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Qualcomm eyes pieces of Intel’s struggling chip business

Qualcomm is reportedly exploring the possibility of acquiring parts of Intel’s chip design business to enhance its product portfolio. This potential move comes as Intel faces financial difficulties, prompting the company to consider divesting certain business units and assets, according to a Reuters report.

Qualcomm has been evaluating various parts of Intel’s design operations, with a particular interest in Intel’s client PC design business. However, other segments, such as Intel’s server division, are seen as less relevant to Qualcomm’s strategic goals, the report said citing sources.

While Qualcomm has not yet approached Intel regarding a deal, the discussions have been ongoing for months, and the plans could still change, insiders noted.

Queries to Qualcomm and Intel remained unanswered.

Acquiring Intel’s chip design business could offer Qualcomm a strategic opportunity to diversify its product offerings and expand beyond its mobile chip dominance. With AI becoming increasingly important, Intel’s expertise in PC chips could complement Qualcomm’s push into AI-driven computing.

“Qualcomm’s increasing interest in the PC chip business is no surprise,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “This addition will strengthen the Qualcomm-Microsoft relationship and build on the Surface Laptop and Pro tablet series.”

He further added, “The convergence of mobile and personal computing devices offers Qualcomm a significant opportunity to create optimized experiences across device types.”

It will be great news for Qualcomm who can now enter the PC segment, especially in the AI world, said Titus M, practice director at Everest Group. “However, the exact forms and shape remain unknown and are now down to pure speculations.”

This move comes as Qualcomm aims to strengthen its Snapdragon X series, which as Gogia notes would “sit very well” with Intel’s design capabilities, enhancing Qualcomm’s position in the AI PC space. Gogia also highlights Qualcomm’s potential to disrupt the market with aggressive pricing, posing a significant challenge to competitors like AMD and Apple.

Intel’s financial struggles and ongoing restructuring

Intel, facing mounting financial challenges, posted disappointing results in its second quarter, leading to a 15% staff reduction and a suspension of dividend payments. The company’s PC client business, a cornerstone of its operations, saw an 8% drop in revenue last year as the overall PC market weakened. Executives are now banking on the introduction of AI features in PCs to spur consumer upgrades.

Titus weighed in on Intel’s position, noting that the PC segment remains vital for the company. “Even with the increased internal pressure, it does not make sense for Intel to lose its most dominant sector in the form of PCs,” Titus explained. He emphasized the importance of innovation in the semiconductor industry, especially as AI markets are poised for significant growth.

“I highly doubt Intel will be ready to sell its AI-focused designs right when the market is about to boom,” Titus added.

This week, Intel launched its Lunar Lake chip, designed to power AI applications, as part of its push to regain a competitive edge. However, Intel outsourced significant portions of the chip’s fabrication to TSMC, a shift from its historical reliance on in-house production.

Qualcomm’s expansion strategy

With the potential acquisition, Qualcomm could significantly expand its footprint in the PC chip market, which is becoming increasingly intertwined with AI-driven computing. Gogia adds that such a deal would “allow Qualcomm to carve out a niche for supporting devices that allow AI tasks to be run without an internet connection.” This could further strengthen Qualcomm’s relationship with Microsoft, especially as both companies continue to explore opportunities in AI PCs.

Qualcomm may also be interested in Intel’s server and HPC segments, opined Neil Shah, VP for Research at Counterpoint Research. “This is a key market where Qualcomm is not yet playing, but where Intel is struggling against NVIDIA and AMD,” he said adding that Intel’s Altera (FPGA) and Movidius (Visual Processing Units) would help fill gaps in Qualcomm’s portfolio.

However, the speculation surrounding this potential acquisition highlights that Intel may still be considering other strategies to cut costs while maintaining its market presence.

“Intel will have to decide which business is long-term lucrative and which is not, especially in a highly competitive environment with both AMD and Arm-based servers gaining ground,” Shah said.

Broader implications

The acquisition, if realized, could also alter competitive dynamics in the semiconductor market. Faisal Kawoosa, Founder and Lead Analyst at Techarc, noted that Qualcomm is well-positioned to capitalize on growth opportunities in the PC sector.

“With Intel’s design capabilities under the belt, Qualcomm can be aggressive about this market and become a strategic partner to Microsoft,” Kawoosa said. He also pointed out that Intel’s core competencies in high-performance processors may be better suited for the server market, where the company could refocus its efforts against the rising dominance of Nvidia in AI and server segments.

Kawoosa added that the acquisition would serve Qualcomm well in a market where “laptops still have huge potential to grow even in markets like India, where penetration is very low.” He said this move could help Qualcomm become a stronger player in the PC and AI sectors.

