In Computer Science Engineering, design patterns are implemented to architect a software design solution. Implementing industrial best practices for object-oriented programming and code implementation require design patterns at a higher-level as an abstract solution. Most of the functions in Python are first-class citizens. The biological cognitive models based on the way the complex computations work in the brain inspire the architecture for neural networks for machine learning and deep learning. The connectionism in the human brain is implemented through artificial neural networks and massively parallel distributed processing with a wide range of capabilities for the models of memory, attention span, semantic representation, language, inception formation, and cognitive reasoning.

MARKETING isn't what it used to be with technology as an option in the toolbox. Conventional approaches that worked just a few years back are largely ineffective now. As the use of artificial intelligence (AI) in marketing rises, so do data privacy concerns. Despite that, AI may be exactly what will overcome data privacy concerns in marketing for businesses. What needs to be clear is that it's not AI that's driving most digital marketing campaigns right now. Instead, today's marketing efforts are based on algorithms.

Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account. When Scott pulls into his destination's parking lot, his car bumps into one of several parking signs.

If a woman (or non-female identifying person with a uterus and visions of starting a family) is struggling to conceive and decides to improve their reproductive odds at an IVF clinic, they'll likely interact with a doctor, a nurse, and a receptionist. They will probably never meet the army of trained embryologists working behind closed lab doors to collect eggs, fertilize them, and develop the embryos bound for implantation. One of embryologists' more time-consuming jobs is doing something called "grading" embryos--looking at their morphological features under a microscope and assigning a quality score. Round, even numbers of cells are good. They'll use that information to decide which embryos to implant first. Newer methods, like pulling off a cell to extract its DNA and test for abnormalities, something called preimplantation genetic screening, provide more information.

Worries about deep fakes--machine-manipulated videos of celebrities and world leaders purportedly saying or doing things that they really didn't--are quaint compared to a new threat: doctored images of the Earth itself. China is the acknowledged leader in using an emerging technique called generative adversarial networks to trick computers into seeing objects in landscapes or in satellite images that aren't there, says Todd Myers, automation lead and Chief Information Officer in the Office of the Director of Technology at the National Geospatial-Intelligence Agency. "The Chinese are well ahead of us. This is not classified info," Myers said Thursday at the second annual Genius Machines summit, hosted by Defense One and Nextgov. "The Chinese have already designed; they're already doing it right now, using GANs--which are generative adversarial networks--to manipulate scenes and pixels to create things for nefarious reasons."

The rapid progress and development in artificial intelligence (AI) is prompting desperate speculation about its dual-use applications and security risks. From autonomous weapons systems (AWS) to facial recognition technology to decision-making algorithms, each emerging application of artificial intelligence brings with it both good and bad. It is this dual nature of artificial intelligence technology that is bringing enormous security risks to not only individuals and entities across nations: its government, industries, organizations, and academia (NGIOA) but also the future of humanity. The reality is that any new AI innovation might be used for both beneficial and harmful purposes: any single algorithm that may provide important economic applications might also lead to the production of unprecedented weapons of mass destruction on a scale that is difficult to fathom. As a result, the concerns about artificial intelligence-based automation are growing.

New robots are about to create a buzz in space. Three hovering robots, dubbed Astrobees, will be heading to the International Space Station to help astronauts conduct research, do maintenance and track inventory, NASA said Tuesday in a blog post. Our @Space_Station crew is gaining 3 robotic helpers -- Astrobees! These cube-shaped robots will stay busy, flying around the orbital lab assisting basic tasks & allowing our astronauts more time for science. But how will they do it?

IT organizations are adjusting their plans about how and when to implement artificial intelligence and machine learning initiatives. Moving into production is taking longer than IT leaders may have expected. CIOs have identified artificial intelligence and machine learning as the number one way to achieve "game-changing transformation." That's according to Gartner research VP Svetlana Sicular, who provided a perspective on where the industry is right now in terms of implementation, where we are going, and how soon we might be getting there. Sicular offered her take during a session, The Future of Data Science, Machine Learning, and AI, during the recent Gartner Data and Analytics Summit in Orlando, Florida.

The US Food and Drug Administration has announced that it is preparing to regulate AI systems that can update and improve themselves as they gorge on more training data. The announcement: The agency released a white paper proposing a regulatory framework to decide how medical products that use AI should seek approval before they can go on the market. It is the biggest step the FDA has taken to date toward formalizing oversight of products that use machine learning (ML). The challenge: Machine-learning systems are tricky to regulate because they can continuously update and improve their performance through new training data. In instances where the FDA has approved ML-based medical software before, it has required the algorithms to be "frozen" before commercial deployment and to go through a reapproval process when they are changed.

Amazon's Echo speakers have a broadcast feature that will help you send a message to family members that might be scattered around the house. It's time for homework or to summon the troops for dinner. Sure, you could holler upstairs, but that seems so last century. You save your voice and text them instead. It's not just the kids we are texting – we're also texting each other, at home. So is texting under one roof becoming the modern-day equivalent of the dinner bell or intercom?