All around Meta’s Menlo Park campus, cameras stared at me. I’m not talking about security cameras or my fellow reporters’ DSLRs. I’m not even talking about smartphones. I mean Ray-Ban and Meta’s smart glasses, which Meta hopes we’ll all — one day, in some form — wear.
Technology
NYT Crossword: answers for Saturday, September 28
The New York Times crossword puzzle can be tough! If you’re stuck, we’re here to help with a list of today’s clues and answers.
Technology
Get peace of mind with the Blink Outdoor 4 camera for $40
If you own a home and want a solution to keeping track of the outside of your home, then check out this deal on the Blink Outdoor 4 camera that Amazon is currently offering. There are two parts to this deal that are worth mentioning. Or rather, they’re two different deal options for the same product.
The first deal option is that you can now pick up the Blink Outdoor 4 camera for just $40. This is the lowest this camera has ever been and it typically sits around $87.35 on Amazon. So this is more than half of the regular price and slightly more than half of the typical sale price. That’s quite good. And if you don’t already have a security camera setup for your home, this is definitely worth considering.
The other deal option that Amazon is currently offering, is that you can get the Blink Outdoor 4 camera for free if you purchase a 1-year subscription to the Blink Subscription Plus plan. That plan is $100, but it covers all of your other Blink devices. So this deal option is tailored to people who either already have other Blink devices set up, or plan to buy others in addition to this Blink Outdoor 4.
The Blink Outdoor 4 is a wire-free camera so you don’t have to worry about a complicated installation. The setup is a pretty quick and painless process. It has a 2-year battery life too. Additionally, it has two-way audio so you can talk to people on the other end, enhanced motion control, and it even works with Alexa. You can check the camera’s footage from your phone, where you can also manage its controls and features. But you can also use your voice for that so things stay hands-free.
Technology
Google launches Gemini’s contextual smart replies in Gmail
When Google rolled out Gemini side panels for Gmail and its other Workspace apps, it revealed that its generative AI chatbot will also be able to offer contextual smart replies for its email service in the future. Now, the company has officially released that feature. Smart replies have existed in Gmail since 2017, giving you a quick, albeit impersonal, way to respond to messages, even if you’re in a hurry or on the go. These machine-generated responses are pretty limited, though, and they’re often just one liners to tell the recipient that you understand what they’re saying or that you agree with whatever they’re suggesting.
The new Gemini-generated smart replies take the full content of the email thread into consideration. While you may still have to edit them a bit if you want them to be as close to something you’d write as possible, they are more detailed and more personable. When you get the feature, you’ll see several response options at the bottom of your screen when you reply through the Gmail app. Just hover over each of them to get a detailed preview before choosing one that you think makes for the best response.
You’ll get access to the feature if you have a Gemini Business, Enterprise, Education or Education Premium add-on, or if you have a Google One AI Premium subscription. Google says it could take up to 15 days before you see Gemini’s smart replies in your app — just make sure you’ve ticked on “Smart features and personalization” in your Gmail app’s Settings page.
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Technology
AI is changing enterprise computing — and the enterprise itself
Presented by AMD
This article is part of a VB Special Issue called “Fit for Purpose: Tailoring AI Infrastructure.” Catch all the other stories here.
It’s hard to think of any enterprise technology having a greater impact on business today than artificial intelligence (AI), with use cases including automating processes, customizing user experiences, and gaining insights from massive amounts of data.
As a result, there is a realization that AI has become a core differentiator that needs to be built into every organization’s strategy. Some were surprised when Google announced in 2016 that they would be a mobile-first company, recognizing that mobile devices had become the dominant user platform. Today, some companies call themselves ‘AI first,’ acknowledging that their networking and infrastructure must be engineered to support AI above all else.
Failing to address the challenges of supporting AI workloads has become a significant business risk, with laggards set to be left trailing AI-first competitors who are using AI to drive growth and speed towards a leadership position in the marketplace.
However, adopting AI has pros and cons. AI-based applications create a platform for businesses to drive revenue and market share, for example by enabling efficiency and productivity improvements through automation. But the transformation can be difficult to achieve. AI workloads require massive processing power and significant storage capacity, putting strain on already complex and stretched enterprise computing infrastructures.
