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Cloud Computing solutions, including Software, Infrastructure, Platform, Unified Communications, Mobile, and Content as a Service are well-established and growing. The evolution of these markets will be driven by the complex interaction of all participants, beginning with end customers.

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News

  • Apple is the latest company to get pwned by AI

    It’s happened yet again — this time to Apple. Apple recently had to disable AI-generated news summaries in its News app in iOS 18.3. You can guess why: the AI-driven Notification Summaries for the news and entertainment categories in the app occasionally hallucinated, lied, and spread misinformation.  Sound familiar?  Users complained about the summaries, but Apple acted only after a complaint from BBC News, which told Apple that several of its notifications were improperly summarized. These were major errors in some cases.  The generative AI (genAI) tool incorrectly summarized a BBC headline, falsely claiming that Luigi Mangione, who was charged with murdering UnitedHealthcare CEO Brian Thompson, had shot himself. It inaccurately reported that Luke Littler had won the PDC World Darts Championship hours before the competition had even begun and falsely claimed that Spanish tennis star Rafael Nadal had come out as gay. Apple summarized other real stories with false information: The tool said Israeli Prime Minister Benjamin Netanyahu had been arrested, Pete Hegseth had been fired, that Trump tariffs had triggered inflation (before Donald Trump had re-assumed office), and spewed dozens of other falsehoods.  Apple rolled out the feature not knowing it would embarrass the company and force a retreat — which is amazing when you consider that this happens to every other company that tries to automate genAI information delivery of any kind on a large scale. Microsoft Start’s travel section, for example, published an AI-generated guide for Ottawa that included the Ottawa Food Bank as a “tourist hotspot,” encouraging visitors to come on “an empty stomach.” In September 2023, Microsoft’s news portal MSN ran an AI-generated obituary for former NBA player Brandon Hunter, who had passed away at the age of 42. The obituary headline called Hunter “useless at 42,” while the body of the text said that Hunter had “performed in 67 video games over two seasons.” Microsoft’s news aggregator, MSN, attached an inappropriate AI-generated poll to a Guardianarticle about a woman’s death. The poll asked readers to guess the cause of death, offering options like murder, accident, or suicide. During its first public demo in February 2024, Google’s Bard AI incorrectly claimed that the James Webb Space Telescope had taken the first pictures of a planet outside our solar system—some 16 years after the first extrasolar planets were photographed.  These are just a few examples out of many.  The problem: AI isn’t human The Brandon Hunter example is instructive. The AI knows enough about language to “know” that a person who does something is “useful,” that death means they can no longer do that thing, and that the opposite of “useful” is “useless.” But AI does not have a clue that saying in an obituary that a person’s death makes them “useless” is problematic in the extreme. Chatbots based on Large Language Models (LLMs) are inherently tone-deaf, ignorant of human context, and can’t tell the difference between fact and fiction, between truth and lies. They are, for lack of a better term, sociopaths — unable to tell the difference between the emotional impact of an obituary and a corporate earnings report.  There are several reasons for errors. LLMs are trained on massive datasets that contain errors, biases, or inconsistencies. Even if the data is mostly reliable, it may not cover all possible topics a model is expected to generate content about, leading to gaps in knowledge. Beyond that, LLMs generate responses based on statistical patterns using probability to choose words rather than understanding or thinking. (They’ve been described as next-word prediction machines.) The biggest problem, however, is that AI isn’t human, sentient, or capable of thought.  Another problem: People aren’t AI Most people don’t pay attention to the fact that we don’t actually communicate with complete information.  Here’s a simple example: If I say to my neighbor, “Hey, what’s up?” My neighbor is likely to reply, “Not much. You?” A logic machine would likely respond to that question by describing the layers of the atmosphere, satellites, and the planets and stars beyond. It answered the question factually as it was asked, but the literal content of the question did not contain the actual information sought by the asker.  To answer that simple question in the manner expected, a person has to be a human who is part of a culture and understands verbal conventions — or has to be specifically programmed to respond to such conventions with the correct canned response.  When we communicate, we rely on shared understanding, context, intonation, facial expression, body language, situational awareness, cultural references, past interactions, and many other things. This varies by language. The English language is one of the most literally specific languages in the world, and so a great many other languages will likely have bigger problems with human-machine communication.  