According to a Bloomberg report by Mark Gurman, “Apple isn’t paying OpenAI as part of the partnership.”

Instead, the Cupertino-based company is leveraging its massive user base and device ecosystem as currency.

“Apple believes pushing OpenAI’s brand and technology to hundreds of millions of its devices is of equal or greater value than monetary payments,” Gurman’s sources explained.

Gurman notes that OpenAI could find a silver lining by encouraging Apple users to subscribe to ChatGPT Plus, priced at $20 per month. If subscribers sign up through Apple devices, the iPhone maker will likely even claim a commission.

Apple’s AI strategy extends beyond OpenAI. The company is reportedly in talks to offer Google’s Gemini chatbot as an additional option later this year, signalling its intent to provide users with diverse AI experiences without necessarily having to make such major investments itself.

The long-term vision for Apple involves capturing a slice of the revenue generated from monetising chatbot results on its operating systems. This move anticipates a shift in user behaviour, with more people relying on AI assistants rather than traditional search engines like Google.

While Apple’s AI plans are ambitious, challenges remain. The report highlights that the company has yet to secure a deal with a local Chinese provider for chatbot features, though discussions with local firms like Baidu and Alibaba are underway. Initially, Apple Intelligence will be limited to US English, with expanded language support planned for the following year.

The Apple-OpenAI deal represents a novel approach to collaboration in the AI space, where brand exposure and technological integration are valued as much as, if not more than, direct financial compensation.

See also: Musk ends OpenAI lawsuit while slamming Apple’s ChatGPT plans

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Musk had initially sued OpenAI in March 2024, alleging breach of contracts, unfair business practices, and failure in fiduciary duty. He claimed that his contributions to the company were made “in exchange for and in reliance on promises that those assets were irrevocably dedicated to building AI for public benefit, with only safety as a countervailing concern.”

The lawsuit sought remedies for “breach of contract, promissory estoppel, breach of fiduciary duty, unfair business practices, and accounting,” as well as specific performance, restitution, and damages.

However, Musk’s filings to withdraw the case provided no explanation for abandoning the lawsuit. OpenAI had previously called Musk’s claims “incoherent” and that his inability to produce a contract made his breach claims difficult to prove, stating that documents provided by Musk “contradict his allegations as to the alleged terms of the agreement.”

The withdrawal of the lawsuit comes at a time when Musk is strongly opposing Apple’s plans to integrate ChatGPT into its operating systems.

During Apple’s keynote event announcing Apple Intelligence for iOS 18, iPadOS 18, and macOS Sequoia, Musk threatened to ban Apple devices from his companies, calling the integration “an unacceptable security violation.”

Despite assurances from Apple and OpenAI that user data would only be shared with explicit consent and that interactions would be secure, Musk questioned Apple’s ability to ensure data security, stating, “Apple has no clue what’s actually going on once they hand your data over to OpenAI. They’re selling you down the river.”

Since bringing the lawsuit against OpenAI, Musk has also created his own AI company, xAI, and secured over $6 billion in funding for his plans to advance the Grok chatbot on his social network, X.

While Musk’s reasoning for dropping the OpenAI lawsuit remains unclear, his actions suggest a potential shift in focus towards advancing his own AI endeavours while continuing to vocalise his criticism of OpenAI through social media rather than the courts.

See also: DuckDuckGo releases portal giving private access to AI models

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Microsoft, Google, Baidu, and others have all raced to make very public AI announcements over the past month. Apple held its own AI event earlier this month but it was a staff-only affair.

Apple’s low-key AI event was notable as being the first to be held in-person at the Steve Jobs Theatre since the pandemic began. Other than that, it wasn’t particularly newsworthy—which is somewhat newsworthy in itself.

Most AI solutions rely on the cloud for processing. Google is moving an increasing amount to on-device but Apple, for better or worse, has made a big deal about its on-device AI strategy.

One of the ways that Apple markets itself as differing from rivals is its privacy-first approach. The firm collects minimal data and processes it on-device. That approach has worked great for Apple but the company may begin to struggle as it requires more data and processing power—something we may already be seeing.

Siri is widely perceived to be the third most capable virtual assistant behind Google and Alexa. Apple currently has no answer to the ChatGPT and Bard chatbots unveiled by Microsoft and Google respectively.

