From 2008-2010 we built an experimental personalized news system where readers subscribe to organized channels of topical information that are curated by experts. AI technology was employed to efficiently present the right information to each reader and to radically reduce the workload of curators. The system went through three implementation cycles and processed over 20 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system.
Operational deployment in your business process is where AI, machine learning and predictive algorithms actually start generating measurable results and ROI for your organization. Therefore, the faster you are able deploy and use these "intelligent" models in your IT environment, the more your business will reap in the benefits of smarter decisions. In the past, the operational deployment of AI, machine learning and predictive algorithms used to be a tedious, labor- and time-intensive task. Predictive and machine learning models, once built by the data science team, needed to be manually re-coded for enterprise deployment in operational IT systems. Only then predictive models could be used to effectively score new data in real-time streaming or big data batch applications.
It's no surprise that Amazon Web Services is way ahead of the world with continuous integration and continuous deployment of software, especially since it advertises itself as a go-to place for organizations seeking to put CI/CD into full practice. The online services giant has taken its own internal CI/CD practices to the next level, however, making it essentially a completely "hands-off" operation. At AWS, changes in microservices are automatically deployed to production "multiple times a day by continuous deployment pipelines," according to Clare Liguori, a principal software engineer at AWS. This pipeline-centered strategy is key to its ability to keep pumping out code. In a recent post, she explains how Amazon moves software through its phases rapidly and automatically.
Artificial intelligence and machine learning has the potential to boost many, many areas of the enterprise. As explored in my recent post, it is capable of accelerating and adding intelligence to supply chain management, human resources, sales, marketing and finance. The inevitable impact of AI on IT departments was touched on in a recent survey of 2,280 business leaders from MIT Sloan Management Review and SAS, which finds that in these early days of AI, IT professionals will be feeling the greatest impact -- both from a career and an operational point of view.. CIOs, chief data officers, and chief analytics officers will be on the front lines of AI implementations, the study finds. IT road maps, software development, deployment processes, and data environments are likely to be transformed in the near future. Most IT managers report that they are still developing foundational capabilities for AI -- cloud or data center infrastructure, cybersecurity, data management, development processes and workflow.
The last few years have seen the chatbot industry slowly but surely become a global phenomenon. In 2016, the chatbot market was worth approximately $700 million. Growing at a CAGR of over 24%, the Global Chatbot Market is now expected to reach $ 1.25 billion by 2025 according to a recent report. Innovations in artificial intelligence and machine learning technologies are increasingly enhancing chatbots features and thus driving the market demand. Unlike human assistants, chatbots provide instant responses, process complex questions, enhance analytics, boost customer engagement and are less costly.