Our human conventions for communication are very unlikely to align with genAI tools for a very long time.  That’s why frequent AI chatbot users often feel like the software sometimes willfully evades their questions.  The biggest problem: Tech companies can be hubristic What’s really astonishing to me is that companies keep doing this. And by “this,” I mean rolling out unsupervised automated content-generating systems that deliver one-to-many content on a large scale. And scale is precisely the difference.  If a single user prompts ChatGPT and gets a false or ridiculous answer, they are likely to shrug and try again, sometimes chastising the bot for its error, for which the chatbot is programmed to apologize and try again. No harm, no foul.  But when an LLM spits out a wrong answer for a million people, that’s a problem, especially in Apple’s case, where no doubt many users are just reading the summary instead of the whole story. “Wow, Israeli Prime Minister Benjamin Netanyahu was arrested. Didn’t see that coming,” and now some two-digit percentage of those users are walking around believing misinformation.  Each tech company believes they have better technology than the others.  Google thought: Sure, that happened to Microsoft, but our tech is better.  Apple thought: Sure, it happened to Google, but our tech is better. Tech companies: No, your technology is not better. The current state of LLM technology is what it is — and we have definitely not reached the point where genAI chatbots can reliably handle a job like this.  What Apple’s error teaches us There’s a right way and a wrong way to use LLM-based chatbots. The right way is to query with intelligent prompts, ask the question in several ways, and always fact-check the responses before using or believing that information.  Chatbots are great for brainstorming, providing quick information that isn’t important, or being a mere starting point for research that leads you to legitimate sources.  But using LLM-based chatbots to write content unsupervised at scale? It’s very clear that this is the road to embarrassment and failure.  The moral of the story is that genAI is still too unpredictable to reliably represent a company in one-to-many communications of any kind at scale.  rrorSo, make sure this doesn’t happen with any project under your purview. Setting up any public-facing content-producing project meant to communicate information to large numbers of people should be a hard, categorical “no” until further notice.  AI is not human, can’t think, and it will confuse your customers and embarrass your company if you give it a public-facing role.

  • Copilot AI comes to Microsoft 365 plans: Everything you need to know

    If you’re a Microsoft 365 subscriber with a Personal or Family subscription, Microsoft just flipped a switch and activated Copilot AI features for your account. It’s a new part of your subscription, along with that 1TB of OneDrive storage and access to Office apps like Word and Excel. But there are some big catches — including a price hike and some limits. Microsoft’s latest changes follow in Google’s footsteps, with AI features appearing in the standard subscription for much less than you’d pay for those AI features separately — but with the standard subscription price also going up at the same time. Let’s dive into how Microsoft just transformed some of the world’s most popular productivity apps, what you can do now — and how you can avoid paying more. Want to keep track of the latest Windows and Office developments in just a few minutes each week? Sign up for my free Windows Intelligence newsletter. Plus, get free Windows Field Guide downloads as a bonus when you sign up! How Copilot works in Microsoft 365 plans First things first, the basics: Microsoft announced that all Microsoft 365 Personal and Microsoft 365 Family plans now include Copilot features as of Jan. 16 in “most markets worldwide.” This won’t affect you when using a Microsoft 365 plan provided by a workplace — businesses still have to pay separately for AI features — but it will affect your individual plans. And plenty of professionals do pay for their own Microsoft 365 subscriptions. (I should know; I’m one of them!) In other words, if you pay for Microsoft 365 and use apps like Word, Excel, PowerPoint, OneNote, and Outlook, you’ll now find the Copilot button popping up in these applications. Previously, you had to pay $20 per month for a Copilot Pro subscription to unlock these features. Here’s the first big catch: The newly expanded paid 365 plans don’t give you unlimited access to Microsoft’s AI features. Instead, you get a monthly allotment of credits that Microsoft says “…should be enough for most subscribers.” In practice, that appears to be 60 credits per month — meaning you can use AI features 60 times per month. After that, you’ll need to pay for a $20-per-month Copilot Pro subscription to keep using those AI features. You’ll see an informational pop-up window the first time you open an app like Word.Chris Hoffman, IDG Note: These AI credits are actually shared across various other Microsoft apps — including for AI image generation in Designer, Paint, and Photos and text-editing work in Notepad. They’re not just for Word, Excel, and PowerPoint. Plus, again, Microsoft is raising its 365 subscription prices, with Copilot bundled into the mix. They’re going up by $3 per month in the US, though the exact price increase will vary by country. For the yearly plans in the US, Microsoft 