The AI revolution is happening right now, and there’s never been a better time to jump in.
So, what’s stopping you?
Start experimenting, building, and sharing what you learn. The future belongs to the bold and you’re already ahead of the pack.
You Don’t Need to Master All 9, Just Start with One
Here’s your shortcut,
1. Pick a skill that excites you
2. Watch a 10-minute tutorial
3. Use an AI tool on something you actually care about
4. Share your result online
You’ll learn faster than you expect and you’ll position yourself ahead of 95% of people still stuck on step 0.
Which AI skill are you itching to try first? Already dabbling, or just getting started? Drop your thoughts, share your questions, and let’s help each other win in this new age of AI.
If this sparked an idea, share your thoughts below or ask any questions about getting started with AI. Let’s build the future together!
If you found this guide helpful and would like to support more content like this, you can buy me a coffee here.
Every bit of support helps me keep creating tutorials and sharing what I learn!
Written by Pasindu Rangana
B.Sc. (Hons.) in Computer Engineering (UG), Computer Vision & AI, Photographer. linkedin.com/in/pasindu-rangana/
Published in Mr. Plan ₿ Publication
Welcome to Mr. Plan ₿ Publication! A space for both beginners and experienced writers to promote their articles. Discover the secrets to a strong presence and amplify the impact of your words! 🚀📝 #MediumTips #WritersCommunity
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人工智能公司泡沫化危機？ -- Nick Lichtenberg
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下文蠻有看頭和存檔備查價值；作者大量旁徵博引之外，它還穿插了一小段「技術-經濟互動簡史」；兩者在技術性報導中並不多見。不過，人工智能技術潛能和人工智能企業前景到底是風光無限，還是前途多舛？下文並未提供定論；相信絕大多數人也都只有能力觀望、揣測、或摸瞎。
對人工智能有濃厚興趣的朋友，不妨參看：此文--該欄2025/08/16、此文--該欄2025/08/11、和本欄2025/08/24、2025/07/16、2025/06/12等篇貼文。
‘It’s almost tragic’: Bubble or not, the AI backlash is validating what one researcher and critic has been saying for years
Nick Lichtenberg, 08/24/25
First it was the release of GPT-5 that OpenAI “totally screwed up,” according to Sam Altman. Then Altman followed that up by saying the B-word at a dinner with reporters. “When bubbles happen, smart people get overexcited about a kernel of truth,” The Verge reported on comments by the OpenAI CEO. Then it was the sweeping MIT survey that put a number on what so many people seem to be feeling: a whopping 95% of generative AI pilots at companies are failing.
A tech sell-off ensued, as rattled investors sent the value of the S&P 500 down by $1 trillion. Given the increasing dominance of that index by tech stocks that have largely transformed into AI stocks, it was a sign of nerves that the AI boom was turning into dotcom bubble 2.0. To be sure, fears about the AI trade aren’t the only factor moving markets, as evidenced by the S&P 500 snapping a five-day losing streak on Friday after Jerome Powell’s quasi-dovish comments at Jackson Hole, Wyoming, as even the hint of openness from the Fed chair toward a September rate cut set markets on a tear.
Gary Marcus has been warning of the limits of large language models (LLMs) since 2019 and warning of a potential bubble and problematic economics since 2023. His words carry a particularly distinctive weight. The cognitive scientist turned longtime AI researcher has been active in the machine learning space since 2015, when he founded Geometric Intelligence. That company was acquired by Uber in 2016, and Marcus left shortly afterward, working at other AI startups while offering vocal criticism of what he sees as dead-ends in the AI space.
Still, Marcus doesn’t see himself as a “Cassandra,” (沒人相信的預言家) and he’s not trying to be, he told Fortune in an interview. Cassandra, a figure from Greek tragedy, was a character who uttered accurate prophecies but wasn’t believed until it was too late. “I see myself as a realist and as someone who foresaw the problems and was correct about them.”
Marcus attributes the wobble in markets to GPT-5 above all. It’s not a failure, he said, but it’s “underwhelming,” a “disappointment,” and that’s “really woken a lot of people up. You know, GPT-5 was sold, basically, as AGI (人工全方位智能), and it just isn’t,” he added, referencing artificial general intelligence, a hypothetical AI with human-like reasoning abilities. “It’s not a terrible model, it’s not like it’s bad,” he said, but “it’s not the quantum leap that a lot of people were led to expect.”
Marcus said this shouldn’t be news to anyone paying attention, as he argued in 2022 that “deep learning is hitting a wall.” To be sure, Marcus has been wondering openly on his Substack on when the generative AI bubble will deflate. He told Fortune that “crowd psychology” is definitely taking place, and he thinks every day about the John Maynard Keynes quote: “The market can stay irrational longer than you can stay solvent,” or Looney Tunes’s Wile E. Coyote following Road Runner off the edge of a cliff and hanging in midair, before falling down to Earth.
“That’s what I feel like,” Marcus says. “We are off the cliff. This does not make sense. And we get some signs from the last few days that people are finally noticing.”