>>Don’t miss our special issue: Fit for Purpose: Tailoring AI Infrastructure.<<
In addition to centralized data center resources, most AI deployments have multiple touchpoints across user devices including desktops, laptops, phones and tablets. AI is increasingly being used on edge and endpoint devices, enabling data to be collected and analyzed close to the source, for greater processing speed and reliability. For IT teams, a large part of the AI discussion is about infrastructure cost and location. Do they have enough processing power and data storage? Are their AI solutions located where they run best — at on-premises data centers or, increasingly, in the cloud or at the edge?
How enterprises can succeed at AI
If you want to become an AI-first organization, then one of the biggest challenges is building the specialized infrastructure that this requires. Few organizations have the time or money to build massive new data centers to support power-hungry AI applications.
The reality for most businesses is that they will have to determine a way to adapt and modernize their data centers to support an AI-first mentality.
But where do you start? In the early days of cloud computing, cloud service providers (CSPs) offered simple, scalable compute and storage — CSPs were considered a simple deployment path for undifferentiated business workloads. Today, the landscape is dramatically different, with new AI-centric CSPs offering cloud solutions specifically designed for AI workloads and, increasingly, hybrid AI setups that span on-premises IT and cloud services.
AI is a complex proposition and there’s no one-size-fits-all solution. It can be difficult to know what to do. For many organizations, help comes from their strategic technology partners who understand AI and can advise them on how to create and deliver AI applications that meet their specific objectives — and will help them grow their businesses.
With data centers, often a significant part of an AI application, a key element of any strategic partner’s role is enabling data center modernization. One example is the rise in servers and processors specifically designed for AI. By adopting specific AI-focused data center technologies, it’s possible to deliver significantly more compute power through fewer processors, servers, and racks, enabling you to reduce the data center footprint required by your AI applications. This can increase energy efficiency and also reduce the total cost of investment (TCO) for your AI projects.
A strategic partner can also advise you on graphics processing unit (GPU) platforms. GPU efficiency is key to AI success, particularly for training AI models, real-time processing or decision-making. Simply adding GPUs won’t overcome processing bottlenecks. With a well implemented, AI-specific GPU platform, you can optimize for the specific AI projects you need to run and spend only on the resources this requires. This improves your return on investment (ROI), as well as the cost-effectiveness (and energy efficiency) of your data center resources.
Similarly, a good partner can help you identify which AI workloads truly require GPU-acceleration, and which have greater cost effectiveness when running on CPU-only infrastructure. For example, AI Inference workloads are best deployed on CPUs when model sizes are smaller or when AI is a smaller percentage of the overall server workload mix. This is an important consideration when planning an AI strategy because GPU accelerators, while often critical for training and large model deployment, can be costly to obtain and operate.
Data center networking is also critical for delivering the scale of processing that AI applications require. An experienced technology partner can give you advice about networking options at all levels (including rack, pod and campus) as well as helping you to understand the balance and trade-off between different proprietary and industry-standard technologies.
What to look for in your partnerships
Your strategic partner for your journey to an AI-first infrastructure must combine expertise with an advanced portfolio of AI solutions designed for the cloud and on-premises data centers, user devices, edge and endpoints.
AMD, for example, is helping organizations to leverage AI in their existing data centers. AMD EPYC(TM) processors can drive rack-level consolidation, enabling enterprises to run the same workloads on fewer servers, CPU AI performance for small and mixed AI workloads, and improved GPU performance, supporting advanced GPU accelerators and minimize computing bottlenecks. Through consolidation with AMD EPYC™ processors data center space and power can be freed to enable deployment of AI-specialized servers.
The increase in demand for AI application support across the business is putting pressure on aging infrastructure. To deliver secure and reliable AI-first solutions, it’s important to have the right technology across your IT landscape, from data center through to user and endpoint devices.
Enterprises should lean into new data center and server technologies to enable them to speed up their adoption of AI. They can reduce the risks through innovative yet proven technology and expertise. And with more organizations embracing an AI-first mindset, the time to get started on this journey is now.
Robert Hormuth is Corporate Vice President, Architecture & Strategy — Data Center Solutions Group, AMD
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact
Servers computers
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Technology
Welcome to Meta’s future, where everyone wears cameras
I visited Meta for this year’s Connect conference, where just about every hardware product involved cameras. They’re on the Ray-Ban Meta smart glasses that got a software update, the new Quest 3S virtual reality headset, and Meta’s prototype Orion AR glasses. Orion is what Meta calls a “time machine”: a functioning example of what full-fledged AR could look like, years before it will be consumer-ready.
