Adobe wants to make it easier to store, access, and “remix” marketing assets with the addition of a new content hub in Adobe Experience Manager (AEM). 

The content hub is accessible as part of AEM Assets, Adobe’s digital asset management tool, and connects with GenStudio — a separate app for managing marketing content. Unveiled last year, GenStudio is currently in trial by customers.

The content hub has several main features.

Users can search and browse for assets using smart tags that automatically provide key terms for an image. Here, AI is used to detect the contents, such as objects featured, settings (such as “outdoors”), or colors.

Adobe’s lightweight content creation and editing app Express is integrated to make changes to existing assets. Express can be opened directly from within the content hub to make quick changes, creating variations to meet different use cases. This also enables access to Firefly generative AI features in Express that Adobe said will make it even easier to change content. 

“That becomes a really awesome tool for marketers in downstream cases to quickly make a change…without having to flip through five different software [applications],” said Haresh Kumar, senior director for strategy and product marketing for Adobe Experience Manager. Any change made to an asset can then be sent for approval before being used, he said, shortening a process that could otherwise take days.  

Within the content hub, users can also manage permissions for sensitive assets and apply governance controls to ensure AI-generated content meets brand standards. The hub also contains analytics to track asset usage across an organization and understand how content is used. 

Organizations typically store 44TB of data in their digital asset management system, according to a recent IDC survey, with another 143TB scattered across individual user devices, corporate shared drives, and social media platforms, said Marci Maddox, research vice president for digital experience strategies at IDC. 

“Adobe’s Content Hub addresses this issue by promoting reuse and reducing inadvertent duplication of creative efforts,” said Maddox. “By breaking down content silos and fostering collaboration, the content hub empowers teams to work more efficiently, leverage existing assets effectively, and ultimately, deliver a more cohesive brand experience with digital media.”

The creation of digital assets is vital for an organization to tell a “personalized, contextual and relevant story to an increasingly discerning audience,” said Liz Miller, vice president and principal analyst at Constellation Research. “It can also be pure chaos. This is why features like Content Hub are a proving to be a welcome resource for customer experience teams not traditionally part of a digital asset management workflow.”

Miller said that the hub complements the broader AEM Assets application, which serves as the “big firepower solution” when it comes to digital asset management. 

“Content Hub acknowledges that not every nail requires the biggest hammer out there,” she said. “For informal engagement hubs, they need a hammer…, just a smaller, easier one to wield.”

The Content Hub is now generally available to Adobe AEM Assets customers.

More Adobe news:

• Adobe introduces AI assistant to help enterprises exploit data held in PDFs
• Adobe to adapt terms of service on genAI training after customer backlash
• Adobe Experience Platform gets AI assistant for customer data insights

OpenAI has been testing its ChatGPT Mac desktop app among paying subscribers for the last few weeks. Now, it has opened its new desktop app to anyone with a Mac.

Coming so soon after Apple announced at WWDC that ChatGPT will be integrated within Siri and some apps across its platforms, the introduction must be a sign of the times. Apple’s decision to support ChatGPT and other generative AI (genAI) platforms across its ecosystem should expose millions of users to tools they might not have yet tried out.

A nice integration for Mac users

If you’ve already used ChatGPT on a browser or through other applications, you’ll be familiar with what it can accomplish. The smart chatbot can help get things done, source information, improve your work, and much more. (Though, as always, be careful of any hallucinations or errors that might crop up.)

The integration seems to be a solid combination of the ease-of-use of the Mac and the powerful tools within ChatGPT. In effect, that means the tools within the chatbot are easily available from almost anywhere on your Mac.

It is just the beginning, of course, as Apple has already promised deeper integration between its platforms and ChatGPT as an adjunct to Apple Intelligence. Announced at this month’s developer conference, Apple described plenty of uses for the technology, including in Writing tools.

What can the ChatGPT app do on a Mac?

In a series of short videos shared via OpenAI’s Twitter/X feed, the company described some of what you can achieve. On your Mac, the Chat GPT app can:

• Respond to the well-known and familiar Option-Space shortcut to invoke a ChatGPT query.
• Take a screenshot of a specific window or your entire screen to request feedback or advice on what is there — for example, when seeking to improve code.
• Search past ChatGPT conversations for specific items.
• Quickly and easily ask about anything on your computer using copy and paste.
• And of course, as you engage in conversation, you can refine and improve the exchange to produce more useful results.