Building warning signs
The bubble talk began heating up in July, when Apollo Global Management’s chief economist, Torsten Slok, widely read and influential on Wall Street, issued a striking calculation while falling short of declaring a bubble. “The difference between the IT bubble in the 1990s and the AI bubble today is that the top 10 companies in the S&P 500 today are more overvalued than they were in the 1990s,” he wrote, warning that the forward P/E ratios and staggering market capitalizations of companies such as Nvidia, Microsoft, Apple, and Meta had “become detached from their earnings.”
In the weeks since, the disappointment of GPT-5 was an important development, but not the only one. Another warning sign is the massive amount of spending on data centers to support all the theoretical future demand for AI use. Slok has tackled this subject as well, finding that data center investments’ contribution to GDP growth has been the same as consumer spending over the first half of 2025, which is notable since consumer spending makes up 70% of GDP. (The Wall Street Journal‘s Christopher Mims had offered the calculation weeks earlier.) Finally, on August 19, former Google CEO Eric Schmidt co-authored a widely discussed New York Times op-ed on August 19, arguing that “it is uncertain how soon artificial general intelligence can be achieved.”
This is a significant about-face, according to political scientist Henry Farrell, who argued in the Financial Times in January that Schmidt was a key voice shaping the “New Washington Consensus,” predicated in part on AGI being “right around the corner.” On his Substack, Farrell said Schmidt’s op-ed shows that his prior set of assumptions are “visibly crumbling away,” while caveating that he had been relying on informal conversations with people he knew in the intersection of D.C. foreign policy and tech policy. Farrell’s title for that post: “The twilight of tech unilateralism.” He concluded: “If the AGI bet is a bad one, then much of the rationale for this consensus falls apart. And that is the conclusion that Eric Schmidt seems to be coming to.”
Finally, the vibe is shifting in the summer of 2025 into a mounting AI backlash. Darrell West warned in Brookings in May that the tide of both public and scientific opinion would soon turn against AI’s masters of the universe. Soon after, Fast Company predicted the summer would be full of “AI slop.” By early August, Axios had identified the slang “clunker” being applied widely to AI mishaps, particularly in customer service gone awry.
History says: short-term pain, long-term gain
John Thornhill of the Financial Times offered some perspective on the bubble question, advising readers to brace themselves for a crash, but to prepare for a future “golden age” of AI nonetheless. He highlights the data center buildout—a staggering $750 billion investment from Big Tech over 2024 and 2025, and part of a global rollout projected to hit $3 trillion by 2029. Thornhill turns to financial historians for some comfort and some perspective. Over and over, it shows that this type of frenzied investment typically triggers bubbles, dramatic crashes, and creative destruction—but that eventually durable value is realized.
He notes that Carlota Perez documented this pattern in Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages. She identified AI as the fifth technological revolution to follow the pattern begun in the late 18th century, as a result of which the modern economy now has railroad infrastructure and personal computers, among other things. Each one had a bubble and a crash at some point. Thornhill didn’t cite him in this particular column, but Edward Chancellor documented similar patterns in his classic Devil Take The Hindmost, a book notable not just for its discussions of bubbles but for predicting the dotcom bubble before it happened.
Owen Lamont of Acadian Asset Management cited Chancellor in November 2024, when he argued that a key bubble moment had been passed: an unusually large number of market participants saying that prices are too high, but insisting that they’re likely to rise further.
Wall Street is cautious, but not calling a bubble
Wall Street banks are largely not calling for a bubble. Morgan Stanley released a note recently seeing huge efficiencies ahead for companies as a result of AI: $920 billion per year for the S&P 500. UBS, for its part, concurred with the caution flagged in the news-making MIT research. It warned investors to expect a period of “capex indigestion” accompanying the data center buildout, but it also maintained that AI adoption is expanding far beyond expectations, citing growing monetization from OpenAI’s ChatGPT, Alphabet’s Gemini, and AI-powered CRM systems.
Bank of America Research wrote a note in early August, before the launch of GPT-5, seeing AI as part of a worker productivity “sea change” that will drive an ongoing “innovation premium” for S&P 500 firms. Head of U.S. Equity Strategy Savita Subramanian essentially argued that the inflation wave of the 2020s taught companies to do more with less, to turn people into processes, and that AI will turbo-charge this. “I don’t think it’s necessarily a bubble in the S&P 500,” she told Fortune in an interview, before adding, “I think there are other areas where it’s becoming a little bit bubble-like.”
Subramanian mentioned smaller companies and potentially private lending as areas “that potentially have re-rated too aggressively.” She’s also concerned about the risk of companies diving into data centers to such a great extent, noting that this represents a shift back toward an asset-heavier approach, instead of the asset-light approach that increasingly distinguishes top performance in the U.S. economy.
