It's been a rough few months for Mobileye, maker of the assisted driving platform used in cars from automaker Tesla. The two have been at odds over what caused recent Tesla accidents, with the car maker blaming Mobileye's system. Tesla is now developing its own platform for assisted driving, and Mobileye -- having denied responsibility for the Tesla accidents -- is moving on. It is designing a next-generation chip called EyeQ5, which will be used as the brains for fully autonomous cars by 2020. The chip will have a new 64-bit MIPS CPU from Imagination Technologies, which has been dealing with struggles of its own.

TDSP provides recommendations for managing shared analytics and storage infrastructure, including cloud file systems for storing datasets, databases, Big Data clusters (Hadoop, Spark), machine learning services, etc., both on the cloud and on-premises. This is where raw and processed datasets are stored, enabling reproducible analysis. It also avoids duplication, which could lead to inconsistencies and additional infrastructure costs. Scripts are provided to provision the shared resources, track them and allow each team member to connect to those resources securely. Our data science team uses the Microsoft Data Science Virtual Machine as our cloud development environment.

A couple of weeks ago I wrote an article on simple linear regression, which I would recommend reading before proceeding to read this one. Machine learning is a very interesting topic and I have been studying it on my free time. I hope this article sparks your interest in the subject or helps continue fuel it. In simple linear regression there is a one-to-one relationship between the input variable and the output variable. But in multiple linear regression, as the name implies there is a many-to-one relationship, instead of just using one input variable, you use several.

We've been hearing all artificial intelligence, all the time from the Customer Relationship Management (CRM) industry over the last several weeks. Microsoft is the latest to trumpet its AI capabilities for sales people with the general availability of Dynamics 365 coming on November 1st. Microsoft announced last summer that it was going to be combining its ERP and CRM into a unified solution, and this is the culmination of that announcement. Like many large organizations, Microsoft tends to deliver the news in waves -- it's coming, it's in beta, it's here. While the news smacks of "look at me too," Microsoft points out it has been working on AI long before its biggest competitors like Salesforce and Oracle, which recently announced their own AI capabilities at their respective customer conferences, Dreamforce and Oracle Open World. Microsoft has built in a couple of intelligence features into the release designed specifically for sales and service personnel.

Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.

Nearly 140 private companies working to advance artificial intelligence technologies have been acquired since 2011, with over 40 acquisitions taking place in 2016 alone (as of 10/7/2016). Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant this month with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant.

Machine learning is a very interesting field in Computer Science that has ranked really high on my to-learn list for a long while now. With so many updates from RxJava, Testing, Android N, Android Studio and other Android goodies, I haven't been able to dedicate time to learn it. I was very excited to discover that Machine Learning can now implemented by anyone in their Android Apps based on the Mobile Vision APIs from Google without needing to have prior knowledge in the field. All you need is to know is how to use APIs. There are a lot of APIs for Machine Learning on the cloud and mobile, but in this series I'm going to focus only on the Mobile Vision APIs since those are created specifically for Android developers.

Marc Vollenweider is the CEO and co-founder of Evalueserve. As a former partner at McKinsey & Company in Zurich and India, Marc developed a deep interest in the world of data analytics, particularly how human minds and smart machines can successfully complement each other. He saw the potential for companies to enhance productivity, shorten their time-to-market, improve quality, and gain new capabilities by leveraging mind machine. Marc's fascination with innovation has led to the establishment of a series of internal ventures at Evalueserve. He is the co-inventor of the digital model of InsightBee, Evalueserve's new pay-as-you-go research and analytics solution.

The Zen monks of Plum Village monastery had staked out a small space on the green lawn between the hulking bunkers of Moscone North and South, the expo halls at the epicenter of Dreamforce '16. They were steering cross-legged peace-seekers down the path to mindfulness. As I passed by, I liked to imagine that the meditating attendees were seeking solace after three days of trying to wrap their heads around Artificial Intelligence--overwhelmingly the topic of the week. And yet Salesforce had made it all look such fun. With a touch of movie magic, the brands two co-founders Marc Benioff and Parker Harris could be seen on the keynote stage chatting with a cartoon representation of the German genius.

The British government has been slammed for failing to respond to the coming advancements in robotics and artificial intelligence (A.I.), changes that will have a far-reaching impact on both the country and world. The science and technology select committee, a group of 10 members of parliament (MPs) that scrutinizes the government's science policies, warned on Wednesday that not enough is being done to prepare for the social and ethical issues that will arise from robots and A.I. "Artificial intelligence has some way to go before we see systems and robots as portrayed in the creative arts such as Star Wars," Dr Tania Mathias, interim chair of the committee and a Conservative MP, said in a statement. "But science fiction is slowly becoming science fact, and robotics and A.I. look destined to play an increasing role in our lives over the coming decades." As technologies progress, British politicians are slowly growing fearful that a complacent response could cause mass social upheaval if not properly managed. Labour MP and innovation spokesperson Jon Trickett warned at his party's conference last month that "we will have to make [technology] our servant," while Conservative MP and Brexit secretary David Davis told his party's conference that manual laborers are being replaced by robots.