Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to access given longstanding privacy, and strict data sharing policies. By manipulating image datasets in the pixel or feature space, existing data augmentation techniques represent one of the effective ways to improve the quantity and diversity of training data. Here, we look to advance augmentation techniques by building upon the emerging success of text-to-image diffusion probabilistic models in augmenting the training samples of our macroscopic skin disease dataset. We do so by enabling fine-grained control of the image generation process via input text prompts. We demonstrate that this generative data augmentation approach successfully maintains a similar classification accuracy of the visual classifier even when trained on a fully synthetic skin disease dataset. Similar to recent applications of generative models, our study suggests that diffusion models are indeed effective in generating high-quality skin images that do not sacrifice the classifier performance, and can improve the augmentation of training datasets after curation.

During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several sectors. Consequently, the implications that these generative models have in the industry and society are enormous, as several job positions may be transformed. For example, Generative AI is capable of transforming effectively and creatively texts to images, like the DALLE-2 model; text to 3D images, like the Dreamfusion model; images to text, like the Flamingo model; texts to video, like the Phenaki model; texts to audio, like the AudioLM model; texts to other texts, like ChatGPT; texts to code, like the Codex model; texts to scientific texts, like the Galactica model or even create algorithms like AlphaTensor. This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently.

As previously reported in this space, the Court of Appeal for the Federal Circuit has ruled that an AI machine cannot be an inventor because it is not a "natural person." You can read those posts here and here. On November 11, 2022, a group of plaintiffs filed suit in the Northern District of California against several defendants, including GitHub, Inc., Microsoft Corporation, and OpenAI, Inc. and related companies to OpenAI. The issue stems from a product called Copilot and a product integrated into Copilot called Codex. To provide some context of the issue, some backstory may help.

Launched in November last year, ChatGPT made global news for its ease of answering even complex questions in a conversational manner. The algorithm that powers the chatbot, GPT3.5 is built by Open AI and is trained to learn what humans mean when they ask a question. The algorithm uses large language models to predict what words will come next and uses human feedback to follow directions and provide responses that are satisfactory. It is this ability of ChatGPT that makes it a threat to the search engine business of Google. As per the revelations made by Jason Calacanis, entrepreneur, investor, and more recently known for being one of the people in Elon Musk's inner circle at Twitter, OpenAI's future app currently has a search function and a thread history that can be seen in the samples released so far.

Get3D is a new generative AI model from NVIDIA that can create 3D shapes. The model was recently added to NVIDIA's Omniverse platform. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning advancements in a concise and easy-to-understand format. The new model was recently added to NVIDIA's marquee Omniverse platform.. "NVIDIA's Get3D is a Generative AI Model for 3D Shapes" is published by Jesus Rodriguez.

If Microsoft leans more towards building in functionality that's present in ChatGPT, the conversational AI that made headlines last year, then Outlook could compose entire emails based on simple queries. Imagine Outlook writing an email to your colleagues explaining you're unwell, based purely on a "write an email to my team explaining I'm out sick" query. Microsoft is also reportedly planning to launch a version of Bing that uses ChatGPT to answer search queries. This new feature could be available as soon as March, in a bid to make Bing more competitive with Google.

OpenAI's recently-released ChatGPT (Generative Pre-trained Transformer) had been making headlines due to the potential to push forward conversational artificial intelligence. This has led some technology commentators to question whether the technology could surpass Google as the most popular search engine. Certainly interest in the chatbot is increasing. Analysis of Google Trends data shows a clear spike in interest over the past month or so, coinciding with the app's release, both in OpenAI and the ChatGPT solution itself. However, the app itself proves of greater interest to the public. Nepal appears to demonstrate the highest overall interest in the solution and company, ranking first in an index of Google Trends data for OpenAI, ChatGPT and various derivatives and related search terms.

Check out all the on-demand sessions from the Intelligent Security Summit here. And late today, Semafor reported that Microsoft, which invested $1 billion in OpenAI in 2019, is in talks to invest another $10 billion in the company. The stream of Microsoft news made me wonder: How would these apps-on-steroids, used by billions of companies globally, change how we work? Especially once Google gets fully in the game, integrating its own generative AI capabilities into Google Workspace? Will AI become as mundane in our day-to-day work lives as the humble spreadsheet?

In less than 6 weeks' time, ChatGPT has taken the world by storm. If you haven't heard of ChatGPT – specifically ChatGPT3 – at this point, here's the low-down: It's a question-and-answer computer program which uses artificial intelligence and machine learning to create not only intelligent answers but writes it in a "human-like way." It's really quite astonishing how well it answers questions, and for that reason, it's already changed the world. That's why OpenAI's ChatGPT is valued at $29 billion, according to the Washington Post. Per the Post's reporting, OpenAI is in talks with investors currently and looks to be valued at nearly $30 billion.

AI revolution is not the future, it is happening now. Many organizations like World Economic Forum or IBM recognize AI as the primary technology that will drive the 4th Industrial Revolution. It will fundamentally alter the way we live, work, and relate to one another. AI can be biased, produce unethical and even dangerous results, and generate incorrect or misleading information. However, it is evolving at an incredible rate and these issues are likely to improve over time.