If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Ernst & Young LLP (EY) announced that the Department of Health and Human Services (HHS) has awarded a $49 million IDIQ (indefinite delivery indefinite quantity) contract vehicle to the EY US Government & Public Sector practice for intelligent automation and artificial intelligence (IAAI) products and services. The Health and Human Services Program Support Center is managing a government-wide contract vehicle for IAAI services. The intent of the vehicle is to promote innovation in this space through funding support for rapid prototyping and proof of concepts leveraging robotic process automation, natural language processing, machine learning, artificial intelligence and blockchain. "We're honored we've been selected and are presented with the opportunity to share our IAAI capabilities to help HHS and other agencies transform their operations," said Mike Herrinton, Partner and US Government & Public Sector Leader at Ernst & Young LLP. "EY implements digital solutions with modern technologies that can help agencies unlock the potential of their data and assets, and change the way customers interact with the government."
Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some examples of AI-based applications that already affect us every day. Deep learning in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition has been systematically improving the state of the art for a while now. However, the reasons that make deep neural networks (DNN) so powerful are only heuristically understood, i.e. we know only from experience that we can achieve excellent results by using large datasets and following specific training protocols. Recently, one possible explanation was proposed, based on a remarkable analogy between a physics-based conceptual framework called renormalization group (RG) and a type of neural network known as a restricted Boltzmann machine (RBM).
Bankers are rushing to take Oxford University's courses on fintech, blockchain strategy, algorithmic trading, and artificial intelligence before robots take their jobs. More than 9,000 people from upwards of 135 countries have taken the online open courses, which focus on digital transformation in business, at the university's Saïd Business School, a spokesperson told Markets Insider. The fintech course, the first of five to be launched, has run 12 times and attracted nearly 4,300 students in less than two years. The average age of participants across the courses is 39, and two-thirds of them came from the financial services sector, suggesting experienced professionals are returning to school to understand how their industry is being disrupted and learn the skills needed to weather the changes. Bankers' fears of being replaced by robots are well founded.
The R and Python programming languages are primary citizens for data science on the Azure AI Platform. These are the most common languages for performing data preparation, transformation, training and operationalization of machine learning models; the core components for one's digital transformation leveraging AI. Yet they are fundamentally different in many aspects, directly affecting not only deployed solutions IT architectures but also but also corporate strategies for developer skills and product supportability. This series of articles is designed help you understand the options your company and customers have to support and evolve their R strategy. As with all of our blog posts, please share your questions, comments, and feedback.
Antonio Piraino is Chief Technology Officer at ScienceLogic, where he guides the company's IT management vision and product strategy. Recently, Gartner announced its top 10 strategic technology trends for 2019. It is a nice list, touching on digital transformation trends that range from empowered edge computing to artificial intelligence-driven autonomous things. But while Gartner's trends sound great in annual reports and Forbes articles, operationally, most enterprises aren't properly (or digitally) prepared to adopt these trends. Today's pace of business and the disorderly data that's needed to make sense of it all.
In December 2014, I asked whether we were at the beginning of "the end of the Hadoop bubble." I kept updating my Hadoop bubble watch (here and here) through the much-hyped IPOs of Hortonworks and Cloudera. The question was whether an open-source distributed storage technology which Google invented (and quickly replaced with better tools) could survive as a business proposition at a time when enterprises have moved rapidly to adopting the cloud and "AI"--advanced machine learning or deep learning. In January 2019, perennially unprofitable Hortonworks closed an all-stock $5.2 billion merger with Cloudera. In May 2019, another Hadoop-based provider, MapR, announced that it would shut down if it were unable to find a buyer or a new source of funding.
Artificial Intelligence (AI) is nothing new. The field of AI research was founded in 1956. To date, this field has been always covered with huge expectations. Surely, we are in such a hype phase, but Google CEO Sundar Pichai is also right, when he says AI is bigger than the invention of fire and electricity. As one of the largest industrial nations, Germany addresses this big promise and published a AI strategy this month (Nov, 2018).
As artificial intelligence re-writes business models, how will its application and adoption revolutionize business and commerce further? From the production and marketing era to the relationship and intelligence era, business models have been evolving over the centuries. Over the years, the rise of artificial intelligence (AI) has fundamentally transformed the very meaning of ideas, innovation, and inventions. As a result, business models are evolving further. As we witness businesses across industries undergo a profound and dramatic shift in the relative balance of intelligence power, AI applications and adoption are offering each business entity as many new opportunities as it does challenges.
Effect of Population Based Augmentation applied to images, which differs at different percentages into training. In this blog post we introduce Population Based Augmentation (PBA), an algorithm that quickly and efficiently learns a state-of-the-art approach to augmenting data for neural network training. PBA matches the previous best result on CIFAR and SVHN but uses one thousand times less compute, enabling researchers and practitioners to effectively learn new augmentation policies using a single workstation GPU. You can use PBA broadly to improve deep learning performance on image recognition tasks. We discuss the PBA results from our recent paper and then show how to easily run PBA for yourself on a new data set in the Tune framework.
As enterprises continue their rush to digitally transform their organizations, they're investing big in retooling their software development pipelines, new artificial intelligence (AI) and robotic process automation (RPA) technologies and low-code development platforms, as well as putting considerable money into customer engagement software. Yet, the extent of success following these investments remains unclear and varies significantly from industry to industry. To shine a light on the digital transformation efforts currently underway, as well as the degree of their success, we've collected a number of fascinating trends in the most essential areas of digital transformation. According to the consultancy KMPG, enterprise investments in AI, machine learning, and RPA will rise to $232 billion by 2025 from $12.4 billion in 2018. "Nearly two-thirds of respondents indicate plans to fully implement RPA within three years. As for cognitive automation, nearly half noted intentions to use these approaches at scale within 3 years, while 29% indicated selective reliance on cognitive automation capabilities. Some 10% said they would launch pilots and proof of concept projects," according to the report.