“I mean, this is new,” she said. “Tech used to be very asset-light and just spent money on R&D and innovation, and now they’re spending money to build out these data centers,” adding that she sees it as potentially marking the end of their asset-light, high-margin existence and basically transforming them into “very asset-intensive and more manufacturing-like than they used to be.” From her perspective, that warrants a lower multiple in the stock market. When asked if that is tantamount to a bubble, if not a correction, she said “it’s starting to happen in places,” and she agrees with the comparison to the railroad boom.
The math and the ghost in the machine
Gary Marcus also cited the fundamentals of math as a reason that he’s concerned, with nearly 500 AI unicorns being valued at $2.7 trillion. “That just doesn’t make sense relative to how much revenue is coming [in],” he said. Marcus cited OpenAI reporting $1 billion in revenue in July, but still not being profitable. Speculating, he extrapolated that to OpenAI having roughly half the AI market, and offered a rough calculation that it means about $25 billion a year of revenue for the sector, “which is not nothing, but it costs a lot of money to do this, and there’s trillions of dollars [invested].”
So if Marcus is correct, why haven’t people been listening to him for years? He said he’s been warning people about this for years, too, calling it the “gullibility gap” in his 2019 book Rebooting AI and arguing in The New Yorker in 2012 that deep learning was a ladder that wouldn’t reach the moon. For the first 25 years of his career, Marcus trained and practiced as a cognitive scientist, and learned about the “anthropomorphization people do. … [they] look at these machines and make the mistake of attributing to them an intelligence that is not really there, a humanness that is not really there, and they wind up using them as a companion, and they wind up thinking that they’re closer to solving these problems than they actually are.” He said he thinks the bubble inflating to its current extent is in large part because of the human impulse to project ourselves onto things, something a cognitive scientist is trained not to do.
These machines might seem like they’re human, but “they don’t actually work like you,” Marcus said, adding, “this entire market has been based on people not understanding that, imagining that scaling was going to solve all of this, because they don’t really understand the problem. I mean, it’s almost tragic.”
Subramanian, for her part, said she thinks “people love this AI technology because it feels like sorcery. It feels a little magical and mystical … the truth is it hasn’t really changed the world that much yet, but I don’t think it’s something to be dismissed.” She’s also become really taken with it herself. “I’m already using ChatGPT more than my kids are. I mean, it’s kind of interesting to see this. I use ChatGPT for everything now.”
This story was originally featured on Fortune.com
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MIT報告：人工智能試行計畫失敗率高達95% -- Sheryl Estrada
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請參考：
Sam Altman admits OpenAI ‘totally screwed up’ its GPT-5 launch and says the company will spend trillions of dollars on data centers
MIT report: 95% of generative AI pilots at companies are failing
Sheryl Estrada, 08/18/25
Good morning. Companies are betting on AI—yet nearly all enterprise pilots are stuck at the starting line.
The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDA initiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.
Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.
To unpack these findings, I spoke with Aditya Challapally, the lead author of the report, and a research contributor to project NANDA at MIT.
“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.
But for 95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained.
The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
What’s behind successful AI deployments?
How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.
This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025. Yet, MIT’s research suggests companies see far more failures when going solo.
Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.
Other key factors for success include empowering line managers—not just central AI labs—to drive adoption, and selecting tools that can integrate deeply and adapt over time.
Workforce disruption is already underway, especially in customer support and administrative roles. Rather than mass layoffs, companies are increasingly not backfilling positions as they become vacant. Most changes are concentrated in jobs previously outsourced due to their perceived low value.
The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.
Looking ahead, the most advanced organizations are already experimenting with agentic AI systems that can learn, remember, and act independently within set boundaries—offering a glimpse at how the next phase of enterprise AI might unfold.
Sheryl Estrada, sheryl.estrada@fortune.com
This story was originally featured on Fortune.com
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人工智能效應：高科技企業今年已裁撤5萬職位-Cryptopolitan
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請參考：
* Enterprises Push Ahead with AI-Powered Job Replacement Despite Risks
* This CEO laid off nearly 80% of his staff because they refused to adopt AI fast enough. 2 years later, he says he’d do it again
60歲以下的白領階層員工需要提高警覺。由於薪資積累以及生產力降低(由於腦力/體力衰退)等因素，40歲以上的「老」員工更是屬於「高風險群」；上述兩個因素是我親身經驗之談。
Around 50,000 tech jobs replaced by AI as companies restructure for the future
Cryptopolitan, 08/14/25
Around 50,000 tech job roles have been replaced with AI as companies like Oracle, Intel, Microsoft, and Nextdoor restructured their workforces. The nationwide layoffs have been attributed to firms pursuing large-scale investments in artificial intelligence and managing the AI-related costs.
Oracle confirmed changes in its cloud division, cutting off more than 150 workers in Seattle, the base of its cloud operations. Some of the affected roles include engineering and support positions. Oracle informed staff that performance factors contributed to their release. However, the company continues to hire in other business units.
Tech companies slash jobs to boost AI and cloud spending
Oracle failed to provide an official statement regarding the number of employees laid off. However, the company stated in June that restructuring and workforce changes occur periodically concerning company strategies, operational reorganizations, and performance reviews. The report revealed that the actions would result in high near-term costs and decreased productivity as employees seek to adjust to the new structure.