How to use slicers in Excel

Spreadsheets’ greatest strength — the wealth of data they contain — also makes them nearly indecipherable at a glance. That’s why Microsoft provides numerous ways to filter, format, and highlight data in Excel.

In previous articles, we’ve explained how to use conditional formatting, tables, and PivotTables and PivotCharts to show the most important data in a spreadsheet. In this Excel tutorial, we’ll cover slicers.

What is an Excel slicer?

A slicer is an easy-to-use tool that lets you filter and dynamically change data based on your selected criteria. It’s a great tool for drilling down on information that you want to focus on. Once you’ve set up a slicer in an Excel worksheet, you (or anyone viewing the spreadsheet) can simply click buttons in the slicer to zero in on one or more particular groups of data within the larger data set.

In Excel, both tables and PivotTables include built-in filtering tools, but they can be a little clunky to use. Slicers offer a more user-friendly way to filter data, making them especially useful for spreadsheets you’re sharing with co-workers, executives, or clients.

Where can you use slicers in Excel?

You can apply slicers to any table or PivotTable in Excel. What’s more, you can create multiple slicers for the same table or PivotTable, so anyone viewing the sheet can see which subsets of data you want them to focus on, and then they can click on the slicer buttons to further home in on specific data.

You can also use slicers to filter the data in charts. And if you have more than one PivotTable based on the same data set, you can use the same slicer for all the PivotTables.

In this article, we will walk through how to create and format slicers, use them to filter data, and connect them to multiple PivotTables. We’ll give instructions for Excel for Windows, but the steps are very similar if you’re using Excel in macOS or on the web.

If you want to follow along with the demo, the sample data is below. Simply copy and paste it into a blank Excel file to get started.

YearCategoryProductSales (US Dollars)
2019ClothingSocks80,000
2018AccessoriesChains50,000
2020AccessoriesNecklaces40,000
2018EquipmentBasketballs30,000
2020EquipmentSoccer Balls20,000
2019ClothingPants30,000
2018EquipmentFootballs40,000
2018AccessoriesRings60,000
2019EquipmentSoccer Balls30,000
2018ClothingUnderwear30,000
2020EquipmentBasketballs50,000
2019AccessoriesChains80,000
2020ClothingUnderwear25,000
2020ClothingSocks30,000
2018ClothingHat45,000
2018EquipmentSoccer Balls35,000
2017ClothingSocks40,000
2020AccessoriesRings70,000
2019ClothingShirts30,000
2018ClothingPants30,000

How to create and format slicers

To begin, highlight the entire table. Then, in the Ribbon toolbar at the top of the screen, select Insert and then Table. On the popup that appears, make sure “My table has headers” is checked and select OK.

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Now that we have a table, simply click on any cell in the table and then select Insert > Slicer. The popup that appears lets you select which slicers you want to create, with each option corresponding to one of the headers in your table. In this case, select all the checkmarks and click OK.

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Four slicers appear on the sheet. You can spread them out on the page so they are easier to read.

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Notice that the buttons within each slicer reflect the data in the table. For instance, there are four different years that appear in various rows in column A. Those four years are represented as buttons in the Year slicer. Likewise, all the categories from column B appear in the Category slicer, and so on.

You can change each slicer’s colors to make it easier to differentiate among them or just for aesthetic reasons. To do so, click one of the slicers, click the Slicer tab on the Ribbon toolbar, and select a new color from gallery that appears. In our example, we’ll click the Category slicer and select the orange color scheme.

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As a final formatting task, change the colors of the remaining slicers to match the image below:

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How to filter data with slicers

Now we’ll demonstrate the power of slicers in a table. To begin, let’s filter the table to show only data related to 2018 equipment sales.

To do this, click 2018 in the year slicer. This will deselect 2017, 2019, and 2020, leaving only 2018 selected. Then in the Category slicer, click Equipment to deselect everything except Equipment.

The result? The table now shows only three rows, all of which contain equipment sales from 2018.

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If you want to show more data in the table, you can select multiple items within a slicer. For example, in the Category slicer, click Clothing, hold down the Ctrl key in Windows or the ⌘ key on a Mac, and then click Equipment. With both of those items selected, our example table now shows all clothing and equipment sales for 2018.

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When you apply filters to a table, all the original data is still there; it’s just hidden from view. To remove the filters you added, simply click the icon of the red X over a funnel on the top right of each slicer. When you do, all the original data reappears in the table.

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Click the red X icon to remove the filters applied by the slicer.

