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Hitching a struggling media industry to the wagon of AI wont serve our interests in the long run

Before we start, I want to let you know that a human wrote this article. The same cant be said for many articles from News Corp, which is reportedly using generative AI to produce 3,000 Australian news stories per week. It isnt alone. Media corporations around the world are increasingly using AI to generate content.

By now, I hope its common knowledge that large language models such as GPT-4 do not produce facts; rather, they predict language. We can think of ChatGPT as an automated mansplaining machine often wrong, but always confident. Even with assurances of human oversight, we should be concerned when material generated this way is repackaged as journalism. Aside from the issues of inaccuracy and misinformation, it also makes for truly awful reading.

Content farms are nothing new; media outlets were publishing trash long before the arrival of ChatGPT. What has changed is the speed, scale and spread of this chaff. For better or worse, News Corp has huge reach across Australia so its use of AI warrants attention. The generation of this material appears to be limited to local service information churned out en masse, such as stories about where to find the cheapest fuel or traffic updates. Yet we shouldnt be too reassured because it does signal where things might be headed.

In January, tech news outlet CNET was caught publishing articles generated by AI that were riddled with errors. Since then, many readers have been bracing themselves for an onslaught of AI generated reporting. Meanwhile, CNET workers and Hollywood writers alike are unionising and striking in protest of (among other things) AI-generated writing, and they are calling for better protections and accountability regarding the use of AI. So, is it time for Australian journalists to join the call for AI regulation?

The use of generative AI is part of a broader shift of mainstream media organisations towards acting like digital platforms that are data-hungry, algorithmically optimised, and desperate to monetise our attention. Media corporations opposition to crucial reforms to the Privacy Act, which would help impede this behaviour and better protect us online, makes this strategy abundantly clear. The longstanding problem of dwindling profits in traditional media in the digital economy has led some outlets to adopt digital platforms surveillance capitalism business model. After all, if you cant beat em, join em. Adding AI generated content into the mix will make things worse, not better.

What happens when the web becomes dominated by so much AI generated content that new models are trained not on human-made material, but on AI outputs? Will we be left with some kind of cursed digital ouroboros eating its own tail?

Its what Jathan Sadowski has dubbed Habsburg AI, referring to an infamously inbred European royal dynasty. Habsburg AI is a system that is so heavily trained on the outputs of other generative AIs that it becomes an inbred mutant, replete with exaggerated, grotesque features.

As it turns out, research suggests that large language models, like the one that powers ChatGPT, quickly collapse when the data they are trained on is created by other AIs instead of original material from humans. Other research found that without fresh data, an autophagous loop is created, doomed to a progressive decline in the quality of content. One researcher said were about to fill the internet with blah. Media organisations using AI to generate a huge amount of content are accelerating the problem. But maybe this is cause for a dark optimism; rampant AI generated content could seed its own destruction.

AI in the media doesnt have to be bad news. There are other AI applications that could benefit the public. For example, it can improve accessibility by helping with tasks such as transcribing audio content, generating image descriptions, or facilitating text-to-speech delivery. These are genuinely exciting applications.

Hitching a struggling media industry to the wagon of generative AI and surveillance capitalism wont serve Australias interests in the long run. People in regional areas deserve better, genuine, local reporting, and Australian journalists deserve protection from the encroachment of AI on their jobs. Australia needs a strong, sustainable and diverse media to hold those in power to account and keep people informed rather than a system that replicates the woes exported from Silicon Valley.

Samantha Floreani is a digital rights activist and writer based in Naarm

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Is artificial intelligence a threat to journalism or will the technology destroy itself? - The Guardian

Amidst the excitement surrounding ChatGPT and the impressive power and potential of artificial intelligence (AI), the impact on the environment has been somewhat overlooked.

Analysts predict that AI's carbon footprint could be as bad if not worse than bitcoin mining, which currently generates more greenhouse gases than entire countries.

Record-shattering heat across land, sky, and seas suggests this is the last thing our fragile life support systems need.

Currently, the entire IT industry is responsible for around 2 percent of global CO2 emissions. If the AI industry continues along its current trajectory, it will consume 3.5 percent of global electricity by 2030, predicts consulting firm Gartner.

"Fundamentally speaking, if you do want to save the planet with AI, you have to consider also the environmental footprint," Sasha Luccioni, an ethics researcher at the open-source machine learning platform Hugging Face, told The Guardian.