Oracle’s stock has traded near record highs recently following the growth in its cloud business. In June, it announced an agreement with OpenAI to secure 4.5 gigawatts of data center power capacity in the U.S. The deal forms part of initiatives to support AI workloads that require large compute power capacity. The database pioneer has committed tens of billions towards constructing new data centers that meet AI-related tasks. In its Q2 results, it reported a negative free cash flow reflecting the scale of investment it has entered into.
Big tech firms are making similar moves industry-wide. Microsoft has laid off approximately 15,000 employees since the beginning of 2025. The affected roles include teams across engineering, sales, and support. Amazon and Meta have also laid off staff in 2025. Scale AI, a San Francisco-based firm specializing in AI data labelling, Nextdoor, the neighborhood-focused social networking platform, and Intel have all announced reductions in staff this year. Since January, the industry-wide layoffs have contributed to approximately 50,000 jobs lost across the technology sector.
In June, Microsoft outlined plans to lay off multiple roles following the 6,000 job cuts in May. At least 22,000 jobs had already been cut in 2025 while 150,000 jobs were lost in 2024 across hundreds of companies. The job cuts occur as companies rush to expand spending in AI development and infrastructure. Amazon and Meta are investing more than $100 billion in AI.
Industry-wide job cuts spread across multiple geographic locations
Oracle stated that strategy changes and performance outcomes influence workforce changes. It also noted that although such measures are intended to provide financial caution, they may involve transitional costs and short-term productivity issues.
Industry analysts have noted that layoffs have affected various roles, from entry-level technical positions to senior management. The effects of the layoffs have been distributed across different locations, with much concentration in the Seattle area, the San Francisco Bay Area, and Austin, Texas.
Amid the layoffs, companies continue to recruit in other business units. Oracle’s cloud division indicates that its recruiting positions are aligned with priority growth areas. Roles related to AI infrastructure development, data centre operations, and high-demand enterprise software services are some of the key areas receiving employees.
Cryptopolitan reported that about 41% of executives expect the tech workforce to shrink by 2030 due to AI automation. The report noted Nvidia’s CEO Jensen Huang’s remarks that while AI will make some jobs obsolete, it will also create new ones, and industries could grow if fresh ideas keep emerging.
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5個妙招讓ChatGPT發揮淋漓盡至-- Sejalbaranwal
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別只看不練！立刻現學現賣！
I Use These 5 ChatGPT Prompts Daily - You'll Wish You Knew These Sooner
Sejalbaranwal, 07/29/25
There's always a need to understand the truth. But sometimes, it hits you only after you have wasted a whole month.
I spent hours- no, days- just starting at the screen.
Trying to write blogs, emails, LinkedIn posts, create course layouts, and even summarize my study notes.
Even simple decisions, like scheduling my day, became time-consuming.
Why? Because I was stuck in the old-school mindset:
Do it all yourself. Work harder, not smarter.
And it led to just one thing: burnout.
In just 10 days, I was completely exhausted.
The process of thinking, writing, rewriting and finding someone to give feedback. it drained me.
I knew about ChatGPT. But I didn't use it.
Why? Because of overconfidence.
Because I thought: "AI won't help unless I work hard myself."
And yes, if you give vague prompts (指令), ChatGPT will give vague answers.
But sometimes, we have to fall flat on our face to learn the truth.
One Month Wasted.
Then a Day That Changed Everything.
I realized: working all day is not the same as being productive.
I was getting nothing done. No satisfaction. No results.
Then one day, I asked ChatGPT the right questions.
And suddenly, things started shifting.
Want to know how?
Here are 5 mind-blowing prompts that changed how I work. These will unlock your productivity too.
1. Writing Faster Than a Bullet Train
First, I took inspiration from the amazing Medium writers.
I started creating first drafts in no time.
Here are the magic prompts:
1. Provide me with underrated angles of the given topic.
2. Give the blog outline in AIDA format (Attention, Interest, Desire, Action).
3. Summarize real-life case studies and statistics.
ChatGPT gives everything in seconds. But I don't copy-paste. Here's how I use it:
I arrange the content in my style of writing.
I prefer short, powerful sentences.
I still do thorough research.
So no, AI doesn't replace my thinking - it accelerates it.
2. Email Writing - It Hurts
Screenshot of the GPT writing emails. 請至原網頁觀看螢幕截圖
Writing like I speak? Easy. Writing to sound professional? Not my cup of tea.
So I started using GPT Email Writer.
I just type my message and ask:
Write this in a professional tone.
Done. Polished. Clear. Respectful.
It literally saves me from awkward backspacing for hours.
3. Codeless Websites on the Rise
Everyone's talking about building websites.
Few talk about doing it without writing a single line of code.
After my coder friend let me down, I went to YouTube:
How to build a website without coding?
I discovered DeepSite- a web development tool powered by AI. I typed this prompt:
Create a portfolio website that looks modern, impressive, and up-to-date for a content writer.