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Slicers can also be used to filter the data displayed on charts generated from the same table. To illustrate this, we’ll add a chart to the spreadsheet. Highlight the entire table, go to the Ribbon’s Insert tab, click the pie chart icon, and select the first 2D pie chart in the popup.

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Now, you will have a pie chart that shows all of the data presented in the table.

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Not very useful, is it? With so many different items displayed in the chart, the viewer is overwhelmed and can glean no insights from it.

Let’s filter this data using the slicers we created to show just 2020 accessory sales. The result is much easier to understand quickly:

Shimon Brathwaite


To delete the chart, right-click it and select Cut.

If you want to remove any slicer from your spreadsheet, first clear the filters on your slicers to restore the table back to normal. Then delete the slicers by right-clicking each one and selecting the Remove option.

removing a slicer

Removing a slicer

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Remove all the slicers, leaving only the table you created initially.

How to use slicers with PivotTables

In addition to using slicers on regular tables, you can also use them on PivotTables. To begin, highlight your table of data, go to Insert in the Ribbon toolbar, and select PivotTable. Select OK on the popup.

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You will be directed to a new page with a blank PivotTable on the left and a PivotTable Fields sidebar on the right. To populate the information, first check the checkbox next to Sales (US Dollars) in the sidebar. This places it in the Values area at the lower right. Next, drag the Year item down to the columns area in the sidebar. Then drag Category and Product down to the Rows area — place Category first and Product second.

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These actions populate the PivotTable on the left. The sales data is grouped first by category, then by product, with each year’s data appearing in a separate column. (For more details about working with PivotTables, see our PivotTables tutorial.)

Now follow the same steps as before to add a slicer to this PivotTable. Click anywhere on the table, go to the Insert tab and select Slicer. Check all the checkboxes and select OK.

Four slicers appear on the sheet. You can now use the slicers to filter the data in the PivotTable just as you did the data in the regular table earlier.

Shimon Brathwaite

As always, you can click the X icon at the upper right of the slicer to remove its filtering.

How to use slicers across multiple PivotTables

Now, there is a unique feature that can be used when you have two or more PivotTables in an Excel workbook. Slicers can be used across multiple PivotTables as long as they are based on the same data set.

So let’s try this: return to your original worksheet, create a second PivotTable, and populate the data. In the real world, you likely wouldn’t want to create a second PivotTable that’s identical to the first one; PivotTables are generally used to focus on particular subsets of data. So when you create the second PivotTable for our demo, check the Sales (US Dollars) checkbox, then drag Category and Year to the Rows area.

Shimon Brathwaite

Then, return to the first PivotTable you created, which already has slicers. Select the Year slicer, navigate to the Slicer tab in the Ribbon, and select Report Connections. (Alternatively, you can right-click the Year slicer and select Report Connections from the menu.) In the popup that appears, check the checkmark next to the second PivotTable you created and hit OK.

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Now, any change that you make with the slicer in the first sheet will be transferred to the other sheet as well. Try it out by changing the Year slicer to include only 2018.

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The connected PivotTable is now filtered for 2018 only.

Shimon Brathwaite

In this way, you can have multiple sheets with different views and data visualizations that dynamically change and remain in sync with one another.

If you ever need to disconnect a slicer, simply select that slicer, open the Report Connections dialog box, and deselect the PivotTable that you want to disconnect.

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Disconnecting a PivotTable from a slicer.

Shimon Brathwaite

And that’s all you need to know to get started with slicers. Time to start slicing your data!

Further reading:

Now that Qualcomm’s interested, will Apple buy (a little more) Intel?

Qualcomm is allegedly sniffing at the beleaguered remains of Intel and may try to acquire parts of the company. Will Apple make its own counterproposal?

Apple’s decision to abandon its processors in favor of Apple Silicon reflected a wider malaise. Try as it might, Intel found itself unable to accelerate processor development to the same extent as Apple found it could with ARM-based Apple Silicon chips, and what began as a jubilant relationship expired. Intel had saved Apple from the PowerPC chip disaster but couldn’t keep the pace with modern mobile processors.

The rest, as they say, is history.

Intel shudders, Qualcomm ascends

Qualcomm, meanwhile, knows a good idea when it sees one and has been dancing in Apple’s shadow with its own move to manufacture ARM-based processors. Those fast chips are picking up vendor sales at Intel’s expense. 

But it seems Qualcomm wants to take things a step further, which is why it has been exploring the possibility of acquiring parts of Intel’s design business, particularly the PC design business. 

A deal has not been reached — Reuters tells us Intel says it is “deeply committed” to its PC business — and Qualcomm hasn’t approached Intel to discuss its plans. In other words, all or none of this could be true.

