Category: Artificial Intelligence

Investors displayed cautious optimism as the crypto market stayed neutral for the previous few days. Today the Fear and Greed Index is showing 67, which indicates greed. This is the perfect moment for investors to diversify their portfolios, with cryptos already showing massive growth potential. 

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$SUBBD Tokenomics Uncovered

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In order for the project to keep evolving, 20% is set aside for the project development, while 18% of the tokens will be directed towards the Stock Exchange Liquidity. The remaining tokens will be divided between airdrops (10%), community rewards (7%), staking rewards (5%), cash balance (5%), and creators’ rewards (5%)

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SUBBD Roadmap Revealed

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I’m a writing professor who sees artificial intelligence as more of an opportunity for students, rather than a threat.

That sets me apart from some of my colleagues, who fear that AI is accelerating a glut of superficial content, impeding critical thinking and hindering creative expression. They worry that students are simply using it out of sheer laziness or, worse, to cheat.

Perhaps that’s why so many students are afraid to admit that they use ChatGPT.

In The New Yorker magazine, historian D. Graham Burnett recounts asking his undergraduate and graduate students at Princeton whether they’d ever used ChatGPT. No one raised their hand.

“It’s not that they’re dishonest,” he writes. “It’s that they’re paralyzed.”

Students seem to have internalized the belief that using AI for their coursework is somehow wrong. Yet, whether my colleagues like it or not, most college students are using it.

A February 2025 report from the Higher Education Policy Institute in the U.K. found that 92% of university students are using AI in some form. As early as August 2023 – a mere nine months after ChatGPT’s public release – more than half of first-year students at Kennesaw State University, the public research institution where I teach, reported that they believed that AI is the future of writing.

It’s clear that students aren’t going to magically stop using AI. So I think it’s important to point out some ways in which AI can actually be a useful tool that enhances, rather than hampers, the writing process.

Helping with the busywork

A February 2025 OpenAI report on ChatGPT use among college-aged users found that more than one-quarter of their ChatGPT conversations were education-related.

The report also revealed that the top five uses for students were writing-centered: starting papers and projects (49%); summarizing long texts (48%); brainstorming creative projects (45%); exploring new topics (44%); and revising writing (44%).

These figures challenge the assumption that students use AI merely to cheat or write entire papers.

Instead, it suggests they are leveraging AI to free up more time to engage in deeper processes and metacognitive behaviors – deliberately organizing ideas, honing arguments and refining style.

If AI allows students to automate routine cognitive tasks – like information retrieval or ensuring that verb tenses are consistent – it doesn’t mean they’re thinking less. It means their thinking is changing.

Of course, students can misuse AI if they use the technology passively, reflexively accepting its outputs and ideas. And overreliance on ChatGPT can erode a student’s unique voice or style.

However, as long as students learn how to use AI intentionally, this shift can be seen as an opportunity, rather than a loss,

Clarifying the creative vision

It has also become clear that AI, when used responsibly, can augment human creativity.

For example, science comedy writer Sarah Rose Siskind recently gave a talk to Harvard students about her creative process. She spoke about how she uses ChatGPT to brainstorm joke setups and explore various comedic scenarios, which allows her to focus on crafting punchlines and refining her comedic timing.

Note how Siskin used AI in ways that didn’t supplant the human touch. Instead of replacing her creativity, AI amplified it by providing structured and consistent feedback, giving her more time to polish her jokes.

Another example is the Rhetorical Prompting Method, which I developed alongside fellow Kennesaw State University researchers. Designed for university students and adult learners, it’s a framework for conversing with an AI chatbot, one that emphasizes the importance of agency in guiding AI outputs.

When writers use precise language to prompt, critical thinking to reflect, and intentional revision to sculpt inputs and outputs, they direct AI to help them generate content that aligns with their vision.

There’s still a process

The Rhetorical Prompting Method mirrors best practices in process writing, which encourages writers to revisit, refine and revise their drafts.

When using ChatGPT, though, it’s all about thoughtfully revisiting and revising prompts and outputs.

For instance, say a student wants to create a compelling PSA for social media to encourage campus composting. She considers her audience. She prompts ChatGPT to draft a short, upbeat message in under 50 words that’s geared to college students.

Reading the first output, she notices it lacks urgency. So she revises the prompt to emphasize immediate impact. She also adds some additional specifics that are important to her message, such as the location of an information session. The final PSA reads:

“Every scrap counts! Join campus composting today at the Commons. Your leftovers aren’t trash – they’re tomorrow’s gardens. Help our university bloom brighter, one compost bin at a time.”

The Rhetorical Prompting Method isn’t groundbreaking; it’s riffing on a process that’s been tested in the writing studies discipline for decades. But I’ve found that it works by directing writers how to intentionally prompt.

I know this because we asked users about their experiences. In an ongoing study, my colleagues and I polled 133 people who used the Rhetorical Prompting Method for their academic and professional writing:

• 92% reported that it helped them evaluate writing choices before and during their process.

