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                    [title] => Apple Looks to AI to End iPhone Slump - The Wall Street Journal
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                    [url] => https://www.wsj.com/tech/ai/apple-looks-to-ai-to-end-iphone-slump-570a3279
                    [date] => Mon, 09 Sep 2024 01:00:00 GMT
                    [short_description] => Apple Looks to AI to End iPhone Slump  The Wall Street JournalApple to unveil iPhone 16 and ‘Apple Intelligence’ AI features  The GuardianCan Apple’s iPhones Pass the A.I. Test?  The New York Times
                    [text] => Apple Looks to AI to End iPhone Slump  The Wall Street Journal
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                    [title] => South Korea summit to target 'blueprint' for using AI in the military - Reuters
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                    [url] => https://www.reuters.com/world/asia-pacific/south-korea-summit-target-blueprint-using-ai-military-2024-09-09/
                    [date] => Mon, 09 Sep 2024 04:16:23 GMT
                    [short_description] => South Korea summit to target 'blueprint' for using AI in the military  Reuters
                    [text] => South Korea summit to target 'blueprint' for using AI in the military  Reuters
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                    [title] => Another Artificial Intelligence (AI) Stock Just Hiked Its Dividend, This Time by a Whopping 17%
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                    [url] => https://finance.yahoo.com/news/another-artificial-intelligence-ai-stock-093000979.html
                    [date] => Sun, 08 Sep 2024 09:30:00 GMT
                    [short_description] => Another Artificial Intelligence (AI) Stock Just Hiked Its Dividend, This Time by a Whopping 17%  Yahoo Finance
                    [text] => 'Tis the season. The weather gets cooler, kids go back to school, and many technology companies raise their dividends for the year ahead.

While artificial intelligence (AI) technology stocks aren't necessarily known for high dividend payouts, they are known for growth. So if you can find a winning AI name that also pays a dividend, a small payout today could look much, much bigger in five, 10, or 20 or more years.

Two weeks ago, one semiconductor manufacturing company raised its dividend by 15%. But not to be outdone, one of its peers bested it last Tuesday -- by raising its quarterly payout by 17%.

And yet, there are reasons I'm more bullish on the first company, if I had to pick between them.

KLA Corporation delivers an early Christmas

KLA Corporation (NASDAQ: KLAC), which makes semiconductor capital (semicap) equipment, announced Tuesday that it would raise its quarterly payout to shareholders by 17%, from $1.45 per share to $1.70, good for an annual dividend of $6.80. That's an impressive increase, especially since it marks KLA's 15th consecutive year of annual dividend increases.

The new heightened dividend only yields about 0.9% at the current stock price. But that shouldn't necessarily stop dividend investors from looking at KLA. If a company has the ability to outpace inflation with dividend raises for years, it can make for a great long-term holding and wind up paying you a lot in retirement.

KLA's outsized payout increase is a sign of optimism about AI growth, which should be celebrated. But two of its peers also just raised their dividends, and appear to have greater capacity to raise theirs even more in the years ahead.

How KLA is different from Lam and Applied Materials

KLA Corporation is a leader in process control and metrology equipment, which is used to inspect chips and wafers for defects at many points along the semiconductor manufacturing process. These machines are in high demand as chip designs get ever smaller and more complex, especially in the age of AI. Like many large semiconductor equipment names, KLA dominates its respective niche, with well over 50% market share for certain processes.

KLA's dominant position, and the stability of process control and diagnostics relative to other equipment segments, get its stock a more favorable valuation in terms of a higher multiple. The companies that dominate the etch and deposition market, Lam Research (NASDAQ: LRCX) and Applied Materials (NASDAQ: AMAT), tend to trade at lower multiples, even though all three companies have similar long-term growth prospects, profitability, and cyclicality. Lam just raised its dividend by 15% two weeks ago. Applied Materials seems set for a big increase when it raises its dividend (as is typical) in March; six months ago, Applied raised its dividend by 23%.

Which semicap dividends seem primed for the most growth?

To be sure, KLA has proven itself to be a great dividend grower, and should continue to be. But despite its outsized raise today, it may be a little more constrained in how much it can grow its payout, compared to Lam Research or Applied Materials.

This is due to a variety of factors, including overall valuation, current payout ratio (the portion of earnings paid out as dividends), and leverage on the balance sheet:

Data source: Yahoo! Finance. Forward payout ratio = forward dividend divided by current-year earnings estimates.

Now, all three of these look like great companies to own for dividend growth investors, with high margins and relatively low payout ratios.

But KLA does trade at a higher valuation, although it has more than $2 billion in net debt, while both Lam Research and Applied Materials have hefty net cash positions (more cash than debt). It also has a high trailing price-to-earnings (P/E) ratio at 36, much higher than its two peers, and a higher current payout ratio than Lam. So the forward estimates above already assume a lot of growth for KLA's earnings.

Clearly, KLA's dominance of its niche, evidenced by its higher operating margin, is tantalizing for investors. But by the same token, KLA is given credit for that with its higher valuation.

Meanwhile, its earnings power has shown itself to be similar, in terms of growth and cyclicality, to that of the other two companies:

Finally, while KLA has a dominant position now, it's beginning to see some strong competition in the metrology market from both Applied Materials and newcomer Onto Innovation. So while it has higher margins and seems safer at the moment, there probably isn't as much room for market-share or margin increases. Investors are simply assuming that both will continue to rise.

KLA is still a great stock, it's just not preferable to the other two

KLA's big dividend increase is another indicator of the bright long-term future of semicap equipment stocks, especially in the age of AI. No doubt it would be a great stock to own for the long term, and should continue growing its payout above the rate of inflation.

However, given their lower valuations, better balance sheets, and similar growth prospects, Lam or Applied would be my current preferences for future dividend growth.

Should you invest $1,000 in KLA right now?

Before you buy stock in KLA, consider this:

The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and KLA wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years.

Consider when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $630,099!*

Stock Advisor provides investors with an easy-to-follow blueprint for success, including guidance on building a portfolio, regular updates from analysts, and two new stock picks each month. The Stock Advisor service has more than quadrupled the return of S&P 500 since 2002*.

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*Stock Advisor returns as of September 3, 2024

