Yes, you can pay $50,000 to clone a pet. But others are using the technology to rescue endangered species. This week, we heard that Tom Brady had his dog cloned. The former quarterback revealed that his Junie is actually a clone of Lua, a pit bull mix that died in 2023. Brady's announcement follows those of celebrities like Paris Hilton and Barbra Streisand, who also famously cloned their pet dogs. But some believe there are better ways to make use of cloning technologies.

A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.

The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

In my previous posts, I described what is Active Learning (introduction to Active Learning and the main approaches) and how one can implement such a pipeline in a simple and generic way (architecture for Active Learning pipeline). This post is based on a talk I gave at Reversim Summit 2021. I want to jump a few steps forward and talk about how I evaluate my Active Learning models' results and how it helped me to catch a huge bug. In general, when we talk about Active Learning, we talk about creating a Data Selector. An automatic algorithm that will be able to choose a subset from our unlabeled data and will give our model the largest performance gain, so it will be able to learn and improve its accuracy.

While the human touch will always prevail, you should know that there are many ways in which we can all utilize artificial intelligence (AI) wisely in order to run more efficient and effective PR campaigns. As technology continues to evolve, many companies are turning to artificial intelligence as a means to help streamline repetitive processes and find efficiencies where they can. The public relations sector is no different, with PR firms looking for ways to use artificial intelligence for certain aspects of the business that can benefit from AI functions. The idea of using AI can be worrisome for people working in PR as they are concerned about their jobs being replaced by programmable technology, but that is not the case. Public relations firms can benefit from the use of AI, particularly when it comes to the sheer volume of information they need to sort through.

You are free to share this article under the Attribution 4.0 International license. A form of artificial intelligence designed to interpret a combination of retinal images successfully identified a group of patients known to have Alzheimer's disease, researchers report. The findings suggest the approach could one day be used as a predictive tool, according to the new study. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals.

The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

A form of artificial intelligence designed to interpret a combination of retinal images was able to successfully identify a group of patients who were known to have Alzheimer's disease, suggesting the approach could one day be used as a predictive tool, according to an interdisciplinary study from Duke University. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

DONE wisely, artificial intelligence "can be the best thing ever for humanity", says the fundamental physicist turned AI researcher Max Tegmark in our interview this week (see "If we do it wisely, AI can be the best thing ever for humanity"). We subscribe wholeheartedly to his assessment. Seldom has there been a technology with such an obvious power to improve our lot – or one with such obvious dangers. The risks are potentially existential.

Note: all the links below take you to other articles by the same author. With all the gratuitous anthropomorphization infecting the machine learning (ML) and artificial intelligence (AI) space, many businessfolk are tricked into thinking of AI as an objective, impartial colleague that knows all the right answers. Here's a quick demo that shows you why that's a terrible misconception. A task that practically every AI student has to suffer through is building a system that classifies images as "cat" (photo contains a cat) or "not-cat" (no cat to be seen). The reason this is a classic AI task is that recognizing objects is a task that's relatively easy for humans to perform, but it's really hard for us to say how we do it (so it's difficult to code explicit rules that describe "catness").