"It doesn't make sense to burn a forest and then use AI to track deforestation."

Open.AI spends an estimated US$700,000 per day on computing costs alone in order to deliver its chatbot service to more than 100 million users worldwide.

The popularity of Microsoft-backed ChatGPT has set off an arms race between the tech giants, with Google and Amazon quickly deploying resources to generate natural language processing systems of their own.

Many companies have banned the use of ChatGPT but are developing their own AI in-house.

Like cryptocurrency mining, AI depends on high-powered graphics processing units to crunch data. ChatGPT is powered by gigantic data centers using tens of thousands of these energy-hungry computer chips.

The total environmental impact of ChatGPT and other AI systems is complex to calculate, and much of the information required to do so is not available to researchers.

"Obviously these companies don't like to disclose what model they are using and how much carbon it emits," computer scientist Roy Schwartz from the Hebrew University of Jerusalem told Bloomberg.

It's also hard to predict exactly how much AI will scale up over the next few years, or how energy-efficient it will become.

Researchers have estimated that training GPT-3, the predecessor of ChatGPT, on a database of more than 500 billion words would have taken 1,287 megawatt hours of electricity and 10,000 computer chips.

The same amount of energy would power around 121 homes for a year in the United States.

This training process would have produced around 550 tonnes of carbon dioxide, which is equivalent to flying from Australia to the UK 33 times.

GPT-4, the version released in July, was trained on 570 times more parameters than GPT-3, suggesting it might use more energy than its predecessors.

Another language model called BLOOM was found to consume 433 megawatt hours of electricity when it was trained on 1.6 terabytes of data.

If the growth of the AI sector is anything like cryptocurrency, it's only going to become more energy-intensive over time.

Bitcoin now consumes 66 times more energy than it did in 2015, so much energy that China and New York have banned cryptocurrency mining.

Computers must complete lengthy calculations to mine crypto, and it can take up to a month to earn a single bitcoin.

Bitcoin mining burns through 137 million megawatt hours a year of electricity, with a carbon footprint that is almost as large as New Zealand.

Innovation and protecting Earth's limited resources require a careful balancing act.

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AI Keeps Using More And More Energy. Where Will It End? - ScienceAlert

Shares of Marvell Technology (MRVL 0.55%) have been red-hot on the stock market over the past three months, surging more than 70% thanks to the company's fiscal 2024 first-quarter results, which were releasedat the end of May.

The chipmaker shot up nearly 30% in a single session following its earnings report for the period ended April 29, as it beat Wall Street's expectations on revenue and earnings, and pointedout that its "revenue growth will accelerate in the second half of the fiscal year." Artificial intelligence (AI) is going to play an important role in boosting Marvell's growth, as management said on the company's May earnings conference call.

That's why investors looking for a stock to benefit from the rapid adoption of AI technology may want to act before the company releases its fiscal 2024 second-quarter results on Aug. 24.

Marvell Technology's fiscal Q2 results likely won't be great. The company, whose chips are deployed in multiple end markets such as data centers, telecom infrastructure, enterprise networking, and automotive, expects revenue of $1.33 billion for the three months that ended in July 2023. It expects non-GAAP earnings of $0.32 per share for the quarter.

Those numbers point toward a substantial decline over the prior-year period's adjusted earnings of $0.57 per share on revenue of $1.52 billion. But this hasn't deterred investors from buying Marvell stock hand over fist in the past couple of months due to one reason -- AI. Marvell CEO Matt Murphy is expecting AI to contribute significantly to Marvell's business. He said he expects"Marvell's overall AI revenue to at least double in fiscal 2024," and it won't be surprising to see this catalyst help the company deliver stronger-than-expected growth.

But this is just the beginning, as Murphy added on the company's previous conference call: "In aggregate, we foresee our overall AI revenue to at least double again next year. In other words, we are forecasting an AI revenue growth CAGR over 100% over the fiscal 2023 to 2025 timeframe."

However, it is worth noting that AI could drive Marvell's growth for years to come as the growing adoption of this technology is going to create the need for more storage and faster connections within data centers. Murphy explained this on the earnings call:

To give you an idea, the latest dual CPU server in a cloud data center today can drive up to 200 gigabits per second of IO and contains the network interfaces to support that bandwidth. In contrast, an example of an advanced AI system containing eight accelerators can drive close to 30 terabits of full duplex bandwidth. That's hundreds of times more bandwidth required to connect these systems together.