You do gain access to standard Voice Mode (so you can speak to ChatGPT). But OpenAI has already promised a new version of Voice Mode powered by GPT-4o will be introduced in the coming weeks. That version will introduce the new audio and video capabilities introduced within GPT-4o.

“Whether you want to brainstorm a new idea for your company, prepare for an interview or have a topic you’d like to discuss, tap the headphone icon in the bottom right corner of the desktop app to start a voice conversation,” the company said.

The trickle becomes a flood

While ethical, environmental, and trust issues continue to delay the kind of rapid deployment of AI aficionados had originally anticipated, there’s little doubt usage will accelerate as platforms like Apple’s weave support inside them.

For most enterprises, this demands implementation of device management systems to help protect against unapproved use of these tools, particularly around confidential data. Apple is responding to this need with device management controls in macOS to constrain use of Apple Intelligence/OpenAI. It is already possible to manage app installs on Macs using MDM systems.

The new app is available for Apple Silicon (M-series) Macs running macOS 14 or later. Similar apps are already available for iPads and iPhones, but not Windows, where they’re expected later this year. You can download the Mac app here. 

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OpenAI has delayed the release of ChatGPT’s much-anticipated new Voice Mode feature, saying it needs another month” to refine the technology before offering it to a limited group of users in an alpha test.

“We had planned to start rolling this out in alpha to a small group of ChatGPT Plus users in late June, but need one more month to reach our bar to launch,” the  company said in social media platform X.

It said it “needs one more month to reach our bar to launch.”

OpenAI was more optimistic back in May, when it showcased Voice Mode during the Spring Update event at which it launched the faster and more capable GPT-4o large language model.

“We plan to launch a new Voice Mode with these new capabilities in an alpha in the coming weeks, with early access for Plus users as we roll out more broadly,” it said then, referring to users of its $20/month ChatGPT Plus subscription service.

With the introduction of GPT-4o, OpenAI said it was able to cut the voice response to time around 320 milliseconds, from 5.4 seconds for GPT-4, creating a more natural and real-time conversational experience.

Safety and scalability concerns take center stage

OpenAI gave two reasons for the launch delay: safety and scalability.

It emphasized its commitment to responsible AI development and the need for the model to effectively “detect and refuse certain content.” This suggests concerns about potential misuse of the technology for generating harmful or offensive speech.

Scalability also appears to be a hurdle. OpenAI said it aims to ensure the feature functions smoothly for millions of users while maintaining real-time responsiveness. This requires robust infrastructure capable of handling the increased processing demands.

“Exact timelines depend on meeting our high safety and reliability bar,” the company added in the post. “We are also working on rolling out the new video and screen sharing capabilities we demoed separately, and will keep you posted on that timeline.”

More competition for ChatGPT

OpenAI’s delay in Voice Mode rollout creates an interesting scenario in the burgeoning field of AI voice capabilities.

Competitors like Anthropic, with its Claude 3.5 Sonnet model, have already showcased voice-enabled interaction during demos.

Similarly, Google’s AI research arm, DeepMind, has been making strides in voice-based AI with its LaMDA language model,.

“Anthropic has joined this year’s intense AI race with models designed to compete head-on with recent announcements from OpenAI and Google,” said Neil Shah, VP for research and partner at Counterpoint Research. “Generative AI is a blue ocean opportunity, and each company, including Anthropic and OpenAI, will need to target specific use cases and segments. Anthropic, for example, is focusing on coding, writing, and workflow optimization.”

Beyond dedicated AI models, large language models such as Bard (Google AI) and Jurassic-1 Jumbo (AI21 Labs) are also constantly evolving, with some incorporating basic functionalities for voice interaction and response generation.

Even Microsoft’s Copilot programming assistant has begun to integrate voice-based guidance for developers.

OpenAI’s iterative approach: safety first

OpenAI’s decision to prioritize safety and scalability reflects a cautious yet responsible approach. Launching a powerful voice-enabled AI requires careful consideration of potential risks and ensuring the technology can handle widespread use without compromising performance.

“As part of our iterative deployment strategy, we’ll start the alpha with a small group of users to gather feedback and expand based on what we learn,” said the company.  

This iterative approach allows them to refine the model based on real-world user interactions and mitigate potential issues before a wider release.

While the delay may disappoint some users eager to experience Voice Mode, it does show a certain caution in the face of recent criticism of OpenAI’s attitude to safety. It has been working to restore confidence in that area with a series of appointments to its new safety and security committee.

