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'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
What is the most impressive task that AI can accomplish today?
We all interact with AI products, for example, through an in-home voice-activated personal assistant or receiving a product recommendation on your favorite e-commerce website. It is abundantly clear that AI is omnipresent, driving innovation and influencing how businesses run and compete. AI has been known for nearly six decades. However, AI was thrust to the forefront of the new industrial revolution only recently. AI is the dominant driving force for modernizing a seemingly never-ending list of industries and functions such as communications, healthcare, media, education, audit, taxation, and operations. In short, the current and future use of AI in innovation can change how people live, work, play, and even think.
Below are a few examples of what AI can accomplish today.
Natural Language Understanding and Generation. AI can process natural language, and it can read and comprehend what the intent is in documents, news articles, blogs, books, and emails. Furthermore, it can generate summary information from processing the text and label the text with topics. The technology can detect a question in text and group similar questions together. Note that humans have a lot of variation in speech, as someone can ask the same question differently. Similarly, AI can use existing unstructured data to find answers to these questions. The ability to process unstructured text and respond to the text has further been helped by the technology to hear using state-of-the-art pattern recognition speech-to-text (STT) techniques. STT application programming interfaces can convert spoken words by humans into text in real-time. Generating human-like speech is an excellent accomplishment of speech synthesis techniques. Speech synthesis is the artificial creation of human speech where a text to speech (TTS) system converts natural language text into speech. This synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database, and the quality of these speech synthesizers has been becoming more and more similar to the human voice every year. Text to speech technologies leverage Neural Network models to deliver a human-like, engaging, and personalized user experience. This capability of using existing unstructured databases and grouping questions and answers facilitates responding to those questions being asked in real-time. So not only is AI understanding, but it also generates natural language and appropriately customizes responses into a specific answer based on the context by identifying the sentiment of the question. These capabilities result in creating an engaging user experience in achieving a great degree of containment in virtual voice assistants with interactions that mimic a human conversational style. The ability to be empathetic is critical. For example, "I would like to reschedule my flight" and "I just broke with my girlfriend, I'm rebooking my flight to leave early" would be different because the contexts in both these situations are different.
Advanced Inference Engines. AI facilitates incorporating rapid and advanced inferences, and inference engines are part of the decision systems that apply logical rules to the knowledge base to deduce new information. For example, inference engines based on Bayesian Belief Networks are used for automated breast cancer detection support tools. AI is a viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results, and imaging study findings. In the example above, the work brings essential roles such as Radiologists, Image Processing Scientists, Database Specialists, and Applied Mathematicians on a common platform. By exploiting conditional independencies entailed by influence chains, it is possible to represent extensive cause-effect relationships using little space. It is often possible to perform probabilistic inference among the features in an acceptable amount of time. The inference engines are critical in recommendation systems, such as suggesting which movie to watch next and deciding which price point will maximize the revenue.