As AI data centers are equipped with multipleaccelerators to tackle massive workloads, Marvell expects this technology to significantly increase its revenue opportunity in the long run. This explains why Marvell has significantly bumped its revenue expectations from AI-specific chips that will be deployed in the cloud, statingthat "the relative proportion of projected lifetime revenue from AI has increased from approximately 20% in our prior forecast to well over half today."

The good part is that catalysts such as AI could play an important role in boosting Marvell's top and bottom lines. The following chart shows that Marvell's growth is expected to pick up from the next fiscal year.

MRVL Revenue Estimates for Current Fiscal Year data by YCharts

Even better, analysts are expecting Marvell's earnings to increaseat an annual rate of 14% for the next five years. However, it won't be surprising to see Marvell deliver faster growth than what analysts are looking for given the fast-growing nature of the AI chip market. Next Move Strategy Consulting estimates that the global AI chip market could be worth $304 billion in 2030 compared to just under $29 billion last year.

Marvell stock is now tradingat 38 times forward earnings. While that's well above the forward earnings multiple of 16 at the end of 2022, this semiconductor stock still looks like a good bet thanks to a couple of reasons.

First, Marvell's forward earnings multiple is relatively cheaper than other AI stocks. Nvidia, for instance, tradesat 55 times forward earnings. Micron Technology, which is another company that could win big from the AI revolution, has a forward earnings multiple of77.

Second, the potential acceleration in Marvell's growth and the solid long-term prospects driven by AI could make the stock look cheap in the long run. As such, if you're looking to add an AI stock to your portfolio, it may be a good idea to consider going long Marvell stock as a strong set of earnings later this month could send the stock higher and make it more expensive.

Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Nvidia. The Motley Fool recommends Marvell Technology. The Motley Fool has a disclosure policy.

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This AI Stock Could Soar Big-Time This Month, and It Is a Screaming ... - The Motley Fool

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Bots are usually the last thing you want to encounter on a dating app while swiping through endless photos of people standing atop mountains, but Tinder is preparing to harness the power of AI for the good of your profile.

The app, which already uses artificial intelligence in its matching algorithms, has started testing new customer-facing tools. One sifts through your photos and selects the best five to display (hopefully with the good sense to avoid the ambiguous group shot), addressing a pain point where many users get flustered when building their profile.

Its just the beginning of the AI dating revolution. Tinders parent company, Match Group, which also owns Hinge and OKCupid, revealed plans in its recent earnings announcement to integrate new AI features across its brandsnot just to help build profiles but also to highlight why someone may be a good match.

And the company cant control whether daters bring AI into the mix themselves: A recent UK study found that over 50% of single men would use chatbots to help chat up potential dates.

Lo-fi alternatives: As dating apps go increasingly high-tech, some singles are turning to more staid Google Docs and creating long-form date-me docs to find romance outside the app grind.AR

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AI is getting into the dating game - Morning Brew

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All organizations are eager to harness the productivity gains of generative AI, starting with ChatGPT, despite the security threat of their confidential data being leaked into large language models (LLMs). CISOs tell VentureBeat theyre split on the issue, with AI governance becoming a hot topic in risk management discussions with boards of directors.

Alex Philips, CIO at National Oilwell Varco (NOV), told VentureBeat in an interview earlier this year that hes taking an education-centric approach to keep his board of directors up to date on the latest advantages, risks and current state of gen AI technologies. Philips says having an ongoing educational process helps set expectations about what gen AI can and cant do, and helps NOV put guardrails in place to avert confidential data leaks.

Several healthcare CISOs and CIOs are restricting ChatGPT access across all research and development, pricing and licensing business units. VentureBeat has learned that CISOs are divided on if and how they manage the security threat of confidential data finding its way into LLMs. Not having gen AI as a research tool is a competitive disadvantage healthcare providers are willing to go without as the risks to their intellectual property, pricing and licensing are too great.

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The challenge is to keep confidential data secure while allowing employees to be more productive using gen AI and ChatGPT at the browser, app and API levels. Cloud data loss prevention (DLP) platform Nightfall AI today announced the first data security platform for gen AI that spans API browser and Software-as-a-Service (SaaS) application gen AI protection.

Designed to take on the productivity paradox CISOs and CIOs are facing when it comes to gen AI in their organizations, Nightfall AIs platform is the first DLP platform that scales across the three top threat vectors CISOs need the most help securing when gen AI and ChatGPT are in use across their organizations. The goal is to enable organizations to securely use AIs benefits while protecting sensitive data and reducing risk.

