The accelerated development of Artificial Intelligence in recent years has produced a number of solid use cases. There areas have undergone massive research, testing, application in many practical areas at large scale and continue to improve with further usages, as is natural to AI capabilities. The big four platform providers IBM, Google, Amazon and Microsoft are likely to provide AI capabilities in the form of cloud platforms. Here you utilize AI capabilities as-a-service without having to download, install and configure Artificial Intelligence libraries, let alone develop your own. You can easily work with test data and run some experiments before you scale your solutions.
Let’s take IBM Watson, which offers the following areas of ready-to-use capabilities (as of June 2017) as a service:
- Conversation – Add a natural language interface to your application to automate interactions with your end users.
- Document conversion – The IBM Watson™ Document conversion service converts a single HTML, PDF, or Microsoft Word™ document into a normalized HTML, plain text, or a set of JSON-formatted answer units that can be used with other Watson services.
- Language translation – Dynamically translate news, patents, or conversational documents. Instantly publish content in multiple languages.
- Natural language classifiers – The Natural Language Classifier service applies cognitive computing techniques to return the best matching classes for a sentence or phrase.
- Natural language understanding – Analyze text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, semantic roles, using natural language understanding.
- Personality insights – Watson Personality Insights: Personality Insights derives insights from transactional and social media data to identify psychological traits which determine purchase decisions, intent and behavioral trait.
- Retrieve and rank – The IBM Watson Retrieve and Rank service helps users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data.
- Tone analyzer – Humans exhibit a range of ‘tones’ such as joy, sadness, anger, and agreeableness, in daily communications. Tone Analyzer uses cognitive linguistic analysis to identify a variety of tones at both the sentence and document level. This insight can then be used/applied to refine and improve communications.
- Speech to text – The Speech to Text service converts the human voice into written word. It can be used anywhere there is a need to bridge the gap between the spoken word and their written form, including voice control of embedded systems, transcription of meetings and conference calls, and dictation of email and notes.
- Text to speech – The Text to Speech service processes text and natural language to generate synthesized audio output complete with appropriate cadence and intonation.
- Visual recognition – Find meaning in visual content! Analyze images for scenes, objects, faces, and other content.
- Discovery – Add a cognitive search and content analytics engine to applications to identify patterns, trends and actionable insights that drive better decision-making.
These capabilities are available to you instantly. Now back to our example of Tesla. Their built-in cameras are capable of constantly taking pictures and videos. AI can be trained to understand those pictures. What is observed in the picture? Are there any obstacles on the road? For AI to learn computer vision, it must be fed with training data. Based on the training data and feedback on correct interpretation, the AI can learn to identify objects more precisely than humans can. Take 3D simulators. These are used to train AI faster. Over time the AI-based algorithm improves and can be made available to more cars.
Let’s look at another example in the areas of customer interaction. Let’s say your website offers a support chat to assist and guide customers on finding the right information or contact. This can be based on AI capabilities. Here the training data can consist of responses from well-delivered customer interactions. These can be used to train the AI on how to communicate well. Once the AI takes over, the confidence level of a given customer interaction decides whether a human support agent jumps into the conversation, or whether the AI completes the conversation. Just try it, it is a fun experiment. And AI can speak several languages.