There are many excellent books and articles describing those topics and how they can be implemented in various software frameworks, and those descriptions will not be repeated here. There also are many articles on Big Tech implementing AI at scale. But how do "regular" organizations implement AI projects successfully, especially within an existing portfolio of solutions? In the BLOG@CACM post "Anna Karenina on Development Methodologies," I described how the famous opening line "happy families are all alike, unhappy families are unhappy each in their own way" applies to software development. This post will describe in a similar vein the development behaviors with the highest chance of success for AI efforts.

The following is a 3-part deep dive on applications of edge CV for Enterprises. Part one [this article] talks about what new powerful business outcomes CV can unlock for Enterprises and why Enterprises should invest in CV. Part two describes the key CV technologies in the industry to unlock those use cases. Part three describes some of the headwinds in mass adoption of CV technologies and strategies that Enterprises can adopt to overcome those. Computer vision is a sub domain of the field of Artificial Intelligence that is aimed at helping machines to identify and understand content in digital video or images. Simply put, CV enabled machines to "see" the world like we humans do and use that knowledge to augment human efforts.

One in five enterprises sees modernizing its data infrastructure for AI as the top AI initiative to ... [ ] gain a competitive edge today. These and many other fascinating insights are from Deloitte Insights' Survey: State of AI in the Enterprise, Third Edition: Thriving in the Era of Pervasive AI. A PDF of the study is available for download here (28 pp., PDF, no opt-in). Deloitte surveyed 2,737 IT and line-of-business executives between October and December 2019 from nine countries. To qualify for the study, all responding executives needed to have experience evaluating, budgeting for and implementing AI investments.

Deutsche Bank has announced a multi-year innovation partnership with NVIDIA to accelerate the use of artificial intelligence (AI) and machine learning (ML) in the financial services sector. Combining Deutsche Bank's financial industry expertise with NVIDIA's leadership in AI and accelerated computing will hasten the development of a broad range of regulatory-compliant AI-powered services. The partnership will support Deutsche Bank's cloud transformation journey, for example by using AI and ML to simplify and accelerate cloud migration decisions. "AI, ML and data will be a game changer in banking, and our partnership with NVIDIA is further evidence that we are committed to redefining what is possible for our clients," says Christian Sewing, CEO, Deutsche Bank. "This partnership is a significant step forward in our AI and ML ambitions. It will help us take a leading position in the usage of these technologies in financial services," adds Bernd Leukert, Deutsche Bank's Management Board Member responsible for Technology, Data and Innovation.

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course.

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Digital transformation (DX) is not just a differentiator but a matter of survival in the modern business world. And while a maelstrom of technologies, including cloud computing, mobility, IoT, and AR-VR, are delivering value and creating better experiences for customers, studies show that Artificial Intelligence (AI) will play the most crucial role in driving businesses through DX. That said, while leading tech companies such as Amazon, Netflix, and Google use AI and Machine Learning (ML) at scale in their core business processes, small and medium-sized enterprises often struggle to expand their machine learning projects beyond a small pilot scope. In a Deloitte survey involving 2,875 executives from 11 top economies, 79% of leading digital transformers (as against 49% of the starters) stated that their AI initiatives are essential to their market competitiveness. As a catalyst for digital transformation, AI helps companies become more data-driven, accelerate innovation, strengthen decision-making and increase productivity with better resource utilization in crucial business functions and domains.

Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news. We conjecture that such issues result from representing entities as static embeddings of superficial words, while neglecting to model their ever-changing states, i.e., the information they carry, as the text unfolds. Therefore, we extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. We propose a contrastive framework to learn the state representations in a discrete space, and insert additional attention layers into the decoder to better exploit these states. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines with the guidance of meaningful entity states.

The first two decades of the 21st century have been marked by exponential advances in technology that were once considered elements of a science fiction movie script. Technologies like Artificial intelligence (AI) and Cloud Computing--have stood the test of time and have become mainstream. In this article, we'll look at what these technologies are and how their combination has been a landscape-changing force in the world of modern technology. Simply put, artificial intelligence is the simulation of human intelligence by machines. The integration of artificial intelligence into business allows it to perceive and observe the environment and generate optimal results accordingly--very similar to how people operate, although much faster.

Many organizations are struggling with artificial intelligence deployments despite believing that AI will be critical to business success over the next five years, according to a report by Deloitte.… The 5th Edition of Deloitte's State of AI in the Enterprise report is based on a survey of 2,620 business leaders from organizations around the globe, all of whom are responsible for AI technology spending or managing its implementation. According to the authors, the AI race (if such a thing ever existed) is no longer about adopting AI or automating processes for efficiency, but has now moved on to realizing value, driving outcomes, and unleashing the potential of AI to drive new opportunities. However, the topline findings are that many organizations are struggling with "middling results" despite increased deployment activity since the last edition of the report. According to Deloitte, 79 percent of respondents claimed to have achieved full-scale deployment of three or more types of AI applications, up from 62 percent last year. But also up was the percentage of those rating their organizations as "underachievers" – 22 percent in this report compared with 17 percent last time.