One of the primary uses for machine learning over the years has been web search. The threat that a ChatGPT-integrated Bing poses to Google reportedly set off the alarm bells over at Mountain View and led to the frantic (and “botched”) announcement of Bard.

Apple has reportedly been working on its own search engine but the company’s ethos against data collection could be holding it back from launching a product that can go toe-to-toe against Google and Bing.

At its AI event this month, Apple appeared set on rallying employees and convincing them it isn’t falling behind. Apple’s AI chief told attendees that “machine learning is moving faster than ever” and that Apple has talent that is “truly at the forefront.”

That doesn’t sound like a company that is particularly confident.

“While that may be Apple’s belief, I haven’t heard of anything — for consumers — that is a game changer coming out of the summit,” wrote Bloomberg’s Mark Gurman in the latest edition of his Power On newsletter.

“For those wondering, I don’t believe Apple previewed a ChatGPT/New Bing competitor or anything of the sort.”

Apple isn’t known to rush products to market and it’s not surprising that we’re not getting any major announcements ahead of WWDC. However, this staff-only event – and Gurman’s report – certainly gives the impression that Apple knows it’s not as well-positioned as its rivals when it comes to AI.

For now, Apple looks quite happy to sit out of the spotlight when it comes to AI. This year, all the attention will be firmly on its mixed-reality headset. However, questions will certainly be raised in the coming years about whether Apple is an AI leader unless it can silence the critics.

(Photo by Oscar Keys on Unsplash)

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Stable Diffusion is an AI image generator similar to DALL-E. Users can input a text prompt and the AI will produce an image that’s often far better than what most of us mere mortals can do.

Apple is a supporter of the Stable Diffusion project and posted an update on its machine learning blog this week about how it’s improving the performance on Macs.

“Beyond image generation from text prompts, developers are also discovering other creative uses for Stable Diffusion, such as image editing, in-painting, out-painting, super-resolution, style transfer and even color palette generation,” wrote Apple.

“With the growing number of applications of Stable Diffusion, ensuring that developers can leverage this technology effectively is important for creating apps that creatives everywhere will be able to use.”

Apple highlights there are many reasons for people to want to run Stable Diffusion locally instead of a server, including:

• Safeguarding privacy — User data remains on-device.
• More flexibility — Users don’t require an internet connection.
• Reduced cost — Users can eliminate server-related costs.

Apple says that it has released optimisations to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, along with code to help get started on M-based devices.

Following the optimisations, a baseline M2 Macbook Air can generate an image using a 50 inference steps Stable Diffusion model in under 18 seconds. Arguably more impressively, even an M1 iPad Pro can do the job in under 30 seconds.

The release also features a Python package for converting Stable Diffusion models from PyTorch to Core ML using diffusers and coremltools, as well as a Swift package to deploy the models.

Detailed instructions on benchmarking and deployment are available on the Core ML Stable Diffusion repo here.

(Image Credit: Apple)

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Ian Goodfellow is a renowned machine learning researcher. Goodfellow invented generative adversarial networks (GANs), developed a system for Google Maps that transcribes addresses from Street View car photos, and more.

In a departure note to his team at Apple, Goodfellow cited the company’s much-criticised lack of flexibility in its work policies.

Many companies were forced into supporting remote work during the pandemic and many have since decided to keep flexible working due to the recruitment advantages, mental/physical health benefits, lowering the impact of rocketing fuel costs, improved productivity, and reduced office space costs.

Apple planned for employees to work from the office on Mondays, Tuesdays, and Thursdays, starting this month. However, following backlash, on Tuesday the company put the plan on hold—officially citing rising Covid cases.

Goodfellow already decided to hand in his resignation and head to a company with more forward-looking, modern working policies.

The machine learning researcher had worked for Apple since 2019. Prior to Apple, Goodfellow had previously worked for Google as a senior research scientist.

Goodfellow is now reportedly returning to Google, albeit to its DeepMind subsidiary. Google is currently approving requests from most employees seeking to work from home.

More departures are expected from Apple if it proceeds with its return-to-office mandate.