Microsoft broke European Union competition law by bundling Teams with other Office 365 applications, the European Commission said in a preliminary finding published Tuesday. If Microsoft fails to defend itself, the company could face billions of dollars in fines and other penalties.

The EU’s antitrust regulator said it had sent Microsoft a statement of objections outlining its finding that the company had given its Teams communications service an unfair advantage by bundling it with subscriptions to Office 365 and Microsoft 365, the suite of software-as-a-service applications that also includes Word, Excel, and PowerPoint.

Teams enables workforce collaboration via video and chat and surged in popularity during the Covid-19 pandemic, reaching over 300 million global users in 2023.

“The Commission preliminarily finds that Microsoft is dominant worldwidein the market for SaaS productivity applications for professional use,” it said in a news release posted Tuesday.

The company’s practice since at least April 2019 to tie Teams to its core SaaS productivity apps has restricted market competition for similar products as well as defended Microsoft’s own “market position in productivity software as well as its suites-centric model from competing suppliers of individual software,” the Commission concluded.

“In particular, the Commission is concerned that Microsoft may have granted Teams a distribution advantage by not giving customers the choice whether or not to acquire access to Teams when they subscribe to their SaaS productivity applications,” the Commission’s said.

Interoperability limits between competing offerings and Microsoft’s apps also bolstered Microsoft’s advantage and prevented its rivals from competing, and in turn innovating, to the detriment of customers in the European Economic Area, the Commission said, referring to the 27 EU countries plus Iceland, Norway and Liechtenstein.

Investigation a year in the making

The decision follows a nearly year-long investigation that started last July and was sparked by a July 2020 competition complaint against the software giant by then enterprise messaging application Slack, which has since been bought by Salesforce.

Microsoft first unbundled Teams from its Office suites in the European Economic Area and Switzerland in August 2023 to try to appease EU officials during the investigation, then extended that move worldwide in April.

Neither effort apparently did anything to dissuade the Commission from considering Microsoft in violation of competition rules, a decision that was foreshadowed when the Competition said in May that it planned to include Teams in its inivestigation of Microsoft for anticompetitive practices despite the company’s concessions.

For its part, Microsoft plans to continue to address remaining concerns the Commission has over Teams as it awaits further decisions by the Commission, such as what, if any, financial consequences the company will face.

“Having unbundled Teams and taken initial interoperability steps, we appreciate the additional clarity provided today and will work to find solutions to address the Commission’s remaining concerns,” Brad Smith, Microsoft vice chair and president, said in a statement emailed to Computerworld.

The road ahead

Microsoft now gets a chance to present its defense. However, if it’s unable to sway the Commission from its preliminary decision, it could be forced to pay a fine of up to 10% of its annual worldwide revenue under EU law, and the Commission may also impose remedies to end the infringement.

“This EU chargesheet proves what was feared: Microsoft didn’t do enough to provide a level playing field to the Team’s competitors,” noted Pareekh Jain, CEO of EIIRTrend & Pareekh Consulting.

It now has its work cut out for it to unbundle Teams and create interoperability for competitors’ software to avoid penalties and damages, Jain noted.

It’s about a decade since EU regulators last levelled anti-trust charges against Microsoft: Its failure to offer Windows 7 users a choice of browser drew a €561 million (then about $731 million) fine.

To date, Microsoft has racked up around €2.2 billion ($2.4 billion) in fines for tying or bundling products together in a way that was deemed anti-competitive by EU regulators.

The rush to build global cloud infrastructure to support artificial intelligence (AI) has turned Nvidia into a top three tech stock and will likely nurture innovation in processor design and energy creation — but Apple may need to serve itself.

Apple needs to bring more for less

The IEA says global electricity demand to drive data centers will more than double by 2026. That’s even as consumption of AI services such as OpenAI spikes when devices (including new iPhones) gain easy access to them.

To satisfy demand, tech firms must tweak more computational performance and additional energy efficiency from the chips they put inside both servers and edge devices. But this push for efficiency and low power reflects what Apple has been trying to do with its own silicon development for years. Apple Silicon chips were way ahead of the wider industry on both metrics (at least for consumer devices) long before generative AI (genAI) climbed through the Overton Window. 

While attention on server processors rests with Nvidia, the M4 processor inside the iPad Pro could be seen as harbinger of what is to come. At 38 trillion operations per second, we know it has the fastest Neural Engine you can get. Ever modest, Apple has described the M4 iPad as the best AI PC money can buy, and those same processors are also coming to servers.