Billy Duberstein and/or his clients have positions in Applied Materials, KLA, Lam Research, and Onto Innovation. The Motley Fool has positions in and recommends Applied Materials and Lam Research. The Motley Fool has a disclosure policy.
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                    [url] => https://www.cnbc.com/2024/09/09/this-swiss-chip-stock-is-set-ride-the-ai-boom-analysts-say-and-one-gives-it-75percent-upside.html
                    [date] => Sun, 08 Sep 2024 23:32:18 GMT
                    [short_description] => This Swiss chip stock is set ride the AI boom, analysts say — and one gives it 75% upside  CNBC
                    [text] => Swiss company VAT Group is poised to benefit significantly from the growth in artificial intelligence, according to analysts at a number of investment banks. Investment firm Vontobel highlighted the company as a key player in the AI chips supply chain. The stock is listed in Switzerland and also trades in the U.S . Vontobel's analyst said that AI could be "the biggest technology shift of our lifetimes," with the global semiconductor industry projected to reach $1 trillion by 2030. "We estimate that AI-related semiconductors will account for 30-40% of the total chip market by 2027," said Michael Foeth, senior equity analyst at Vontabel, in a note to clients on Aug. 5. "Semiconductor equipment technology is enabling this inflection," he added, naming VAT as a key stock set to benefit. VAT Group Vontobel has a "buy" rating on VAT Group, with a price target of 540 Swiss Francs ($641), representing a potential 35% upside. The company, headquartered in Haag, Switzerland, specializes in the development and manufacture of vacuum valves used in semiconductor production. Their products are used by other semiconductor supply chain companies, such as Lam Research , to set up so-called "ultra-clean rooms" for chip production. VAC.N-CH 1Y line Vontobel is not alone in its bullish stance on VAT. Investment bank Jefferies' Olivia Honychurch has a price target of 700 Swiss francs on the stock, indicating an upside of 75%. UBS also notes potential risks associated with ongoing AI-trade tensions between the United States and China, which could impact up to 10% of global semiconductor equipment capital expenditure. FactSet estimates that 25% of the sector's total revenue is derived from China, while U.S. sales comprise 22%. However, RBC Capital Markets — the most bearish among those covering VAT — upgraded the stock to "sector perform" from "underperform" in August, citing confidence in the wafer fab equipment (WFE) market. "We remain overall confident on the WFE market outlook and VAT's role within. Estimates drop somewhat (lower high-margin China business and FX headwinds) without impairing the underlying story," said RBC's Sebastian Kuenne. Comet, Inficon Vontobel also highlighted Comet and Inficon as potential beneficiaries of the AI boom, albeit with more measured expectations. The bank has a "hold" rating on Comet, a provider of x-ray and radio frequency technology, with a price target of 345 Swiss francs, indicating a 10% upside. Inficon, which specializes in making instruments for gas analysis and measurement, also has a "hold" rating from Vontobel, with a price target of 1,270 Swiss francs, suggesting an 8% upside.
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                    [title] => The Newest Artificial Intelligence (AI) Stock in the S&P 500 Is Up 370% Since 2023. Wall Street Says Avoid It.
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                    [url] => https://www.fool.com/investing/2024/09/09/new-ai-stock-sp-500-up-370-since-2023-avoid-it/
                    [date] => Mon, 09 Sep 2024 08:06:00 GMT
                    [short_description] => The Newest Artificial Intelligence (AI) Stock in the S&P 500 Is Up 370% Since 2023. Wall Street Says Avoid It.  The Motley Fool
                    [text] => The S&P 500 (^GSPC -1.73%) measures the performance of 500 companies that meet specific eligibility requirements, including positive earnings over the last four quarters, a market value of at least $18 billion, and a sufficiently liquid stock. The S&P 500 is generally considered to be the best barometer for the overall U.S. stock market.

Palantir Technologies (PLTR 0.56%) will join the benchmark index on Monday, Sept. 23, the date of the next quarterly rebalancing. Palantir shares have skyrocketed 370% since January 2023, a period that essentially coincides with the generative artificial intelligence boom started by ChatGPT.

However, Wall Street analysts are less than optimistic, despite Palantir's increasingly strong position in artificial intelligence software. The stock carries a median 12-month price target of $28 per share, implying 12% downside from its current share price of $32. Here's what investors should know.

Palantir is a recognized leader in artificial intelligence platforms

Palantir specializes in data analytics. Its platform lets businesses collect data, develop machine learning models, and integrate those digital assets into an ontology. The Palantir ontology defines the relationship between those assets and real-world objects, and it lets users surface information through analytical applications that improve decision-making. Management says its ontology-based architecture is its key differentiator.

The Palantir platform comprises several software products. Foundry is used for data processing and analytics, AIP (Artificial Intelligence Platform) lets customers use large language models and generative artificial intelligence within Foundry, and Apollo ensures Foundry is consistently updated across all IT environments. Palantir also provides an alternative to Foundry (called Gotham) originally designed for sensitive government data.

Forrester Research recently recognized Palantir as a leader in artificial intelligence and machine learning platforms. "Palantir is quietly becoming one of the largest players in this market," analysts wrote. Palantir received the highest score for its current product offering, but three competitors -- Alphabet, Databricks, and C3.ai -- received higher scores for their product development strategy.

Palantir's revenue growth continued to accelerate in the second quarter

Palantir reported solid financial results in the second quarter, beating estimates on the top and bottom lines. Revenue increased 27% to $678 million, marking the fifth consecutive sequential acceleration in sales growth. Meanwhile, non-GAAP net income increased 80% to $0.09 per diluted share.

Additionally, Management guided for 25% to 26% revenue growth in the third quarter, comfortably topping the 22% growth analysts anticipated. "The persistent and unbridled demand for our software, for an effective enterprise platform that makes artificial intelligence capabilities useful to large institutions, shows no signs of relenting," CEO Alex Karp wrote in his latest shareholder letter.

Palantir stock trades at an outrageous valuation

Looking ahead, Wall Street expects Palantir's adjusted earnings to increase at 22% annually through 2025. Without additional context, that estimate sounds pretty good. However, Palantir shares currently trade at 100 times adjusted earnings, an outrageous multiple in context. Those figures give a PEG ratio of 4.5.

For perspective, Nvidia currently trades at 47 times adjusted earnings and Wall Street expects its bottom line to grow at 49% annually over the next six quarters, which is the same timeframe I quoted for Palantir. Those figures give a PEG ratio slightly below 1.

Here's what that means: If the market afforded Palantir the same PEG multiple it affords Nvidia, Palantir stock would plunge 30%. Personally, I would avoid the stock until it trades at a more reasonable valuation. That does not mean Palantir shares will nosedive tomorrow. The stock could continue climbing higher in the near term. But without a dramatic acceleration in earnings, I think shares are headed toward a sharp correction at some point in the future
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                    [title] => Stratipath and Roche Join Forces to Help Expand Access to AI-Powered Precision Diagnostics - Yahoo Finance
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                    [date] => Mon, 09 Sep 2024 08:05:00 GMT
                    [short_description] => Stratipath and Roche Join Forces to Help Expand Access to AI-Powered Precision Diagnostics  Yahoo Finance
                    [text] => Stratipath and Roche Join Forces to Help Expand Access to AI-Powered Precision Diagnostics  Yahoo Finance
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                    [title] => There’s a fix for AI-generated essays. Why aren’t we using it?
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                    [url] => https://www.vox.com/future-perfect/370419/chatgpt-schools-ai-cheating-plagiarism-detection
                    [date] => Sat, 07 Sep 2024 11:00:00 GMT
                    [short_description] => There’s a fix for AI-generated essays. Why aren’t we using it?  Vox.com
                    [text] => It’s the start of the school year, and thus the start of a fresh round of discourse on generative AI’s new role in schools. In the space of about three years, essays have gone from a mainstay of classroom education everywhere to a much less useful tool, for one reason: ChatGPT. Estimates of how many students use ChatGPT for essays vary, but it’s commonplace enough to force teachers to adapt.

While generative AI has many limitations, student essays fall into the category of services that they’re very good at: There are lots of examples of essays on the assigned topics in their training data, there’s demand for an enormous volume of such essays, and the standards for prose quality and original research in student essays are not all that high.

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Right now, cheating on essays via the use of AI tools is hard to catch. A number of tools advertise they can verify that text is AI-generated, but they’re not very reliable. Since falsely accusing students of plagiarism is a big deal, these tools would have to be extremely accurate to work at all — and they simply aren’t.

AI fingerprinting with technology

But there is a technical solution here. Back in 2022, a team at OpenAI, led by quantum computing researcher Scott Aaronson, developed a “watermarking” solution that makes AI text virtually unmistakable — even if the end user changes a few words here and there or rearranges text. The solution is a bit technically complicated, but bear with me, because it’s also very interesting.

At its core, the way that AI text generation works is that the AI “guesses” a bunch of possible next tokens given what appears in a text so far. In order not to be overly predictable and produce the same repetitive output constantly, AI models don’t just guess the most probable token — instead, they include an element of randomization, favoring “more likely” completions but sometimes selecting a less likely one.