“Everything happened with us working from home all day, and now we have to go back to the office, sit in traffic for two hours, and hire people to take care of kids at home,” a different former Apple employee told Bloomberg.

Every talented AI researcher like Goodfellow that leaves Apple is a potential win for Google and other companies.

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70 percent of the 2,341 respondents across 30 business sectors picked AR as disrupting their industry most out of a selection of seven emerging technologies: AI, cybersecurity, cloud computing, IoT, blockchain, and 5G.

Filipe Oliveira, Senior Analyst at GlobalData, commented: “This change in how people see AR will likely be long term, and not just a temporary blip. It is clear that people are warming towards the technology, even if they don’t believe that it will make a big difference tomorrow.” 

AI wins some ground back when it comes to confidence in the technology. 57 percent of the respondents believe that AI will live up to all of its promises compared to just 26 percent for AR.

Along those same lines, 31 percent believe “The technology is hyped, but I can see a use for it” for AI, while a huge 50 percent report the same for AR.

Apple’s decision to add a LiDAR sensor to its latest mobile devices was seen as an important step towards the mass adoption of AR. Excitement is also growing around so-called “metaverses” that converge virtually-enhanced physical reality with physically-persistent shared virtual spaces.

SenseTime, one of China’s leading AI companies, announced earlier this week that it had partnered with BilibiliWorld to create a metaverse. The experience leverages SenseTime’s AI and mixed reality technologies to enable players to enjoy role-playing games that seamlessly blend reality with virtuality.

Facebook CEO Mark Zuckerberg recently said the company “will effectively transition from people seeing us as primarily being a social media company to being a metaverse company”. As the owner of Oculus, Zuckerberg’s plans for the future of Facebook will likely make people think of a large virtual space similar to that depicted in Ernest Cline’s Ready Player One novel and the 2018 film adaptation.

Some people have expressed concern about a large centralised company such as Facebook having control over such a potentially ubiquitous world and the content they consume. Many believe that an open-source decentralised version is vital:

Zuckerberg, for his part, claims that no one company will run the metaverse and it will be an “embodied internet” that is operated by many different players.

Decentraland is an early example of what a truly decentralised virtual space could look like. The platform makes use of a DAO (Decentralised Autonomous Organisation) to make policy decisions such as what content is allowed in addition to taking advantage of the NFT (Non-Fungible Token) trend to offer exclusive in-world items.

AR and AI are both important emerging technologies that can often go hand-in-hand, but it’s clear that the latter is losing its perspective among professionals as having the biggest impact on their industries over the coming years.

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Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple’s decision to introduce LiDAR in its recent devices has given AR a boost but it’s clear that more needs to be done to make applications more useful.

A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or “automagically,” the company would probably say) detect objects in AR.

The first proposed use of the technology would be for Apple’s own Measure app.

Measure’s previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

The patent suggests machine learning could be used for object recognition in Measure to help users simply point their devices at an object and have its measurements automatically presented in AR.

Specifically, Apple’s patent suggests displaying a “measurement of the object determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.”

This simplicity benefit over a traditional tape measure would likely drive greater adoption.

Machine learning is already used for a number of object recognition and labelling tasks within Apple’s ecosystem. Image editor Pixelmator Pro, for example, uses it to automatically label layers.

Apple’s implementation suggests an object is measured “by first generating a 3D bounding box for the object based on the depth data”. This boundary box is then refined “using various neural networks and refining algorithms described herein.”

Not all objects are measured the same so Apple suggests that a neural network could also step in here to determine what could be useful for the user. For example, “a seat height for chairs, a display diameter for TVs, a table diameter for round tables, a table length for rectangular tables, and the like.”

To accomplish what Apple envisions here, a lot of models will need to be trained for all objects. However, there are many of the more everyday items that could be supported early on—with more added over time.

“One model may be trained and used to determine measurements for chair type objects (e.g., determining a seat height, arm length, etc.),” Apple wrote, “and another model may be trained and used to determine measurements for TV type objects (e.g., determining a diagonal screen size, greatest TV depth, etc.)”

Five inventors are credited with the patent: Amit Jain, Aditya Sankar; Qi Shan, Alexandre Da Veiga, and Shreyas V Joshi.

