Summary: What do you need to do to get an entry level job in data science? This article is written for anyone who is considering becoming a data scientist. That includes young people just starting their bachelor's degrees and folks in the first two or three years of their careers who want to make the switch. It's not for folks who know they are going to pursue one of the new Master's in Data Science or Ph.D. candidates. It's for folks looking for entry level jobs that are specifically on the data science career ladder.

Cornell University wants to help whales avoid getting hit by ships, so it is working on an algorithm that uses audio recordings to alert ships to the whales' whereabouts. Dassault Systèmes is creating a 3D model of a human heart that will allow surgeons to test the performance of pacemakers before opening up patients. Sure, machine learning has already had a significant impact on the worlds of science and culture, and in life, but it will be years before CIOs need to start worrying about enterprise machine learning applications ... right? "If CIOs invested in machine learning three years ago, they would have wasted their money," Olley says. "But if they wait another three years, they will never catch up."

Google wants to put its artificial intelligence technology to use in top hospitals. Earlier this week, the search giant announced it would work with the U.K.'s National Health Service, or NHS, to alert staff to patients at risk of serious complications due to kidney failure. Details about the technology are fairly thin on the ground at this stage. But it is known that Google DeepMind recently acquired an app called Hark, which is a task management app that aims to replace paper-based systems and pagers. Hark was developed over four years by a team at Imperial College London, which is one of the U.K.'s top medical schools.

Austin-based SparkCognition, a startup applying artificial intelligence and machine learning to the cybersecurity market, has raised 6M in a Series B funding round. The funding came from CME Ventures, Verizon Ventures, The Entrepreneurs' Fund (TEF), and Alameda Ventures. The startup--led by Amir Husain--said the Series B was oversusbcribed. SparkCognition says it has developed machine learning technology which it is applying to both Internet-of-Things (IoT) and cyber security, to automatically detect and identify attacks in IoT infrastructure.

Austin-based SparkCognition, a startup applying artificial intelligence and machine learning to the cybersecurity market, has raised 6M in a Series B funding round. The funding came from CME Ventures, Verizon Ventures, The Entrepreneurs' Fund (TEF), and Alameda Ventures. The startup--led by Amir Husain--said the Series B was oversusbcribed. SparkCognition says it has developed machine learning technology which it is applying to both Internet-of-Things (IoT) and cyber security, to automatically detect and identify attacks in IoT infrastructure.

Artificial Intelligence has been the topic de jour lately with every corner of intellectual thought sounding in on the perils, and the potential rewards, of synthesizing a machine intelligence that could successfully perform any intellectual task that a human can. Elon Musk, Bill Gates and even Stephen Hawking have all suggested that an AI with this sort of general intelligence (also known as Strong AI or Full AI) could bring about an apocalypse that sees an end to human civilization, or even an end to the human race. There's no doubt that Strong AI is the subject of intense research by DARPA, MIT, Berkeley, IBM, Google and many others. But it's hard not to notice that despite all the anxiety, Strong AI today lives only in the imagination of science fiction writers and in the hopes and dreams of research scientist. At the prestigious "Future of AI" conference in San Juan this January, the estimates for when an AI might emerge vacillated wildly from 5 years to a hundred years in our future--its variables are that unknown.

A revolution in artificial intelligence is currently sweeping through computer science. The technique is called deep learning and it's affecting everything from facial and voice to fashion and economics. But one area that has not yet benefitted is natural language processing--the ability to read a document and then answer questions about it. That's partly because deep learning machines must first learn their trade from vast databases that are carefully annotated for the purpose. However, these simply do not exist in sufficient size to be useful.

Drive.ai is the 13th company to be granted a license to test autonomous vehicles on public roads in California. This is exciting news, especially because we had no idea that Drive.ai even existed until just last week. The company has been in stealth mode for the past year, working on applying deep learning techniques to self-driving cars. We spoke with two of Drive.ai's Its core team is made up of experts with a wealth of experience developing deep learning systems in all kinds of different domains, including natural language processing, computer vision, and (most recently) autonomous driving. "This team helped pioneer how to scale deep learning, which is one of the reasons why deep learning has been successful as of late," says Tandon, the company's CEO.

Always wanted to compete in a Kaggle competition, but not sure where to get started? Together with the team at Kaggle, we have developed a free interactive Machine Learning tutorial in Python that can be used in your Kaggle competitions! Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using Python and Machine Learning. DataCamp's interactive UI makes it easy to follow along from start to random forest! In this Machine Learning tutorial, you will gradually learn how basic machine learning techniques can help you to make better predictions.

Artificial Intelligence (AI) is invading our lives everyday. The basics of "if then else" logic among programming constructs allows a computer to make a decision based on inputs. When it becomes "magical" is when the complexity of that decision making begins to go beyond the simple algorithm or software application and begins to "think" in certain ways. Gamers will be very familiar with this concept as they play against the "AI" in their games. AI is now invading the world of investing.