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Keeping up with an industry as fast-moving asAIis a tall order. So until an AI can do it for you, heres a handy roundup of the last weeks stories in the world of machine learning, along with notable research and experiments we didnt cover on their own.

If it wasnt obvious already, the competitive landscape in AI particularly the subfield known as generative AI is red-hot. And its getting hotter. This week, Dropbox launched its first corporate venture fund, Dropbox Ventures, which the company said would focus on startups building AI-powered products that shape the future of work. Not to be outdone, AWS debuted a $100 million program to fund generative AI initiatives spearheaded by its partners and customers.

Theres a lot of money being thrown around in the AI space, to be sure.Salesforce Ventures, Salesforces VC division,plansto pour $500 million into startups developing generative AI technologies. Workdayrecently added $250 million to its existing VC fund specifically to back AI and machine learning startups. And Accenture and PwC have announced that they plan to invest $3 billion and $1 billion, respectively, in AI.

But one wonders whether money is the solution to the AI fields outstanding challenges.

In an enlightening panel during a Bloomberg conference in San Francisco this week, Meredith Whittaker, the president of secure messaging app Signal, made the case that the tech underpinning some of todays buzziest AI apps is becoming dangerously opaque. She gave an example of someone who walks into a bank and asks for a loan.

That person can be denied for the loan and have no idea that theres a system in [the] back probably powered by some Microsoft API that determined, based on scraped social media, that I wasnt creditworthy, Whittaker said. Im never going to know [because] theres no mechanism for me to know this.

Its not capital thats the issue. Rather, its the current power hierarchy, Whittaker says.

Ive been at the table for like, 15 years, 20 years. Ive been at the table. Being at the table with no power is nothing, she continued.

Of course, achieving structural change is far tougher than scrounging around for cash particularly when the structural change wont necessarily favor the powers that be. And Whittaker warns what might happen if there isnt enough pushback.

As progress in AI accelerates, the societal impacts also accelerate, and well continue heading down a hype-filled road toward AI, she said, where that power is entrenched and naturalized under the guise of intelligence and we are surveilled to the point [of having] very, very little agency over our individual and collective lives.

That should give the industry pause. Whether it actually will is another matter. Thats probably something that well hear discussed when she takes the stage at Disrupt in September.

Here are the other AI headlines of note from the past few days:

This week was CVPR up in Vancouver, Canada, and I wish I could have gone because the talks and papers look super interesting. If you can only watch one, check out Yejin Chois keynote about the possibilities, impossibilities, and paradoxes of AI.

The UW professor and MacArthur Genius grant recipient first addressed a few unexpected limitations of todays most capable models. In particular, GPT-4 is really bad at multiplication. It fails to find the product of two three-digit numbers correctly at a surprising rate, though with a little coaxing it can get it right 95% of the time. Why does it matter that a language model cant do math, you ask? Because the entire AI market right now is predicated on the idea that language models generalize well to lots of interesting tasks, including stuff like doing your taxes or accounting. Chois point was that we should be looking for the limitations of AI and working inward, not vice versa, as it tells us more about their capabilities.

The other parts of her talk were equally interesting and thought-provoking. You can watch the whole thing here.

Rod Brooks, introduced as a slayer of hype, gave an interesting history of some of the core concepts of machine learning concepts that only seem new because most people applying them werent around when they were invented! Going back through the decades, he touches on McCulloch, Minsky, even Hebb and shows how the ideas stayed relevant well beyond their time. Its a helpful reminder that machine learning is a field standing on the shoulders of giants going back to the postwar era.

Many, many papers were submitted to and presented at CVPR, and its reductive to only look at the award winners, but this is a news roundup, not a comprehensive literature review. So heres what the judges at the conference thought was the most interesting:

VISPROG, from researchers at AI2, is a sort of meta-model that performs complex visual manipulation tasks using a multi-purpose code toolbox. Say you have a picture of a grizzly bear on some grass (as pictured) you can tell it to just replace the bear with a polar bear on snow and it starts working. It identifies the parts of the image, separates them visually, searches for and finds or generates a suitable replacement, and stitches the whole thing back again intelligently, with no further prompting needed on the users part. The Blade Runner enhance interface is starting to look downright pedestrian. And thats just one of its many capabilities.

Planning-oriented autonomous driving, from a multi-institutional Chinese research group, attempts to unify the various pieces of the rather piecemeal approach weve taken to self-driving cars. Ordinarily theres a sort of stepwise process of perception, prediction, and planning, each of which might have a number of sub-tasks (like segmenting people, identifying obstacles, etc). Their model attempts to put all these in one model, kind of like the multi-modal models we see that can use text, audio, or images as input and output. Similarly this model simplifies in some ways the complex inter-dependencies of a modern autonomous driving stack.

DynIBaR shows a high-quality and robust method of interacting with video using dynamic Neural Radiance Fields, or NeRFs. A deep understanding of the objects in the video allows for things like stabilization, dolly movements, and other things you generally dont expect to be possible once the video has already been recorded. Again enhance. This is definitely the kind of thing that Apple hires you for, and then takes credit for at the next WWDC.

DreamBooth you may remember from a little earlier this year when the projects page went live. Its the best system yet for, theres no way around saying it, making deepfakes. Of course its valuable and powerful to do these kinds of image operations, not to mention fun, and researchers like those at Google are working to make it more seamless and realistic. Consequences later, maybe.

The best student paper award goes to a method for comparing and matching meshes, or 3D point clouds frankly its too technical for me to try to explain, but this is an important capability for real world perception and improvements are welcome. Check out the paper here for examples and more info.

Just two more nuggets: Intel showed off this interesting model, LDM3D, for generating 3D 360 imagery like virtual environments. So when youre in the metaverse and you say put us in an overgrown ruin in the jungle it just creates a fresh one on demand.

And Meta released a voice synthesis tool called Voicebox thats super good at extracting features of voices and replicating them, even when the input isnt clean. Usually for voice replication you need a good amount and variety of clean voice recordings, but Voicebox does it better than many others, with less data (think like 2 seconds). Fortunately theyre keeping this genie in the bottle for now. For those who think they might need their voice cloned, check out Acapela.

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This week in AI: Big tech bets billions on machine learning tools - TechCrunch