In seconds, my website was ready. And honestly? It looked great. You should try it - you'll be surprised how professional it feels.
4. Creating Images Like a Pro
We often give vague prompts like:
Create a picture of a sunset below the horizon with a river.
Press enter or click to view image in full size 請至原網頁觀看AI圖片
But that's not how to get the best out of image-generation AI. Here's the improved version:
Generate a scenic sunset over a mountain range with a river having profound depths. The sky is filled with my mysterious, stormy clouds. The atmosphere looks fiery with fierce reds and vivid orange. The scene should feels overwhelming - like real nature.
Press enter or click to view image in full size 請至原網頁觀看AI圖片
The result? Mind-blowing.
Detail, colors, emotions - all come alive.
Lesson: Precision prompts = pro-quality visuals.
5. Started My Course Creation Journey
Press enter or click to view image in full size 請至原網頁觀看視頻和螢幕截圖
Creating a full course?
It takes months - I thought.
Then I read about a collaboration between Blue Carrot and GenEd AI: They created two courses - one traditionally, and one with AI. Both got similar ratings. But the AI course? It was done in just 1 week. It was 25 times faster.
I was triggered by this article in a good way.
I created two test videos. The AI-assisted one got selected.
Here's the prompt that helped me plan my course:
Give me stepwise ideas for publishing a Course on Udemy. I'll share my research on how competitors are structuring theirs. Your job is to arrange everything in sequence. Provide a table that breaks down each session clearly.
It worked. And I am not turning back.
Now It's Your Turn
Try any one of these prompts today.
See the shift. Feel the productivity.
Don't just save this post - use it.
Copy the prompts. Edit them to fit your focus on creativity. Because the truth is simple:
Smart work doesn't replace hard work. It just makes it worth it.
Bye for now.
This article was published on July 29th, 2025 in learnaitoprofit.com.
Written by Sejalbaranwal
Get articles on AI, Productivity, important skills with a touch of personal experiences. My stories will take you in different dimension.
Published in LearnAItoprofit.com
Artificial Intelligence Tools For Writers. How to use Ai Tools and Generative AI for passive income for your side hustle or home business.
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「深度求索」簡介 -- iKala
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DeepSeek基本資訊
DeepSeek 是什麼？
DeepSeek 是一款由中國 AI 公司「深度求索」開發的大型語言模型，旨在為企業、開發者及個人用戶提供高效且具成本優勢的 AI 解決方案。DeepSeek 擅長自然語言處理，能執行對話、文本生成、翻譯、程式碼編寫等多種任務，提升學習、工作與創意內容生成的效率。
DeepSeek 的主要功能有哪些？
DeepSeek 提供多樣化功能，主要包括 DeepSeek Chat 和 DeepSeek Coder。
DeepSeek Chat 是一個對話式 AI 應用，可以像真人一樣與你互動，回答問題、提供建議、和你聊天。
DeepSeek Coder 則是一個程式碼生成工具，可以幫助開發者快速編寫、測試和 debug 程式碼。
此外，DeepSeek 還具備文本生成、翻譯、摘要等多種功能，應用範圍廣泛。
DeepSeek 的應用領域有哪些？
DeepSeek 應用廣泛，涵蓋以下領域：
* 教育： 作為智能學習助手，提供解題與學習建議。
* 金融： 分析市場數據，支援風險評估與投資決策。
* 客服： 作為智能客服，快速回應客戶問題，提升服務效率。
* 內容創作與媒體： 支援文章撰寫、新聞摘要、社交媒體內容生成等。
DeepSeek 是否為開源模型？可以部署在本地環境嗎？
DeepSeek 提供 開源版本，允許開發者自由使用、修改和優化模型，以滿足特定需求。用戶可以在 本地環境(地端)部署 DeepSeek，而無需依賴雲端服務，從而提升數據隱私與安全性。目前，開發者可透過 Ollama 等工具輕鬆安裝和運行 DeepSeek 模型，實現 離線運行，確保敏感數據不會外洩，同時降低長期運算成本。這種開源與本地部署的彈性，使 DeepSeek 成為企業及開發者在 AI 應用上更具掌控性的選擇。
DeepSeek比較與選擇
DeepSeek 與 OpenAI 在效能上有何差異？成本優勢如何？
* 模型對應：
** DeepSeek V3 效能接近 OpenAI GPT-4o，特別在中文 NLP 任務上表現突出。
** DeepSeek R1 則相當於 OpenAI o3-mini，適用於一般文本生成與推理需求。
* 開放性與部署：