The Apple connection

In context, Intel is encountering tough headwinds, prompting deep layoffs and a pause in dividend payments. The company’s PC client business declined 8% last year, reflecting weak PC sales across the board.

Apple’s Mac sales kicked against this trend, increasing 20.8% year on year in Q2 against a PC industry average 3% growth — mostly attributable to Apple’s extra million Mac sales.

With Apple expected to introduce incredibly performant M4 Macs this side of Christmas, all of which will be capable of running Apple Intelligence with built-in AI support, Cupertino is counting on its PC sales growth to continue. The company is unique in that it offers a completely compatible range of AI-supporting products in every key form factor (Mac, tablet, smartphone). No one else has this.

Qualcomm competes

Qualcomm wants a slice of that market, too. Its newly introduced Snapdragon processors are winning praise across the PC media for their low power use and high performance (though these still lag behind Apple Silicon in many respects).

All the same, as a business it may well have learned from Apple’s integrated approach to product design. A strategic Intel acquisition would give it an opportunity to begin building its own platform ecosystem, or at least make additional cash through hardware sales on its own account. Though it must be noted that most PCs capable of running AI cost more than some of Apple’s systems (e.g., the Mac mini) that are also capable of doing so.

The myth that PCs are cheaper is an enduring one, but you get what you pay for, and Crowdstrike showed us the risks of that platform.

But why wouldn’t Qualcomm want to grab a larger slice of the PC industry pie?

More than a modem

There is a clear competitive relationship between Apple and Qualcomm. Not so long ago, Apple settled outstanding litigation between the two companies in order to begin using Qualcomm’s 5G chips in its devices. The iPhone manufacturer had hoped to build its own 5G modems with the help of Intel, but that plan didn’t bear fruit.

In the end, Apple acquired Intel’s modem design unit and a big bucket full of related mobile patents for a billion dollars. It’s fair to say modem development has proved a struggle, but Apple is now expected to introduce its first 5G modems as soon as 2025

When it does, Apple will no longer be dependent on Qualcomm.

But, given that Qualcomm has its own chip design talent, will Apple want it to emerge as a hardware competitor? What is the risk that some key patents used by Apple may suddenly migrate from Intel to Qualcomm if such a deal does take place? After all, one of the key disputes between both firms has been around patent licensing costs.

All of this is speculation, of course: Qualcomm may never bid for Intel’s PC business. But if it does, and if Apple doesn’t like it, then it may be instructional to note that Qualcomm’s market cap currently stands at $182 billion, in contrast to Intel’s $82.7 billion and Apple’s eye-watering $3.38 trillion.

That difference in financial capital hints at what could be a dramatic bidding war, but almost certainly suggests regulatory investigation whoever seals the deal, if such an event happens at all. 

Please follow me on LinkedInMastodon, or join me in the AppleHolic’s bar & grill group on MeWe.

European AI treaty adds uncertainty for CIOs, but few specifics

An AI usage treaty, negotiated by representatives of 57 countries, was unveiled Thursday, but its language is so overarching that it’s unclear if enterprise CIOs will need to do anything differently to comply.

This mostly European effort adds to a lengthy list of AI global compliance efforts on top of many new legal attempts to govern AI in the United States. The initial signatories were Andorra, Georgia, Iceland, Norway, the Republic of Moldova, San Marino, and the United Kingdom, as well as Israel, the United States of America, and the European Union.

In its announcement, the Council of Europe said, “there are serious risks and perils arising from certain activities within the lifecycle of artificial intelligence such as discrimination in a variety of contexts, gender inequality, the undermining of democratic processes, impairing human dignity or individual autonomy, or the misuses of artificial intelligence systems by some States for repressive purposes, in violation of international human rights law.”

What the treaty says

The treaty, dubbed Framework Convention on artificial intelligence and human rights, democracy, and the rule of law, did emphasize that companies must make it clear to users whether or not they are communicating with a human or an AI.

Companies under the treaty must give “notice that one is interacting with an artificial intelligence system and not with a human being” as well as “carry out risk and impact assessments in respect of actual and potential impacts on human rights, democracy and the rule of law.”

Entities must also document everything they can about AI usage and be ready to make it available to anyone who asks about it. The agreement says that entities must “document the relevant information regarding AI systems and their usage and to make it available to affected persons. The information must be sufficient to enable people concerned to challenge the decision(s) made through the use of the system or based substantially on it, and to challenge the use of the system itself” and to be able to “lodge a complaint to competent authorities.”

Double standard

One observer in the treaty negotiation process, Francesca Fanucci, a legal specialist at ECNL (European Center for Not-for-Profit Law Stichting), described the effort as having been “watered down”, mostly in dealing with private companies and national security. 