The watermarking works at this stage. Instead of having the AI generate the next token according to random selection, it has the AI use a nonrandom process: favoring next tokens that get a high score in an internal “scoring” function OpenAI invented. It might, for example, favor words with the letter V just slightly, so that text generated with this scoring rule will have 20 percent more Vs than normal human text (though the actual scoring functions are more complicated than this). Readers wouldn’t normally notice this — in fact, I edited this newsletter to increase the number of Vs in it, and I doubt this variation in my normal writing stood out.

Similarly, the watermarked text will not, at a glance, be different from normal AI output. But it would be straightforward for OpenAI, which knows the secret scoring rule, to evaluate whether a given body of text gets a much higher score on that hidden scoring rule than human-generated text ever would. If, for example, the scoring rule were my above example about the letter V, you could run this newsletter through a verification program and see that it has about 90 Vs in 1,200 words, more than you’d expect based on how often V is used in English. It’s a clever, technically sophisticated solution to a hard problem, and OpenAI has had a working prototype for two years.

So if we wanted to solve the problem of AI text masquerading as human-written text, it’s very much solvable. But OpenAI hasn’t released their watermarking system, nor has anyone else in the industry. Why not?

It’s all about competition

If OpenAI — and only OpenAI — released a watermarking system for ChatGPT, making it easy to tell when generative AI had produced a text, this wouldn’t affect student essay plagiarism in the slightest. Word would get out fast, and everyone would just switch over to one of the many AI options available today: Meta’s Llama, Anthropic’s Claude, Google’s Gemini. Plagiarism would continue unabated, and OpenAI would lose a lot of its user base. So it’s not shocking that they would keep their watermarking system under wraps.

In a situation like this, it might seem appropriate for regulators to step in. If every generative AI system is required to have watermarking, then it’s not a competitive disadvantage. This is the logic behind a bill introduced this year in the California state Assembly, known as the California Digital Content Provenance Standards, which would require generative AI providers to make their AI-generated content detectable, along with requiring providers to label generative AI and remove deceptive content. OpenAI is in favor of the bill — not surprisingly, as they’re the only generative AI provider known to have a system that does this. Their rivals are mostly opposed.

I’m broadly in favor of some kind of watermarking requirements for generative AI content. AI can be incredibly useful, but its productive uses don’t require it to pretend to be human-created. And while I don’t think it’s the place of government to ban newspapers from replacing us journalists with AI, I certainly don’t want outlets to misinform readers about whether the content they’re reading was created by real humans.

Though I’d like some kind of watermarking obligation, I am not sure it’s possible to implement. The best of the “open” AI models that have been released (like the latest Llama), models that you can run yourself on your own computer, are very high quality — certainly good enough for student essays. They’re already out there, and there’s no way to go back and add watermarking to them because anyone can run the current versions, whatever updates are applied in future versions. (This is among the many ways I have complicated feelings about open models. They enable an enormous amount of creativity, research, and discovery — and they also make it impossible to do all kinds of common-sense anti-impersonation or anti-child sexual abuse material measures that we otherwise might really like to have.)

So even though watermarking is possible, I don’t think we can count on it, which means we’ll have to figure out how to address the ubiquity of easy, AI-generated content as a society. Teachers are already switching to in-class essay requirements and other approaches to cut down on student cheating. We’re likely to see a switch away from college admissions essays as well — and, honestly, it’ll be good riddance, as those were probably never a good way to select students.

But while I won’t mourn much over the college admissions essay, and while I think teachers are very much capable of finding better ways to assess students, I do notice some troubling trends in the whole saga. There was a simple way to let us harness the benefits of AI without obvious downsides like impersonation and plagiarism, yet AI development happened so fast that society more or less just let the opportunity pass us by. Individual labs could do it, but they won’t because it’d put them at a competitive disadvantage — and there isn’t likely to be a good way to make everyone do it.

In the school plagiarism debate, the stakes are low. But the same dynamic reflected in the AI watermarking debate — where commercial incentives stop companies from self-regulating and the pace of change stops external regulators from stepping in until it’s too late — seems likely to remain as the stakes get higher.
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                    [url] => https://www.cnet.com/tech/services-and-software/chatgpt-glossary-45-ai-terms-that-everyone-should-know/
                    [date] => Sun, 08 Sep 2024 15:13:00 GMT
                    [short_description] => ChatGPT Glossary: 45 AI Terms That Everyone Should Know  CNET
                    [text] => When ChatGPT launched in late 2022, it completely changed how people's relationship with technology. Suddenly, online searches were agentive, meaning you could have a conversation in natural language with a chatbot and it would respond with novel answers, much like a human would. It was so transformative that Google, Meta, Microsoft and Apple quickly began integrating AI into its suite of products.

But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on country of origin is cool, but the potential of generative AI could completely reshape economies. That could be worth $4.4 trillion to the global economy annually, according to McKinsey Global Institute, which is why you should expect to hear more and more about artificial intelligence.

It's showing up in a dizzying array of products -- a short, short list includes Google's Gemini, Microsoft's Copilot, Anthropic's Claude, the Perplexity AI search tool and gadgets from Humane and Rabbit. You can read our reviews and hands-on evaluations of those and other products, along with news, explainers and how-to posts, at our new AI Atlas hub.

As people become more accustomed to a world intertwined with AI, new terms are popping up everywhere. So whether you're trying to sound smart over drinks or impress in a job interview, here are some important AI terms you should know.

This glossary will be regularly updated.

—

artificial general intelligence, or AGI: A concept that suggests a more advanced version of AI than we know today, one that can perform tasks much better than humans while also teaching and advancing its own capabilities.

AI ethics: Principles aimed at preventing AI from harming humans, achieved through means like determining how AI systems should collect data or deal with bias.

AI safety: An interdisciplinary field that's concerned with the long-term impacts of AI and how it could progress suddenly to a super intelligence that could be hostile to humans.

algorithm: A series of instructions that allows a computer program to learn and analyze data in a particular way, such as recognizing patterns, to then learn from it and accomplish tasks on its own.

alignment: Tweaking an AI to better produce the desired outcome. This can refer to anything from moderating content to maintaining positive interactions toward humans.

anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a chatbot is more humanlike and aware than it actually is, like believing it's happy, sad or even sentient altogether.

artificial intelligence, or AI: The use of technology to simulate human intelligence, either in computer programs or robotics. A field in computer science that aims to build systems that can perform human tasks.

autonomous agents: An AI model that have the capabilities, programming and other tools to accomplish a specific task. A self-driving car is an autonomous agent, for example, because it has sensory inputs, GPS and driving algorithms to navigate the road on its own. Stanford researchers have shown that autonomous agents can develop their own cultures, traditions and shared language.

bias: In regards to large language models, errors resulting from the training data. This can result in falsely attributing certain characteristics to certain races or groups based on stereotypes.

chatbot: A program that communicates with humans through text that simulates human language.

ChatGPT: An AI chatbot developed by OpenAI that uses large language model technology.

cognitive computing: Another term for artificial intelligence.

data augmentation: Remixing existing data or adding a more diverse set of data to train an AI.

deep learning: A method of AI, and a subfield of machine learning, that uses multiple parameters to recognize complex patterns in pictures, sound and text. The process is inspired by the human brain and uses artificial neural networks to create patterns.

diffusion: A method of machine learning that takes an existing piece of data, like a photo, and adds random noise. Diffusion models train their networks to re-engineer or recover that photo.

emergent behavior: When an AI model exhibits unintended abilities.

end-to-end learning, or E2E: A deep learning process in which a model is instructed to perform a task from start to finish. It's not trained to accomplish a task sequentially but instead learns from the inputs and solves it all at once.

ethical considerations: An awareness of the ethical implications of AI and issues related to privacy, data usage, fairness, misuse and other safety issues.

foom: Also known as fast takeoff or hard takeoff. The concept that if someone builds an AGI that it might already be too late to save humanity.

generative adversarial networks, or GANs: A generative AI model composed of two neural networks to generate new data: a generator and a discriminator. The generator creates new content, and the discriminator checks to see if it's authentic.

generative AI: A content-generating technology that uses AI to create text, video, computer code or images. The AI is fed large amounts of training data, finds patterns to generate its own novel responses, which can sometimes be similar to the source material.