Apple is now in the server market

The company’s Private Cloud Compute (PCC) system consists of proprietary Apple servers running Apple Silicon chips to provide responses to Apple Intelligence queries.  

While these are only being situated in US data centers at this time (as that’s the only place Apple Intelligence will be made available on launch), it’s easy to expect the company will deploy these highly secure systems globally *except in the EU in the coming months. 

It must. As Apple Intelligence launches globally, Apple will find itself needing to ramp up its international server infrastructure to meet the demands for AI its billion or so customers might make. 

But one data center at a time, server by server, Apple is already in the server market. Motivated by privacy, these Apple servers also meet wider industry needs around energy consumption and performance requirements.

It makes sense for Apple to expand this provision, perhaps even to offer highly secure, low energy server services to enterprise users, but it’s more likely to drive its streaming services while lowering energy consumption.

There’s another chip coming

It is worth noting that Apple hasn’t yet hit a performance ceiling. Very likely to be tumbling off production lines right now, the next iPhone chip is expected to be a 3nm processor. This might deliver even faster Neural Engine performance than you get inside the M4, which means the next iPhone will be capable of handling even bigger calculations at higher speeds for less energy than Apple’s best available current chip delivers now. Apple also has a road map toward 2nm chips, which will maintain that pattern of performance and energy efficiency.

All of this means the company already has a road map toward processor performance that it can now apply to the server market it abandoned in 2011, when it discontinued the Xserve. 

This is true corporate social responsibility

Apple already knows it isn’t good enough to just put high-performance chips inside servers and edge devices if they consume vast quantities of energy. To mitigate this, the company has already invested hundreds of millions of dollars in reducing energy consumption across its entire ecosystem, including major investments in renewable energy supply. It will not turn back time.

With that in mind, it will want to widen its ecosystem of low power, high performance iCloud servers, and when doing so it makes sense for it adopt those servers across its other online services over time. After all, if it can build servers and services that can be delivered at lower energy requirements without compromise on performance, why wouldn’t it do so?

For Apple, adoption of these may be an easy win in terms of its environmental performance data. But at what point will these systems become a service offering in their own right? What value could they unlock for the company?

However the eventual story ends, it’s interesting that by focusing on energy efficiency and computational performance for iPhone chips (and the PA Semi purchase), Apple put itself in a good position to meet the then-unseen challenges of server-based AI — and the space between the lines suggests we’re not near the end of that particular story just yet….

Please follow me on Mastodon, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe.

Apple’s decision not to introduce Apple intelligence, iPhone Mirroring, or SharePlay Screen Sharing in the European Union this year isn’t surprising, and reflects concerns around privacy far more than being a response to Europe’s decision to act against Apple’s App Store compliance.

The news basically is that Apple has confirmed it will not introduce the Apple Intelligence features it announced at WWDC in EU nations because it has concerns around the application of the Digital Markets Act (DMA). 

Apple Intelligence delayed, App Store gets a DMA slap

“Due to the regulatory uncertainties brought about by the Digital Markets Act, we do not believe that we will be able to roll out three of these [new] features — iPhone Mirroring, SharePlay Screen Sharing enhancements, and Apple Intelligence — to our EU users this year,” Apple told the Financial Times.

At the same time Apple made its announcement, the EU itself announced it will begin to take enforcement action against the company for breach of the DMA. Europe is concerned about elements of Apple’s offer to developers and the fees it charges the small number of developers who are the most successful on the store, arguing this stifles competition. Apple says it has made changes and is “confident” its plans align with that law.

It’s worth noting that the EU recently proposed an incredibly intrusive surveillance law that would break end-to-end encryption. While it looks like those proposals may have been shelved, Apple might have decided to stall while it waits to see what kind of shabby surveillance laws do get passed. 

Privacy or convenience? It’s up to EU

If you think about it, the beauty of Apple Intelligence is that it uses information your device has collected about you in order to function. But the risk of that information existing — even on your device — is that under the DMA, it’s not certain the EU won’t insist on that data, your data, being opened up to competitors. 

That’s a lot of information.

Apple is committed to keeping that information private and secure, but once it exists and is on the device in some form, I expect the company is concerned the DMA could force it to open up the information to third parties who want to compete with their own AI. As we’ve seen since the invention of the Internet, not every company is legitimate, ethical, or trustworthy, and even those that are might not have enough clout to invest in the world’s best security teams to maintain safety on their platforms.

