The 2023 Google I/O: It’s all about AI, don’t cha know

As longstanding readers may already recall, I regularly cover the yearly Apple Worldwide Developers Conference, with the 2023 version scheduled for next month, June 5-9 to be exact. Stay tuned for this year’s iteration of my ongoing event analysis! Beginning this year, EDN is also kicking off a planned yearly coverage cadence from yours truly for Google’s developer conference, called Google I/O (or is it parent company Alphabet’s? I’ll use the more recognizable “Google” lingo going forward in this writeup). Why, might you ask? Well:

Google’s Linux-derived Android and ChromeOS operating systems are best known for their implementations, respectively, in the company’s and partners’ smartphones and tablets, and in netbooks (i.e., Chromebooks) and nettops (Chromeboxes). But the OSs’ open-source foundations also render them applicable elsewhere. This aspiration is also the case for the Linux-abandoning but still open-source Fuschia O/S sibling (successor?).
Although Google’s been developing coprocessor ICs ever since the Pixel 2 smartphone generation’s Visual Core, with added neural network processing capabilities in the Pixel 4’s Neural Core, the company significantly upped its game beginning with the Pixel 6 generation with full-featured Tensor SoCs, supplanting the application processors from Qualcomm used in prior Pixel phone generations. And beginning in 2016, Google has also developed and productized multiple generations of Tensor Processing Units (TPUs) useful in accelerating deep learning inference and (later also) training functions, initially for the “cloud” and more recently expanding to network edge nodes.
Speaking of deep learning and other AI operations, they unsurprisingly were a regularly repeated topic at Wednesday morning’s keynote and, more generally, throughout the multi-day event. Google has long internally developed various AI technologies and products based on them—the company invented the transformer (the “T” in “GPT”) deep learning model technique now commonly used in natural language processing, for example—but productizing those research projects gained further “code red” urgency when Microsoft, in investment partnership with OpenAI, added AI-based enhancements to its Bing search service, which competes with Google’s core business. AI promises, as I’ve written before, to revolutionize how applications and the functions they’re based on are developed, implemented and updated. So, Google’s ongoing work in this area should be of interest even if your company isn’t one of Google’s partner or customers.

AI everywhere

Let’s focus on that last bullet first in diving into the details of what the company rolled out this week. AI is a category rife with buzzwords and hype, which a planned future post by me will attempt to dissect and describe in more detail. For purposes of this piece, acting among other things as a preamble, I’ll try to keep things simple. The way I look at AI is by splitting up the entire process into four main steps:

Input
Analysis and identification
Appropriate-response discernment, and
Output

Take, for example, a partially-to-fully autonomous car in forward motion, in front of which another vehicle, a person or some other object has just seemingly appeared:

Visible light image sensors, radar, LiDAR, IR and/or other sensing technologies detect the object’s presence and discern details such as its size, shape, distance, speed (and acceleration-or-deceleration trend) and path of travel.
All of this “fused” sensor-generated data is passed on a processing subsystem, which determines what the object is including whether it’s a “false positive” (glare or dirt on a camera lens, for example, or fog or other environmental effects).
That same or a subsequent processing subsystem further down the “chain” then determines what the appropriate response, if any, should be.
Possible outputs of the analysis and response algorithms, beyond “nothing”, are actions such as automated takeover of acceleration, braking and steering to prevent a collision, and visual, audible, vibration and other alerts for the vehicle driver and other occupants.

Much media attention of late is focused on large language models (LLMs), whether text-only or audible in conjunction with speech-to-text (voice input) and text-to-speech (output) conversion steps. This attention is understandable, as language is an already-familiar means by which we interact with each other, and therefore is also a natural method of interacting with an AI system.

Note, however, that LLMs represent only steps 1 and 4 of my intentionally oversimplified process. While you can use them as a natural-language I/O scheme for a search engine, as Microsoft has done with OpenAI’s ChatGPT in Bing, or as Google is now beta-testing, you can also use an LLM input in combination with generative AI to create a synthesized still image, video clip, music track (such as MusicLM, which Google announced this week) or even code snippet (Google’s just-announced Codey and Studio Bot, for example), whose output paths include data files, displays and speakers.

This brief-but-spectacular discernment will, I hope, help you sort out the flurry of AI-based and enhanced technology and product announcements that Google made this week. One of the highlights was version 2 of PaLM (Pathways Language Model), the latest version of the company’s core LLM, which has seemingly superceded its BERT predecessor. When Microsoft announced its OpenAI partnership and ChatGPT-based products at the beginning of this year, it didn’t immediately reveal that they were already running on the latest GPT-4-based version of ChatGPT; OpenAI’s GPT-4 unveil came more than a month later.