Google Gemini: An AI chatbot by Google that functions similarly to ChatGPT but pulls information from the current web, whereas ChatGPT is limited to data until 2021 and isn't connected to the internet.

guardrails: Policies and restrictions placed on AI models to ensure data is handled responsibly and that the model doesn't create disturbing content.

hallucination: An incorrect response from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct. The reasons for this aren't entirely known. For example, when asking an AI chatbot, "When did Leonardo da Vinci paint the Mona Lisa?" it may respond with an incorrect statement saying, "Leonardo da Vinci painted the Mona Lisa in 1815," which is 300 years after it was actually painted.

large language model, or LLM: An AI model trained on mass amounts of text data to understand language and generate novel content in human-like language.

machine learning, or ML: A component in AI that allows computers to learn and make better predictive outcomes without explicit programming. Can be coupled with training sets to generate new content.

Microsoft Bing: A search engine by Microsoft that can now use the technology powering ChatGPT to give AI-powered search results. It's similar to Google Gemini in being connected to the internet.

multimodal AI: A type of AI that can process multiple types of inputs, including text, images, videos and speech.

natural language processing: A branch of AI that uses machine learning and deep learning to give computers the ability to understand human language, often using learning algorithms, statistical models and linguistic rules.

neural network: A computational model that resembles the human brain's structure and is meant to recognize patterns in data. Consists of interconnected nodes, or neurons, that can recognize patterns and learn over time.

overfitting: Error in machine learning where it functions too closely to the training data and may only be able to identify specific examples in said data but not new data.

paperclips: The Paperclip Maximiser theory, coined by philosopher Nick Boström of the University of Oxford, is a hypothetical scenario where an AI system will create as many literal paperclips as possible. In its goal to produce the maximum amount of paperclips, an AI system would hypothetically consume or convert all materials to achieve its goal. This could include dismantling other machinery to produce more paperclips, machinery that could be beneficial to humans. The unintended consequence of this AI system is that it may destroy humanity in its goal to make paperclips.

parameters: Numerical values that give LLMs structure and behavior, enabling it to make predictions.

prompt: The suggestion or question you enter into an AI chatbot to get a response.

prompt chaining: The ability of AI to use information from previous interactions to color future responses.

stochastic parrot: An analogy of LLMs that illustrates that the software doesn't have a larger understanding of meaning behind language or the world around it, regardless of how convincing the output sounds. The phrase refers to how a parrot can mimic human words without understanding the meaning behind them.

style transfer: The ability to adapt the style of one image to the content of another, allowing an AI to interpret the visual attributes of one image and use it on another. For example, taking the self-portrait of Rembrandt and re-creating it in the style of Picasso.

temperature: Parameters set to control how random a language model's output is. A higher temperature means the model takes more risks.

text-to-image generation: Creating images based on textual descriptions.

tokens: Small bits of written text that AI language models process to formulate their responses to your prompts. A token is equivalent to four characters in English, or about three-quarters of a word.

training data: The datasets used to help AI models learn, including text, images, code or data.

transformer model: A neural network architecture and deep learning model that learns context by tracking relationships in data, like in sentences or parts of images. So, instead of analyzing a sentence one word at a time, it can look at the whole sentence and understand the context.

Turing test: Named after famed mathematician and computer scientist Alan Turing, it tests a machine's ability to behave like a human. The machine passes if a human can't distinguish the machine's response from another human.

weak AI, aka narrow AI: AI that's focused on a particular task and can't learn beyond its skill set. Most of today's AI is weak AI.
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                    [title] => A.I. Isn’t Magic, but Can It Be ‘Agentic’?
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                    [url] => https://www.nytimes.com/2024/09/06/business/artificial-intelligence-agentic.html
                    [date] => Fri, 06 Sep 2024 09:04:18 GMT
                    [short_description] => A.I. Isn’t Magic, but Can It Be ‘Agentic’?  The New York Times
                    [text] => Spend enough time talking to tech executives and you might come away thinking generative artificial intelligence — the technology behind ChatGPT and other services that can create text, video and images — is about to upend every aspect of our lives.

A.I. co-pilots, assistants and agents promise to boost productivity with helpful suggestions and shortcuts. Today, A.I. tools can do simple, specific tasks like craft emails, make presentations or write code, but soon, tech executives say, they will usher us into a sort of efficiency nirvana, where digital assistants anticipate and deliver our every need before we have to ask.

One problem: Tech’s hype machine moves faster than anyone can actually build the tech. The technologists are not waiting for it to catch up to its promises — they’re already on to the next thing. And what comes after A.I. agents? Agentic A.I.

How it’s pronounced

/ā-jənt-ĭk/

It is a fancified way to say something acts like an agent. Unlike chatbots, which require a human to type in a prompt before it can spit out a response, agentic A.I. can act on its own. A customer could create a complex goal, like predicting which factory machines will need maintenance or booking a trip, and the A.I. would automatically complete the required tasks.

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                    [date] => Mon, 09 Sep 2024 04:01:18 GMT
                    [short_description] => AI exuberance masks broad weakness in tech sector, say investors  Financial Times
                    [text] => Enthusiasm about artificial intelligence masks weakness across most of the technology sector, with many companies “still in a recession” following a slowdown that started in 2022, according to investors and analysis of recent financial reports.

Massive share price gains for large companies that were predicted to be early beneficiaries of AI, such as Nvidia and Microsoft, helped to erase memories of a dreadful 2022, when the tech-dominated Nasdaq Composite tumbled by almost a third.

Beneath the surface, however, many tech businesses that do not focus on AI have struggled to regain momentum.

“When you look at technology outside of AI, there’s not that much happening,” said Tony Kim, head of technology investing in BlackRock’s fundamental equities division. “Many [sub]-sectors are still in a recession. The only thing that has been really growing has been AI.”

More traditional tech areas such as software, IT consulting and the production of electronic equipment for other sectors such as manufacturing and the auto industry have faced difficulties, including weak demand and the hangover from overexpansion and overstocking of inventories during the coronavirus pandemic. Some have also directly suffered from the growth of AI, as customers with limited budgets redirect investment.

Dustin Moskovitz, the Facebook co-founder who is now chief executive of Asana, last week summed up the situation for many companies as the business software group scaled back its forecasts for the rest of the year.

“What we’re seeing in tech is still kind of the unwinding of the over-hiring and overspending that we saw at the beginning of the pandemic,” he told analysts. “And then that all couples with what I think is massive uncertainty in the economic environment. And then, also, just with how AI is going to play out.”

Recent financial reports show the majority of large tech companies have been growing more slowly than in the past, while many smaller ones are actively shrinking.

Groups in the S&P 500 IT sub-index increased revenues by an average of 6.9 per cent over the past 12 months, according to Bloomberg data, compared with a five-year average of 10 per cent. About three-quarters of companies grew more slowly than their recent average.

Earnings per share increased by an average of 16 per cent in the past 12 months, down from 21 per cent over the past five years.

The weakness is more obvious in small cap indices, where there is no boost from megacap groups. In the Russell 2000, technology was the second-worst performing sector in terms of revenue growth in the second quarter, according to data from LSEG. Revenue fell 6.1 per cent year on year, while profits were down 2.8 per cent.