365 Family goes from $100 to $130 per year, and Microsoft 365 Personal goes from $70 to $100 per year. This is the first time Microsoft has raised prices since launching the subscription service — originally called Office 365 — back in 2013. While it’s true that Microsoft is using these AI features as a way to hike prices, these subscriptions were overdue for a price increase anyway, and it’s nice to at least get something out of it. (In my opinion, between the 1TB of OneDrive storage and access to Office apps, it’s still a good value.) It’s worth noting that this Copilot change is only for Microsoft 365 plans. If you buy a more traditional “one-time purchase” version of Office like Office 2024, your setup isn’t changing — and you won’t have access to these newer AI features. Using Copilot AI in Microsoft 365 apps With the new adjustments in place, Copilot AI is easy to find in Office apps: You’ll find a Copilot icon on the ribbon, or you can also select some text and click the little Copilot icon that appears next to it, or just press Alt+i. Then you can prompt Copilot to write or rewrite text in a document for you. You could also ask it questions about the document you’re viewing from the Copilot sidebar. For more information on exactly how Copilot works in these Office apps, check out my Copilot Pro review from last year. The new built-in Copilot features are exactly the same as what you get with Copilot Pro; the only difference is that you’re limited to 60 uses per month in the 365 setup. If you run out of credits, Microsoft will encourage you to upgrade to Copilot Pro. In a way, then, these AI features are a bit of a “trial” for Copilot Pro. To check how many credits you have left, you can click the little menu icon in the Copilot sidebar in an Office app and then click “AI credit balance.” This will take you to your Microsoft 365 account subscription page, where you can see a running balance of the AI credits you’ve used. Your AI credit balance is just a few clicks away.Chris Hoffman, IDG Generating images with Microsoft Designer The same credit system also applies to Microsoft Designer, which is a useful AI image-generation tool. (At our newsletter-focused small business The Intelligence, we use Microsoft Designer to create some feature image illustrations for our articles — we’re writers, not visual artists!) That means with any paid Personal or Family 365 plan, you can opt to use your 60 monthly AI image credits directly within Designer, too. This is actually quite a downgrade: Previously, everyone got 15 credits per day for AI image generations. Now, subscribers get a total of 60 credits per month, while free accounts only get 15 credits per month. If you need more than that, you can upgrade to the $20-a-month Copilot Pro plan, which gives you many more AI image generations in Designer and beyond. (Microsoft says you get “at least 10x more credits” for Designer with Copilot Pro, compared to the 15-credits-per-month free setup — so roughly 150 credits per month, then, compared to the 60 monthly credits in the base 365 subscription.) AI tools are expensive to create and operate, and companies have lost a lot of money on them. It’s no surprise to see many AI tools offering less for free and looking for more payment from their users; that’s what’s happening here. How to avoid the AI features (and costs) entirely There are ways to avoid the Microsoft 365 subscription price increases, if you don’t anticipate using them and don’t want to pay for them. (The price increase doesn’t take effect until your next subscription renewal, by the way.) If you already have a Microsoft 365 subscription, you can keep your old subscription price and opt out of the AI features “for a limited time.” Microsoft says you can switch by canceling your subscription and choosing one of the “Classic” plans during the cancelation process. Here are Microsoft’s instructions. You could also buy “perpetual licenses” of Office instead of using the more prominently offered subscriptions. In other words, with a one-time purchase of Office 2024, you could use Office for a few years for that one-time purchase price. It’s not as good a deal as it sounds — that one-time purchase price will only get you access to Office apps like Word and Excel on a single computer, and you won’t have access to the 1TB of OneDrive storage. (Plus, while your license will be good in perpetuity, Microsoft will stop delivering security updates for Office 2024 in October 2029.) You can also buy Microsoft 365 subscription keys from other retailers. Without getting into the weeds too far here, it’s worth noting that days after Microsoft implemented the subscription price increase, Amazon is still selling Microsoft 365 Personal subscriptions for $70 and Microsoft 365 Family subscriptions for $100 — the old prices. But these are the standard plans and include those AI features. That’s a bargain. Of course, you could also turn to other office suites — the web-based Google Docs, the open-source LibreOffice, or the Apple-focused iWork suite — but Word, Excel, and PowerPoint are the business standard for a reason. And even with these AI-adding price increases, getting that 1TB of OneDrive storage at those prices is still a great deal. Want more insights and helpful tips? Sign up for my free Windows Intelligence newsletter! You’ll also get three new things to try every Friday and free copies of Paul Thurrott’s Windows Field Guides as a special welcome gift.