A Pandora’s box chock-full of trouble

I get the sense that Apple’s decision to hold back on Apple Intelligence in the EU reflects the ongoing battle between the two entities as Europe forces Apple to open up a little. Given the source of this speculation, that might be correct, but the analysis misses what’s really at stake: once you have all your personal information turned into usable data on your device, every spook, hacker, fraudster, blackmailer, censor, despot, cop, or secret service operative is going to want to take a look.

That means any weakness in protecting that information opens a Pandora’s box of misanthropy — affecting consumers, corroding trust, and enabling surveillance at a scale no one has seen ever before on our sadly ailing planet. 

Could the EU end up without Apple Intelligence? 

It feels possible EU might never get Apple Intelligence.

Apple says: “We are committed to collaborating with the European Commission (EC) in an attempt to find a solution that would enable us to deliver these features to our EU customers without compromising their safety.”

According to the Financial Times, Apple seeks “clarity” from the European Commission regarding the level of access it would need to grant to third parties over Apple Intelligence features in order to be DMA-compliant.

However, rather than providing any insight into requirements, an EU rep said companies like Apple are, “Welcome to offer their services in Europe, provided that they comply with our rules aimed at ensuring fair competition.” Which is, of course, what Apple is asking for, it just wants to know how those rules will be applied to its service before launch, rather than working with decisions made after the event.

What isn’t yet clear is the extent to which other AI providers might be affected. Is it possible the European Commission might have just created an obstacle to AI deployment?

Who has the energy for this?

And, of course, the big conversation everyone should be having concerning artificial intelligence is one Europe’s regulators don’t appear to be addressing at all — the energy consumption of AI servers. Combined, the world’s data centers now consume more power in a year than the entire Italian nation, and this is set to increase exponentially. Perhaps waiting until privacy, security, and energy challenges are solved makes sense after all?

One more thing is also certain: That with the removal of these three features the temptation to upgrade to iOS 18 among European users will be lower than ever before, given they comprise the majority of improvements to the OS.

Please follow me on Mastodon, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe.

Data centers are draining more electricity from global power grids than ever before because of generative artificial intelligence (genAI) and general AI processing needs. 

The compute capacity to train large language models, the platforms on which generative AI (gen) and AI run, is now roughly doubling every nine months, according to Epoch AI, an AI research institute. The International Energy Agency forecast that global data center electricity demand will more than double from 2022 to 2026, in large part because of AI and cryptocurrency.

That insatiable demand for energy has tech companies scrambling for alternative sources of energy as well as ways to reduce the energy needs of AI technologies.

One potential emerging solution to the AI-compute dilemma is quantum computing, which vastly surpasses today’s binary computing systems in processing capabilities and energy consumption. Studies have shown quantum computing can increase the performance of AI neural networks for tasks such as natural language processing and image analysis.

“Quantum computing definitely augments the power of AI. For example, AI and quantum computing used together can accelerate drug discovery and personalized pharmaceuticals by years. Quantum computing supports AI-based simulation of clinical drug trials so that the trials take one hour instead of ten years,” said Avivah Litan, a vice president analyst at Gartner.

For example, in February, Insilico Medicine and the University of Toronto announced they’d demonstrated the first instance of a generative model running on quantum hardware outperforming state-of-the-art classical models in generating viable cancer drug candidates.

What is quantum computing?

In classical computers, bits programmed as units of data have a possible value of one or zero — hence the term binary code. In quantum computers, data units are programmed with quantum bits, known as qubits, which can represent a one, a zero, or a combination of both zero and one at the same time. At a high level, that trait enables quantum computers to be faster and better at fundamental processing tasks than data processing on classical computing systems that use GPUs or CPUs.

For example, Google’s Quantum AI division built a supercomputer based on its Sycamore quantum processor. Each chip currently holds 70 qubits and can reportedly complete in seconds what would take a CPU- or GPU-based supercomputer of similar size decades to process.

“Quantum artificial intelligence with better algorithms… are faster and more accurate,” CompTIA, a global, nonprofit IT association, stated in a blog.

Commercial quantum platforms, such as Microsoft Azure Quantum, AWS Braket, Google Cirq, and others, allow cloud providers to use quantum comuting as compute service offerings.

