What impact will artificial intelligence (AI) have on the workforce? Will smart machines really replace a large number of people in a variety of jobs? These questions have been on the minds of a lot of people of late -- especially as AI becomes even more advanced. Clearly the technology will take away the need for some functions that are now performed by humans. But there's good reason to believe that AI will actually create a lot of new jobs as well -- at least in some areas of the economy.

The weight of the automotive and tech industries is fully behind the move toward self-driving cars. Cars with "limited autonomy"--i.e., the ability to drive themselves under certain conditions (level 3) or within certain geofenced locations (level 4)--should be on our roads within the next five years. But a completely autonomous vehicle--capable of driving anywhere, any time, with human input limited to telling it just a destination--remains a more distant goal. To make that happen, cars are going to need to know exactly where they are in the world with far greater precision than currently possible with technology like GPS. And that means new maps that are far more accurate than anything you could buy at the next gas station--not that a human would be able to read them anyway.

Illegal, Unreported and Unregulated (IUU) fishing is becoming a major issue around the world . In general, IUU fishing is a broad term encapsulating many different scenarios (i.e. For the purposes of this blog, we'll just limit out discussion to Illegal fishing – i.e. fishing uses practices that are against the law, fishing in areas where it is not allowed, or taking animals which are not allowed to be taken. In this blog, I'm simply going to present some code demonstrating the simulation of a training dataset. The training dataset consists of 3000 fictional ships that engage in fishing activities. First, let's load up the libraries and set variables with our base categories Create names for our 3000 ships here.

Data science today is a lot like the Wild West: there's endless opportunity and excitement, but also a lot of chaos and confusion. If you're new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. With this O'Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics. In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners. Alice is a technical leader in the field of Machine Learning.

Robots are interacting with patients in medical facilities, handling material in warehouses, working in manufacturing, and helping ill children attend school--all with a hand from the Cloud. Indeed, by linking to the Cloud, robots are bringing homebound students into classrooms, hallways and cafeterias to socialize with their friends and continue learning--in school. "When you allow the student to actually be there, move around, go to classes and go to recess, you return a sense of control," said Daniel Theobald, chief innovation officer and co-founder of Vecna, a Massachusetts-based company that has created the VGo Robotic Telepresence. The VGo robot is essentially a virtual student who is present in the classroom and interacts in all the usual ways, even able to raise a hand (so to speak) to respond to questions in real time. But its creators hope you won't think of it as just a fancy Skype or FaceTime.

Google Cloud announces a Machine Learning Startup Competition in collaboration with venture capital firms Data Collective and Emergence Capital. The competition brings together promising early-stage startups using machine learning for an opportunity to receive up to $1M in Google Cloud Platform (GCP) credits and the potential to receive up to $1M of equity investment from our partners, Data Collective and Emergence Capital. Startups invited to participate in our final pitch-off event will also have an opportunity to meet and receive feedback from our supporting partners, a16z, Greylock Partners, GV, Kleiner Perkins Caufield & Byers and Sequoia Capital. In keeping with Google's spirit of openness, teams don't need to use GCP to be eligible for the competition. U.S.-based startups working in any sector -- from health to financial services and from retail to mobile apps -- are encouraged to apply.

Bengaluru-based Microsoft Accelerator has selected 14 startups for its startup accelerator programme. It is the tenth batch for Microsoft's Accelerator Programme. Selected startups belong to the realm of AI and machine learning and will undergo an intensive programme to increase their'enterprise readiness quotient.' The cohort has been selected from VC portfolios – including Inventus Capital Partners, Ideaspring Capital, Accel Partners, IDG Ventures and Pi Ventures, based on their nominations. They were selected based upon founder experience and industry expertise, products, and traction, as per an official statement.

AI is no longer the future--it's now here in our living rooms and cars and, often, our pockets. As the technology has continued to expand its role in our lives, an important question has emerged: What level of trust can--and should--we place in these AI systems? To explore this question, we spoke to 30 AI scientists and leading thinkers. They told us that building trust in AI will require a significant effort to instill in it a sense of morality, operate in full transparency and provide education about the opportunities it will create for business and consumers. And, they say, this effort must be collaborative across scientific disciplines, industries and government.

Artificial intelligence has become as meaningless a description of technology as "all natural" is when it refers to fresh eggs. At least, that's the conclusion reached by Devin Coldewey, a Tech Crunch contributor. AI has become a popular buzzword, he said, precisely because it's so poorly defined. Marketers use it to create an impression of competence and to more easily promote "intelligent" capabilities as trends change. The popularity of the AI buzzword, however, "has to do at least partly with the conflation of neural networks with artificial intelligence," he said.

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