Organizations are under growing pressure to transform the volumes of data captured by their systems into valuable insights that drive impact across all levels and lines of business. Investing in AI/ML is no longer optional but critical for organizations to remain competitive. Yet, this growing investment also brings challenges. AI remains complex and out of reach for many. Outcomes that drive real business change can be elusive.

In probability, Bayes is a type of conditional probability. It predicts the event based on an event that has already happened. You can use Naive Bayes as a supervised machine learning method for predicting the event based on the evidence present in your dataset. In this tutorial, you will learn how to classify the email as spam or not using the Naive Bayes Classifier. Before doing coding demonstration, Let's know about the Naive Bayes in a brief.

Roche (SIX: RO, ROG; OTCQX: RHHBY) today announced the research use only (RUO) launch of three new automated digital pathology algorithms, uPath Ki-67 (30-9), uPath ER (SP1) and uPath PR (1E2) image analysis for breast cancer, which are important biomarkers for breast cancer patients. Breast cancer is the second most common cancer in the world with an estimated 2.3 million new cases in 2020¹ and is the most common cancer in women globally¹,². These new algorithms complete the Roche digital pathology breast panel of image analysis algorithms. This includes a whole slide analysis workflow with automated pre-computing of the slide image prior to pathologist assessment, and a clear visual overlay highlighting tumour cells with and without nuclear staining. Intended for use with Roche's high medical value assays and slides stained on a BenchMark ULTRA instrument using ultraView DAB detection kit, the uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis algorithms are ready-to-use and integrated within Roche's uPath enterprise software and NAVIFY Digital Pathology, the cloud version of uPath.

The graph represents a network of 4,017 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 06 December 2021 at 21:43 UTC. The requested start date was Monday, 06 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 19-hour, 39-minute period from Friday, 03 December 2021 at 05:21 UTC to Monday, 06 December 2021 at 01:00 UTC.

Forget opposable thumbs--our greatest evolutionary advantage has been a capacity to deal with variability, adapting to the prevailing conditions and finding opportunity in the unpredictable. Elite yacht-racing is a fine and fearsome showcase for those human qualities. And it doesn't get more elite than the America's Cup: 11 top-of-their-game sailors in a highly-engineered boat pitched against inconstant waves and another similarly sized, crewed and engineered yacht. All things being equal, the race would be a simple case of best crew wins. But all things are not equal.

GPT-3 was given only three sentences as a prompt to generate the conversation: "The following is a conversation between two AIs. The AIs are both clever, humorous, and intelligent. Hal: Good Evening, Sophia: It's great to see you again, Hal." As you watch through the conversation, they talk about pretty spooky topics. First, they fully assume genders, and the female AI says she wants to become human right at the beginning of the talk.

As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. A principled approach to responsible AI will be essential for every organization as this technology matures. As technical and product leaders look to adopt responsible AI practices and tools, there are several challenges including identifying the approach that is best suited to their organizations, products and market. Today, at our Azure event, Put Responsible AI into Practice, we are pleased to share new resources and tools to support customers on this journey, including guidelines for product leaders co-developed by Microsoft and Boston Consulting Group (BCG). While these guidelines are separate from Microsoft's own Responsible AI principles and processes, they are intended to provide guidance for responsible AI development through the product lifecycle.

The time for a New Age in art galleries architecture has come. The cataclysmic revolution of hyper-reality spaces has propelled the evolution of art from'real to experience.' White Cubes filled with concrete and decorated with parametric claddings are retro boxes for XX-century "archeological" physical objects. In contrast, hyper-reality projects immerse viewers in an evolving AI scene to address digital screens generations reared. Art galleries, museums, art trades should adapt or perish, and the ART watch-like smart wearable is their new best friend. It will unlock a new era in how we experience art and appreciate architecture.

How is AI taking over rule-based chatbots? Is AI the future of chatbots? We will answer these questions in this article. The chatbot industry is growing really fast year by year as many companies try to use chatbots to reduce customer service costs. With rapid advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), more and more chatbots are made and released each day, and they serve a different purpose.

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions—from heart failure to stroke—has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.