  • For the first time ever, I wish Google would act more like Amazon

    Fair warning: This isn’t your average article about what’s happening with all the newfangled AI hullabaloo in this weird and wild world of ours. Nope — there’ll be no “oohing” and “ahhing” or talk about how systems like Gemini and ChatGPT and their brethren are, like, totally gonna revolutionize the world and change life as we know it. Instead, I want to look at the state of these generative AI systems through as practical and realistic of a lens as possible — focusing purely on how they work right now and what they’re able to accomplish. And with that in mind, my friend, there’s no way around it: These things seriously suck. Sorry for the bluntness, but for Goog’s sake, someone’s gotta say it. For all their genuinely impressive technological feats and all the interesting ways they’re able to help with mundane work tasks, Google’s Gemini and other such generative AI systems are doing us all a major disservice in one key area — and everyone seems content in looking the other way and pretending it isn’t a problem. That’s why I was so pleasantly surprised to see that one tech giant seemingly isn’t taking the bait and is instead lagging behind and taking its time to get this right instead of rushing it out half-baked, like everyone else. It’s the antithesis to the strategy we’re seeing play out from Google and virtually every other tech player right now. And my goodness, is it ever a refreshing contrast. [Get level-headed insight in your inbox with my Android Intelligence newsletter. Three new things to know and try each Friday!] The Google Gemini Bizarro World I won’t keep you waiting: The company that’s getting it right, at least in terms of its process and philosophy, is none other than Amazon. I’ll be the first to admit: I’m typically not a huge fan of Amazon or its approach. But within this specific area, it really is creating a model for how tech companies should be thinking about these generative AI systems. My revelation comes via a locked-down article that went mostly unnoticed at The Financial Times last week. The report’s all about how Amazon is scrambling to upgrade its Alexa virtual assistant with generative AI and relaunch it as a powerful “agent” for offering up complex answers and completing all kinds of online tasks. More of the same, right? Sure sounds that way — but hang on: There’s a twist. Allow me to quote a pertinent passage from behind the paywall for ya: Rohit Prasad, who leads the artificial general intelligence (AGI) team at Amazon, told the Financial Times the voice assistant still needed to surmount several technical hurdles before the rollout. This includes solving the problem of “hallucinations” or fabricated answers, its response speed or “latency,” and reliability.  “Hallucinations have to be close to zero,” said Prasad. “It’s still an open problem in the industry, but we are working extremely hard on it.”  (Insert exaggerated record-scratch sound effect here.) Wait — what? Did we read that right?! Let’s look to another passage to confirm: One former senior member of the Alexa team said while LLMs were very sophisticated, they came with risks, such as producing answers that were “completely invented some of the time.” “At the scale that Amazon operates, that could happen large numbers of times per day,” they said, damaging its brand and reputation. Well, tickle me tootsies and call me Tito. Someone actually gives a damn. If the contrast here still isn’t apparent, let me spell it out: These large-language-model systems — the type of technology under the hood of Gemini, ChatGPT, and pretty much every other generative AI service we’ve seen show up over the past year or two — they don’t really know anything, in any human-like sense. They work purely by analyzing massive amounts of data, observing patterns within that data, and then using sophisticated statistics to predict what word is likely to come next in any scenario — relying on all the info they’ve ingested as a guide. Or, put into layman’s terms: They have no idea what they’re saying or if it’s right. They’re just coughing up characters based on patterns and probability. And that gets us to the core problem with these systems and why, as I put it so elegantly a moment ago, they suck. As I mused whilst explaining why Gemini is, in many ways, the new Google+ recently: The reality … is that large-language models like Gemini and ChatGPT are wildly impressive at a very small set of specific, limited tasks. They work wonders when it comes to unambiguous data processing, text summarizing, and other low-level, closely defined and clearly objective chores. That’s great! They’re an incredible new asset for those sorts of purposes. But everyone in the tech industry seems to be clamoring to brush aside an extremely real asterisk to that — and that’s the fact that Gemini, ChatGPT, and other such systems simply don’t belong everywhere. They aren’t at all reliable as “creative” tools or tools intended to parse information and provide specific, factual answers. And we, as actual human users of the services associated with this stuff, don’t need this type of technology everywhere — and might even be actively harmed by having it forced into so many places where it doesn’t genuinely belong. That, m’dear, is a pretty pressing problem. Allow me to borrow a quote collected by my Computerworld colleague Lucas Mearian in a thoroughly reported analysis of how, exactly, these