** DeepSeek 採用 開源架構，可本地部署與客製化，提供更大的彈性與數據掌控權。
** OpenAI 則提供 封閉式 API 服務，適合雲端應用，無需維護基礎設施。
* 成本考量：
** DeepSeek 開源版本 可降低長期運行成本，特別適合企業自建運算資源或需要本地部署的應用。
** OpenAI API 提供穩定的雲端服務，但以使用量計費，長期成本可能較高。
DeepSeek 與其他 AI 模型（如 ChatGPT、Google Gemini）的差異？
DeepSeek、ChatGPT 與 Google Gemini 各具特色：
* DeepSeek： 在中文處理方面優化，符合中文語言習慣，並具備成本效益。
* ChatGPT： 擅長英文內容生成與創意寫作，適合開放式對話應用。
* Google Gemini： 強化知識檢索與資訊處理，適合需要龐大資料支持的場景。選擇 AI 模型時可根據需求與應用場景進行比較。
DeepSeek 隱私與安全
DeepSeek 是否安全可靠？
DeepSeek 重視用戶數據的隱私與安全，採取多層次的保護措施，包括數據加密、身份驗證與漏洞檢測，並遵循相關數據保護法規。然而，與所有技術一樣，仍存在潛在風險，建議企業根據自身需求搭配額外的安全防護措施，以確保數據的完整性與安全性。
DeepSeek 的隱私政策？
DeepSeek 的隱私政策強調數據使用的透明性，僅收集必要的用戶數據，如設備資訊、操作系統、IP 位址及訪問時間，主要用於系統維護與服務優化。DeepSeek 承諾不會未經授權使用個人數據，並採取嚴格的安全控制措施。然而，根據不同市場法規及應用場景，建議用戶詳細了解隱私政策內容，並定期檢視數據使用狀況，以降低潛在風險。
為何部分國家或機構對 DeepSeek 保持警戒，甚至考慮限制其使用？
隨著 DeepSeek 迅速成為全球熱門 AI 模型，部分國家對其安全性與潛在風險保持關注。例如：
* 美國海軍 已基於安全考量，要求人員避免使用 DeepSeek 模型，以防機密外洩。
* 台灣數發部 也發布公告，公務機關需限制使用 DeepSeek 相關 AI 產品，以確保數據安全。
* 義大利、法國、愛爾蘭 等歐洲國家則開始對 DeepSeek 進行調查，以評估其合規性與影響力。
這些國家與企業的擔憂主要來自於：
1. 數據安全風險：若 AI 服務部署於國外伺服器，可能存在資訊外洩風險。
2. 內容生成合規性：DeepSeek 的開源特性讓第三方可修改與訓練模型，可能導致難以監管的內容產出。
3. 產業競爭影響：部分大型 AI 企業可能會受到 DeepSeek 低成本策略的衝擊，因此市場動態正在變化。
DeepSeek 技術解析
DeepSeek 的模型架構與混合專家架構（MoE）是什麼？
DeepSeek 採用了 混合專家架構（Mixture of Experts, MoE），這是一種先進的深度學習技術，能夠提升模型的處理效率與準確性。MoE 的核心思想是將模型劃分為多個「專家模組」，每個模組負責處理特定類型的輸入。當模型接收到指令時，一個門控網絡（Gating Network）會根據輸入內容，自動選擇最適合的專家模組進行運算，從而實現高效能的推理與生成能力。這種架構讓 DeepSeek 在處理複雜任務時能更有效分配資源，減少不必要的計算開銷，並在多樣化應用場景下保持高效運行。
DeepSeek 如何利用強化學習（RL）來增強模型能力？
DeepSeek 使用 強化學習（Reinforcement Learning, RL） 技術來進一步提升模型的準確性與適應性。例如：
* RLHF, Reinforcement Learning from Human Feedback（人類回饋強化學習）：透過人類標註數據來訓練 AI，使其回應更符合使用者需求。
* RLAIF, Reinforcement Learning from AI Feedback（AI 自己評估回應）：利用 AI 自動評估與調整回應質量，加快學習速度。 這些技術讓 DeepSeek 能夠在語言理解、邏輯推理與生成內容上持續進化，提供更準確且高效的回應。
DeepSeek 的訓練成本和效能如何？
DeepSeek 透過高效訓練方法與 MoE 架構，降低成本並提升效能。DeepSeek 的目標是讓更多人能夠使用 AI 技術，因此在成本控制方面做了很多努力。具體的訓練成本和效能數據可能因不同的模型版本和訓練數據而有所不同。
DeepSeek 整合與應用
DeepSeek是否能與其他應用程式整合？
DeepSeek 提供了 API 介面，開發者可以將 DeepSeek 的功能整合到各種應用程式中。這意味著您可以將 DeepSeek 的強大 AI 能力應用於您的產品和服務中，例如將 DeepSeek Chat 整合到您的客服系統中，或將 DeepSeek Coder 整合到您的開發工具中。透過 API 整合，DeepSeek 可以為您的應用程式帶來更多價值和創新。
DeepSeek 的推理能力如何？
DeepSeek 具有出色的推理能力，可以根據用戶提出的問題，進行深入的分析和理解，並給出合理的答案。DeepSeek 的推理能力得益於其強大的語言模型和先進的算法。DeepSeek 不僅可以回答簡單的問題，還可以處理複雜的邏輯推理和知識推理任務。
DeepSeek 與雲端市場
DeepSeek 在雲端市場的角色？
DeepSeek 在雲端市場中扮演著多重角色，它既是強大的 AI 模型供應商，提供 DeepSeek Chat 和 DeepSeek Coder 等模型，也與多家雲端服務商建立合作關係，將模型部署在這些平台上，共同推動 AI 技術的應用。同時，DeepSeek 也以其高性能和低成本的優勢，成為市場的有力競爭者，促使整個雲端 AI 市場不斷發展和創新。
DeepSeek 的崛起對 AI 產業與雲端市場有何影響？
DeepSeek 的興起正在改變 AI 產業的市場格局，主要影響包括：
1. 開源生態系統壯大：許多企業開始關注開源 AI，並投入資源進行客製化開發。
2. 新穎架構與成本優勢：DeepSeek 提供高效能但相對低成本的解決方案，使企業能夠降低 AI 服務開銷。
3. 大型 AI 企業價格競爭：隨著 DeepSeek 進入市場，OpenAI、Google、Anthropic 等 AI 大廠可能會調整價格或推出新的策略來應對競爭。
4. 政府與企業監管提升：部分國家已開始對 AI 產品進行更嚴格的審查，以確保技術的安全性與合法合規。
企業與開發者如何應對？
企業可以與 DeepSeek 或其他 AI 模型供應商合作，將其技術整合到自己的產品和服務中，提升競爭力。同時，企業應重視 AI 人才的培養和引進。開發者可以積極參與到 DeepSeek 的開源生態中，利用其提供的 API 和工具，開發出更多創新應用。此外，企業和開發者都應關注 AI 倫理與安全問題，確保 AI 技術的健康發展。
如果您對人工智慧解決方案有任何疑問或需求，iKala 作為 AI 專家，可以為您提供專業支持，協助您選擇最適合的技術，以提升企業效能與競爭力。歡迎聯繫我們！
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人工智能：杞人憂天還是夢魘成真 -- Matt Egan
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The ‘godfather of AI’ reveals the only way humanity can survive superintelligent AI
Matt Egan, CNN, 08/14/25