“The formulation of principles and obligations in this convention is so overbroad and fraught with caveats that it raises serious questions about their legal certainty and effective enforceability,” she told Reuters.

The final document does explicitly exclude national securities matters: “Matters relating to national defence do not fall within the scope of this Convention.”

In an interview with Computerworld, Fanucci said that the final version of the treaty treats businesses very differently than governments.

The treaty “establishes obligations for State Parties, not for private actors directly. This treaty imposes on the State Parties to apply its rules to the public sector, but to choose if and how to apply them in their national legislation to the private sector. This is a compromise reached with the countries who specifically asked to have the private sector excluded, among these were the US, Canada, Israel and the UK,” Fanucci said. “They are practically allowed to place a reservation to the treaty.”

“This double standard is disappointing,” she added.

Lack of specifics

Tim Peters, an officer of compliance firm Enghouse Systems in Canada, was one of many who applauded the idea and intent of the treaty while questioning its specifics.

“The Council of Europe’s AI treaty is a well-intentioned but fundamentally flawed attempt to regulate a rapidly evolving space with yesterday’s tools. Although the treaty touts itself as technology-neutral, this neutrality may be its Achilles’ heel,” Peters said. “AI is not a one-size-fits-all solution, and attempting to apply blanket rules that govern everything from customer service bots to autonomous weapons could stifle innovation and push Europe into a regulatory straitjacket.”

Peters added that this could ultimately undermine enterprise AI efforts. 

“Enterprise IT executives should be concerned about the unintended consequences: stifling their ability to adapt, slowing down AI development, and driving talent and investment to more AI-friendly regions,” Peters said. “Ultimately, this treaty could create a competitive divide between companies playing it safe in Europe and those pushing boundaries elsewhere. Enterprises that want to thrive need to think critically about the long-term impact of this treaty, not just on AI ethics, but on their ability to innovate.”

Another industry executive, Trustible CTO Andrew Gamino-Cheong, also questioned the agreement’s lack of specifics.

“The actual contents of the treaty aren’t particularly strong and are mostly high level statements of principles. But I think it’s mostly an effort for countries to unify in asserting their rights as sovereign entities over the digital world. For some context on what I mean, I see what’s happening with Elon Musk and Brazil as a good example of the challenges governments face with tech,” Gamino-Cheong said. “It is technologically difficult to block Starlink in Brazil, which can in turn allow access to X, which is able to set its own content rules and dodge what Brazil wants them to do. Similarly, even though Clearview AI doesn’t legally operate in the EU, their having EU citizens’ data is enough for GDPR lawsuits against them there.”

Ernst & Young managing director Brian Levine addressed questions about the enforceability of this treaty, especially with companies in the United States, even though the US was one of the signatories. It is not uncommon for American companies to ignore European fines and penalties

“One step at a time. You can’t enforce shared rules and norms until you first reach agreement on what the rules and norms are,” Levine said. “We are rapidly exiting the ‘Wild West’ phase of AI. Get ready for the shift from too little regulation and guidance to too much.”

The treaty will enter into force “on the first day of the month following the expiration of a period of three months after the date on which five signatories, including at least three Council of Europe member states, have ratified it,” the announcement said. 

GenAI could make the Apple Watch a powerful healthcare tool

Generative AI (genAI) features added to an existing Apple Watch health app may light the path toward personalized and data-led healthcare for patients with Parkinson’s disease. The StrivePD app is made by Rune Labs, a California-based entity focused on delivering next-generation care for people with neurological disorders.

StrivePD has been enhanced with new genAI-created clinical reporting tools that provide in-depth data on a patient and the progression of their disease, and it delivers personalized educational content to patients, caregivers, and clinicians to improve outcomes.

What is StrivePD and how does it help?

The reports, allegedly HIPAA-compliant and shared with patients via email, are structured so patients get good insight into where they are with the disease, including summaries of their medication compliance, exercise, and symptom fluctuations. The app also delivers coaching in the form of exercise suggestions and tips around sleep patterns, and draws on data gathered by the Apple Watch (along with information shared by the patient).

In theory, the combined solution should help patients while also equipping medical professionals with deeper information they can use to guide treatment. 

It could even enable Parkinson’s patients to access care in the first place “The unfortunate reality is there is a structural shortage of specialists who can treat Parkinson’s, and the problem is getting worse,” said Rune Labs CEO Brian Pepin. “Most Parkinson’s patients struggle to get adequate access to care.”

Changing lives, one focused LLM at a time

It should be noted that the Rune Labs solution was in 2022 given the go ahead by the US Food and Drug Administration (FDA) to collect patient symptom data through measurements made by Apple Watch.

