The last year has seen several exciting AI innovations in the field of artificial intelligence, including complex multimodal models and a thriving open-source landscape.
However, as corporations focus more on real-world activities rather than experimentation, views are growing more complex and mature, even while generative artificial intelligence continues to enthrall the tech world. This year’s trends show how AI innovation and implementation tactics are becoming more sophisticated and cautious while keeping an eye on safety, ethics, and the changing legal environment.
That was the year governments started to take AI danger seriously and the year chatbots went viral for the first time. These advancements weren’t so much fresh inventions as they were concepts and technology that were coming of age after a protracted gestation period.
Multimodality
Although the term “multimodality” may seem technical, it’s important to know that it refers to an AI system’s capacity to handle a wide variety of data kinds.
This year marked the very first time that robust multimodal AI models were made available to the general public. The initial of these, GPT-4 from OpenAI, lets users upload graphics in addition to text inputs. With its ability to “see” images, GPT-4 offers a plethora of options. For instance, you could ask it to decide what to have for supper based on a picture of what’s in your refrigerator. OpenAI released the capability for users to communicate with ChatGPT via voice and text in September.
Announced in December, Google DeepMind’s most recent model, Gemini, is also capable of processing audio and images. In a Google launch trailer, the model was shown using a post-it note with a line drawing to identify a duck. In the same video, Gemini came up with a vision of a pink and blue plush octopus after seeing a picture of pink and blue fabric and asked what they could make.
Sam Altman, the CEO of OpenAI, stated in a recent interview that one of the main things to look out for in the company’s new models for 2019 will be multimodality.
Multimodality has benefits beyond making models more practical a wealth of new data sets, including pictures, videos, and audio files, that provide more details about the globe than just text may be used to train the models.
Many of the world’s leading AI businesses hold the view that these models will become more powerful or capable as a result of this additional training data. It is a step toward “artificial general intelligence,” the kind of system that can equal human intellect, producing labor that is economically valuable and leading to new scientific discoveries. This is the hope held by many AI scientists.
Constitutional AI
How to integrate AI innovation with human values is one of the most important unsolved issues in the field. If these machines outsmart and surpass human intelligence, there could be catastrophic consequences for our species some even predict complete extinction unless they are somehow restrained by laws that prioritize human well-being.
If the AI’s response was beneficial, safe, and conformed to OpenAI’s list of content guidelines, human raters would evaluate it and award it the computer-generated version.
Text-to-video
The rapidly increasing popularity of text-to-video solutions is one obvious result of the hundreds of millions that have been invested in AI this year. Text-to-image technologies had just begun to take shape a year ago; today, several businesses can convert texts into moving pictures with ever-increasing precision.
A Brooklyn-based AI innovation video business wants to enable everyone to become a filmmaker. In a feature it calls video-to-video, its most recent model, Gen-2, enables users to alter the style of an already-existing video in response to a text prompt in addition to creating new videos from text.
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