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Revelations, innovations and questions about AI unfolded in VentureBeat’s news coverage this week. Deep learning turned 10 and insights from the field’s top leaders like Yann LeCun and Geoffrey Hinton predict that there’s no sign of slowdown for deep learning anytime soon.
Meanwhile, Melanie Mitchell, professor at the Santa Fe Institute, warned technical decision-makers that across the board, AI still needs three essential capabilities to continue meaningful advancements in the field: To understand concepts, to form abstractions and to draw analogies.
To Mitchell’s point, explainable AI is on the rise and developing rapidly to address some of these concerns — and MLops is in the driver’s seat for several solutions, including from the likes of: Domino Data Lab, Qwak, ZenML and others. More work is yet to be done in the space, but research is ongoing.
Speaking of research — this week, Meta announced that its AI research framework, PyTorch, is moving out from under its purview and becoming part of the Linux Foundation. Zuckerberg noted that while the company still plans to fund PyTorch, Meta plans to take steps toward distinctly separating itself from PyTorch in the coming year.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
In other news, Apple’s debut of iOS 16 shed new light on what other tech giants may do going forward in the vein of going passwordless. In its latest software update, Apple users can now use biometrics across iPhone, iPad and Mac devices to sign in more easily — with their biometrics info synched via iCloud.
Here’s more from our top five tech stories of the week:
- 10 years later, deep learning ‘revolution’ rages on, say AI pioneers Hinton, LeCun and Li
Artificial intelligence (AI) pioneer Geoffrey Hinton, one of the trailblazers of the deep learning “revolution” that began a decade ago, says that the rapid progress in AI will continue to accelerate.
In an interview before the 10-year anniversary of key neural network research that led to a major AI breakthrough in 2012, Hinton and other leading AI luminaries fired back at some critics who say deep learning has “hit a wall.”
Other AI path breakers, including Yann LeCun, head of AI and chief scientist at Meta and Stanford University professor Fei-Fei Li, agree with Hinton that the results from the groundbreaking 2012 research on the ImageNet database pushed deep learning into the mainstream and have sparked a massive momentum that will be hard to stop.
- Apple iOS 16: Passkeys brings passwordless authentication mainstream
When it comes to security, passwords often aren’t an asset, but a liability. They provide cybercriminals with an entry point to protected information which they can exploit with phishing scams and social engineering attempts, to manipulate users into handing over personal information.
With 15 billion passwords exposed online, something needs to change. Many providers are positing that the solution to this problem is to get rid of passwords altogether.
Now, as Apple iOS 16 launches today alongside macOS Ventura, users will be able to log in with Passkeys on iPhone, iPad and Mac, using biometric authentication options like Touch ID and Face ID, which are synched across the iCloud keychain.
- 3 essential abilities AI is missing
As the AI community puts a growing focus and resources toward data-driven, deep learning–based approaches, Melanie Mitchell, professor at the Santa Fe Institute, warns that what seems to be a human-like performance by neural networks is, in fact, a shallow imitation that misses key components of intelligence.
Despite progress in deep learning, some of its problems remain. Among them, she says, are three essential capabilities: To understand concepts, to form abstractions and to draw analogies.
What is for sure is that as AI becomes more prevalent in applications we use every day, it will be important to create robust systems that are compatible with human intelligence and work — and fail — in predictable ways.
- Why the explainable AI market is growing rapidly
Powered by digital transformation, there seems to be no ceiling to the heights organizations will reach in the next few years. One of the notable technologies helping enterprises scale these new heights is artificial intelligence (AI).
As AI advances, there has still been the persistent problem of trust: AI is still not fully trusted by humans. At best, it’s under intense scrutiny and we’re still a long way from the human-AI synergy.
- PyTorch has a new home: Meta announces independent foundation
Meta announced today that its artificial intelligence (AI) research framework, PyTorch, has a new home. It’s moving to an independent PyTorch Foundation, which will be part of the nonprofit Linux Foundation, a technology consortium with a core mission of collaborative development of open-source software.
Despite being freed of direct oversight, Meta said it intends to continue using Pytorch as its primary AI research platform and will “financially support it accordingly.” Though, Zuckerberg did note that the company plans to maintain “a clear separation between the business and technical governance” of the foundation.
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