Apple’s patent is another example of how machine learning can be combined with other technologies to add real utility and ultimately improve lives. There’s no telling when, or even if, Apple will release an updated Measure app based on this patent—but it seems more plausible in the not-so-distant future than many of the company’s patents.

(Image Credit: Apple)

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While still technically in pre-release, the Mac-optimised TensorFlow fork supports native hardware acceleration on Mac devices with M1 or Intel chips through Apple’s ML Compute framework.

The new TensorFlow release boasts of an over 10x speed improvement for common training tasks. While impressive, it has to be taken in the context that the GPU was not previously used for training tasks. 

A look at the benchmarks still indicates a substantial gap between the Intel and M1-based Macs across various machine learning models:

In a blog post, Pankaj Kanwar, Tensor Processing Units Technical Program Manager at Google, and Fred Alcober, TensorFlow Product Marketing Lead at Google, wrote:

“These improvements, combined with the ability of Apple developers being able to execute TensorFlow on iOS through TensorFlow Lite, continue to showcase TensorFlow’s breadth and depth in supporting high-performance ML execution on Apple hardware.”

We can only hope that running these workloads doesn’t turn MacBooks into expensive frying pans—but the remarkable efficiency they’ve displayed so far gives little cause for concern.

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Voysis specialises in improving digital assistants inside online shopping applications wherein the software could respond more precisely to voice commands. According to the start-up, its technology could narrow product search results by processing shopping phrases like “I need a new LED TV” and “My budget is $1,000.” This AI was provided to other companies for use in their own applications and voice assistants.

The acquisition of Voysis would give Siri an edge to perform better than the Google Assistant, which many in the industry say has a notable lead in natural language comprehension and processing.

Voysis uses an AI-based method called WaveNets to create more human-like computer speech. In 2018, co-founder Peter Cahill had said that his company managed to shrink its system to the point where, once the AI is trained, the software uses as little as 25MB of memory, which makes it easier to run on smartphones without an internet connection.

This is not the first instance where Apple has made a big bet in this space. In January, Apple acquired Seattle-based edge AI specialist Xnor.ai for approximately $200m. Xnor.ai is the same company that once powered the person-detection feature on Wyze’s popular cameras. The Cupertino-based tech giant has been on something of an AI acquisition spree in recent years. A report by CBInsights found that Apple acquired more AI firms (20) than any other leading tech company in 2019.

This time last year, Apple acquired AI company Laserlike to add to its growing roster of in-house talent. Laserlike is known for its AI-powered app which makes it easier for users to follow news topics. Most notably, it was founded by former Google engineers.

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If you recognise Xnor.ai, it’s likely because the company’s technology once powered the person-detection feature on Wyze’s popular cameras. Xnor.ai abruptly cancelled their contract with Wyze back in November – and now we know why.

In layman’s terms, edge computing means the processing is done on-device. On-device computation has significant advantages when it comes to reducing latency, improving performance, and retaining privacy.

https://www.youtube.com/watch?v=FG31XxX7ra8

Apple makes a big deal out of its privacy credentials. When people are becoming increasingly concerned about the data collection practices of competitors like Google, Amazon, and Microsoft, it’s a strategy that works.

Compared to all three of those cloud-focused competitors, many believe Apple has fallen behind when it comes to AI. With its Xnor.ai acquisition, Apple can boost its AI capabilities while saying that it’s protecting customers’ privacy by not sending it all up to the cloud like its rivals often do.

Xnor.ai started its journey as part of the nonprofit Allen Institute for AI. In 2017, the company spun off as its own entity headed by CEO Ali Farhadi.

While small change compared to what Apple acquired it for, Xnor.ai had already raised significant capital. In early 2017, it raised $2.7 million. In 2018, it raised $12 million. Both rounds were led by Madrona Venture Group.

Apple has been on something of an AI acquisition spree in recent years. In fact, a report by CBInsights last year found that Apple acquired more AI firms (20) than any other leading tech company in 2019. Google took second place with 14 acquisitions, followed by Microsoft with 10.

With the IoT becoming the next big thing, companies like Apple are looking to AI for ways to differentiate their products from competitors. Apple’s $200 million for Xnor.ai may seem a lot on paper, but it will almost certainly pay off in the long run.

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