“Generative AI is masking a cyclical downturn in a lot of other core sectors,” said Ted Mortonson, a tech strategist at RW Baird. “Everyone is hoping things get better in the next few quarters, though hope is not an investment strategy.”

Even within subsectors that have been caught up in the AI enthusiasm such as semiconductors, some business lines have been struggling. Brice Hill, chief financial officer at chip equipment supplier Applied Materials, told analysts last month that “we’re seeing particularly strong pull related to AI and data centre computing”, but there were “pockets of weakness in the auto and industrial end-markets”.

“Everywhere you look on the industrial side it’s similar,” said John Barr, a portfolio manager at Needham Funds who has invested in several semiconductor companies including Applied Materials. “Current growth is not so great, so what we’re looking for are companies that have a stable business and are investing in something new.”

Investor exuberance around AI-focused companies has faded since the early summer, leading many commentators to predict a prolonged rotation of investor attention away from Big Tech stocks towards sectors such as financial services and industrials.

Some tech specialists are hoping for a similar intra-industry rotation from the biggest AI stocks to more unloved corners of the industry. While few companies are predicting the sort of triple-digit growth that Nvidia has reported in recent quarters, there are signs that some of the worst-performing parts of the tech sector are turning a corner.

“I think we are seeing a stabilisation — things have stopped getting worse in those more macro-sensitive areas, and if rates go down then that will help,” said Tony Wang, portfolio manager for T Rowe Price’s science and technology fund.

“I feel like the idea that AI is the only thing that is working has been the case for the last two years. I’m not sure it will be the case for the next two.”
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Title: Apple Looks to AI to End iPhone Slump - The Wall Street Journal
Content: Apple Looks to AI to End iPhone Slump  The Wall Street Journal
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Title: South Korea summit to target 'blueprint' for using AI in the military - Reuters
Content: South Korea summit to target 'blueprint' for using AI in the military  Reuters
Image URL: https://lh3.googleusercontent.com/J6_coFbogxhRI9iM864NL_liGXvsQp2AupsKei7z0cNNfDvGUmWUy20nuUhkREQyrpY4bEeIBuc=s0-w300
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Title: Another Artificial Intelligence (AI) Stock Just Hiked Its Dividend, This Time by a Whopping 17%
Content: 'Tis the season. The weather gets cooler, kids go back to school, and many technology companies raise their dividends for the year ahead.

While artificial intelligence (AI) technology stocks aren't necessarily known for high dividend payouts, they are known for growth. So if you can find a winning AI name that also pays a dividend, a small payout today could look much, much bigger in five, 10, or 20 or more years.

Two weeks ago, one semiconductor manufacturing company raised its dividend by 15%. But not to be outdone, one of its peers bested it last Tuesday -- by raising its quarterly payout by 17%.

And yet, there are reasons I'm more bullish on the first company, if I had to pick between them.

KLA Corporation delivers an early Christmas

KLA Corporation (NASDAQ: KLAC), which makes semiconductor capital (semicap) equipment, announced Tuesday that it would raise its quarterly payout to shareholders by 17%, from $1.45 per share to $1.70, good for an annual dividend of $6.80. That's an impressive increase, especially since it marks KLA's 15th consecutive year of annual dividend increases.

The new heightened dividend only yields about 0.9% at the current stock price. But that shouldn't necessarily stop dividend investors from looking at KLA. If a company has the ability to outpace inflation with dividend raises for years, it can make for a great long-term holding and wind up paying you a lot in retirement.

KLA's outsized payout increase is a sign of optimism about AI growth, which should be celebrated. But two of its peers also just raised their dividends, and appear to have greater capacity to raise theirs even more in the years ahead.

How KLA is different from Lam and Applied Materials

KLA Corporation is a leader in process control and metrology equipment, which is used to inspect chips and wafers for defects at many points along the semiconductor manufacturing process. These machines are in high demand as chip designs get ever smaller and more complex, especially in the age of AI. Like many large semiconductor equipment names, KLA dominates its respective niche, with well over 50% market share for certain processes.

KLA's dominant position, and the stability of process control and diagnostics relative to other equipment segments, get its stock a more favorable valuation in terms of a higher multiple. The companies that dominate the etch and deposition market, Lam Research (NASDAQ: LRCX) and Applied Materials (NASDAQ: AMAT), tend to trade at lower multiples, even though all three companies have similar long-term growth prospects, profitability, and cyclicality. Lam just raised its dividend by 15% two weeks ago. Applied Materials seems set for a big increase when it raises its dividend (as is typical) in March; six months ago, Applied raised its dividend by 23%.

Which semicap dividends seem primed for the most growth?

To be sure, KLA has proven itself to be a great dividend grower, and should continue to be. But despite its outsized raise today, it may be a little more constrained in how much it can grow its payout, compared to Lam Research or Applied Materials.

This is due to a variety of factors, including overall valuation, current payout ratio (the portion of earnings paid out as dividends), and leverage on the balance sheet:

Data source: Yahoo! Finance. Forward payout ratio = forward dividend divided by current-year earnings estimates.

Now, all three of these look like great companies to own for dividend growth investors, with high margins and relatively low payout ratios.

But KLA does trade at a higher valuation, although it has more than $2 billion in net debt, while both Lam Research and Applied Materials have hefty net cash positions (more cash than debt). It also has a high trailing price-to-earnings (P/E) ratio at 36, much higher than its two peers, and a higher current payout ratio than Lam. So the forward estimates above already assume a lot of growth for KLA's earnings.

Clearly, KLA's dominance of its niche, evidenced by its higher operating margin, is tantalizing for investors. But by the same token, KLA is given credit for that with its higher valuation.

Meanwhile, its earnings power has shown itself to be similar, in terms of growth and cyclicality, to that of the other two companies:

Finally, while KLA has a dominant position now, it's beginning to see some strong competition in the metrology market from both Applied Materials and newcomer Onto Innovation. So while it has higher margins and seems safer at the moment, there probably isn't as much room for market-share or margin increases. Investors are simply assuming that both will continue to rise.

KLA is still a great stock, it's just not preferable to the other two

KLA's big dividend increase is another indicator of the bright long-term future of semicap equipment stocks, especially in the age of AI. No doubt it would be a great stock to own for the long term, and should continue growing its payout above the rate of inflation.

However, given their lower valuations, better balance sheets, and similar growth prospects, Lam or Applied would be my current preferences for future dividend growth.

Should you invest $1,000 in KLA right now?