This makes it a recognized solution that could in the future become a poster child for the potential of genAI to deliver life-changing health benefits when deployed in such focused domains. (Turns out there’s a lot more to genAI than automating job applications and creating amusing images — data analysis at this level could yield profound benefits in terms of healthcare results and patient autonomy.)

Apple should be looking at this

I’d be very, very surprised if Apple’s health teams were not themselves already exploring ways in which to combine the data gathered by their own sensors and services with focused large language models (LLMs) to provide similar benefits. It’s a natural progression from the accurate exercise tracking tools the company has already deployed, including but not limited to swimming and wheelchair activity sensors.

The existence of that kind of highly personalized data and the also existing connection between Apple’s devices and patient medical data opens up interesting possibilities for LLM-augmented health and services that extend beyond Apple Fitness.

In that sense, the Rune Labs announcement could prophesize future health-related services that combine genAI with the vast quantity of personal data Apple’s ecosystem already gathers.

What’s happening in Apple R&D?

Apple CEO Tim Cook has frequently claimed that Apple will in the end be remembered for the work it is doing in health. Given the entire company is now shoulders to the wheel in the push to put AI in everything, it is unlikely its health teams aren’t at least trying to book some internal R&D time to explore how it can be applied in that sector.

If the Rune Labs solution actually delivers on its promises, Apple’s health teams will at least have an argument to justify that investment. But Apple aside, tools like these that empower better patient care and encourage personal autonomy are among the bright spots for a technology so many people fear may be a dystopian fin de siècle. 

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Copilot+ PCs that use Intel and AMD chips coming in November

Copilot+ PCs — special Windows computers equipped with the latest AI functions — first arrived in May. But only hardware using Qualcomm Snapdragon X Elite or X Plus chips were designated as ready for Copilot+ by Microsoft

As of November, new PCs that run processors from Intel and AMD will also be covered. Specifically, they’ll be equipped with Intel Core Ultra 200V or AMD Ryzen AI 300 chips, which are considered powerful enough to run compute-intensive AI functions.

More information about the upcoming Copilot+ PCs can be found on the Windows Experience blog .

The source code for Android 15 is now available to developers

Google has posted the source code for Android 15 on the Android Open Source Project (AOSP), which means developers around the world can now produce their own versions of the operating system if they wish.

More information, including the latest details on the Android Studio and Jetpack Compose tools, is available on the Android Developers Blog. (For those who prefer video, Spotlight Week offers a new clip about Android 15 daily.)

Android 15 is due to roll out to Pixel users in the next few weeks, followed by Samsung, Honor, iQOO, Lenovo, Motorola, Nothing, Oneplus, Oppo, Realme, Sharp, Sony, Tecno, Vivo and Xiaomi.

Australia pushes for AI rules, focusing on oversight and accountability

Australia has outlined plans for new AI regulations, focusing on human oversight and transparency as the technology spreads rapidly across business and everyday life.

The country’s Industry and Science Minister, Ed Husic, on Thursday, introduced ten voluntary AI guidelines and launched a month-long consultation to assess whether these measures should be made mandatory in high-risk areas.

Apple’s planned chatbot should have no ‘personality’

Apple is reportedly developing a new AI digital assistant expected to be integrated into its upcoming robotic devices. Based on generative AI (genAI) and more advanced than Siri, the new assistant will have a “human-like” AI “personality.”

The new assistant could replace Siri on HomePod, iPhones, iPads, or Macs and, most likely and intriguingly, on a new robotic desktop screen that follows and faces you while interacting or while you’re using it for a FaceTime call, according to Bloomberg’s Mark Gurman. Speech might be the main or sole interface.

The prospect fills me with dread.

The history of personality” failures

Personal computing’s past is littered with the virtual corpses of chatbots and assistants with “personality.” Microsoft, for example, has never stopped trying.

In 1995, it introduced the Microsoft Bob assistant, which conspicuously tried too hard to be personable; users mostly found it condescending and irritating.

Microsoft tried again in 1997 with Clippy, an anthropomorphic paper clip designed to have a personality. It landed like a thud, and critics slammed it for its irritating personality and intrusive interruptions.

Microsoft engineers in China released the experimental Xiaoice (pronounced “Shao-ice,” meaning “Little Bing”) in 2014. The chatbot prioritizes “emotional intelligence” and “empathy.” It uses advanced natural language processing and deep learning to continuously improve its conversational abilities. Microsoft built Xiaoice on what the company calls an “Empathetic Computing Framework.”

As of 2020, Xiaoice had attracted over 660 million active users globally, making it the world’s most popular personality chatbot. It’s been deployed on more than 40 platforms in countries such as China, Japan, and Indonesia, as well as previously in the US and India.

