If you use an AI image or text generator to make a work of "art," does it belong to you? That's a huge question hanging over the heads of anyone tempted to use AI tools for commercial products. Crucially, simply plugging prompts into an AI image generator or text generator does NOT meet this burden. Because the author (or artist, or other relevant creative term) of a work is defined as "the person who translates an idea into a fixed, tangible expression," an AI system cannot meet this burden, even though it's using input from a human to generate its output. Commenting on established case law, the report says that "…the Supreme Court has made clear that originality is required, not just time and effort."

Instead of writing myself a bland, general introductory paragraph about what generative AI and ChatGPT are, I asked ChatGPT to explain that to me. Generative AI refers to artificial intelligence systems that are able to generate new content that is similar in style or content to a given input. This can include tasks such as generating text, images, or audio. Generative AI systems are fascinating because they have the potential to create new and creative content that can be used in a variety of applications, such as generating news articles, creating art, or even generating realistic-sounding dialogue for virtual assistants. One of the main milestones in the development of generative AI has been the development of large language models, such as GPT-3 (short for "Generative Pre-trained Transformer 3").

Dec 15 (Reuters) - ChatGPT, the new chatbot that is the talk of Silicon Valley, can spit out haikus, crack jokes in Italian and may soon be the scourge of teachers everywhere facing fake essays generated by the AI-powered technology. But a question it can't fully answer is this: How will OpenAI make money? The research organization, co-founded by Elon Musk and investor Sam Altman and backed by $1 billion in funding from Microsoft Corp (MSFT.O), is expecting its business to surge. Three sources briefed on OpenAI's recent pitch to investors said the organization expects $200 million in revenue next year and $1 billion by 2024. The forecast, first reported by Reuters, represents how some in Silicon Valley are betting the underlying technology will go far beyond splashy and sometimes flawed public demos.

Generative Adversarial Networks (GAN-s) -- sounds complicated, doesn't it? It is a lot simpler than it sounds. In this article, I will intuitively explain how those programs work, what they are used for, and my view on their future applications. Without further ado, let's get into it. The cheater wants to print banknotes that are indistinguishable from real money.

Patients regularly continue assessment or treatment in other facilities than they began them in, receiving their previous imaging studies as a CD-ROM and requiring clinical staff at the new hospital to import these studies into their local database. However, between different facilities, standards for nomenclature, contents, or even medical procedures may vary, often requiring human intervention to accurately classify the received studies in the context of the recipient hospital's standards. In this study, the authors present MOMO (MOdality Mapping and Orchestration), a deep learning-based approach to automate this mapping process utilizing metadata substring matching and a neural network ensemble, which is trained to recognize the 76 most common imaging studies across seven different modalities. A retrospective study is performed to measure the accuracy that this algorithm can provide. To this end, a set of 11,934 imaging series with existing labels was retrieved from the local hospital's PACS database to train the neural networks. A set of 843 completely anonymized external studies was hand-labeled to assess the performance of our algorithm. Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted. In comparison to a commercial product (96.20% predictive power, 82.86% accuracy, 1.36% minor errors), a neural network ensemble alone performs the classification task with less accuracy (99.05% predictive power, 72.69% accuracy, 10.3% minor errors). However, MOMO outperforms either by a large margin in accuracy and with increased predictive power (99.29% predictive power, 92.71% accuracy, 2.63% minor errors).

As I noted recently, organizations often find the biggest success through small steps with artificial intelligence. There are many examples of this at work, but Linux offers a great one. Linux started out as a student desktop experiment before it creeped slowly into companies as a reliable print server before eventually taking over the data center and the cloud (and Mars--it's on both the Chinese and U.S. rovers there). Incremental steps can add up to big things. In the area of food production, it needs to.

Boston Dynamics has made a name for itself through fascinating videos of biped and quadruped robots doing backflips, opening doors, and dancing to Uptown Funk. Now, it has revealed its latest gadget: A robot that looks like a huge overhead projector on wheels. It's called Stretch, it doesn't do backflips, it doesn't dance, and it's made to do one task: moving boxes. But this could, in fact, become the most successful commercial product of Boston Dynamics and turn it into a profitable company. Stretch has a box-like base with a set of wheels that can move in all directions. On top of the base are a large robotic arm and a perception mast.

Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball? Or providing each user a set of search results that have shown to be the most likely to yield a conversion? Recommending a product through a web campaign that can be most effective to prompt an engagement? This is where artificial intelligence is most effective for digital marketers.

Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball? Or providing each user a set of search results that have shown to be the most likely to yield a conversion? Recommending a product through a web campaign that can be most effective to prompt an engagement? This is where artificial intelligence is most effective for digital marketers.

A few months ago, I had a conversation with several researchers from a prominent AI company in Toronto, and their company philosophy was that everybody should write production-grade code and even be able to deploy it. It made me think about a lot of stuff. AI teams are specifically interesting because they simultaneously require at least two disciplines: software/hardware engineering and scientific discovery. So how does one work towards success and creating a cohesive team, or teams, of researchers and software engineers who work together and create great products? The product being either pure research in the context of an enterprise trying to gain a competitive edge in terms of intellectual property, or applied research geared more towards a commercial product in a given vertical, or a hybrid of both.