Geoffrey Hinton, known as the “godfather of AI,” fears the technology he helped build could wipe out humanity — and “tech bros” are taking the wrong approach to stop it.
Hinton, a Nobel Prize-winning computer scientist and a former Google executive, has warned in the past that there is a 10% to 20% chance that AI wipes out humans. On Tuesday, he expressed doubts about how tech companies are trying to ensure humans remain “dominant” over “submissive” AI systems.
“That’s not going to work. They’re going to be much smarter than us. They’re going to have all sorts of ways to get around that,” Hinton said at Ai4, an industry conference in Las Vegas.
In the future, Hinton warned, AI systems might be able to control humans just as easily as an adult can bribe 3-year-old with candy. This year has already seen examples of AI systems willing to deceive, cheat and steal to achieve their goals. For example, to avoid being replaced, one AI model tried to blackmail an engineer about an affair it learned about in an email.
Instead of forcing AI to submit to humans, Hinton presented an intriguing solution: building “maternal instincts” into AI models, so “they really care about people” even once the technology becomes more powerful and smarter than humans.
AI systems “will very quickly develop two subgoals, if they’re smart: One is to stay alive… (and) the other subgoal is to get more control,” Hinton said. “There is good reason to believe that any kind of agentic AI will try to stay alive.”
That’s why it is important to foster a sense of compassion for people, Hinton argued. At the conference, he noted that mothers have instincts and social pressure to care for their babies.
“The right model is the only model we have of a more intelligent thing being controlled by a less intelligent thing, which is a mother being controlled by her baby,” Hinton said.
‘The only good outcome’
Hinton said it’s not clear to him exactly how that can be done technically but stressed it’s critical researchers work on it.
“That’s the only good outcome. If it’s not going to parent me, it’s going to replace me,” he said. “These super-intelligent caring AI mothers, most of them won’t want to get rid of the maternal instinct because they don’t want us to die.”
Hinton is known for his pioneering work on neural networks, which helped pave the way to today’s AI boom. In 2023, he stepped down from Google and started speaking out about the dangers of AI.
Not everyone is on board with Hinton’s mother AI approach.
Fei-Fei Li, known as the “godmother of AI” for her pioneering work in the field, told CNN on Wednesday that she respectfully disagrees with Hinton, her longtime friend.
“I think that’s the wrong way to frame it,” Li, the co-founder and CEO of spatial intelligence startup World Labs, said during a fireside chat at Ai4.
Instead, Li is calling for “human-centered AI that preserves human dignity and human agency.”
“It’s our responsibility at every single level to create and use technology in the most responsible way. And at no moment, not a single human should be asked or should choose to let go of our dignity,” Li said. “Just because a tool is powerful, as a mother, as an educator and as an inventor, I really believe this is the core of how AI should be centered.”
Emmett Shear, who briefly served as interim CEO of ChatGPT owner OpenAI, said he’s not surprised that some AI systems have tried to blackmail humans or bypass shutdown orders.
