Will Content Marketing Die To Robotic AI? Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Will Content Marketing Die To Robotic AI?

Ford Motor Co. intends to have a fully driverless vehicle -- no steering wheel, no pedals -- on the road within five years. The car will initially be used for commercial ride-hailing or ride-sharing services; sales to consumers will come later. "This is a transformational moment in our industry and it is a transformational moment for our company," said CEO Mark Fields, as he announced the plan at Ford's Silicon Valley campus in Palo Alto, California. Ford's approach to the autonomous car breaks from many other companies, like Mercedes-Benz and Tesla Motors, which plan to gradually add self-driving capability to traditional cars. Just last month, BMW AG, Intel Corp. and the automotive camera maker Mobileye announced a plan to put an autonomous vehicle with a steering wheel on the road by 2021.

A compact computer called Euclid from Intel should make the development of robots much easier. Euclid looks much like the Kinect camera for Xbox consoles, but it's a self-contained PC that can be the guts of a robot. It's possible to install the Euclid computer where the "eyes" of a human-like robot would be typically placed. Intel demonstrated the Euclid computer in a robot moving on stage during CEO Brian Krzanich's keynote at the Intel Developer Forum on Tuesday. Euclid has a 3D RealSense camera that can serve as the eyes in a robot, capturing images in real-time.

It's no secret that there is a wide gender gap in the tech industry. According to the Center for the Study of the Workplace, women represent around 20 percent of engineering graduates, but just 11 percent of practicing software engineers. Unconscious bias is one of the primary drivers of this disparity, which has led many of Silicon Valley's leading tech companies to introduce unconscious bias training to their employees. However, it's fair to say that its machine learning algorithms need it more. In humans, unconscious biases are ingrained assumptions about particular personal attributes (including race or gender) that can influence decision making without the decision maker being explicitly aware.

The amount of data in pathology images has previously been too vast for researchers to process easily, but that's changing thanks to advanced machine learning. A group of researchers from Stanford University were able to more accurately predict lung cancer prognoses by grabbing images from the Cancer Genome Atlas from patients with the disease, and then through those train a computer software program to pinpoint characteristics in the images previously unable to be seen by the human eye, according to an announcement. Their research was published in Nature Communications. Once the researchers could home in on those specific characteristics, they were able to figure out the cancer subtype, as well as how long a patient would live with that diagnosis. "Ultimately this technique will give us insight into the molecular mechanisms of cancer by connecting important pathological features with outcome data," Michael Snyder, Ph.D., a professor and chair of genetics at Stanford, said in the announcement.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.