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Billy Duberstein and/or his clients have positions in Applied Materials, KLA, Lam Research, and Onto Innovation. The Motley Fool has positions in and recommends Applied Materials and Lam Research. The Motley Fool has a disclosure policy.
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Title: This Swiss chip stock is set ride the AI boom, analysts say — and one gives it 75% upside
Content: Swiss company VAT Group is poised to benefit significantly from the growth in artificial intelligence, according to analysts at a number of investment banks. Investment firm Vontobel highlighted the company as a key player in the AI chips supply chain. The stock is listed in Switzerland and also trades in the U.S . Vontobel's analyst said that AI could be "the biggest technology shift of our lifetimes," with the global semiconductor industry projected to reach $1 trillion by 2030. "We estimate that AI-related semiconductors will account for 30-40% of the total chip market by 2027," said Michael Foeth, senior equity analyst at Vontabel, in a note to clients on Aug. 5. "Semiconductor equipment technology is enabling this inflection," he added, naming VAT as a key stock set to benefit. VAT Group Vontobel has a "buy" rating on VAT Group, with a price target of 540 Swiss Francs ($641), representing a potential 35% upside. The company, headquartered in Haag, Switzerland, specializes in the development and manufacture of vacuum valves used in semiconductor production. Their products are used by other semiconductor supply chain companies, such as Lam Research , to set up so-called "ultra-clean rooms" for chip production. VAC.N-CH 1Y line Vontobel is not alone in its bullish stance on VAT. Investment bank Jefferies' Olivia Honychurch has a price target of 700 Swiss francs on the stock, indicating an upside of 75%. UBS also notes potential risks associated with ongoing AI-trade tensions between the United States and China, which could impact up to 10% of global semiconductor equipment capital expenditure. FactSet estimates that 25% of the sector's total revenue is derived from China, while U.S. sales comprise 22%. However, RBC Capital Markets — the most bearish among those covering VAT — upgraded the stock to "sector perform" from "underperform" in August, citing confidence in the wafer fab equipment (WFE) market. "We remain overall confident on the WFE market outlook and VAT's role within. Estimates drop somewhat (lower high-margin China business and FX headwinds) without impairing the underlying story," said RBC's Sebastian Kuenne. Comet, Inficon Vontobel also highlighted Comet and Inficon as potential beneficiaries of the AI boom, albeit with more measured expectations. The bank has a "hold" rating on Comet, a provider of x-ray and radio frequency technology, with a price target of 345 Swiss francs, indicating a 10% upside. Inficon, which specializes in making instruments for gas analysis and measurement, also has a "hold" rating from Vontobel, with a price target of 1,270 Swiss francs, suggesting an 8% upside.
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Title: The Newest Artificial Intelligence (AI) Stock in the S&P 500 Is Up 370% Since 2023. Wall Street Says Avoid It.
Content: The S&P 500 (^GSPC -1.73%) measures the performance of 500 companies that meet specific eligibility requirements, including positive earnings over the last four quarters, a market value of at least $18 billion, and a sufficiently liquid stock. The S&P 500 is generally considered to be the best barometer for the overall U.S. stock market.

Palantir Technologies (PLTR 0.56%) will join the benchmark index on Monday, Sept. 23, the date of the next quarterly rebalancing. Palantir shares have skyrocketed 370% since January 2023, a period that essentially coincides with the generative artificial intelligence boom started by ChatGPT.

However, Wall Street analysts are less than optimistic, despite Palantir's increasingly strong position in artificial intelligence software. The stock carries a median 12-month price target of $28 per share, implying 12% downside from its current share price of $32. Here's what investors should know.

Palantir is a recognized leader in artificial intelligence platforms

Palantir specializes in data analytics. Its platform lets businesses collect data, develop machine learning models, and integrate those digital assets into an ontology. The Palantir ontology defines the relationship between those assets and real-world objects, and it lets users surface information through analytical applications that improve decision-making. Management says its ontology-based architecture is its key differentiator.

The Palantir platform comprises several software products. Foundry is used for data processing and analytics, AIP (Artificial Intelligence Platform) lets customers use large language models and generative artificial intelligence within Foundry, and Apollo ensures Foundry is consistently updated across all IT environments. Palantir also provides an alternative to Foundry (called Gotham) originally designed for sensitive government data.

Forrester Research recently recognized Palantir as a leader in artificial intelligence and machine learning platforms. "Palantir is quietly becoming one of the largest players in this market," analysts wrote. Palantir received the highest score for its current product offering, but three competitors -- Alphabet, Databricks, and C3.ai -- received higher scores for their product development strategy.

Palantir's revenue growth continued to accelerate in the second quarter

Palantir reported solid financial results in the second quarter, beating estimates on the top and bottom lines. Revenue increased 27% to $678 million, marking the fifth consecutive sequential acceleration in sales growth. Meanwhile, non-GAAP net income increased 80% to $0.09 per diluted share.

Additionally, Management guided for 25% to 26% revenue growth in the third quarter, comfortably topping the 22% growth analysts anticipated. "The persistent and unbridled demand for our software, for an effective enterprise platform that makes artificial intelligence capabilities useful to large institutions, shows no signs of relenting," CEO Alex Karp wrote in his latest shareholder letter.

Palantir stock trades at an outrageous valuation

Looking ahead, Wall Street expects Palantir's adjusted earnings to increase at 22% annually through 2025. Without additional context, that estimate sounds pretty good. However, Palantir shares currently trade at 100 times adjusted earnings, an outrageous multiple in context. Those figures give a PEG ratio of 4.5.

For perspective, Nvidia currently trades at 47 times adjusted earnings and Wall Street expects its bottom line to grow at 49% annually over the next six quarters, which is the same timeframe I quoted for Palantir. Those figures give a PEG ratio slightly below 1.

Here's what that means: If the market afforded Palantir the same PEG multiple it affords Nvidia, Palantir stock would plunge 30%. Personally, I would avoid the stock until it trades at a more reasonable valuation. That does not mean Palantir shares will nosedive tomorrow. The stock could continue climbing higher in the near term. But without a dramatic acceleration in earnings, I think shares are headed toward a sharp correction at some point in the future
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Title: Stratipath and Roche Join Forces to Help Expand Access to AI-Powered Precision Diagnostics - Yahoo Finance
Content: Stratipath and Roche Join Forces to Help Expand Access to AI-Powered Precision Diagnostics  Yahoo Finance
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Title: There’s a fix for AI-generated essays. Why aren’t we using it?
Content: It’s the start of the school year, and thus the start of a fresh round of discourse on generative AI’s new role in schools. In the space of about three years, essays have gone from a mainstay of classroom education everywhere to a much less useful tool, for one reason: ChatGPT. Estimates of how many students use ChatGPT for essays vary, but it’s commonplace enough to force teachers to adapt.

While generative AI has many limitations, student essays fall into the category of services that they’re very good at: There are lots of examples of essays on the assigned topics in their training data, there’s demand for an enormous volume of such essays, and the standards for prose quality and original research in student essays are not all that high.

This story was first featured in the Future Perfect newsletter.

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Right now, cheating on essays via the use of AI tools is hard to catch. A number of tools advertise they can verify that text is AI-generated, but they’re not very reliable. Since falsely accusing students of plagiarism is a big deal, these tools would have to be extremely accurate to work at all — and they simply aren’t.

AI fingerprinting with technology

But there is a technical solution here. Back in 2022, a team at OpenAI, led by quantum computing researcher Scott Aaronson, developed a “watermarking” solution that makes AI text virtually unmistakable — even if the end user changes a few words here and there or rearranges text. The solution is a bit technically complicated, but bear with me, because it’s also very interesting.

At its core, the way that AI text generation works is that the AI “guesses” a bunch of possible next tokens given what appears in a text so far. In order not to be overly predictable and produce the same repetitive output constantly, AI models don’t just guess the most probable token — instead, they include an element of randomization, favoring “more likely” completions but sometimes selecting a less likely one.

The watermarking works at this stage. Instead of having the AI generate the next token according to random selection, it has the AI use a nonrandom process: favoring next tokens that get a high score in an internal “scoring” function OpenAI invented. It might, for example, favor words with the letter V just slightly, so that text generated with this scoring rule will have 20 percent more Vs than normal human text (though the actual scoring functions are more complicated than this). Readers wouldn’t normally notice this — in fact, I edited this newsletter to increase the number of Vs in it, and I doubt this variation in my normal writing stood out.

Similarly, the watermarked text will not, at a glance, be different from normal AI output. But it would be straightforward for OpenAI, which knows the secret scoring rule, to evaluate whether a given body of text gets a much higher score on that hidden scoring rule than human-generated text ever would. If, for example, the scoring rule were my above example about the letter V, you could run this newsletter through a verification program and see that it has about 90 Vs in 1,200 words, more than you’d expect based on how often V is used in English. It’s a clever, technically sophisticated solution to a hard problem, and OpenAI has had a working prototype for two years.