“This keeps happening. This is not going to stop happening,” Shear, the CEO of AI alignment startup Softmax, said at the Ai4 conference. “AIs today are relatively weak, but they’re getting stronger really fast.”
Shear said that rather than trying to instill human values into AI systems, a smarter approach would be to forge collaborative relationships between humans and AI.
AI is accelerating faster than expected
Many experts believe AIs will achieve superintelligence, also known as artificial general intelligence, or AGI, in the coming years.
Hinton said he used to think it could take 30 years to 50 years to achieve AGI but now sees this moment coming sooner.
“A reasonable bet is sometime between five and 20 years,” he said.
While Hinton remains concerned about what could go wrong with AI, he is hopeful the technology will pave the way to medical breakthroughs.
“We’re going to see radical new drugs. We are going to get much better cancer treatment than the present,” he said. For instance, he said AI will help doctors comb through and correlate the vast amounts of data produced by MRI and CT scans.
However, Hinton does not believe AI will help humans achieve immortality.
“I don’t believe we’ll live forever,” Hinton said. “I think living forever would be a big mistake. Do you want the world run by 200-year-old white men?”
Asked if there’s anything he would have done differently in his career if he knew how fast AI would accelerate, Hinton said he regrets solely focusing on getting AI to work.
“I wish I’d thought about safety issues, too,” he said.
For more CNN news and newsletters create an account at CNN.com
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人工智能與智慧財產權外一章 - Dade Hayes
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借機會吃川普豆腐。請參考本欄2025/06/28貼文。
Robert Thomson, CEO Of Rupert Murdoch’s News Corp, Waggishly Notes That Donald Trump Is Among Authors Hurt By “Blatant Theft” Of AI: “The Art Of The Deal Has Become The Art Of The Steal”
Dade Hayes, 08/06/25
Robert Thomson, CEO of Rupert Murdoch’s News Corp., which was sued last month by Donald Trump, found a crafty way to bring Trump back into the conversation Tuesday.
The feisty and puckish exec, whose signature communiqués often include alliteration and high-flown language, cited Trump in the company’s fiscal fourth quarter earnings report. Trump’s suit was prompted by an enterprise report in News Corp’s Wall Street Journal exploring ties between the president and convicted pedophile Jeffrey Epstein.
Thomson’s reference cleverly avoided any direct reference to Epstein or the lawsuit, and it came after some signs of a thaw in the hot-and-cold relations between Murdoch and Trump.
In the company’s earnings release and in prepared remarks on a subsequent conference call with investors, Thomson noted the irony of Trump as an IP holder being victimized by AI. That circumstance has developed even as the president has made several moves to support major tech firms in their development of AI. Last January, he hosted OpenAI chief Sam Altman, Oracle chairman Larry Ellison and others at the White House to announce Project Stargate, an initiative targeting $500 billion in AI infrastructure spending in the U.S.
Publishers and media companies like News Corp have selectively made deals with AI firms in an effort to wring revenue from the incursion of the technology. They also have been willing to explore their legal options to fight against what they consider to be theft of their property, as in News Corp’s lawsuit last fall against major AI firm Perplexity. The suit claims that the Jeff Bezos-backed purveyor of chatbots and large-language models was trained in part on News Corp properties like the Journal, the New York Post and works published by its HarperCollins book division.
“The AI age must cherish the value of intellectual property if we are collectively to realize our potential,” Thomson said in the earnings release. “Much is made of the competition with China, but America’s advantage is ingenuity and creativity, not bits and bytes, not watts but wit. To undermine that comparative advantage by stripping away IP rights is to vandalize our virtuosity.
“Even the President of the United States is not immune to this blatant theft. The President’s books are still reporting healthy sales, but are being consumed by AI engines which profit from his thoughts by cannibalizing his concepts, thus undermining future sales of his books. Suddenly, The Art of the Deal has become The Art of the Steal.”
During the call, Thompson said Trump’s books (including the still-commercial 1987 title Art of the Deal) are being victimized by the very technology the president is championing.
“Is it fair that creators are having their works purloined? Is it just that the President of the United States is being ripped off?” Thomson asked.
“Companies are spending tens of billions on data centers, tens of billions on chips, tens of billions on energy generation – these same companies need to spend tens of millions or more on the content crucial for their success. And they need to ensure that the content eco-system remains healthy, that there is a vast range of varied and verifiable sources, and that a deeply derivative Woke AI does not become the default pathway to digital decay.
“In the meantime, we will fight to protect the intellectual property of our authors and journalists, and continue to woo and to sue companies that violate the most basic property rights.”
Asked during a Q&A period with Wall Street analysts during the call about any negatives from AI summaries provided by Google and other AI players, which are starting to siphon web traffic away from publishers, Thomson said nothing material has been noted as of yet. He said the company’s approach to AI companies is a “woo-and-sue” combination. Just like computing and energy resources, AI companies need large amounts of legally cleared content to feed into their models. “AI runs on IP,” Thomson said.
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