Similarly, although Google announced its Bard AI-based chatbot back in early February, it waited until this week (in conjunction with revealing service enhancements and the end of the prior public-access waitlist) to reveal that Bard was PaLM 2-based. And like Microsoft, Google is adding LLM- and more general AI-based enhancements to its Workspace office suite, branding them as Duet. Bigger picture, there’s the Labs, where Google will going-forward be rolling out various AI-based “experiments” for the public to try before they “go gold” (or are eventually canned), including the aforementioned search enhancements.

A new mainstream smartphone

Roughly a half-year after launching each new high-end Pixel smartphone offering, Google unveils a more cost-effective and somewhat feature-reduced mainstream “a” derivative. The company’s followed this pattern ever since 2019’s Pixel 3a, and “a” Pixel phones have been my “daily drivers” ever since. The Pixel 7a is the latest-and-greatest, coming in at $500, roughly $100 lower-priced than the Pixel 7, and normally I’d be planning on transitioning to it once my Pixel 4a 5G times out and falls off the supported-device list later this year…but Ars Technica also makes compelling ongoing arguments for the Pixel 6a (which I also own and planned on using as a backup), which continues to be sold and whose price has been cut by $100 to $350. Now that Google’s using its own Tensor SoCs, as I mentioned earlier, the company promises security updates for five years, and the Pixel 6a was launched only a year ago. The biggest arguments in favor of the Pixel 7 line, ironically, are that its cellular radio subsystem is seemingly less buggy than with Pixel 6 precursors, and that its fingerprint-unlock scanning also seems more reliable.

A tablet revisit

I was quite enamored with my Google-branded, ASUS-developed and Android-based Nexus 7 tablet of a half-decade-plus back, and apparently I wasn’t the only one. Its multiple successors, including the ChromeOS-based Pixel Slate, didn’t replicate its success, but Google’s trying again to recapture its past glory with the new Pixel Tablet. It’s based on the same Tensor G2 SoC that powers the entire Pixel 7 line, including the just-introduced 7a mentioned previously in this piece, and Google curiously seems to be positioning it as (among other things) a successor to its Home (now Nest) Hub products, along with an optional $129.99 docking station (complete with speakers and charging capabilities). The screen size (11”) is heftier than I’d prefer bedside but spot-on elsewhere in the home. And at $500, it’s priced competitively with Apple iPad alternatives. If at first you don’t succeed, try, try again? We shall see if this time’s the charm.

Gambling on Folding

Google’s reveal of its first foldable smartphone, the aptly named Pixel Fold, is bittersweet on a personal level. A bit more than a year ago, I told you about my experiences with Microsoft’s also-Android-based first-generation Surface Duo, for which initial reviews were quite abysmal but which improved greatly thanks to software evolutions:

Unfortunately, things returned to “bad” (if not “worse”) shortly thereafter. There have been no significant software-experience enhancements since the Android 12L update, only Android security patches in rough cadence with their releases by Google. To date, specifically, both Surface Duo generations have yet to receive otherwise-mainstream Android 13; ironically, this week Google rolled out the second public beta of Android 14. And even the security patches are increasingly getting delayed; March’s didn’t show up until month end, and April’s didn’t arrive until just a couple of days ago (May, mind you), after Google released the May security updates! The dual-screen Surface Duo 3 was reportedly canceled in January, and more generally, rumor has it that the team within Microsoft has been gutted and essentially disbanded.

With that as a backdrop, what do I think of a Samsung-reminiscent foldable with a $1,800 (starting) price tag? Google probably won’t sell many of them at that price, but the company has arguably got deep enough pockets that it doesn’t need to do so at least for this initial go-around. You had to know, after all, that when Google announced it was developing a widescreen variant of its showcase Android O/S, it wasn’t doing so just out of the goodness of its own heart for its licensees: it had product plans of its own. Specifics include the same Tensor G2 SoC as that found on the Pixel 7 smartphone line and the Pixel Tablet, a 7.6” (unfolded) 1840 x 2208-pixel OLED display, and 12 GBytes of system DRAM along with both 256 GByte and 512 GByte flash memory storage options. Microsoft’s Surface Duo misfires aside, I remain bullish on the foldable form factor (and remain amused that I am, given my historical fondness for small-screen smartphones), and once again I’m seemingly not alone.

But wait, there’s more

I’ve hit what I think are the highlights, but there’s plenty more that came out of Shoreline Amphitheater this week; Googlers themselves even came up with a list of 100 things they announced. I’ll briefly touch on just one more; the way-cool (IMHO) Project Starline hologram-based virtual conferencing booth system announced two years ago:

has now been significantly slimmed down and otherwise simplified:

With that, I’ll close here in order to avoid crossing the 2,000-word threshold which would undoubtedly ensure that my colleague and friend Aalyia would never speak to me again (just kidding…I think…). What else caught your eyes and ears at Google I/O this year? Let me know in the comments!

—Brian Dipert is the Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

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The 2023 Google I/O: It’s all about AI, don’t cha know

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