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) …
Summary: This is the third in our series on chatbots. In this installment we'll look at the best practice dos and don'ts as described by a number of successful chatbot developers. In our first article we covered the chatbot basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. The second article focused on the universal NLU front ends for all chatbots and some of the technical definitions and programming particulars necessary to understand how these really function. In this article, we've scoured the internet for advice from successful chatbot developers to provide some useful best practices, or at least some valuable dos and don'ts.
The hypothesis of this experiment considers the use of some variants of unsupervised learning models to discover relationships between atomic elements based on a few of chemical and physical matter attributes. Moreover, these techniques calculate clusters of categories based on their numerical attributes. The research problem drives also to an element clustering that could offer a new atomic distribution based on the inferred functions processed by the machine learning processes. The goal is to present an organisation of elements based on the clustering calculation applied on a specific set of atomic properties.
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to show how companies develop, deploy, and dynamically update these applications and how this data-first approach is fundamentally different from traditional applications. He covered examples of how leading companies have identified ways to simplify data streams in a publish-and-subscribe framework (for example, how focusing on a stream of electronic medical records simplified the deployment of real-time applications for hospitals, clinics, and insurance companies). He also detailed how a data-first approach can lead to rapid deployment of additional real-time applications as well as centralize and simplify many data management and administration tasks.
Machine learning and big data tools similar to those that power popular digital assistants like Alexa and Siri can enable banks and insurance companies to rationalize their operations and cost structures and, longer term, help gain insights about customer needs and identify new sources of incremental revenue. Bank call centers have traditionally been focused on customer satisfaction by responding to routine requests for assistance at minimal cost. They have been run as cost centers, with average call hold time their key metric. Machine learning (ML), Natural Language Processing (NLP) and Robotic Process Automation (RPA) help to develop and automate repetitive tasks and flows. Then Predictive Analytics facilitates building models that are not explicitly programmed.
Amazon Web Services announced a series of updates to its Recognition service, which provides machine learning-based computer vision capabilities to cloud customers. The system will now be able to detect and recognize text in images so customers can feed in signs and documents and get the contents of those images back for further processing. That means Recognition can be used for making images of the physical world more intelligible by systems that are only built for processing textual data. Customers will also be able to perform real-time face searches across collections of millions of faces. For example, Rekognition could be used to verify that an image of a person matches another one on file in an existing database of up to tens of millions of images, with sub-second latency.
Human-like artificial intelligence is still a long way off, but Greg Brockman believes the time to start thinking about its safety is now. That's why, after helping to build the online-payments firm Stripe, he cofounded OpenAI along with Elon Musk and others. The nonprofit research group focuses on making sure AI continues to benefit humanity even as it increases in sophistication. Brockman plays many roles at the firm, from recruiting to helping researchers test new learning algorithms. In the long term, he says, a general AI system will need something akin to a sense of shame to prevent it from misbehaving.
Artificial Intelligence (AI) has quickly become a driving force in retail, with Forrester Research predicting earlier this year that investments into AI would triple before 2018. These trends in consumer marketing have primed the pump for widespread use of AI and insight-based marketing in B2B relationships. In fact, recent studies of business buyers show that almost two-thirds of them fully expect AI to anticipate their needs in the near future. One of the most recognizable examples of B2C AI-driven marketing is preemptive marketing, which includes things like the movies Netflix recommends to you based on your viewing history and ratings, and the products companies like Amazon suggest based on your past purchases. Another example most people are familiar with is targeted advertising.
Attended by CTOs, CIOs, IT Directors, Heads of Innovation, General Counsel, Managing Partners and Technology Leaders, join us to find out everything you need to know right now about AI in the professional services market. The event content is written by our Advisory Board and Alternative Events, the UK's leading event company focused on professional services technology.
The government of Karnataka has decided to partner US tech giant Microsoft to use artificial intelligence (AI) for digital agriculture. The collaboration intends to empower smallholder farmers with technology-oriented solutions that will help them increase income using ground-breaking, cloud-based technologies, machine learning and advanced analytics. The collaboration will experiment with the Karnataka Agricultural Price Commission (KAPC), department of agriculture to help improve price forecasting practices to benefit farmers. Microsoft, with guidance from KAPC, is attempting to develop a multi-variant agricultural commodity price forecasting model considering the following datasets--historical sowing area, production, yield, weather datasets and other related datasets as relevant. For this season, Tur crop has been identified for this prediction model.