Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.

Scientists have developed a new artificial intelligence system that can more effectively extract data from the vast wealth of information present on the internet. The data necessary to answer myriad questions - about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results - may all be online in form of plain text. However, extracting data from plain text and organising it for quantitative analysis may be prohibitively time consuming. Researchers from Massachusetts Institute of Technology (MIT) in the US developed a new approach to information extraction. Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators.

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Google Translate is rolling out a major upgrade that promises more human-like language translations. Google is bullish on its Neural Machine Translation technology, claiming that it's a bigger upgrade to the service than everything that's been accomplished in the last ten years combined. The company is rolling out the improvements to eight language pairs in Google search, the Translate apps, and the website. You'll find the new technology behind translations between English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish. Google says that makes up more than 35 percent of all language queries.

Last month at Dreamforce, the ultimate Salesforce conference, there was a lot of talk about artificial intelligence and use cases for businesses. Will artificial intelligence be a game changer in the way we interact with our prospective customers? How far do bots go before human engagement takes over? In some ways, artificial intelligence has already changed the world of sales with examples all around us. When Amazon recommends new books or products based on past purchases or when we see a friendly chatbot pop on the screen when visiting a website – these use cases rely on data analysis to predict what you might be interested in, ideally offering timely and helpful information that improves the customer experience.

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If you're not using big data, you're about to fail fast How Google's new Cloud Jobs API uses machine learning to help companies fill jobs Why machine learning could change customer engagement, as we know it. Is Machine Learning Making Your Data Scientists Obsolete? 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.

As I wrote in March of this year, the Databricks service is an excellent product for data scientists. It has a full assortment of ingestion, feature selection, model building, and evaluation functions, plus great integration with data sources and excellent scalability. The Databricks service provides a superset of Spark as a cloud service. Databricks the company was founded by the original developer of Spark, Matei Zaharia, and others from U.C. Berkeley's AMPLab. Meanwhile, Databricks continues to be a major contributor to the Apache Spark project. In this review, I'll discuss Spark ML, the open source machine learning library for Spark.

As I found myself unexpectedly snowed in this weekend, I decided to take on a weekend project for fun. While searching for something to catch my fancy, I ran across an old Slate article claiming that they found a foolproof strategy for finding Waldo in the classic "Where's Waldo?" book series. Now, I'm no Waldo-spotting expert, but even I could tell that the strategy they proposed there is far from perfect. That's when I decided what my weekend project would be: I was going to pull out every machine learning trick in my tool box to compute the optimal search strategy for finding Waldo. I was going to crush Slate's supposed foolproof strategy and carve a trail of defeated Waldo-searchers in my wake.

Microsoft chief executive Satya Nadella has revealed the Australian government will be among the first in the world to deploy artificial intelligence "bots" to deal with inquiries from citizens. As the tech giant looks to lead in the new era of cognitive computing, Mr Nadella told a technology developers' conference in Sydney on Wednesday morning that increasingly intelligent technology would allow human-like interaction with complex business systems. Mr Nadella told The Australian Financial Review after the speech that the Department of Human Services had completed the largest deployment of the Windows 10 operating system in the Asia-Pacific region and was now working on AI-powered applications. He said the DHS was working to create a smart software-driven conversational bot, which could help in reducing the amount of time citizens had to wait to speak to human staff.