Microsoft researchers modeled Xiaoice to present as a teenage girl, leading many Chinese users to form strong emotional connections with it. Disturbingly, some 25% of Xiaoice users have told the chatbot, “I love you,” with millions of users forming what they think is a “relationship” with Xiaoice — at the expense of pursuing relationships with other people.

In 2016, Microsoft launched a chatbot called “Tay.” It was targeted at 18- to 24-year-olds and trained on social media posts, mainly Twitter. Within 24 hours of launch, the chatbot started posting racist, sexist, and anti-Semitic remarks and content favoring conspiracy theories and genocidal ideologies. (Again, trained on Twitter.)

Microsoft apologized and pulled the plug on “Tay.”

Other personality-centric chatbots have emerged over the years:

  • Replika: An AI chatbot that learns from interactions to become a personalized friend, mentor, or even romantic partner. Critics have slammed Replika for sexual content, even with minors, and also for claiming bonkers experiences, such as seeing supernatural entities.
  • Kuki (Mitsuku): Known for its conversational abilities, Kuki has won multiple Loebner Prize Turing Tests. It is designed to engage users in natural dialogues, but can also spout random nonsense.
  • Rose: A chatbot with a backstory and personality developed to provide engaging user interactions, but the conversation is fake, inconsistent, and unrelated to previous conversations.
  • BlenderBot: Developed by Meta, BlenderBot is designed to blend different conversational skills and engage users in meaningful conversations, but has tended to lie and hallucinate.
  • Eviebot: An AI companion with emotional understanding capabilities designed to engage users in meaningful conversations. Responses can be cryptic, unsettling, and even manipulative.
  • SimSimi: One of the earliest chatbots, SimSimi engages users in casual conversations and supports multiple languages, but can be vulgar and highly inappropriate.
  • Chai AI: Allows users to create and interact with personalized chatbot companions, offering a stream of AI personalities based on user preferences. The chatbot has offended many users with sexualized or dark content.
  • Inworld: Provides tools for users to create distinct personality chatbots, including those based on celebrities. This tool has often been used for creative, deceptive, and harmful personas.
  • AIBliss: A virtual girlfriend chatbot that develops different characteristics as users interact. Experts have warned that, like Xiaoice, some users have obsessed over their relationship with the bot at the expense of real, human relationships.

Pi in the sky

Closer to home, AI chatbots vary in the degree to which they prioritize “personality.” You’ll find a chatbot called Pi at the maximum “personality” end of the spectrum.

You can leave Pi running on your phone and start conversations with it whenever you like. The chatbot is chatty and conversational to the extreme. It also uses a lot of natural-sounding pauses, and it even takes breaths as it speaks. Most of the time, it will respond to your question or comment and end its wordy monologue with a question of its own. Pi comes with a variety of voices you can choose from. I pick voice #4, which sounds like a very California woman, complete with vocal fry.

Though I’m amazed with Pi, I don’t use it much. While the voice is natural, the conversationality feels forced and tone-deaf. It just won’t shut up, and I end up just turning it off after the 10th question it asks. In truth, I want a chatbot that answers my questions, not one that tries to get me to answer its questions.

Pi is also overly ingratiating, constantly telling me how insightful, thoughtful, or funny my inane responses are.

Why, Apple? Why?

I’m prepared to conclude that every single personality-centric chatbot ever produced has failed. So why does Apple think it can succeed?

Many already dislike Siri because of how the company has implemented the assistant’s personality. Specific prompts can elicit corny jokes and other useless content.

While writing this column, I asked Siri, “What are the three laws of robotics?” Its reply was: “Something about obeying people and never hurting them. I would never hurt anyone.”

In this case, Siri responded with a canned personality instead of answering the question. This doesn’t always happen, but it’s an example of how Apple might approach its generative AI chatbot personality.

I can’t imagine Apple thinks Siri’s personality is popular, nor do I believe the company has seen personality-focused chatbots in the wild and found something worth emulating. “Personality” in chatbots is a novelty act, a parlor trick, that can be fun for 10 minutes but then grates on the nerves after a few encounters.

What we need instead of personality

Natural, casual human conversation is far beyond the capacity of today’s most advanced AI. It requires nuance, compassion, empathy, subtly, and a capacity for perceiving and expressing “tone.”

Writing a formal missive, letter, scientific paper, or essay is far, far easier for AI than casual chit-chat with a friend.

Another problem is that personality chatbots are liars. They express emotions they don’t have, make vocal intonations based on thoughts they don’t have, and often claim experiences they never had.