So if we wanted to solve the problem of AI text masquerading as human-written text, it’s very much solvable. But OpenAI hasn’t released their watermarking system, nor has anyone else in the industry. Why not?

It’s all about competition

If OpenAI — and only OpenAI — released a watermarking system for ChatGPT, making it easy to tell when generative AI had produced a text, this wouldn’t affect student essay plagiarism in the slightest. Word would get out fast, and everyone would just switch over to one of the many AI options available today: Meta’s Llama, Anthropic’s Claude, Google’s Gemini. Plagiarism would continue unabated, and OpenAI would lose a lot of its user base. So it’s not shocking that they would keep their watermarking system under wraps.

In a situation like this, it might seem appropriate for regulators to step in. If every generative AI system is required to have watermarking, then it’s not a competitive disadvantage. This is the logic behind a bill introduced this year in the California state Assembly, known as the California Digital Content Provenance Standards, which would require generative AI providers to make their AI-generated content detectable, along with requiring providers to label generative AI and remove deceptive content. OpenAI is in favor of the bill — not surprisingly, as they’re the only generative AI provider known to have a system that does this. Their rivals are mostly opposed.

I’m broadly in favor of some kind of watermarking requirements for generative AI content. AI can be incredibly useful, but its productive uses don’t require it to pretend to be human-created. And while I don’t think it’s the place of government to ban newspapers from replacing us journalists with AI, I certainly don’t want outlets to misinform readers about whether the content they’re reading was created by real humans.

Though I’d like some kind of watermarking obligation, I am not sure it’s possible to implement. The best of the “open” AI models that have been released (like the latest Llama), models that you can run yourself on your own computer, are very high quality — certainly good enough for student essays. They’re already out there, and there’s no way to go back and add watermarking to them because anyone can run the current versions, whatever updates are applied in future versions. (This is among the many ways I have complicated feelings about open models. They enable an enormous amount of creativity, research, and discovery — and they also make it impossible to do all kinds of common-sense anti-impersonation or anti-child sexual abuse material measures that we otherwise might really like to have.)

So even though watermarking is possible, I don’t think we can count on it, which means we’ll have to figure out how to address the ubiquity of easy, AI-generated content as a society. Teachers are already switching to in-class essay requirements and other approaches to cut down on student cheating. We’re likely to see a switch away from college admissions essays as well — and, honestly, it’ll be good riddance, as those were probably never a good way to select students.

But while I won’t mourn much over the college admissions essay, and while I think teachers are very much capable of finding better ways to assess students, I do notice some troubling trends in the whole saga. There was a simple way to let us harness the benefits of AI without obvious downsides like impersonation and plagiarism, yet AI development happened so fast that society more or less just let the opportunity pass us by. Individual labs could do it, but they won’t because it’d put them at a competitive disadvantage — and there isn’t likely to be a good way to make everyone do it.

In the school plagiarism debate, the stakes are low. But the same dynamic reflected in the AI watermarking debate — where commercial incentives stop companies from self-regulating and the pace of change stops external regulators from stepping in until it’s too late — seems likely to remain as the stakes get higher.
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Title: ChatGPT Glossary: 45 AI Terms That Everyone Should Know
Content: When ChatGPT launched in late 2022, it completely changed how people's relationship with technology. Suddenly, online searches were agentive, meaning you could have a conversation in natural language with a chatbot and it would respond with novel answers, much like a human would. It was so transformative that Google, Meta, Microsoft and Apple quickly began integrating AI into its suite of products.

But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on country of origin is cool, but the potential of generative AI could completely reshape economies. That could be worth $4.4 trillion to the global economy annually, according to McKinsey Global Institute, which is why you should expect to hear more and more about artificial intelligence.

It's showing up in a dizzying array of products -- a short, short list includes Google's Gemini, Microsoft's Copilot, Anthropic's Claude, the Perplexity AI search tool and gadgets from Humane and Rabbit. You can read our reviews and hands-on evaluations of those and other products, along with news, explainers and how-to posts, at our new AI Atlas hub.

As people become more accustomed to a world intertwined with AI, new terms are popping up everywhere. So whether you're trying to sound smart over drinks or impress in a job interview, here are some important AI terms you should know.

This glossary will be regularly updated.

—

artificial general intelligence, or AGI: A concept that suggests a more advanced version of AI than we know today, one that can perform tasks much better than humans while also teaching and advancing its own capabilities.

AI ethics: Principles aimed at preventing AI from harming humans, achieved through means like determining how AI systems should collect data or deal with bias.

AI safety: An interdisciplinary field that's concerned with the long-term impacts of AI and how it could progress suddenly to a super intelligence that could be hostile to humans.

algorithm: A series of instructions that allows a computer program to learn and analyze data in a particular way, such as recognizing patterns, to then learn from it and accomplish tasks on its own.

alignment: Tweaking an AI to better produce the desired outcome. This can refer to anything from moderating content to maintaining positive interactions toward humans.

anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a chatbot is more humanlike and aware than it actually is, like believing it's happy, sad or even sentient altogether.

artificial intelligence, or AI: The use of technology to simulate human intelligence, either in computer programs or robotics. A field in computer science that aims to build systems that can perform human tasks.

autonomous agents: An AI model that have the capabilities, programming and other tools to accomplish a specific task. A self-driving car is an autonomous agent, for example, because it has sensory inputs, GPS and driving algorithms to navigate the road on its own. Stanford researchers have shown that autonomous agents can develop their own cultures, traditions and shared language.

bias: In regards to large language models, errors resulting from the training data. This can result in falsely attributing certain characteristics to certain races or groups based on stereotypes.

chatbot: A program that communicates with humans through text that simulates human language.

ChatGPT: An AI chatbot developed by OpenAI that uses large language model technology.

cognitive computing: Another term for artificial intelligence.

data augmentation: Remixing existing data or adding a more diverse set of data to train an AI.

deep learning: A method of AI, and a subfield of machine learning, that uses multiple parameters to recognize complex patterns in pictures, sound and text. The process is inspired by the human brain and uses artificial neural networks to create patterns.

diffusion: A method of machine learning that takes an existing piece of data, like a photo, and adds random noise. Diffusion models train their networks to re-engineer or recover that photo.

emergent behavior: When an AI model exhibits unintended abilities.

end-to-end learning, or E2E: A deep learning process in which a model is instructed to perform a task from start to finish. It's not trained to accomplish a task sequentially but instead learns from the inputs and solves it all at once.

ethical considerations: An awareness of the ethical implications of AI and issues related to privacy, data usage, fairness, misuse and other safety issues.

foom: Also known as fast takeoff or hard takeoff. The concept that if someone builds an AGI that it might already be too late to save humanity.

generative adversarial networks, or GANs: A generative AI model composed of two neural networks to generate new data: a generator and a discriminator. The generator creates new content, and the discriminator checks to see if it's authentic.

generative AI: A content-generating technology that uses AI to create text, video, computer code or images. The AI is fed large amounts of training data, finds patterns to generate its own novel responses, which can sometimes be similar to the source material.

Google Gemini: An AI chatbot by Google that functions similarly to ChatGPT but pulls information from the current web, whereas ChatGPT is limited to data until 2021 and isn't connected to the internet.

guardrails: Policies and restrictions placed on AI models to ensure data is handled responsibly and that the model doesn't create disturbing content.

hallucination: An incorrect response from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct. The reasons for this aren't entirely known. For example, when asking an AI chatbot, "When did Leonardo da Vinci paint the Mona Lisa?" it may respond with an incorrect statement saying, "Leonardo da Vinci painted the Mona Lisa in 1815," which is 300 years after it was actually painted.

large language model, or LLM: An AI model trained on mass amounts of text data to understand language and generate novel content in human-like language.

machine learning, or ML: A component in AI that allows computers to learn and make better predictive outcomes without explicit programming. Can be coupled with training sets to generate new content.

