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) …
In this blog post, we will go through how to train MNIST using distributed Tensorflow* and Kubeflow* from scratch. Machine learning (ML) and deep learning (DL) have been around for more than half a century now, yet it is just as of late that these ideas have begun to flourish--thanks to advancements in compute capabilities and the deluge of data. This is due, essentially, to the fact that ML/DL algorithms need vast amounts of information to register the desired level of accuracy. Likewise, this high volume of data requires high processing power so it can yield the expected intelligence and knowledge. With the emergence of Cloud and other distributed frameworks, we started to treat a set number of servers as "cattle versus pets" in an attempt to utilize their collective assets for storage and computation.
The future of AI will require facing rapid change, vagueness, and difficulty. We need to be prepared for different adaptations of the future. There is no way to know what path the development of AI will take. What exactly is Artificial Intelligence (AI)? AI is the science that attempts to create intelligent robots.
Mobile apps downloading crossed all the figures to date. App Annie reports a 10% increase in mobile apps download and 22% increase in money spent on app development. In this scenario, imagine Artificial Intelligence being leveraged to accelerate the process. It establishes the idea of rising trend of mobile app development. Today, there will be hardly a B2B company without custom mobile application.
The first version of Vymo only solved a basic need. Salespeople around the world found it tedious to report sales data. Without sales data, managers and leaders couldn't forecast accurately or help their teams achieve targets, which impacted their topline directly. So, we built a mobile-first solution to detect all sales activities and then, based on sales data, our solution did what a manager would do. This is quite contrary to the general perception of artificial intelligence (AI) solving -- or exacerbating, depending on whose views you subscribe to on the matter -- all of the world's most complex problems.
It's increasingly likely that the first publicly acknowledged, artificial intelligence (AI)-based network attack will occur in 2018. Administrators who recognize the potential effect of machine learning (ML) on the future of network and application security management are beginning to strengthen their understanding in this area. Vendors are also becoming more aware, simplifying observation tools to mitigate existing and future risk. In the age-old battle of good versus evil, the question now is: who will bring AI and ML to the fight first? Consider a dedicated hacker who intends to breach a corporate environment.
It's clear: Artificial intelligence has transformed the way we live. According to PwC, 55 percent of consumers would prefer to receive new media recommendations from AI -- a development that illuminates how much we've integrated the technology into our lives. Google, Amazon, and Microsoft are just a few of the obvious innovators embracing bot-powered business functions, but others are also taking notice. Artificial intelligence's ability to synthesize and analyze data can easily improve business operations for many industries, including hospitality, restaurants, and travel. Such markets experience success when they revise their customer experience or marketing strategies with machine learning and chatbots.
This is the first in a five-part series exploring the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the machine learning potential of combining different advanced technologies. Deep learning and complex machine learning has quickly become one of the most important computationally intensive applications for a wide variety of fields. The combination of large data sets, high-performance computational capabilities, and evolving and improving algorithms has enabled many successful applications which were previously difficult or impossible to consider. This series explores the challenges of deep learning training and inference, and discusses the benefits of a comprehensive approach for combining CPU, GPU, and FPGA technologies, along with the appropriate software frameworks in a unified deep learning architecture.
The development of Artificial Intelligence is one to the most important events in recent human history. The final outcome of it, is still to be determined. At a moment when many might see the development of AI as potentially more threatening than beneficial, a growing coalition of researchers and innovators around the world tries to make sure that the opposite is the case. This movement towards "AI for Good", is gaining significant momentum and brings up relevant questions just at the right time. It also drives business and innovation in areas where Artificial Intelligence is used as a promising tool for sustainable development.
Intellibot uses a novel approach to Robotic Process Automation. While existing RPA solutions use slow, difficult and error prone techniques of interrogating screen elements and setting match rules, our robots interact with a screen like humans do. Intellibot uses Computer Vision Engine to identify screen elements such as applications, buttons, menus and textboxes. This makes Intellibot compatible with all technologies, including Citrix, Java, .Net and Web. Since there is no dependency on the underlying application code, computer vision based bots are more reliable in production environments.
Kai-Fu Lee is the founder and CEO of Sinovation Ventures, a Chinese technology venture investment firm. He was named one of Time magazine's 100 most influential people in the world in 2013. Before founding Sinovation Ventures, he was president of Google China and previously held executive positions at Microsoft, SGI, and Apple. While in Vancouver attending the TED conference, Lee sat down with Martin Reeves, director of the BCG Henderson Institute, to talk about the impact of artificial intelligence on companies, industries, and nations. Drawing from his new book AI Superpowers: China, Silicon Valley, and the New World Order -- which will be released in September 2018 -- he discussed the case for the regulation of AI applications, how AI affects company and national competitiveness, and how CEOs might be underestimating the effect of AI on the future of work. A transcript of the conversation follows. We hear all sorts of extreme predictions about the possibilities for AI.