large-language models work: “Hallucinations happen because LLMs, in their in most vanilla form, don’t have an internal state representation of the world,” said Jonathan Siddharth, CEO of Turing, a Palo Alto, California company that uses AI to find, hire, and onboard software engineers remotely. “There’s no concept of fact. They’re predicting the next word based on what they’ve seen so far — it’s a statistical estimate.” And there we have it. That’s why Gemini, ChatGPT, and other such systems so frequently serve up inaccurate info and present it as fact — something that’s endlessly amusing to see examples of, sure, but that’s also an extremely serious issue. What’s more, it’s only growing more and more prominent as these systems show up everywhere and increasingly overshadow traditional search methods within Google and beyond. And that brings us back to Amazon’s seemingly accidental accomplishment. Amazon and Google: A tale of two AI journeys What’s especially interesting about the slow-moving state of Amazon’s Alexa AI rollout is how it’s being presented as a negative by most market-watchers. Back to that same Financial Times article I quoted a moment ago, the conclusion is unambiguous: In June, Mihail Eric, a former machine learning scientist at Alexa and founding member of its “conversational modelling team,” said publicly that Amazon had “dropped the ball” on becoming “the unequivocal market leader in conversational AI” with Alexa. But, ironically, that’s exactly where I see Amazon doing something admirable and creating that striking contrast between its efforts and those of Google and others in the industry. The reality is that all these systems share those same foundational flaws. Remember: By the very nature of the technology, generative-AI-provided answers are woefully inconsistent and unreliable. And yet, Google’s been going in overdrive to get Gemini into every possible place and get us all in the habit of relying on it for almost every imaginable purpose — including those where it simply isn’t reliable. (Remember my analogy from a minute ago? Yuuuuuup.) In doing so, it’s chasing short-term market gains at the cost of long-term trust. All other variables aside, being wrong or misleading with basic information 20% of the time — or, heck, even just 10% of the time — is a pretty substantial problem. I’ve said it before, and I’ll say it again: If something is inaccurate or unreliable 10% of the time, it’s useful precisely 0% of the time. And to be clear, the stakes here couldn’t be higher. In terms of their answer-offering and info-providing capabilities, Gemini and other such systems are being framed and certainly perceived as magical answer machines. Most people aren’t treating ’em with a hefty degree of skepticism and taking the time to ask all the right questions, verify answers, and so on. They’re asking questions, seeing or hearing answers, and then assuming they’re right. And by golly, are they getting an awful lot of confidently stated inaccuracies as a result — something that, as we established a moment ago, is likely inevitable with this type of technology in its current state. On some level, Google is clearly aware of this. The company had been developing the technology behind Gemini for years before rushing it out into the world following the success and attention around ChatGPT’s initial rollout — but, as had been said in numerous venues over time, it hadn’t felt like it was mature enough to be ready for public use. So what changed? Not the nature of the technology — nope; by all counts, it was just the competitive pressure that forced Google to say “screw it, it’s good enough” and go all-in with systems that weren’t and still aren’t ready for primetime, at least with all of their promoted purposes. And that, my fellow accuracy-obsessed armadillo, is where Amazon is getting it right. Rather than just rushing to replace Alexa with some new half-baked replacement, the company is actually waiting until it feels like it’s got the new system ready — with reliability, yes, but also with branding and a consistent-seeming user experience. (Anyone who’s been trying to navigate the comically complex web of Gemini and Assistant on Android — and beyond — can surely relate!) Whether Amazon will keep up this pattern or eventually relent and go the “good enough” route remains to be seen. Sooner or later, investor pressure may force it to follow Google’s path and put its next-gen answer agent out there, even if it in all likelihood still isn’t ready by any reasonable standard. For now, though, man: I can’t help but applaud the fact that the company’s taking its time instead of prematurely fumbling to the finish line like everyone else. And I can’t help but wish Google would have taken that same path, too, rather than doing its usual Google Thang™ and forcing some undercooked new concept into every last nook and cranny — no matter the consequences. Maybe, hopefully, this’ll all settle out in some sensible way and turn into a positive in the future. For the moment, though, Google’s strategy sure seems like more of a minus than a plus for us, as users of its most important products — and especially in this arena, it sure seems like getting it right should mean more than getting it out into the world quickly, flaws and all and at any cost. Get plain-English perspective on the news that matters with my free Android Intelligence newsletter — three things to know and try in your inbox each Friday.

