'The Five' co-hosts discuss new AI bot ChatGPT and the impact artificial intelligence will have on future jobs. Church leaders and volunteers will soon have access to an artificial intelligence platform that aims to shave hours off their day-to-day tasks by generating content from sermons to engage fellow Christians when they are not in the pews. Upcoming platform Pulpit AI, founded by Michael Whittle, is expected to launch later this summer and will serve as a tool for Christian leaders looking to take the tedious work out of crafting religious blog posts, devotionals and prayer guides and social media posts. "We want to help pastors of small to medium-sized churches be able to make content for their congregations to interact with throughout the week and on social media," Whittle told Fox News Digital. "We think every pastor should, if they want, have a digital signal to their congregations beyond the sermon. "Most small to medium-sized churches have small or completely volunteer staff, so they have zero operational leverage when it comes to media and resources for their church," he added. "If we can help a church media team get past the blank page, we can not only save them crazy amounts of time, we can help every church become a resourcing church for their people." 'AI JESUS' TALKS DATING, RELATIONSHIPS, MORALS -- EVEN OFFERS VIDEO-GAMING TIPS A congregant reads a referred passage from her Bible during services at Highland Colony Baptist Church in Ridgeland, Mississippi, Nov. 29, 2020. Puplit AI "doesn't and never will" generate sermons, instead it serves as a tool where the user uploads a sermon or religious podcast in order to repurpose it into "social media highlights, blog posts, discussion questions, and the other content churches use to reach their congregations and communities day in and day out," Whittle said. "Pulpit AI analyzes long form audio and video, then repurposes that into various forms of content," Whittle said. "Pulpit AI's output is taken directly from the source material.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Can artificial intelligence (AI) be legally listed as an inventor? After all, if AI can legally invent products, the number of patents on drug-discovery tools will shoot up fast. The issue is currently before a United States court. The U.S. Court of Appeals heard arguments on that question again last week, and the ruling could affect the pace of AI technology development, particularly within the pharmaceutical and life science industries.

Ubisoft reportedly plans to repurpose an Assassin's Creed Valhalla expansion into a standalone release. According to Bloomberg, the company is working on a game codenamed "Rift." What started life as DLC for the latest entry in the company's long-running historical franchise apparently morphed into a full game sometime late last year. Per Bloomberg, the game will star Basim Ibn Ishaq (pictured above), a character that appears in Valhalla. What's more, it won't be a massive open-world game and will instead focus more on stealth gameplay.

The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice.

Before we start, if you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! If you have been trying to build machine learning models with high accuracy; but never tried Transfer Learning, this article will change your life. At least, it did mine!

Using multiple nodes and parallel computing algorithms has become a principal tool to improve training and execution times of deep neural networks as well as effective collective intelligence in sensor networks. In this paper, we consider the parallel implementation of an already-trained deep model on multiple processing nodes (a.k.a. workers) where the deep model is divided into several parallel sub-models, each of which is executed by a worker. Since latency due to synchronization and data transfer among workers negatively impacts the performance of the parallel implementation, it is desirable to have minimum interdependency among parallel sub-models. To achieve this goal, we propose to rearrange the neurons in the neural network and partition them (without changing the general topology of the neural network), such that the interdependency among sub-models is minimized under the computations and communications constraints of the workers. We propose RePurpose, a layer-wise model restructuring and pruning technique that guarantees the performance of the overall parallelized model. To efficiently apply RePurpose, we propose an approach based on $\ell_0$ optimization and the Munkres assignment algorithm. We show that, compared to the existing methods, RePurpose significantly improves the efficiency of the distributed inference via parallel implementation, both in terms of communication and computational complexity.

Artificial intelligence has led to some major changes in the field of marketing. The Content Marketing Institute has talked about some of the biggest ways that AI is driving changes for marketers. Influencer marketing, in particular, is evolving with advances in AI. Smart marketers can find ways to use AI to resolve some of the biggest influencer marketing mistakes they will encounter. Influencer marketing has become synonymous with brand building.

Many believe that AI has the potential to complement the scientific community in major breakthroughs, especially in developing vaccines for COVID-19. Why is AI so important in the COVID-19 era? And how important it can be for drug discoveries in these times? Traditionally, the drug discovery process is very long (typically it takes 8-10 years from research, development to market), complicated and expensive. AI could play an important role by expediting the overall test process from years to months by evaluating different scenarios across parameters at once.

With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50 novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Drug repurposing is about investigating existing drugs for new therapeutic purposes which can potentially speed up drug development 1 . With a large number of existing drugs, it is important to quickly and accurately identify promising candidates for new indications. Especially in facing COVID-19 pandemic today, drug repurposing become particularly relevant as a potentially much faster way to discover effective and safe drugs for treating COVID-19. Deep learning has recently demonstrated its superior performance than classic methods to assist computational drug discovery 2, 3, thanks to its expressive power in extracting, processing and extrapolating patterns in molecular data.

Waymo will build its autonomous vehicles in Detroit. CEO John Krafcik wrote Tuesday in a Medium post that the company will repurpose an existing facility in Motor City with the goal of being operational by mid-2019. Back in January, the company announced it had chosen southeast Michigan as the location of its new facility for the mass production of L4 autonomous vehicles, the first of its kind in the world. The company will create anywhere between 100 to 400 jobs as a result of the venture, according to The Detroit Free Press. Waymo will also receive incentives from the Michigan Economic Development Corporation.