Apple has reportedly purchased artificial intelligence and machine learning startup Turi over the weekend, another signal that the world's biggest tech firms see a bright future in AI applications. The acquisition was first reported by the tech blog GeekWire. Turi, a Seattle-based company, offers a selection of tools aimed at helping developers easily scale machine learning applications, including Predictive Services, a server product for hosting and managing machine learning models, and GraphLab Create, an extensible machine learning framework that enables developers and data scientists to build and deploy intelligent applications and services. Apple responded to the publication with its standard comment explaining that the company buys smaller technology companies from time to time, and does not generally discuss the purpose or plans behind the deal. In October 2015, Apple acquired Perceptio, which has developed deep learning and AI capabilities that could be added to Siri.

Overview We are looking for talents who can use NLP&ML to make a text classification program for us. Classification standard: There are 22 main classes about different industries according to our classification standard. Under every main class are some subclasses. Every inputted text should be put into at least one of the 170 classes. A text can be put into 2 classes (maximum) if the two classes are both related.

According to the BBC, there is a mere 3% chance that solicitors' jobs could be replaced by artificial intelligence and automation. For family lawyers, the risks should be even lower because particularly human skills of empathy and negotiation are integral to your work. That BBC article's a year old, and artificial intelligence is shaking up the law. In Melbourne, Settify is a new family law firm that has embraced artificial intelligence. Settify is so machine-based that it can offer many of its services, including initial advice, free.

Imagine that you were tasked with indexing (categorizing and summarizing) the contents of a large collection of randomly formatted text documents to make them easily accessible throughout your company. Perhaps this use case contains legal documents as well as medical records (with various levels of sensitivity). Suppose that the size of this collection contained over 100 million documents that you had to analyze. Besides categorizing each document as medical or legal, you must also assign them to subcategories like cardiology or probate. Finally, you must capture the meaning of each document for reporting and analytics.

Tim Cook believes AI is the future of smartphones, sees "enormous opportunity" for Apple Stanford programs prepare underrepresented high schoolers for careers in science, engineering ... Trader Poll: Do you fear artificial intelligence?

From time to time, the Singularity Hub editorial team unearths a gem from the archives and wants to share it all over again. It's usually a piece that was popular back then and we think is still relevant now. This is one of those articles. It was originally published August 10, 2010. We hope you enjoy it!

Making sense of data can involve a wide variety of tools, and IBM is hoping to make data scientists' lives easier by putting them all in one place. The company on Tuesday released what it calls Data Science Experience, a new development environment in the cloud for real-time, high-performance analytics. Based on data-processing framework Apache Spark, Data Science Experience is designed to speed and simplify the process of embedding data and machine learning into cloud applications. Included in the new offering are tools such as RStudio and Jupyter Notebooks. Developers can tap Python, R and Scala; they can also view sample notebooks and watch tutorials while they code. Additional tools focus on data preparation and cleaning, visualization, prescriptive analytics, data connections, and scheduling jobs.

Mayhem ruled the day when seven AIs clashed here last week -- a bot named Mayhem that, along with its competitors, proved that machines can now quickly find many types of security vulnerabilities hiding in vast amounts of code. Sponsored by the Defense Advanced Research Projects Agency, or DARPA, the first-of-its-kind contest sought to explore how artificial intelligence and automation might help find security and design flaws that bad actors use to penetrate computer networks and steal data. Mayhem, built by the For All Secure team out of Carnegie Mellon University, so outclassed its competition that it won even though it was inoperable for about half of the contest's 96 270-second rounds. Mayhem pivoted between two autonomous methods of finding bugs and developing ways to exploit them. Under one method, dubbed symbolic execution, Mayhem tries to figure out how a target program works by systematically replacing sample inputs with classes of inputs.

The Data Science Institute (DSI) at Columbia University and Bloomberg LP are pleased to announce a workshop on "Machine Learning in Finance". The workshop will be held at Columbia University under the auspices of the Financial and Business Analytics Center, one of the constituent centers in the DSI, and the Center for Financial Engineering.

Machine Learning sounds like something out of an 1980s movie about a deceiving robot we end up fighting against to defend the existence of the planet. That may explain the distrust people have with it. How understandable is this distrust? Computers monitoring and absorbing information, learning and adapting to become better? Add to this distrust the purported technological singularity: the moment when artificial intelligence surpasses the human in any practical competency.