Synthesia is a startup using AI to create synthetic avatar videos for marketing purposes. After an initial round of funding in April 2021, it announced a new $5O million round led by Kleiner Perkins last December, with participation from GV and existing investors Firstmark Capital, LDV Capital, Seedcamp and MMC Ventures. Synthesia was founded in 2017 by a team of researchers and entrepreneurs from UCL, Stanford, TUM and Cambridge who want to replace cameras with code, make everyone a creator and for synthetic media to progress in an impactful and ethical way." Synthetic media refers to video, image, text and voice that have been wholly or partially generated by computers. The ability of AI-driven systems to generate audiovisual content is a development made possible by the latest advances in deep learning. Synthesia uses deep learning to create visual chatbots, product videos, and sales videos for clients without actors, camera crews, studios, or cameras. A video production is expensive, requires studios, actors, cameras, post-production can be complex. Synthesia offers the realization of videos in a few minutes from a text, integrated avatars if the customer has not created one himself, he chooses a real or synthetic voice and the video is ready, with a possible translation in 50 languages. Afterwards, it is possible to modify the content of the video, to add text, images… Since the fundraising in April, Synthesia has added features that allow users to create their own animated talkers even more easily, and the platform now has 1,000 custom avatars in use. Riparbelli cited Ernst & Young as an example of a customer. The company has 35 partners with their own avatars, creating videos for internal and customer communications. Companies looking to make video creation easier with AI and avatars still need to increase realism, but they also need to ensure user safety and the credibility of their own platforms. An Israeli company called D-ID demonstrated its technology at Disrupt 2021, which can take a still image of a person and turn it into video content. "We will not offer our software for public use.

Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly focus on Generative Adversarial Networks (GANs) while paying less attention to Autoregressive Models, Variational Autoencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs' latent-space structure and interpolation ability could effectively ground the interdisciplinary learning of AI, creative arts, and philosophy. Thus, we designed a lesson to teach high school students about VAEs. We developed a web-based game and used Plato's cave, a philosophical metaphor, to introduce how VAEs work. We used a Google Colab notebook for students to re-train VAEs with their hand-written digits to consolidate their understandings. Finally, we guided the exploration of creative VAE tools such as SketchRNN and MusicVAE to draw the connection between what they learned and real-world applications. This paper describes the lesson design and shares insights from the pilot studies with 22 students. We found that our approach was effective in teaching students about a novel AI concept.

AI's strength and influence have already been demonstrated. With each passing day, the field of artificial intelligence continues to evolve and improve. Because of the huge potential that it has on the world's most pressing issues, tech corporations and researchers are pouring money into developing new technologies and along with the COVID-19 epidemic approaching its third year, it's no surprise that the biomedical community has remained focused on diagnosing and treating the disease. Let's take a look back at some of the major AI breakthroughs and innovations in medical sciences that made headlines this year as we approach the conclusion of 2021. Using a dataset of text-image pairs, OpenAI developed DALL.E, a 12-billion parameter version of GPT-3 trained to produce images from text descriptions.

We took a photo of a pineapple and used it to generate artworks in the styles of other famous artworks. Pieces in this series look great on their own, and even better when matched with other pieces from the series. Displaying the same subject in different styles really shows off the power of generative AI art.

Enterprises continued to accelerate the adoption of AI and machine learning to solve product and business challenges and improve revenues in 2021. Meanwhile, AI startups have experienced significant growth, roping in major investments to improve their product offerings and meet the growing demand for AI solutions across sectors. In fact, data from CB Insights Research shows that while the number of equity funding deals in the global AI space this year is just slightly less than the last (2,384 deals in 2021 versus 2,450 in 2020), the amount of capital invested has almost doubled to $68 billion. As we head into 2022, here's a quick look back at the milestones that shaped the AI space over the past 12 months. To start the year, OpenAI announced DALL-E, a multimodal AI system that generated images from text.

With the advent of new techniques, robust systems that can understand the relationships not only between words but words and photos, videos, and audio became possible. At the same time, policymakers -- growing increasingly wary of AI's potential harm -- proposed rules aimed at mitigating the worst of AI's effects, including discrimination. Meanwhile, AI research labs -- while signaling their adherence to "responsible AI" -- rushed to commercialize their work, either under pressure from corporate parents or investors. But in a bright spot, organizations ranging from the U.S. National Institutes of Standards and Technology (NIST) to the United Nations released guidelines laying the groundwork for more explainable AI, emphasizing the need to move away from "black-box" systems in favor of those whose reasoning is transparent. As for what 2022 might hold, the renewed focus on data engineering -- designing the datasets used to train, test, and benchmark AI systems -- that emerged in 2021 seems poised to remain strong.

Generative A.I. is the present and future of A.I. and deep learning, and it will touch every part of our lives. It is the part of A.I that is closer to our unique human capability of creating, imagining and inventing. By doing this course, you gain advanced knowledge and practical experience in the most promising part of A.I., deep learning, data science and advanced technology. The course takes you on a fascinating journey in which you learn gradually, step by step, as we code together a range of generative architectures, from basic to advanced, until we reach multimodal A.I, where text and images are connected in incredible ways to produce amazing results. At the beginning of each section, I explain the key concepts in great depth and then we code together, you and me, line by line, understanding everything, conquering together the challenge of building the most promising A.I architectures of today and tomorrow.

Enterprises continued to accelerate the adoption of AI and machine learning to solve product and business challenges and improve revenues in 2021. Meanwhile, AI startups have experienced significant growth, roping in major investments to improve their product offerings and meet the growing demand for AI solutions across sectors. In fact, data from CB Insights Research shows that while the number of equity funding deals in the global AI space this year is just slightly less than the last (2,384 deals in 2021 versus 2,450 in 2020), the amount of capital invested has almost doubled to $68 billion. As we head into 2022, here's a quick look back at the milestones that shaped the AI space over the past 12 months. To start the year, OpenAI announced DALL-E, a multimodal AI system that generated images from text.

In this tutorial we will be building a Javascript helper chatbot using OpenAIs GPT-3 engine. This chatbot will be capable of answering all your Javascript related queries. Generative Pre-trained Transformer 3 (GPT-3) is a new language model created by OpenAI that is able to generate written text of such quality that is often difficult to differentiate from text written by a human. You need an OpenAI GPT-3 API key with codex engine access for testing out the code in this blog. At the time I'm writing this, OpenAI is running a beta program for GPT-3, and you can signup for the beta program here.

What follows is an unedited dialogue between the two most powerful artificial intelligences available to the public; GPT-3 and J1-Jumbo. OpenAI and AI21 labs, the startups that created them, have set up public playgrounds (OpenAI API and AI21 Studio) for people to test the systems. Users can input text and the models generate a completion according to context and conditioning. To define the direction and style of the conversation I wrote a short prompt including the roles, personalities, and a few exchanges (few-shot setting). It conditioned the models to continue talking as two people would.