An artificial intelligence (AI) judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificial intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.

Business owners, be forewarned: The AccTech (accounting technology) bots are taking over. These bots are lines of code that grab information and communicate with humans about your business operations. They know (almost) everything before you've even whispered the thought, and they might want to take your bookkeeper's job -- or maybe just work alongside her. I had a recent conversation with Jan Haugo, CEO and vice president of the Institute of Certified Bookkeepers USA (ICBUSA), who emphasized all the ways emerging technologies will transform the role of small business bookkeeping. In particular, we discussed how machine learning and artificial intelligence will enable accountants to interact with bots the same way they would with a human co-worker.

How Can Lean Six Sigma Help Machine Learning? Can Natural Language Processing Boost Clinical Documentation? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

The world's fastest computer runs a Chinese chip, and that fact hasn't escaped notice by the U.S. government. So how does the U.S. government bludgeon the Chinese chip threat? A new U.S. government working group aims to encourage domestic companies to use homegrown chip technology and resist the urge to buy inexpensive Chinese semiconductors. The White House this week established the Semiconductor Working Group, a private-public advisory group that will create policy and research guidelines for semiconductor development. The ultimate goal is to retain U.S. leadership in semiconductor technology.

Data science is the fastest growing category, and machine learning tops the list of fastest growing skills for freelance workers, according to a new study by Upwork. In its new report, The Upwork Skills Index, Upwork identifies the top 20 skills desired in the freelance market. The index is part of a quarterly series that Upwork, the largest freelancing website, publishes. The index calculates growth rates based on freelancer billings through Upwork in the third quarter 2016 versus the third quarter 2015. "The freelance workfore is the best indicator of new skill trends," noted Upwork CEO Stephane Kasriel.

The Perceptron algorithm is the simplest type of artificial neural network. It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks. In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python. How To Implement The Perceptron Algorithm From Scratch In Python Photo by Les Haines, some rights reserved. This section provides a brief introduction to the Perceptron algorithm and the Sonar dataset to which we will later apply it.

If you're following tech news, you'll see mentions of IBM Watson nearly everywhere in the past few weeks. Is it HAL 9000, a Turing test waiting to be passed? Or is it a collection of standard-ish machine learning tools paired with professional services implementations and a top-class sales/marketing org? IBM is consciously choosing to brand this varied work as Watson. It might confound tech pros who know that Jeopardy and chatbots are very different domains.

Machine learning is a hot topic in the software industry right now. More and more companies are taking advantage of this artificial intelligence technique to train machines on their data and make predictions. Researchers from MIT's Computer Science and Artificial Laboratory (CSAIL) want to take it a step further by revealing how a machine makes those insights. If you are using a machine learning system in the medical field to help diagnose the symptoms or illness of a patient, you want to make sure that the system is correct. Similarly, if you are a business that uses machine learning to make investment decisions, you want to make sure you can justify the cost.

Hence, the objective of all the machine learning algorithms is to estimate a predictive model that best generalizes to a particular type of data. Therefore, for solving a problem by machine learning, it is imperative to have a large number of examples that can be used by the learning algorithm to understand the system's behavior and similar kind of predictions can be generated by the system when the machine learning algorithm is presented with new examples of data. Although the learning task is not easy, with a better understanding of the different components of the machine learning and how they interact with each other, things will become clearer. In the subsequent posts, we will look at how the machine learning algorithms can be used to solve real-world problems.

October was filled with stories on future-influencing technologies and trends. Below are links to stories and/or presentations in three key categories: AI, AR/VR and predictive analytics. A cumulative scan of foresight news: Artificial Intelligence Monday: Meet the artists embracing artificial intelligence. The mondo spacecraft Juno has arrived in orbit to spy on Jupiter. Machine learning is the tail that wags the AI dog.