The Nightfall for GenAI data security platform consists of three products that include:

Nightfall for ChatGPT. Nightfall AIs browser-based solution provides real-time scanning and redaction of sensitive data entered by employees into chatbots before exposure. Providing a browser-based extension is one of the less obtrusive ways to protect data because its a technique that lends itself well to minimizing the impact on users experiences. Nightfall AI CEO Isaac Madan told VentureBeat that the experiences users have with Nightfall for ChatGPT formed the foundation of the products design goals.

Madan says the initial browsers supported include Apple Safari, Google Chrome and Microsoft Edge.

Eric Cohen, Vice President of Security at Genesys, considers Nightfall for ChatGPT a breakthrough in providing colleagues in Genesys with access to gen AI products while reducing the risk. Cohen told VentureBeat that the ideal is for Nightfall AI to take a collaborative approach to help users self-remediate data risks without requiring them to be generative AI experts.

Nightfall for LLMs: APIs are one of Nightfall AIs core strengths, reflected in how theyve taken on the challenge of enabling enterprises at scale to achieve productivity gains from gen AI. Nightfall for LLMs is a developer API that detects and redacts data developers input to train LLMs combined into a software development kit (SDK). Many industry leaders have already integrated these APIs into their workflows.

Cohen told VentureBeat that Nightfall AIs API strategy provides the customizability and flexibility Genesys needs to scale gen AI protection across their organization and tech stacks. Nightfall AI also provides insights into redaction rates, adding greater insights and learning into how gen AI can be securely used for greater productivity, he said.

Nightfall for SaaS: Nightfall for SaaS provides data leak prevention directly within the workflows of popular SaaS applications, allowing companies to detect and redact sensitive data as third-party AI systems are processing it. This prevents sensitive information from being exposed in chatbot conversations, documents, cloud storage and other SaaS apps. Nightfall for SaaS has been implemented by MovableInk, Aarons and Klaviyo, who need to secure customer data within their SaaS ecosystems. By natively leveraging Nightfalls DLP capabilities within these apps, these companies can leverage third-party AI while maintaining control and visibility into their sensitive data.

All of these products are available today to explore. Nightfall for ChatGPT is available on the Google Chrome store as part of a 14-day free trial Nightfall AI offers.

Securing the future of generative AIs productivity gains

Cohen told VentureBeat that gen AIs productivity is integral to enabling Genesys to continue excelling for their clients. Generative AI offers significant productivity gains for organizations across teams but until Nightfall AI, there was a lack of security products that allowed us to use these tools safely, he said. Cohen found Nightfall AI while actively researching DLP solutions to solve a data privacy problem Genesys was facing. The customizability of Nightfalls data rules presented an advantage over other options he had looked into.

CISOs tell VentureBeat they have three main concerns regarding adopting GenAI as a research and productivity platform. First, theyre concerned that employees will include sensitive data (such as software credentials or customer PII) in chatbot prompts. Second, theyre worried that employees might inadvertently expose confidential company data using SaaS apps such as Notion that use third-party AI sub-processors such as Anthropic. Lastly, their third concern revolves around engineers and data scientists using confidential data to build and train their LLMs. This last concern is underscored by a recent incident where users tricked ChatGPT into generating active API keys for Windows.

GenAI has the potential to offer substantial productivity benefits for employers and employees, but the lack of a complete DLP solution is impeding the safe adoption of AI, said Madan. As a result, many organizations have either completely blocked these tools, or have resorted to using multiple security products as a patchwork solution to mitigate the risk. This struggle ultimately drove the creation of Nightfalls latest innovation: Nightfall for GenAI.

Frederic Kerrest, cofounder and executive vice chairman of Okta, commended Nightfall and compares its latest initiatives to Oktas early days. When using Nightfall, I have seen many similarities with our early vision at Okta, where we centralized user access and management security for all cloud apps. Nightfall is now doing the same for data security across generative AI and the cloud.

Early adopters like Genesys highlight the benefits of Nightfalls customizable data rules and remediation insights that help users self-correct. For CISOs, the platform provides the visibility and control needed to leverage AI while maintaining data security confidently. The availability of Nightfalls gen AI-focused platform marks an important milestone in realizing AIs potential.

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Protecting data in the era of generative AI: Nightfall AI launches ... - VentureBeat