• 75% said that they were able to maintain their authentic voice while using AI assistance.

• 89% responded that it helped them think critically about their writing.

The data suggests that learners take their writing seriously. Their responses reveal that they are thinking carefully about their writing styles and strategies. While this data is preliminary, we continue to gather responses in different courses, disciplines and learning environments.

All of this is to say that, while there are divergent points of view over when and where it’s appropriate to use AI, students are certainly using it. And being provided with a framework can help them think more deeply about their writing.

AI, then, is not just a tool that’s useful for trivial tasks. It can be an asset for creativity. If today’s students – who are actively using AI to write, revise and explore ideas – see AI as a writing partner, I think it’s a good idea for professors to start thinking about helping them learn the best ways to work with it.

By Khari Johnson, CalMatters

This story was originally published by CalMatters. Sign up for their newsletters.

California’s first-in-the-nation privacy agency is retreating from an attempt to regulate artificial intelligence and other forms of computer automation.

The California Privacy Protection Agency was under pressure to back away from draft rules. Business groups, lawmakers, and Gov. Gavin Newsom said they would be costly to businesses, potentially stifle innovation, and usurp the authority of the legislature, where proposed AI regulations have proliferated. In a unanimous vote last week, the agency’s board watered down the rules, which impose safeguards on AI-like systems.

Agency staff estimate that the changes reduce the cost for businesses to comply in the first year of enforcement from $834 million to $143 million and predict that 90% percent of businesses initially required to comply will no longer have to do so.

The retreat marks an important turn in an ongoing and heated debate over the board’s role. Created following the passage of state privacy legislation by lawmakers in 2018 and voters in 2020, the agency is the only body of its kind in the United States. 

The draft rules have been in the works for more than three years, but were revisited after a series of changes at the agency in recent months, including the departure of two leaders seen as pro-consumer, including Vinhcent Le, a board member who led the AI rules drafting process, and Ashkan Soltani, the agency’s executive director.

Consumer advocacy groups worry that the recent shifts mean the agency is deferring excessively to businesses, particularly tech giants.

The changes approved last week mean the agency’s draft rules no longer regulate behavioral advertising, which targets people based on profiles built up from their online activity and personal information. In a prior draft of the rules, businesses would have had to conduct risk assessments before using or implementing such advertising.

Behavioral advertising is used by companies like Google, Meta, and TikTok and their business clients. It can perpetuate inequality, pose a threat to national security, and put children at risk. 

The revised draft rules also eliminate use of the phrase “artificial intelligence” and narrow the range of business activity regulated as “automated decisionmaking,” which also requires assessments of the risks in processing personal information and the safeguards put in place to mitigate them.

Supporters of stronger rules say the narrower definition of “automated decisionmaking” allows employers and corporations to opt out of the rules by claiming that an algorithmic tool is only advisory to human decision making.

“My one concern is that if we’re just calling on industry to identify what a risk assessment looks like in practice, we could reach a position by which they’re writing the exam by which they’re graded,” said board member Brandie Nonnecke during the meeting.

“The CPPA is charged with protecting the data privacy of Californians, and watering down its proposed rules to benefit Big Tech does nothing to achieve that goal,“ said Sacha Haworth, executive director of Tech Oversight Project, an advocacy group focused on challenging policy that reinforces Big Tech power, said in a statement to CalMatters. “By the time these rules are published, what will have been the point?”

The draft rules retain some protections for workers and students in instances when a fully automated system determines outcomes in finance and lending services, housing, and health care without a human in the decisionmaking loop.

Businesses and the organizations that represent them made up 90% of comments about the draft rules before the agency held listening sessions across the state last year, Soltani said in a meeting last year.

In April, following pressure from business groups and legislators to weaken the rules, a coalition of nearly 30 unions, digital rights, and privacy groups wrote a letter together urging the agency to continue work to regulate AI and protect consumers, students, and workers. 

Roughly a week later, Gov. Newsom intervened, sending the agency a letter stating that he agreed with critics that the rules overstepped the agency’s authority and supported a proposal to roll them back.

Newsom cited Proposition 24, the 2020 ballot measure that paved the way for the agency. “The agency can fulfill its obligations to issue the regulations called for by Proposition 24 without venturing into areas beyond its mandate,” the governor wrote.

The original draft rules were great, said Kara Williams, a law fellow at the advocacy group Electronic Privacy Information Center. On a phone call ahead of the vote, she added that ”with each iteration they’ve gotten weaker and weaker, and that seems to correlate pretty directly with pressure from the tech industry and trade association groups so that these regulations are less and less protective for consumers.”

The public has until June 2 to comment on the alteration to draft rules. Companies must comply with automated decisionmaking rules by 2027.

Prior to voting to water down its own regulation last week, at the same meeting the agency board voted to throw its support behind four draft bills in the California Legislature, including one that protects the privacy of people who connect computing devices to their brain and another that prohibits the collection of location data without permission.

