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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At the heart of AI systems are statistical models that have no concept of social inequality, fairness, or hardships. In Cathy O'Neil's book, Weapons of Math Destruction (WMD), she points out that big data is discriminating nearly at every juncture of our society and pummeling the poor at each opportunity. Her book points to many avenues of misuse of data, but most offensive is through the use of proxies. Data statistics that are designed for one purpose but are repurposed to be used for economic or convenience sake. There are a number of examples of this.

California released a proposal this week amending some of its strict regulations for autonomous vehicles. If all goes as planned, the state will allow self-driving vehicles to operate without a human driver behind the wheel starting next year. The proposal eliminates some of the hoops automakers have to jump through to test self-driving cars on public roads, reports the Los Angeles Times. Testers no longer need to obtain permission from each locality their cars drive through, and instead only need to inform each location of their plans. And rather than creating an emergency plan with each group of law enforcement officials, companies would only need to file a communications plan going forward.