But on Meta’s campus, at least, the Ray-Bans were already everywhere. It was a different kind of time machine: a glimpse into CEO Mark Zuckerberg’s future world where glasses are the new phones.
I’m conflicted about it.
Meta really wants to put cameras on your face. The glasses, which follow 2021’s Ray-Ban Stories, are apparently making inroads on that front, as Zuckerberg told The Verge sales are going “very well.” They aren’t full-fledged AR glasses since they have no screen to display information, though they’re becoming more powerful with AI features. But they’re perfect for what the whole Meta empire is built on: encouraging people to share their lives online.
The glasses come in a variety of classic Ray-Ban styles, but for now, it’s obvious users aren’t just wearing glasses. As I wandered the campus, I spotted the telltale signs on person after person: two prominent circle cutouts at the edges of their glasses, one for a 12MP ultrawide camera and the other for an indicator light.
This light flashes when a user is taking photos and videos, and it’s generally visible even in sunlight. In theory, that should have put my mind at ease: if the light wasn’t on, I could trust nobody was capturing footage of me tucking into some lunch before my meetings.
But as I talked with people around campus, I was always slightly on edge. I found myself keenly aware of those circles, checking to see if somebody was filming me when I wasn’t paying attention. The mere potential of a recording would distract me from conversations, inserting a low hum of background anxiety.
When I put a pair on for myself, the situation changed
Then, when I put a pair on for myself, the situation suddenly changed. As a potential target of recording, I’d been hesitant, worried I might be photographed or filmed as a byproduct of making polite eye contact. With the glasses on my own face, though, I felt that I should be recording more. There’s something really compelling about the experience of a camera right at the level of your eyes. By just pressing a button on the glasses, I could take a photo or video of anything I was seeing at exactly the angle I was seeing it. No awkward fumble of pulling out my phone and hoping the moment lasted. There might be no better way to share my reality with other people.
Meta’s smart glasses have been around for a few years now, and I’m hardly the first person — or even the first person at The Verge — to be impressed by them. But this was the first time I’d seen these glasses not as early adopter tech, but as a ubiquitous product like a phone or smartwatch. I got a hint of how this seamless recording would work at scale, and the prospect is both exciting and terrifying.
The camera phone was a revolution in its own right, and we’re still grappling with its social effects. Nearly anyone can now document police brutality or capture a fleeting funny moment, but also take creepshots and post them online or (a far lesser offense, to be clear) annoy people at concerts. What will happen when even the minimal friction of pulling a phone out drops away, and billions of people can immediately snap a picture of anything they see?
Personally, I can see how incredibly useful this would be to capture candid photos of my new baby, who is already starting to recognize when a phone is taking a picture of her. But it’s not hard to imagine far more malicious uses. Sure, you might think that we all got used to everyone pointing their phone cameras at everything, but I’m not exactly sure that’s a good thing; I don’t like that there’s a possibility I end up in somebody’s TikTok just because I stepped outside the house. (The rise of sophisticated facial recognition makes the risks even greater.) With ubiquitous glasses-equipped cameras, I feel like there’s an even greater possibility that my face shows up somewhere on the internet without my permission.
There are also clear risks to integrating cameras into what is, for many people, a nonnegotiable vision aid. If you already wear glasses and switch to prescription smart glasses, you’ll either have to carry a low-tech backup or accept that they’ll stay on in some potentially very awkward places, like a public bathroom. The current Ray-Ban Meta glasses are largely sunglasses, so they’re probably not most people’s primary set. But you can get them with clear and transition lenses, and I bet Meta would like to market them more as everyday specs.
Of course, there’s no guarantee most people will buy them. The Ray-Ban Meta glasses are pretty good gadgets now, but I was at Meta’s campus meeting Meta employees to preview Meta hardware for a Meta event. It’s not surprising Meta’s latest hardware was commonplace, and it doesn’t necessarily tell us much about what people outside that world want.
Camera glasses have been just over the horizon for years now. Remember how magical I said taking pictures of what’s right in front of your eyes is? My former colleague Sean O’Kane relayed almost the exact same experience with Snap Spectacles back in 2016.
But Meta is the first company to make a credible play for mainstream acceptance. They’re a lot of fun — and that’s what scares me a little.
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