Microsoft Bing: A search engine by Microsoft that can now use the technology powering ChatGPT to give AI-powered search results. It's similar to Google Gemini in being connected to the internet.

multimodal AI: A type of AI that can process multiple types of inputs, including text, images, videos and speech.

natural language processing: A branch of AI that uses machine learning and deep learning to give computers the ability to understand human language, often using learning algorithms, statistical models and linguistic rules.

neural network: A computational model that resembles the human brain's structure and is meant to recognize patterns in data. Consists of interconnected nodes, or neurons, that can recognize patterns and learn over time.

overfitting: Error in machine learning where it functions too closely to the training data and may only be able to identify specific examples in said data but not new data.

paperclips: The Paperclip Maximiser theory, coined by philosopher Nick Boström of the University of Oxford, is a hypothetical scenario where an AI system will create as many literal paperclips as possible. In its goal to produce the maximum amount of paperclips, an AI system would hypothetically consume or convert all materials to achieve its goal. This could include dismantling other machinery to produce more paperclips, machinery that could be beneficial to humans. The unintended consequence of this AI system is that it may destroy humanity in its goal to make paperclips.

parameters: Numerical values that give LLMs structure and behavior, enabling it to make predictions.

prompt: The suggestion or question you enter into an AI chatbot to get a response.

prompt chaining: The ability of AI to use information from previous interactions to color future responses.

stochastic parrot: An analogy of LLMs that illustrates that the software doesn't have a larger understanding of meaning behind language or the world around it, regardless of how convincing the output sounds. The phrase refers to how a parrot can mimic human words without understanding the meaning behind them.

style transfer: The ability to adapt the style of one image to the content of another, allowing an AI to interpret the visual attributes of one image and use it on another. For example, taking the self-portrait of Rembrandt and re-creating it in the style of Picasso.

temperature: Parameters set to control how random a language model's output is. A higher temperature means the model takes more risks.

text-to-image generation: Creating images based on textual descriptions.

tokens: Small bits of written text that AI language models process to formulate their responses to your prompts. A token is equivalent to four characters in English, or about three-quarters of a word.

training data: The datasets used to help AI models learn, including text, images, code or data.

transformer model: A neural network architecture and deep learning model that learns context by tracking relationships in data, like in sentences or parts of images. So, instead of analyzing a sentence one word at a time, it can look at the whole sentence and understand the context.

Turing test: Named after famed mathematician and computer scientist Alan Turing, it tests a machine's ability to behave like a human. The machine passes if a human can't distinguish the machine's response from another human.

weak AI, aka narrow AI: AI that's focused on a particular task and can't learn beyond its skill set. Most of today's AI is weak AI.
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Title: A.I. Isn’t Magic, but Can It Be ‘Agentic’?
Content: Spend enough time talking to tech executives and you might come away thinking generative artificial intelligence — the technology behind ChatGPT and other services that can create text, video and images — is about to upend every aspect of our lives.

A.I. co-pilots, assistants and agents promise to boost productivity with helpful suggestions and shortcuts. Today, A.I. tools can do simple, specific tasks like craft emails, make presentations or write code, but soon, tech executives say, they will usher us into a sort of efficiency nirvana, where digital assistants anticipate and deliver our every need before we have to ask.

One problem: Tech’s hype machine moves faster than anyone can actually build the tech. The technologists are not waiting for it to catch up to its promises — they’re already on to the next thing. And what comes after A.I. agents? Agentic A.I.

How it’s pronounced

/ā-jənt-ĭk/

It is a fancified way to say something acts like an agent. Unlike chatbots, which require a human to type in a prompt before it can spit out a response, agentic A.I. can act on its own. A customer could create a complex goal, like predicting which factory machines will need maintenance or booking a trip, and the A.I. would automatically complete the required tasks.

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Title: AI exuberance masks broad weakness in tech sector, say investors
Content: Enthusiasm about artificial intelligence masks weakness across most of the technology sector, with many companies “still in a recession” following a slowdown that started in 2022, according to investors and analysis of recent financial reports.

Massive share price gains for large companies that were predicted to be early beneficiaries of AI, such as Nvidia and Microsoft, helped to erase memories of a dreadful 2022, when the tech-dominated Nasdaq Composite tumbled by almost a third.

Beneath the surface, however, many tech businesses that do not focus on AI have struggled to regain momentum.

“When you look at technology outside of AI, there’s not that much happening,” said Tony Kim, head of technology investing in BlackRock’s fundamental equities division. “Many [sub]-sectors are still in a recession. The only thing that has been really growing has been AI.”

More traditional tech areas such as software, IT consulting and the production of electronic equipment for other sectors such as manufacturing and the auto industry have faced difficulties, including weak demand and the hangover from overexpansion and overstocking of inventories during the coronavirus pandemic. Some have also directly suffered from the growth of AI, as customers with limited budgets redirect investment.

Dustin Moskovitz, the Facebook co-founder who is now chief executive of Asana, last week summed up the situation for many companies as the business software group scaled back its forecasts for the rest of the year.

“What we’re seeing in tech is still kind of the unwinding of the over-hiring and overspending that we saw at the beginning of the pandemic,” he told analysts. “And then that all couples with what I think is massive uncertainty in the economic environment. And then, also, just with how AI is going to play out.”

Recent financial reports show the majority of large tech companies have been growing more slowly than in the past, while many smaller ones are actively shrinking.

Groups in the S&P 500 IT sub-index increased revenues by an average of 6.9 per cent over the past 12 months, according to Bloomberg data, compared with a five-year average of 10 per cent. About three-quarters of companies grew more slowly than their recent average.

Earnings per share increased by an average of 16 per cent in the past 12 months, down from 21 per cent over the past five years.

The weakness is more obvious in small cap indices, where there is no boost from megacap groups. In the Russell 2000, technology was the second-worst performing sector in terms of revenue growth in the second quarter, according to data from LSEG. Revenue fell 6.1 per cent year on year, while profits were down 2.8 per cent.

“Generative AI is masking a cyclical downturn in a lot of other core sectors,” said Ted Mortonson, a tech strategist at RW Baird. “Everyone is hoping things get better in the next few quarters, though hope is not an investment strategy.”

Even within subsectors that have been caught up in the AI enthusiasm such as semiconductors, some business lines have been struggling. Brice Hill, chief financial officer at chip equipment supplier Applied Materials, told analysts last month that “we’re seeing particularly strong pull related to AI and data centre computing”, but there were “pockets of weakness in the auto and industrial end-markets”.

“Everywhere you look on the industrial side it’s similar,” said John Barr, a portfolio manager at Needham Funds who has invested in several semiconductor companies including Applied Materials. “Current growth is not so great, so what we’re looking for are companies that have a stable business and are investing in something new.”

Investor exuberance around AI-focused companies has faded since the early summer, leading many commentators to predict a prolonged rotation of investor attention away from Big Tech stocks towards sectors such as financial services and industrials.

Some tech specialists are hoping for a similar intra-industry rotation from the biggest AI stocks to more unloved corners of the industry. While few companies are predicting the sort of triple-digit growth that Nvidia has reported in recent quarters, there are signs that some of the worst-performing parts of the tech sector are turning a corner.

“I think we are seeing a stabilisation — things have stopped getting worse in those more macro-sensitive areas, and if rates go down then that will help,” said Tony Wang, portfolio manager for T Rowe Price’s science and technology fund.

“I feel like the idea that AI is the only thing that is working has been the case for the last two years. I’m not sure it will be the case for the next two.”
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