  • Perplexity launches Sonar API, enabling enterprise AI search integration

    Perplexity has introduced an API service named Sonar that would allow developers and enterprises to embed the company’s generative AI search technology into their applications. The company has rolled out two initial tiers – a more affordable and faster option called Sonar, and a higher-priced tier, Sonar Pro, tailored for handling more complex queries. In a blog post, Perplexity described Sonar API as “lightweight, affordable, fast, and simple to use,” noting that it includes features such as citations and the ability to customize sources. The company said the API is ideal for businesses requiring streamlined question-and-answer functionalities optimized for speed. For enterprises with more complex requirements, Perplexity will offer the Sonar Pro API, which supports multi-step queries, a larger context window for handling longer and more detailed searches, and higher extensibility. It will also provide approximately twice the number of citations per search compared to the standard Sonar API, the company said. Competing with bigger players The launch positions Perplexity as a stronger, more direct competitor to larger players such as OpenAI and Google, offering its real-time, web-connected search capabilities to users. “Perplexity’s real-time data retrieval and citation-backed responses cater to enterprise demands for reliable, transparent, and actionable information,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “By prioritizing verifiability and up-to-date insights, it offers a specialized approach that sets it apart from broader conversational models like GPT-4 and Claude, which serve a wider range of use cases but may lack the tailored features enterprises need for effective decision-making and compliance management.” However, significant challenges remain in areas such as data privacy, depth, scale, and audit trails before any company can establish itself as a leader in the field. Although real-time information capabilities offer clear advantages, they also introduce additional complexities to an already intricate landscape. “Enterprises tend to take cautious steps when exploring new technologies like enterprise research, especially when handling sensitive company data,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “Beyond concerns around data privacy, security, and compliance, any large company would first evaluate accuracy and performance. Whether Perplexity can outpace Google and OpenAI in this space will depend on the aggressiveness of Google’s and other competitors’ strategies.” Real-time information and search is an area where Google excels. To stay competitive, Perplexity will need to explore other differentiators, such as compliance, Gogia said. Use cases for enterprises Analysts note that Perplexity’s latest offerings have diverse applications, ranging from basic tasks such as identifying service management issues in IT functions to broader use by leaders in supply chain, finance, sales, marketing, and operations. “Sonar Pro, [especially] caters to the needs of compliance-driven industries such as healthcare, BFSI, and energy by offering robust tools for security reporting, portfolio management, and seamless DevOps integration,” Ram said. “These capabilities not only enhance operational efficiency but also ensure adherence to regulatory standards, positioning it as an indispensable solution for improving software development practices.” But for the tool to succeed, significant customization may be necessary, tailored to specific industries and individual companies. Initially, and in the near term, use cases are expected to remain highly customized before gradually evolving into industry-standard templates, all while adhering to local compliance requirements, according to Gogia. “Early adopters may come from industries like banking and manufacturing with a mature data posture, which is an essential foundation for any such deployment,” Gogia said. “Architectural maturity is another key requirement for success, as it enables the embedding and native use of such a tool within the architecture.”

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