“Think of these platforms as quantum computing marketplaces whereby the cloud service providers have partnered with multiple quantum computing vendors to provide access to their hardware, software, QSDKs [Quantum software development kits], etc.,” said Heather West, a research manager with IDC.

“Most of these cloud service providers have not, and thus do not, provide access to their own quantum systems, the exception being Google. AI is not a part or related to these offerings,” she added.

As with any technology, along with the positives there are negatives associated with quantum computing. For example, quantum computing poses a serious threat to the cybersecurity systems relied on by virtually every company, according to CompTIA. The current standard for encryption algorithms, such as RSA or SSL/TLS, relies on the complexity in factoring large numbers into primes, and that’s the type of problem quantum computers are great at solving, CompTIA said.

Startups and established companies continue to accelerate their advances in the quantum computing space. Big tech companies such as Alibaba, Amazon, IBM, Google, and Microsoft have already launched commercial quantum-computing cloud services. Two years ago, Goldman Sachs said it planned to introduce quantum algorithms to price financial instruments as soon as 2026. Honeywell anticipates that quantum will form a $1 trillion industry in the decades ahead.

Quantum computing, meet genAI

Some say quantum computing is a natural partner for genAI and can reduce its energy demands.

For example, Sumitomo Mitsui Trust Bank in Japan is using quantum computing to run genAI-powered programs for financial simulation models of future market movements. The bank partnered with Zapata AI, a genAI company that was spun out of Harvard University’s quantum computing lab in 2017.

Christopher Savoie, Zapata AI’s CEO, sees linear algebra (quantum math) as the solution to perform all kinds of AI tasks, including chatbots such as ChatGPT.

“We’re throwing an obscene the amount of GPU energy at chatbots right now. Are we getting that much business value right now from it? We’re hitting a wall: when are we going to make money with that?” said Savoie, who is a molecular biophysicist.

Savoie pointed to the study done by Insilico Medicine and the University of Toronto, which Zapata participated in.

“When we used this quantum-based model… we were able to develop cancer drugs the other models didn’t,” Savoie said. “We used quantum models to determine what drugs would block this cancer protein and then non-quantum models. The quantum models found two capable drugs that we synthesized and showed they blocked the cancer protein.

“So, it’s qualitatively better,” he continued. “It’s cheaper, faster, and better — better in that we get faster answers. That’s important in drug discovery. You’re saving a lot of money for pharmaceutial companies if you get your answer the first run around. Or you have a more accurate modeling of trading behavior for a bank.”

Zapata AI’s Orquestra platform was specifically designed to run any AI or machine learning model, including more traditional neural networks as well as the company’s proprietary tensor networks.

Tensor networks can be used to model any quantum circuit and run it on today’s classical computers, giving users an on ramp to the potential benefits of future quantum computers, according to Zapata AI. Tensor networks also come with their own advantages for AI today, including more accurate, efficient, and expressive AI models.

“Every quantum circuit can be written as a tensor product, which means we can do things on GPUs that quantum computers will eventually be faster at doing. Zapata and others have shown that quantum math is better at getting better answers in the context of generative AI,” Savoie.

Specifically, Savoie said, quantum statistics can enhance genAI models’ ability to extrapolate missing information and generate new, high-quality information from big data. Generating genuinely new and high-quality data is very important for industrial use cases, he said. 

Early days yet

IDC’s West said quantum computing fits with complex problem solving, but it’s “not a big data solution.” Quantum computing will be useful for solving specific types of problems, she said.

In quantum computing, a qubit begins in a binary state of 0 or 1, but through a process known as annealing, the qubits become entangled, allowing them to represent many possible answers, always with minimum energy. The process occurs in microseconds.

“Quantum annealers are best suited for optimization problems,” West said. “The complex algebraic/factorization problems include some QML [quantum machine learning] problems, but not all AI problems will be suitable for quantum. Research is being conducted to determine how to integrate AI into [quantum computing] and [quantum] into AI to optimize the compute resources needed to solve some of these problems.”

In large part, quantum computing is in very early stages of development, West noted. That’s because the hardware still needs considerable improvements for gate-based models that allow for the​ execution of quantum‌ algorithms. By applying various gates ​sequentially, complex computations can be carried out.

“There are not any real-world applications for this type of system,” West said. “These systems are only useful for small-scale experimentation and debugging. Quantum [computing is] currently being used for solve some scientific and business optimization problems. It is still too early for the integration of AI. Right now, it is only a hypothetical and experimental.”