This article was originally published on CalMatters and was republished under the Creative Commons Attribution-NonCommercial-NoDerivatives license.

Story Continues

Artificial intelligence (AI) has become a major tailwind for technology businesses over the last couple of years. But just how big of a factor is the AI boom for the world’s largest enterprises?

Consider semiconductor powerhouse Nvidia (NVDA -0.02%) as a prime example. Exactly two years ago, Nvidia’s market capitalization was $700 billion. Today, it is worth north of $2.7 trillion — trailing only Microsoft and Apple as the world’s most valuable companies.

Over those same two years, e-commerce and cloud computing behemoth Amazon (AMZN -0.48%) added just shy of $1 trillion to its own market value. While Amazon trails Nvidia’s valuation today, I think the company could be worth much more than the semiconductor giant by next decade.

Let’s explore how Amazon’s business is transforming thanks to the AI revolution. More importantly, I’ll break down why I think the stock is a no-brainer buying opportunity right now for investors to buy and hold for the long term.

How Amazon is jump-starting its business

Amazon reports its revenue across six major categories: online stores (e-commerce), physical stores, third-party seller services, advertising, subscriptions, and Amazon Web Services (AWS).

E-commerce, brick-and-mortar storefronts, and third-party seller services all touch the retail industry where Amazon has its roots. Over the years, the company did a good job of branching out beyond retail and getting involved in higher-margin opportunities through advertising, Prime subscriptions, and cloud computing (AWS).

While AI has the potential to disrupt all of Amazon’s operational segments, AWS and retail are the two that have me most encouraged.

On the retail side, Amazon is investing heavily in AI robotics. Essentially, the company is outfitting its fulfillment centers with robotic processes that can bring a new level of automation and efficiency to packaging and shipping services. In turn, Amazon should be able to reduce labor costs in its warehouses over time, resulting in greater profitability for its core retail operation.

Over the last couple of years, AWS has been going through something of a renaissance thanks to its $8 billion investment into AI start-up Anthropic. Amazon first partnered with Anthropic in September 2023. At the end of 2023’s third quarter, AWS was operating at a $92 billion annual revenue run rate and with an operating income margin of roughly 30%. As of the end of 2025’s first quarter, AWS’ revenue run rate is over $117 billion while its operating income margin is nearly 39%.

That combination of accelerating revenue and widening profit margins is lucrative. Amazon is also making some major moves to bolster the AWS platform — notably through the development of custom chipsets and heavy investments in data center infrastructure.

Why Nvidia’s business might start to slow down

Amazon is in the early phases of developing its own AI chips. Its “Magnificent Seven” cohorts Microsoft, Meta Platforms, and Alphabet are pursuing similar ambitions.

While I don’t think this will be catastrophic for Nvidia, it likely won’t help it in the long run. As it stands today, each of these megacap technology companies works closely with Nvidia. But as more high-end AI chip options enter the market, theoretically, these businesses won’t need to rely on Nvidia as heavily.

Already, its chipmaking rival Advanced Micro Devices has already been able to win over the likes of Oracle, Meta, and Microsoft as major buyers of its own competing GPUs, so investors might want to prepare for less robust growth in Nvidia’s sales and profits down the road.

Amazon stock is trading at a light valuation… and that could soon change

If the introduction of more chips that rival its GPUs ends up becoming a headwind for Nvidia, and if its revenue and profit gains become less inspiring for growth investors, then I think it’s highly likely that the company’s valuation multiples could compress. On the flip side, Amazon appears to be in the early stages of taking advantage of new opportunities for accelerated growth, particularly in AWS and its retail marketplaces.

Nevertheless, Amazon’s valuation trends suggest that investors are not yet placing much of a premium at all on the company’s efficiency improvements or their long-run potential. When you account for how much the company’s operating profits have grown over the last several years relative to its market cap gains, Amazon looks dirt cheap.

If it keeps up its current trajectory, though, the company should be in a position to continue accelerating sales while minting billions in cash flow in the process. This has me optimistic that Amazon’s valuation could begin to witness notable expansion over the next several years relative to peers such as Nvidia — ultimately becoming a much larger company by 2030.

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The internet search giant submitted the patent application in December, but it was only published publicly by the China National Intellectual Property Administration on Tuesday.

The translation method works by collecting various types of data from a target animal, including “voice, body language, behavioural changes and other biological signs”, according to the application posted on the agency’s website. It then determines the “emotion” by analysing the data before converting it into a target language.

The technology utilises several subfields of AI, including machine learning, which enables computers to learn from large data sets; deep learning, which uses neural networks to analyse voice and movement characteristics; and natural language processing, which applies computational approaches to the analysis and synthesis of natural language and speech, according to Baidu.

If a voice does not match any historical emotional record, the team would manually label the voice data, update the sample and adjust the model parameters.

The publication of the patent application does not mean it has been granted yet. The process of assessing the application could take a year in the best-case scenario, according to You Yunting, a senior partner at Shanghai Debund Law Firm.