AI Weekly: Digging into DALL-E 2 for the enterprise

Welcome to the newest version of AI Weekly 

As I end my first week at VentureBeat, it’s an ideal alternative to introduce myself: I’m Sharon Goldman, a senior editor and author protecting AI for know-how decision-makers. 

Based mostly in central New Jersey (exit 10), I’ve reported on business-to-business (B2B) know-how for over a decade, writing for publications together with CIO, Forbes.com, Insider, Shopper Advertising and marketing, Adweek and CMSWire.

I selected a busy AI information cycle to get on board at VentureBeat. Actually, the debut of DALL-E 2, OpenAI’s new AI mannequin, which makes use of superior deep-learning strategies to generate and edit photorealistic photos just by comprehending textual content directions, has been the topic of chatter for 2 weeks now. That features each rhapsodic responses round DALL-E 2’s functionality to create wonderful pictures of avocado-shaped teapots and chairs, in addition to loud considerations about potential digital fakes picture era and the unfold of misinformation. 

As Ben Dickson explained here, “DALL-E 2 is a ‘generative mannequin,’ a particular department of machine studying that creates complicated output as a substitute of performing prediction or classification duties on enter knowledge. You present DALL-E 2 with a textual content description, and it generates a picture that matches the outline.”

What units DALL-E 2 aside from different generative fashions, he continued, is “its functionality to take care of semantic consistency within the photos it creates.” I needed to know what this all means for enterprise enterprise, so I reached out for feedback from a few consultants: 

Lastly, in a VentureBeat column this week, Sahor Mor, a product supervisor at Stripe, explored how DALL-E 2’s highly effective text-to-image mannequin could be used to generate datasets to unravel computer vision’s biggest challenges. 

“Pc imaginative and prescient AI purposes can fluctuate from detecting benign tumors in CT scans to enabling self-driving vehicles, but what’s frequent to all is the necessity for ample knowledge,” Mor wrote. “DALL-E 2 is one more thrilling analysis consequence from OpenAI that opens the door to new sorts of purposes. Producing large datasets to deal with one in every of laptop imaginative and prescient’s greatest bottlenecks – knowledge – is only one instance.” 

Some consultants, nonetheless, preserve there’s the hazard of over-hyping DALL-E 2. “It’s essential to not conflate the flexibility to generate reasonable photos from textual content with “understanding,” Peter Stone, president, founder and director of the Studying Brokers Analysis Group (LARG) throughout the AI Laboratory within the division of laptop science on the College of Texas at Austin, informed VentureBeat. “I don’t consider DALLE-2 as making important advances (past present fashions) in the direction of the long-term objectives of many individuals within the area of AI – it doesn’t give me any extra confidence than I had earlier than that every one of AI could be solved with neural networks alone.” 

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Thanks for studying,

Sharon

Twitter: @sharongoldman