Active learning has been studied extensively as a method for efficient data col- lection. Among the many approaches in literature, Expected Error Reduction (EER) Roy & McCallum (2001) has been shown to be an effective method for ac- tive learning: select the candidate sample that, in expectation, maximally decreases the error on an unlabeled set. However, EER requires the model to be retrained for every candidate sample and thus has not been widely used for modern deep neural networks due to this large computational cost. In this paper we reformulate EER under the lens of Bayesian active learning and derive a computationally efficient version that can use any Bayesian parameter sampling method (such as Gal & Ghahramani (2016)). We then compare the empirical performance of our method using Monte Carlo dropout for parameter sampling against state of the art methods in the deep active learning literature. Experiments are performed on four standard benchmark datasets and three WILDS datasets (Koh et al., 2021).

Google and it's very own version of Chatbot Bard is soon to be released to the public. Recently Google AI engineers stated that it's opening up access to Bard, the Google's very own AI-powered chatbot that's a rival to services released by competitor Microsoft and it's OpenAI. Google is starting with users in the US and UK, who can go to the Bard site to sign up for the waiting list. "We've learned a lot so far by testing Bard, and the next critical step in improving it is to get feedback from more people," stated Google's Sissie Hsiao and Eli Collins. Google has announced Bard and the company went into "code red" following the release of the OpenAI's ChatGPT late last year to garner it's own AI Chat.

The AI field took a turn with the release of powerful Generative Artifical Intelligence (AI) models, and as a result, the world is seeing the automation of some skills around creativity and imagination sooner than many expected. For some organizations, Generative AI holds valuable potential for higher order opportunities, like new services and business models. Deloitte offers a method for selecting Generative AI use cases, as well as some next steps for business leaders in the Age of With . Generative AI has captured attention in global media and the public square, prompting questions and discussions around this transformative technology. Businesses, research organizations, and even lay users are experimenting with Generative AI, and given the excitement and interest, it is important to look more closely at the potential capabilities and implications for business.

HIMSS 2023 kicked off Monday in Chicago. Follow our live blog for the latest updates and happenings from the conference. Epic Systems is working with Microsoft to integrate generative AI technology into its electronic health record software for the first time, the companies said Monday. The announcement was made in conjunction with the first day of the HIMSS conference, which is being held in Chicago this week. Health systems using Epic's EHR system will be able to run generative AI solutions through Microsoft's OpenAI Azure Service.

Elon Musk Creates new AI Company to rival OpenAI as Elon Musk announced his plans to enter in the Artificial Intelligence (AI) market of worth more than US$ 125 billion, globally. Since, the global AI market is expected to hit US$ 1,591 billion by 2030 with a registered CAGR of 38.1% from 2022 to 2030, Elon Musk has planned to enter this arena. The boss of world's leading companies like Twitter, Telsa, StarLink, and SpaceX has announced a new venture and as he has registered a company called'X.AI'. It is reveled that the new subsidiary will be the home of efforts to build new AI based tools similar to ChatGPT that is currently owned by OpenAI. To accelerate with X.AI, Musk is reported that he is assembling a team of AI experts and researchers as he is discussing with with investors of SpaceX and Tesla about putting money into this new venture.

AutoGPT: Everything You Need To Know Can Robots and Humans Combat Extinction Together?

Over the past few decades, the advances in computational resources and computer science, combined with next-generation sequencing and other emerging omics techniques, ushered in a new era of biology, allowing for sophisticated analysis of complex biological data. Bioinformatics is evolving as an integrative field between computer science and biology, that allows the representation, storage, management, analysis and investigation of numerous data types with diverse algorithms and computational tools. The bioinformatics approaches include sequence analysis, comparative genomics, molecular evolution studies and phylogenetics, protein and RNA structure prediction, gene expression and regulation analysis, and biological network analysis, as well as the genetics of human diseases, in particular, cancer, and medical image analysis [1,2,3]. Machine learning (ML) is a field in computer science that studies the use of computers to simulate human learning by exploring patterns in the data and applying self-improvement to continually enhance the performance of learning tasks. ML algorithms can be roughly divided into supervised learning algorithms, which learn to map input example into their respective output, and unsupervised learning algorithms, which identify hidden patterns in unlabeled data. The advances made in machine-learning over the past decade transformed the landscape of data analysis [4,5,6].

Since the launch of OpenAI's GPT-4 API last month to beta testers, a loose group of developers has been experimenting with making agent-like ("agentic") implementations of the AI model that attempt to carry out multistep tasks with as little human intervention as possible. These homebrew scripts can loop, iterate, and spin-off new instances of an AI model as needed. Two experimental open source projects, in particular, have captured much attention on social media, especially among those who hype AI projects relentlessly: Auto-GPT, created by Toran Bruce Richards, and BabyAGI, created by Yohei Nakajima. They need a lot of human input and hand-holding along the way, so they're not yet as autonomous as promised. But they represent early steps toward more complex chaining AI models that could potentially be more capable than a single AI model working alone.

Google went into a "panic" after Samsung, the world's second-largest manufacturer of smartphones and a user of the company's Android operating system, said it was considering offering Bing as the default search engine for users, according to a report published by the New York Times on Sunday. The tech giant is scrambling to catch up to rivals Microsoft in the adoption of A.I. services. The threat to Google's $162 billion business is reportedly pushing the company to revamp its search products, including creating an entirely new A.I.-powered search engine. Google's drive to compete contrasts with comments from Alphabet CEO Sundar Pichai in an interview, also aired on Sunday, in which he suggested that he wanted to avoid a rush to release new A.I. products for fear of how they might affect society. In the interview with CBS's 60 Minutes, Alphabet CEO Pichai called artificial intelligence "the most profound technology humanity is working on," adding that it was "more profound than fire or electricity or anything that we've done in the past."

The arrival of ChatGPT, however, feels different. Most new technologies have transformed the products of creative agencies, but ChatGPT is a product that could transform the strategic and creative process itself. Agencies and consultancies should therefore be seriously considering the potential value that this tool can bring to their businesses. One of the helpful ways to go about this is to frame ChatGPT as a thinking partner and to explore the two basic thinking styles that underpin the practice of innovation and strategy formation: divergent and convergent thinking. This approach can help us to understand the benefits of ChatGPT, and how we can start capitalizing on them.