People don’t like to be lied to. What we need instead of a profane, inappropriate, ingratiating, boring liar is something useful.

The human factor in elocution and tone should be calibrated to be unnoticeable — neither overly “real” nor robotic-sounding. If you can program for empathy, empathize with my situation and objectives, not my emotions.

We want personalization, not personality. We want agency, not tone-deafness. We want a powerful tool that magnifies our abilities, not a “friend.”

Who knows? Apple might surprise everyone with a popular “personality” robot voice that doesn’t repel or confuse people. But I doubt it.

Nobody’s ever done it. Nobody should attempt it.

GenAI vendors’ self-destructive habit of overpromising

One of the ongoing issues in enterprise IT is the gap between perception and reality. But when it comes to generative AI (genAI), vendors are about to discover that there is a big price to pay for overpromising. 

Not only are corporate execs dealing with disappointment and a lack of meaningful ROI, but the same senior non-tech leaders (think CFOs, CEOs, COOs, and some board members) who pushed for the technology before it was ready are the ones who will quickly resist deployment efforts down the road. The irony is that those later rollouts will more likely deliver on long-promised benefits. A little “AI-sales-rep-who-cried-wolf” goes a long way.

There are plenty of enterprise examples of genAI ROI not happening, but perhaps the best illustration of the conundrum involves Apple’s upcoming iPhone rollout and consumers.

Apple will add AI (it’s branded Apple Intelligence) to some of its iPhone 16 line to function on-device. In theory, on-device access might accelerate AI responses (compared with the cloud), and it could allow Siri to grab information seamlessly from all installed apps. 

If you buy into the argument, this setup could eventually change the dynamics of apps. Why wait for a weather app to launch and tell you the hourly forecast if Siri can do it easier and faster? 

For example, I have an app solely to tell me the humidity level and no fewer than a half-dozen communications apps (WhatsApp, Webex, Signal, etc.), plus apps that can directly message me (LinkedIn, X, and Facebook) —  all in addition to text messages, emails, and transcribed voicemails. Why should I have to mess with all of that?

In theory, Apple Intelligence could consolidate all of those bits and bytes of information and deliver my communications and updates in a consistent format.

But this is where reality gets in the way of tech dreams. As friend and fellow tech Jason Perlow writes, Apple is delivering a slimmed-down version of genAI in a way that could fuel more disappointment.

“Unlike typical iOS or MacOS feature upgrades, Apple Intelligence loads a downsized version of Apple’s Foundation Models, a home-grown large language model (LLM) with approximately 3 billion parameters,” Perlow wrote. “While impressive, this is tiny compared to models like GPT-3.5 and GPT-4, which boast hundreds of billions of parameters. Even Meta’s open source Llama 3, which you can run on a desktop computer, has 8 billion parameters.”

On top of that, Apple Intelligence will grab as much as 2GB of RAM, which means users will either need more RAM than they want or deal with performance slowdowns in other iPhone functions. Then there’s the potential drag on battery performance which, again, threatens to undermine everything else on the device.

Bottom line: Not only will this initial rollout likely eat battery and RAM for breakfast, but it will be smaller and therefore less powerful than most other genAI deployments. That’s a recipe for buyer remorse.

Then there is the issue of app developers. First, it will take some time for them to work with the Apple API and deliver versions of their apps that play nicely with Apple Intelligence. Other developers may question whether it is even in their interest to embrace Apple Intelligence. Once they enable Apple to effortlessly grab their data and deliver it via Siri, doesn’t the value of their standalone app diminish? And doesn’t that undermine their monetization strategies? 

Why look at ads on a movie-ticket or concert venue app when Siri can deliver the needed info directly?

Research firm IDC looked recently at those Apple-promised capabilities and predicted they could initally boost phone sales. “Initially” is the key word. People often buy based on tech promises, then talk things over with others and decide whether to buy a future phone (or keep the one they just got) based on their actual experience.

This brings us back to enterprise IT and genAI. Business execs who pushed for genAI rollouts before the technology was ready are unlikely to be patient and realistic waiting for the solid results to surface.

And then, just when meaningful ROI is likely to arrive (roughly two or three years from now), they’ll have moved on, feeling burned by early deployments and unwilling to be fooled again. 

GenAI has great potential to push near-term sales with unrealistic promises — a self-destructive marketing approach, whether you’re OpenAI, Microsoft, Google, or Amazon selling to enterprise CIOs or Apple selling to consumers. 

Overpromising is a dangerous and foolhardy strategy. And yet, with enterprise genAI sales these days, overpromising isn’t a side course — it’s the main course. It’s unlikely to prove appetizing for anyone.