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) …
Today's topic is … well, the same as the last one. Last time, we explained what Q Learning is and how to use the Bellman equation to find the Q-values and as a result the optimal policy. Later, we introduced Deep Q Networks and how instead of computing all the values of the Q-table, we let a Deep Neural Network learn to approximate them. Deep Q Networks take as input the state of the environment and output a Q value for each possible action. The maximum Q value determines, which action the agent will perform.
IBM has announced AI OpenScale, a service that aims to bring visibility and explainability of AI models for enterprises. When it comes to adopting AI for business use, there are multiple concerns among enterprise customers. Lack of visibility of the model, unwanted bias, interoperability among tools and frameworks, compliance in building and consuming AI models are some of the critical issues with AI. IBM AI OpenScale provides explanations into how AI models are making decisions, and automatically detects and mitigates bias to produce fair, trusted outcomes. It attempts to bring confidence to enterprises by addressing the challenges involved in adopting artificial intelligence.
The impact that AI will have on different sectors of the economy is a widely debated topic. It comes as no surprise since leading technological innovations have always been met with fear and uncertainty. According to a study reported by Forbes, in 2016, something around US$8 billion to US$12 billion was invested in the development of AI worldwide. It's now difficult to imagine a job in near future that ultimately smart computers won't be able to do. So, with the advancement of AI, it's important to know where we stand and how it can alter the future.
IBM has announced AI OpenScale, a service that aims to bring visibility and explainability of AI models for enterprises. When it comes to adopting AI for business use, there are multiple concerns among enterprise customers. Lack of visibility of the model, unwanted bias, interoperability among tools and frameworks, compliance in building and consuming AI models are some of the critical issues with AI. IBM AI OpenScale provides explanations into how AI models are making decisions, and automatically detect and mitigate bias to produce fair, trusted outcomes. It attempts to bring confidence to enterprises by addressing the challenges involved in adopting artificial intelligence.
The future – how it will be? Here are the best strategies if you want to succeed in looking into it. You can best predict the future after reading the below article. Looking into the future is difficult. But with predictive analysis based on historical data combined with analytical tools we can predict the future.
In an ever-increasingly digitized world, traditional cyber defense methods are no longer adequate to counter the current cyber threats, according to cybersecurity expert Samer Omar. "The increased likelihood of artificial intelligence being used by adversaries has pushed companies to continue to implement methods of detection and deception in an effort to provide counter-intelligence," he explained. Furthermore, security threat intelligence analyst Martin Giles stated that AI for cybersecurity is a hot new thing -- and a dangerous gamble. Machine learning, and artificial intelligence can help guard against cyberattacks, but hackers can foil security algorithms by targeting the data they train on and the warning flags they look for. The cybersecurity expert added there had been a steady increase in sales of cybersecurity solutions which leverage machine learning and artificial intelligence technologies enabling them to instantly detect any malicious behavior on the network, quickly respond to incidents and reduce impacts of a breach.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.
We live in a society under construction, and we are on the brink of an infrastructure paradigm shift. This is not sensationalist click-bait: it's the logic conclusion of some simple measurable observations. Here is why: Communication, energy, and transport (which are the basis of our economy and society at large) are changing rapidly -- and to the core -- due to breakthroughs in several technology fields. This shift is known as the second, third or even the fourth industrial revolution, depending on who you talk to. Either way, it is per definition a revolution if communication, energy, and transport are changed drastically simultaneously.
A new artificial intelligence system developed by researchers at the University of Washington uses patient data to predict whether patients are at risk of abnormally low blood oxygen (hypoxia) during surgery. University of Washington (UW) researchers have developed an artificial intelligence (AI) system that uses patient data to predict whether patients are at risk of abnormally low blood oxygen (hypoxia) during surgery. The Prescience system also provides users with real-world explanations to support and explain its predictions. In collaboration with physicians, UW's Su-In Lee and colleagues trained Prescience on about 50,000 patient files, so the program could analyze data such as patient age and weight to calculate the likelihood of hypoxemia prior to surgery. The system also uses real-time data during surgery to predict when patients are in danger of hypoxemia, and a new AI model helps Prescience provide doctors a concise description of the prediction's underlying factors.
Vaimal is a machine learning add-in that allows you to train and deploy machine learning algorithms without programming. You can make predictions on new data using models that are trained on historical data. Vaimal allows you to create decision trees, support vector machines and neural networks all within Excel . It also includes more powerful ensemble methods to combine models for even better predictive performance. The easy to use interface allows you to focus on your data without worrying about learning mundane programming tasks required with common machine learning platforms.